R-universe search: sigma

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

16.2 match 81 stars 10.94 score 792 scripts

mu-sigma

HVT:Constructing Hierarchical Voronoi Tessellations and Overlay Heatmaps for Data Analysis

Facilitates building topology preserving maps for data analysis.

Maintained by "Mu Sigma, Inc.". Last updated 1 months ago.

23.4 match 4 stars 6.26 score 1 scripts

cefasrepres

EcoEnsemble:A General Framework for Combining Ecosystem Models

Fit and sample from the ensemble model described in Spence et al (2018): "A general framework for combining ecosystem models"<doi:10.1111/faf.12310>.

Maintained by Michael A. Spence. Last updated 1 months ago.

cpp

21.5 match 1 stars 6.07 score 19 scripts

paulnorthrop

revdbayes:Ratio-of-Uniforms Sampling for Bayesian Extreme Value Analysis

Provides functions for the Bayesian analysis of extreme value models. The 'rust' package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of 'revdbayes' is similar to the 'evdbayes' package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.

Maintained by Paul J. Northrop. Last updated 7 months ago.

analysis bayesian extreme extreme-value-statistics extremes generalized-pareto-distribution gev inference nhpp point-process posterior predictive rcpp value openblas cpp

16.9 match 4 stars 7.62 score 58 scripts 4 dependents

emilopezcano

SixSigma:Six Sigma Tools for Quality Control and Improvement

Functions and utilities to perform Statistical Analyses in the Six Sigma way. Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control), you can manage several Quality Management studies: Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis, etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2] and "Quality Control with R" [ISBN 978-3-319-24046-6], are also included in the package.

Maintained by Emilio L. Cano. Last updated 2 years ago.

quality-control quality-improvement six-sigma spc

12.9 match 15 stars 7.82 score 169 scripts 1 dependents

benjaminhlina

nichetools:Complementary Package to 'nicheROVER' and 'SIBER'

Provides functions complementary to packages 'nicheROVER' and 'SIBER' allowing the user to extract Bayesian estimates from data objects created by the packages 'nicheROVER' and 'SIBER'. Please see the following publications for detailed methods on 'nicheROVER' and 'SIBER' Hansen et al. (2015) <doi:10.1890/14-0235.1>, Jackson et al. (2011) <doi:10.1111/j.1365-2656.2011.01806.x>, and Layman et al. (2007) <doi:10.1890/0012-9658(2007)88[42:CSIRPF]2.0.CO;2>, respectfully.

Maintained by Benjamin L. Hlina. Last updated 3 days ago.

jags cpp

14.2 match 2 stars 6.39 score 17 scripts

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 2 days ago.

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

6.3 match 196 stars 14.31 score 984 scripts 11 dependents

calvagone

campsismod:Generic Implementation of a PK/PD Model

A generic, easy-to-use and expandable implementation of a pharmacokinetic (PK) / pharmacodynamic (PD) model based on the S4 class system. This package allows the user to read/write a pharmacometric model from/to files and adapt it further on the fly in the R environment. For this purpose, this package provides an intuitive API to add, modify or delete equations, ordinary differential equations (ODE's), model parameters or compartment properties (like infusion duration or rate, bioavailability and initial values). Finally, this package also provides a useful export of the model for use with simulation packages 'rxode2' and 'mrgsolve'. This package is designed and intended to be used with package 'campsis', a PK/PD simulation platform built on top of 'rxode2' and 'mrgsolve'.

Maintained by Nicolas Luyckx. Last updated 1 months ago.

12.6 match 5 stars 6.64 score 42 scripts 1 dependents

dnychka

fields:Tools for Spatial Data

For curve, surface and function fitting with an emphasis on splines, spatial data, geostatistics, and spatial statistics. The major methods include cubic, and thin plate splines, Kriging, and compactly supported covariance functions for large data sets. The splines and Kriging methods are supported by functions that can determine the smoothing parameter (nugget and sill variance) and other covariance function parameters by cross validation and also by restricted maximum likelihood. For Kriging there is an easy to use function that also estimates the correlation scale (range parameter). A major feature is that any covariance function implemented in R and following a simple format can be used for spatial prediction. There are also many useful functions for plotting and working with spatial data as images. This package also contains an implementation of sparse matrix methods for large spatial data sets and currently requires the sparse matrix (spam) package. Use help(fields) to get started and for an overview. The fields source code is deliberately commented and provides useful explanations of numerical details as a companion to the manual pages. The commented source code can be viewed by expanding the source code version and looking in the R subdirectory. The reference for fields can be generated by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development of this package was supported in part by the National Science Foundation Grant 1417857, the National Center for Atmospheric Research, and Colorado School of Mines. See the Fields URL for a vignette on using this package and some background on spatial statistics.

Maintained by Douglas Nychka. Last updated 9 months ago.

fortran

5.8 match 15 stars 12.60 score 7.7k scripts 295 dependents

moshagen

semPower:Power Analyses for SEM

Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM).

Maintained by Morten Moshagen. Last updated 1 months ago.

10.8 match 8 stars 6.30 score 18 scripts

aphalo

photobiologyPlants:Plant Photobiology Related Functions and Data

Provides functions for quantifying visible (VIS) and ultraviolet (UV) radiation in relation to the photoreceptors Phytochromes, Cryptochromes, and UVR8 which are present in plants. It also includes data sets on the optical properties of plants. Part of the 'r4photobiology' suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

Maintained by Pedro J. Aphalo. Last updated 2 months ago.

11.3 match 5.52 score 55 scripts

azure

azuremlsdk:Interface to the 'Azure Machine Learning' 'SDK'

Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

Maintained by Diondra Peck. Last updated 3 years ago.

amlcompute azure azure-machine-learning azureml dsi machine-learning rstudio sdk-r

6.6 match 106 stars 8.91 score 221 scripts

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 12 days ago.

cpp openmp

3.3 match 312 stars 16.77 score 3.7k scripts 24 dependents

arni-magnusson

scape:Statistical Catch-at-Age Plotting Environment

Import, plot, and diagnose results from statistical catch-at-age models, used in fisheries stock assessment.

Maintained by Arni Magnusson. Last updated 5 months ago.

9.6 match 5.72 score 35 scripts

jsilve24

fido:Bayesian Multinomial Logistic Normal Regression

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

Maintained by Justin Silverman. Last updated 18 days ago.

cpp openmp

6.6 match 20 stars 8.31 score 103 scripts

stan-dev

rstanarm:Bayesian Applied Regression Modeling via Stan

Estimates previously compiled regression models using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the customary R syntax with a formula and data.frame plus some additional arguments for priors.

Maintained by Ben Goodrich. Last updated 9 months ago.

bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics multilevel-models rstan rstanarm stan statistical-modeling cpp

3.3 match 393 stars 15.68 score 5.0k scripts 13 dependents

robinhankin

elliptic:Weierstrass and Jacobi Elliptic Functions

A suite of elliptic and related functions including Weierstrass and Jacobi forms. Also includes various tools for manipulating and visualizing complex functions.

Maintained by Robin K. S. Hankin. Last updated 12 days ago.

5.6 match 3 stars 9.31 score 54 scripts 79 dependents

pln-team

PLNmodels:Poisson Lognormal Models

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Maintained by Julien Chiquet. Last updated 4 days ago.

count-data multivariate-analysis network-inference pca poisson-lognormal-model openblas cpp

5.4 match 56 stars 9.50 score 226 scripts

novartis

RBesT:R Bayesian Evidence Synthesis Tools

Tool-set to support Bayesian evidence synthesis. This includes meta-analysis, (robust) prior derivation from historical data, operating characteristics and analysis (1 and 2 sample cases). Please refer to Weber et al. (2021) <doi:10.18637/jss.v100.i19> for details on applying this package while Neuenschwander et al. (2010) <doi:10.1177/1740774509356002> and Schmidli et al. (2014) <doi:10.1111/biom.12242> explain details on the methodology.

Maintained by Sebastian Weber. Last updated 2 months ago.

bayesian clinical historical-data meta-analysis cpp

6.3 match 22 stars 7.87 score 115 scripts 4 dependents

circulosmeos

bytescircle:Statistics About Bytes Contained in a File as a Circle Plot

Shows statistics about bytes contained in a file as a circle graph of deviations from mean in sigma increments. The function can be useful for statistically analyze the content of files in a glimpse: text files are shown as a green centered crown, compressed and encrypted files should be shown as equally distributed variations with a very low CV (sigma/mean), and other types of files can be classified between these two categories depending on their text vs binary content, which can be useful to quickly determine how information is stored inside them (databases, multimedia files, etc).

Maintained by Roberto S. Galende. Last updated 3 years ago.

byte-values bytes circle deviation plot sigma

11.8 match 3 stars 4.18 score 1 scripts

gsucarrat

gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.

Maintained by Genaro Sucarrat. Last updated 8 months ago.

6.8 match 8 stars 6.89 score 73 scripts 3 dependents

tsmodels

tsdistributions:Location Scale Standardized Distributions

Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.

Maintained by Alexios Galanos. Last updated 4 months ago.

distributions finance probability-distribution probability-distributions statistical-distributions timeseries cpp

7.0 match 4 stars 6.66 score 19 scripts 2 dependents

insongkim

concordance:Product Concordance

A set of utilities for matching products in different classification codes used in international trade research. It supports concordance between the Harmonized System (HS0, HS1, HS2, HS3, HS4, HS5, HS6, HS combined), the Standard International Trade Classification (SITC1, SITC2, SITC3, SITC4), the North American Industry Classification System (NAICS 2002, 2007, 2012, 2017, combined), as well as the Broad Economic Categories (BEC4), the International Standard of Industrial Classification (ISIC2, ISIC3, ISIC3.1, ISIC4), and the Standard Industrial Classification (SIC). It also provides code nomenclature/descriptions look-up, product code look-up based on user-specified keywords, Rauch classification look-up (via concordance to SITC2), trade elasticity look-up (via concordance to HS0 or SITC3 codes), upstreamness/downstreamness look-up (via concordance to ISIC3 and NAICS codes), and intermediateness look-up (via product descriptions).

Maintained by Steven Liao. Last updated 7 months ago.

7.2 match 52 stars 5.98 score 73 scripts

biodiverse

unmarked:Models for Data from Unmarked Animals

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

Maintained by Ken Kellner. Last updated 1 days ago.

openblas cpp openmp

3.3 match 4 stars 13.03 score 652 scripts 12 dependents

federico-m-stefanini

convergEU:Monitoring Convergence of EU Countries

Indicators and measures by country and time describe what happens at economic and social levels. This package provides functions to calculate several measures of convergence after imputing missing values. The automated downloading of Eurostat data, followed by the production of country fiches and indicator fiches, makes possible to produce automated reports. The Eurofound report (<doi:10.2806/68012>) "Upward convergence in the EU: Concepts, measurements and indicators", 2018, is a detailed presentation of convergence.

Maintained by Federico M. Stefanini. Last updated 2 years ago.

8.6 match 1 stars 4.95 score 89 scripts

metrumresearchgroup

mrgsolve:Simulate from ODE-Based Models

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

Maintained by Kyle T Baron. Last updated 1 months ago.

mrgsolve ode openblas cpp

3.8 match 138 stars 10.90 score 1.2k scripts 3 dependents

pecanproject

PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling

Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.

Maintained by Alexey Shiklomanov. Last updated 2 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants fortran jags cpp

4.3 match 216 stars 9.68 score 132 scripts

klausherrmann

multIntTestFunc:Provides Test Functions for Multivariate Integration

Provides implementations of functions that can be used to test multivariate integration routines. The package covers six different integration domains (unit hypercube, unit ball, unit sphere, standard simplex, non-negative real numbers and R^n). For each domain several functions with different properties (smooth, non-differentiable, ...) are available. The functions are available in all dimensions n >= 1. For each function the exact value of the integral is known and implemented to allow testing the accuracy of multivariate integration routines. Details on the available test functions can be found at on the development website.

Maintained by Klaus Herrmann. Last updated 6 months ago.

9.5 match 4.30 score 1 scripts

alexiosg

rugarch:Univariate GARCH Models

ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Maintained by Alexios Galanos. Last updated 3 months ago.

cpp

3.3 match 26 stars 12.13 score 1.3k scripts 15 dependents

edvanburen

twosigma:DE Analysis for Single-Cell RNA-Sequencing Data

Implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.

Maintained by Eric Van Buren. Last updated 2 years ago.

10.5 match 8 stars 3.60 score 3 scripts

cran

gaston:Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models

Manipulation of genetic data (SNPs). Computation of GRM and dominance matrix, LD, heritability with efficient algorithms for linear mixed model (AIREML). Dandine et al <doi:10.1159/000488519>.

Maintained by Hervé Perdry. Last updated 1 years ago.

zlib cpp

6.3 match 5 stars 6.02 score 12 dependents

thomaschln

sgraph:Network Visualization Using 'sigma.js'

Interactive visualizations of graphs created with the 'igraph' package using a 'htmlwidgets' wrapper for the 'sigma.js' network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.

Maintained by Thomas Charlon. Last updated 4 months ago.

7.0 match 5.32 score 1 scripts 2 dependents

bquast

HomomorphicEncryption:BFV, BGV, CKKS Schema for Fully Homomorphic Encryption

Implements the Brakerski-Fan-Vercauteren (BFV, 2012) <https://eprint.iacr.org/2012/144>, Brakerski-Gentry-Vaikuntanathan (BGV, 2014) <doi:10.1145/2633600>, and Cheon-Kim-Kim-Song (CKKS, 2016) <https://eprint.iacr.org/2016/421.pdf> schema for Fully Homomorphic Encryption. The included vignettes demonstrate the encryption procedures.

Maintained by Bastiaan Quast. Last updated 1 years ago.

6.8 match 1 stars 5.52 score 39 scripts

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 3 days ago.

cpp

1.8 match 647 stars 20.69 score 35k scripts 1.5k 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 9 days ago.

4.5 match 11 stars 7.57 score 188 scripts 3 dependents

smoeding

usl:Analyze System Scalability with the Universal Scalability Law

The Universal Scalability Law (Gunther 2007) <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and software scalability. It uses system capacity as a function of load to forecast the scalability for the system.

Maintained by Stefan Moeding. Last updated 2 years ago.

scalability universal-scalability-law usl

5.1 match 36 stars 6.32 score 117 scripts

ropensci

beautier:'BEAUti' from R

'BEAST2' (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. 'BEAUti 2' (which is part of 'BEAST2') is a GUI tool that allows users to specify the many possible setups and generates the XML file 'BEAST2' needs to run. This package provides a way to create 'BEAST2' input files without active user input, but using R function calls instead.

Maintained by Richèl J.C. Bilderbeek. Last updated 23 days ago.

bayesian beast beast2 beauti phylogenetic-inference phylogenetics

3.7 match 13 stars 8.76 score 198 scripts 5 dependents

openpharma

mmrm:Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

Maintained by Daniel Sabanes Bove. Last updated 10 days ago.

cpp

2.5 match 138 stars 12.15 score 113 scripts 4 dependents

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

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

kenaho1

asbio:A Collection of Statistical Tools for Biologists

Contains functions from: Aho, K. (2014) Foundational and Applied Statistics for Biologists using R. CRC/Taylor and Francis, Boca Raton, FL, ISBN: 978-1-4398-7338-0.

Maintained by Ken Aho. Last updated 2 months ago.

4.1 match 5 stars 7.32 score 310 scripts 3 dependents

pitakakariki

simr:Power Analysis for Generalised Linear Mixed Models by Simulation

Calculate power for generalised linear mixed models, using simulation. Designed to work with models fit using the 'lme4' package. Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.

Maintained by Peter Green. Last updated 2 years ago.

3.0 match 71 stars 9.87 score 756 scripts

ggobi

GGally:Extension to 'ggplot2'

The R package 'ggplot2' is a plotting system based on the grammar of graphics. 'GGally' extends 'ggplot2' by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Maintained by Barret Schloerke. Last updated 10 months ago.

1.8 match 597 stars 16.15 score 17k scripts 154 dependents

flr

FLCore:Core Package of FLR, Fisheries Modelling in R

Core classes and methods for FLR, a framework for fisheries modelling and management strategy simulation in R. Developed by a team of fisheries scientists in various countries. More information can be found at <http://flr-project.org/>.

Maintained by Iago Mosqueira. Last updated 10 days ago.

fisheries flr fisheries-modelling

3.3 match 16 stars 8.78 score 956 scripts 23 dependents

amices

mice:Multivariate Imputation by Chained Equations

Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.

Maintained by Stef van Buuren. Last updated 6 days ago.

chained-equations fcs imputation mice missing-data missing-values multiple-imputation multivariate-data cpp

1.7 match 462 stars 16.50 score 10k scripts 154 dependents

suyusung

arm:Data Analysis Using Regression and Multilevel/Hierarchical Models

Functions to accompany A. Gelman and J. Hill, Data Analysis Using Regression and Multilevel/Hierarchical Models, Cambridge University Press, 2007.

Maintained by Yu-Sung Su. Last updated 5 months ago.

2.3 match 25 stars 12.38 score 3.3k scripts 89 dependents

moshejasper

kindisperse:Simulate and Estimate Close-Kin Dispersal Kernels

Functions for simulating and estimating kinship-related dispersal. Based on the methods described in M. Jasper, T.L. Schmidt., N.W. Ahmad, S.P. Sinkins & A.A. Hoffmann (2019) <doi:10.1111/1755-0998.13043> "A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito". Assumes an additive variance model of dispersal in two dimensions, compatible with Wright's neighbourhood area. Simple and composite dispersal simulations are supplied, as well as the functions needed to estimate parent-offspring dispersal for simulated or empirical data, and to undertake sampling design for future field studies of dispersal. For ease of use an integrated Shiny app is also included.

Maintained by Moshe-Elijah Jasper. Last updated 5 months ago.

7.4 match 1 stars 3.70 score 5 scripts

anhoej

qicharts2:Quality Improvement Charts

Functions for making run charts, Shewhart control charts and Pareto charts for continuous quality improvement. Included control charts are: I, MR, Xbar, S, T, C, U, U', P, P', and G charts. Non-random variation in the form of minor to moderate persistent shifts in data over time is identified by the Anhoej rules for unusually long runs and unusually few crossing [Anhoej, Olesen (2014) <doi:10.1371/journal.pone.0113825>]. Non-random variation in the form of larger, possibly transient, shifts is identified by Shewhart's 3-sigma rule [Mohammed, Worthington, Woodall (2008) <doi:10.1136/qshc.2004.012047>].

Maintained by Jacob Anhoej. Last updated 1 months ago.

2.9 match 39 stars 9.04 score 122 scripts 2 dependents

agalecki

nlmeU:Datasets and Utility Functions Enhancing Functionality of 'nlme' Package

Datasets and utility functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models: A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.

Maintained by Andrzej Galecki. Last updated 3 years ago.

5.1 match 5.08 score 135 scripts 6 dependents

ramnathv

htmlwidgets:HTML Widgets for R

A framework for creating HTML widgets that render in various contexts including the R console, 'R Markdown' documents, and 'Shiny' web applications.

Maintained by Carson Sievert. Last updated 1 years ago.

1.3 match 791 stars 19.05 score 7.4k scripts 3.1k dependents

isaakiel

oxcAAR:Interface to 'OxCal' Radiocarbon Calibration

A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.

Maintained by Hinz Martin. Last updated 2 years ago.

3.9 match 21 stars 6.54 score 55 scripts

melff

mclogit:Multinomial Logit Models, with or without Random Effects or Overdispersion

Provides estimators for multinomial logit models in their conditional logit and baseline logit variants, with or without random effects, with or without overdispersion. Random effects models are estimated using the PQL technique (based on a Laplace approximation) or the MQL technique (based on a Solomon-Cox approximation). Estimates should be treated with caution if the group sizes are small.

Maintained by Martin Elff. Last updated 3 months ago.

2.3 match 23 stars 11.03 score 262 scripts 4 dependents

patperry

mbest:Moment-Based Estimation for Hierarchical Models

Fast moment-based hierarchical model fitting. Implements methods from the papers "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017) and "Fitting a Deeply Nested Hierarchical Model to a Large Book Review Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry (2018).

Maintained by Patrick O. Perry. Last updated 7 years ago.

4.5 match 25 stars 5.47 score 13 scripts 2 dependents

loicym

multibreakeR:Tests for a Structural Change in Multivariate Time Series

Flexible implementation of a structural change point detection algorithm for multivariate time series. It authorizes inclusion of trends, exogenous variables, and break test on the intercept or on the full vector autoregression system. Bai, Lumsdaine, and Stock (1998) <doi:10.1111/1467-937X.00051>.

Maintained by Loic Marechal. Last updated 2 years ago.

6.6 match 3.70 score 3 scripts

bioc

limma:Linear Models for Microarray and Omics Data

Data analysis, linear models and differential expression for omics data.

Maintained by Gordon Smyth. Last updated 6 days ago.

exonarray geneexpression transcription alternativesplicing differentialexpression differentialsplicing genesetenrichment dataimport bayesian clustering regression timecourse microarray micrornaarray mrnamicroarray onechannel proprietaryplatforms twochannel sequencing rnaseq batcheffect multiplecomparison normalization preprocessing qualitycontrol biomedicalinformatics cellbiology cheminformatics epigenetics functionalgenomics genetics immunooncology metabolomics proteomics systemsbiology transcriptomics

1.8 match 13.81 score 16k scripts 585 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 9 hours ago.

openblas cpp openmp

2.8 match 16 stars 8.44 score 104 scripts 1 dependents

revelle

psych:Procedures for Psychological, Psychometric, and Personality Research

A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the <https://personality-project.org/r/> web page.

Maintained by William Revelle. Last updated 3 months ago.

1.7 match 52 stars 13.94 score 29k scripts 317 dependents

cvxgrp

CVXR:Disciplined Convex Optimization

An object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided, both commercial and open source.

Maintained by Anqi Fu. Last updated 4 months ago.

cpp

1.8 match 207 stars 12.89 score 768 scripts 51 dependents

r-forge

robustbase:Basic Robust Statistics

"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.

Maintained by Martin Maechler. Last updated 4 months ago.

fortran openblas

1.7 match 13.33 score 1.7k scripts 480 dependents

jgx65

hierfstat:Estimation and Tests of Hierarchical F-Statistics

Estimates hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution(1998), 52:950). Tests via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G (Goudet et al. (1996) <https://academic.oup.com/genetics/article/144/4/1933/6017091>). Estimates genetic diversity statistics for haploid and diploid genetic datasets in various formats, including inbreeding and coancestry coefficients, and population specific F-statistics following Weir and Goudet (2017) <https://academic.oup.com/genetics/article/206/4/2085/6072590>.

Maintained by Jerome Goudet. Last updated 4 months ago.

devtools fstatistics gwas hierfstat kinship population-genetics population-genomics quantitative-genetics simulations

2.0 match 25 stars 10.94 score 560 scripts 4 dependents

mqbssppe

fabMix:Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).

Maintained by Panagiotis Papastamoulis. Last updated 1 years ago.

openblas cpp openmp

10.4 match 2.09 score 41 scripts 1 dependents

cran

shotGroups:Analyze Shot Group Data

Analyzes shooting data with respect to group shape, precision, and accuracy. This includes graphical methods, descriptive statistics, and inference tests using standard, but also non-parametric and robust statistical methods. Implements distributions for radial error in bivariate normal variables. Works with files exported by 'OnTarget PC/TDS', 'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran', as well as with custom data files in text format. Supports inference from range statistics such as extreme spread. Includes a set of web-based graphical user interfaces.

Maintained by Daniel Wollschlaeger. Last updated 2 years ago.

8.7 match 2.48 score 1 dependents

tomkellygenetics

graphsim:Simulate Expression Data from 'igraph' Networks

Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph' structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.

Maintained by S. Thomas Kelly. Last updated 3 years ago.

benchmarking gene-expression gene-regulatory-networks genetics genomic-data-analysis genomics graph-algorithms igraph-networks joss ngs-analysis simulated-data simulation-modeling

4.1 match 24 stars 5.08 score 2 scripts

iame-researchcenter

PFIM:Population Fisher Information Matrix

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) <doi:10.1093/biomet/84.2.429>, Retout S, Comets E, Samson A, Mentré F (2007) <doi:10.1002/sim.2910>, Bazzoli C, Retout S, Mentré F (2009) <doi:10.1002/sim.3573>, Le Nagard H, Chao L, Tenaillon O (2011) <doi:10.1186/1471-2148-11-326>, Combes FP, Retout S, Frey N, Mentré F (2013) <doi:10.1007/s11095-013-1079-3> and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) <doi:10.1016/j.cmpb.2021.106126>.

Maintained by Romain Leroux. Last updated 5 months ago.

7.5 match 2.78 score 9 scripts

johncoene

sigmajs:Interface to 'Sigma.js' Graph Visualization Library

Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.

Maintained by John Coene. Last updated 4 years ago.

htmlwidgets network-visualization sigmajs

3.5 match 72 stars 5.92 score 77 scripts

bioc

PROcess:Ciphergen SELDI-TOF Processing

A package for processing protein mass spectrometry data.

Maintained by Xiaochun Li. Last updated 5 months ago.

immunooncology massspectrometry proteomics

3.3 match 6.04 score 552 scripts

ices-tools-prod

icesAdvice:Functions Related to ICES Advice

A collection of functions that facilitate computational steps related to advice for fisheries management, according to ICES guidelines. These include methods for calculating reference points and model diagnostics.

Maintained by Colin Millar. Last updated 1 years ago.

3.9 match 7 stars 5.14 score 66 scripts 2 dependents

hwborchers

numbers:Number-Theoretic Functions

Provides number-theoretic functions for factorization, prime numbers, twin primes, primitive roots, modular logarithm and inverses, extended GCD, Farey series and continued fractions. Includes Legendre and Jacobi symbols, some divisor functions, Euler's Phi function, etc.

Maintained by Hans W. Borchers. Last updated 2 years ago.

3.3 match 2 stars 6.02 score 370 scripts 70 dependents

vabar

vibass:Valencia International Bayesian Summer School

Materials for the introductory course on Bayesian inference. Practicals, data and interactive apps.

Maintained by Facundo Muñoz. Last updated 8 months ago.

bayesian-inference teaching

3.6 match 7 stars 5.40 score 2 scripts

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

2.0 match 173 stars 9.65 score 203 scripts 2 dependents

ropensci

GLMMcosinor:Fit a Cosinor Model Using a Generalized Mixed Modeling Framework

Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.

Maintained by Rex Parsons. Last updated 4 months ago.

3.3 match 1 stars 5.77 score 22 scripts

cran

REAT:Regional Economic Analysis Toolbox

Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.

Maintained by Thomas Wieland. Last updated 4 years ago.

5.2 match 3 stars 3.62 score 140 scripts

dicook

nullabor:Tools for Graphical Inference

Tools for visual inference. Generate null data sets and null plots using permutation and simulation. Calculate distance metrics for a lineup, and examine the distributions of metrics.

Maintained by Di Cook. Last updated 1 months ago.

1.8 match 57 stars 10.38 score 370 scripts 2 dependents

marie-perrotdockes

BlockCov:Estimation of Large Block Covariance Matrices

Computation of large covariance matrices having a block structure up to a permutation of their columns and rows from a small number of samples with respect to the dimension of the matrix. The method is described in the paper Perrot-Dockès et al. (2019) <arXiv:1806.10093>.

Maintained by Marie Perrot-Dockès. Last updated 6 years ago.

9.1 match 2.00 score 6 scripts

mmaechler

CLA:Critical Line Algorithm in Pure R

Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) <doi:10.2307/2975974>. Care has been taken for correctness in light of previous buggy implementations.

Maintained by Martin Maechler. Last updated 8 months ago.

6.7 match 2.70 score 8 scripts

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 21 days ago.

1.7 match 4 stars 10.50 score 346 scripts 71 dependents

atsa-es

MARSS:Multivariate Autoregressive State-Space Modeling

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

Maintained by Elizabeth Eli Holmes. Last updated 1 years ago.

multivariate-timeseries state-space-models statistics time-series

1.7 match 52 stars 10.34 score 596 scripts 3 dependents

lukejharmon

geiger:Analysis of Evolutionary Diversification

Methods for fitting macroevolutionary models to phylogenetic trees Pennell (2014) <doi:10.1093/bioinformatics/btu181>.

Maintained by Luke Harmon. Last updated 2 years ago.

openblas cpp

2.3 match 1 stars 7.84 score 2.3k scripts 28 dependents

trackage

trip:Tracking Data

Access and manipulate spatial tracking data, with straightforward coercion from and to other formats. Filter for speed and create time spent maps from tracking data. There are coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects can be created from raw or grouped data frames, and from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.

Maintained by Michael D. Sumner. Last updated 8 months ago.

2.3 match 13 stars 7.72 score 137 scripts 1 dependents

bioc

scMultiSim:Simulation of Multi-Modality Single Cell Data Guided By Gene Regulatory Networks and Cell-Cell Interactions

scMultiSim simulates paired single cell RNA-seq, single cell ATAC-seq and RNA velocity data, while incorporating mechanisms of gene regulatory networks, chromatin accessibility and cell-cell interactions. It allows users to tune various parameters controlling the amount of each biological factor, variation of gene-expression levels, the influence of chromatin accessibility on RNA sequence data, and so on. It can be used to benchmark various computational methods for single cell multi-omics data, and to assist in experimental design of wet-lab experiments.

Maintained by Hechen Li. Last updated 5 months ago.

singlecell transcriptomics geneexpression sequencing experimentaldesign

2.4 match 23 stars 7.15 score 11 scripts

antonio-pgarcia

evoper:Evolutionary Parameter Estimation for 'Repast Simphony' Models

The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with 'Repast Simphony' Agent-Based framework (<https://repast.github.io/>) and with NetLogo (<https://ccl.northwestern.edu/netlogo/>) which are the most used frameworks for Agent-based modeling.

Maintained by Antonio Prestes Garcia. Last updated 5 years ago.

openjdk

4.3 match 6 stars 3.92 score 28 scripts

snthomas99

clinDR:Simulation and Analysis Tools for Clinical Dose Response Modeling

Bayesian and ML Emax model fitting, graphics and simulation for clinical dose response. The summary data from the dose response meta-analyses in Thomas, Sweeney, and Somayaji (2014) <doi:10.1080/19466315.2014.924876> and Thomas and Roy (2016) <doi:10.1080/19466315.2016.1256229> Wu, Banerjee, Jin, Menon, Martin, and Heatherington(2017) <doi:10.1177/0962280216684528> are included in the package. The prior distributions for the Bayesian analyses default to the posterior predictive distributions derived from these references.

Maintained by Neal Thomas. Last updated 2 years ago.

9.0 match 1 stars 1.85 score 71 scripts

robinhankin

emulator:Bayesian Emulation of Computer Programs

Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.

Maintained by Robin K. S. Hankin. Last updated 8 months ago.

2.0 match 4 stars 8.27 score 56 scripts 17 dependents

bioc

gcrma:Background Adjustment Using Sequence Information

Background adjustment using sequence information

Maintained by Z. Wu. Last updated 5 months ago.

microarray onechannel preprocessing

2.3 match 7.28 score 164 scripts 11 dependents

bioc

MsQuality:MsQuality - Quality metric calculation from Spectra and MsExperiment objects

The MsQuality provides functionality to calculate quality metrics for mass spectrometry-derived, spectral data at the per-sample level. MsQuality relies on the mzQC framework of quality metrics defined by the Human Proteom Organization-Proteomics Standards Initiative (HUPO-PSI). These metrics quantify the quality of spectral raw files using a controlled vocabulary. The package is especially addressed towards users that acquire mass spectrometry data on a large scale (e.g. data sets from clinical settings consisting of several thousands of samples). The MsQuality package allows to calculate low-level quality metrics that require minimum information on mass spectrometry data: retention time, m/z values, and associated intensities. MsQuality relies on the Spectra package, or alternatively the MsExperiment package, and its infrastructure to store spectral data.

Maintained by Thomas Naake. Last updated 2 months ago.

metabolomics proteomics massspectrometry qualitycontrol mass-spectrometry qc

3.0 match 7 stars 5.45 score 2 scripts

glsnow

TeachingDemos:Demonstrations for Teaching and Learning

Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.

Maintained by Greg Snow. Last updated 1 years ago.

2.3 match 7.18 score 760 scripts 13 dependents

andrewljackson

SIBER:Stable Isotope Bayesian Ellipses in R

Fits bi-variate ellipses to stable isotope data using Bayesian inference with the aim being to describe and compare their isotopic niche.

Maintained by Andrew Jackson. Last updated 10 months ago.

community-ecology ecology niche-modelling stable-isotopes jags cpp

1.8 match 36 stars 9.13 score 187 scripts 1 dependents

egenn

rtemis:Machine Learning and Visualization

Advanced Machine Learning and Visualization. Unsupervised Learning (Clustering, Decomposition), Supervised Learning (Classification, Regression), Cross-Decomposition, Bagging, Boosting, Meta-models. Static and interactive graphics.

Maintained by E.D. Gennatas. Last updated 1 months ago.

data-science data-visualization machine-learning machine-learning-library visualization

2.3 match 145 stars 7.09 score 50 scripts 2 dependents

adrian-bowman

sm:Smoothing Methods for Nonparametric Regression and Density Estimation

This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.

Maintained by Adrian Bowman. Last updated 1 years ago.

fortran

2.3 match 1 stars 6.99 score 732 scripts 36 dependents

tesselle

kairos:Analysis of Chronological Patterns from Archaeological Count Data

A toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.

Maintained by Nicolas Frerebeau. Last updated 13 days ago.

chronology matrix-seriation archaeology archaeological-science

3.3 match 4.66 score 11 scripts 1 dependents

venelin

POUMM:The Phylogenetic Ornstein-Uhlenbeck Mixed Model

The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.

Maintained by Venelin Mitov. Last updated 4 years ago.

cpp

3.0 match 4 stars 4.94 score 22 scripts

lucapresicce

spBPS:Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

Maintained by Luca Presicce. Last updated 5 months ago.

openblas cpp openmp

3.3 match 4.40 score 10 scripts

fernandalschumacher

skewlmm:Scale Mixture of Skew-Normal Linear Mixed Models

It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.

Maintained by Fernanda L. Schumacher. Last updated 2 months ago.

3.3 match 6 stars 4.43 score 10 scripts

cran

EDISON:Network Reconstruction and Changepoint Detection

Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty.

Maintained by Frank Dondelinger. Last updated 9 years ago.

5.5 match 2 stars 2.62 score 1 dependents

bioc

immunoClust:immunoClust - Automated Pipeline for Population Detection in Flow Cytometry

immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples.

Maintained by Till Soerensen. Last updated 4 months ago.

clustering flowcytometry singlecell cellbasedassays immunooncology gsl cpp

3.3 match 4.38 score 4 scripts

philipberg

baldur:Bayesian Hierarchical Modeling for Label-Free Proteomics

Statistical decision in proteomics data using a hierarchical Bayesian model. There are two regression models for describing the mean-variance trend, a gamma regression or a latent gamma mixture regression. The regression model is then used as an Empirical Bayes estimator for the prior on the variance in a peptide. Further, it assumes that each measurement has an uncertainty (increased variance) associated with it that is also inferred. Finally, it tries to estimate the posterior distribution (by Hamiltonian Monte Carlo) for the differences in means for each peptide in the data. Once the posterior is inferred, it integrates the tails to estimate the probability of error from which a statistical decision can be made. See Berg and Popescu for details (<doi:10.1101/2023.05.11.540411>).

Maintained by Philip Berg. Last updated 7 months ago.

cpp

3.5 match 1 stars 4.00 score 8 scripts

bergsmat

nonmemica:Create and Evaluate NONMEM Models in a Project Context

Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See 'package?nonmemica'.

Maintained by Tim Bergsma. Last updated 2 months ago.

3.0 match 4 stars 4.58 score 45 scripts

asgr

magicaxis:Pretty Scientific Plotting with Minor-Tick and Log Minor-Tick Support

Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.

Maintained by Aaron Robotham. Last updated 5 months ago.

2.0 match 9 stars 6.84 score 184 scripts 7 dependents

skranz

RTutor:Interactive R problem sets with automatic testing of solutions and automatic hints

Interactive R problem sets with automatic testing of solutions and automatic hints

Maintained by Sebastian Kranz. Last updated 1 years ago.

economics learn-to-code problem-set rstudio rtutor shiny teaching

2.3 match 205 stars 5.83 score 111 scripts 1 dependents

nmautoverse

NMdata:Preparation, Checking and Post-Processing Data for PK/PD Modeling

Efficient tools for preparation, checking and post-processing of data in PK/PD (pharmacokinetics/pharmacodynamics) modeling, with focus on use of Nonmem. Attention is paid to ensure consistency, traceability, and Nonmem compatibility of Data. Rigorously checks final Nonmem datasets. Implemented in 'data.table', but easily integrated with 'base' and 'tidyverse'.

Maintained by Philip Delff. Last updated 3 days ago.

nonmem pharmacometrics

1.7 match 17 stars 7.69 score 88 scripts 2 dependents

cran

pmxcode:Create Pharmacometric Models

Provides a user interface to create or modify pharmacometric models for various modeling and simulation software platforms.

Maintained by Sebastien Bihorel. Last updated 16 days ago.

4.0 match 1 stars 3.18 score

wasquith

copBasic:General Bivariate Copula Theory and Many Utility Functions

Extensive functions for bivariate copula (bicopula) computations and related operations for bicopula theory. The lower, upper, product, and select other bicopula are implemented along with operations including the diagonal, survival copula, dual of a copula, co-copula, and numerical bicopula density. Level sets, horizontal and vertical sections are supported. Numerical derivatives and inverses of a bicopula are provided through which simulation is implemented. Bicopula composition, convex combination, asymmetry extension, and products also are provided. Support extends to the Kendall Function as well as the Lmoments thereof. Kendall Tau, Spearman Rho and Footrule, Gini Gamma, Blomqvist Beta, Hoeffding Phi, Schweizer- Wolff Sigma, tail dependency, tail order, skewness, and bivariate Lmoments are implemented, and positive/negative quadrant dependency, left (right) increasing (decreasing) are available. Other features include Kullback-Leibler Divergence, Vuong Procedure, spectral measure, and Lcomoments for inference, maximum likelihood, and AIC, BIC, and RMSE for goodness-of-fit.

Maintained by William Asquith. Last updated 1 months ago.

2.4 match 5.34 score 139 scripts 5 dependents

jaredsmurray

bcf:Causal Inference for a Binary Treatment and Continuous Outcome using Bayesian Causal Forests

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <https://projecteuclid.org/journals/bayesian-analysis/volume-15/issue-3/Bayesian-Regression-Tree-Models-for-Causal-Inference--Regularization-Confounding/10.1214/19-BA1195.full> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.

Maintained by Jared S. Murray. Last updated 1 years ago.

openblas cpp

1.5 match 41 stars 8.12 score 46 scripts

lucas-castillo

samplr:Compare Human Performance to Sampling Algorithms

Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.

Maintained by Lucas Castillo. Last updated 3 days ago.

openblas cpp

2.0 match 2 stars 6.02 score 25 scripts

fbertran

OneTwoSamples:Deal with One and Two (Normal) Samples

We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length.

Maintained by Frederic Bertrand. Last updated 2 years ago.

4.9 match 2.43 score 27 scripts

r-forge

distrEllipse:S4 Classes for Elliptically Contoured Distributions

Distribution (S4-)classes for elliptically contoured distributions (based on package 'distr').

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

3.3 match 3.51 score 18 scripts

otryakhin-dmitry

rlfsm:Simulations and Statistical Inference for Linear Fractional Stable Motions

Contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.

Maintained by Dmitry Otryakhin. Last updated 3 years ago.

cpp

3.8 match 3.00 score 20 scripts

eliaskrainski

INLAspacetime:Spatial and Spatio-Temporal Models using 'INLA'

Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2023) <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. Details are provided in the available vignettes and from the URL bellow.

Maintained by Elias Teixeira Krainski. Last updated 3 days ago.

openblas

1.6 match 4 stars 7.05 score 56 scripts

hugogogo

natural:Estimating the Error Variance in a High-Dimensional Linear Model

Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.

Maintained by Guo Yu. Last updated 7 years ago.

2.5 match 1 stars 4.48 score 9 scripts

skranz

RoundingMatters:Tools for adjusting for rounding problems in metastudies about p-hacking and publication bias

Tools for adjusting for rounding problems in metastudies about p-hacking and publication bias

Maintained by Sebastian Kranz. Last updated 4 years ago.

6.5 match 1.70 score 8 scripts

cran

ActivityIndex:Activity Index Calculation using Raw 'Accelerometry' Data

Reads raw 'accelerometry' from 'GT3X+' data and plain table data to calculate Activity Index from 'Bai et al.' (2016) <doi:10.1371/journal.pone.0160644>. The Activity Index refers to the square root of the second-level average variance of the three 'accelerometry' axes.

Maintained by Jiawei Bai. Last updated 4 years ago.

4.1 match 2.70 score

cran

refineR:Reference Interval Estimation using Real-World Data

Indirect method for the estimation of reference intervals using Real-World Data ('RWD'). It takes routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and uses sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples. This distribution can then be used to derive reference intervals. Furthermore, the package offers functions for printing and plotting the results of the algorithm. See ?refineR for a more comprehensive description of the features. Version 1.0 of the algorithm is described in detail in 'Ammer et al. (2021)' <doi:10.1038/s41598-021-95301-2>. Additional guidance on the usage of the algorithm is given in 'Ammer et al. (2023)' <doi:10.1093/jalm/jfac101>.

Maintained by Tatjana Ammer. Last updated 7 months ago.

5.1 match 1 stars 2.18 score 15 scripts

luisgruber

bayesianVARs:MCMC Estimation of Bayesian Vectorautoregressions

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

Maintained by Luis Gruber. Last updated 4 months ago.

bayesian time-series vectorautoregression openblas cpp

2.0 match 9 stars 5.43 score 9 scripts

modeloriented

localModel:LIME-Based Explanations with Interpretable Inputs Based on Ceteris Paribus Profiles

Local explanations of machine learning models describe, how features contributed to a single prediction. This package implements an explanation method based on LIME (Local Interpretable Model-agnostic Explanations, see Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>) in which interpretable inputs are created based on local rather than global behaviour of each original feature.

Maintained by Przemyslaw Biecek. Last updated 3 years ago.

1.8 match 14 stars 6.16 score 23 scripts

minnage

ui:Uncertainty Intervals and Sensitivity Analysis for Missing Data

Implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters when outcome is missing not at random (non-ignorable missingness) introduced in Genbaeck, M., Stanghellini, E., de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbaeck, M., Ng, N., Stanghellini, E., de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>; and (ii) double robust and outcome regression estimators of average causal effects (on the treated) with possibly unobserved confounding introduced in Genbaeck, M., de Luna, X. (2018) <doi:10.1111/biom.13001>.

Maintained by Minna Genbaeck. Last updated 5 years ago.

3.8 match 2.78 score 151 scripts

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

1.8 match 7 stars 5.99 score 47 scripts 1 dependents

danforthcenter

pcvr:Plant Phenotyping and Bayesian Statistics

Analyse common types of plant phenotyping data, provide a simplified interface to longitudinal growth modeling and select Bayesian statistics, and streamline use of 'PlantCV' output. Several Bayesian methods and reporting guidelines for Bayesian methods are described in Kruschke (2018) <doi:10.1177/2515245918771304>, Kruschke (2013) <doi:10.1037/a0029146>, and Kruschke (2021) <doi:10.1038/s41562-021-01177-7>.

Maintained by Josh Sumner. Last updated 5 days ago.

1.5 match 4 stars 6.99 score 39 scripts

cran

hbim:Hill/Bliss Independence Model for Combination Vaccines

Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

Maintained by Michael P. Fay. Last updated 2 years ago.

4.5 match 2.30 score

kestrel99

pmxTools:Pharmacometric and Pharmacokinetic Toolkit

Pharmacometric tools for common data analytical tasks; closed-form solutions for calculating concentrations at given times after dosing based on compartmental PK models (1-compartment, 2-compartment and 3-compartment, covering infusions, zero- and first-order absorption, and lag times, after single doses and at steady state, per Bertrand & Mentre (2008) <http://lixoft.com/wp-content/uploads/2016/03/PKPDlibrary.pdf>); parametric simulation from NONMEM-generated parameter estimates and other output; and parsing, tabulating and plotting results generated by Perl-speaks-NONMEM (PsN).

Maintained by Justin Wilkins. Last updated 7 months ago.

nonmem pharmacokinetics simulation

1.6 match 30 stars 6.40 score 84 scripts

stla

jacobi:Jacobi Theta Functions and Related Functions

Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported.

Maintained by Stéphane Laurent. Last updated 1 years ago.

elliptic-functions theta-functions cpp

2.5 match 2 stars 4.01 score 103 scripts

bgctw

lognorm:Functions for the Lognormal Distribution

The lognormal distribution (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051%5B0341:lndats%5D2.0.co;2>) can characterize uncertainty that is bounded by zero. This package provides estimation of distribution parameters, computation of moments and other basic statistics, and an approximation of the distribution of the sum of several correlated lognormally distributed variables (Lo 2013 <doi:10.12988/ams.2013.39511>) and the approximation of the difference of two correlated lognormally distributed variables (Lo 2012 <doi:10.1155/2012/838397>).

Maintained by Thomas Wutzler. Last updated 4 years ago.

1.8 match 6 stars 5.73 score 59 scripts

modeloriented

live:Local Interpretable (Model-Agnostic) Visual Explanations

Interpretability of complex machine learning models is a growing concern. This package helps to understand key factors that drive the decision made by complicated predictive model (so called black box model). This is achieved through local approximations that are either based on additive regression like model or CART like model that allows for higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>. More details can be found in Staniak, Biecek (2018) <doi:10.32614/RJ-2018-072>.

Maintained by Mateusz Staniak. Last updated 6 years ago.

iml interpretability lime machine-learning model-visualization visual-explanations xai

1.8 match 35 stars 5.59 score 55 scripts

pachadotdev

LSTS:Locally Stationary Time Series

A set of functions that allow stationary analysis and locally stationary time series analysis.

Maintained by Mauricio Vargas. Last updated 1 years ago.

1.8 match 3 stars 5.54 score 51 scripts 5 dependents

cran

roots:Reconstructing Ordered Ontogenic Trajectories

A set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data.

Maintained by Wajid Jawaid. Last updated 8 years ago.

3.6 match 2.70 score

alstat

ALUES:Agricultural Land Use Evaluation System

Evaluates land suitability for different crops production. The package is based on the Food and Agriculture Organization (FAO) and the International Rice Research Institute (IRRI) methodology for land evaluation. Development of ALUES is inspired by similar tool for land evaluation, Land Use Suitability Evaluation Tool (LUSET). The package uses fuzzy logic approach to evaluate land suitability of a particular area based on inputs such as rainfall, temperature, topography, and soil properties. The membership functions used for fuzzy modeling are the following: Triangular, Trapezoidal and Gaussian. The methods for computing the overall suitability of a particular area are also included, and these are the Minimum, Maximum and Average. Finally, ALUES is a highly optimized library with core algorithms written in C++.

Maintained by Al-Ahmadgaid B. Asaad. Last updated 3 years ago.

agricultural-modelling agriculture cpp

1.5 match 11 stars 6.38 score 55 scripts

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

2.1 match 4.61 score 18 scripts

simulatr

simrel:Simulation of Multivariate Linear Model Data

Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts. This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.

Maintained by Raju Rimal. Last updated 2 years ago.

bivariate-simulation multivariate-simulation relevant-predictor-components simulated-data simulation univariate-simulation

1.9 match 3 stars 4.78 score 40 scripts

cran

stpm:Stochastic Process Model for Analysis of Longitudinal and Time-to-Event Outcomes

Utilities to estimate parameters of the models with survival functions induced by stochastic covariates. Miscellaneous functions for data preparation and simulation are also provided. For more information, see: (i)"Stochastic model for analysis of longitudinal data on aging and mortality" by Yashin A. et al. (2007), Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>; (ii) "Health decline, aging and mortality: how are they related?" by Yashin A. et al. (2007), Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.

Maintained by Ilya Y. Zhbannikov. Last updated 3 years ago.

openblas cpp openmp

3.3 match 2.70 score

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 1 months ago.

fortran cpp openmp

1.6 match 21 stars 5.40 score 17 scripts

dkesada

dbnR:Dynamic Bayesian Network Learning and Inference

Learning and inference over dynamic Bayesian networks of arbitrary Markovian order. Extends some of the functionality offered by the 'bnlearn' package to learn the networks from data and perform exact inference. It offers three structure learning algorithms for dynamic Bayesian networks: Trabelsi G. (2013) <doi:10.1007/978-3-642-41398-8_34>, Santos F.P. and Maciel C.D. (2014) <doi:10.1109/BRC.2014.6880957>, Quesada D., Bielza C. and Larrañaga P. (2021) <doi:10.1007/978-3-030-86271-8_14>. It also offers the possibility to perform forecasts of arbitrary length. A tool for visualizing the structure of the net is also provided via the 'visNetwork' package.

Maintained by David Quesada. Last updated 9 months ago.

bayesian-networks dynamic-bayesian-networks forecasting inference time-series cpp

1.7 match 55 stars 5.01 score 37 scripts

przechoj

gips:Gaussian Model Invariant by Permutation Symmetry

Find the permutation symmetry group such that the covariance matrix of the given data is approximately invariant under it. Discovering such a permutation decreases the number of observations needed to fit a Gaussian model, which is of great use when it is smaller than the number of variables. Even if that is not the case, the covariance matrix found with 'gips' approximates the actual covariance with less statistical error. The methods implemented in this package are described in Graczyk et al. (2022) <doi:10.1214/22-AOS2174>.

Maintained by Adam Przemysław Chojecki. Last updated 8 months ago.

covariance-estimation machine-learning normal-distribution

1.3 match 6 stars 6.40 score 31 scripts

asgr

hyper.fit:N-Dimensional Hyperplane Fitting with Errors

High level functions for hyperplane fitting (hyper.fit()) and visualising (hyper.plot2d() / hyper.plot3d()). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.

Maintained by Aaron Robotham. Last updated 9 months ago.

2.8 match 7 stars 3.02 score 15 scripts

statnmap

GeoDist:Constrained distance calculation and associated geotools

This package allows the calculation of distances that are constrained by frontiers, islands, mountains, ... These distances are then implemented in classical geotools like kriging with a modified version of geoR functions.

Maintained by Sébastien Rochette. Last updated 5 years ago.

1.7 match 7 stars 4.94 score 251 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

1.8 match 1 stars 4.69 score 33 scripts

permaverse

nevada:Network-Valued Data Analysis

A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" <doi:10.1111/rssc.12463>.

Maintained by Aymeric Stamm. Last updated 3 months ago.

openblas cpp

1.9 match 6 stars 4.48 score 7 scripts

certara-jcraig

Certara.RDarwin:Interface for 'pyDarwin' Machine Learning Pharmacometric Model Development

Utilities that support the usage of 'pyDarwin' (<https://certara.github.io/pyDarwin/>) for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine.

Maintained by James Craig. Last updated 19 days ago.

4.9 match 1.70 score

sujit-sahu

bmstdr:Bayesian Modeling of Spatio-Temporal Data with R

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Maintained by Sujit K. Sahu. Last updated 1 years ago.

bayesian modelling spatio-temporal-data cpp

1.6 match 15 stars 4.95 score 12 scripts

flr

AAP:Aarts and Poos Stock Assessment Model that Estimates Bycatch

FLR version of Aarts and Poos stock assessment model.

Maintained by Iago Mosqueira. Last updated 1 years ago.

3.0 match 2.70 score 5 scripts

cran

OPI:Open Perimetry Interface

Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters) first started as an open source project with support of Haag-Streit in 2010. It specifies basic functions that allow many visual field tests to be constructed. As of February 2022 it is fully implemented on the Haag-Streit Octopus 900 and 'CrewT ImoVifa' ('Topcon Tempo') with partial implementations on the Centervue Compass, Kowa AP 7000 and Android phones. It also has a cousin: the R package 'visualFields', which has tools for analysing and manipulating visual field data.

Maintained by Andrew Turpin. Last updated 8 months ago.

3.3 match 1 stars 2.48 score

svdpas

horseshoe:Implementation of the Horseshoe Prior

Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals.

Maintained by Stephanie van der Pas. Last updated 6 years ago.

2.2 match 1 stars 3.57 score 41 scripts 3 dependents

fboehm

qtl2pleio:Testing Pleiotropy in Multiparental Populations

We implement an adaptation of Jiang & Zeng's (1995) <https://www.genetics.org/content/140/3/1111> likelihood ratio test for testing the null hypothesis of pleiotropy against the alternative hypothesis, two separate quantitative trait loci. The test differs from that in Jiang & Zeng (1995) <https://www.genetics.org/content/140/3/1111> and that in Tian et al. (2016) <doi:10.1534/genetics.115.183624> in that our test accommodates multiparental populations.

Maintained by Frederick J Boehm. Last updated 4 years ago.

multiparental-populations quantitative-genetics quantitative-trait cpp

1.8 match 5 stars 4.41 score 26 scripts

bayerse

esreg:Joint Quantile and Expected Shortfall Regression

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Maintained by Sebastian Bayer. Last updated 2 years ago.

expected-shortfall quantile-regression value-at-risk openblas cpp

2.3 match 2 stars 3.52 score 11 scripts 1 dependents

johanngb

ruv:Detect and Remove Unwanted Variation using Negative Controls

Implements the 'RUV' (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.

Maintained by Johann Gagnon-Bartsch. Last updated 6 years ago.

1.8 match 2 stars 4.36 score 94 scripts 7 dependents

mingzehuang

latentcor:Fast Computation of Latent Correlations for Mixed Data

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <arXiv:1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Maintained by Mingze Huang. Last updated 2 years ago.

data-analysis data-mining data-processing data-science data-structures machine-learning mixed-types statistics

1.1 match 16 stars 6.65 score 46 scripts 1 dependents

eddelbuettel

RcppFastAD:'Rcpp' Bindings to 'FastAD' Auto-Differentiation

The header-only 'C++' template library 'FastAD' for automatic differentiation <https://github.com/JamesYang007/FastAD> is provided by this package, along with a few illustrative examples that can all be called from R.

Maintained by Dirk Eddelbuettel. Last updated 6 months ago.

cpp

1.7 match 10 stars 4.40 score 4 scripts

mlysy

nicheROVER:Niche Region and Niche Overlap Metrics for Multidimensional Ecological Niches

Implementation of a probabilistic method to calculate 'nicheROVER' (_niche_ _r_egion and niche _over_lap) metrics using multidimensional niche indicator data (e.g., stable isotopes, environmental variables, etc.). The niche region is defined as the joint probability density function of the multidimensional niche indicators at a user-defined probability alpha (e.g., 95%). Uncertainty is accounted for in a Bayesian framework, and the method can be extended to three or more indicator dimensions. It provides directional estimates of niche overlap, accounts for species-specific distributions in multivariate niche space, and produces unique and consistent bivariate projections of the multivariate niche region. The article by Swanson et al. (2015) <doi:10.1890/14-0235.1> provides a detailed description of the methodology. See the package vignette for a worked example using fish stable isotope data.

Maintained by Martin Lysy. Last updated 1 years ago.

1.1 match 9 stars 6.80 score 47 scripts 1 dependents

monty-se

PINstimation:Estimation of the Probability of Informed Trading

A comprehensive bundle of utilities for the estimation of probability of informed trading models: original PIN in Easley and O'Hara (1992) and Easley et al. (1996); Multilayer PIN (MPIN) in Ersan (2016); Adjusted PIN (AdjPIN) in Duarte and Young (2009); and volume-synchronized PIN (VPIN) in Easley et al. (2011, 2012). Implementations of various estimation methods suggested in the literature are included. Additional compelling features comprise posterior probabilities, an implementation of an expectation-maximization (EM) algorithm, and PIN decomposition into layers, and into bad/good components. Versatile data simulation tools, and trade classification algorithms are among the supplementary utilities. The package provides fast, compact, and precise utilities to tackle the sophisticated, error-prone, and time-consuming estimation procedure of informed trading, and this solely using the raw trade-level data.

Maintained by Montasser Ghachem. Last updated 5 months ago.

clustering-analysis expectation-maximisation-algorithm hierarchical-clustering information-asymmetry market-microstructure maximum-likelihood-estimation mixture-distributions poisson-distribution

1.1 match 36 stars 6.48 score 14 scripts

cran

svplots:Sample Variance Plots (Sv-Plots)

Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating the squared deviations from sample variance. Besides indicating the contribution of squared deviations for the sample variability, these plots are capable of detecting characteristics of the distribution such as symmetry, skewness and outliers. A remarkable graphical method based on Sv-plot2 can determine the decision on testing hypotheses over one or two population means. In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.

Maintained by Uditha Amarananda Wijesuriya. Last updated 4 years ago.

3.5 match 2.00 score 4 scripts

georgekoliopanos

modgo:MOck Data GeneratiOn

Generation of mock data from a real dataset using rank normal inverse transformation.

Maintained by George Koliopanos. Last updated 9 months ago.

1.8 match 1 stars 4.00 score 3 scripts

sqyu

genscore:Generalized Score Matching Estimators

Implementation of the Generalized Score Matching estimator in Yu et al. (2019) <http://jmlr.org/papers/v20/18-278.html> for non-negative graphical models (truncated Gaussian, exponential square-root, gamma, a-b models) and univariate truncated Gaussian distributions. Also includes the original estimator for untruncated Gaussian graphical models from Lin et al. (2016) <doi:10.1214/16-EJS1126>, with the addition of a diagonal multiplier.

Maintained by Shiqing Yu. Last updated 5 years ago.

density-estimation graphical-models interaction-models score-matching undirected-graphs

1.7 match 1 stars 4.18 score 3 scripts 1 dependents

egpivo

SpatPCA:Regularized Principal Component Analysis for Spatial Data

Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.

Maintained by Wen-Ting Wang. Last updated 7 months ago.

admm covariance-estimation eigenfunctions lasso matrix-factorization pca rcpparmadillo rcppparallel regularization spatial spatial-data-analysis splines openblas cpp openmp

1.3 match 20 stars 5.53 score 17 scripts

flaviobarros

IQCC:Improved Quality Control Charts

Builds statistical control charts with exact limits for univariate and multivariate cases.

Maintained by Flavio Barros. Last updated 6 years ago.

quality-control

1.7 match 2 stars 3.92 score 28 scripts 1 dependents

marlonecobos

nichevol:Tools for Ecological Niche Evolution Assessment Considering Uncertainty

A collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species' niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>, Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>, Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>, Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>, Barve et al. (2011) <doi:10.1111/ecog.02671>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and Cobos et al. (2021) <doi:10.1111/jav.02868>.

Maintained by Marlon E. Cobos. Last updated 2 years ago.

1.7 match 14 stars 3.85 score 2 scripts

thomaschln

kgraph:Knowledge Graphs Constructions and Visualizations

Knowledge graphs enable to efficiently visualize and gain insights into large-scale data analysis results, as p-values from multiple studies or embedding data matrices. The usual workflow is a user providing a data frame of association studies results and specifying target nodes, e.g. phenotypes, to visualize. The knowledge graph then shows all the features which are significantly associated with the phenotype, with the edges being proportional to the association scores. As the user adds several target nodes and grouping information about the nodes such as biological pathways, the construction of such graphs soon becomes complex. The 'kgraph' package aims to enable users to easily build such knowledge graphs, and provides two main features: first, to enable building a knowledge graph based on a data frame of concepts relationships, be it p-values or cosine similarities; second, to enable determining an appropriate cut-off on cosine similarities from a complete embedding matrix, to enable the building of a knowledge graph directly from an embedding matrix. The 'kgraph' package provides several display, layout and cut-off options, and has already proven useful to researchers to enable them to visualize large sets of p-value associations with various phenotypes, and to quickly be able to visualize embedding results. Two example datasets are provided to demonstrate these behaviors, and several live 'shiny' applications are hosted by the CELEHS laboratory and Parse Health, as the KESER Mental Health application <https://keser-mental-health.parse-health.org/> based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Maintained by Thomas Charlon. Last updated 25 days ago.

1.3 match 4.85 score

harveyklyne

drape:Doubly Robust Average Partial Effects

Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.

Maintained by Harvey Klyne. Last updated 4 months ago.

1.6 match 2 stars 4.00 score 4 scripts

mikabr

jglmm:Generalized Mixed-Effects Models in Julia

R package for interfacing with Julia's MixedModels library to fit generalized linear mixed-effects models, similar to the lme4 package in R (<http://dmbates.github.io/MixedModels.jl/latest/>).

Maintained by Mika Braginsky. Last updated 2 years ago.

1.8 match 6 stars 3.32 score 35 scripts

bristol-vaccine-centre

testerror:Uncertainty in Multiplex Panel Testing

Provides methods to support the estimation of epidemiological parameters based on the results of multiplex panel tests.

Maintained by Robert Challen. Last updated 12 months ago.

1.8 match 1 stars 3.40 score 4 scripts

polehalieutique

EcoTroph:An implementation of the EcoTroph Ecosystem modelling approach

An approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.

Maintained by Jerome Guitton. Last updated 2 years ago.

2.0 match 1 stars 2.93 score 17 scripts

cran

MRTSampleSizeBinary:Sample Size Calculator for MRT with Binary Outcomes

Provides a sample size calculator for micro-randomized trials (MRTs) with binary outcomes based on Cohn et al. (2023) <doi:10.1002/sim.9748>. Also provides a power calculator when the sample size is input by the user.

Maintained by Tianchen Qian. Last updated 1 years ago.

1.7 match 3.26 score 36 scripts

leerichardson

swdft:Sliding Window Discrete Fourier Transform (SWDFT)

Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides statistical methods based on the SWDFT, and graphical tools to display the outputs.

Maintained by Lee F. Richardson. Last updated 6 years ago.

2.0 match 1 stars 2.70 score 6 scripts

numbersman77

bpp:Computations Around Bayesian Predictive Power

Implements functions to update Bayesian Predictive Power Computations after not stopping a clinical trial at an interim analysis. Such an interim analysis can either be blinded or unblinded. Code is provided for Normally distributed endpoints with known variance, with a prominent example being the hazard ratio.

Maintained by Kaspar Rufibach. Last updated 22 days ago.

1.7 match 3.12 score 19 scripts

cran

PPLasso:Prognostic Predictive Lasso for Biomarker Selection

We provide new tools for the identification of prognostic and predictive biomarkers. For further details we refer the reader to the paper: Zhu et al. Identification of prognostic and predictive biomarkers in high-dimensional data with PPLasso. BMC Bioinformatics. 2023 Jan 23;24(1):25.

Maintained by Wencan Zhu. Last updated 2 years ago.

2.6 match 2.00 score

catherineschramm

KSPM:Kernel Semi-Parametric Models

To fit the kernel semi-parametric model and its extensions. It allows multiple kernels and unlimited interactions in the same model. Coefficients are estimated by maximizing a penalized log-likelihood; penalization terms and hyperparameters are estimated by minimizing leave-one-out error. It includes predictions with confidence/prediction intervals, statistical tests for the significance of each kernel, a procedure for variable selection and graphical tools for diagnostics and interpretation of covariate effects. Currently it is implemented for continuous dependent variables. The package is based on the paper of Liu et al. (2007), <doi:10.1111/j.1541-0420.2007.00799.x>.

Maintained by Catherine Schramm. Last updated 5 years ago.

2.3 match 2.26 score 18 scripts

cran

HeterFunctionalData:Test of No Main and/or Interaction Effects in Functional Data

Distribution free heteroscedastic tests for functional data. The following tests are included in this package: test of no main treatment or contrast effect and no simple treatment effect given in Wang, Higgins, and Blasi (2010) <doi:10.1016/j.spl.2009.11.016>, no main time effect, and no interaction effect based on original observations given in Wang and Akritas (2010a) <doi:10.1080/10485250903171621> and tests based on ranks given in Wang and Akritas (2010b) <doi:10.1016/j.jmva.2010.03.012>.

Maintained by Haiyan Wang. Last updated 5 years ago.

5.1 match 1.00 score

adamtclark

ecostatscale:Statistical Scaling Functions for Ecological Systems

Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters <DOI:10.1111/ele.13760>. Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details.

Maintained by Adam Clark. Last updated 1 years ago.

2.0 match 3 stars 2.48 score

giabaio

bmhe:This Package Creates a Set of Functions Useful for Bayesian modelling

A set of utility functions that can be used to post-process BUGS or JAGS objects as well as other to facilitate various Bayesian modelling activities (including in HTA).

Maintained by Gianluca Baio. Last updated 11 days ago.

bayesian-statistics bugs cost-effectiveness-analysis jags tidyverse

1.7 match 2 stars 3.00 score 7 scripts

ethan-alt

surbayes:Bayesian Analysis of Seemingly Unrelated Regression Models

Implementation of the direct Monte Carlo approach of Zellner and Ando (2010) <doi:10.1016/j.jeconom.2010.04.005> to sample from posterior of Seemingly Unrelated Regression (SUR) models. In addition, a Gibbs sampler is implemented that allows the user to analyze SUR models using the power prior.

Maintained by Ethan Alt. Last updated 5 years ago.

openblas cpp openmp

1.8 match 2.70 score 1 scripts

tkrisztin

estimateW:Estimation of Spatial Weight Matrices

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial Durbin (SDM), and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

Maintained by Tamas Krisztin. Last updated 2 years ago.

openblas cpp

1.8 match 2.70 score 2 scripts

danielturek

nimbleSCR:Spatial Capture-Recapture (SCR) Methods Using 'nimble'

Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

Maintained by Daniel Turek. Last updated 2 years ago.

1.1 match 4.29 score 388 scripts

cran

mbrdr:Model-Based Response Dimension Reduction

Functions for model-based response dimension reduction. Usual dimension reduction methods in multivariate regression focus on the reduction of predictors, not responses. The response dimension reduction is theoretically founded in Yoo and Cook (2008) <doi:10.1016/j.csda.2008.07.029>. Later, three model-based response dimension reduction approaches are proposed in Yoo (2016) <doi:10.1080/02331888.2017.1410152> and Yoo (2019) <doi:10.1016/j.jkss.2019.02.001>. The method by Yoo and Cook (2008) is based on non-parametric ordinary least squares, but the model-based approaches are done through maximum likelihood estimation. For two model-based response dimension reduction methods called principal fitted response reduction and unstructured principal fitted response reduction, chi-squared tests are provided for determining the dimension of the response subspace.

Maintained by Jae Keun Yoo. Last updated 3 years ago.

4.7 match 1.00 score

gilberto-sassi

geoFKF:Kriging Method for Spatial Functional Data

A Kriging method for functional datasets with spatial dependency. This functional Kriging method avoids the need to estimate the trace-variogram, and the curve is estimated by minimizing a quadratic form. The curves in the functional dataset are smoothed using Fourier series. The functional Kriging of this package is a modification of the method proposed by Giraldo (2011) <doi:10.1007/s10651-010-0143-y>.

Maintained by Gilberto Sassi. Last updated 4 years ago.

openblas cpp openmp

1.7 match 1 stars 2.70 score 1 scripts

rabilon

merror:Accuracy and Precision of Measurements

N>=3 methods are used to measure each of n items. The data are used to estimate simultaneously systematic error (bias) and random error (imprecision). Observed measurements for each method or device are assumed to be linear functions of the unknown true values and the errors are assumed normally distributed. Pairwise calibration curves and plots can be easily generated. Unlike the 'ncb.od' function, the 'omx' function builds a one-factor measurement error model using 'OpenMx' and allows missing values, uses full information maximum likelihood to estimate parameters, and provides both likelihood-based and bootstrapped confidence intervals for all parameters, in addition to Wald-type intervals.

Maintained by Richard A. Bilonick. Last updated 2 years ago.

3.7 match 1.23 score 17 scripts

antongagin

BBEST:Bayesian Estimation of Incoherent Neutron Scattering Backgrounds

We implemented a Bayesian-statistics approach for subtraction of incoherent scattering from neutron total-scattering data. In this approach, the estimated background signal associated with incoherent scattering maximizes the posterior probability, which combines the likelihood of this signal in reciprocal and real spaces with the prior that favors smooth lines. The description of the corresponding approach could be found at Gagin and Levin (2014) <DOI:10.1107/S1600576714023796>.

Maintained by Anton Gagin. Last updated 4 years ago.

2.3 match 2.00 score 4 scripts

cran

adana:Adaptive Nature-Inspired Algorithms for Hybrid Genetic Optimization

The Genetic Algorithm (GA) is a type of optimization method of Evolutionary Algorithms. It uses the biologically inspired operators such as mutation, crossover, selection and replacement.Because of their global search and robustness abilities, GAs have been widely utilized in machine learning, expert systems, data science, engineering, life sciences and many other areas of research and business. However, the regular GAs need the techniques to improve their efficiency in computing time and performance in finding global optimum using some adaptation and hybridization strategies. The adaptive GAs (AGA) increase the convergence speed and success of regular GAs by setting the parameters crossover and mutation probabilities dynamically. The hybrid GAs combine the exploration strength of a stochastic GAs with the exact convergence ability of any type of deterministic local search algorithms such as simulated-annealing, in addition to other nature-inspired algorithms such as ant colony optimization, particle swarm optimization etc. The package 'adana' includes a rich working environment with its many functions that make possible to build and work regular GA, adaptive GA, hybrid GA and hybrid adaptive GA for any kind of optimization problems. Cebeci, Z. (2021, ISBN: 9786254397448).

Maintained by Erkut Tekeli. Last updated 3 years ago.

4.3 match 1.00 score 9 scripts

kzst

mxcc:Maxwell Control Charts

Computes Control limits, coefficients of control limits, various performance metrics and depicts control charts for monitoring Maxwell-distributed quality characteristics.

Maintained by Zsolt T. Kosztyan. Last updated 8 days ago.

1.6 match 2.60 score

cran

WRestimates:Sample Size, Power and CI for the Win Ratio

Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) <doi:10.1002/sim.9297>.

Maintained by Autumn ODonnell. Last updated 1 years ago.

2.0 match 2.00 score

julierennes

denoiseR:Regularized Low Rank Matrix Estimation

Estimate a low rank matrix from noisy data using singular values thresholding and shrinking functions. Impute missing values with matrix completion. The method is described in <arXiv:1602.01206>.

Maintained by Julie Josse. Last updated 5 years ago.

2.3 match 1.72 score 52 scripts

bernhardklar

lancor:Statistical Inference via Lancaster Correlation

Implementation of the methods described in Holzmann, Klar (2024) <doi:10.48550/arXiv.2303.17872>. Lancaster correlation is a correlation coefficient which equals the absolute value of the Pearson correlation for the bivariate normal distribution, and is equal to or slightly less than the maximum correlation coefficient for a variety of bivariate distributions. Rank and moment-based estimators and corresponding confidence intervals are implemented, as well as independence tests based on these statistics.

Maintained by Bernhard Klar. Last updated 11 months ago.

2.3 match 1.70 score

dxy99999

rbbnp:A Bias Bound Approach to Non-Parametric Inference

A novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.

Maintained by Xinyu DAI. Last updated 1 years ago.

2.3 match 1.70 score

prajual

bqror:Bayesian Quantile Regression for Ordinal Models

Package provides functions for estimating Bayesian quantile regression with ordinal outcomes, computing the covariate effects, model comparison measures, and inefficiency factor. The generic ordinal model with 3 or more outcomes (labeled OR1 model) is estimated by a combination of Gibbs sampling and Metropolis-Hastings algorithm. Whereas an ordinal model with exactly 3 outcomes (labeled OR2 model) is estimated using Gibbs sampling only. For each model framework, there is a specific function for estimation. The summary output produces estimates for regression quantiles and two measures of model comparison — log of marginal likelihood and Deviance Information Criterion (DIC). The package also has specific functions for computing the covariate effects and other functions that aids either the estimation or inference in quantile ordinal models. Rahman, M. A. (2016).“Bayesian Quantile Regression for Ordinal Models.” Bayesian Analysis, II(I): 1-24 <doi: 10.1214/15-BA939>. Yu, K., and Moyeed, R. A. (2001). “Bayesian Quantile Regression.” Statistics and Probability Letters, 54(4): 437–447 <doi: 10.1016/S0167-7152(01)00124-9>. Koenker, R., and Bassett, G. (1978).“Regression Quantiles.” Econometrica, 46(1): 33-50 <doi: 10.2307/1913643>. Chib, S. (1995). “Marginal likelihood from the Gibbs output.” Journal of the American Statistical Association, 90(432):1313–1321, 1995. <doi: 10.1080/01621459.1995.10476635>. Chib, S., and Jeliazkov, I. (2001). “Marginal likelihood from the Metropolis-Hastings output.” Journal of the American Statistical Association, 96(453):270–281, 2001. <doi: 10.1198/016214501750332848>.

Maintained by Prajual Maheshwari. Last updated 3 years ago.

1.9 match 2.01 score 4 scripts

gianmarco-v

SIRE:Finding Feedback Effects in SEM and Testing for Their Significance

Provides two main functionalities. 1 - Given a system of simultaneous equation, it decomposes the matrix of coefficients weighting the endogenous variables into three submatrices: one includes the subset of coefficients that have a causal nature in the model, two include the subset of coefficients that have a interdependent nature in the model, either at systematic level or induced by the correlation between error terms. 2 - Given a decomposed model, it tests for the significance of the interdependent relationships acting in the system, via Maximum likelihood and Wald test, which can be built starting from the function output. For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and Faliva and Zoia (1994) <doi:10.1007/BF02589041>.

Maintained by Gianmarco Vacca. Last updated 6 years ago.

1.9 match 2.00 score 5 scripts

cran

RNAseqNet:Log-Linear Poisson Graphical Model with Hot-Deck Multiple Imputation

Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in <doi:10.1093/bioinformatics/btx819>.

Maintained by Nathalie Vialaneix. Last updated 1 years ago.

1.8 match 2.00 score 10 scripts

shwasoo

DIFM:Dynamic ICAR Spatiotemporal Factor Models

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models 'DIFM' package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

Maintained by Hwasoo Shin. Last updated 11 months ago.

openblas cpp openmp

1.8 match 2.00 score 2 scripts

cran

RegCombin:Partially Linear Regression under Data Combination

We implement linear regression when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked, based on D'Haultfoeuille, Gaillac, Maurel (2022) <doi:10.3386/w29953>. The package allows for common regressors observed in both datasets, and for various shape constraints on the effect of covariates on the outcome of interest. It also provides the tools to perform a test of point identification. See the associated vignette <https://github.com/cgaillac/RegCombin/blob/master/RegCombin_vignette.pdf> for theory and code examples.

Maintained by Christophe Gaillac. Last updated 1 years ago.

3.4 match 1 stars 1.00 score

pvkabaila

ciuupi:Confidence Intervals Utilizing Uncertain Prior Information

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Mainzer, R. and Kabaila, P. (2019) <doi:10.32614/RJ-2019-026>, and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Maintained by Paul Kabaila. Last updated 9 months ago.

1.6 match 2.00 score 8 scripts

neonira

wyz.code.offensiveProgramming:Wizardry Code Offensive Programming

Allows to turn standard R code into offensive programming code. Provides code instrumentation to ease this change and tools to assist and accelerate code production and tuning while using offensive programming code technics. Should improve code robustness and quality. Function calls can be easily verified on-demand or in batch mode to assess parameter types and length conformities. Should improve coders productivity as offensive programming reduces the code size due to reduced number of controls all along the call chain. Should speed up processing as many checks will be reduced to one single check.

Maintained by Fabien Gelineau. Last updated 1 years ago.

1.1 match 2.95 score 4 scripts 3 dependents

mariushofert

nvmix:Multivariate Normal Variance Mixtures

Functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.

Maintained by Marius Hofert. Last updated 1 years ago.

1.1 match 1 stars 2.90 score 40 scripts

paleolimbot

exifr:EXIF Image Data in R

Reads EXIF data using ExifTool <https://exiftool.org> and returns results as a data frame. ExifTool is a platform-independent Perl library plus a command-line application for reading, writing and editing meta information in a wide variety of files. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR, FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

Maintained by Dewey Dunnington. Last updated 4 years ago.

0.5 match 35 stars 6.24 score 150 scripts 2 dependents

joshobrien

exiftoolr:ExifTool Functionality from R

Reads, writes, and edits EXIF and other file metadata using ExifTool <https://exiftool.org/>, returning read results as a data frame. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, Lyrics3, as well as the maker notes of many digital cameras by Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

Maintained by Joshua OBrien. Last updated 2 months ago.

exiftool image-processing metadata-extraction

0.5 match 23 stars 5.95 score 51 scripts 1 dependents

jfwambaugh

invivoPKfit:Fits Toxicokinetic Models to In Vivo PK Data Sets

Takes in vivo toxicokinetic concentration-time data and fits parameters of 1-compartment and 2-compartment models for each chemical. These methods are described in detail in "Informatics for Toxicokinetics" (submitted).

Maintained by John Wambaugh. Last updated 2 months ago.

1.1 match 2.60 score 4 scripts

bernice0321

MultiOrd:Generation of Multivariate Ordinal Variates

A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006) <DOI:10.1080/10629360600569246>.

Maintained by Ran Gao. Last updated 4 years ago.

2.0 match 1.48 score 9 scripts 1 dependents

edmhlin

BAYSTAR:On Bayesian Analysis of Threshold Autoregressive Models

Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.

Maintained by Edward M.H. Lin. Last updated 3 years ago.

2.3 match 2 stars 1.30 score 4 scripts

corinne-rahel

dmlalg:Double Machine Learning Algorithms

Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially Linear Mixed-Effects Models with Repeated Measurements". First part: our goal is to perform inference for the linear parameter in partially linear models with confounding variables. The standard DML estimator of the linear parameter has a two-stage least squares interpretation, which can lead to a large variance and overwide confidence intervals. We apply regularization to reduce the variance of the estimator, which produces narrower confidence intervals that are approximately valid. Nuisance terms can be flexibly estimated with machine learning algorithms. Second part: our goal is to estimate and perform inference for the linear coefficient in a partially linear mixed-effects model with DML. Machine learning algorithms allows us to incorporate more complex interaction structures and high-dimensional variables.

Maintained by Corinne Emmenegger. Last updated 3 years ago.

1.7 match 1 stars 1.70 score 3 scripts

vitara-p

icmm:Empirical Bayes Variable Selection via ICM/M Algorithm

Empirical Bayes variable selection via ICM/M algorithm for normal, binary logistic, and Cox's regression. The basic problem is to fit high-dimensional regression which sparse coefficients. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. More information can be found in the papers listed in the URL below.

Maintained by Vitara Pungpapong. Last updated 4 years ago.

2.3 match 1.28 score 19 scripts

neonira

wyz.code.rdoc:Wizardry Code Offensive Programming R Documentation

Allows to generate on-demand or by batch, any R documentation file, whatever is kind, data, function, class or package. It populates documentation sections, either automatically or by considering your input. Input code could be standard R code or offensive programming code. Documentation content completeness depends on the type of code you use. With offensive programming code, expect generated documentation to be fully completed, from a format and content point of view. With some standard R code, you will have to activate post processing to fill-in any section that requires complements. Produced manual page validity is automatically tested against R documentation compliance rules. Documentation language proficiency, wording style, and phrasal adjustments remains your job.

Maintained by Fabien Gelineau. Last updated 3 years ago.

1.1 match 2.70 score 1 scripts

paolomaranzano

SCDA:Spatially-Clustered Data Analysis

Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.

Maintained by Paolo Maranzano. Last updated 5 months ago.

1.6 match 1.79 score 31 scripts

sbshah10

discharge:Fourier Analysis of Discharge Data

Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.

Maintained by Samarth Shah. Last updated 6 years ago.

1.8 match 1.56 score 36 scripts

cran

WMAP:Weighted Meta-Analysis with Pseudo-Populations

Implementation of integrative weighting approaches for multiple observational studies and causal inferences. The package features three weighting approaches, each representing a special case of the unified weighting framework, introduced by Guha and Li (2024) <doi:10.1093/biomtc/ujae070>, which includes an extension of inverse probability weights for data integration settings.

Maintained by Subharup Guha. Last updated 4 months ago.

2.0 match 1.30 score

bernice0321

BinNor:Simultaneous Generation of Multivariate Binary and Normal Variates

Generating multiple binary and normal variables simultaneously given marginal characteristics and association structure based on the methodology proposed by Demirtas and Doganay (2012) <DOI:10.1080/10543406.2010.521874>.

Maintained by Ran Gao. Last updated 4 years ago.

2.0 match 1.28 score 19 scripts

denglinsui

EMSNM:EM Algorithm for Sigmoid Normal Model

It provides a method based on EM algorithm to estimate the parameter of a mixture model, Sigmoid-Normal Model, where the samples come from several normal distributions (also call them subgroups) whose mean is determined by co-variable Z and coefficient alpha while the variance are homogeneous. Meanwhile, the subgroup each item belongs to is determined by co-variables X and coefficient eta through Sigmoid link function which is the extension of Logistic Link function. It uses bootstrap to estimate the standard error of parameters. When sample is indeed separable, removing estimation with abnormal sigma, the estimation of alpha is quite well. I used this method to explore the subgroup structure of HIV patients and it can be used in other domains where exists subgroup structure.

Maintained by Linsui Deng. Last updated 6 years ago.

2.5 match 1.00 score

clavie3009

WLogit:Variable Selection in High-Dimensional Logistic Regression Models using a Whitening Approach

It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.

Maintained by Wencan Zhu. Last updated 2 years ago.

1.3 match 2.00 score 3 scripts