This package provides statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution).

This package provides utilities for working with Google APIs. This includes functions and classes for handling common credential types and for preparing, executing, and processing HTTP requests.

This package provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are developed using C++ code and of the OpenMP API.

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

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 data structures and algorithms for k-ary relations with arbitrary domains, featuring relational algebra, predicate functions, and fitters for consensus relations.

This package provides a graphical display of a correlation matrix or general matrix. It also contains some algorithms to do matrix reordering. In addition, corrplot is good at details, including choosing color, text labels, color labels, layout, etc.

This package allows you to create Q-Q and Manhattan plots for GWAS data from PLINK results.

This package lets you estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and compute average partial effects. Incidental parameter bias can be reduced with an asymptotic bias correction proposed by Fernandez-Val (2009) <doi:10.1016/j.jeconom.2009.02.007>.

This light-weight package helps you track and visualize the progress of parallel versions of vectorized R functions of the mc*apply family.

This package does local optimization using two derivatives and trust regions. Guaranteed to converge to local minimum of objective function.

This package provides tools to infer the code style (which style rules are followed and which ones are not) from one package and use it to check another. This makes it easier to find and correct the most important problems first.

This package provides tools for defensive programming. It is inspired by purrr mappers and based on rlang. Attempt extends and facilitates defensive programming by providing a consistent grammar, and a set of functions for common tests and conditions. Attempt only depends on rlang, and focuses on speed, so it can be integrated with other functions and used in the data analysis.

Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method.

This package provides a fast dimensionality reduction method scalable to large numbers of samples. Landmark Multi-Dimensional Scaling (LMDS) is an extension of classical Torgerson MDS, but rather than calculating a complete distance matrix between all pairs of samples, only the distances between a set of landmarks and the samples are calculated.

This package provides a parallel estimation of the mutual information based on entropy estimates from k-nearest neighbors distances and algorithms for the reconstruction of gene regulatory networks.

Stringr is a consistent, simple and easy to use set of wrappers around the fantastic stringi package. All function and argument names (and positions) are consistent, all functions deal with "NA"'s and zero length vectors in the same way, and the output from one function is easy to feed into the input of another.

This package provides a set of tools for post processing the outcomes of species distribution modeling exercises. It includes novel methods for comparing models and tracking changes in distributions through time. It further includes methods for visualizing outcomes, selecting thresholds, calculating measures of accuracy and landscape fragmentation statistics, etc.

When testing multiple hypotheses simultaneously, this package provides functionality to calculate a lower bound for the number of correct rejections (as a function of the number of rejected hypotheses), which holds simultaneously -with high probability- for all possible number of rejections. As a special case, a lower bound for the total number of false null hypotheses can be inferred. Dependent test statistics can be handled for multiple tests of associations. For independent test statistics, it is sufficient to provide a list of p-values.

This is a package for the manipulation of genetic data (SNPs). Computation of genetic relationship matrix (GRM) and dominance matrix, linkage disequilibrium (LD), and heritability with efficient algorithms for linear mixed models (AIREML).

The two main functionalities of this package are creating mock objects (functions) and selectively intercepting calls to a given function that originate in some other function. It can be used with any testing framework available for R. Mock objects can be injected with either this package's own stub function or a similar with_mock facility present in the testthat package.

This package contains a set of functions for working with Random Number Generators (RNGs). In particular, it defines a generic S4 framework for getting/setting the current RNG, or RNG data that are embedded into objects for reproducibility. Notably, convenient default methods greatly facilitate the way current RNG settings can be changed.

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 package provides functions for working with magnetic resonance images. It supports reading and writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH); interactive and non-interactive visualization; flexible image manipulation; metadata and sparse image handling.