This package allows users to test characteristics of common R objects.

This package provides a flexible approach to Bayesian optimization / model based optimization building on the bbotk package. The mlr3mbo is a toolbox providing both ready-to-use optimization algorithms as well as their fundamental building blocks allowing for straightforward implementation of custom algorithms. Single- and multi-objective optimization is supported as well as mixed continuous, categorical and conditional search spaces. Moreover, using mlr3mbo for hyperparameter optimization of machine learning models within the mlr3 ecosystem is straightforward via mlr3tuning.

LIGER is a package for integrating and analyzing multiple single-cell datasets, developed and maintained by the Macosko lab. It relies on integrative non-negative matrix factorization to identify shared and dataset-specific factors.

This package provides multiple pairwise tests.

This package provides plotting functions for posterior analysis, model checking, and MCMC diagnostics. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling.

This package provides tools to fit Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, and a simulation module for various binary data matrices.

This package lets you replace the standard x-axis in ggplots with a combination matrix to visualize complex set overlaps. UpSet has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using ggplot2. In addition it can convert any categorical axis into a combination matrix axis.

This package provides tools to create a lightweight Shiny wrapper for the css-loaders created by Luke Hass https://github.com/lukehaas/css-loaders. Wrapping a Shiny output will automatically show a loader when the output is (re)calculating.

This package contains a collection of various functions to assist in R programming, such as tools to assist in developing, updating, and maintaining R and R packages, calculating the logit and inverse logit transformations, tests for whether a value is missing, empty or contains only NA and NULL values, and many more.

This package simplifies regression tests by comparing objects produced by test code with earlier versions of those same objects. If objects are unchanged the tests pass, otherwise execution stops with error details. If in interactive mode, tests can be reviewed through the provided interactive environment.

This package offers quick statistical hypothesis testing for matrix rows/columns. The main goals are speed through vectorization, detailed and user-friendly output, and compatibility with tests implemented in R.

This package provides a statistical method to impute the missing values in accelerometer data. The methodology includes both parametric and semi-parametric multiple imputations under the zero-inflated Poisson lognormal model. It also provides multiple functions to preprocess the accelerometer data previous to the missing data imputation. These include detecting the wearing and the non-wearing time, selecting valid days and subjects, and creating plots.

ZeroMQ is a well-known library for high-performance asynchronous messaging in scalable, distributed applications. This package provides high level R wrapper functions to easily utilize ZeroMQ. The main focus is on interactive client/server programming frameworks. A few wrapper functions compatible with rzmq are also provided.

The wordspace package turns R into an interactive laboratory for empirical research on distributional semantic models (DSM). It consists of a small set of carefully designed functions, most of which

• encapsulate non-trivial R operations in a user-friendly manner or

• provide efficient and memory-lean C implementations of key operations.

This package provides syntax highlighting of R code, specifically designed for the needs of RMarkdown packages like pkgdown, hugodown, and bookdown. It includes linking of function calls to their documentation on the web, and automatic translation of ANSI escapes in output to the equivalent HTML.

The empirical transition matrix (etm) package estimates the matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space using the Aalen-Johansen estimator.

This package provides Map, Reduce and Filter variants to generate jobs on batch computing systems like PBS/Torque, LSF, SLURM and Sun Grid Engine. Multicore and SSH systems are also supported.

This package provides an R implementation of the Octave package signal, containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

This package provides the means to compile user-supplied C++ functions with Rcpp and retrieve an XPtr that can be passed to other C++ components.

This package provides a fast and user-friendly implementation of nonparametric estimators for censored event history (survival) analysis with the Kaplan-Meier and Aalen-Johansen methods.

This package provides tools to calculate exact and approximate theory experimental designs for D, A, and I criteria. Very large designs may be created. Experimental designs may be blocked or blocked designs created from a candidate list, using several criteria. The blocking can be done when whole and within plot factors interact.

This package provides a set of R bindings for the Selenium 2.0 WebDriver (see https://selenium.dev/documentation/en/ for more information) using the JsonWireProtocol (see https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol for more information). Selenium 2.0 WebDriver allows driving a web browser natively as a user would either locally or on a remote machine using the Selenium server it marks a leap forward in terms of web browser automation. Selenium automates web browsers (commonly referred to as browsers). Using RSelenium you can automate browsers locally or remotely.

This package provides the Molecular Signatures Database (MSigDB) gene sets typically used with the Gene Set Enrichment Analysis (GSEA) software in a standard R data frame with key-value pairs. Included are the original human gene symbols and Entrez IDs as well as the equivalents for various frequently studied model organisms such as mouse, rat, pig, fly, and yeast.

This package provides an interface from R to Python modules, classes, and functions. When calling into Python, R data types are automatically converted to their equivalent Python types. When values are returned from Python to R they are converted back to R types.