Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) . More details can also be found in the paper linked to via the URL below.

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) .

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.

Working with Dynamic Models for Agriculture and Environment

R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRAE), David Makowski (INRAE), James W. Jones (U.of Florida), Francois Brun (ACTA), 2019.

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.

Shared Memory Multithreading

This project extends 'R' with a mechanism for efficient parallel data access by utilizing 'C++' shared memory. Large data objects can be accessed and manipulated directly from 'R' without redundant copying, providing both speed and memory efficiency. Memshare was published in Thrun, M.C., Märte J.: "Memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE" (2026), R Journal, .

Quick Serialization of R Objects

Provides functions for quickly writing and reading any R object to and from disk.

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