This package implements various procedures for finding multiple change-points. Two methods make use of dynamic programming and pruning, with no distributional assumptions other than the existence of certain absolute moments in one method. Hierarchical and exact search methods are included. All methods return the set of estimated change-points as well as other summary information.

This package provides wrappers on regexpr and gregexpr to return the match results in tidy data frames.

This is a package for pretty-printing R code without changing the user's formatting intent.

This package provides simple and secure authentication mechanism for single Shiny applications. Credentials are stored in an encrypted SQLite database.

This package provides a set of estimators for models and (robust) covariance matrices, and tests for panel data econometrics, including within/fixed effects, random effects, between, first-difference, nested random effects as well as instrumental-variable (IV) and Hausman-Taylor-style models, panel generalized method of moments (GMM) and general FGLS models, mean groups (MG), demeaned MG, and common correlated effects (CCEMG) and pooled (CCEP) estimators with common factors, variable coefficients and limited dependent variables models. Test functions include model specification, serial correlation, cross-sectional dependence, panel unit root and panel Granger (non-)causality. Typical references are general econometrics text books such as Baltagi (2021), Econometric Analysis of Panel Data (<doi:10.1007/978-3-030-53953-5>), Hsiao (2014), Analysis of Panel Data (<doi:10.1017/CBO9781139839327>), and Croissant and Millo (2018), Panel Data Econometrics with R (<doi:10.1002/9781119504641>).

This package provides classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).

This package contains three main functions including stddiff.numeric(), stddiff.binary() and stddiff.category(). These are used to calculate the standardized difference between two groups. It is especially used to evaluate the balance between two groups before and after propensity score matching.

This package extends simulation, distribution, quantile and density functions to univariate and multivariate parametric extreme value distributions, and provides fitting functions which calculate maximum likelihood estimates for univariate and bivariate maxima models, and for univariate and bivariate threshold models.

This package implements an opinionated framework for building a production- ready Shiny application. Golem contains a series of tools like dependency management, version management, easy installation and deployment or documentation management.

This package provides a set of tools to perform Quantitative Trait Locus (QTL) analysis in experimental crosses. It is a reimplementation of the R/qtl package to better handle high-dimensional data and complex cross designs. Broman et al. (2018) <doi:10.1534/genetics.118.301595>.

Hnswlib is a C++ library for approximate nearest neighbors. This package provides a minimal R interface by relying on the Rcpp package.

latex2exp parses and converts LaTeX math formulas to R's plotmath expressions, used to enter mathematical formulas and symbols to be rendered as text, axis labels, etc. throughout R's plotting system.

This is a framework for fitting multiple caret models. It uses the same re-sampling strategy as well as creating ensembles of such models. Use caretList to fit multiple models and then use caretEnsemble to combine them greedily or caretStack to combine them using a caret model.

This package provides a toolkit for working with Biological Observation Matrix (BIOM) files. Features include reading/writing all BIOM formats, rarefaction, alpha diversity, beta diversity (including UniFrac), summarizing counts by taxonomic level, and sample subsetting. Standalone functions for reading, writing, and subsetting phylogenetic trees are also provided.

This package analyzes gene expression (time series) data with focus on the inference of gene networks. In particular, GeneNet implements the methods of Schaefer and Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007) for learning large-scale gene association networks (including assignment of putative directions).

The mlr3 package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core mlr3 packages.

This package provides kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods kernlab includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

This package infers directional Conservative causal core (gene) networks (C3NET). This is a version of the algorithm C3NET with directional network.

This is software accompanying the book 'Applied Smoothing Techniques for Data Analysis---The Kernel Approach with S-Plus Illustrations', Oxford University Press. It provides smoothing methods for nonparametric regression and density estimation

The CommonMark specification defines a rationalized version of markdown syntax. This package uses the cmark reference implementation for converting markdown text into various formats including HTML, LaTeX and groff man. In addition, it exposes the markdown parse tree in XML format. The latest version of this package also adds support for Github extensions including tables, autolinks and strikethrough text.

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>.

The Rsolnp package implements a general non-linear augmented Lagrange multiplier method solver, a sequential quadratic programming (SQP) based solver).

This package uses the node library is-my-json-valid or ajv to validate JSON against a JSON schema. Drafts 04, 06 and 07 of JSON schema are supported.

This package provides coroutines for R, a family of functions that can be suspended and resumed later on. This includes async functions (which await) and generators (which yield). Async functions are based on the concurrency framework of the promises package. Generators are based on a dependency free iteration protocol defined in coro and are compatible with iterators from the reticulate package.