R-universe search: topic:openmp

rcppcore

RcppArmadillo:'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library

'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

Maintained by Dirk Eddelbuettel. Last updated 2 hours ago.

armadillo c-plus-plus rcpp rcpparmadillo openblas cpp openmp

200 stars 18.85 score 1.9k scripts 3.4k dependents

dmlc

xgboost:Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Maintained by Jiaming Yuan. Last updated 1 days ago.

distributed-systems gbdt gbm gbrt machine-learning xgboost cpp openmp

27k stars 17.45 score 115 dependents

glmmtmb

glmmTMB:Generalized Linear Mixed Models using Template Model Builder

Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.

Maintained by Mollie Brooks. Last updated 2 days ago.

cpp openmp

314 stars 16.84 score 3.7k scripts 25 dependents

sebkrantz

collapse:Advanced and Fast Data Transformation

A C/C++ based package for advanced data transformation and statistical computing in R that is extremely fast, class-agnostic, robust and programmer friendly. Core functionality includes a rich set of S3 generic grouped and weighted statistical functions for vectors, matrices and data frames, which provide efficient low-level vectorizations, OpenMP multithreading, and skip missing values by default. These are integrated with fast grouping and ordering algorithms (also callable from C), and efficient data manipulation functions. The package also provides a flexible and rigorous approach to time series and panel data in R. It further includes fast functions for common statistical procedures, detailed (grouped, weighted) summary statistics, powerful tools to work with nested data, fast data object conversions, functions for memory efficient R programming, and helpers to effectively deal with variable labels, attributes, and missing data. It is well integrated with base R classes, 'dplyr'/'tibble', 'data.table', 'sf', 'units', 'plm' (panel-series and data frames), and 'xts'/'zoo'.

Maintained by Sebastian Krantz. Last updated 6 days ago.

data-aggregation data-analysis data-manipulation data-processing data-science data-transformation econometrics high-performance panel-data scientific-computing statistics time-series weighted weights cpp openmp

672 stars 16.68 score 708 scripts 99 dependents

markvanderloo

stringdist:Approximate String Matching, Fuzzy Text Search, and String Distance Functions

Implements an approximate string matching version of R's native 'match' function. Also offers fuzzy text search based on various string distance measures. Can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q- gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences. This package is built for speed and runs in parallel by using 'openMP'. An API for C or C++ is exposed as well. Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.

Maintained by Mark van der Loo. Last updated 4 months ago.

openmp

327 stars 15.54 score 2.0k scripts 179 dependents

kosukeimai

MatchIt:Nonparametric Preprocessing for Parametric Causal Inference

Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)

Maintained by Noah Greifer. Last updated 14 days ago.

cpp openmp

220 stars 15.03 score 2.4k scripts 21 dependents

philchalmers

mirt:Multidimensional Item Response Theory

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.

Maintained by Phil Chalmers. Last updated 22 hours ago.

irt mirt openblas cpp openmp

212 stars 14.93 score 2.5k scripts 40 dependents

lrberge

fixest:Fast Fixed-Effects Estimations

Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.

Maintained by Laurent Berge. Last updated 7 months ago.

cpp openmp

394 stars 14.69 score 3.8k scripts 26 dependents

r-lidar

lidR:Airborne LiDAR Data Manipulation and Visualization for Forestry Applications

Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

Maintained by Jean-Romain Roussel. Last updated 2 months ago.

als forestry las laz lidar point-cloud remote-sensing openblas cpp openmp

623 stars 14.47 score 844 scripts 8 dependents

jkrijthe

Rtsne:T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation

An R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).

Maintained by Jesse Krijthe. Last updated 10 months ago.

openblas cpp openmp

256 stars 14.01 score 4.4k scripts 233 dependents

asgr

imager:Image Processing Library Based on 'CImg'

Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R. The package wraps 'CImg', <http://cimg.eu>, a simple, modern C++ library for image processing.

Maintained by Aaron Robotham. Last updated 4 days ago.

libx11 fftw3 tiff cpp openmp

17 stars 13.53 score 2.4k scripts 44 dependents

biodiverse

unmarked:Models for Data from Unmarked Animals

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Maintained by Ken Kellner. Last updated 9 days ago.

openblas cpp openmp

4 stars 13.02 score 652 scripts 12 dependents

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

32 stars 12.71 score 17k scripts 7.8k dependents

covaruber

sommer:Solving Mixed Model Equations in R

Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.

Maintained by Giovanny Covarrubias-Pazaran. Last updated 2 days ago.

average-information mixed-models rcpparmadillo openblas cpp openmp

44 stars 12.63 score 300 scripts 10 dependents

rfastofficial

Rfast:A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.

Maintained by Manos Papadakis. Last updated 29 days ago.

openblas cpp openmp

147 stars 12.54 score 1.2k scripts 166 dependents

bioc

SparseArray:High-performance sparse data representation and manipulation in R

The SparseArray package provides array-like containers for efficient in-memory representation of multidimensional sparse data in R (arrays and matrices). The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray. Each subclass uses its own internal representation of the nonzero multidimensional data: the "COO layout" and the "SVT layout", respectively. SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix and array objects in base R. In particular, they suppport most of the "standard matrix and array API" defined in base R and in the matrixStats package from CRAN.

Maintained by Hervé Pagès. Last updated 10 days ago.

infrastructure datarepresentation bioconductor-package core-package openmp

9 stars 12.47 score 79 scripts 1.2k dependents

privefl

bigsnpr:Analysis of Massive SNP Arrays

Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.

Maintained by Florian Privé. Last updated 22 days ago.

big-data bioinformatics memory-mapped-file parallel-computing polygenic-scores population-structure-inference snp-data statistical-methods openblas zlib cpp openmp

200 stars 11.44 score 1.5k scripts 3 dependents

nlmixr2

rxode2:Facilities for Simulating from ODE-Based Models

Facilities for running simulations from ordinary differential equation ('ODE') models, such as pharmacometrics and other compartmental models. A compilation manager translates the ODE model into C, compiles it, and dynamically loads the object code into R for improved computational efficiency. An event table object facilitates the specification of complex dosing regimens (optional) and sampling schedules. NB: The use of this package requires both C and Fortran compilers, for details on their use with R please see Section 6.3, Appendix A, and Appendix D in the "R Administration and Installation" manual. Also the code is mostly released under GPL. The 'VODE' and 'LSODA' are in the public domain. The information is available in the inst/COPYRIGHTS.

Maintained by Matthew L. Fidler. Last updated 1 months ago.

fortran openblas cpp openmp

40 stars 11.24 score 220 scripts 13 dependents

markvanderloo

gower:Gower's Distance

Compute Gower's distance (or similarity) coefficient between records. Compute the top-n matches between records. Core algorithms are executed in parallel on systems supporting OpenMP.

Maintained by Mark van der Loo. Last updated 10 months ago.

openmp

29 stars 11.19 score 66 scripts 391 dependents

mlampros

ClusterR:Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.

Maintained by Lampros Mouselimis. Last updated 9 months ago.

affinity-propagation cpp11 gmm kmeans kmedoids-clustering mini-batch-kmeans rcpparmadillo openblas cpp openmp

84 stars 11.08 score 640 scripts 24 dependents

stephenslab

mashr:Multivariate Adaptive Shrinkage

Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.

Maintained by Peter Carbonetto. Last updated 5 months ago.

openblas gsl cpp openmp

91 stars 11.04 score 624 scripts 3 dependents

grunwaldlab

poppr:Genetic Analysis of Populations with Mixed Reproduction

Population genetic analyses for hierarchical analysis of partially clonal populations built upon the architecture of the 'adegenet' package. Originally described in Kamvar, Tabima, and Grünwald (2014) <doi:10.7717/peerj.281> with version 2.0 described in Kamvar, Brooks, and Grünwald (2015) <doi:10.3389/fgene.2015.00208>.

Maintained by Zhian N. Kamvar. Last updated 11 months ago.

clonality genetic-analysis genetic-distances minimum-spanning-networks multilocus-genotypes multilocus-lineages population-genetics populations openmp

69 stars 10.84 score 672 scripts

zdebruine

RcppML:Rcpp Machine Learning Library

Fast machine learning algorithms including matrix factorization and divisive clustering for large sparse and dense matrices.

Maintained by Zach DeBruine. Last updated 2 years ago.

clustering matrix-factorization nmf rcpp rcppeigen sparse-matrix cpp openmp

107 stars 10.66 score 125 scripts 50 dependents

norskregnesentral

shapr:Prediction Explanation with Dependence-Aware Shapley Values

Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying 'Python' wrapper ('shaprpy') is available through the GitHub repository.

Maintained by Martin Jullum. Last updated 1 days ago.

explainable-ai explainable-ml rcpp rcpparmadillo shapley openblas cpp openmp

154 stars 10.59 score 175 scripts 1 dependents

privefl

bigstatsr:Statistical Tools for Filebacked Big Matrices

Easy-to-use, efficient, flexible and scalable statistical tools. Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more <doi:10.1093/bioinformatics/bty185>.

Maintained by Florian Privé. Last updated 7 months ago.

big-data large-matrices memory-mapped-file parallel-computing statistical-methods openblas cpp openmp

180 stars 10.59 score 394 scripts 16 dependents

jenniniku

gllvm:Generalized Linear Latent Variable Models

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Maintained by Jenni Niku. Last updated 1 hours ago.

cpp openmp

52 stars 10.56 score 176 scripts 1 dependents

bioc

DECIPHER:Tools for curating, analyzing, and manipulating biological sequences

A toolset for deciphering and managing biological sequences.

Maintained by Erik Wright. Last updated 17 days ago.

clustering genetics sequencing dataimport visualization microarray qualitycontrol qpcr alignment wholegenome microbiome immunooncology geneprediction openmp

10.55 score 1.1k scripts 14 dependents

wrathematics

float:32-Bit Floats

R comes with a suite of utilities for linear algebra with "numeric" (double precision) vectors/matrices. However, sometimes single precision (or less!) is more than enough for a particular task. This package extends R's linear algebra facilities to include 32-bit float (single precision) data. Float vectors/matrices have half the precision of their "numeric"-type counterparts but are generally faster to numerically operate on, for a performance vs accuracy trade-off. The internal representation is an S4 class, which allows us to keep the syntax identical to that of base R's. Interaction between floats and base types for binary operators is generally possible; in these cases, type promotion always defaults to the higher precision. The package ships with copies of the single precision 'BLAS' and 'LAPACK', which are automatically built in the event they are not available on the system.

Maintained by Drew Schmidt. Last updated 19 days ago.

float-matrix hpc linear-algebra matrix fortran openblas openmp

46 stars 10.53 score 228 scripts 42 dependents

bioc

miloR:Differential neighbourhood abundance testing on a graph

Milo performs single-cell differential abundance testing. Cell states are modelled as representative neighbourhoods on a nearest neighbour graph. Hypothesis testing is performed using either a negative bionomial generalized linear model or negative binomial generalized linear mixed model.

Maintained by Mike Morgan. Last updated 5 months ago.

singlecell multiplecomparison functionalgenomics software openblas cpp openmp

357 stars 10.49 score 340 scripts 1 dependents

bioc

celda:CEllular Latent Dirichlet Allocation

Celda is a suite of Bayesian hierarchical models for clustering single-cell RNA-sequencing (scRNA-seq) data. It is able to perform "bi-clustering" and simultaneously cluster genes into gene modules and cells into cell subpopulations. It also contains DecontX, a novel Bayesian method to computationally estimate and remove RNA contamination in individual cells without empty droplet information. A variety of scRNA-seq data visualization functions is also included.

Maintained by Joshua Campbell. Last updated 1 months ago.

singlecell geneexpression clustering sequencing bayesian immunooncology dataimport cpp openmp

147 stars 10.47 score 256 scripts 2 dependents

david-cortes

isotree:Isolation-Based Outlier Detection

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

Maintained by David Cortes. Last updated 7 days ago.

anomaly-detection imputation isolation-forest outlier-detection cpp openmp

206 stars 10.43 score 115 scripts 6 dependents

hmjianggatech

huge:High-Dimensional Undirected Graph Estimation

Provides a general framework for high-dimensional undirected graph estimation. It integrates data preprocessing, neighborhood screening, graph estimation, and model selection techniques into a pipeline. In preprocessing stage, the nonparanormal(npn) transformation is applied to help relax the normality assumption. In the graph estimation stage, the graph structure is estimated by Meinshausen-Buhlmann graph estimation or the graphical lasso, and both methods can be further accelerated by the lossy screening rule preselecting the neighborhood of each variable by correlation thresholding. We target on high-dimensional data analysis usually d >> n, and the computation is memory-optimized using the sparse matrix output. We also provide a computationally efficient approach, correlation thresholding graph estimation. Three regularization/thresholding parameter selection methods are included in this package: (1)stability approach for regularization selection (2) rotation information criterion (3) extended Bayesian information criterion which is only available for the graphical lasso.

Maintained by Haoming Jiang. Last updated 3 years ago.

cpp openmp

12 stars 10.29 score 608 scripts 19 dependents

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

83 stars 10.26 score 368 scripts 1 dependents

gaynorr

AlphaSimR:Breeding Program Simulations

The successor to the 'AlphaSim' software for breeding program simulation [Faux et al. (2016) <doi:10.3835/plantgenome2016.02.0013>]. Used for stochastic simulations of breeding programs to the level of DNA sequence for every individual. Contained is a wide range of functions for modeling common tasks in a breeding program, such as selection and crossing. These functions allow for constructing simulations of highly complex plant and animal breeding programs via scripting in the R software environment. Such simulations can be used to evaluate overall breeding program performance and conduct research into breeding program design, such as implementation of genomic selection. Included is the 'Markovian Coalescent Simulator' ('MaCS') for fast simulation of biallelic sequences according to a population demographic history [Chen et al. (2009) <doi:10.1101/gr.083634.108>].

Maintained by Chris Gaynor. Last updated 5 months ago.

breeding genomics simulation openblas cpp openmp

47 stars 10.22 score 534 scripts 2 dependents

l-ramirez-lopez

prospectr:Miscellaneous Functions for Processing and Sample Selection of Spectroscopic Data

Functions to preprocess spectroscopic data and conduct (representative) sample selection/calibration sampling.

Maintained by Leonardo Ramirez-Lopez. Last updated 11 days ago.

chemometrics derivatives infrared near-infrared nir pedometrics preprocessing resample sampling signal soil-spectroscopy spectroscopy openblas cpp openmp

42 stars 10.22 score 326 scripts 4 dependents

zarquon42b

Rvcg:Manipulations of Triangular Meshes Based on the 'VCGLIB' API

Operations on triangular meshes based on 'VCGLIB'. This package integrates nicely with the R-package 'rgl' to render the meshes processed by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is an open source portable C++ templated library for manipulation, processing and displaying with OpenGL of triangle and tetrahedral meshes. The library, composed by more than 100k lines of code, is released under the GPL license, and it is the base of most of the software tools of the Visual Computing Lab of the Italian National Research Council Institute ISTI <https://vcg.isti.cnr.it/>, like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options determined by the configure script as well as to work with the header files included by 'RcppEigen'.

Maintained by Stefan Schlager. Last updated 5 months ago.

cpp openmp

25 stars 10.20 score 195 scripts 29 dependents

stewid

SimInf:A Framework for Data-Driven Stochastic Disease Spread Simulations

Provides an efficient and very flexible framework to conduct data-driven epidemiological modeling in realistic large scale disease spread simulations. The framework integrates infection dynamics in subpopulations as continuous-time Markov chains using the Gillespie stochastic simulation algorithm and incorporates available data such as births, deaths and movements as scheduled events at predefined time-points. Using C code for the numerical solvers and 'OpenMP' (if available) to divide work over multiple processors ensures high performance when simulating a sample outcome. One of our design goals was to make the package extendable and enable usage of the numerical solvers from other R extension packages in order to facilitate complex epidemiological research. The package contains template models and can be extended with user-defined models. For more details see the paper by Widgren, Bauer, Eriksson and Engblom (2019) <doi:10.18637/jss.v091.i12>. The package also provides functionality to fit models to time series data using the Approximate Bayesian Computation Sequential Monte Carlo ('ABC-SMC') algorithm of Toni and others (2009) <doi:10.1098/rsif.2008.0172>.

Maintained by Stefan Widgren. Last updated 16 days ago.

data-driven epidemiology high-performance-computing markov-chain mathematical-modelling gsl openmp

35 stars 10.09 score 227 scripts

zarquon42b

Morpho:Calculations and Visualisations Related to Geometric Morphometrics

A toolset for Geometric Morphometrics and mesh processing. This includes (among other stuff) mesh deformations based on reference points, permutation tests, detection of outliers, processing of sliding semi-landmarks and semi-automated surface landmark placement.

Maintained by Stefan Schlager. Last updated 5 months ago.

openblas cpp openmp

51 stars 10.01 score 218 scripts 13 dependents

kogalur

randomForestSRC:Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC)

Fast OpenMP parallel computing of Breiman's random forests for univariate, multivariate, unsupervised, survival, competing risks, class imbalanced classification and quantile regression. New Mahalanobis splitting for correlated outcomes. Extreme random forests and randomized splitting. Suite of imputation methods for missing data. Fast random forests using subsampling. Confidence regions and standard errors for variable importance. New improved holdout importance. Case-specific importance. Minimal depth variable importance. Visualize trees on your Safari or Google Chrome browser. Anonymous random forests for data privacy.

Maintained by Udaya B. Kogalur. Last updated 6 days ago.

openmp

124 stars 10.01 score 1.2k scripts 11 dependents

mlampros

OpenImageR:An Image Processing Toolkit

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

filtering gabor-feature-extraction gabor-filters hog-features image image-hashing processing rcpparmadillo recognition slic slico superpixels openblas cpp openmp

60 stars 9.86 score 358 scripts 8 dependents

pbreheny

biglasso:Extending Lasso Model Fitting to Big Data

Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like 'glmnet' and 'ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.

Maintained by Patrick Breheny. Last updated 23 days ago.

bigdata lasso out-of-core parallel-computing cpp openmp

113 stars 9.84 score 74 scripts 1 dependents

jasonjfoster

roll:Rolling and Expanding Statistics

Fast and efficient computation of rolling and expanding statistics for time-series data.

Maintained by Jason Foster. Last updated 2 months ago.

algorithms rcpp statistics openblas cpp openmp

116 stars 9.76 score 318 scripts 13 dependents

coatless-rpkg

sitmo:Parallel Pseudo Random Number Generator (PPRNG) 'sitmo' Header Files

Provided within are two high quality and fast PPRNGs that may be used in an 'OpenMP' parallel environment. In addition, there is a generator for one dimensional low-discrepancy sequence. The objective of this library to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and 'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the header files inside of 'sitmo' instead of including a copy of each engine within their individual package. Lastly, the package contains example implementations using the 'sitmo' package and three accompanying vignette that provide additional information.

Maintained by James Balamuta. Last updated 1 years ago.

parallel random-generation rcpp cpp openmp

7 stars 9.75 score 15 scripts 201 dependents

jolars

SLOPE:Sorted L1 Penalized Estimation

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. 2015). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.

Maintained by Johan Larsson. Last updated 14 days ago.

generalized-linear-models slope sparse-regression cpp openmp

17 stars 9.62 score 75 scripts 3 dependents

donaldrwilliams

BGGM:Bayesian Gaussian Graphical Models

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Maintained by Philippe Rast. Last updated 3 months ago.

bayes-factors bayesian-hypothesis-testing gaussian-graphical-models openblas cpp openmp

55 stars 9.61 score 102 scripts 1 dependents

schochastics

netrankr:Analyzing Partial Rankings in Networks

Implements methods for centrality related analyses of networks. While the package includes the possibility to build more than 20 indices, its main focus lies on index-free assessment of centrality via partial rankings obtained by neighborhood-inclusion or positional dominance. These partial rankings can be analyzed with different methods, including probabilistic methods like computing expected node ranks and relative rank probabilities (how likely is it that a node is more central than another?). The methodology is described in depth in the vignettes and in Schoch (2018) <doi:10.1016/j.socnet.2017.12.003>.

Maintained by David Schoch. Last updated 2 months ago.

network-analysis network-centrality openblas cpp openmp

49 stars 9.56 score 91 scripts 2 dependents

ropensci

aorsf:Accelerated Oblique Random Forests

Fit, interpret, and compute predictions with oblique random forests. Includes support for partial dependence, variable importance, passing customized functions for variable importance and identification of linear combinations of features. Methods for the oblique random survival forest are described in Jaeger et al., (2023) <DOI:10.1080/10618600.2023.2231048>.

Maintained by Byron Jaeger. Last updated 7 days ago.

data-science oblique random-forest survival openblas cpp openmp

58 stars 9.29 score 60 scripts 1 dependents

alarm-redist

redist:Simulation Methods for Legislative Redistricting

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <arXiv:1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <arXiv:2011.02288>.

Maintained by Christopher T. Kenny. Last updated 2 months ago.

geospatial gerrymandering redistricting sampling openblas cpp openmp

68 stars 9.17 score 259 scripts

mlampros

KernelKnn:Kernel k Nearest Neighbors

Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

cpp11 distance-metric kernel-methods knn rcpparmadillo openblas cpp openmp

17 stars 9.16 score 54 scripts 13 dependents

2005m

kit:Data Manipulation Functions Implemented in C

Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.

Maintained by Morgan Jacob. Last updated 7 months ago.

openmp

58 stars 9.11 score 92 scripts 5 dependents

david-cortes

MatrixExtra:Extra Methods for Sparse Matrices

Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.

Maintained by David Cortes. Last updated 9 months ago.

csr sparse-matrix openblas cpp openmp

20 stars 9.08 score 84 scripts 29 dependents

dexter-psychometrics

dexter:Data Management and Analysis of Tests

A system for the management, assessment, and psychometric analysis of data from educational and psychological tests.

Maintained by Jesse Koops. Last updated 17 days ago.

openblas cpp openmp

8 stars 8.97 score 135 scripts 2 dependents

bhklab

mRMRe:Parallelized Minimum Redundancy, Maximum Relevance (mRMR)

Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.

Maintained by Benjamin Haibe-Kains. Last updated 4 years ago.

cpp openmp

19 stars 8.95 score 105 scripts 2 dependents

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 2 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

30 stars 8.93 score 146 scripts 1 dependents

wrathematics

coop:Co-Operation: Fast Covariance, Correlation, and Cosine Similarity Operations

Fast implementations of the co-operations: covariance, correlation, and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes.

Maintained by Drew Schmidt. Last updated 3 years ago.

openblas openmp

35 stars 8.92 score 214 scripts 16 dependents

bioc

BayesSpace:Clustering and Resolution Enhancement of Spatial Transcriptomes

Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed.

Maintained by Matt Stone. Last updated 5 months ago.

software clustering transcriptomics geneexpression singlecell immunooncology dataimport openblas cpp openmp

123 stars 8.89 score 278 scripts 1 dependents

usccana

netdiffuseR:Analysis of Diffusion and Contagion Processes on Networks

Empirical statistical analysis, visualization and simulation of diffusion and contagion processes on networks. The package implements algorithms for calculating network diffusion statistics such as transmission rate, hazard rates, exposure models, network threshold levels, infectiousness (contagion), and susceptibility. The package is inspired by work published in Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN: 9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.

Maintained by George Vega Yon. Last updated 4 months ago.

contagion diffusion-network network-analysis network-visualization openblas cpp openmp

88 stars 8.88 score 217 scripts

atmoschem

vein:Vehicular Emissions Inventories

Elaboration of vehicular emissions inventories, consisting in four stages, pre-processing activity data, preparing emissions factors, estimating the emissions and post-processing of emissions in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>. Before using VEIN you need to know the vehicular composition of your study area, in other words, the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to start with the project to download a template to create a structure of directories and scripts.

Maintained by Sergio Ibarra-Espinosa. Last updated 21 hours ago.

atmoschem atmospheric-chemistry atmospheric-science atmospheric-sciences emissions emissions-model vehicular-emissions-inventories vein fortran openmp

46 stars 8.73 score 137 scripts

helske

seqHMM:Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series

Designed for fitting hidden (latent) Markov models and mixture hidden Markov models for social sequence data and other categorical time series. Also some more restricted versions of these type of models are available: Markov models, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and hidden Markov models. Models are estimated using maximum likelihood via the EM algorithm and/or direct numerical maximization with analytical gradients. All main algorithms are written in C++ with support for parallel computation. Documentation is available via several vignettes in this page, and the paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).

Maintained by Jouni Helske. Last updated 2 years ago.

categorical-data em-algorithm hidden-markov-models hmm mixture-markov-models time-series openblas cpp openmp

98 stars 8.52 score 92 scripts 1 dependents

jameslamb

lightgbm:Light Gradient Boosting Machine

Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.

Maintained by James Lamb. Last updated 1 months ago.

cpp openmp

1 stars 8.47 score 1.6k scripts 6 dependents

dcgerard

updog:Flexible Genotyping for Polyploids

Implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.

Maintained by David Gerard. Last updated 1 years ago.

openblas cpp openmp

28 stars 8.45 score 83 scripts 2 dependents

collinerickson

GauPro:Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Maintained by Collin Erickson. Last updated 12 days ago.

openblas cpp openmp

16 stars 8.44 score 104 scripts 1 dependents

byoungman

evgam:Generalised Additive Extreme Value Models

Methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.

Maintained by Ben Youngman. Last updated 15 days ago.

openblas cpp openmp

6 stars 8.43 score 82 scripts 12 dependents

kss2k

modsem:Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)

Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). <doi:10.1207/S15328007SEM0801_3>. "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). <doi:10.1007/BF02296338>. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). <doi:10.1080/00273170701710205>. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). <doi:10.1080/10705511.2010.488999>. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). <doi:10.1207/s15328007sem1304_1>. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). <doi:10.1037/1082-989X.9.3.275>. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." <https://www.statmodel.com/>. Rosseel Y (2012). <doi:10.18637/jss.v048.i02>. "'lavaan': An R Package for Structural Equation Modeling."

Maintained by Kjell Solem Slupphaug. Last updated 1 days ago.

interaction-effect interaction-effects latent-moderated-structural-equations lavaan-syntax lms moderation qml quasi-maximum-likelihood rlang rlanguage sem structural-equation-modeling structural-equation-models openblas cpp openmp

6 stars 8.41 score 54 scripts

kisungyou

Rdimtools:Dimension Reduction and Estimation Methods

We provide linear and nonlinear dimension reduction techniques. Intrinsic dimension estimation methods for exploratory analysis are also provided. For more details on the package, see the paper by You and Shung (2022) <doi:10.1016/j.simpa.2022.100414>.

Maintained by Kisung You. Last updated 2 years ago.

dimension-estimation dimension-reduction manifold-learning subspace-learning openblas cpp openmp

52 stars 8.37 score 186 scripts 8 dependents

nlmixr2

nlmixr2est:Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

Maintained by Matthew Fidler. Last updated 11 days ago.

openblas cpp openmp

9 stars 8.33 score 26 scripts 9 dependents

uofuepibio

epiworldR:Fast Agent-Based Epi Models

A flexible framework for Agent-Based Models (ABM), the 'epiworldR' package provides methods for prototyping disease outbreaks and transmission models using a 'C++' backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents' features, providing great complexity for the model dynamics. Furthermore, 'epiworldR' is ideal for simulation studies featuring large populations.

Maintained by Andrew Pulsipher. Last updated 23 days ago.

abm agent-based-modeling covid-19 epidemics epidemiology r-programming rpack rpkg seir seir-model simulation sir sir-model cpp openmp

9 stars 8.33 score 58 scripts 1 dependents

jsilve24

fido:Bayesian Multinomial Logistic Normal Regression

Provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. 'fido' replaces the previous package 'stray'.

Maintained by Justin Silverman. Last updated 30 days ago.

cpp openmp

20 stars 8.31 score 103 scripts

drizopoulos

JMbayes2:Extended Joint Models for Longitudinal and Time-to-Event Data

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Maintained by Dimitris Rizopoulos. Last updated 23 days ago.

competing-risks longitudinal-analysis mixed-models multi-state personalized-medicine precision-medicine prediction-model survival-models openblas cpp openmp

84 stars 8.27 score 264 scripts 2 dependents

valentint

tclust:Robust Trimmed Clustering

Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011) <doi:10.1007/s11222-010-9194-z> and others.

Maintained by Valentin Todorov. Last updated 1 months ago.

openblas cpp openmp

3 stars 8.26 score 72 scripts 3 dependents

lbelzile

mev:Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Maintained by Leo Belzile. Last updated 5 months ago.

extreme-value-statistics likelihood-functions max-stable simulation threshold-selection openblas cpp openmp

14 stars 8.21 score 94 scripts 4 dependents

alexiosg

rmgarch:Multivariate GARCH Models

Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.

Maintained by Alexios Galanos. Last updated 3 months ago.

openblas cpp openmp

14 stars 8.11 score 294 scripts 1 dependents

rfastofficial

Rfast2:A Collection of Efficient and Extremely Fast R Functions II

A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions. References: Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.

Maintained by Manos Papadakis. Last updated 1 years ago.

openblas cpp openmp

38 stars 8.09 score 75 scripts 26 dependents

lbelzile

TruncatedNormal:Truncated Multivariate Normal and Student Distributions

A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.

Maintained by Leo Belzile. Last updated 29 days ago.

gaussian student-distributions truncated openblas cpp openmp

8 stars 8.08 score 116 scripts 18 dependents

mrc-ide

dust:Iterate Multiple Realisations of Stochastic Models

An Engine for simulation of stochastic models. Includes support for running stochastic models in parallel, either with shared or varying parameters. Simulations are run efficiently in compiled code and can be run with a fraction of simulated states returned to R, allowing control over memory usage. Support is provided for building bootstrap particle filter for performing Sequential Monte Carlo (e.g., Gordon et al. 1993 <doi:10.1049/ip-f-2.1993.0015>). The core of the simulation engine is the 'xoshiro256**' algorithm (Blackman and Vigna <arXiv:1805.01407>), and the package is further described in FitzJohn et al 2021 <doi:10.12688/wellcomeopenres.16466.2>.

Maintained by Rich FitzJohn. Last updated 8 days ago.

cpp openmp

18 stars 8.07 score 60 scripts 3 dependents

dschuhmacher

transport:Computation of Optimal Transport Plans and Wasserstein Distances

Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.

Maintained by Dominic Schuhmacher. Last updated 6 months ago.

cpp openmp

5 stars 8.07 score 414 scripts 22 dependents

xiaolei-lab

rMVP:Memory-Efficient, Visualize-Enhanced, Parallel-Accelerated GWAS Tool

A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can (1) effectively process large data, (2) rapidly evaluate population structure, (3) efficiently estimate variance components several algorithms, (4) implement parallel-accelerated association tests of markers three methods, (5) globally efficient design on GWAS process computing, (6) enhance visualization of related information. 'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).

Maintained by Xiaolei Liu. Last updated 2 months ago.

openblas cpp openmp

290 stars 8.06 score 38 scripts

acorg

Racmacs:Antigenic Cartography Macros

A toolkit for making antigenic maps from immunological assay data, in order to quantify and visualize antigenic differences between different pathogen strains as described in Smith et al. (2004) <doi:10.1126/science.1097211> and used in the World Health Organization influenza vaccine strain selection process. Additional functions allow for the diagnostic evaluation of antigenic maps and an interactive viewer is provided to explore antigenic relationships amongst several strains and incorporate the visualization of associated genetic information.

Maintained by Sam Wilks. Last updated 9 months ago.

openblas cpp openmp

21 stars 8.06 score 362 scripts

rsparapa

BART:Bayesian Additive Regression Trees

Bayesian Additive Regression Trees (BART) provide flexible nonparametric modeling of covariates for continuous, binary, categorical and time-to-event outcomes. For more information see Sparapani, Spanbauer and McCulloch <doi:10.18637/jss.v097.i01>.

Maintained by Rodney Sparapani. Last updated 9 months ago.

cpp openmp

14 stars 8.05 score 474 scripts 10 dependents

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

223 stars 8.00 score 282 scripts

kosukeimai

fastLink:Fast Probabilistic Record Linkage with Missing Data

Implements a Fellegi-Sunter probabilistic record linkage model that allows for missing data and the inclusion of auxiliary information. This includes functionalities to conduct a merge of two datasets under the Fellegi-Sunter model using the Expectation-Maximization algorithm. In addition, tools for preparing, adjusting, and summarizing data merges are included. The package implements methods described in Enamorado, Fifield, and Imai (2019) ''Using a Probabilistic Model to Assist Merging of Large-scale Administrative Records'' <doi:10.1017/S0003055418000783> and is available at <https://imai.fas.harvard.edu/research/linkage.html>.

Maintained by Ted Enamorado. Last updated 1 years ago.

cpp openmp

279 stars 7.98 score 95 scripts 1 dependents

yixuan

recosystem:Recommender System using Matrix Factorization

R wrapper of the 'libmf' library <https://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender system using matrix factorization. It is typically used to approximate an incomplete matrix using the product of two matrices in a latent space. Other common names for this task include "collaborative filtering", "matrix completion", "matrix recovery", etc. High performance multi-core parallel computing is supported in this package.

Maintained by Yixuan Qiu. Last updated 2 years ago.

matrix-factorization recommender-system cpp openmp

84 stars 7.97 score 101 scripts 6 dependents

ocbe-uio

BayesMallows:Bayesian Preference Learning with the Mallows Rank Model

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

Maintained by Oystein Sorensen. Last updated 2 months ago.

mallows-model openblas cpp openmp

21 stars 7.91 score 36 scripts 1 dependents

stocnet

goldfish:Statistical Network Models for Dynamic Network Data

Tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

Maintained by Alvaro Uzaheta. Last updated 7 months ago.

dynam network-modelling rem statistical-network-analysis openblas cpp openmp

61 stars 7.91 score 44 scripts

davidrusi

mombf:Model Selection with Bayesian Methods and Information Criteria

Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).

Maintained by David Rossell. Last updated 2 months ago.

openblas cpp openmp

7 stars 7.89 score 73 scripts 1 dependents

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 16 hours ago.

openblas cpp openmp

9 stars 7.86 score 42 scripts

jdtuck

fdasrvf:Elastic Functional Data Analysis

Performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework (Srivastava et al., 2011 <doi:10.48550/arXiv.1103.3817> and Tucker et al., 2014 <DOI:10.1016/j.csda.2012.12.001>). This framework allows for elastic analysis of functional data through phase and amplitude separation.

Maintained by J. Derek Tucker. Last updated 1 months ago.

openblas cpp openmp

13 stars 7.79 score 83 scripts 3 dependents

cristiancastiglione

sgdGMF:Estimation of Generalized Matrix Factorization Models via Stochastic Gradient Descent

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione, Segers, Clement, Risso (2024, <https://arxiv.org/abs/2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski, Hui, Warton, Hastie (2022, <http://jmlr.org/papers/v23/20-1104.html>).

Maintained by Cristian Castiglione. Last updated 23 days ago.

openblas cpp openmp

10 stars 7.75 score 108 scripts

eco-hydro

phenofit:Extract Remote Sensing Vegetation Phenology

The merits of 'TIMESAT' and 'phenopix' are adopted. Besides, a simple and growing season dividing method and a practical snow elimination method based on Whittaker were proposed. 7 curve fitting methods and 4 phenology extraction methods were provided. Parameters boundary are considered for every curve fitting methods according to their ecological meaning. And 'optimx' is used to select best optimization method for different curve fitting methods. Reference: Kong, D., (2020). R package: A state-of-the-art Vegetation Phenology extraction package, phenofit version 0.3.1, <doi:10.5281/zenodo.5150204>; Kong, D., Zhang, Y., Wang, D., Chen, J., & Gu, X. (2020). Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology. Journal of Geophysical Research: Biogeosciences, 125(8), e2020JG005636. <doi:10.1029/2020JG005636>; Kong, D., Zhang, Y., Gu, X., & Wang, D. (2019). A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 155, 13–24; Zhang, Q., Kong, D., Shi, P., Singh, V.P., Sun, P., 2018. Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982–2013). Agric. For. Meteorol. 248, 408–417. <doi:10.1016/j.agrformet.2017.10.026>.

Maintained by Dongdong Kong. Last updated 2 months ago.

phenology remote-sensing openblas cpp openmp

78 stars 7.71 score 332 scripts

jonclayden

RNiftyReg:Image Registration Using the 'NiftyReg' Library

Provides an 'R' interface to the 'NiftyReg' image registration tools <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration are supported, in two and three dimensions.

Maintained by Jon Clayden. Last updated 6 months ago.

image-registration medical-imaging transformations cpp openmp

43 stars 7.69 score 50 scripts 5 dependents

coatless-rpkg

RcppEnsmallen:Header-Only C++ Mathematical Optimization Library for 'Armadillo'

'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later.

Maintained by James Joseph Balamuta. Last updated 4 months ago.

armadillo cpp11 ensmallen optimization rcpp rcpparmadillo openblas cpp openmp

31 stars 7.67 score 1 scripts 14 dependents

nsaph-software

CausalGPS:Matching on Generalized Propensity Scores with Continuous Exposures

Provides a framework for estimating causal effects of a continuous exposure using observational data, and implementing matching and weighting on the generalized propensity score. Wu, X., Mealli, F., Kioumourtzoglou, M.A., Dominici, F. and Braun, D., 2022. Matching on generalized propensity scores with continuous exposures. Journal of the American Statistical Association, pp.1-29.

Maintained by Naeem Khoshnevis. Last updated 9 months ago.

cpp openmp

24 stars 7.67 score 39 scripts

bsvars

bsvars:Bayesian Estimation of Structural Vector Autoregressive Models

Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

Maintained by Tomasz Woźniak. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

46 stars 7.67 score 32 scripts 1 dependents

nlmixr2

lotri:A Simple Way to Specify Symmetric, Block Diagonal Matrices

Provides a simple mechanism to specify a symmetric block diagonal matrices (often used for covariance matrices). This is based on the domain specific language implemented in 'nlmixr2' but expanded to create matrices in R generally instead of specifying parts of matrices to estimate. It has expanded to include some matrix manipulation functions that are generally useful for 'rxode2' and 'nlmixr2'.

Maintained by Matthew L. Fidler. Last updated 6 months ago.

openblas cpp openmp

6 stars 7.67 score 18 scripts 14 dependents

mlampros

textTinyR:Text Processing for Small or Big Data Files

It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.

Maintained by Lampros Mouselimis. Last updated 1 years ago.

bh boost cpp11 processing rcpp rcpparmadillo text openblas cpp openmp

39 stars 7.64 score 244 scripts 1 dependents

bioc

ggsc:Visualizing Single Cell and Spatial Transcriptomics

Useful functions to visualize single cell and spatial data. It supports visualizing 'Seurat', 'SingleCellExperiment' and 'SpatialExperiment' objects through grammar of graphics syntax implemented in 'ggplot2'.

Maintained by Guangchuang Yu. Last updated 5 months ago.

dimensionreduction geneexpression singlecell software spatial transcriptomics visualization openblas cpp openmp

47 stars 7.59 score 18 scripts

bioc

scde:Single Cell Differential Expression

The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).

Maintained by Evan Biederstedt. Last updated 5 months ago.

immunooncology rnaseq statisticalmethod differentialexpression bayesian transcription software analysis bioinformatics heterogenity ngs single-cell transcriptomics openblas cpp openmp

173 stars 7.53 score 141 scripts

rezamoammadi

BDgraph:Bayesian Structure Learning in Graphical Models using Birth-Death MCMC

Advanced statistical tools for Bayesian structure learning in undirected graphical models, accommodating continuous, ordinal, discrete, count, and mixed data. It integrates recent advancements in Bayesian graphical models as presented in the literature, including the works of Mohammadi and Wit (2015) <doi:10.1214/14-BA889>, Mohammadi et al. (2021) <doi:10.1080/01621459.2021.1996377>, Dobra and Mohammadi (2018) <doi:10.1214/18-AOAS1164>, and Mohammadi et al. (2023) <doi:10.48550/arXiv.2307.00127>.

Maintained by Reza Mohammadi. Last updated 7 months ago.

openblas cpp openmp

8 stars 7.46 score 223 scripts 7 dependents

blasern

rdist:Calculate Pairwise Distances

A common framework for calculating distance matrices.

Maintained by Nello Blaser. Last updated 2 years ago.

openblas cpp openmp

17 stars 7.45 score 203 scripts 11 dependents

jonclayden

mmand:Mathematical Morphology in Any Number of Dimensions

Provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for finding connected components, resampling, filtering, smoothing and other image processing-style operations.

Maintained by Jon Clayden. Last updated 1 years ago.

image-processing morphology resampling cpp openmp

37 stars 7.42 score 223 scripts 9 dependents

jonathancornelissen

highfrequency:Tools for Highfrequency Data Analysis

Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>). The DOI in the CITATION is for a new Journal of Statistical Software publication that will be registered after publication on CRAN. A working paper version can be found on SSRN: <doi:10.2139/ssrn.3917548>.

Maintained by Kris Boudt. Last updated 2 years ago.

openblas cpp openmp

152 stars 7.37 score 286 scripts

tommyjones

tidylda:Latent Dirichlet Allocation Using 'tidyverse' Conventions

Implements an algorithm for Latent Dirichlet Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>, using style conventions from the 'tidyverse', Wickham et al. (2019)<doi:10.21105/joss.01686>, and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>. Fitting is done via collapsed Gibbs sampling. Also implements several novel features for LDA such as guided models and transfer learning.

Maintained by Tommy Jones. Last updated 2 months ago.

cpp openmp

41 stars 7.36 score 53 scripts

pneuvial

adjclust:Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

Maintained by Pierre Neuvial. Last updated 6 months ago.

clustering featureextraction gwas hi-c hierarchical-clustering linkage-disequilibrium cpp openmp

16 stars 7.35 score 13 scripts 2 dependents

david-cortes

outliertree:Explainable Outlier Detection Through Decision Tree Conditioning

Outlier detection method that flags suspicious values within observations, constrasting them against the normal values in a user-readable format, potentially describing conditions within the data that make a given outlier more rare. Full procedure is described in Cortes (2020) <doi:10.48550/arXiv.2001.00636>. Loosely based on the 'GritBot' <https://www.rulequest.com/gritbot-info.html> software.

Maintained by David Cortes. Last updated 3 months ago.

anomaly-detection outlier-detection cpp openmp

58 stars 7.34 score 21 scripts 2 dependents

gagolews

genieclust:Fast and Robust Hierarchical Clustering with Noise Points Detection

A retake on the Genie algorithm (Gagolewski, 2021 <DOI:10.1016/j.softx.2021.100722>), which is a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 <DOI:10.1016/j.ins.2016.05.003>). It is now faster and more memory efficient; determining the whole cluster hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only a minute or so. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (e.g., Gini and Bonferroni), external cluster validity measures (e.g., the normalised clustering accuracy, the adjusted Rand index, the Fowlkes-Mallows index, and normalised mutual information), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.

Maintained by Marek Gagolewski. Last updated 8 days ago.

cluster-analysis clustering clustering-algorithm data-analysis data-mining data-science genie hdbscan hierarchical-clustering hierarchical-clustering-algorithm machine-learning machine-learning-algorithms mlpack nmslib python python3 sparse cpp openmp

61 stars 7.33 score 13 scripts 5 dependents

kharchenkolab

conos:Clustering on Network of Samples

Wires together large collections of single-cell RNA-seq datasets, which allows for both the identification of recurrent cell clusters and the propagation of information between datasets in multi-sample or atlas-scale collections. 'Conos' focuses on the uniform mapping of homologous cell types across heterogeneous sample collections. For instance, users could investigate a collection of dozens of peripheral blood samples from cancer patients combined with dozens of controls, which perhaps includes samples of a related tissue such as lymph nodes. This package interacts with data available through the 'conosPanel' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/conos>. The size of the 'conosPanel' package is approximately 12 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

batch-correction scrna-seq single-cell-rna-seq openblas cpp openmp

205 stars 7.33 score 258 scripts

biodiverse

spOccupancy:Single-Species, Multi-Species, and Integrated Spatial Occupancy Models

Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.

Maintained by Jeffrey Doser. Last updated 1 months ago.

openblas cpp openmp

59 stars 7.31 score 204 scripts

ropensci

melt:Multiple Empirical Likelihood Tests

Performs multiple empirical likelihood tests. It offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' 'C++' library and 'RcppEigen' interface, with 'OpenMP' for parallel computation. Details of the testing procedures are provided in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. A companion paper by Kim, MacEachern, and Peruggia (2024) <doi:10.18637/jss.v108.i05> is available for further information. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Maintained by Eunseop Kim. Last updated 11 months ago.

cpp openmp

12 stars 7.24 score 84 scripts

alexanderlange53

svars:Data-Driven Identification of SVAR Models

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Maintained by Alexander Lange. Last updated 2 years ago.

openblas cpp openmp

46 stars 7.22 score 130 scripts

nicchr

cheapr:Simple Functions to Save Time and Memory

Fast and memory-efficient (or 'cheap') tools to facilitate efficient programming, saving time and memory. It aims to provide 'cheaper' alternatives to common base R functions, as well as some additional functions.

Maintained by Nick Christofides. Last updated 1 days ago.

cpp openmp

19 stars 7.21 score 73 scripts 2 dependents

kkholst

targeted:Targeted Inference

Various methods for targeted and semiparametric inference including augmented inverse probability weighted (AIPW) estimators for missing data and causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>), variable importance and conditional average treatment effects (CATE) (van der Laan (2006) <doi:10.2202/1557-4679.1008>), estimators for risk differences and relative risks (Richardson et al. (2017) <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).

Maintained by Klaus K. Holst. Last updated 2 months ago.

causal-inference double-robust estimation semiparametric-estimation statistics openblas cpp openmp

11 stars 7.20 score 30 scripts 1 dependents

jhorzek

lessSEM:Non-Smooth Regularization for Structural Equation Models

Provides regularized structural equation modeling (regularized SEM) with non-smooth penalty functions (e.g., lasso) building on 'lavaan'. The package is heavily inspired by the ['regsem'](<https://github.com/Rjacobucci/regsem>) and ['lslx'](<https://github.com/psyphh/lslx>) packages.

Maintained by Jannik H. Orzek. Last updated 1 years ago.

lasso psychometrics regularization regularized-structural-equation-model sem structural-equation-modeling openblas cpp openmp

7 stars 7.19 score 223 scripts

jalilian

ETAS:Modeling Earthquake Data Using 'ETAS' Model

Fits the space-time Epidemic Type Aftershock Sequence ('ETAS') model to earthquake catalogs using a stochastic 'declustering' approach. The 'ETAS' model is a 'spatio-temporal' marked point process model and a special case of the 'Hawkes' process. The package is based on a Fortran program by 'Jiancang Zhuang' (available at <http://bemlar.ism.ac.jp/zhuang/software.html>), which is modified and translated into C++ and C such that it can be called from R. Parallel computing with 'OpenMP' is possible on supported platforms.

Maintained by Abdollah Jalilian. Last updated 7 months ago.

cpp openmp

24 stars 7.18 score 21 scripts 1 dependents

ericgiunta

Colossus:"Risk Model Regression and Analysis with Complex Non-Linear Models"

Performs survival analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grant 80NSSC19M0161 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

Maintained by Eric Giunta. Last updated 7 days ago.

cpp openmp

1 stars 7.10 score 36 scripts

aalfons

robustHD:Robust Methods for High-Dimensional Data

Robust methods for high-dimensional data, in particular linear model selection techniques based on least angle regression and sparse regression. Specifically, the package implements robust least angle regression (Khan, Van Aelst & Zamar, 2007; <doi:10.1198/016214507000000950>), (robust) groupwise least angle regression (Alfons, Croux & Gelper, 2016; <doi:10.1016/j.csda.2015.02.007>), and sparse least trimmed squares regression (Alfons, Croux & Gelper, 2013; <doi:10.1214/12-AOAS575>).

Maintained by Andreas Alfons. Last updated 9 months ago.

openblas cpp openmp

10 stars 7.10 score 174 scripts 8 dependents

mlampros

elmNNRcpp:The Extreme Learning Machine Algorithm

Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

armadillo elm extreme-learning-machine rcpparmadillo openblas cpp openmp

14 stars 7.06 score 39 scripts 7 dependents

rwehrens

kohonen:Supervised and Unsupervised Self-Organising Maps

Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.

Maintained by Ron Wehrens. Last updated 2 years ago.

cpp openmp

9 stars 7.05 score 724 scripts 14 dependents

loelschlaeger

fHMM:Fitting Hidden Markov Models to Financial Data

Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.

Maintained by Lennart Oelschläger. Last updated 5 days ago.

finance hidden-markov-models cpp openmp

17 stars 7.04 score 5 scripts

doccstat

fastcpd:Fast Change Point Detection via Sequential Gradient Descent

Implements fast change point detection algorithm based on the paper "Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis" by Xianyang Zhang, Trisha Dawn <https://proceedings.mlr.press/v206/zhang23b.html>. The algorithm is based on dynamic programming with pruning and sequential gradient descent. It is able to detect change points a magnitude faster than the vanilla Pruned Exact Linear Time(PELT). The package includes examples of linear regression, logistic regression, Poisson regression, penalized linear regression data, and whole lot more examples with custom cost function in case the user wants to use their own cost function.

Maintained by Xingchi Li. Last updated 10 days ago.

change-point-detection cpp custom-function gradient-descent lasso linear-regression logistic-regression offline pelt penalized-regression poisson-regression quasi-newton statistics time-series warm-start fortran openblas cpp openmp

22 stars 7.00 score 7 scripts

drizopoulos

JMbayes:Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Maintained by Dimitris Rizopoulos. Last updated 4 years ago.

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

59 stars 6.97 score 80 scripts

insitro

AllelicSeries:Allelic Series Test

Implementation of gene-level rare variant association tests targeting allelic series: genes where increasingly deleterious mutations have increasingly large phenotypic effects. The COding-variant Allelic Series Test (COAST) operates on the benign missense variants (BMVs), deleterious missense variants (DMVs), and protein truncating variants (PTVs) within a gene. COAST uses a set of adjustable weights that tailor the test towards rejecting the null hypothesis for genes where the average magnitude of effect increases monotonically from BMVs to DMVs to PTVs. See McCaw ZR, O’Dushlaine C, Somineni H, Bereket M, Klein C, Karaletsos T, Casale FP, Koller D, Soare TW. (2023) "An allelic series rare variant association test for candidate gene discovery" <doi:10.1016/j.ajhg.2023.07.001>.

Maintained by Zachary McCaw. Last updated 2 months ago.

openblas cpp openmp

13 stars 6.97 score 8 scripts

james-thorson-noaa

dsem:Fit Dynamic Structural Equation Models

Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."

Maintained by James Thorson. Last updated 17 days ago.

cpp openmp

11 stars 6.90 score 24 scripts

lcrawlab

mvMAPIT:Multivariate Genome Wide Marginal Epistasis Test

Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this package, we present the 'multivariate MArginal ePIstasis Test' ('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact – thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search based methods. Our proposed 'mvMAPIT' builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized genome-wide association studies. Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>. Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.

Maintained by Julian Stamp. Last updated 5 months ago.

cpp epistasis epistasis-analysis gwas gwas-tools linear-mixed-models mapit mvmapit variance-components openblas cpp openmp

11 stars 6.90 score 17 scripts 1 dependents

furrer-lab

abn:Modelling Multivariate Data with Additive Bayesian Networks

The 'abn' R package facilitates Bayesian network analysis, a probabilistic graphical model that derives from empirical data a directed acyclic graph (DAG). This DAG describes the dependency structure between random variables. The R package 'abn' provides routines to help determine optimal Bayesian network models for a given data set. These models are used to identify statistical dependencies in messy, complex data. Their additive formulation is equivalent to multivariate generalised linear modelling, including mixed models with independent and identically distributed (iid) random effects. The core functionality of the 'abn' package revolves around model selection, also known as structure discovery. It supports both exact and heuristic structure learning algorithms and does not restrict the data distribution of parent-child combinations, providing flexibility in model creation and analysis. The 'abn' package uses Laplace approximations for metric estimation and includes wrappers to the 'INLA' package. It also employs 'JAGS' for data simulation purposes. For more resources and information, visit the 'abn' website.

Maintained by Matteo Delucchi. Last updated 17 days ago.

bayesian-network binomial categorical-data gaussian grouped-datasets mixed-effects multinomial multivariate poisson structure-learning gsl openblas cpp openmp jags

6 stars 6.88 score 90 scripts

mrc-ide

dust2:Next Generation dust

Experimental sources for the next generation of dust, which will properly adopt the particle filter, have support for partial parameter updates, support for multiple parameter sets and hopefully better GPU/MPI support.

Maintained by Rich FitzJohn. Last updated 21 hours ago.

cpp openmp

6.87 score 32 scripts 2 dependents

david-cortes

cmfrec:Collective Matrix Factorization for Recommender Systems

Collective matrix factorization (a.k.a. multi-view or multi-way factorization, Singh, Gordon, (2008) <doi:10.1145/1401890.1401969>) tries to approximate a (potentially very sparse or having many missing values) matrix 'X' as the product of two low-dimensional matrices, optionally aided with secondary information matrices about rows and/or columns of 'X', which are also factorized using the same latent components. The intended usage is for recommender systems, dimensionality reduction, and missing value imputation. Implements extensions of the original model (Cortes, (2018) <arXiv:1809.00366>) and can produce different factorizations such as the weighted 'implicit-feedback' model (Hu, Koren, Volinsky, (2008) <doi:10.1109/ICDM.2008.22>), the 'weighted-lambda-regularization' model, (Zhou, Wilkinson, Schreiber, Pan, (2008) <doi:10.1007/978-3-540-68880-8_32>), or the enhanced model with 'implicit features' (Rendle, Zhang, Koren, (2019) <arXiv:1905.01395>), with or without side information. Can use gradient-based procedures or alternating-least squares procedures (Koren, Bell, Volinsky, (2009) <doi:10.1109/MC.2009.263>), with either a Cholesky solver, a faster conjugate gradient solver (Takacs, Pilaszy, Tikk, (2011) <doi:10.1145/2043932.2043987>), or a non-negative coordinate descent solver (Franc, Hlavac, Navara, (2005) <doi:10.1007/11556121_50>), providing efficient methods for sparse and dense data, and mixtures thereof. Supports L1 and L2 regularization in the main models, offers alternative most-popular and content-based models, and implements functionality for cold-start recommendations and imputation of 2D data.

Maintained by David Cortes. Last updated 3 months ago.

cold-start collaborative-filtering collective-matrix-factorization openblas openmp

120 stars 6.84 score 23 scripts

nepem-ufsc

pliman:Tools for Plant Image Analysis

Tools for both single and batch image manipulation and analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>). The tools can be used for the quantification of leaf area, object counting, extraction of image indexes, shape measurement, object landmark identification, and Elliptical Fourier Analysis of object outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package also provides a comprehensive pipeline for generating shapefiles with complex layouts and supports high-throughput phenotyping of RGB, multispectral, and hyperspectral orthomosaics. This functionality facilitates field phenotyping using UAV- or satellite-based imagery.

Maintained by Tiago Olivoto. Last updated 16 hours ago.

openblas fftw3 cpp openmp

11 stars 6.76 score 476 scripts

r-lidar

lasR:Fast and Pipeable Airborne LiDAR Data Tools

Fast and pipeable airborne lidar processing tools. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, normalization, individual tree segmentation and other manipulations in a powerful and versatile processing chain.

Maintained by Jean-Romain Roussel. Last updated 1 months ago.

gdal cpp openmp

19 stars 6.75 score 26 scripts

s3alfisc

fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models

Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.

Maintained by Alexander Fischer. Last updated 2 years ago.

clustered-standard-errors linear-regression-models wild-bootstrap wild-cluster-bootstrap openblas cpp openmp

25 stars 6.69 score 109 scripts 2 dependents

mlizhangx

NAIR:Network Analysis of Immune Repertoire

Pipelines for studying the adaptive immune repertoire of T cells and B cells via network analysis based on receptor sequence similarity. Relate clinical outcomes to immune repertoires based on their network properties, or to particular clusters and clones within a repertoire. Yang et al. (2023) <doi:10.3389/fimmu.2023.1181825>.

Maintained by Brian Neal. Last updated 2 months ago.

cpp openmp

7 stars 6.66 score 27 scripts

e-nakama

RhpcBLASctl:Control the Number of Threads on 'BLAS'

Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL'). And possible to control the number of threads in 'OpenMP'. Get a number of logical cores and physical cores if feasible.

Maintained by Ei-ji Nakama. Last updated 2 years ago.

openmp

6.65 score 552 scripts 103 dependents

davidbolin

excursions:Excursion Sets and Contour Credibility Regions for Random Fields

Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.

Maintained by David Bolin. Last updated 10 days ago.

gsl cpp openmp

3 stars 6.64 score 40 scripts 1 dependents

bioc

bayNorm:Single-cell RNA sequencing data normalization

bayNorm is used for normalizing single-cell RNA-seq data.

Maintained by Wenhao Tang. Last updated 5 months ago.

immunooncology normalization rnaseq singlecell sequencing scrnaseq cpp openmp

9 stars 6.59 score 36 scripts

stscl

sdsfun:Spatial Data Science Complementary Features

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

Maintained by Wenbo Lv. Last updated 26 days ago.

geoinformatics spatial-data-analysis spatial-data-science spatial-statistics openblas cpp openmp

16 stars 6.58 score 6 scripts 8 dependents

agnesdeng

mixgb:Multiple Imputation Through 'XGBoost'

Multiple imputation using 'XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2023) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

Maintained by Yongshi Deng. Last updated 2 months ago.

cpp openmp

23 stars 6.58 score 82 scripts

mdsteiner

EFAtools:Fast and Flexible Implementations of Exploratory Factor Analysis Tools

Provides functions to perform exploratory factor analysis (EFA) procedures and compare their solutions. The goal is to provide state-of-the-art factor retention methods and a high degree of flexibility in the EFA procedures. This way, for example, implementations from R 'psych' and 'SPSS' can be compared. Moreover, functions for Schmid-Leiman transformation and the computation of omegas are provided. To speed up the analyses, some of the iterative procedures, like principal axis factoring (PAF), are implemented in C++.

Maintained by Markus Steiner. Last updated 3 months ago.

openblas cpp openmp

10 stars 6.58 score 83 scripts 1 dependents

craddm

eegUtils:Utilities for Electroencephalographic (EEG) Analysis

Electroencephalography data processing and visualization tools. Includes import functions for 'BioSemi' (.BDF), 'Neuroscan' (.CNT), 'Brain Vision Analyzer' (.VHDR), 'EEGLAB' (.set) and 'Fieldtrip' (.mat). Many preprocessing functions such as referencing, epoching, filtering, and ICA are available. There are a variety of visualizations possible, including timecourse and topographical plotting.

Maintained by Matt Craddock. Last updated 5 months ago.

eeg eeg-analysis eeg-data eeg-signals eeg-signals-processing openblas cpp openmp

106 stars 6.54 score 82 scripts

american-institutes-for-research

wCorr:Weighted Correlations

Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.

Maintained by Paul Bailey. Last updated 2 years ago.

openblas cpp openmp

6.54 score 118 scripts 8 dependents

jmgirard

circumplex:Analysis and Visualization of Circular Data

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

Maintained by Jeffrey Girard. Last updated 5 months ago.

circular circumplex data-analysis ggplot2 interpersonal psychology rcpparmadillo tidyverse openblas cpp openmp

11 stars 6.54 score 52 scripts

ausgis

geocomplexity:Mitigating Spatial Bias Through Geographical Complexity

The geographical complexity of individual variables can be characterized by the differences in local attribute variables, while the common geographical complexity of multiple variables can be represented by fluctuations in the similarity of vectors composed of multiple variables. In spatial regression tasks, the goodness of fit can be improved by incorporating a geographical complexity representation vector during modeling, using a geographical complexity-weighted spatial weight matrix, or employing local geographical complexity kernel density. Similarly, in spatial sampling tasks, samples can be selected more effectively by using a method that weights based on geographical complexity. By optimizing performance in spatial regression and spatial sampling tasks, the spatial bias of the model can be effectively reduced.

Maintained by Wenbo Lv. Last updated 5 months ago.

geographical-complexity geospatial-analysis spatial-regression spatial-relations spatial-sampling spatial-statistics openblas cpp openmp

19 stars 6.53 score 12 scripts

scmethods

scregclust:Reconstructing the Regulatory Programs of Target Genes in scRNA-Seq Data

Implementation of the scregclust algorithm described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3> which reconstructs regulatory programs of target genes in scRNA-seq data. Target genes are clustered into modules and each module is associated with a linear model describing the regulatory program.

Maintained by Felix Held. Last updated 2 months ago.

clustering regulatory-programs scrna-seq-analysis cpp openmp

10 stars 6.50 score 21 scripts

tvpham

iq:Protein Quantification in Mass Spectrometry-Based Proteomics

An implementation of the MaxLFQ algorithm by Cox et al. (2014) <doi:10.1074/mcp.M113.031591> in a comprehensive pipeline for processing proteomics data in data-independent acquisition mode (Pham et al. 2020 <doi:10.1093/bioinformatics/btz961>). It offers additional options for protein quantification using the N most intense fragment ions, using all fragment ions, and a wrapper for the median polish algorithm by Tukey (1977, ISBN:0201076160). In general, the tool can be used to integrate multiple proportional observations into a single quantitative value.

Maintained by Thang Pham. Last updated 27 days ago.

cpp openmp

27 stars 6.49 score 25 scripts

thongphamthe

PAFit:Generative Mechanism Estimation in Temporal Complex Networks

Statistical methods for estimating preferential attachment and node fitness generative mechanisms in temporal complex networks are provided. Thong Pham et al. (2015) <doi:10.1371/journal.pone.0137796>. Thong Pham et al. (2016) <doi:10.1038/srep32558>. Thong Pham et al. (2020) <doi:10.18637/jss.v092.i03>. Thong Pham et al. (2021) <doi:10.1093/comnet/cnab024>.

Maintained by Thong Pham. Last updated 1 years ago.

complex-networks fit-get-richer general-preferential-attachment minorize-maximization preferential-attachment rich-get-richer scale-free temporal-networks cpp openmp

17 stars 6.47 score 70 scripts

bachmannpatrick

CLVTools:Tools for Customer Lifetime Value Estimation

A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.

Maintained by Patrick Bachmann. Last updated 4 months ago.

clv customer-lifetime-value customer-relationship-management openblas gsl cpp openmp

55 stars 6.47 score 12 scripts

tilburgnetworkgroup

remify:Processing and Transforming Relational Event History Data

Efficiently processes relational event history data and transforms them into formats suitable for other packages. The primary objective of this package is to convert event history data into a format that integrates with the packages in 'remverse' and is compatible with various analytical tools (e.g., computing network statistics, estimating tie-oriented or actor-oriented social network models). Second, it can also transform the data into formats compatible with other packages out of 'remverse'. The package processes the data for two types of temporal social network models: tie-oriented modeling framework (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) and actor-oriented modeling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>).

Maintained by Giuseppe Arena. Last updated 2 months ago.

cpp openmp

4 stars 6.45 score 39 scripts 1 dependents

kaskr

RTMBp:'R' Bindings for 'TMB'

Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMBp' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.

Maintained by Kasper Kristensen. Last updated 1 months ago.

openblas cpp openmp

51 stars 6.44 score 1 scripts

helske

bssm:Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.

Maintained by Jouni Helske. Last updated 7 months ago.

bayesian-inference cpp markov-chain-monte-carlo particle-filter state-space time-series openblas cpp openmp

42 stars 6.43 score 11 scripts

bioc

SynExtend:Tools for Working With Synteny Objects

Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects.

Maintained by Nicholas Cooley. Last updated 15 days ago.

genetics clustering comparativegenomics dataimport fortran openmp

1 stars 6.42 score 77 scripts

ygeunkim

bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Maintained by Young Geun Kim. Last updated 28 days ago.

bayesian bayesian-econometrics bvar eigen forecasting har pybind11 python rcppeigen time-series vector-autoregression cpp openmp

6 stars 6.42 score 25 scripts

bioc

SpliceWiz:interactive analysis and visualization of alternative splicing in R

The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across conditions. SpliceWiz features a shiny-based GUI facilitating interactive data exploration of results including gene ontology enrichment. It is performance optimized with multi-threaded processing of BAM files and a new COV file format for fast recall of sequencing coverage. Overall, SpliceWiz streamlines AS analysis, enabling reliable identification of functionally relevant AS events for further characterization.

Maintained by Alex Chit Hei Wong. Last updated 16 days ago.

software transcriptomics rnaseq alternativesplicing coverage differentialsplicing differentialexpression gui sequencing cpp openmp

16 stars 6.41 score 8 scripts

pbreheny

plmmr:Penalized Linear Mixed Models for Correlated Data

Fits penalized linear mixed models that correct for unobserved confounding factors. 'plmmr' infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), 'plmmr' eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of 'PLINK' files are also supplied. For examples, see the package homepage. <https://pbreheny.github.io/plmmr/>.

Maintained by Patrick J. Breheny. Last updated 8 days ago.

cpp openmp

5 stars 6.41 score 10 scripts

lbb220

GWmodel:Geographically-Weighted Models

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.

Maintained by Binbin Lu. Last updated 7 months ago.

openblas cpp openmp

18 stars 6.38 score 266 scripts 4 dependents

saviviro

sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Maintained by Savi Virolainen. Last updated 28 days ago.

openblas cpp openmp

4 stars 6.36 score 41 scripts

ailich

GLCMTextures:GLCM Textures of Raster Layers

Calculates grey level co-occurrence matrix (GLCM) based texture measures (Hall-Beyer (2017) <https://prism.ucalgary.ca/bitstream/handle/1880/51900/texture%20tutorial%20v%203_0%20180206.pdf>; Haralick et al. (1973) <doi:10.1109/TSMC.1973.4309314>) of raster layers using a sliding rectangular window. It also includes functions to quantize a raster into grey levels as well as tabulate a glcm and calculate glcm texture metrics for a matrix.

Maintained by Alexander Ilich. Last updated 2 months ago.

openblas cpp openmp

12 stars 6.33 score 20 scripts 2 dependents

pachadotdev

economiccomplexity:Computational Methods for Economic Complexity

A wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. This package provides standard matrix and graph output that can be used seamlessly with other packages. See <doi:10.21105/joss.01866> for a summary of these methods and its evolution in literature.

Maintained by Mauricio Vargas Sepulveda. Last updated 3 months ago.

economic-complexity eigenvalues eigenvectors graphs international-trade matrix networks recursive-algorithm openblas cpp openmp

39 stars 6.32 score 18 scripts

spatialnous

alcyon:Spatial Network Analysis

Interface package for 'sala', the spatial network analysis library from the 'depthmapX' software application. The R parts of the code are based on the 'rdepthmap' package. Allows for the analysis of urban and building-scale networks and provides metrics and methods usually found within the Space Syntax domain. Methods in this package are described by K. Al-Sayed, A. Turner, B. Hillier, S. Iida and A. Penn (2014) "Space Syntax methodology", and also by A. Turner (2004) <https://discovery.ucl.ac.uk/id/eprint/2651> "Depthmap 4: a researcher's handbook".

Maintained by Petros Koutsolampros. Last updated 10 hours ago.

cpp openmp

2 stars 6.32 score 13 scripts

ibidat

nn2poly:Neural Network Weights Transformation into Polynomial Coefficients

Implements a method that builds the coefficients of a polynomial model that performs almost equivalently as a given neural network (densely connected). This is achieved using Taylor expansion at the activation functions. The obtained polynomial coefficients can be used to explain features (and their interactions) importance in the neural network, therefore working as a tool for interpretability or eXplainable Artificial Intelligence (XAI). See Morala et al. 2021 <doi:10.1016/j.neunet.2021.04.036>, and 2023 <doi:10.1109/TNNLS.2023.3330328>.

Maintained by Pablo Morala. Last updated 2 months ago.

cpp openmp

10 stars 6.32 score 23 scripts

hughparsonage

hutilscpp:Miscellaneous Functions in C++

Provides utility functions that are simply, frequently used, but may require higher performance that what can be obtained from base R. Incidentally provides support for 'reverse geocoding', such as matching a point with its nearest neighbour in another array. Used as a complement to package 'hutils' by sacrificing compilation or installation time for higher running speeds. The name is a portmanteau of the author and 'Rcpp'.

Maintained by Hugh Parsonage. Last updated 8 days ago.

openmp

10 stars 6.31 score 113 scripts 2 dependents

highlanderlab

SIMplyBee:'AlphaSimR' Extension for Simulating Honeybee Populations and Breeding Programmes

An extension of the 'AlphaSimR' package (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of individual bees that form a colony, which includes a queen, fathers (drones the queen mated with), virgin queens, workers, and drones. Multiple colony can be merged into a population of colonies, such as an apiary or a whole country of colonies. Functions enable operations on castes, colony, or colonies, to ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with respect to genomes and genetic and phenotype values is available and further extended for honeybees, including haplo-diploidy, complementary sex determiner locus, colony events (swarming, supersedure, etc.), and colony phenotype values.

Maintained by Jana Obšteter. Last updated 6 months ago.

cpp openmp

2 stars 6.24 score 18 scripts

miicteam

miic:Learning Causal or Non-Causal Graphical Models Using Information Theory

Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

Maintained by Franck Simon. Last updated 6 months ago.

cpp openmp

27 stars 6.22 score 69 scripts

bsvars

bsvarSIGNs:Bayesian SVARs with Sign, Zero, and Narrative Restrictions

Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

Maintained by Xiaolei Wang. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

13 stars 6.21 score 10 scripts

jacobseedorff21

BranchGLM:Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms

Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.

Maintained by Jacob Seedorff. Last updated 6 months ago.

generalized-linear-models regression statistics subset-selection variable-selection openblas cpp openmp

7 stars 6.20 score 30 scripts

gzt

MixMatrix:Classification with Matrix Variate Normal and t Distributions

Provides sampling and density functions for matrix variate normal, t, and inverted t distributions; ML estimation for matrix variate normal and t distributions using the EM algorithm, including some restrictions on the parameters; and classification by linear and quadratic discriminant analysis for matrix variate normal and t distributions described in Thompson et al. (2019) <doi:10.1080/10618600.2019.1696208>. Performs clustering with matrix variate normal and t mixture models.

Maintained by Geoffrey Thompson. Last updated 6 months ago.

openblas cpp openmp

3 stars 6.19 score 29 scripts 3 dependents

stscl

spEDM:Spatial Empirical Dynamic Modeling

Inferring causal associations in cross-sectional earth system data through empirical dynamic modeling (EDM), with extensions to convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023)<doi:10.1016/j.fmre.2023.01.007>.

Maintained by Wenbo Lv. Last updated 3 days ago.

causal-inference cpp empirical-dynamic-modeling geoinformatics geospatial-causality spatial-statistics openblas cpp openmp

17 stars 6.16 score 2 scripts

joeguinness

GpGp:Fast Gaussian Process Computation Using Vecchia's Approximation

Functions for fitting and doing predictions with Gaussian process models using Vecchia's (1988) approximation. Package also includes functions for reordering input locations, finding ordered nearest neighbors (with help from 'FNN' package), grouping operations, and conditional simulations. Covariance functions for spatial and spatial-temporal data on Euclidean domains and spheres are provided. The original approximation is due to Vecchia (1988) <http://www.jstor.org/stable/2345768>, and the reordering and grouping methods are from Guinness (2018) <doi:10.1080/00401706.2018.1437476>. Model fitting employs a Fisher scoring algorithm described in Guinness (2019) <doi:10.48550/arXiv.1905.08374>.

Maintained by Joseph Guinness. Last updated 5 months ago.

openblas cpp openmp

10 stars 6.16 score 160 scripts 6 dependents

biodiverse

spAbundance:Univariate and Multivariate Spatial Modeling of Species Abundance

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.

Maintained by Jeffrey Doser. Last updated 3 days ago.

openblas cpp openmp

17 stars 6.15 score 43 scripts 1 dependents

joliencremers

bpnreg:Bayesian Projected Normal Regression Models for Circular Data

Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.

Maintained by Jolien Cremers. Last updated 1 years ago.

openblas cpp openmp

14 stars 6.15 score 101 scripts

astamm

fdacluster:Joint Clustering and Alignment of Functional Data

Implementations of the k-means, hierarchical agglomerative and DBSCAN clustering methods for functional data which allows for jointly aligning and clustering curves. It supports functional data defined on one-dimensional domains but possibly evaluating in multivariate codomains. It supports functional data defined in arrays but also via the 'fd' and 'funData' classes for functional data defined in the 'fda' and 'funData' packages respectively. It currently supports shift, dilation and affine warping functions for functional data defined on the real line and uses the SRVF framework to handle boundary-preserving warping for functional data defined on a specific interval. Main reference for the k-means algorithm: Sangalli L.M., Secchi P., Vantini S., Vitelli V. (2010) "k-mean alignment for curve clustering" <doi:10.1016/j.csda.2009.12.008>. Main reference for the SRVF framework: Tucker, J. D., Wu, W., & Srivastava, A. (2013) "Generative models for functional data using phase and amplitude separation" <doi:10.1016/j.csda.2012.12.001>.

Maintained by Aymeric Stamm. Last updated 2 months ago.

openblas cpp openmp

5 stars 6.14 score 31 scripts 1 dependents

helske

Rlibeemd:Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.

Maintained by Jouni Helske. Last updated 2 years ago.

c decomposition eemd emd time-series gsl cpp openmp

39 stars 6.14 score 17 scripts 14 dependents

dylanb95

statespacer:State Space Modelling in 'R'

A tool that makes estimating models in state space form a breeze. See "Time Series Analysis by State Space Methods" by Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details about the algorithms implemented.

Maintained by Dylan Beijers. Last updated 2 years ago.

cpp dynamic-linear-model forecasting gaussian-models kalman-filter mathematical-modelling state-space statistical-inference statistical-models structural-analysis time-series openblas cpp openmp

15 stars 6.14 score 37 scripts

jwiley

brmsmargins:Bayesian Marginal Effects for 'brms' Models

Calculate Bayesian marginal effects, average marginal effects, and marginal coefficients (also called population averaged coefficients) for models fit using the 'brms' package including fixed effects, mixed effects, and location scale models. These are based on marginal predictions that integrate out random effects if necessary (see for example <doi:10.1186/s12874-015-0046-6> and <doi:10.1111/biom.12707>).

Maintained by Joshua F. Wiley. Last updated 2 months ago.

openblas cpp openmp

20 stars 6.14 score 42 scripts

biometris

statgenGWAS:Genome Wide Association Studies

Fast single trait Genome Wide Association Studies (GWAS) following the method described in Kang et al. (2010), <doi:10.1038/ng.548>. One of a series of statistical genetic packages for streamlining the analysis of typical plant breeding experiments developed by Biometris.

Maintained by Bart-Jan van Rossum. Last updated 4 months ago.

genetics gwas openblas cpp openmp

14 stars 6.14 score 15 scripts 3 dependents

sales-lab

parmigene:Parallel Mutual Information Estimation for Gene Network Reconstruction

Parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks (Sales et al, 2011 <doi:10.1093/bioinformatics/btr274>).

Maintained by Gabriele Sales. Last updated 5 months ago.

openmp

6 stars 6.14 score 38 scripts 4 dependents

mbant

BayesSUR:Bayesian Seemingly Unrelated Regression Models in High-Dimensional Settings

Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

Maintained by Zhi Zhao. Last updated 1 days ago.

openblas cpp openmp

8 stars 6.11 score 3 scripts

mkln

meshed:Bayesian Regression with Meshed Gaussian Processes

Fits Bayesian regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2024) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Maintained by Michele Peruzzi. Last updated 8 months ago.

bayesian mcmc multivariate regression spatial spatiotemporal openblas cpp openmp

13 stars 6.11 score 49 scripts

bioc

QUBIC:An R package for qualitative biclustering in support of gene co-expression analyses

The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape).

Maintained by Yu Zhang. Last updated 5 months ago.

statisticalmethod microarray differentialexpression multiplecomparison clustering visualization geneexpression network bioconductor-package bioconductor-packages cpp openmp

3 stars 6.10 score 14 scripts 1 dependents

jeremygelb

geocmeans:Implementing Methods for Spatial Fuzzy Unsupervised Classification

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed. The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Maintained by Jeremy Gelb. Last updated 4 months ago.

clustering cmeans fuzzy-classification-algorithms spatial-analysis spatial-fuzzy-cmeans unsupervised-learning cpp openmp

28 stars 6.10 score 90 scripts

sentometricsresearch

sentometrics:An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction

Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.

Maintained by Samuel Borms. Last updated 4 years ago.

nlp prediction sentiment-analysis text-mining time-series openblas cpp openmp

83 stars 6.09 score 49 scripts

kisungyou

ADMM:Algorithms using Alternating Direction Method of Multipliers

Provides algorithms to solve popular optimization problems in statistics such as regression or denoising based on Alternating Direction Method of Multipliers (ADMM). See Boyd et al (2010) <doi:10.1561/2200000016> for complete introduction to the method.

Maintained by Kisung You. Last updated 4 years ago.

openblas cpp openmp

6 stars 6.08 score 15 scripts 9 dependents

bioc

mgsa:Model-based gene set analysis

Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology.

Maintained by Sebastian Bauer. Last updated 5 months ago.

pathways go genesetenrichment openmp

5 stars 6.08 score 12 scripts

pachadotdev

capybara:Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815> and Gaure (2013) <doi:10.1016/j.csda.2013.03.024>.

Maintained by Mauricio Vargas Sepulveda. Last updated 3 days ago.

cpp11 econometrics linear-models openblas cpp openmp

13 stars 6.07 score

xiaooupan

conquer:Convolution-Type Smoothed Quantile Regression

Estimation and inference for conditional linear quantile regression models using a convolution smoothed approach. In the low-dimensional setting, efficient gradient-based methods are employed for fitting both a single model and a regression process over a quantile range. Normal-based and (multiplier) bootstrap confidence intervals for all slope coefficients are constructed. In high dimensions, the conquer method is complemented with flexible types of penalties (Lasso, elastic-net, group lasso, sparse group lasso, scad and mcp) to deal with complex low-dimensional structures.

Maintained by Xiaoou Pan. Last updated 2 years ago.

openblas cpp openmp

21 stars 6.06 score 17 scripts 5 dependents

dakep

pense:Penalized Elastic Net S/MM-Estimator of Regression

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The methods are proposed in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <https://projecteuclid.org/euclid.aoas/1574910036>. The package implements the extensions and algorithms described in Kepplinger, D. (2020) <doi:10.14288/1.0392915>.

Maintained by David Kepplinger. Last updated 8 months ago.

linear-regression pense regression robust-regresssion robust-statistics openblas cpp openmp

4 stars 6.06 score 48 scripts

jakobraymaekers

cellWise:Analyzing Data with Cellwise Outliers

Tools for detecting cellwise outliers and robust methods to analyze data which may contain them. Contains the implementation of the algorithms described in Rousseeuw and Van den Bossche (2018) <doi:10.1080/00401706.2017.1340909> (open access) Hubert et al. (2019) <doi:10.1080/00401706.2018.1562989> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1080/00401706.2019.1677270> (open access), Raymaekers and Rousseeuw (2021) <doi:10.1007/s10994-021-05960-5> (open access), Raymaekers and Rousseeuw (2021) <doi:10.52933/jdssv.v1i3.18> (open access), Raymaekers and Rousseeuw (2022) <arXiv:2207.13493> (open access) Rousseeuw (2022) <doi:10.1016/j.ecosta.2023.01.007> (open access). Examples can be found in the vignettes: "DDC_examples", "MacroPCA_examples", "wrap_examples", "transfo_examples", "DI_examples", "cellMCD_examples" , "Correspondence_analysis_examples", and "cellwise_weights_examples".

Maintained by Jakob Raymaekers. Last updated 1 years ago.

openblas cpp openmp

2 stars 6.06 score 54 scripts 16 dependents

hughparsonage

grattan:Australian Tax Policy Analysis

Utilities to cost and evaluate Australian tax policy, including fast projections of personal income tax collections, high-performance tax and transfer calculators, and an interface to common indices from the Australian Bureau of Statistics. Written to support Grattan Institute's Australian Perspectives program, and related projects. Access to the Australian Taxation Office's sample files of personal income tax returns is assumed.

Maintained by Hugh Parsonage. Last updated 1 years ago.

australian-analysts tax openmp

25 stars 6.04 score 124 scripts

ahaeusser

echos:Echo State Networks for Time Series Modeling and Forecasting

Provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Maintained by Alexander Häußer. Last updated 24 days ago.

echo-state-networks fable fabletools forecast forecasting recurrent-neural-networks reservoir-computing ridge-regression time-series openblas cpp openmp

12 stars 6.03 score 8 scripts

jaredhuling

oem:Orthogonalizing EM: Penalized Regression for Big Tall Data

Solves penalized least squares problems for big tall data using the orthogonalizing EM algorithm of Xiong et al. (2016) <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the functions cv.oem() and xval.oem() are for cross validation, the latter being an accelerated cross validation function for linear models. The big.oem() function allows for out of memory fitting. A description of the underlying methods and code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.

Maintained by Jared Huling. Last updated 8 months ago.

group-lasso lasso machine-learning mcp oem oem-algorithm penalized-regression scad variable-selection openblas cpp openmp

27 stars 6.02 score 26 scripts 1 dependents

thiloklein

matchingMarkets:Analysis of Stable Matchings

Implements structural estimators to correct for the sample selection bias from observed outcomes in matching markets. This includes one-sided matching of agents into groups as well as two-sided matching of students to schools. The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem, the college admissions problem, and the house allocation problem.

Maintained by Thilo Klein. Last updated 5 years ago.

openblas cpp openmp openjdk

40 stars 5.99 score 49 scripts

xiaolei-lab

simer:Data Simulation for Life Science and Breeding

Data simulator including genotype, phenotype, pedigree, selection and reproduction in R. It simulates most of reproduction process of animals or plants and provides data for GS (Genomic Selection), GWAS (Genome-Wide Association Study), and Breeding. For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>. For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.

Maintained by Xiaolei Liu. Last updated 1 days ago.

cpp openmp

36 stars 5.96 score 2 scripts

jameel-institute

daedalus:Model Health, Social, and Economic Costs of a Pandemic

Model the health, education, and economic costs of directly transmitted respiratory virus pandemics, under different scenarios of prior vaccine investment and reactive interventions, using the 'DAEDALUS' integrated health-economics model adapted from Haw et al. (2022) <doi.org/10.1038/s43588-022-00233-0>.

Maintained by Pratik Gupte. Last updated 1 hours ago.

decision-support epidemiological-models health-economics pandemic-preparedness public-health rcpp sdg-3 cpp openmp

4 stars 5.95 score 8 scripts

polkas

miceFast:Fast Imputations Using 'Rcpp' and 'Armadillo'

Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.

Maintained by Maciej Nasinski. Last updated 2 months ago.

cpp fast fast-imputations grouping imputation imputations matrix mro multiple-imputation rcpp rcpparmadillo vif weighting openblas cpp openmp

20 stars 5.94 score 29 scripts

comeetie

greed:Clustering and Model Selection with the Integrated Classification Likelihood

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Maintained by Etienne Côme. Last updated 2 years ago.

openblas cpp openmp

14 stars 5.94 score 41 scripts

bioc

normr:Normalization and difference calling in ChIP-seq data

Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions.

Maintained by Johannes Helmuth. Last updated 5 months ago.

bayesian differentialpeakcalling classification dataimport chipseq ripseq functionalgenomics genetics multiplecomparison normalization peakdetection preprocessing alignment cpp openmp

11 stars 5.93 score 13 scripts

caetanods

ratematrix:Bayesian Estimation of the Evolutionary Rate Matrix

The Evolutionary Rate Matrix is a variance-covariance matrix which describes both the rates of trait evolution and the evolutionary correlation among multiple traits. This package has functions to estimate these parameters using Bayesian MCMC. It is possible to test if the pattern of evolutionary correlations among traits has changed between predictive regimes painted along the branches of the phylogenetic tree. Regimes can be created a priori or estimated as part of the MCMC under a joint estimation approach. The package has functions to run MCMC chains, plot results, evaluate convergence, and summarize posterior distributions.

Maintained by Daniel Caetano. Last updated 2 years ago.

openblas cpp openmp

10 stars 5.91 score 18 scripts 1 dependents

l-ramirez-lopez

resemble:Memory-Based Learning in Spectral Chemometrics

Functions for dissimilarity analysis and memory-based learning (MBL, a.k.a local modeling) in complex spectral data sets. Most of these functions are based on the methods presented in Ramirez-Lopez et al. (2013) <doi:10.1016/j.geoderma.2012.12.014>.

Maintained by Leonardo Ramirez-Lopez. Last updated 2 years ago.

chemoinformatics chemometrics infrared-spectroscopy lazy-learning local-regression machine-learning memory-based-learning nir pedometrics soil-spectroscopy spectral-data spectral-library spectroscopy openblas cpp openmp

20 stars 5.91 score 27 scripts

cmjt

stelfi:Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder

Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).

Maintained by Charlotte M. Jones-Todd. Last updated 5 months ago.

cpp openmp

8 stars 5.90 score 4 scripts

cran

dotCall64:Enhanced Foreign Function Interface Supporting Long Vectors

Provides .C64(), which is an enhanced version of .C() and .Fortran() from the foreign function interface. .C64() supports long vectors, arguments of type 64-bit integer, and provides a mechanism to avoid unnecessary copies of read-only and write-only arguments. This makes it a convenient and fast interface to C/C++ and Fortran code.

Maintained by Reinhard Furrer. Last updated 6 months ago.

fortran openmp

5.90 score 439 dependents

hmjianggatech

SAM:Sparse Additive Modelling

Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.

Maintained by Haoming Jiang. Last updated 3 years ago.

cpp openmp

6 stars 5.86 score 20 scripts 4 dependents

jamesyang007

adelie:Group Lasso and Elastic Net Solver for Generalized Linear Models

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver, a wide range of matrix classes that can exploit special structure to allow large-scale inputs, and an assortment of generalized linear model classes for fitting various types of data. The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.

Maintained by Trevor Hastie. Last updated 28 days ago.

cpp openmp

6 stars 5.86 score 3 scripts

vdblab

FLORAL:Fit Log-Ratio Lasso Regression for Compositional Data

Log-ratio Lasso regression for continuous, binary, and survival outcomes with (longitudinal) compositional features. See Fei and others (2024) <doi:10.1016/j.crmeth.2024.100899>.

Maintained by Teng Fei. Last updated 1 months ago.

openblas cpp openmp

12 stars 5.85 score 13 scripts