This package implements the RUV (Remove Unwanted Variation) algorithms. These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data. The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well. The algorithms require the user to specify a set of negative control variables, as described in the references. The algorithms included in this package are RUV-2, RUV-4, RUV-inv, RUV-rinv, RUV-I, and RUV-III, along with various supporting algorithms.

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

This package provides functions for converting, importing, and drawing PostScript pictures in R plots.

This package provides ACE and AVAS methods for choosing regression transformations.

This package provides a suite of custom R Markdown formats and templates for authoring journal articles and conference submissions.

This package provides various themes, palettes, and other functions that are used to customise ggplots to look like they were made in GraphPad Prism. The Prism-look is achieved with theme_prism() and scale_fill|colour_prism(), axes can be changed with custom guides like guide_prism_minor(), and significance indicators added with add_pvalue().

This package provides a method to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree.

This package provides an efficient interface to MPI by utilizing S4 classes and methods with a focus on Single Program/Multiple Data (SPMD) parallel programming style, which is intended for batch parallel execution.

This package provides functions for animations in statistics, covering topics in probability theory, mathematical statistics, multivariate statistics, non-parametric statistics, sampling survey, linear models, time series, computational statistics, data mining and machine learning. These functions may be helpful in teaching statistics and data analysis. Also provided in this package are a series of functions to save animations to various formats, e.g. GIF, HTML pages, PDF, and videos. PDF animations can be inserted into Sweave / knitr easily.

This package provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census, including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. The package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways.

This package provides utilities for computing measures to assess model quality, which are not directly provided by R's base or stats packages. These include e.g. measures like r-squared, intraclass correlation coefficient, root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.

This package provides a simple, consistent interface to working with XML files in R. It is built on top of the libxml2 C library.

This package provides improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

This package proposes a new file format named gson for storing gene set and related information, and provides read, write and other utilities to process this file format.

Tidyft is an extension of data.table. It uses modifification by reference whenever possible. This toolkit is designed for big data analysis in high-performance desktop or laptop computers. The syntax of the package is similar or identical to tidyverse.

Generalized Additive Mixed Modeling (GAMM; Lin & Zhang, 1999) as implemented in the R package mgcv is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).

This package implements multitaper spectral estimation techniques using prolate spheroidal sequences (Slepians) and sine tapers for time series analysis. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.

This package contains the Rook specification and convenience software for building and running Rook applications. A Rook application is an R reference class object that implements a call method or an R closure that takes exactly one argument, an environment, and returns a list with three named elements: the status, the headers, and the body.

This package provides a set of predicates and assertions for checking the properties of dates and times. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

For outlier detection in small and normally distributed samples the ratio test of Dixon (Q-test) can be used. Density, distribution function, quantile function and random generation for Dixon's ratio statistics are provided as wrapper functions. The core applies McBane's Fortran functions that use Gaussian quadrature for a numerical solution.

This package provides basic wavelet routines for time series (1D), image (2D) and array (3D) analysis. The code provided here is based on wavelet methodology developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).

The R package ggplot2 is a plotting system based on the grammar of graphics. GGally extends ggplot2 by adding several functions to reduce the complexity of combining geometric objects with transformed data. Some of these functions include a pairwise plot matrix, a two group pairwise plot matrix, a parallel coordinates plot, a survival plot, and several functions to plot networks.

Ggplot2 is an implementation of the grammar of graphics in R. It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, and you can still build up a plot step by step from multiple data sources. It also implements a sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

This package provides functions for applying a wide range of fisheries stock assessment methods.