This package provides an interface to Amazon Web Services machine learning services, including SageMaker managed machine learning service, natural language processing, speech recognition, translation, and more.

This package provides a functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

This package allows for the imputation of the last largest censored observantions. This method brings less bias and more efficient estimates for AFT models.

This package provides a set of tools to help explain which variables are most important in a random forests. Various variable importance measures are calculated and visualized in different settings in order to get an idea on how their importance changes depending on our criteria (Hemant Ishwaran and Udaya B. Kogalur and Eiran Z. Gorodeski and Andy J. Minn and Michael S. Lauer (2010) <doi:10.1198/jasa.2009.tm08622>, Leo Breiman (2001) <doi:10.1023/A:1010933404324>).

This package contains several basic utility functions including: moving (rolling, running) window statistic functions, read/write for GIF and ENVI binary files, fast calculation of AUC, LogitBoost classifier, base64 encoder/decoder, round-off-error-free sum and cumsum, etc.

This package provides procedures for fitting a principal curve to a data matrix in arbitrary dimensions.

This package provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets. It performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set.

This package provides functions to make zebra-striped tables (tables with alternating row colors) in LaTeX and HTML formats easily from data.frame, matrix, lm, aov, anova, glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.

This package contains some functions to help users (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. Functions in this package are supposed to be efficient and easy to use.

This package provides a fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.

This package contains functionality for performing the following methods of p-value aggregation: Fisher's method, the Lancaster method (weighted Fisher's method), and Sidak correction.

Extracts plain text from Rich Text Format (RTF) file.

This package provides JSON parsing capability through the Rapidjson library.

This package provides tools to more conveniently perform tasks associated with add-on packages. pacman conveniently wraps library and package related functions and names them in an intuitive and consistent fashion. It seeks to combine functionality from lower level functions which can speed up workflow.

This package provides MathJax and macros to enable its use within Rd files for rendering equations in the HTML help files.

This package provides tools to fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.

This package provides an R implementation of an extension of the BayeScan software for codominant markers, adding the option to group individual SNPs into pre-defined blocks. A typical application of this new approach is the identification of genomic regions, genes, or gene sets containing one or more SNPs that evolved under directional selection.

This package allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.

R-coop offers implementations of covariance, correlation and cosine similarity. The implementations are fast and memory-efficient and their use is resolved automatically based on the input data, handled by R's S3 methods. Full descriptions of the algorithms and benchmarks are available in the package vignettes.

This package provides a cross-platform command-line argument parser written purely in R with no external dependencies. It is useful with the Rscript front-end and facilitates turning an R script into an executable script.

This package provides a header-only C++ library is provided with support for dates, time zones, ISO weeks, Julian dates, and Islamic dates. date offers extensive date and time functionality for the C++11, C++14 and C++17 standards. A slightly modified version has been accepted (along with tz.h) as part of C++20. This package regroups all header files from the upstream repository so that other R packages can use them in their C++ code.

This is an unofficial package aimed at automating the import of LISREL output in R.

This package provides tools to accurately benchmark and analyze execution times for R expressions.

This package provides alternative implementations of some base R functions, including sort, order, and match. The functions are simplified but can be faster or have other advantages.