Parametric Statistical Modelling and Inference for the 'spatstat' Family

Functionality for parametric statistical modelling and inference for 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'.) Supports parametric modelling, formal statistical inference, and model validation. Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.

Biodiversity Assessment Tools

Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) .

Interface for Masked Autoregressive Flows

Interfaces the Python library 'zuko' implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) and Papamakarios, Pavlakou and Murray (2017) .

Population Fisher Information Matrix

Evaluate or optimize designs for nonlinear mixed effects models using the Fisher Information matrix. Methods used in the package refer to Mentré F, Mallet A, Baccar D (1997) , Retout S, Comets E, Samson A, Mentré F (2007) , Bazzoli C, Retout S, Mentré F (2009) , Le Nagard H, Chao L, Tenaillon O (2011) , Combes FP, Retout S, Frey N, Mentré F (2013) and Seurat J, Tang Y, Mentré F, Nguyen TT (2021) .

GPL Palettes Copied from 'Gimp' and 'Inkscape'

A set of palettes imported from 'Gimp' distributed under GPL3 (< https://www.gimp.org/about/COPYING>), and 'Inkscape' distributed under GPL2 (< https://inkscape.org/about/license/>).

Perform Factorial Analysis from 'FactoMineR' with a Shiny Application

Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.

Coalescent-Based Simulation of Ecological Communities

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) . The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.

R Interface to 'Keras'

Interface to 'Keras' < https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.

Data Only: Tools for Approximate Bayesian Computation (ABC)

Contains data which are used by functions of the 'abc' package.

Tools for Eurostat Open Data

Tools to download data from the Eurostat database < https://ec.europa.eu/eurostat> together with search and manipulation utilities.