This package offers functionality for taking methtuple or Bismark outputs to calculate ASM scores and compute DAMEs regions. It also offers nice visualization of methyl-circle plots.

This package provides a test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc.

This package provides enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). It also provides multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.).

This package calls the Jupyter script nbconvert to create vignettes from notebooks. Those notebooks (.ipynb files) are files containing rich text, code, and its output. Code cells can be edited and evaluated interactively.

The lpSolveAPI package provides an R interface to lp_solve, a MILP, solver with support for pure linear, (mixed) integer/binary, semi-continuous and SOS models.

This package provides statistical models of biased sampling in the form of univariate and multivariate noncentral hypergeometric distributions, including Wallenius' noncentral hypergeometric distribution and Fisher's noncentral hypergeometric distribution (also called extended hypergeometric distribution).

This package carries out a mapping between assorted color spaces including RGB, HSV, HLS, CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB. Qualitative, sequential, and diverging color palettes based on HCL colors are provided.

This package is a Ruby parser library for Gemtext (hypertext format which is intended to serve as the native response format of the Gemini file transfer protocol) and produces a document object of various nodes.

This r-rbenchmark package is inspired by the Perl module Benchmark, and is intended to facilitate benchmarking of arbitrary R code. The library consists of just one function, benchmark, which is a simple wrapper around system.time. Given a specification of the benchmarking process (counts of replications, evaluation environment) and an arbitrary number of expressions, benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.

This package provides a S4 class has been created such that complex operations can be executed on each cell of a raster map. The raster of objects contains a raster map with the addition of a list of generic objects: one object for each raster cells. It allows to write few lines of R code for complex map algebra. Two environmental applications about frequency analysis of raster map of precipitation and creation of a raster map of soil water retention curves have been presented.

This package creates JavaScript charts. The charts can be included in Shiny apps and R markdown documents, or viewed from the R console and RStudio viewer. Based on the JavaScript library amCharts 4 and the R packages htmlwidgets and reactR'. Currently available types of chart are: vertical and horizontal bar chart, radial bar chart, stacked bar chart, vertical and horizontal Dumbbell chart, line chart, scatter chart, range area chart, gauge chart, boxplot chart, pie chart, and 100% stacked bar chart.

The Australian Statistical Geography Standard ('ASGS') is a set of shapefiles by the Australian Bureau of Statistics. This package provides an interface to those shapefiles, as well as methods for converting coordinates to shapefiles.

This package provides a toolbox to read all R files inside a package and automatically generate @importFrom roxygen2 tags in the right place. Includes a shiny application to review the changes before applying them.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

This package provides a system of functions and data aiming to apply quantitative analyses to forest ecology, silviculture and decision-making. Besides, the package helps to carry out data management, exploratory analysis, and model assessment.

This package provides a tool for transforming coordinates in a color space to common color names using data from the Royal Horticultural Society and the International Union for the Protection of New Varieties of Plants.

Clustering categorical sequences by means of finite mixtures with Markov model components is the main utility of ClickClust. The package also allows detecting blocks of equivalent states by forward and backward state selection procedures.

Classical cryptography methods for words and brief phrases. Substitution, transposition and concealment (null) ciphers are available, like Caesar, Vigenère, Atbash, affine, simple substitution, Playfair, rail fence, Scytale, single column, bifid, trifid, and Polybius ciphers.

Helps to describe a data frame in hand. Has been developed during PhD work of the maintainer. More information may be obtained from Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) <doi:10.1111/2041-210X.12918>. The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

This package provides a Shiny web application for energy industry analytics. Take an overview of the industry, measure Key Performance Indicators, identify changes in the industry over time, and discover new relationships in the data.

It provides a custom ggplot2 geom to add day/night patterns to plots. It visually distinguishes daytime and nighttime periods. It is useful for visualizing data that spans multiple days and for highlighting diurnal patterns.

This package provides a variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.

Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals.