Multivariate Polynomials with Rational Coefficients

Symbolic calculation and evaluation of multivariate polynomials with rational coefficients. This package is strongly inspired by the 'spray' package. It provides a function to compute Gröbner bases (reference ). It also includes some features for symmetric polynomials, such as the Hall inner product. The header file of the C++ code can be used by other packages. It provides the templated class 'Qspray' that can be used to represent and to deal with multivariate polynomials with another type of coefficients.

Get Spanish Origin-Destination Data

Gain seamless access to origin-destination (OD) data from the Spanish Ministry of Transport, hosted at < https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad>. This package simplifies the management of these large datasets by providing tools to download zone boundaries, handle associated origin-destination data, and process it efficiently with the 'duckdb' database interface. Local caching minimizes repeated downloads, streamlining workflows for researchers and analysts. Extensive documentation is available at < https://ropenspain.github.io/spanishoddata/index.html>, offering guides on creating static and dynamic mobility flow visualizations and transforming large datasets into analysis-ready formats.

Statistical Bias Correction Kit

Implementation of several recent multivariate bias correction methods with a unified interface to facilitate their use. A description and comparison between methods can be found in .

Colors for all

Color palettes for all people, including those with color vision deficiency. Popular color palette series have been organized by type and have been scored on several properties such as color-blind-friendliness and fairness (i.e. do colors stand out equally?). Own palettes can also be loaded and analysed. Besides the common palette types (categorical, sequential, and diverging) it also includes cyclic and bivariate color palettes. Furthermore, a color for missing values is assigned to each palette.

Diversity Measures on Tripartite Graphs

Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure.

Provides a Function to Calculate Prize Winner Indices Based on Bibliometric Data

A function 'PWI()' that calculates prize winner indices based on bibliometric data is provided. The default is the 'Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.

Structured Covariances Estimators for Pairwise and Spatial Covariates

Implements estimators for structured covariance matrices in the presence of pairwise and spatial covariates. Metodiev, Perrot-Dockès, Ouadah, Fosdick, Robin, Latouche & Raftery (2025) .

A Simple Data Science Challenge System

A simple data science challenge system using R Markdown and 'Dropbox' < https://www.dropbox.com/>. It requires no network configuration, does not depend on external platforms like e.g. 'Kaggle' < https://www.kaggle.com/> and can be easily installed on a personal computer.

Regression and Classification Tools

Tools for linear, nonlinear and nonparametric regression and classification. Novel graphical methods for assessment of parametric models using nonparametric methods. One vs. All and All vs. All multiclass classification, optional class probabilities adjustment. Nonparametric regression (k-NN) for general dimension, local-linear option. Nonlinear regression with Eickert-White method for dealing with heteroscedasticity. Utilities for converting time series to rectangular form. Utilities for conversion between factors and indicator variables. Some code related to "Statistical Regression and Classification: from Linear Models to Machine Learning", N. Matloff, 2017, CRC, ISBN 9781498710916.

Variable Selection Using Random Forests

Three steps variable selection procedure based on random forests. Initially developed to handle high dimensional data (for which number of variables largely exceeds number of observations), the package is very versatile and can treat most dimensions of data, for regression and supervised classification problems. First step is dedicated to eliminate irrelevant variables from the dataset. Second step aims to select all variables related to the response for interpretation purpose. Third step refines the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purpose. Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015) < https://journal.r-project.org/articles/RJ-2015-018/>.