This package implements the Figueiredo machine learning algorithm for adaptive sparsity and the Wong algorithm for adaptively sparse Gaussian geometric models.

Tree based algorithms can be improved by introducing boosting frameworks. LightGBM is one such framework, based on Ke, Guolin et al. (2017). This package offers an R interface to work with it. It is designed to be distributed and efficient with the following goals:

1. Faster training speed and higher efficiency;

2. lower memory usage;

3. better accuracy;

4. parallel learning supported; and

5. capable of handling large-scale data.

This package provides helper functions with a consistent interface to coerce and verify the types and shapes of values for input checking.

Learn vector representations of words by continuous bag of words and skip-gram implementations of the word2vec algorithm. The techniques are detailed in the paper "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al. (2013), available at <arXiv:1310.4546>.

This package provides kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, and two-sample hypothesis testing.

This package provides functions to query the main R repository to find the versions that r-release and r-oldrel refer to, and also all previous R versions and their release dates.

This package provides well-known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.

This package provides an iteration of the DEoptim function. It performs global optimization by differential evolution.

This is a package for computation and visualization of the empirical attainment function (EAF) for the analysis of random sets in multi-criterion optimization.

This package provides more controls on the option values such as validation and filtering on the values, making options invisible or private.

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 package implements density, distribution functions, quantile functions and random generation functions for a large number of univariate and multivariate distributions.

This package supports multiple precision arithmetic (big integers and rationals, prime number tests, matrix computation), "arithmetic without limitations" using the GNU Multiple Precision library.

This package provides functions for importing and handling text files and formatted text files with additional meta-data, such including .csv, .tab, .json, .xml, .html, .pdf, .doc, .docx, .rtf, .xls, .xlsx, and others.

This package creates a lightweight way to add markdown helpfiles to Shiny apps, using modal dialog boxes, with no need to observe each help button separately.

This is a package for the analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported.

This package provides an S4 implementation of Eq. (3) and Eq. (7) by David J. Hand and Robert J. Till (2001) <DOI:10.1023/A:1010920819831>.

This package provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

Alabama stands for Augmented Lagrangian Adaptive Barrier Minimization Algorithm; it is used for optimizing smooth nonlinear objective functions with constraints. Linear or nonlinear equality and inequality constraints are allowed.

This package implements a simple key-value style database where character string keys are associated with data values that are stored on the disk. A simple interface is provided for inserting, retrieving, and deleting data from the database. Utilities are provided that allow filehash databases to be treated much like environments and lists are already used in R. These utilities are provided to encourage interactive and exploratory analysis on large datasets.

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

This package provides tools for creating, viewing, and assessing qualitative palettes with many (20-30 or more) colors. See Coombes and colleagues (2019) https://doi:10.18637/jss.v090.c01.

This is a port of the type guesser from the readr package, the so-called readr first edition parsing engine, now superseded by vroom.

This package contains functions to implement the methodology and considerations laid out by Marks et al. in the article "Measuring abnormality in high dimensional spaces: applications in biomechanical gait analysis". Using high-dimensional datasets to measure a subject's overall level of abnormality as compared to a reference population is often needed in outcomes research.