This package provides a collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses.

Matrix algebra using the Eigen C++ library: determinant, rank, inverse, pseudo-inverse, kernel and image, QR decomposition, Cholesky decomposition, Schur decomposition, Hessenberg decomposition, linear least-squares problems. Also provides matrix functions such as exponential, logarithm, power, sine and cosine. Complex matrices are supported.

Construct the admissible exact intervals for the binomial proportion, the Poisson mean and the total number of subjects with a certain attribute or the total number of the subjects for the hypergeometric distribution. Both one-sided and two-sided intervals are of interest. This package can be used to calculate the intervals constructed methods developed by Wang (2014) <doi:10.5705/ss.2012.257> and Wang (2015) <doi:10.1111/biom.12360>.

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.

This package provides a comprehensive collection of utility functions for data analysis and visualization in R. The package provides 55+ functions for data manipulation, file handling, color palette management, bioinformatics workflows, plotting, and package management. Features include void value handling, custom infix operators, flexible file I/O, and publication-ready visualizations with sensible defaults. Implementation follows tidyverse principles (Wickham et al. (2019) <doi:10.21105/joss.01686>) and incorporates best practices from the R community.

This package provides a goodness-of-fit test for elliptical distributions with diagnostic capabilities. Gilles R. Ducharme, Pierre Lafaye de Micheaux (2020) <doi:10.1016/j.jmva.2020.104602>.

Tool for Environment-Wide Association Studies (EnvWAS / EWAS) which are repeated analysis. It includes three functions. One function for linear regression, a second for logistic regression and a last one for generalized linear models.

Survival analysis is employed to model time-to-event data. This package examines the relationship between survival and one or more predictors, termed as covariates, which can include both treatment variables (e.g., season of birth, represented by indicator functions) and continuous variables. To this end, the Cox-proportional hazard (Cox-PH) model, introduced by Cox in 1972, is a widely applicable and commonly used method for survival analysis. This package enables the estimation of the effect of randomization for the treatment variable to account for potential confounders, providing adjustment when estimating the association with exposure. It accommodates both fixed and time-dependent covariates and computes survival probabilities for lactation periods in dairy animals. The package is built upon the algorithm developed by Klein and Moeschberger (2003) <DOI:10.1007/b97377>.

This package performs analysis of polynomial regression in simple designs with quantitative treatments.

The cointegration based support vector regression model enables researchers to use data obtained from the cointegrating vector as input in the support vector regression model.

Take the examples written in your documentation of functions and use them to create shells (skeletons which must be manually completed by the user) of test files to be tested with the testthat package. Sort of like python doctests for R.

Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. (2022) <doi:10.1371/journal.pcbi.1010618>.

This package creates text, LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.

Support functions for R-based "EQUALCompareImages - Compare similarity between and within images" shiny application which allow researchers without coding skills or expertise in image comparison algorithms to compare images. Gurusamy,K (2025)<doi:10.5281/zenodo.16994047>.

Reads water network simulation data in Epanet text-based .inp and .rpt formats into R. Also reads results from Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See <http://www2.epa.gov/water-research/epanet> for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems.

This package provides functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise direct estimation, the model-based unit-level approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010) <doi:10.1002/cjs.10051>), the area-level model (see "Estimates of income for small places: An application of James-Stein procedures to Census Data" by Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>) and various extensions of it (adjusted variance estimation methods, log and arcsin transformation, spatial, robust and measurement error models), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel. For a detailed description of the package and the methods used see "The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators" by Kreutzmann et al. (2019) <doi:10.18637/jss.v091.i07> and the second package vignette "A Framework for Producing Small Area Estimates Based on Area-Level Models in R".

This package provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), <doi:10.32942/X29K9P>. These models were originally implemented in the EpiPv GitHub package.

Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.

Allows users to model and draw inferences from extreme value inflated count data, and to evaluate these models and compare to non extreme-value inflated counterparts. The package is built to be compatible with standard presentation tools such as broom', tidy', and modelsummary'.

Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly.

End-member modelling analysis of grain-size data is an approach to unmix a data set's underlying distributions and their contribution to the data set. EMMAgeo provides deterministic and robust protocols for that purpose.

Convenience functions for implementing extended two-way fixed effect regressions a la Wooldridge (2021, 2023) <doi:10.2139/ssrn.3906345>, <doi:10.1093/ectj/utad016>.

Finding life outside the planet Earth several is the ultimate goal of an astrobiologist. Using known astronomical measurements and assumptions the probability of extraterrestrial life existence could be estimated. Equations such as the Drake equation (1961) as stated in the paper of Molina (2019) <arXiv:1912.01783>, Seager (2013) <https://www.space.com/22648-drake-equation-alien-life-seager.html> and Foucher et al, (2017) <doi:10.3390/life7040040> are included in the extraterrestrial package.