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.

Algebraic procedures for analyses of multiple social networks are delivered with this package. multiplex makes possible, among other things, to create and manipulate multiplex, multimode, and multilevel network data with different formats. Effective ways are available to treat multiple networks with routines that combine algebraic systems like the partially ordered semigroup with decomposition procedures or semiring structures with the relational bundles occurring in different types of multivariate networks. multiplex provides also an algebraic approach for affiliation networks through Galois derivations between families of the pairs of subsets in the two domains of the network with visualization options.

This package performs the Baumgartner-Weiss-Schindler two-sample test of equal probability distributions (doi:10.2307/2533862). It also performs similar rank-based tests for equal probability distributions due to Neuhauser (doi:10.1080/10485250108832874) and Murakami (doi:10.1080/00949655.2010.551516).

This package enables you to define a command-line interface by just giving it a description in the specific format.

This package performs approximate bayesian computation (ABC) model choice and parameter inference via random forests. This machine learning tool named random forests (RF) can conduct selection among the highly complex models covered by ABC algorithms.

This package provides an R-based solution for symbolic differentiation. It admits user-defined functions as well as function substitution in arguments of functions to be differentiated. Some symbolic simplification is part of the work.

The clusterGeneration package provides functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

The ability to tune models is important. tune contains functions and classes to be used in conjunction with other tidymodels packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.

This package provides functions that implement the known population median test.

The gdtools package provides functionalities to get font metrics and to generate base64 encoded string from raster matrix.

This package provides numerical simulations, and visualizations, of Hubbell's Unified Neutral Theory of Biodiversity (UNTB).

This package provides an interface to use SPARQL to pose SELECT or UPDATE queries to an end-point.

The R kernel for the Jupyter environment executes R code which the front-end (Jupyter Notebook or other front-ends) submits to the kernel via the network.

This package is a micro-package for getting your IP address, either the local/internal or the public/external one. Currently only IPv4 addresses are supported.

The smurf package contains the implementation of the Sparse Multi-type Regularized Feature (SMuRF) modeling algorithm to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood. Next to the fitting procedure, following functionality is available:

• Selection of the regularization tuning parameter lambda using three different approaches: in-sample, out-of-sample or using cross-validation.

• S3 methods to handle the fitted object including visualization of the coefficients and a model summary.

This package creates scatterpie plots, especially useful for plotting pies on a map.

This package provides maximally selected rank statistics with several p-value approximations.

This package provides a minimal set of predicates and assertions used by the assertive package. This is mainly for use by other package developers who want to include run-time testing features in their own packages.

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

Zero-variance control variates (ZV-CV) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. This method has been extended to higher dimensions using regularisation. This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.

This package provides a collection of HTML, JavaScript, and CSS assets that dynamically generate beautiful documentation from a Swagger compliant API.

This package implements faster versions of base R functions (e.g. mean, standard deviation, covariance, weighted mean), mostly written in C++, along with miscellaneous functions for various purposes (e.g. create the histogram with fitted probability density function or probability mass function curve, create the body mass index groups, assess the linearity assumption in logistic regression).

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

This package proposes a new file format named gson for storing gene set and related information, and provides read, write and other utilities to process this file format.