Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).

This package contains functionality for performing the following methods of p-value aggregation: Fisher's method, the Lancaster method (weighted Fisher's method), and Sidak correction.

The SciViews svGUI package eases the management of Graphical User Interfaces (GUI) in R. It is independent from any particular GUI widgets. It centralizes info about GUI elements currently used, and it dispatches GUI calls to the particular toolkits in use in function of the context.

This package computes model II simple linear regression using ordinary least squares (OLS), major axis (MA), standard major axis (SMA), and ranged major axis (RMA).

This package performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. It includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012 <doi:10.1093/molbev/MSR262>; Leblois et al., 2014 <doi:10.1093/molbev/msu212>; and see URL). The latter functions are used for prediction of likelihood surfaces and implied likelihood ratio confidence intervals, and for exploration of predictor space of the surface. Prediction of the response is based on ordinary Kriging (with residual error) of the input. Estimation of smoothing parameters is performed by generalized cross-validation.

This package provides five omnibus tests for testing the composite hypothesis of normality.

This package uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

This package provides tools for shrunken centroids regularized discriminant analysis for the purpose of classifying high dimensional data.

This package provides functions for Bayesian A/B testing including prior elicitation options based on Kass and Vaidyanathan (1992) doi:10.1111/j.2517-6161.1992.tb01868.x.

The DHARMa package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as JAGS, STAN, or BUGS can be processed as well. The resulting residuals are standardized to values between 0 and 1 and can be interpreted as intuitively as residuals from a linear regression. The package also provides a number of plot and test functions for typical model misspecification problems, such as over/underdispersion, zero-inflation, and residual spatial, phylogenetic and temporal autocorrelation.

The package offers functions for analyzing and interactively exploring large-scale single-cell RNA-seq datasets. Pagoda2 primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. pagoda2 was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos.

This is a package for fast and user-friendly estimation of econometric models with multiple fixed-effects. It includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018). It further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.

This package provides a developer-facing interface to Arrow Database Connectivity (ADBC) for the purposes of driver development, driver testing, and building high-level database interfaces for users. ADBC is an API standard for database access libraries that uses Arrow for result sets and query parameters.

The purpose of this package is to factor out logic and math common to Item Factor Analysis fitting, diagnostics, and analysis. It is envisioned as core support code suitable for more specialized IRT packages to build upon. Complete access to optimized C functions is made available with R_RegisterCCallable().

This package simulates the process of installing a package and then attaching it. This is a key part of the devtools package as it allows you to rapidly iterate while developing a package.

This package provides a mutation analysis tool that discovers cancer driver genes with frequent mutations in protein signalling sites such as post-translational modifications (phosphorylation, ubiquitination, etc). The Poisson generalized linear regression model identifies genes where cancer mutations in signalling sites are more frequent than expected from the sequence of the entire gene. Integration of mutations with signalling information helps find new driver genes and propose candidate mechanisms to known drivers.

This package provides an implementation of sparse linear discriminant analysis, which is a supervised classification method for multiple classes. Various novel optimization approaches to this problem are implemented including alternating direction method of multipliers (ADMM), proximal gradient (PG) and accelerated proximal gradient (APG). Functions for performing cross validation are also supplied along with basic prediction and plotting functions. Sparse zero variance discriminant (SZVD) analysis is also included in the package.

This package provides functions and datasets from book Companion to Applied regression, Second Edition, Sage, 2011.

This package provides a set of tools to permute multisets without loops or hash tables and to generate integer partitions. Cool-lex order is similar to colexicographical order.

This package provides tools for generating random assignments for common experimental designs and random samples for common sampling designs.

This package calls the Jupyter script nbconvert to create vignettes from notebooks. Those notebooks (.ipynb files) are files containing rich text, code, and its output. Code cells can be edited and evaluated interactively.

This package provides graphical scales that map data to aesthetics, and provides methods for automatically determining breaks and labels for axes and legends.

This package implements density, distribution functions, quantile functions and random generation functions for a large number of univariate and multivariate distributions.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.