An R and Repast integration tool for running individual-based (IbM) simulation models developed using Repast Simphony Agent-Based framework directly from R code supporting multicore execution. This package integrates Repast Simphony models within R environment, making easier the tasks of running and analyzing model output data for automated parameter calibration and for carrying out uncertainty and sensitivity analysis using the power of R environment.

This package provides a set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created rigr to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. rigr output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.

This package implements the P-model (Stocker et al., 2020 <doi:10.5194/gmd-13-1545-2020>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

This package provides tools for preprocessing and processing canopy photographs with support for raw data reading. Provides methods to address variability in sky brightness and to mitigate errors from image acquisition in non-diffuse light. Works with all types of fish-eye lenses, and some methods also apply to conventional lenses.

This package provides a robust alternative to the aJIVE (angle based Joint and Individual Variation Explained) method (Feng et al 2018: <doi:10.1016/j.jmva.2018.03.008>) for the estimation of joint and individual components in the presence of outliers in multi-source data. It decomposes the multi-source data into joint, individual and residual (noise) contributions. The decomposition is robust to outliers and noise in the data. The method is illustrated in Ponzi et al (2021) <arXiv:2101.09110>.

Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.

STK++ <http://www.stkpp.org> is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen'-like API), regression, dimension reduction, etc. The integration of the library to R is using Rcpp'. The rtkore package includes the header files from the STK++ core library. All files contain only template classes and/or inline functions. STK++ is licensed under the GNU LGPL version 2 or later. rtkore (the stkpp integration into R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details.

This package implements random variables by means of S4 classes and methods.

Makes easier the creation of R package or research compendium (i.e. a predefined files/folders structure) so that users can focus on the code/analysis instead of wasting time organizing files. A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with testthat', and build and deploy website using pkgdown'). This package heavily relies on the R packages devtools and usethis and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.

This package provides a general routine, envMU, which allows estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model. This package implements response envelopes, partial response envelopes, envelopes in the predictor space, heteroscedastic envelopes, simultaneous envelopes, scaled response envelopes, scaled envelopes in the predictor space, groupwise envelopes, weighted envelopes, envelopes in logistic regression, envelopes in Poisson regression envelopes in function-on-function linear regression, envelope-based Partial Partial Least Squares, envelopes with non-constant error covariance, envelopes with t-distributed errors, reduced rank envelopes and reduced rank envelopes with non-constant error covariance. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing. Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.

Helps fisheries scientists collect measurements from calcified structures and back-calculate estimated lengths at previous ages using standard procedures and models. This is intended to replace much of the functionality provided by the now out-dated fishBC software (<https://fisheries.org/bookstore/all-titles/software/70317/>).

The RMM fits Revenue Management Models using the RDE(Robust Demand Estimation) method introduced in the paper by <doi:10.2139/ssrn.3598259>, one of the customer choice-based Revenue Management Model. Furthermore, it is possible to select a multinomial model as well as a conditional logit model as a model of RDE.

Implementation of the MaxRank normalization method, which enables standardization of Rank Abundance Distributions (RADs) to a specified number of ranks. Rank abundance distributions are widely used in biology and ecology to describe species abundances, and are mathematically equivalent to complementary cumulative distribution functions (CCDFs) used in physics, linguistics, sociology, and other fields. The method is described in Saeedghalati et al. (2017) <doi:10.1371/journal.pcbi.1005362>.

Supporting decision making involving multiple criteria. Annice Najafi, Shokoufeh Mirzaei (2025) RMCDA: The Comprehensive R Library for applying multi-criteria decision analysis methods, Volume 24, e100762 <doi:10.1016/j.simpa.2025.100762>.

Generates both total- and level-specific R-squared measures from Rights and Sterbaâ s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.

Resampling Stats (http://www.resample.com) is an add-in for running randomization tests in Excel worksheets. The workflow is (1) to define a statistic of interest that can be calculated from a data table, (2) to randomize rows ad/or columns of a data table to simulate a null hypothesis and (3) and to score the value of the statistic from many randomizations. The relative frequency distribution of the statistic in the simulations is then used to infer the probability of the observed value be generated by the null process (probability of Type I error). This package intends to translate this logic for R for teaching purposes. Keeping the original workflow is favored over performance.

BaseX <https://basex.org> is a XML database engine and a compliant XQuery 3.1 processor with full support of W3C Update Facility'. This package is a full client-implementation of the client/server protocol for BaseX and provides functionalities to create, manipulate and query on XML-data.

This package implements the Simulating Optimal FUNctioning framework for site-scale simulations of ecosystem processes, including model calibration. It contains Fortran 90 modules for the P-model (Stocker et al. (2020) <doi:10.5194/gmd-13-1545-2020>), SPLASH (Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>) and BiomeE (Weng et al. (2015) <doi:10.5194/bg-12-2655-2015>).

Indirect method for the estimation of reference intervals (RIs) using Real-World Data ('RWD') and methods for comparing and verifying RIs. Estimates RIs by applying advanced statistical methods to routine diagnostic test measurements, which include both pathological and non-pathological samples, to model the distribution of non-pathological samples. This distribution is then used to derive reference intervals and support RI verification, i.e., deciding if a specific RI is suitable for the local population. The package also provides functions for printing and plotting algorithm results. See ?refineR for a detailed description of features. Version 1.0 of the algorithm is described in Ammer et al. (2021) <doi:10.1038/s41598-021-95301-2>. Additional guidance is in Ammer et al. (2023) <doi:10.1093/jalm/jfac101>. The verification method is described in Beck et al. (2025) <doi:10.1515/cclm-2025-0728>.

This package provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.

Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).

Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions. Includes transport routines in porous media, in estuaries, and in bodies with variable shape.

Applies quality control to daily precipitation observations; reconstructs the original series by estimating precipitation in missing values; and creates gridded datasets of daily precipitation.