Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.

Construct and evaluate directed tree structures that model the process of occurrence of genetic alterations during carcinogenesis as described in Szabo, A. and Boucher, K (2002) <doi:10.1016/S0025-5564(02)00086-X>.

This extension of the poems pattern-oriented modeling (POM) framework provides a collection of modules and functions customized for paleontological time-scales, and optimized for single-generation transitions and large populations, across multiple generations.

Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly shiny interface.

This package provides a toolkit for working with TOML files in R while preserving formatting, comments, and structure. tomledit enables serialization of R objects such as lists, data.frames, numeric, logical, and date vectors.

Interface to the API for TreeBASE <http://treebase.org> from R. TreeBASE is a repository of user-submitted phylogenetic trees (of species, population, or genes) and the data used to create them.

This package provides an API to work with Redatam (see <https://redatam.org>) databases in both formats: RXDB (new format) and DICX (old format) and running Redatam programs written in SPC language. It's a wrapper around Redatam core and provides functions to open/close a database (redatam_open()/redatam_close()), list entities and variables from the database (redatam_entities(), redatam_variables()) and execute a SPC program and gets the results as data frames (redatam_query(), redatam_run()).

An expansion of R's stats random wishart matrix generation. This package allows the user to generate singular, Uhlig and Harald (1994) <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate wishart matrices with fractional degrees of freedom, Adhikari (2008) <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility modeling. Users can also use this package to create random covariance matrices.

Extends the functionality of the RTMB <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While RTMB enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.

Full 64-bit resolution date and time functionality with nanosecond granularity is provided, with easy transition to and from the standard POSIXct type. Three additional classes offer interval, period and duration functionality for nanosecond-resolution timestamps.

This package provides distance-based parametric bootstrap tests for clustering with spatial neighborhood information. It implements some distance measures, clustering of presence-absence, abundance and multilocus genetical data for species delimitation, nearest neighbor based noise detection.

This package is a r-ggplot2 extension that provides flipped components:

1. horizontal versions of r-ggplot2 stats and r-ggplot2 geoms;

2. vertical versions of r-ggplot2 positions.

This package provides integration of the Google Repo tool with emacs. It displays the output of the repo status command in a buffer and launches Magit from the status buffer for the project at point.

This package contains a MonadRef type class that abstracts over the details of manipulating references, allowing one to write code that can operate in either the ST monad or the IO monad.

This package provides a client for BEDbase. bedbaser provides access to the API at api.bedbase.org. It also includes convenience functions to import BED files into GRanges objects and BEDsets into GRangesLists.

Resampling is a standard step in particle filtering and in sequential Monte Carlo. This package implements the chopthin resampler, which keeps a bound on the ratio between the largest and the smallest weights after resampling.

This package provides tools for the fitting and cross validation of exact conditional logistic regression models with lasso and elastic net penalties. Uses cyclic coordinate descent and warm starts to compute the entire path efficiently.

API to the database of CRAN package downloads from the RStudio CRAN mirror'. The database itself is at <http://cranlogs.r-pkg.org>, see <https://github.com/r-hub/cranlogs.app> for the raw API'.

Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses RcppArmadillo for fast evaluation. It is also possible to export the code to be used in other packages at C++ level.

This package provides functions for plotting probability density functions, distribution functions, survival functions, hazard functions and computing distribution moments. The implementation is inspired by Delignette-Muller and Dutang (2015) <doi:10.18637/jss.v064.i04>.

Allows get address and port of the free proxy server, from one of two services <http://gimmeproxy.com/> or <https://getproxylist.com/>. And it's easy to redirect your Internet connection through a proxy server.

Spatio-temporal radial basis functions (optimization, prediction and cross-validation), summary statistics from cross-validation, Adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.

Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.

Saves a ggplot object into multiple files, each with a layer added incrementally. Generally to be used in presentation slides. Flexible enough to allow different file types for the final complete plot, and intermediate builds.