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Cramer-von Mises Goodness-of-Fit Tests
It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Mini Charts for Interactive Maps
Add and modify small charts on an interactive map created with package 'leaflet'. These charts can be used to represent at same time multiple variables on a single map.
Search Contributed R Packages, Sort by Package
Search contributed R packages, sort by package.
Computes the Variance-Covariance Matrix of Multidimensional Parameters Using M-Estimation
Provides a flexible framework for estimating the variance-covariance matrix of estimated parameters. Estimation relies on unbiased estimating functions to compute the empirical sandwich variance. (i.e., M-estimation in the vein of Tsiatis et al. (2019)
Black Box Optimization and Exploration of Parameter Space
Performs prediction of a response function from simulated response values, allowing black-box optimization of functions estimated with some error. Includes a simple user interface for such applications, as well as more specialized functions designed to be called by the Migraine software (Rousset and Leblois, 2012
Population Genetic Data Analysis Using Genepop
Makes the Genepop software available in R. This software implements a mixture of traditional population genetic methods and some more focused developments: it computes exact tests for Hardy-Weinberg equilibrium, for population differentiation and for genotypic disequilibrium among pairs of loci; it computes estimates of F-statistics, null allele frequencies, allele size-based statistics for microsatellites, etc.; and it performs analyses of isolation by distance from pairwise comparisons of individuals or population samples.
Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Sensory Data Analysis
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).
Multi-Fidelity Emulator for Computer Experiments with Tunable Fidelity Levels
Multi-Fidelity emulator for data from computer simulations of the
same underlying system but at different input locations and fidelity level,
where both the input locations and fidelity level can be continuous. Active
Learning can be performed with an implementation of the Integrated Mean Square
Prediction Error (IMSPE) criterion developed by Boutelet and Sung (2025,
Working with Dynamic Models for Agriculture and Environment
R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.