R-universe search: gaussian

luca-scr

mclust:Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.

Maintained by Luca Scrucca. Last updated 11 months ago.

fortran openblas

58.5 match 21 stars 12.23 score 6.6k scripts 587 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 6 days ago.

openblas cpp openmp

56.7 match 16 stars 8.40 score 104 scripts 1 dependents

r-forge

GeneralizedHyperbolic:The Generalized Hyperbolic Distribution

Functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, normal inverse Gaussian distribution and generalized inverse Gaussian distribution, including fitting of these distributions to data. Linear models with hyperbolic errors may be fitted using hyperblmFit.

Maintained by David Scott. Last updated 3 months ago.

46.4 match 1 stars 8.39 score 124 scripts 27 dependents

vbaliga

gaussplotR:Fit, Predict and Plot 2D Gaussians

Functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either 'ggplot2' or base R plotting.

Maintained by Vikram B. Baliga. Last updated 4 years ago.

2d-gaussian gaussian gaussian-fit gaussian-interpolation gaussian-plot gaussian-volume plotting

69.2 match 4 stars 5.10 score 21 scripts

jeremyroos

gmgm:Gaussian Mixture Graphical Model Learning and Inference

Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.

Maintained by Jérémy Roos. Last updated 3 years ago.

bayesian-networks gaussian-mixture-models inference machine-learning probabilistic-graphical-models

88.2 match 5 stars 3.40 score 7 scripts

gmgeorg

LambertW:Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data

Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.

Maintained by Georg M. Goerg. Last updated 1 years ago.

gaussianize gaussianize-data heavy-tailed heavy-tailed-distributions leptokurtosis normal-distribution normalization skewed-data statistics cpp

27.8 match 10 stars 8.17 score 78 scripts 13 dependents

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 6 months ago.

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

29.8 match 42 stars 6.43 score 11 scripts

eahouseman

RPMM:Recursively Partitioned Mixture Model

Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures. This is a model-based clustering algorithm that returns a hierarchy of classes, similar to hierarchical clustering, but also similar to finite mixture models.

Maintained by E. Andres Houseman. Last updated 8 years ago.

43.7 match 4.34 score 78 scripts 7 dependents

epiforecasts

EpiNow2:Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Estimates the time-varying reproduction number, rate of spread, and doubling time using a range of open-source tools (Abbott et al. (2020) <doi:10.12688/wellcomeopenres.16006.1>), and current best practices (Gostic et al. (2020) <doi:10.1101/2020.06.18.20134858>). It aims to help users avoid some of the limitations of naive implementations in a framework that is informed by community feedback and is actively supported.

Maintained by Sebastian Funk. Last updated 24 days ago.

backcalculation covid-19 gaussian-processes open-source reproduction-number stan cpp

14.9 match 120 stars 11.88 score 210 scripts

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

16.1 match 10 stars 10.67 score 3.6k scripts 169 dependents

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 26 days ago.

openblas cpp openmp

21.7 match 11 stars 7.74 score 83 scripts 3 dependents

santagos

dad:Three-Way / Multigroup Data Analysis Through Densities

The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.

Maintained by Pierre Santagostini. Last updated 4 months ago.

31.2 match 5.33 score 92 scripts

gdancik

mlegp:Maximum Likelihood Estimates of Gaussian Processes

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

Maintained by Garrett M. Dancik. Last updated 3 years ago.

openblas

23.2 match 1 stars 6.80 score 75 scripts 21 dependents

trevorhastie

glmnet:Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see <doi:10.18637/jss.v033.i01> and <doi:10.18637/jss.v039.i05>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<doi:10.18637/jss.v106.i01>). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.

Maintained by Trevor Hastie. Last updated 2 years ago.

fortran cpp

10.3 match 82 stars 15.15 score 22k scripts 736 dependents

mastoffel

rptR:Repeatability Estimation for Gaussian and Non-Gaussian Data

Estimating repeatability (intra-class correlation) from Gaussian, binary, proportion and Poisson data.

Maintained by Martin Stoffel. Last updated 6 months ago.

16.9 match 17 stars 8.53 score 112 scripts 2 dependents

chrhennig

fpc:Flexible Procedures for Clustering

Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.

Maintained by Christian Hennig. Last updated 6 months ago.

15.1 match 11 stars 9.25 score 2.6k scripts 70 dependents

opengeos

whitebox:'WhiteboxTools' R Frontend

An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.

Maintained by Andrew Brown. Last updated 5 months ago.

geomorphometry geoprocessing geospatial gis hydrology remote-sensing rstudio

14.4 match 173 stars 9.65 score 203 scripts 2 dependents

davidbolin

rSPDE:Rational Approximations of Fractional Stochastic Partial Differential Equations

Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

Maintained by David Bolin. Last updated 8 days ago.

18.2 match 11 stars 7.57 score 188 scripts 3 dependents

vmoprojs

GeoModels:Procedures for Gaussian and Non Gaussian Geostatistical (Large) Data Analysis

Functions for Gaussian and Non Gaussian (bivariate) spatial and spatio-temporal data analysis are provided for a) (fast) simulation of random fields, b) inference for random fields using standard likelihood and a likelihood approximation method called weighted composite likelihood based on pairs and b) prediction using (local) best linear unbiased prediction. Weighted composite likelihood can be very efficient for estimating massive datasets. Both regression and spatial (temporal) dependence analysis can be jointly performed. Flexible covariance models for spatial and spatial-temporal data on Euclidean domains and spheres are provided. There are also many useful functions for plotting and performing diagnostic analysis. Different non Gaussian random fields can be considered in the analysis. Among them, random fields with marginal distributions such as Skew-Gaussian, Student-t, Tukey-h, Sin-Arcsin, Two-piece, Weibull, Gamma, Log-Gaussian, Binomial, Negative Binomial and Poisson. See the URL for the papers associated with this package, as for instance, Bevilacqua and Gaetan (2015) <doi:10.1007/s11222-014-9460-6>, Bevilacqua et al. (2016) <doi:10.1007/s13253-016-0256-3>, Vallejos et al. (2020) <doi:10.1007/978-3-030-56681-4>, Bevilacqua et. al (2020) <doi:10.1002/env.2632>, Bevilacqua et. al (2021) <doi:10.1111/sjos.12447>, Bevilacqua et al. (2022) <doi:10.1016/j.jmva.2022.104949>, Morales-Navarrete et al. (2023) <doi:10.1080/01621459.2022.2140053>, and a large class of examples and tutorials.

Maintained by Moreno Bevilacqua. Last updated 2 months ago.

fortran openblas glibc

31.8 match 3 stars 4.17 score 83 scripts

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

13.7 match 55 stars 9.64 score 102 scripts 1 dependents

huizezhang-sherry

ferrn:Facilitate Exploration of touRR optimisatioN

Diagnostic plots for optimisation, with a focus on projection pursuit. These show paths the optimiser takes in the high-dimensional space in multiple ways: by reducing the dimension using principal component analysis, and also using the tour to show the path on the high-dimensional space. Several botanical colour palettes are included, reflecting the name of the package. A paper describing the methodology can be found at <https://journal.r-project.org/archive/2021/RJ-2021-105/index.html>.

Maintained by H. Sherry Zhang. Last updated 9 days ago.

25.2 match 6 stars 5.16 score 20 scripts

mingdeyu

dgpsi:Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations

Interface to the 'python' package 'dgpsi' for Gaussian process, deep Gaussian process, and linked deep Gaussian process emulations of computer models and networks using stochastic imputation (SI). The implementations follow Ming & Guillas (2021) <doi:10.1137/20M1323771> and Ming, Williamson, & Guillas (2023) <doi:10.1080/00401706.2022.2124311> and Ming & Williamson (2023) <doi:10.48550/arXiv.2306.01212>. To get started with the package, see <https://mingdeyu.github.io/dgpsi-R/>.

Maintained by Deyu Ming. Last updated 29 days ago.

deep-gaussian-processes emulation gaussian-processes surrogate-models

21.6 match 5.99 score 76 scripts

r-forge

pcalg:Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Maintained by Markus Kalisch. Last updated 6 months ago.

openblas cpp

17.4 match 7.32 score 700 scripts 19 dependents

helske

KFAS:Kalman Filter and Smoother for Exponential Family State Space Models

State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.

Maintained by Jouni Helske. Last updated 6 months ago.

dynamic-linear-model exponential-family fortran gaussian-models state-space time-series openblas

11.5 match 64 stars 10.97 score 242 scripts 16 dependents

bioc

PrInCE:Predicting Interactomes from Co-Elution

PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE.

Maintained by Michael Skinnider. Last updated 5 months ago.

proteomics systemsbiology networkinference

19.5 match 8 stars 6.38 score 25 scripts

mthrun

AdaptGauss:Gaussian Mixture Models (GMM)

Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.

Maintained by Michael Thrun. Last updated 2 years ago.

cpp

20.2 match 1 stars 6.12 score 25 scripts 5 dependents

tsuchiya-lab

dsdp:Density Estimation with Semidefinite Programming

The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository <https://github.com/tsuchiya-lab/dsdp/>.

Maintained by Satoshi Kakihara. Last updated 2 years ago.

density-estimation semidefinite-programming fortran openblas

32.2 match 3.70 score 2 scripts

paulojus

geoR:Analysis of Geostatistical Data

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.

15.7 match 10 stars 7.57 score 1.8k scripts 12 dependents

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 29 days ago.

brain mri mrs mrshub spectroscopy fortran

13.8 match 24 stars 8.55 score 81 scripts

rstudio

keras3:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Maintained by Tomasz Kalinowski. Last updated 3 days ago.

8.7 match 845 stars 13.57 score 264 scripts 2 dependents

gjmvanboxtel

gsignal:Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Maintained by Geert van Boxtel. Last updated 2 months ago.

signal-processing signals cpp

11.6 match 24 stars 10.03 score 133 scripts 34 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 15 days ago.

gaussian student-distributions truncated openblas cpp openmp

13.9 match 8 stars 8.38 score 116 scripts 18 dependents

venelin

PCMBase:Simulation and Likelihood Calculation of Phylogenetic Comparative Models

Phylogenetic comparative methods represent models of continuous trait data associated with the tips of a phylogenetic tree. Examples of such models are Gaussian continuous time branching stochastic processes such as Brownian motion (BM) and Ornstein-Uhlenbeck (OU) processes, which regard the data at the tips of the tree as an observed (final) state of a Markov process starting from an initial state at the root and evolving along the branches of the tree. The PCMBase R package provides a general framework for manipulating such models. This framework consists of an application programming interface for specifying data and model parameters, and efficient algorithms for simulating trait evolution under a model and calculating the likelihood of model parameters for an assumed model and trait data. The package implements a growing collection of models, which currently includes BM, OU, BM/OU with jumps, two-speed OU as well as mixed Gaussian models, in which different types of the above models can be associated with different branches of the tree. The PCMBase package is limited to trait-simulation and likelihood calculation of (mixed) Gaussian phylogenetic models. The PCMFit package provides functionality for inference of these models to tree and trait data. The package web-site <https://venelin.github.io/PCMBase/> provides access to the documentation and other resources.

Maintained by Venelin Mitov. Last updated 10 months ago.

15.3 match 6 stars 7.56 score 85 scripts 3 dependents

nsaph-software

GPCERF:Gaussian Processes for Estimating Causal Exposure Response Curves

Provides a non-parametric Bayesian framework based on Gaussian process priors for estimating causal effects of a continuous exposure and detecting change points in the causal exposure response curves using observational data. Ren, B., Wu, X., Braun, D., Pillai, N., & Dominici, F.(2021). "Bayesian modeling for exposure response curve via gaussian processes: Causal effects of exposure to air pollution on health outcomes." arXiv preprint <doi:10.48550/arXiv.2105.03454>.

Maintained by Boyu Ren. Last updated 11 months ago.

cpp

18.1 match 9 stars 6.33 score 16 scripts

inlabru-org

inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Maintained by Finn Lindgren. Last updated 2 days ago.

8.8 match 96 stars 12.62 score 832 scripts 6 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 4 months ago.

gsl cpp openmp

17.0 match 3 stars 6.51 score 40 scripts 1 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

9.8 match 84 stars 11.04 score 640 scripts 24 dependents

keefe-murphy

MoEClust:Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.

Maintained by Keefe Murphy. Last updated 10 days ago.

gaussian-mixture-models mixture-of-experts model-based-clustering

16.4 match 7 stars 6.51 score 44 scripts 1 dependents

nicholasjclark

mvgam:Multivariate (Dynamic) Generalized Additive Models

Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.

Maintained by Nicholas J Clark. Last updated 7 hours ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

10.7 match 139 stars 9.85 score 117 scripts

gmcmacran

LRTesteR:Likelihood Ratio Tests and Confidence Intervals

A collection of hypothesis tests and confidence intervals based on the likelihood ratio <https://en.wikipedia.org/wiki/Likelihood-ratio_test>.

Maintained by Greg McMahan. Last updated 6 months ago.

17.8 match 5.83 score 168 scripts

rbgramacy

tgp:Bayesian Treed Gaussian Process Models

Bayesian nonstationary, semiparametric nonlinear regression and design by treed Gaussian processes (GPs) with jumps to the limiting linear model (LLM). Special cases also implemented include Bayesian linear models, CART, treed linear models, stationary separable and isotropic GPs, and GP single-index models. Provides 1-d and 2-d plotting functions (with projection and slice capabilities) and tree drawing, designed for visualization of tgp-class output. Sensitivity analysis and multi-resolution models are supported. Sequential experimental design and adaptive sampling functions are also provided, including ALM, ALC, and expected improvement. The latter supports derivative-free optimization of noisy black-box functions. For details and tutorials, see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) <doi:10.18637/jss.v033.i06>.

Maintained by Robert B. Gramacy. Last updated 6 months ago.

openblas cpp

14.0 match 9 stars 7.36 score 203 scripts 12 dependents

bioc

peakPantheR:Peak Picking and Annotation of High Resolution Experiments

An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained.

Maintained by Arnaud Wolfer. Last updated 5 months ago.

massspectrometry metabolomics peakdetection feature-detection mass-spectrometry

14.2 match 12 stars 6.82 score 23 scripts

mbinois

hetGP:Heteroskedastic Gaussian Process Modeling and Design under Replication

Performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

Maintained by Mickael Binois. Last updated 6 months ago.

cpp

19.7 match 5 stars 4.89 score 260 scripts 2 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 16 days ago.

openblas cpp openmp

7.7 match 147 stars 12.54 score 1.2k scripts 166 dependents

easystats

correlation:Methods for Correlation Analysis

Lightweight package for computing different kinds of correlations, such as partial correlations, Bayesian correlations, multilevel correlations, polychoric correlations, biweight correlations, distance correlations and more. Part of the 'easystats' ecosystem. References: Makowski et al. (2020) <doi:10.21105/joss.02306>.

Maintained by Brenton M. Wiernik. Last updated 11 days ago.

bayesian bayesian-correlations biserial cor correlation correlation-analysis correlations easystats gamma gaussian-graphical-models hacktoberfest matrix multilevel-correlations outliers partial partial-correlations regression robust spearman

6.7 match 439 stars 14.23 score 672 scripts 10 dependents

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 1 days ago.

bayesian-inference brms multilevel-models stan statistical-models

5.7 match 1.3k stars 16.61 score 13k scripts 34 dependents

gamlss-dev

gamlss.dist:Distributions for Generalized Additive Models for Location Scale and Shape

A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The distributions can be continuous, discrete or mixed distributions. Extra distributions can be created, by transforming, any continuous distribution defined on the real line, to a distribution defined on ranges 0 to infinity or 0 to 1, by using a 'log' or a 'logit' transformation respectively.

Maintained by Mikis Stasinopoulos. Last updated 20 days ago.

9.0 match 4 stars 10.50 score 346 scripts 71 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 4 days ago.

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

12.8 match 6 stars 6.94 score 90 scripts

aebilgrau

GMCM:Fast Estimation of Gaussian Mixture Copula Models

Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models.

Maintained by Anders Ellern Bilgrau. Last updated 3 years ago.

clustering gaussian-mixture-models meta-analysis rank unsupervised-cluster-analysis openblas cpp

19.1 match 15 stars 4.62 score 56 scripts

tianxia-jia

mcgf:Markov Chain Gaussian Fields Simulation and Parameter Estimation

Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

Maintained by Tianxia Jia. Last updated 9 months ago.

17.3 match 1 stars 4.82 score 11 scripts

stathin

ggm:Graphical Markov Models with Mixed Graphs

Provides functions for defining mixed graphs containing three types of edges, directed, undirected and bi-directed, with possibly multiple edges. These graphs are useful because they capture fundamental independence structures in multivariate distributions and in the induced distributions after marginalization and conditioning. The package is especially concerned with Gaussian graphical models for (i) ML estimation for directed acyclic graphs, undirected and bi-directed graphs and ancestral graph models (ii) testing several conditional independencies (iii) checking global identification of DAG Gaussian models with one latent variable (iv) testing Markov equivalences and generating Markov equivalent graphs of specific types.

Maintained by Giovanni M. Marchetti. Last updated 1 years ago.

11.6 match 7.07 score 295 scripts 29 dependents

mlr-org

mlr3mbo:Flexible Bayesian Optimization

A modern and flexible approach to Bayesian Optimization / Model Based Optimization building on the 'bbotk' package. 'mlr3mbo' is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using 'mlr3mbo' for hyperparameter optimization of machine learning models within the 'mlr3' ecosystem is straightforward via 'mlr3tuning'. Examples of ready-to-use optimization algorithms include Efficient Global Optimization by Jones et al. (1998) <doi:10.1023/A:1008306431147>, ParEGO by Knowles (2006) <doi:10.1109/TEVC.2005.851274> and SMS-EGO by Ponweiser et al. (2008) <doi:10.1007/978-3-540-87700-4_78>.

Maintained by Lennart Schneider. Last updated 11 days ago.

automl bayesian-optimization bbotk black-box-optimization gaussian-process hpo hyperparameter hyperparameter-optimization hyperparameter-tuning machine-learning mlr3 model-based-optimization optimization optimizer random-forest tuning

9.5 match 25 stars 8.57 score 120 scripts 3 dependents

jeffreyevans

spatialEco:Spatial Analysis and Modelling Utilities

Utilities to support spatial data manipulation, query, sampling and modelling in ecological applications. Functions include models for species population density, spatial smoothing, multivariate separability, point process model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and analysis, auto-logistic modeling, sampling models, cluster optimization, statistical exploratory tools and raster-based metrics.

Maintained by Jeffrey S. Evans. Last updated 12 days ago.

biodiversity conservation ecology r-spatial raster spatial vector

8.5 match 110 stars 9.55 score 736 scripts 2 dependents

arthurleroy

MagmaClustR:Clustering and Prediction using Multi-Task Gaussian Processes with Common Mean

An implementation for the multi-task Gaussian processes with common mean framework. Two main algorithms, called 'Magma' and 'MagmaClust', are available to perform predictions for supervised learning problems, in particular for time series or any functional/continuous data applications. The corresponding articles has been respectively proposed by Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2022) <doi:10.1007/s10994-022-06172-1>, and Arthur Leroy, Pierre Latouche, Benjamin Guedj and Servane Gey (2023) <https://jmlr.org/papers/v24/20-1321.html>. Theses approaches leverage the learning of cluster-specific mean processes, which are common across similar tasks, to provide enhanced prediction performances (even far from data) at a linear computational cost (in the number of tasks). 'MagmaClust' is a generalisation of 'Magma' where the tasks are simultaneously clustered into groups, each being associated to a specific mean process. User-oriented functions in the package are decomposed into training, prediction and plotting functions. Some basic features (classic kernels, training, prediction) of standard Gaussian processes are also implemented.

Maintained by Arthur Leroy. Last updated 3 months ago.

gaussian-processes multi-task-learning multi-task-prediction cpp

16.5 match 14 stars 4.80 score 15 scripts

jongheepark

MCMCpack:Markov Chain Monte Carlo (MCMC) Package

Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.

Maintained by Jong Hee Park. Last updated 7 months ago.

cpp

8.4 match 13 stars 9.40 score 2.6k scripts 150 dependents

rstudio

tfprobability:Interface to 'TensorFlow Probability'

Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

9.1 match 54 stars 8.63 score 221 scripts 3 dependents

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

12.1 match 10 stars 6.16 score 160 scripts 6 dependents

alexkz

kernlab:Kernel-Based Machine Learning Lab

Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

Maintained by Alexandros Karatzoglou. Last updated 7 months ago.

openblas cpp

5.9 match 21 stars 12.26 score 7.8k scripts 487 dependents

hwborchers

pracma:Practical Numerical Math Functions

Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting.

Maintained by Hans W. Borchers. Last updated 1 years ago.

5.8 match 29 stars 12.34 score 6.6k scripts 931 dependents

greta-dev

greta.gp:Gaussian Process Modelling in 'greta'

Provides a syntax to create and combine Gaussian process kernels in 'greta'. You can then them to define either full rank or sparse Gaussian processes. This is an extension to the 'greta' software, Golding (2019) <doi:10.21105/joss.01601>.

Maintained by Nicholas Tierney. Last updated 2 months ago.

11.1 match 19 stars 6.33 score 28 scripts

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

5.4 match 32 stars 12.71 score 17k scripts 7.8k dependents

archaeostat

ArchaeoChron:Bayesian Modeling of Archaeological Chronologies

Provides a list of functions for the Bayesian modeling of archaeological chronologies. The Bayesian models are implemented in 'JAGS' (Plummer 2003). The inputs are measurements with their associated standard deviations and the study period. The output is the MCMC sample of the posterior distribution of the event date with or without radiocarbon calibration.

Maintained by Anne Philippe. Last updated 1 years ago.

archaeology bayesian-statistics geochronology markov-chain radiocarbon-dates jags cpp

18.6 match 3 stars 3.65 score 15 scripts

lbelzile

mig:Multivariate Inverse Gaussian Distribution

Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.

Maintained by Leo Belzile. Last updated 16 days ago.

openblas cpp openmp

14.3 match 4.74 score 1 scripts

hotneim

lg:Locally Gaussian Distributions: Estimation and Methods

An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.

Maintained by Håkon Otneim. Last updated 5 years ago.

16.0 match 4 stars 4.18 score 25 scripts

jtimonen

lgpr:Longitudinal Gaussian Process Regression

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using 'Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

Maintained by Juho Timonen. Last updated 6 months ago.

bayesian-inference gaussian-processes longitudinal-data stan cpp

11.1 match 25 stars 5.94 score 69 scripts

cran

fBasics:Rmetrics - Markets and Basic Statistics

Provides a collection of functions to explore and to investigate basic properties of financial returns and related quantities. The covered fields include techniques of explorative data analysis and the investigation of distributional properties, including parameter estimation and hypothesis testing. Even more there are several utility functions for data handling and management.

Maintained by Georgi N. Boshnakov. Last updated 7 months ago.

9.2 match 2 stars 7.11 score 129 dependents

mlverse

torch:Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Maintained by Daniel Falbel. Last updated 5 days ago.

autograd deep-learning torch cpp

3.9 match 520 stars 16.52 score 1.4k scripts 38 dependents

gksmyth

statmod:Statistical Modeling

A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Also includes advanced generalized linear model functions including Tweedie and Digamma distributional families, secure convergence and exact distributional calculations for unit deviances.

Maintained by Gordon Smyth. Last updated 2 years ago.

fortran

6.6 match 1 stars 9.62 score 2.2k scripts 849 dependents

blasif

cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling

Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.

Maintained by Federico Blasi. Last updated 2 months ago.

covariance-matrix cpp estimation gaussian-processes large-dataset nonstationarity optimization prediction cpp

11.5 match 3 stars 5.48 score 1 scripts

santoroma

CircSpaceTime:Spatial and Spatio-Temporal Bayesian Model for Circular Data

Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions. We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.

Maintained by Mario Santoro. Last updated 6 years ago.

bayesian-statistics circular-statistics projected-gaussian projected-normal spatial-data-analysis spatio-temporal wrapped-gaussian wrapped-normal openblas cpp openmp

15.8 match 7 stars 3.98 score 27 scripts

rudjer

REBayes:Empirical Bayes Estimation and Inference

Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.

Maintained by Roger Koenker. Last updated 9 months ago.

16.0 match 3 stars 3.90 score 27 scripts 1 dependents

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

10.6 match 8 stars 5.90 score 4 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

10.3 match 14 stars 5.94 score 41 scripts

amalan-constat

fitODBOD:Modeling Over Dispersed Binomial Outcome Data Using BMD and ABD

Contains Probability Mass Functions, Cumulative Mass Functions, Negative Log Likelihood value, parameter estimation and modeling data using Binomial Mixture Distributions (BMD) (Manoj et al (2013) <doi:10.5539/ijsp.v2n2p24>) and Alternate Binomial Distributions (ABD) (Paul (1985) <doi:10.1080/03610928508828990>), also Journal article to use the package(<doi:10.21105/joss.01505>).

Maintained by Amalan Mahendran. Last updated 4 months ago.

binomial-outcome-data overdispersion

13.8 match 1 stars 4.44 score 139 scripts

kangjian2016

BayesGPfit:Fast Bayesian Gaussian Process Regression Fitting

Bayesian inferences on nonparametric regression via Gaussian Processes with a modified exponential square kernel using a basis expansion approach.

Maintained by Jian Kang. Last updated 3 years ago.

cpp

13.8 match 3 stars 4.40 score 56 scripts 1 dependents

msesia

knockoff:The Knockoff Filter for Controlled Variable Selection

The knockoff filter is a general procedure for controlling the false discovery rate (FDR) when performing variable selection. For more information, see the website below and the accompanying paper: Candes et al., "Panning for gold: model-X knockoffs for high-dimensional controlled variable selection", J. R. Statist. Soc. B (2018) 80, 3, pp. 551-577.

Maintained by Matteo Sesia. Last updated 3 years ago.

11.3 match 2 stars 5.35 score 248 scripts 5 dependents

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 7 months ago.

bayesian mcmc multivariate regression spatial spatiotemporal openblas cpp openmp

9.8 match 13 stars 6.11 score 49 scripts

dazzimonti

anMC:Compute High Dimensional Orthant Probabilities

Computationally efficient method to estimate orthant probabilities of high-dimensional Gaussian vectors. Further implements a function to compute conservative estimates of excursion sets under Gaussian random field priors.

Maintained by Dario Azzimonti. Last updated 2 years ago.

estimation gaussian orthant probability openblas cpp

15.4 match 3.88 score 6 scripts 5 dependents

giorgilancs

PrevMap:Geostatistical Modelling of Spatially Referenced Prevalence Data

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.

Maintained by Emanuele Giorgi. Last updated 2 years ago.

13.6 match 4.36 score 46 scripts

cran

gmGeostats:Geostatistics for Compositional Analysis

Support for geostatistical analysis of multivariate data, in particular data with restrictions, e.g. positive amounts, compositions, distributional data, microstructural data, etc. It includes descriptive analysis and modelling for such data, both from a two-point Gaussian perspective and multipoint perspective. The methods mainly follow Tolosana-Delgado, Mueller and van den Boogaart (2018) <doi:10.1007/s11004-018-9769-3>.

Maintained by K. Gerald van den Boogaart. Last updated 2 years ago.

openmp

19.7 match 1 stars 3.00 score

bioc

xcms:LC-MS and GC-MS Data Analysis

Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling.

Maintained by Steffen Neumann. Last updated 1 months ago.

immunooncology massspectrometry metabolomics bioconductor feature-detection mass-spectrometry peak-detection cpp

4.1 match 192 stars 14.32 score 984 scripts 11 dependents

shihao-yang

magi:MAnifold-Constrained Gaussian Process Inference

Provides fast and accurate inference for the parameter estimation problem in Ordinary Differential Equations, including the case when there are unobserved system components. Implements the MAGI method (MAnifold-constrained Gaussian process Inference) of Yang, Wong, and Kou (2021) <doi:10.1073/pnas.2020397118>. A user guide is provided by the accompanying software paper Wong, Yang, and Kou (2024) <doi:10.18637/jss.v109.i04>.

Maintained by Shihao Yang. Last updated 9 months ago.

openblas cpp

16.1 match 3.67 score 47 scripts

ropensci

dynamite:Bayesian Modeling and Causal Inference for Multivariate Longitudinal Data

Easy-to-use and efficient interface for Bayesian inference of complex panel (time series) data using dynamic multivariate panel models by Helske and Tikka (2024) <doi:10.1016/j.alcr.2024.100617>. The package supports joint modeling of multiple measurements per individual, time-varying and time-invariant effects, and a wide range of discrete and continuous distributions. Estimation of these dynamic multivariate panel models is carried out via 'Stan'. For an in-depth tutorial of the package, see (Tikka and Helske, 2024) <doi:10.48550/arXiv.2302.01607>.

Maintained by Santtu Tikka. Last updated 18 days ago.

bayesian-inference panel-data stan statistical-models

7.3 match 29 stars 7.92 score 20 scripts

mmaechler

longmemo:Statistics for Long-Memory Processes (Book Jan Beran), and Related Functionality

Datasets and Functionality from 'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall. Estimation of Hurst (and more) parameters for fractional Gaussian noise, 'fARIMA' and 'FEXP' models.

Maintained by Martin Maechler. Last updated 8 months ago.

11.2 match 2 stars 5.10 score 46 scripts 4 dependents

klauschn

ICtest:Estimating and Testing the Number of Interesting Components in Linear Dimension Reduction

For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.

Maintained by Klaus Nordhausen. Last updated 3 years ago.

openblas cpp

13.1 match 4.36 score 63 scripts 4 dependents

cfwp

rags2ridges:Ridge Estimation of Precision Matrices from High-Dimensional Data

Proper L2-penalized maximum likelihood estimators for precision matrices and supporting functions to employ these estimators in a graphical modeling setting. For details, see Peeters, Bilgrau, & van Wieringen (2022) <doi:10.18637/jss.v102.i04> and associated publications.

Maintained by Carel F.W. Peeters. Last updated 1 years ago.

c-plus-plus graphical-models machine-learning networkscience statistics openblas cpp

10.2 match 8 stars 5.60 score 46 scripts

cecileproust-lima

lcmm:Extended Mixed Models Using Latent Classes and Latent Processes

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

Maintained by Cecile Proust-Lima. Last updated 1 months ago.

fortran

4.9 match 62 stars 11.41 score 249 scripts 7 dependents

cran

YEAB:Analyze Data from Analysis of Behavior Experiments

Analyze data from behavioral experiments conducted using 'MED-PC' software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.

Maintained by Emmanuel Alcala. Last updated 1 months ago.

13.8 match 4.00 score

donaldrwilliams

GGMncv:Gaussian Graphical Models with Nonconvex Regularization

Estimate Gaussian graphical models with nonconvex penalties <doi:10.31234/osf.io/ad57p>, including the atan Wang and Zhu (2016) <doi:10.1155/2016/6495417>, seamless L0 Dicker, Huang, and Lin (2013) <doi:10.5705/ss.2011.074>, exponential Wang, Fan, and Zhu <doi:10.1007/s10463-016-0588-3>, smooth integration of counting and absolute deviation Lv and Fan (2009) <doi:10.1214/09-AOS683>, logarithm Mazumder, Friedman, and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>, Lq, smoothly clipped absolute deviation Fan and Li (2001) <doi:10.1198/016214501753382273>, and minimax concave penalty Zhang (2010) <doi:10.1214/09-AOS729>. There are also extensions for computing variable inclusion probabilities, multiple regression coefficients, and statistical inference <doi:10.1214/15-EJS1031>.

Maintained by Donald Williams. Last updated 3 years ago.

openblas cpp

8.8 match 5 stars 6.22 score 22 scripts 2 dependents

mlysy

SuperGauss:Superfast Likelihood Inference for Stationary Gaussian Time Series

Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations. This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300. Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo. The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.

Maintained by Martin Lysy. Last updated 1 months ago.

fftw3 cpp

9.8 match 2 stars 5.60 score 33 scripts 2 dependents

cran

gss:General Smoothing Splines

A comprehensive package for structural multivariate function estimation using smoothing splines.

Maintained by Chong Gu. Last updated 5 months ago.

fortran openblas

8.4 match 3 stars 6.40 score 137 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

8.7 match 15 stars 6.14 score 37 scripts

mitchelloharawild

distributional:Vectorised Probability Distributions

Vectorised distribution objects with tools for manipulating, visualising, and using probability distributions. Designed to allow model prediction outputs to return distributions rather than their parameters, allowing users to directly interact with predictive distributions in a data-oriented workflow. In addition to providing generic replacements for p/d/q/r functions, other useful statistics can be computed including means, variances, intervals, and highest density regions.

Maintained by Mitchell OHara-Wild. Last updated 2 months ago.

probability-distribution statistics vctrs

3.9 match 101 stars 13.50 score 744 scripts 384 dependents

drizopoulos

GLMMadaptive:Generalized Linear Mixed Models using Adaptive Gaussian Quadrature

Fits generalized linear mixed models for a single grouping factor under maximum likelihood approximating the integrals over the random effects with an adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) <doi:10.1080/10618600.1995.10474663>.

Maintained by Dimitris Rizopoulos. Last updated 5 days ago.

generalized-linear-mixed-models mixed-effects-models mixed-models

5.0 match 61 stars 10.37 score 212 scripts 5 dependents

thomasp85

ggfx:Pixel Filters for 'ggplot2' and 'grid'

Provides a range of filters that can be applied to layers from the 'ggplot2' package and its extensions, along with other graphic elements such as guides and theme elements. The filters are applied at render time and thus uses the exact pixel dimensions needed.

Maintained by Thomas Lin Pedersen. Last updated 3 years ago.

filters ggplot2 graphics

5.6 match 170 stars 9.10 score 452 scripts 3 dependents

mlr-org

mlr3extralearners:Extra Learners For mlr3

Extra learners for use in mlr3.

Maintained by Sebastian Fischer. Last updated 4 months ago.

machine-learning mlr3

5.5 match 94 stars 9.16 score 474 scripts

ludvigolsen

cvms:Cross-Validation for Model Selection

Cross-validate one or multiple regression and classification models and get relevant evaluation metrics in a tidy format. Validate the best model on a test set and compare it to a baseline evaluation. Alternatively, evaluate predictions from an external model. Currently supports regression and classification (binary and multiclass). Described in chp. 5 of Jeyaraman, B. P., Olsen, L. R., & Wambugu M. (2019, ISBN: 9781838550134).

Maintained by Ludvig Renbo Olsen. Last updated 8 days ago.

4.9 match 39 stars 10.31 score 492 scripts 5 dependents

ajmcneil

tscopula:Time Series Copula Models

Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Maintained by Alexander McNeil. Last updated 23 days ago.

9.0 match 2 stars 5.53 score 12 scripts

pbiecek

bgmm:Gaussian Mixture Modeling Algorithms and the Belief-Based Mixture Modeling

Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.

Maintained by Przemyslaw Biecek. Last updated 2 years ago.

11.8 match 2 stars 4.22 score 55 scripts 1 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 25 days ago.

libx11 fftw3 tiff cpp openmp

3.5 match 17 stars 13.62 score 2.4k scripts 45 dependents

boennecd

mdgc:Missing Data Imputation Using Gaussian Copulas

Provides functions to impute missing values using Gaussian copulas for mixed data types as described by Christoffersen et al. (2021) <arXiv:2102.02642>. The method is related to Hoff (2007) <doi:10.1214/07-AOAS107> and Zhao and Udell (2019) <arXiv:1910.12845> but differs by making a direct approximation of the log marginal likelihood using an extended version of the Fortran code created by Genz and Bretz (2002) <doi:10.1198/106186002394> in addition to also support multinomial variables.

Maintained by Benjamin Christoffersen. Last updated 2 years ago.

binary gaussian-copula imputation multinomial-variables ordinal semi-parametric fortran openblas cpp openmp

12.6 match 10 stars 3.78 score 12 scripts

anjapago

ocp:Bayesian Online Changepoint Detection

Implements the Bayesian online changepoint detection method by Adams and MacKay (2007) <arXiv:0710.3742> for univariate or multivariate data. Gaussian and Poisson probability models are implemented. Provides post-processing functions with alternative ways to extract changepoints.

Maintained by Andrea Pagotto. Last updated 6 years ago.

11.7 match 1 stars 4.06 score 23 scripts

djbetancourt-gh

funGp:Gaussian Process Models for Scalar and Functional Inputs

Construction and smart selection of Gaussian process models for analysis of computer experiments with emphasis on treatment of functional inputs that are regularly sampled. This package offers: (i) flexible modeling of functional-input regression problems through the fairly general Gaussian process model; (ii) built-in dimension reduction for functional inputs; (iii) heuristic optimization of the structural parameters of the model (e.g., active inputs, kernel function, type of distance). An in-depth tutorial in the use of funGp is provided in Betancourt et al. (2024) <doi:10.18637/jss.v109.i05> and Metamodeling background is provided in Betancourt et al. (2020) <doi:10.1016/j.ress.2020.106870>. The algorithm for structural parameter optimization is described in <https://hal.science/hal-02532713>.

Maintained by Jose Betancourt. Last updated 10 months ago.

12.5 match 4 stars 3.78 score 2 scripts

spatstat

spatstat.model:Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Maintained by Adrian Baddeley. Last updated 6 days ago.

analysis-of-variance cluster-process confidence-intervals cox-process determinantal-point-processes gibbs-process influence leverage model-diagnostics neyman-scott parameter-estimation poisson-process spatial-analysis spatial-modelling spatial-point-processes statistical-inference

5.2 match 5 stars 9.09 score 6 scripts 46 dependents

bnaras

cubature:Adaptive Multivariate Integration over Hypercubes

R wrappers around the cubature C library of Steven G. Johnson for adaptive multivariate integration over hypercubes and the Cuba C library of Thomas Hahn for deterministic and Monte Carlo integration. Scalar and vector interfaces for cubature and Cuba routines are provided; the vector interfaces are highly recommended as demonstrated in the package vignette.

Maintained by Balasubramanian Narasimhan. Last updated 8 months ago.

fortran cpp

4.2 match 12 stars 11.08 score 488 scripts 162 dependents

pbs-assess

sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

Maintained by Sean C. Anderson. Last updated 7 hours ago.

ecology glmm spatial-analysis species-distribution-modelling tmb cpp

4.3 match 203 stars 10.71 score 848 scripts 1 dependents

therneau

survival:Survival Analysis

Contains the core survival analysis routines, including definition of Surv objects, Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models, and parametric accelerated failure time models.

Maintained by Terry M Therneau. Last updated 3 months ago.

2.3 match 400 stars 20.43 score 29k scripts 3.9k 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 10 days ago.

cpp openmp

2.7 match 312 stars 16.77 score 3.7k scripts 24 dependents

cran

flexmix:Flexible Mixture Modeling

A general framework for finite mixtures of regression models using the EM algorithm is implemented. The E-step and all data handling are provided, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering.

Maintained by Bettina Gruen. Last updated 15 days ago.

5.5 match 5 stars 8.19 score 113 dependents

hojsgaard

gRim:Graphical Interaction Models

Provides the following types of models: Models for contingency tables (i.e. log-linear models) Graphical Gaussian models for multivariate normal data (i.e. covariance selection models) Mixed interaction models. Documentation about 'gRim' is provided by vignettes included in this package and the book by Højsgaard, Edwards and Lauritzen (2012, <doi:10.1007/978-1-4614-2299-0>); see 'citation("gRim")' for details.

Maintained by Søren Højsgaard. Last updated 5 months ago.

openblas cpp

7.8 match 2 stars 5.77 score 74 scripts

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

5.5 match 38 stars 8.09 score 75 scripts 26 dependents

sachaepskamp

qgraph:Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.

Maintained by Sacha Epskamp. Last updated 1 years ago.

cpp

3.9 match 69 stars 11.43 score 1.2k scripts 63 dependents

zeemkr

ncpen:Unified Algorithm for Non-convex Penalized Estimation for Generalized Linear Models

An efficient unified nonconvex penalized estimation algorithm for Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit, and Cox proportional hazard regression models. The unified algorithm is implemented based on the convex concave procedure and the algorithm can be applied to most of the existing nonconvex penalties. The algorithm also supports convex penalty: least absolute shrinkage and selection operator (LASSO). Supported nonconvex penalties include smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), truncated LASSO penalty (TLP), clipped LASSO (CLASSO), sparse ridge (SRIDGE), modified bridge (MBRIDGE) and modified log (MLOG). For high-dimensional data (data set with many variables), the algorithm selects relevant variables producing a parsimonious regression model. Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>, Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>, Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>. (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine Graziadio Business School and the National Research Foundation of Korea.)

Maintained by Dongshin Kim. Last updated 6 years ago.

binomial classo cox gaussian high-dimensional-data lasso linear mbridge mcp mlog multinomial nonconvex-penalties poisson scad sridge tlp openblas cpp

11.5 match 8 stars 3.88 score 19 scripts

robinhankin

cmvnorm:The Complex Multivariate Gaussian Distribution

Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.

Maintained by Robin K. S. Hankin. Last updated 4 months ago.

9.7 match 2 stars 4.60 score 7 scripts

gpfda

GPFDA:Gaussian Process for Functional Data Analysis

Functionalities for modelling functional data with multidimensional inputs, multivariate functional data, and non-separable and/or non-stationary covariance structure of function-valued processes. In addition, there are functionalities for functional regression models where the mean function depends on scalar and/or functional covariates and the covariance structure depends on functional covariates. The development version of the package can be found on <https://github.com/gpfda/GPFDA-dev>.

Maintained by Evandro Konzen. Last updated 2 years ago.

openblas cpp openmp

11.7 match 1 stars 3.81 score 36 scripts 1 dependents

span-18

spStack:Bayesian Geostatistics Using Predictive Stacking

Fits Bayesian hierarchical spatial process models for point-referenced Gaussian, Poisson, binomial, and binary data using stacking of predictive densities. It involves sampling from analytically available posterior distributions conditional upon some candidate values of the spatial process parameters and, subsequently assimilate inference from these individual posterior distributions using Bayesian predictive stacking. Our algorithm is highly parallelizable and hence, much faster than traditional Markov chain Monte Carlo algorithms while delivering competitive predictive performance. See Zhang, Tang, and Banerjee (2024) <doi:10.48550/arXiv.2304.12414>, and, Pan, Zhang, Bradley, and Banerjee (2024) <doi:10.48550/arXiv.2406.04655> for details.

Maintained by Soumyakanti Pan. Last updated 9 days ago.

openblas cpp

8.9 match 4.95 score 6 scripts

zrmacc

MGMM:Missingness Aware Gaussian Mixture Models

Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.

Maintained by Zachary McCaw. Last updated 9 months ago.

openblas cpp

8.6 match 4 stars 5.15 score 35 scripts

friendly

matlib:Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics

A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Many of these functions are designed for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D and for rendering matrix expressions and equations in LaTeX.

Maintained by Michael Friendly. Last updated 5 hours ago.

diagrams linear-equations matrix matrix-functions matrix-visualizer vector vignette

3.4 match 65 stars 12.89 score 900 scripts 11 dependents

javzapata

fgm:Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes

Estimates a functional graphical model and a partially separable KL decomposition for a multivariate Gaussian process.

Maintained by Javier Zapata. Last updated 4 years ago.

covariance-estimation functional-data-analysis gaussian-processes graphical-models karhunen-loeve neuroimaging-data neuroscience

12.7 match 4 stars 3.30 score 8 scripts

boost-r

mboost:Model-Based Boosting

Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.

Maintained by Torsten Hothorn. Last updated 4 months ago.

boosting-algorithms gam glm machine-learning mboost modelling r-language tutorials variable-selection openblas

3.3 match 72 stars 12.70 score 540 scripts 27 dependents

marcinjurek

GPvecchia:Scalable Gaussian-Process Approximations

Fast scalable Gaussian process approximations, particularly well suited to spatial (aerial, remote-sensed) and environmental data, described in more detail in Katzfuss and Guinness (2017) <arXiv:1708.06302>. Package also contains a fast implementation of the incomplete Cholesky decomposition (IC0), based on Schaefer et al. (2019) <arXiv:1706.02205> and MaxMin ordering proposed in Guinness (2018) <arXiv:1609.05372>.

Maintained by Marcin Jurek. Last updated 1 years ago.

openblas cpp openmp

9.8 match 4.26 score 61 scripts 2 dependents

spsanderson

TidyDensity:Functions for Tidy Analysis and Generation of Random Data

To make it easy to generate random numbers based upon the underlying stats distribution functions. All data is returned in a tidy and structured format making working with the data simple and straight forward. Given that the data is returned in a tidy 'tibble' it lends itself to working with the rest of the 'tidyverse'.

Maintained by Steven Sanderson. Last updated 5 months ago.

bootstrap density distributions ggplot2 probability r-language simulation statistics tibble tidy

5.3 match 34 stars 7.78 score 66 scripts 1 dependents

vigou3

actuar:Actuarial Functions and Heavy Tailed Distributions

Functions and data sets for actuarial science: modeling of loss distributions; risk theory and ruin theory; simulation of compound models, discrete mixtures and compound hierarchical models; credibility theory. Support for many additional probability distributions to model insurance loss size and frequency: 23 continuous heavy tailed distributions; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions. Support for phase-type distributions commonly used to compute ruin probabilities. Main reference: <doi:10.18637/jss.v025.i07>. Implementation of the Feller-Pareto family of distributions: <doi:10.18637/jss.v103.i06>.

Maintained by Vincent Goulet. Last updated 2 months ago.

openblas

4.4 match 12 stars 9.44 score 732 scripts 35 dependents

ecor

RMAWGEN:Multi-Site Auto-Regressive Weather GENerator

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

Maintained by Emanuele Cordano. Last updated 25 days ago.

7.3 match 3 stars 5.62 score 115 scripts 4 dependents

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

5.3 match 15 stars 7.68 score 59 scripts 4 dependents

cmusso86

recalibratiNN:Quantile Recalibration for Regression Models

Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.

Maintained by Carolina Musso. Last updated 2 months ago.

calibration gaussian-models neural-network probability recalibration regression-models

7.5 match 7 stars 5.39 score 8 scripts

stan-dev

projpred:Projection Predictive Feature Selection

Performs projection predictive feature selection for generalized linear models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>) with or without multilevel or additive terms (Catalina, Bürkner, and Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2023, <arXiv:2301.01660>), and for many other regression models (using the latent projection by Catalina, Bürkner, and Vehtari, 2021, <arXiv:2109.04702>, which can also be applied to most of the former models). The package is compatible with the 'rstanarm' and 'brms' packages, but other reference models can also be used. See the vignettes and the documentation for more information and examples.

Maintained by Frank Weber. Last updated 1 months ago.

bayes bayesian bayesian-inference rstanarm stan statistics variable-selection openblas cpp

4.0 match 112 stars 10.08 score 241 scripts

cran

mclustAddons:Addons for the 'mclust' Package

Extend the functionality of the 'mclust' package for Gaussian finite mixture modeling by including: density estimation for data with bounded support (Scrucca, 2019 <doi:10.1002/bimj.201800174>); modal clustering using MEM (Modal EM) algorithm for Gaussian mixtures (Scrucca, 2021 <doi:10.1002/sam.11527>); entropy estimation via Gaussian mixture modeling (Robin & Scrucca, 2023 <doi:10.1016/j.csda.2022.107582>); Gaussian mixtures modeling of financial log-returns (Scrucca, 2024 <doi:10.3390/e26110907>).

Maintained by Luca Scrucca. Last updated 4 months ago.

cpp

12.6 match 3.18 score 7 scripts

jarod-smithy

baygel:Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models

This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Maintained by Jarod Smith. Last updated 1 years ago.

openblas cpp openmp

14.8 match 2.70 score 2 scripts

cran

noisemodel:Noise Models for Classification Datasets

Implementation of models for the controlled introduction of errors in classification datasets. This package contains the noise models described in Saez (2022) <doi:10.3390/math10203736> that allow corrupting class labels, attributes and both simultaneously.

Maintained by José A. Sáez. Last updated 2 years ago.

19.9 match 2.00 score

dm13450

dirichletprocess:Build Dirichlet Process Objects for Bayesian Modelling

Perform nonparametric Bayesian analysis using Dirichlet processes without the need to program the inference algorithms. Utilise included pre-built models or specify custom models and allow the 'dirichletprocess' package to handle the Markov chain Monte Carlo sampling. Our Dirichlet process objects can act as building blocks for a variety of statistical models including and not limited to: density estimation, clustering and prior distributions in hierarchical models. See Teh, Y. W. (2011) <https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>, among many other sources.

Maintained by Dean Markwick. Last updated 2 years ago.

bayesian bayesian-inference bayesian-statistics dirichlet-process mcmc

5.3 match 58 stars 7.40 score 72 scripts 2 dependents

crisvarin

gcmr:Gaussian Copula Marginal Regression

Likelihood inference in Gaussian copula marginal regression models.

Maintained by Cristiano Varin. Last updated 3 years ago.

21.7 match 3 stars 1.82 score 22 scripts

functionaldata

fdapace:Functional Data Analysis and Empirical Dynamics

A versatile package that provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm. This core algorithm yields covariance and mean functions, eigenfunctions and principal component (scores), for both functional data and derivatives, for both dense (functional) and sparse (longitudinal) sampling designs. For sparse designs, it provides fitted continuous trajectories with confidence bands, even for subjects with very few longitudinal observations. PACE is a viable and flexible alternative to random effects modeling of longitudinal data. There is also a Matlab version (PACE) that contains some methods not available on fdapace and vice versa. Updates to fdapace were supported by grants from NIH Echo and NSF DMS-1712864 and DMS-2014626. Please cite our package if you use it (You may run the command citation("fdapace") to get the citation format and bibtex entry). References: Wang, J.L., Chiou, J., Müller, H.G. (2016) <doi:10.1146/annurev-statistics-041715-033624>; Chen, K., Zhang, X., Petersen, A., Müller, H.G. (2017) <doi:10.1007/s12561-015-9137-5>.

Maintained by Yidong Zhou. Last updated 9 months ago.

cpp

3.4 match 31 stars 11.46 score 474 scripts 25 dependents

danielmork

dlmtree:Bayesian Treed Distributed Lag Models

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to help interpret results.

Maintained by Daniel Mork. Last updated 30 days ago.

fortran cpp openmp

7.1 match 21 stars 5.40 score 17 scripts

grosssbm

blockmodels:Latent and Stochastic Block Model Estimation by a 'V-EM' Algorithm

Latent and Stochastic Block Model estimation by a Variational EM algorithm. Various probability distribution are provided (Bernoulli, Poisson...), with or without covariates.

Maintained by Jean-Benoist Leger. Last updated 9 days ago.

openblas cpp

8.5 match 4 stars 4.51 score 9 dependents

vpnsctl

mixpoissonreg:Mixed Poisson Regression for Overdispersed Count Data

Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.

Maintained by Alexandre B. Simas. Last updated 4 years ago.

count-data diagnostics influence-analysis local-influence negative-binomial-regression poisson-inverse-gaussian-regression

7.0 match 3 stars 5.44 score 23 scripts

cran

GGMselect:Gaussian Graphs Models Selection

Graph estimation in Gaussian Graphical Models, following the method developed by C. Giraud, S. Huet and N. Verzelen (2012) <doi:10.1515/1544-6115.1625>. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.

Maintained by Benjamin Auder. Last updated 4 months ago.

fortran openblas

12.4 match 1 stars 3.08 score 1 dependents

t-kalinowski

keras:R Interface to 'Keras'

Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.

Maintained by Tomasz Kalinowski. Last updated 11 months ago.

3.5 match 10.82 score 10k scripts 54 dependents

lbbe-software

fitdistrplus:Help to Fit of a Parametric Distribution to Non-Censored or Censored Data

Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.

Maintained by Aurélie Siberchicot. Last updated 11 days ago.

2.3 match 54 stars 16.15 score 4.5k scripts 153 dependents

waternumbers

anomalous:Anomaly Detection using the CAPA and PELT Algorithms

Implimentations of the univariate CAPA <doi:10.1002/sam.11586> and PELT <doi:10.1080/01621459.2012.737745> algotithms along with various cost functions for different distributions and models. The modular design, using R6 classes, favour ease of extension (for example user written cost functions) over the performance of other implimentations (e.g. <doi:10.32614/CRAN.package.changepoint>, <doi:10.32614/CRAN.package.anomaly>).

Maintained by Paul Smith. Last updated 3 months ago.

cpp

8.1 match 4.61 score 18 scripts

snoweye

EMCluster:EM Algorithm for Model-Based Clustering of Finite Mixture Gaussian Distribution

EM algorithms and several efficient initialization methods for model-based clustering of finite mixture Gaussian distribution with unstructured dispersion in both of unsupervised and semi-supervised learning.

Maintained by Wei-Chen Chen. Last updated 6 months ago.

openblas

5.0 match 18 stars 7.53 score 123 scripts 2 dependents

swarm-lab

CEC:Cross-Entropy Clustering

Splits data into Gaussian type clusters using the Cross-Entropy Clustering ('CEC') method. This method allows for the simultaneous use of various types of Gaussian mixture models, for performing the reduction of unnecessary clusters, and for discovering new clusters by splitting them. 'CEC' is based on the work of Spurek, P. and Tabor, J. (2014) <doi:10.1016/j.patcog.2014.03.006>.

Maintained by Simon Garnier. Last updated 5 months ago.

clustering cross-entropy openblas cpp

8.8 match 10 stars 4.26 score 18 scripts

donaldrwilliams

GGMnonreg:Non-Regularized Gaussian Graphical Models

Estimate non-regularized Gaussian graphical models, Ising models, and mixed graphical models. The current methods consist of multiple regression, a non-parametric bootstrap <doi:10.1080/00273171.2019.1575716>, and Fisher z transformed partial correlations <doi:10.1111/bmsp.12173>. Parameter uncertainty, predictability, and network replicability <doi:10.31234/osf.io/fb4sa> are also implemented.

Maintained by Donald Williams. Last updated 3 years ago.

10.7 match 6 stars 3.48 score 4 scripts

trn000

norMmix:Direct MLE for Multivariate Normal Mixture Distributions

Multivariate Normal (i.e. Gaussian) Mixture Models (S3) Classes. Fitting models to data using 'MLE' (maximum likelihood estimation) for multivariate normal mixtures via smart parametrization using the 'LDL' (Cholesky) decomposition, see McLachlan and Peel (2000, ISBN:9780471006268), Celeux and Govaert (1995) <doi:10.1016/0031-3203(94)00125-6>.

Maintained by Nicolas Trutmann. Last updated 6 months ago.

gaussian-mixture-models maximum-likelihood-estimation r-language

8.9 match 4.18 score 3 scripts

minhyung-kang

KSD:Goodness-of-Fit Tests using Kernelized Stein Discrepancy

An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <arXiv:1602.03253>.

Maintained by Min Hyung Kang. Last updated 4 years ago.

12.2 match 3.04 score 11 scripts

r-forge

nor1mix:Normal aka Gaussian 1-d Mixture Models

Onedimensional Normal (i.e. Gaussian) Mixture Models (S3) Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities. Efficient random number generation and graphics. Fitting to data by efficient ML (Maximum Likelihood) or traditional EM estimation.

Maintained by Martin Maechler. Last updated 3 months ago.

5.1 match 7.25 score 86 scripts 44 dependents

dsco036

HyperbolicDist:The Hyperbolic Distribution

Maintenance has been discontinued for this package. It has been superseded by 'GeneralizedHyperbolic'. 'GeneralizedHyperbolic' includes all the functionality of 'HyperbolicDist' and more and is based on a more rational design. 'HyperbolicDist' provides functions for the hyperbolic and related distributions. Density, distribution and quantile functions and random number generation are provided for the hyperbolic distribution, the generalized hyperbolic distribution, the generalized inverse Gaussian distribution and the skew-Laplace distribution. Additional functionality is provided for the hyperbolic distribution, including fitting of the hyperbolic to data.

Maintained by David Scott. Last updated 1 years ago.

12.9 match 2.85 score 79 scripts 3 dependents

lme4

lme4:Linear Mixed-Effects Models using 'Eigen' and S4

Fit linear and generalized linear mixed-effects models. The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Maintained by Ben Bolker. Last updated 1 days ago.

cpp

1.8 match 647 stars 20.69 score 35k scripts 1.5k dependents

mhahsler

stream:Infrastructure for Data Stream Mining

A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893, NSF CMMI 1728612, and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.

Maintained by Michael Hahsler. Last updated 3 days ago.

data-stream-clustering datastream stream-mining cpp

3.6 match 39 stars 10.05 score 132 scripts 3 dependents

richardgeveritt

ggsmc:Visualising Output from Sequential Monte Carlo and Ensemble-Based Methods

Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.

Maintained by Richard G Everitt. Last updated 2 months ago.

8.1 match 4.48 score 6 scripts

idsia

bayesRecon:Probabilistic Reconciliation via Conditioning

Provides methods for probabilistic reconciliation of hierarchical forecasts of time series. The available methods include analytical Gaussian reconciliation (Corani et al., 2021) <doi:10.1007/978-3-030-67664-3_13>, MCMC reconciliation of count time series (Corani et al., 2024) <doi:10.1016/j.ijforecast.2023.04.003>, Bottom-Up Importance Sampling (Zambon et al., 2024) <doi:10.1007/s11222-023-10343-y>, methods for the reconciliation of mixed hierarchies (Mix-Cond and TD-cond) (Zambon et al., 2024. The 40th Conference on Uncertainty in Artificial Intelligence, accepted).

Maintained by Dario Azzimonti. Last updated 2 months ago.

reconciliation timeseries

5.0 match 7 stars 7.13 score 40 scripts

r-forge

copula:Multivariate Dependence with Copulas

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

Maintained by Martin Maechler. Last updated 10 days ago.

3.0 match 11.83 score 1.2k scripts 86 dependents

cran

GeneNet:Modeling and Inferring Gene Networks

Analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

Maintained by Korbinian Strimmer. Last updated 3 years ago.

8.8 match 4.03 score 89 scripts 4 dependents

unuran

Runuran:R Interface to the 'UNU.RAN' Random Variate Generators

Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Maintained by Josef Leydold. Last updated 5 months ago.

5.2 match 6.87 score 180 scripts 8 dependents

nano-optics

planar:Multilayer Optics

Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.

Maintained by Baptiste Auguié. Last updated 3 years ago.

openblas cpp

6.0 match 7 stars 5.83 score 65 scripts

haziqj

iprior:Regression Modelling using I-Priors

Provides methods to perform and analyse I-prior regression models. Estimation is done either via direct optimisation of the log-likelihood or an EM algorithm.

Maintained by Haziq Jamil. Last updated 12 months ago.

fisher-information functional gaussian-processes gpr hilbert kernel krein longitudinal multilevel priors random-effects regression reproducing rkhs rkks space cpp

7.5 match 1 stars 4.69 score 33 scripts

cran

GPfit:Gaussian Processes Modeling

A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.

Maintained by Hugh Chipman. Last updated 6 years ago.

7.8 match 1 stars 4.53 score 44 dependents

keefe-murphy

IMIFA:Infinite Mixtures of Infinite Factor Analysers and Related Models

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

Maintained by Keefe Murphy. Last updated 1 years ago.

bayesian-nonparametrics dimension-reduction factor-analysis gaussian-mixture-model model-based-clustering

6.7 match 7 stars 5.25 score 51 scripts

swsoyee

r3dmol:Create Interactive 3D Visualizations of Molecular Data

Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.

Maintained by Wei Su. Last updated 1 years ago.

3d computational-biology computational-chemistry hacktoberfest htmlwidgets molecular-graphics molecular-modeling protein protein-structure visualization

5.5 match 90 stars 6.35 score 166 scripts 1 dependents

claudiofronterre

RiskMap:Geo-Statistical Modeling of Spatially Referenced Data

Provides functions for geo-statistical analysis of both continuous and count data using maximum likelihood methods. The models implemented in the package use stationary Gaussian processes with Matern correlation function to carry out spatial prediction in a geographical area of interest. The underpinning theory of the methods implemented in the package are found in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).

Maintained by Emanuele Giorgi. Last updated 6 months ago.

10.8 match 3.18 score 5 scripts

jingyuhe

bayeslm:Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors

Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <arXiv:1806.05738>.

Maintained by Jingyu He. Last updated 3 years ago.

openblas cpp

6.8 match 9 stars 5.03 score 24 scripts

pulongma

GPBayes:Tools for Gaussian Process Modeling in Uncertainty Quantification

Gaussian processes ('GPs') have been widely used to model spatial data, 'spatio'-temporal data, and computer experiments in diverse areas of statistics including spatial statistics, 'spatio'-temporal statistics, uncertainty quantification, and machine learning. This package creates basic tools for fitting and prediction based on 'GPs' with spatial data, 'spatio'-temporal data, and computer experiments. Key characteristics for this GP tool include: (1) the comprehensive implementation of various covariance functions including the 'Matérn' family and the Confluent 'Hypergeometric' family with isotropic form, tensor form, and automatic relevance determination form, where the isotropic form is widely used in spatial statistics, the tensor form is widely used in design and analysis of computer experiments and uncertainty quantification, and the automatic relevance determination form is widely used in machine learning; (2) implementations via Markov chain Monte Carlo ('MCMC') algorithms and optimization algorithms for GP models with all the implemented covariance functions. The methods for fitting and prediction are mainly implemented in a Bayesian framework; (3) model evaluation via Fisher information and predictive metrics such as predictive scores; (4) built-in functionality for simulating 'GPs' with all the implemented covariance functions; (5) unified implementation to allow easy specification of various 'GPs'.

Maintained by Pulong Ma. Last updated 11 months ago.

gsl cpp openmp

13.8 match 3 stars 2.48 score 3 scripts

saviviro

uGMAR:Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.

Maintained by Savi Virolainen. Last updated 2 months ago.

7.0 match 1 stars 4.88 score 51 scripts

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 15 days ago.

cpp openmp

5.8 match 6 stars 5.86 score 3 scripts

mblumuga

abc.data:Data Only: Tools for Approximate Bayesian Computation (ABC)

Contains data which are used by functions of the 'abc' package.

Maintained by Blum Michael. Last updated 12 months ago.

9.6 match 3.53 score 6 scripts 10 dependents

jhmadsen

DDoutlier:Distance & Density-Based Outlier Detection

Outlier detection in multidimensional domains. Implementation of notable distance and density-based outlier algorithms. Allows users to identify local outliers by comparing observations to their nearest neighbors, reverse nearest neighbors, shared neighbors or natural neighbors. For distance-based approaches, see Knorr, M., & Ng, R. T. (1997) <doi:10.1145/782010.782021>, Angiulli, F., & Pizzuti, C. (2002) <doi:10.1007/3-540-45681-3_2>, Hautamaki, V., & Ismo, K. (2004) <doi:10.1109/ICPR.2004.1334558> and Zhang, K., Hutter, M. & Jin, H. (2009) <doi:10.1007/978-3-642-01307-2_84>. For density-based approaches, see Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002) <doi:10.1007/3-540-47887-6_53>, Jin, W., Tung, A. K. H., Han, J., & Wang, W. (2006) <doi:10.1007/11731139_68>, Schubert, E., Zimek, A. & Kriegel, H-P. (2014) <doi:10.1137/1.9781611973440.63>, Latecki, L., Lazarevic, A. & Prokrajac, D. (2007) <doi:10.1007/978-3-540-73499-4_6>, Papadimitriou, S., Gibbons, P. B., & Faloutsos, C. (2003) <doi:10.1109/ICDE.2003.1260802>, Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) <doi:10.1145/342009.335388>, Kriegel, H.-P., Kröger, P., Schubert, E., & Zimek, A. (2009) <doi:10.1145/1645953.1646195>, Zhu, Q., Feng, Ji. & Huang, J. (2016) <doi:10.1016/j.patrec.2016.05.007>, Huang, J., Zhu, Q., Yang, L. & Feng, J. (2015) <doi:10.1016/j.knosys.2015.10.014>, Tang, B. & Haibo, He. (2017) <doi:10.1016/j.neucom.2017.02.039> and Gao, J., Hu, W., Zhang, X. & Wu, Ou. (2011) <doi:10.1007/978-3-642-20847-8_23>.

Maintained by Jacob H. Madsen. Last updated 6 years ago.

6.7 match 12 stars 5.00 score 56 scripts 1 dependents

luca-scr

ppgmmga:Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

Projection Pursuit (PP) algorithm for dimension reduction based on Gaussian Mixture Models (GMMs) for density estimation using Genetic Algorithms (GAs) to maximise an approximated negentropy index. For more details see Scrucca and Serafini (2019) <doi:10.1080/10618600.2019.1598871>.

Maintained by Luca Scrucca. Last updated 6 months ago.

openblas cpp

8.4 match 2 stars 4.00 score 8 scripts

cran

nlme:Linear and Nonlinear Mixed Effects Models

Fit and compare Gaussian linear and nonlinear mixed-effects models.

Maintained by R Core Team. Last updated 2 months ago.

fortran

2.6 match 6 stars 13.00 score 13k scripts 8.7k dependents

vegandevs

vegan:Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Maintained by Jari Oksanen. Last updated 15 days ago.

ecological-modelling ecology ordination fortran openblas

1.7 match 472 stars 19.41 score 15k scripts 440 dependents

jeremygelb

spNetwork:Spatial Analysis on Network

Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).

Maintained by Jeremy Gelb. Last updated 10 hours ago.

kernel kernel-density-estimation network network-analysis spatial spatial-analysis spatial-data-analysis cpp

4.3 match 38 stars 7.69 score 52 scripts

jiajingz

CopSens:Copula-Based Sensitivity Analysis for Observational Causal Inference

Implements the copula-based sensitivity analysis method, as discussed in Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding <arXiv:2102.09412>, with Gaussian copula adopted in particular.

Maintained by Jiajing Zheng. Last updated 2 years ago.

9.9 match 4 stars 3.30 score 7 scripts

andrewzm

FRK:Fixed Rank Kriging

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Maintained by Andrew Zammit-Mangion. Last updated 6 months ago.

cpp

3.7 match 71 stars 8.70 score 188 scripts 1 dependents

bastian-schaefer

DCSmooth:Nonparametric Regression and Bandwidth Selection for Spatial Models

Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.

Maintained by Bastian Schaefer. Last updated 3 years ago.

openblas cpp openmp

12.0 match 2.70 score 5 scripts

swihart

rmutil:Utilities for Nonlinear Regression and Repeated Measurements Models

A toolkit of functions for nonlinear regression and repeated measurements not to be used by itself but called by other Lindsey packages such as 'gnlm', 'stable', 'growth', 'repeated', and 'event' (available at <https://www.commanster.eu/rcode.html>).

Maintained by Bruce Swihart. Last updated 2 years ago.

fortran

3.9 match 1 stars 8.35 score 358 scripts 70 dependents

jkrijthe

RSSL:Implementations of Semi-Supervised Learning Approaches for Classification

A collection of implementations of semi-supervised classifiers and methods to evaluate their performance. The package includes implementations of, among others, Implicitly Constrained Learning, Moment Constrained Learning, the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic Likelihood estimation, S4VM and WellSVM.

Maintained by Jesse Krijthe. Last updated 1 years ago.

openblas cpp

5.3 match 58 stars 6.05 score 128 scripts 1 dependents

cran

EBEN:Empirical Bayesian Elastic Net

Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models. As a special case of the 'EBglmnet' package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.

Maintained by Anhui Huang. Last updated 5 months ago.

openblas

14.7 match 2.18 score 30 scripts

bioc

destiny:Creates diffusion maps

Create and plot diffusion maps.

Maintained by Philipp Angerer. Last updated 4 months ago.

cellbiology cellbasedassays clustering software visualization diffusion-maps dimensionality-reduction cpp

2.9 match 81 stars 10.94 score 792 scripts

hyu-ub

BayesNetBP:Bayesian Network Belief Propagation

Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.

Maintained by Han Yu. Last updated 2 years ago.

bayesian-networks conditional-gaussian network-inference probabilistic-graphical-models

8.0 match 19 stars 3.98 score 3 scripts

bioboot

bio3d:Biological Structure Analysis

Utilities to process, organize and explore protein structure, sequence and dynamics data. Features include the ability to read and write structure, sequence and dynamic trajectory data, perform sequence and structure database searches, data summaries, atom selection, alignment, superposition, rigid core identification, clustering, torsion analysis, distance matrix analysis, structure and sequence conservation analysis, normal mode analysis, principal component analysis of heterogeneous structure data, and correlation network analysis from normal mode and molecular dynamics data. In addition, various utility functions are provided to enable the statistical and graphical power of the R environment to work with biological sequence and structural data. Please refer to the URLs below for more information.

Maintained by Barry Grant. Last updated 5 months ago.

zlib cpp

3.8 match 5 stars 8.49 score 1.4k scripts 10 dependents

cran

Compositional:Compositional Data Analysis

Regression, classification, contour plots, hypothesis testing and fitting of distributions for compositional data are some of the functions included. We further include functions for percentages (or proportions). The standard textbook for such data is John Aitchison's (1986) "The statistical analysis of compositional data". Relevant papers include: a) Tsagris M.T., Preston S. and Wood A.T.A. (2011). "A data-based power transformation for compositional data". Fourth International International Workshop on Compositional Data Analysis. <doi:10.48550/arXiv.1106.1451> b) Tsagris M. (2014). "The k-NN algorithm for compositional data: a revised approach with and without zero values present". Journal of Data Science, 12(3): 519--534. <doi:10.6339/JDS.201407_12(3).0008>. c) Tsagris M. (2015). "A novel, divergence based, regression for compositional data". Proceedings of the 28th Panhellenic Statistics Conference, 15-18 April 2015, Athens, Greece, 430--444. <doi:10.48550/arXiv.1511.07600>. d) Tsagris M. (2015). "Regression analysis with compositional data containing zero values". Chilean Journal of Statistics, 6(2): 47--57. <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>. e) Tsagris M., Preston S. and Wood A.T.A. (2016). "Improved supervised classification for compositional data using the alpha-transformation". Journal of Classification, 33(2): 243--261. <doi:10.1007/s00357-016-9207-5>. f) Tsagris M., Preston S. and Wood A.T.A. (2017). "Nonparametric hypothesis testing for equality of means on the simplex". Journal of Statistical Computation and Simulation, 87(2): 406--422. <doi:10.1080/00949655.2016.1216554>. g) Tsagris M. and Stewart C. (2018). "A Dirichlet regression model for compositional data with zeros". Lobachevskii Journal of Mathematics, 39(3): 398--412. <doi:10.1134/S1995080218030198>. h) Alenazi A. (2019). "Regression for compositional data with compositional data as predictor variables with or without zero values". Journal of Data Science, 17(1): 219--238. <doi:10.6339/JDS.201901_17(1).0010>. i) Tsagris M. and Stewart C. (2020). "A folded model for compositional data analysis". Australian and New Zealand Journal of Statistics, 62(2): 249--277. <doi:10.1111/anzs.12289>. j) Alenazi A.A. (2022). "f-divergence regression models for compositional data". Pakistan Journal of Statistics and Operation Research, 18(4): 867--882. <doi:10.18187/pjsor.v18i4.3969>. k) Tsagris M. and Stewart C. (2022). "A Review of Flexible Transformations for Modeling Compositional Data". In Advances and Innovations in Statistics and Data Science, pp. 225--234. <doi:10.1007/978-3-031-08329-7_10>. l) Alenazi A. (2023). "A review of compositional data analysis and recent advances". Communications in Statistics--Theory and Methods, 52(16): 5535--5567. <doi:10.1080/03610926.2021.2014890>. m) Tsagris M., Alenazi A. and Stewart C. (2023). "Flexible non-parametric regression models for compositional response data with zeros". Statistics and Computing, 33(106). <doi:10.1007/s11222-023-10277-5>. n) Tsagris. M. (2025). "Constrained least squares simplicial-simplicial regression". Statistics and Computing, 35(27). <doi:10.1007/s11222-024-10560-z>. o) Sevinc V. and Tsagris. M. (2024). "Energy Based Equality of Distributions Testing for Compositional Data". <doi:10.48550/arXiv.2412.05199>.

Maintained by Michail Tsagris. Last updated 2 months ago.

8.7 match 3 stars 3.64 score 4 dependents

crj32

Spectrum:Fast Adaptive Spectral Clustering for Single and Multi-View Data

A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.

Maintained by Christopher R John. Last updated 5 years ago.

clustering spectral-clustering

5.3 match 7 stars 5.99 score 47 scripts 1 dependents

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 2 days ago.

fortran

1.8 match 210 stars 17.61 score 17k scripts 750 dependents

padpadpadpad

rTPC:Fitting and Analysing Thermal Performance Curves

Helps to fit thermal performance curves (TPCs). 'rTPC' contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

Maintained by Daniel Padfield. Last updated 23 days ago.

3.5 match 25 stars 9.07 score 267 scripts

pachadotdev

cpp11armadillo:An 'Armadillo' Interface

Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.48550/arXiv.2408.11074>.

Maintained by Mauricio Vargas Sepulveda. Last updated 24 days ago.

armadillo cpp cpp11 hacktoberfest linear-algebra

3.4 match 9 stars 9.14 score 1 scripts 16 dependents

kisungyou

T4transport:Tools for Computational Optimal Transport

Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.

Maintained by Kisung You. Last updated 2 years ago.

openblas cpp openmp

8.8 match 6 stars 3.48 score 5 scripts

neferkareii

shrinkGPR:Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors

Efficient variational inference methods for fully Bayesian Gaussian Process Regression (GPR) models with hierarchical shrinkage priors, including the triple gamma prior for effective variable selection and covariance shrinkage in high-dimensional settings. The package leverages normalizing flows to approximate complex posterior distributions. For details on implementation, see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.

Maintained by Peter Knaus. Last updated 1 months ago.

8.8 match 1 stars 3.48 score

kbroman

regress:Gaussian Linear Models with Linear Covariance Structure

Functions to fit Gaussian linear model by maximising the residual log likelihood where the covariance structure can be written as a linear combination of known matrices. Can be used for multivariate models and random effects models. Easy straight forward manner to specify random effects models, including random interactions. Code now optimised to use Sherman Morrison Woodbury identities for matrix inversion in random effects models. We've added the ability to fit models using any kernel as well as a function to return the mean and covariance of random effects conditional on the data (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.

Maintained by Karl W Broman. Last updated 2 years ago.

5.1 match 4 stars 5.94 score 146 scripts 1 dependents

bioc

Cardinal:A mass spectrometry imaging toolbox for statistical analysis

Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.

Maintained by Kylie Ariel Bemis. Last updated 3 months ago.

software infrastructure proteomics lipidomics massspectrometry imagingmassspectrometry immunooncology normalization clustering classification regression

3.0 match 47 stars 10.34 score 200 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

4.1 match 8 stars 7.45 score 223 scripts 7 dependents

aefdz

localFDA:Localization Processes for Functional Data Analysis

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elías, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

Maintained by Antonio Elías. Last updated 4 years ago.

classification functional-data-analysis imputation outliers-detection

11.2 match 2.70 score

r-forge

truncreg:Truncated Gaussian Regression Models

Estimation of models for truncated Gaussian variables by maximum likelihood.

Maintained by Yves Croissant. Last updated 7 years ago.

5.6 match 5.33 score 48 scripts 6 dependents

zxw834

BayesianPlatformDesignTimeTrend:Simulate and Analyse Bayesian Platform Trial with Time Trend

Simulating the sequential multi-arm multi-stage or platform trial with Bayesian approach using the 'rstan' package, which provides the R interface for the Stan. This package supports fixed ratio and Bayesian adaptive randomization approaches for randomization. Additionally, it allows for the study of time trend problems in platform trials. There are demos available for a multi-arm multi-stage trial with two different null scenarios, as well as for Bayesian trial cutoff screening. The Bayesian adaptive randomisation approaches are described in: Trippa et al. (2012) <doi:10.1200/JCO.2011.39.8420> and Wathen et al. (2017) <doi:10.1177/1740774517692302>. The randomisation algorithm is described in: Zhao W <doi:10.1016/j.cct.2015.06.008>. The analysis methods of time trend effect in platform trial are described in: Saville et al. (2022) <doi:10.1177/17407745221112013> and Bofill Roig et al. (2022) <doi:10.1186/s12874-022-01683-w>.

Maintained by Ziyan Wang. Last updated 1 years ago.

analysis bayesian-adaptive-randomisation clinial-trial group-sequential-designs multiarm-multistage-trials platform-trials simulation cpp

6.8 match 4.38 score 12 scripts

mayoverse

arsenal:An Arsenal of 'R' Functions for Large-Scale Statistical Summaries

An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models. The primary functions include tableby(), a Table-1-like summary of multiple variable types 'by' the levels of one or more categorical variables; paired(), a Table-1-like summary of multiple variable types paired across two time points; modelsum(), which performs simple model fits on one or more endpoints for many variables (univariate or adjusted for covariates); freqlist(), a powerful frequency table across many categorical variables; comparedf(), a function for comparing data.frames; and write2(), a function to output tables to a document.

Maintained by Ethan Heinzen. Last updated 7 months ago.

baseline-characteristics descriptive-statistics modeling paired-comparisons reporting statistics tableone

2.2 match 225 stars 13.45 score 1.2k scripts 16 dependents

ahb108

rcarbon:Calibration and Analysis of Radiocarbon Dates

Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).

Maintained by Enrico Crema. Last updated 6 months ago.

3.6 match 34 stars 8.14 score 274 scripts 2 dependents

lcbc-uio

galamm:Generalized Additive Latent and Mixed Models

Estimates generalized additive latent and mixed models using maximum marginal likelihood, as defined in Sorensen et al. (2023) <doi:10.1007/s11336-023-09910-z>, which is an extension of Rabe-Hesketh and Skrondal (2004)'s unifying framework for multilevel latent variable modeling <doi:10.1007/BF02295939>. Efficient computation is done using sparse matrix methods, Laplace approximation, and automatic differentiation. The framework includes generalized multilevel models with heteroscedastic residuals, mixed response types, factor loadings, smoothing splines, crossed random effects, and combinations thereof. Syntax for model formulation is close to 'lme4' (Bates et al. (2015) <doi:10.18637/jss.v067.i01>) and 'PLmixed' (Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>).

Maintained by Øystein Sørensen. Last updated 6 months ago.

generalized-additive-models hierarchical-models item-response-theory latent-variable-models structural-equation-models cpp

4.0 match 29 stars 7.33 score 41 scripts

bioc

GWAS.BAYES:Bayesian analysis of Gaussian GWAS data

This package is built to perform GWAS analysis using Bayesian techniques. Currently, GWAS.BAYES has functionality for the implementation of BICOSS (Williams, J., Ferreira, M. A., and Ji, T. (2022). BICOSS: Bayesian iterative conditional stochastic search for GWAS. BMC Bioinformatics), BGWAS (Williams, J., Xu, S., Ferreira, M. A.. (2023) "BGWAS: Bayesian variable selection in linear mixed models with nonlocal priors for genome-wide association studies." BMC Bioinformatics), and GINA. All methods currently are for the analysis of Gaussian phenotypes The research related to this package was supported in part by National Science Foundation awards DMS 1853549, DMS 1853556, and DMS 2054173.

Maintained by Jacob Williams. Last updated 5 months ago.

bayesian assaydomain snp genomewideassociation

7.5 match 3.90 score 8 scripts

kkholst

lava:Latent Variable Models

A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.

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

latent-variable-models simulation statistics structural-equation-models

2.3 match 33 stars 12.85 score 610 scripts 476 dependents

cran

crone:Structural Crystallography in 1d

Functions to carry out the most important crystallographic calculations for crystal structures made of 1d Gaussian-shaped atoms, especially useful for methods development. Main reference: E. Smith, G. Evans, J. Foadi (2017) <doi:10.1088/1361-6404/aa8188>.

Maintained by James Foadi. Last updated 6 years ago.

8.4 match 3.40 score

hanwengutierrez

TAR:Bayesian Modeling of Autoregressive Threshold Time Series Models

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.

Maintained by Hanwen Zhang. Last updated 8 years ago.

10.5 match 5 stars 2.74 score 11 scripts

jarioksa

GO:Gaussian Ordination and Community Simulation

Functions used to produce a manuscript on Unconstrained Gaussian Ordination.

Maintained by Jari Oksanen. Last updated 3 months ago.

8.5 match 3.37 score 117 scripts