This package provides functions for Meta-analysis Burden Test, Sequence Kernel Association Test (SKAT) and Optimal SKAT (SKAT-O) by Lee et al. (2013) <doi:10.1016/j.ajhg.2013.05.010>. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.

This package provides a color picker that can be used as an input in Shiny apps or Rmarkdown documents. The color picker supports alpha opacity, custom color palettes, and many more options. A plot color helper tool is available as an RStudio Addin, which helps you pick colors to use in your plots. A more generic color picker RStudio Addin is also provided to let you select colors to use in your R code.

Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in ggplot2.

This package implements affinity propagation clustering introduced by Frey and Dueck (2007). The package further provides leveraged affinity propagation and an algorithm for exemplar-based agglomerative clustering that can also be used to join clusters obtained from affinity propagation. Various plotting functions are available for analyzing clustering results.

This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.

This package provides utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models, this package implements features like standardization or bootstrapping of parameters and models, feature reduction (feature extraction and variable selection) as well as conversion between indices of effect size.

This package lets you fit pedigree-based mixed-effects models.

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

This package is an R wrapper around the cubature C library for adaptive multivariate integration over hypercubes. This version provides both hcubature and pcubature routines in addition to a vector interface.

This package provides a computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.

This package provides a simple set of wrapper functions for data.table::fread() that allows subsetting or filtering rows and selecting columns of table-formatted files too large for the available RAM.

This package performs search for the global minimum of a very complex non-linear objective function with a very large number of optima.

This package provides power analysis functions along the lines of Cohen (1988).

This package provides a graphical user interface for interactive Markov chain Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a posterior sample. The interface is powered by the Shiny web application framework and works with the output of MCMC programs written in any programming language (and has extended functionality for Stan models fit using the rstan and rstanarm packages).

This package provides infrastructure for seriation with an implementation of several seriation/sequencing techniques to reorder matrices, dissimilarity matrices, and dendrograms. It also provides (optimally) reordered heatmaps, color images and clustering visualizations like dissimilarity plots, and visual assessment of cluster tendency plots (VAT and iVAT).

This package implements both real-valued branches of the Lambert-W function (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for installing the entire GSL.

This package enables construction of continuous and non-contiguous area cartograms.

This package lets you analyze response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.

This package models with sparse and dense matrix matrices, using modular prediction and response module classes.

This package provides procedures to work with classification and regression trees.

This package computes moments of univariate truncated T distribution. There is only one exported function, e_trunct, which should be seen for details.

This package provides a lightweight package to easily manipulate, clean, transform, and prepare your data for analysis. It also forms the data wrangling backend for the packages in the easystats ecosystem.

This package provides functions for kriging and point pattern analysis.

The clusterCrit package provides an implementation of the following indices: Czekanowski-Dice, Folkes-Mallows, Hubert Γ, Jaccard, McNemar, Kulczynski, Phi, Rand, Rogers-Tanimoto, Russel-Rao or Sokal-Sneath. ClusterCrit defines several functions which compute internal quality indices or external comparison indices. The partitions are specified as an integer vector giving the index of the cluster each observation belongs to.