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

Meta-analysis is widely used to summarize estimated effects sizes across multiple statistical tests. Standard fixed and random effect meta-analysis methods assume that the estimated of the effect sizes are statistically independent. Here we relax this assumption and enable meta-analysis when the correlation matrix between effect size estimates is known.

This package provides a %dopar% adapter such that any type of futures can be used as backends for the foreach framework.

Flexible general-purpose toolbox implementing genetic algorithms (GAs) for stochastic optimisation. Binary, real-valued, and permutation representations are available to optimize a fitness function, i.e., a function provided by users depending on their objective function. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.

This package offers extensive tools for phylogenetic analysis. It focuses on phylogenetic comparative biology but also includes methods for visualizing, analyzing, manipulating, reading, writing, and inferring phylogenetic trees. Functions for comparative biology include ancestral state reconstruction, model fitting, and phylogeny and trait data simulation. A broad range of plotting methods includes mapping trait evolution on trees, projecting trees into phenotype space or geographic maps, and visualizing correlated speciation between trees. Additional functions allow for reading, writing, analyzing, inferring, simulating, and manipulating phylogenetic trees and comparative data. Examples include computing consensus trees, simulating trees and data under various models, and attaching species or clades to a tree either randomly or non-randomly. This package provides numerous tools for tree manipulations and analyses that are valuable for phylogenetic research.

This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods. We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables. The R functions allow the measurement errors to be either homoscedastic or heteroscedastic. To make the deconvolution estimators computationally more efficient in R, we adapt the "Fast Fourier Transform" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation. Several methods for the selection of the data-driven smoothing parameter are also provided in the package. See details in: Wang, X.F. and Wang, B. (2011). Deconvolution estimation in measurement error models: The R package decon. Journal of Statistical Software, 39(10), 1-24.

This package extends the out of memory vectors of ff with statistical functions and other utilities to ease their usage.

This is a package for exploratory graphical analysis of multivariate data, specifically gene expression data with different projection methods: principal component analysis, correspondence analysis, spectral map analysis.

This R package provides access to datasets and code published by the data journalism website FiveThirtyEight.

This package provides miscellaneous small tools and utilities. Many of them facilitate the work with matrices, e.g. inserting rows or columns, creating symmetric matrices, or checking for semidefiniteness. Other tools facilitate the work with regression models, e.g. extracting the standard errors, obtaining the number of (estimated) parameters, or calculating R-squared values.

This package provides for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user-level customization and extension, while simplifying cross-class interoperability.

This package provides functions used for local regression, likelihood and density estimation.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

Dichromat collapses red-green or green-blue distinctions to simulate the effects of different types of color-blindness.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

The pls package implements multivariate regression methods: Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and Canonical Powered Partial Least Squares (CPPLS). It supports:

• several algorithms: the traditional orthogonal scores (NIPALS) PLS algorithm, kernel PLS, wide kernel PLS, Simpls, and PCR through svd

• multi-response models (or PLS2)

• flexible cross-validation

• Jackknife variance estimates of regression coefficients

• extensive and flexible plots: scores, loadings, predictions, coefficients, (R)MSEP, R², and correlation loadings

• formula interface, modelled after lm(), with methods for predict, print, summary, plot, update, etc.

• extraction functions for coefficients, scores, and loadings

• MSEP, RMSEP, and R² estimates

• multiplicative scatter correction (MSC)

This package contains routines for logspline density estimation. The function oldlogspline() uses the same algorithm as the logspline package version 1.0.x; i.e., the Kooperberg and Stone (1992) algorithm (with an improved interface). The recommended routine logspline() uses an algorithm from Stone et al (1997).

This package provides grid grobs that fill in a user-defined area with various patterns. It includes enhanced versions of the geometric and image-based patterns originally contained in the ggpattern package as well as original pch, polygon_tiling, regular_polygon, rose, text, wave, and weave patterns plus support for custom user-defined patterns.

This package provides airline on-time data for all flights departing NYC in 2013. It also includes useful metadata on airlines, airports, weather, and planes.

This package provides functionality for creating Quantile-Quantile (QQ) and Probability-Probability (PP) plots with simultaneous testing bands to asses significance of sample deviation from a reference distribution.

This package provides utilities for processing Rd objects and files. Extract argument descriptions and other parts of the help pages of functions.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

This package provides sparse vectors powered by ALTREP (Alternative Representations for R Objects) that behave like regular vectors, and can thus be used in data frames. It also provides tools to convert between sparse matrices and data frames with sparse columns and functions to interact with sparse vectors.

This is a package for the computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate, and multimodal regression.