This package provides methods and algorithms for discrete optimization, e.g. knapsack and subset sum procedures, derivative-free Nelder-Mead and Hooke-Jeeves minimization, and some (evolutionary) global optimization functions.

This package provides an all relevant feature selection wrapper algorithm. It finds relevant features by comparing original attributes' importance with importance achievable at random, estimated using their permuted copies (shadows).

An implementation of functionalities to transform directed graphs that are bound to a set of known forbidden paths. There are several transformations, following the rules provided by Villeneuve and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. The resulting graph is generated in a data-frame format. See rsppfp website for more information, documentation an examples.

Some basic features of MUMPS are wrapped in a class whose methods can be used for sequentially solving a sparse linear system (symmetric or not) with one or many right hand sides (dense or sparse). There is a possibility to do separately symbolic analysis, LU (or LDL^t) factorization and system solving. Third part ordering libraries are included and can be used: PORD, METIS, SCOTCH.

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.

Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.

The CSV library provides a complete interface to CSV files and data. It offers tools to enable you to read and write to and from Strings or IO objects, as needed.

This includes functions for creating 3D and 4D images using WebGL', rgl', and JavaScript commands. This package relies on the X toolkit ('XTK', <https://github.com/xtk/X#readme>).

This package provides debugging tools that let you inspect the intermediate results of a call. The output looks as if we explode a call into its parts hence the package name.

An umbrella package providing a phenotype/genotype data structure and scalable and efficient computational methods for large genomic datasets in combination with several other packages: BEDMatrix', LinkedMatrix', and symDMatrix'.

Can take in images in either .jpg, .jpeg, or .png format and creates a colour palette of the most frequent colours used in the image. Also provides some custom colour palettes.

Estimation of Difference-in-Differences (DiD) estimators from de Chaisemartin and D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> in Heterogeneous Adoption Designs with no stayers but with quasi stayers.

Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.

This package provides a sparse Partial Least Squares implementation which uses soft-threshold estimation of the covariance matrices and therein introduces sparsity. Number of components and regularization coefficients are automatically set.

This package provides tools for post-process, evaluate and visualize results from 3d Meteorological and Air Quality models against point observations (i.e. surface stations) and grid (i.e. satellite) observations.

An implementation of the clustering methods of categorical data discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical data via ensembling dissimilarity matrices. Preprint <arXiv:1506.07930>.

This package provides a research estimation tool for analysts that work with sample-based inventory data from the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program.

Simplify your R data analysis and data visualization workflow by turning your data frame into an interactive Tableau'-like interface, leveraging the graphic-walker JavaScript library and the htmlwidgets package.

Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. Converts images to GIF animations using pngquant's efficient cross-frame palettes and temporal dithering with thousands of colors per frame.

Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.

This package provides functions to download and parse information from INEGI (Official Mexican statistics agency). To learn more about the API, see <https://www.inegi.org.mx/servicios/api_indicadores.html>.

Estimation algorithms for Kullback-Leibler divergence between two probability distributions, based on one or two samples, and including uncertainty quantification. Distributions can be uni- or multivariate and continuous, discrete or mixed.

Change-point detection algorithm with label constraints and a penalty for each change outside of labels. Read TD Hocking, A Srivastava (2023) <doi:10.1007/s00180-022-01238-z> for details.

Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.