Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at http://dmg.org/. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products.

This package provides two convenience functions assert() and test_pkg() to facilitate testing R packages.

This is a supplement to the maps package providing the larger and/or higher-resolution databases.

To make it easy to create CONSORT diagrams for the transparent reporting of participant allocation in randomized, controlled clinical trials. This is done by creating a standardized disposition data, and using this data as the source for the creation a standard CONSORT diagram. Human effort by supplying text labels on the node can also be achieved.

This package provides a collection of regular expression tools associated with the qdap package that may be useful outside of the context of discourse analysis. Tools include removal/extraction/replacement of abbreviations, dates, dollar amounts, email addresses, hash tags, numbers, percentages, citations, person tags, phone numbers, times, and zip codes.

This package provides an R Shiny application to create visual abstracts for original research. A variety of user defined options and formatting are included.

This package provides support for iterators, which allow a programmer to traverse through all the elements of a vector, list, or other collection of data.

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

The Tweedie compound Poisson distribution is a mixture of a degenerate distribution at the origin and a continuous distribution on the positive real line. It has been applied in a wide range of fields in which continuous data with exact zeros regularly arise. The cplm package provides likelihood based and Bayesian procedures for fitting common Tweedie compound Poisson linear models. In particular, models with hierarchical structures or extra zero inflation can be handled. Further, the package implements the Gini index based on an ordered version of the Lorenz curve as a robust model comparison tool involving zero-inflated and highly skewed distributions.

R-tgb provides Bayesian nonstationary regression and treed Gaussian processes. In addition, it provides visualization functions, tree drawing, sensitivity analysis, multi-resolution models, and sequential experimental design tools, including ALM, ALC, and expected improvement for optimizing noisy black-box functions.

This package tests the goodness of fit of a distribution of offspring to the Normal, Poisson, and Gamma distribution and estimates the proportional paternity of the second male (P2) based on the best fit distribution.

This package provides a dependency manager for R projects that allows you to manage the R packages your project depends on in an isolated, portable, and reproducible way.

This package defines S4 classes for single-cell genomic data and associated information, such as dimensionality reduction embeddings, nearest-neighbor graphs, and spatially-resolved coordinates. It provides data access methods and R-native hooks to ensure the Seurat object is familiar to other R users.

This package provides a fully DBI-compliant Rcpp-backed interface to PostgreSQL, a relational database.

This package provides routines for the statistical analysis of landmark shapes, including Procrustes analysis, graphical displays, principal components analysis, permutation and bootstrap tests, thin-plate spline transformation grids and comparing covariance matrices. See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, with Applications in R (2nd Edition), John Wiley and Sons.

This package provides functions for assessing the replication/preservation of a network module's topology across datasets through permutation testing.

This package provides a collection of tools to make working with physical measurements easier. One can convert between metric and imperial units, or calculate a dimension's unknown value from other dimensions' measurements.

With this package it is possible to define parameter spaces, constraints and dependencies for arbitrary algorithms, and to program on such spaces. It also includes statistical designs and random samplers. Objects are implemented as R6 classes.

This package allows you to control the number of threads the BLAS library uses. It is also possible to control the number of threads in OpenMP.

GAMs, GAMMs and other generalized ridge regression with multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. The library includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.

This package is intended to make it easy to create D3 JavaScript network, tree, dendrogram, and Sankey graphs from R using data frames.

This package implements beta regression for modeling beta-distributed dependent variables on the open unit interval (0, 1), e.g., rates and proportions, see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. Moreover, extended-support beta regression models can accommodate dependent variables with boundary observations at 0 and/or 1. For the classical beta regression model, alternative specifications are provided: Bias-corrected and bias-reduced estimation, finite mixture models, and recursive partitioning for beta regression, see <doi:10.18637/jss.v048.i11>.

This package is a collection of search spaces for hyperparameter optimization in the mlr3 ecosystem. It features ready-to-use search spaces for many popular machine learning algorithms. The search spaces are from scientific articles and work for a wide range of data sets.