| hyperopt | 0.1.2 | Distributed Asynchronous Hyperparameter Optimization | Mar 25, 2025 | BSD |
| mpebpm | 0.2.dev1 | This package provides GPU-accelerated inference for the Empirical Bayes Poisson Means (EBPM) problem. This model can be used to model variation in scRNA-seq data due to measurement error, as well as variation in true gene expression values (Sarkar and Stephens 2020).
This implementation readily supports fitting the model for data on the order of 10^6 cells and 10^4 genes in parallel. It also supports fitting multiple EBPM problems per gene in parallel, as arise when e.g., cells have been assigned to groups (clusters). For example, we have used the method to solve 537,678 EBPM problems (54 conditions by 9,957 genes) in parallel in a few minutes (Sarkar et al. 2019). | Mar 25, 2025 | MIT |
| pyplink | 1.3.5 | Python module to read binary Plink files. | Mar 25, 2025 | MIT |
| r-ashr | 2.2.51.dev1 | 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). | Mar 25, 2025 | GPL (>= 3) |
| r-bma | 3.18.14 | Package for Bayesian model averaging and variable selection for linear models, generalized linear models and survival models (cox regression). | Mar 25, 2025 | GPL-2 |
| r-coloc | 4.0.4 | Performs the colocalisation tests described in Plagnol et al (2009) <doi:10.1093/biostatistics/kxn039>, Wallace et al (2013) <doi:10.1002/gepi.21765>, Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>, Wallace (2020) <doi:doi.org/10.1371/journal.pgen.1008720>. | Mar 25, 2025 | GPL-3 |
| r-cowplot | 1.0.0 | Provides various features that help with creating publication-quality figures with 'ggplot2', such as a set of themes, functions to align plots and arrange them into complex compound figures, and functions that make it easy to annotate plots and or mix plots with images. The package was originally written for internal use in the Wilke lab, hence the name (Claus O. Wilke's plot package). It has also been used extensively in the book Fundamentals of Data Visualization. | Mar 25, 2025 | GPL-2 |
| r-descend | 0.0.0.9000 | 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. | Mar 25, 2025 | GPL (>= 2) |
| r-ebnm | 0.1_16 | 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. | Mar 25, 2025 | GPL (>=3) |
| r-etrunct | 0.1 | Computes moments of univariate truncated t distribution. There is only one exported function, e_trunct(), which should be seen for details. | Mar 25, 2025 | MIT + file LICENSE |
| r-expint | 0.1.6 | 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/>. | Mar 25, 2025 | GPL (>= 2) |
| r-fasttopics | 0.5.59.dev1 | Fast algorithms for fitting topic models and non-negative factorizations to count data. | Mar 25, 2025 | MIT |
| r-flashr | 0.6_3 | 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". | Mar 25, 2025 | BSD_3_clause + file LICENSE |
| r-fqtl | 1.4.2.dev1 | Factored QTL estimation | Mar 25, 2025 | LICENSE |
| r-glmpca | 0.1.0.dev1 | 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>. | Mar 25, 2025 | Artistic 2.0 |
| r-grove | 1.1 | Functional denoising and functional ANOVA through wavelet-domain Markov groves. Fore more details see: Ma L. and Soriano J. (2016) Efficient functional ANOVA through wavelet-domain Markov groves. <arXiv:1602.03990v2 [stat.ME]>. | Mar 25, 2025 | GPL-3 |
| r-invgamma | 1.1 | Light weight implementation of the standard distribution functions for the inverse gamma distribution, wrapping those for the gamma distribution in the stats package. | Mar 25, 2025 | GPL-2 |
| r-mashr | 0.2.50 | 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. | Mar 25, 2025 | BSD_3_clause |
| r-mclust | 5.4.3 | 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. | Mar 25, 2025 | GPL (>= 2) |
| r-mixsqp | 0.3.45.dev1 | 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>. | Mar 25, 2025 | MIT + file LICENSE |
| r-mrash | 0.1.dev1 | — | Mar 25, 2025 | — |
| r-mvsusier | 0.0.3.0436.dev1 | A more general implementation of the Sum of SIngle Effects (SuSiE) regression for Bayesian variable selection. | Mar 25, 2025 | MIT |
| r-nnlm | 0.4.4.dev1 | This is a package for Non-Negative Linear Models (NNLM). It implements fast sequential coordinate descent algorithms for non-negative linear regression and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss. Many other features are also implemented, including missing value imputation, domain knowledge integration, designable W and H matrices and multiple forms of regularizations. | Mar 25, 2025 | BSD_2_clause |
| r-nor1mix | 1.2.3 | 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. | Mar 25, 2025 | GPL (>= 2) |
| r-pracma | 2.2.9 | Provides a large number of functions from numerical analysis and linear algebra, numerical optimization, differential equations, time series, plus some well-known special mathematical functions. Uses 'MATLAB' function names where appropriate to simplify porting. | Mar 25, 2025 | GPL (>= 3) |
