This package estimates conditional Akaike information in mixed-effect models. These models are fitted using (g)lmer() from lme4, lme() from nlme, and gamm() from mgcv. The provided functions facilitate the computation of the conditional Akaike information for model evaluation.

This package provides functionality for client-side navigation of the server side file system in shiny apps. In case the app is running locally this gives the user direct access to the file system without the need to "download" files to a temporary location. Both file and folder selection as well as file saving is available.

This package provides tools to more conveniently perform tasks associated with add-on packages. pacman conveniently wraps library and package related functions and names them in an intuitive and consistent fashion. It seeks to combine functionality from lower level functions which can speed up workflow.

This package implements targeted minimum loss-based estimators of counterfactual means and causal effects that are doubly-robust with respect both to consistency and asymptotic normality.

This package provides an R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition. It runs the equivalent of the leidenalg find_partition() function. This package includes the required source code files from the official leidenalg distribution and functions from the R igraph package.

This package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G.

This package provides conditional inference procedures for the general independence problem including two-sample, K-sample (non-parametric ANOVA), correlation, censored, ordered and multivariate problems.

This package provides a base S4 class for comparative methods, incorporating one or more trees and trait data.

The tensor product of two arrays is notionally an outer product of the arrays collapsed in specific extents by summing along the appropriate diagonals. This package allows you to compute the tensor product of arrays.

Access to selected functions found in liblwgeom, the light-weight geometry library used by PostGIS.

This package provides functions for importing, exporting, plotting and other manipulations of bitmapped images.

This package provides density, distribution, quantile and hazard functions of a stable variate, as well as generalized regression models for the parameters of a stable distribution.

This package provides cross-platform utilities for prompting the user for credentials or a passphrase, for example to authenticate with a server or read a protected key.

This package lets you expand factors, characters and other eligible classes into dummy/indicator variables.

This is a package for graphical and statistical analyses of environmental data, with a focus on analyzing chemical concentrations and physical parameters, usually in the context of mandated environmental monitoring. It provides major environmental statistical methods found in the literature and regulatory guidance documents, with extensive help that explains what these methods do, how to use them, and where to find them in the literature. It comes with numerous built-in data sets from regulatory guidance documents and environmental statistics literature. It includes scripts reproducing analyses presented in the book "EnvStats: An R Package for Environmental Statistics" (Millard, 2013, Springer, ISBN 978-1-4614-8455-4, https://link.springer.com/book/10.1007/978-1-4614-8456-1).

This package provides an implementation of the Harmony algorithm for single cell integration. This package includes a standalone Harmony function and interfaces to external frameworks.

This package is designed as an integrated package for genetic data analysis of both population and family data. Currently, it contains functions for sample size calculations of both population-based and family-based designs, probability of familial disease aggregation, kinship calculation, statistics in linkage analysis, and association analysis involving genetic markers including haplotype analysis with or without environmental covariates. Over years, the package has been developed in-between many projects hence also in line with the name (gap).

This package provides a set of fonts. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.

This package provides

1. pseudo random generators, such as general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators)

2. quasi random generators, such as the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence), and

3. some generator tests: the gap test, the serial test, the poker test.

See e.g. Gentle (2003) doi:10.1007/b97336.

This package facilitates mapping by making natural earth map data from https://www.naturalearthdata.com/ more easily available to R users.

This package provides non-statistical utilities used by the software developed by the Statnet Project.

This package provides functions and datasets for the book "Modern Applied Statistics with S" (4th edition, 2002) by Venables and Ripley.

This package provides visualizations for SHAP (SHapley Additive exPlanations) such as waterfall plots, force plots, various types of importance plots, dependence plots, and interaction plots. These plots act on a shapviz object created from a matrix of SHAP values and a corresponding feature dataset. Wrappers for the R packages xgboost, lightgbm, fastshap, shapr, h2o, treeshap, DALEX, and kernelshap are added for convenience. By separating visualization and computation, it is possible to display factor variables in graphs, even if the SHAP values are calculated by a model that requires numerical features. The plots are inspired by those provided by the shap package in Python, but there is no dependency on it.

Feature Selection with Regularized Random Forest. This package is based on the randomForest package by Andy Liaw. The key difference is the RRF() function that builds a regularized random forest. Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener, Regularized random forest for classification by Houtao Deng, Regularized random forest for regression by Xin Guan. Reference: Houtao Deng (2013) <doi:10.48550/arXiv.1306.0237>.