Core Functionality of the 'spatstat' Family

Functionality for data analysis and modelling 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'.) Exploratory 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. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.

A Diceware Passphrase Implementation

The Diceware method can be used to generate strong passphrases. In short, you roll a 6-faced dice 5 times in a row, the number obtained is matched against a dictionary of easily remembered words. By combining together 7 words thus generated, you obtain a password that is relatively easy to remember, but would take several millions years (on average) for a powerful computer to guess.

A DNA Reference Library Manager

Reference database manager offering a set of functions to import, organize, clean, filter, audit and export reference genetic data. Provide functions to download sequence data from NCBI GenBank < https://www.ncbi.nlm.nih.gov/genbank/>. Designed as an environment for semi-automatic and assisted construction of reference databases and to improve standardization and repeatability in barcoding and metabarcoding studies.

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.

Inference Using Simulation

Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated, as described in Rousset et al. (2026) .

Easy Web Scraping

The goal of 'ralger' is to facilitate web scraping in R.

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.

Search Contributed R Packages, Sort by Package

Search contributed R packages, sort by package.

Polishing Content for 'Word' and 'PowerPoint'

Set of functions to polish content for Microsoft 'Word' and 'PowerPoint' into 'OOXML'. Polishing is the conversion of the R object into an 'OOXML' representation of the object that can then be added to 'Word' or 'PowerPoint' files.

Blind Source Separation Methods Based on Joint Diagonalization and Some BSS Performance Criteria

Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given. The package is described in Miettinen, Nordhausen and Taskinen (2017) .