An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package RWeka contains the interface code, the Weka jar is in a separate package RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.

An implementation of a method based on information theory devised for the identification of genes showing a significant variation of expression across multiple conditions. Given expression estimates from any number of RNA-Seq samples and conditions it identifies genes or transcripts with a significant variation of expression across all the conditions studied, together with the samples in which they are over- or under-expressed. Zambelli et al. (2018) <doi:10.1093/nar/gky055>.

An intuitive and explainable metric of Feature Importance for Classification Problems. Resolution Index measures the extent to which a Feature clusters different classes when data is sorted on it. User provides a DataFrame, column name of the Class, sample size and number of iterations used for calculation. Resolution Index for each Feature is returned, which can be effectively used to rank Features and reduce Dimensionality of Training data. For more details on Feature Selection see Theng and Bhoyar (2023) <doi:10.1007/s10115-023-02010-5>.

Enhances the R Optimization Infrastructure ('ROI') package with the SCS solver for solving convex cone problems.

Efficient CRUD interface for the Airtable API <https://airtable.com/developers/web/api>, supporting batch requests and parallel encoding of large data sets.

Includes Resourcecode hindcast database (see <https://resourcecode.ifremer.fr>) configuration data: nodes locations for both the sea-state parameters and the spectra data; examples of time series of 1D and 2D surface elevation variance spectral density.

TROLL is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The rcontroll R package is a wrapper of TROLL'. rcontroll includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the TROLL outputs through tables, figures, and maps taking advantage of other R visualisation packages. rcontroll also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest.

This package provides functions to analyse DNA fragment samples (i.e. derived from RFLP-analysis) and standalone BLAST report files (i.e. DNA sequence analysis).

This package provides functions to calculate several ecological indices of individual and population niche width (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis W, and Roughgarden's WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be quantified by counts of categories, measures of mass or length, or proportions. Monte Carlo resampling procedures are available for hypothesis testing against multinomial null models. Details are provided in Zaccarelli et al. (2013) <doi:10.1111/2041-210X.12079> and associated references.

This package provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.

The goal of readsdr is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by R libraries. The package accomplishes this goal by parsing XMILE files ('Vensim and Stella') models into R objects to construct networks (graph theory); ODE functions for Stan'; and inputs to simulate via deSolve as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

This package provides functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types.

An interface to the Integrated Taxonomic Information System ('ITIS') (<https://www.itis.gov>). Includes functions to work with the ITIS REST API methods (<https://www.itis.gov/ws_description.html>), as well as the Solr web service (<https://www.itis.gov/solr_documentation.html>).

Application of reinsurance treaties to claims portfolios. The package creates a class Claims whose objective is to store claims and premiums, on which different treaties can be applied. A statistical analysis can then be applied to measure the impact of reinsurance, producing a table or graphical output. This package can be used for estimating the impact of reinsurance on several portfolios or for pricing treaties through statistical analysis. Documentation for the implemented methods can be found in "Reinsurance: Actuarial and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant, Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance" by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.

This package provides functions to assist in performing probabilistic record linkage and deduplication: generating pairs, comparing records, em-algorithm for estimating m- and u-probabilities (I. Fellegi & A. Sunter (1969) <doi:10.1080/01621459.1969.10501049>, T.N. Herzog, F.J. Scheuren, & W.E. Winkler (2007), "Data Quality and Record Linkage Techniques", ISBN:978-0-387-69502-0), forcing one-to-one matching. Can also be used for pre- and post-processing for machine learning methods for record linkage. Focus is on memory, CPU performance and flexibility.

Includes data analysis and meta-analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for AB/BA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram),and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi- site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.

This package provides functionality for carrying out estimation with data collected using Respondent-Driven Sampling. This includes Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential Sampling estimator. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, Gile and Handcock (2015) <doi:10.1111/rssa.12091> and Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.

This package provides a fast implementation of the greedy algorithm for the set cover problem using Rcpp'.

Compress local and online images using the reSmush.it API service <https://resmush.it/>.

These functions are especially helpful when writing reports of data analysis using "Sweave".

Make your workflow faster and easier. Easily customizable plots (via ggplot2'), nice APA tables (following the style of the *American Psychological Association*) exportable to Word (via flextable'), easily run statistical tests or check assumptions, and automatize various other tasks.

Work with the Macrostrat (<https://macrostrat.org/>) Web Service (v.2, <https://macrostrat.org/api/v2>) to fetch geological data relevant to the spatial and temporal distribution of sedimentary, igneous, and metamorphic rocks as well as data extracted from them.

Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.

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.