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r-expint
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The exponential integrals E_1(x), E_2(x), E_n(x) and Ei(x), and the incomplete gamma function G(a, x) defined for negative values of its first argument. The package also gives easy access to the underlying C routines through an API; see the package vignette for details. A test package included in sub-directory example_API provides an implementation. C routines derived from the GNU Scientific Library <https://www.gnu.org/software/gsl/>.
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2025-03-25 |
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r-fqtl
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Factored QTL estimation
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2025-03-25 |
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r-rcppprogress
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Allows to display a progress bar in the R console for long running computations taking place in c++ code, and support for interrupting those computations even in multithreaded code, typically using OpenMP.
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2025-03-25 |
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r-varbvs
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Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.
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2025-03-25 |
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r-nor1mix
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Onedimensional Normal Mixture Models Classes, for, e.g., density estimation or clustering algorithms research and teaching; providing the widely used Marron-Wand densities. Efficient random number generation and graphics; now fitting to data by ML (Maximum Likelihood) or EM estimation.
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2025-03-25 |
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r-mashr
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Implements the multivariate adaptive shrinkage (mash) method of Urbut et al (2019) <DOI:10.1038/s41588-018-0268-8> for estimating and testing large numbers of effects in many conditions (or many outcomes). Mash takes an empirical Bayes approach to testing and effect estimation; it estimates patterns of similarity among conditions, then exploits these patterns to improve accuracy of the effect estimates. The core linear algebra is implemented in C++ for fast model fitting and posterior computation.
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2025-03-25 |
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r-rmeta
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Functions for simple fixed and random effects meta-analysis for two-sample comparisons and cumulative meta-analyses. Draws standard summary plots, funnel plots, and computes summaries and tests for association and heterogeneity.
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2025-03-25 |
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r-rcppgsl
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'Rcpp' integration for 'GNU GSL' vectors and matrices The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for scientific computing. It is particularly useful for C and C++ programs as it provides a standard C interface to a wide range of mathematical routines. There are over 1000 functions in total with an extensive test suite. The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data structures and R using concepts from 'Rcpp' which is itself a package that eases the interfaces between R and C++. This package also serves as a prime example of how to build a package that uses 'Rcpp' to connect to another third-party library. The 'autoconf' script, 'inline' plugin and example package can all be used as a stanza to write a similar package against another library.
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2025-03-25 |
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r-glmpca
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Implements a generalized version of principal components analysis (GLM-PCA) for dimension reduction of non-normally distributed data such as counts or binary matrices. Townes FW, Hicks SC, Aryee MJ, Irizarry RA (2019) <doi:10.1101/574574>. Townes FW (2019) <arXiv:1907.02647>.
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2025-03-25 |
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r-mclust
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Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
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2025-03-25 |
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r-descend
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DESCEND deconvolves the true gene expression distribution across cells for UMI scRNA-seq counts. It provides estimates of several distribution based statistics (five distribution measurements and the coefficients of covariates (such as batches or cell size)). Based on the estimation, DESCEND also can perform highly variable selection and differential testing of dispersion and burstiness measurements between two groups of cells with covariates adjustment.
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2025-03-25 |
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r-flashr
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Methods for matrix factorization based on "Empirical Bayes Matrix Factorization" (W. Wang & M. Stephens, 2018, <https://arxiv.org/abs/1802.06931>). The name "flashr" comes from "Factors and Loadings by Adaptive SHrinkage in R".
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2025-03-25 |
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r-softimpute
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Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
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2025-03-25 |
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r-ebnm
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Provides functions to fit the normal means problem using Empirical Bayes. The goal is for these functions to be simple, fast and stable. Currently two models are implemented: the point-normal and point-laplace priors. The point-normal is considerably faster. See functions ebnm_point_normal, ebnm_point_laplace.
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2025-03-25 |
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r-ashr
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The R package 'ashr' implements an Empirical Bayes approach for large-scale hypothesis testing and false discovery rate (FDR) estimation based on the methods proposed in M. Stephens, 2016, "False discovery rates: a new deal", <DOI:10.1093/biostatistics/kxw041>. These methods can be applied whenever two sets of summary statistics---estimated effects and standard errors---are available, just as 'qvalue' can be applied to previously computed p-values. Two main interfaces are provided: ash(), which is more user-friendly; and ash.workhorse(), which has more options and is geared toward advanced users. The ash() and ash.workhorse() also provides a flexible modeling interface that can accomodate a variety of likelihoods (e.g., normal, Poisson) and mixture priors (e.g., uniform, normal).
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2025-03-25 |
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r-mixsqp
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Provides optimization algorithms based on sequential quadratic programming (SQP) for maximum likelihood estimation of the mixture proportions in a finite mixture model where the component densities are known. The algorithms are expected to obtain solutions that are at least as accurate as the state-of-the-art MOSEK interior-point solver (called by function "KWDual" in the 'REBayes' package), and they are expected to arrive at solutions more quickly in large data sets. The algorithms are described in Y. Kim, P. Carbonetto, M. Stephens & M. Anitescu (2012) <arXiv:1806.01412>.
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2025-03-25 |
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r-etrunct
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Computes moments of univariate truncated t distribution. There is only one exported function, e_trunct(), which should be seen for details.
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2025-03-25 |
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r-pscl
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Bayesian analysis of item-response theory (IRT) models, roll call analysis; computing highest density regions; maximum likelihood estimation of zero-inflated and hurdle models for count data; goodness-of-fit measures for GLMs; data sets used in writing and teaching at the Political Science Computational Laboratory; seats-votes curves.
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2025-03-25 |
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r-truncnorm
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Density, probability, quantile and random number generation functions for the truncated normal distribution.
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2025-03-25 |
