This package provides an infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules). It also provides C implementations of the association mining algorithms Apriori and Eclat.

This package provides tools to calculate exact and approximate theory experimental designs for D, A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact.

This package lets you interact with Google Sheets through the Sheets API v4. This package can read and write both the metadata and the cell data in a Sheet.

Customize Bootstrap and Bootswatch themes, like colors, fonts, grid layout, to use in Shiny applications, rmarkdown documents and flexdashboard.

This package provides methods to parse, query and serialize information stored in the Resource Description Framework (RDF). This package supports RDF by implementing an R interface to the Redland RDF C library. In brief, RDF provides a structured graph consisting of Statements composed of Subject, Predicate, and Object Nodes.

This package contains functions to estimate L-moments and trimmed L-moments from the data. It also contains functions to estimate the parameters of the normal polynomial quantile mixture and the Cauchy polynomial quantile mixture from L-moments and trimmed L-moments.

This library lets you place an exclusive or shared lock on a file using the appropriate system call provided by the underlying operating system.

The fit.models function and its associated methods (coefficients, print, summary, plot, etc.) were originally provided in the robust package to compare robustly and classically fitted model objects. The aim of the fit.models package is to separate this fitted model object comparison functionality from the robust package and to extend it to support fitting methods (e.g., classical, robust, Bayesian, regularized, etc.) more generally.

This package provides a set of functions for sparse matrix algebra. Differences with other sparse matrix packages are:

1. it only supports (essentially) one sparse matrix format;

2. it is based on transparent and simple structure(s);

3. it is tailored for MCMC calculations within G(M)RF;

4. and it is fast and scalable (with the extension package spam64).

This package provides tools to create themes and color palettes for the package ggplot2.

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 is a package for converting natural language text into tokens. It includes tokenizers for shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs, characters, shingled characters, lines, tweets, Penn Treebank, regular expressions, as well as functions for counting characters, words, and sentences, and a function for splitting longer texts into separate documents, each with the same number of words. The tokenizers have a consistent interface, and the package is built on the stringi and Rcpp packages for fast yet correct tokenization in UTF-8 encoding.

This package provides a set of little functions that have been found useful to do little odds and ends such as plotting the results of K-means clustering, substituting special text characters, viewing parts of a data.frame, constructing formulas from text and building design and response matrices.

This package creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().

This package provides a set of R functions for identifying and correcting HGNC human gene symbols. In addition, you can identify MGI mouse gene symbols, which have been converted to date format by Excel, withdrawn, or aliased. It also contains functions for reversibly converting between HGNC symbols and valid R names.

The main function of this package is beep(), with the purpose to make it easy to play notification sounds on whatever platform you are on. It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready.

This package provides tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).

This package provides a Database Interface (DBI) compliant driver for R to access PostgreSQL database systems.

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 fast and accurate convolution-type smoothed quantile regression, implemented using Barzilai-Borwein gradient descent with a Huber regression warm start. Confidence intervals for regression coefficients are constructed using multiplier bootstrap.

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.

A treemap is a space-filling visualization of hierarchical structures. This package offers great flexibility to draw treemaps.

With this tool, a user should be able to quickly implement complex random effect models through simple C++ templates. The package combines CppAD (C++ automatic differentiation), Eigen (templated matrix-vector library) and CHOLMOD (sparse matrix routines available from R) to obtain an efficient implementation of the applied Laplace approximation with exact derivatives. Key features are: Automatic sparseness detection, parallelism through BLAS and parallel user templates.

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.