This package provides functions for bitwise operations on integer vectors.

This package lets you import Excel files into R. It supports .xls via the embedded libxls C library and .xlsx via the embedded RapidXML C++ library.

This package provides classes and methods for spatial data; the classes document where the spatial location information resides, for 2D or 3D data. Utility functions are provided, e.g. for plotting data as maps, spatial selection, as well as methods for retrieving coordinates, for subsetting, print, summary, etc.

This package provides high performance container data types such as queues, stacks, deques, dicts and ordered dicts.

This package provides UI widget and layout functions for writing Shiny apps that work well on small screens.

This package provides assorted routines for combinatorics.

This package provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.

This package provides a common framework for optimization of black-box functions for other packages, e.g. mlr3. It offers various optimization methods e.g. grid search, random search and generalized simulated annealing.

This package enables you to define a command-line interface by just giving it a description in the specific format.

This package provides various tools for developers of R packages interfacing with Stan, including functions to set up the required package structure, S3 generics and default methods to unify function naming across Stan-based R packages, and vignettes with recommendations for developers.

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

This is an R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.

This package lets you fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models.

This package provides functions to plot and manipulate multigraphs, signed and valued graphs, bipartite graphs, multilevel graphs, and Cayley graphs with various layout options.

This is a framework for construction and analysis of 2D Monte-Carlo simulations. In addition, this package includes various distributions.

In this package Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.

This package provides functions for fitting general linear structural equation models (with observed and latent variables) using the RAM approach, and for fitting structural equations in observed-variable models by two-stage least squares.

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

Ridgeline plots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in ggplot2.

This package provides infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. It is intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package psychomix and trees based on psychometric models in package psychotree.

Compute time-dependent ROC curve from censored survival data using Kaplan-Meier (KM) or Nearest Neighbor Estimation (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No 2, 2000, PP 337-344)

This package provides utility functions that enhance the parallel package and support the built-in parallel backends of the future package. For example, availableCores gives the number of CPU cores available to your R process as given by R options and environment variables, including those set by job schedulers on high-performance compute clusters. If none is set, it will fall back to parallel::detectCores. Another example is makeClusterPSOCK, which is backward compatible with parallel::makePSOCKcluster while doing a better job in setting up remote cluster workers without the need for configuring the firewall to do port-forwarding to your local computer.

This package lets you build complex Structured Query Language (SQL) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either American National Standards Institute (ANSI) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.

This package enables construction of continuous and non-contiguous area cartograms.