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r-nnlm
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public |
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.
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2023-06-18 |
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vireosnp
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public |
vireoSNP - donor deconvolution for multiplexed scRNA-seq data
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2023-06-18 |
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txg
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public |
The 10x Genomics Cloud CLI is a command line interface (CLI) that allows you to upload FASTQ files to projects in your 10x Genomics account, create projects from the command line, and manage other tasks related to your 10x Genomics account.
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2023-06-18 |
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r-seuratdisk
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public |
The h5Seurat file format is specifically designed for the storage and analysis of multi-modal single-cell and spatially-resolved expression experiments, for example, from CITE-seq or 10X Visium technologies. It holds all molecular information and associated metadata, including (for example) nearest-neighbor graphs, dimensional reduction information, spatial coordinates and image data, and cluster labels. We also support rapid and on-disk conversion between h5Seurat and AnnData objects, with the goal of enhancing interoperability between Seurat and Scanpy.
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2023-06-18 |
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r-topicscore
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Provides implementation of the "Topic SCORE" algorithm that is proposed by Tracy Ke and Minzhe Wang. The singular value decomposition step is optimized through the usage of svds() function in 'RSpectra' package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise error measure in the word-topic matrix A, and an algorithm for recovering the topic-document matrix W given A and D based on quadratic programming. The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
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2023-06-16 |
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r-mvsusier
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public |
A more general implementation of the Sum of SIngle Effects (SuSiE) regression for Bayesian variable selection.
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2023-06-16 |
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r-grove
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public |
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]>.
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2023-06-16 |
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r-coloc
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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>.
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2023-06-16 |
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r-bma
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public |
Package for Bayesian model averaging and variable selection for linear models, generalized linear models and survival models (cox regression).
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2023-06-16 |
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r-rrcov
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public |
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point: principal component analysis (Filzmoser and Todorov (2013), <doi:10.1016/j.ins.2012.10.017>), linear and quadratic discriminant analysis (Todorov and Pires (2007)), multivariate tests (Todorov and Filzmoser (2010) <doi:10.1016/j.csda.2009.08.015>), outlier detection (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>). See also Todorov and Filzmoser (2009) <ISBN-13:978-3838108148>, Todorov and Filzmoser (2010) <doi:10.18637/jss.v032.i03> and Boudt et al. (2019) <doi:10.1007/s11222-019-09869-x>.
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2023-06-16 |
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r-rcpparmadillo
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public |
'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'. Note that Armadillo requires a fairly recent compiler; for the g++ family at least version 4.6.* is required.
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2023-06-16 |
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r-mrash
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public |
No Summary
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2023-06-16 |
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r-invgamma
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public |
Light weight implementation of the standard distribution functions for the inverse gamma distribution, wrapping those for the gamma distribution in the stats package.
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2023-06-16 |
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mpebpm
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public |
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).
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2023-06-16 |
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r-susier
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Implements methods for variable selection in linear regression based on the "Sum of Single Effects" (SuSiE) model, as described in Wang et al (2020) <DOI:10.1101/501114>. These methods provide simple summaries, called "Credible Sets", for accurately quantifying uncertainty in which variables should be selected. The methods are motivated by genetic fine-mapping applications, and are particularly well-suited to settings where variables are highly correlated and detectable effects are sparse. The fitting algorithm, a Bayesian analogue of stepwise selection methods called "Iterative Bayesian Stepwise Selection" (IBSS), is simple and fast, allowing the SuSiE model be fit to large data sets (thousands of samples and hundreds of thousands of variables).
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2023-06-16 |
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r-fasttopics
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Fast algorithms for fitting topic models and non-negative factorizations to count data.
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2023-06-16 |
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r-rcpp
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public |
The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>), the book by Eddelbuettel (2013, <doi:10.1007/978-1-4614-6868-4>) and the paper by Eddelbuettel and Balamuta (2018, <doi:10.1080/00031305.2017.1375990>); see 'citation("Rcpp")' for details.
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2023-06-16 |
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r-cowplot
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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.
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2023-06-16 |
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r-rcppparallel
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High level functions for parallel programming with 'Rcpp'. For example, the 'parallelFor()' function can be used to convert the work of a standard serial "for" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.
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2023-06-16 |
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ucsc-liftover
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public |
Move annotations from one assembly to another
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2023-06-16 |
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pyplink
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Python module to read binary Plink files.
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2023-06-16 |
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scqtl
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public |
This package implements maximum likelihood estimation of the zero-inflated negative binomial model described in:
Abhishek K Sarkar, Po-Yuan Tung, John D. Blischak, Jonathan E. Burnett, Yang I. Li, Matthew Stephens, Yoav Gilad. "Discovery and characterization of variance QTLs in human induced pluripotent stem cells". PLoS Genetics (2019). https://doi.org/10.1371/journal.pgen.1008045
The key idea is that we learn latent point-Gamma distributions for expression per individual/condition per gene, and then perform all downstream analysis on (the parameters of) those distributions.
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2023-06-16 |
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scmodes
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public |
This package collects implementations of single cell expression models, and code to evaluate their performance as described in Sarkar, AK and Stephens, M. "Separating measurement and expression models clarifies confusion in single-cell RNA sequencing analysis"
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2023-06-16 |
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r-pracma
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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.
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2023-06-16 |
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hyperopt
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public |
Distributed Asynchronous Hyperparameter Optimization
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-fqtl
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Factored QTL estimation
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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-rmeta
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public |
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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2023-06-16 |
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r-rcppgsl
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public |
'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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2023-06-16 |
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r-mashr
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public |
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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-pscl
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public |
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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
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r-mclust
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public |
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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2023-06-16 |
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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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2023-06-16 |
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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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2023-06-16 |
