This package provides set of functions aimed at epidemiologists. The package includes commands for measures of association and impact for case control studies and cohort studies. It may be particularly useful for outbreak investigations including univariable analysis and stratified analysis. The functions for cohort studies include the CS(), CSTable() and CSInter() commands. The functions for case control studies include the CC(), CCTable() and CCInter() commands. References - Cornfield, J. 1956. A statistical problem arising from retrospective studies. In Vol. 4 of Proceedings of the Third Berkeley Symposium, ed. J. Neyman, 135-148. Berkeley, CA - University of California Press. Woolf, B. 1955. On estimating the relation between blood group disease. Annals of Human Genetics 19 251-253. Reprinted in Evolution of Epidemiologic Ideas Annotated Readings on Concepts and Methods, ed. S. Greenland, pp. 108-110. Newton Lower Falls, MA Epidemiology Resources. Gilles Desve & Peter Makary, 2007. CSTABLE Stata module to calculate summary table for cohort study Statistical Software Components S456879, Boston College Department of Economics. Gilles Desve & Peter Makary, 2007. CCTABLE Stata module to calculate summary table for case-control study Statistical Software Components S456878, Boston College Department of Economics.

This package provides functions for the method of effect stars as proposed by Tutz and Schauberger (2013) <doi:10.1080/10618600.2012.701379>. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function effectstars there exist methods for special objects, for example for vglm objects from the VGAM package.

Multivariate modeling of data after deflation of interfering effects. EF Mosleth et al. (2021) <doi:10.1038/s41598-021-82388-w> and EF Mosleth et al. (2020) <doi:10.1016/B978-0-12-409547-2.14882-6>.

Addresses tasks along the pipeline from raw data to analysis and visualization for eye-tracking data. Offers several popular types of analyses, including linear and growth curve time analyses, onset-contingent reaction time analyses, as well as several non-parametric bootstrapping approaches. For references to the approach see Mirman, Dixon & Magnuson (2008) <doi:10.1016/j.jml.2007.11.006>, and Barr (2008) <doi:10.1016/j.jml.2007.09.002>.

Create publication-ready regression tables in multiple formats, including Word', HTML', LaTeX', and PDF', from statistical models. Supports lm() and glm() models. Includes options for marginal effects, control variable grouping, and robust standard errors using methods described in Zeileis (2004) <doi:10.18637/jss.v011.i10>. Tables can be exported to Word via flextable or to LaTeX for PDF output.

It allows structuring electoral data of different size and structure to calculate various indicators frequently used in the studies of electoral systems and party systems. Indicators of electoral volatility, electoral disproportionality, party nationalization and the effective number of parties are included.

Estimation of four-fold table cell frequencies (raw data) from risk ratios (relative risks), risk differences and odds ratios. While raw data can be useful for doing meta-analysis, such data is often not provided by primary studies (with summary statistics being solely presented). Therefore, based on summary statistics (namely, risk ratios, risk differences and odds ratios), this package estimates the value of each cell in a 2x2 table according to the equations described in Di Pietrantonj C (2006) <doi:10.1002/sim.2287>.

This package provides a collection of small functions useful for epidemics analysis and infectious disease modelling. This includes computation of basic reproduction numbers from growth rates, generation of hashed labels to anonymize data, and fitting discretized Gamma distributions.

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

The concept of Essential Biodiversity Variables (EBV, <https://geobon.org/ebvs/what-are-ebvs/>) comes with a data structure based on the Network Common Data Form (netCDF). The ebvcube R package provides functionality to easily create, access and visualise this data. The EBV netCDFs can be downloaded from the EBV Data Portal: Christian Langer/ iDiv (2020) <https://portal.geobon.org/>.

Errors in data can be located and removed using validation rules from package validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7.

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

This package creates family objects identical to stats family but for new links.

API wrapper to download statistical information from the Economic Statistics System (ECOS) of the Bank of Korea <https://ecos.bok.or.kr/api/#/>.

Automates feature engineering using evolutionary algorithms inspired by genetic programming. Starting from raw input features, the package evolves candidate transformation recipes through selection, crossover, and mutation, evaluating fitness via cross-validation or train/validation splits with gradient-boosted tree models ('LightGBM or XGBoost'). Built-in transformers include arithmetic, logarithmic, and power operations, interaction terms, target encoding, quantile and log-based binning, principal component analysis, truncated singular value decomposition, Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction, and minimum spanning tree (MST) graph-based clustering. The evolutionary search yields an optimised feature recipe that can be applied to new data for prediction. Methods are described in McInnes et al. (2018) <doi:10.21105/joss.00861>, Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-framework>, Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>, Gagolewski (2021) <doi:10.1016/j.softx.2021.100722>, Gagolewski (2026) <doi:10.32614/CRAN.package.lumbermark>, and Gagolewski (2026) <doi:10.32614/CRAN.package.deadwood>.

Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson (1998) <doi:10.2307/3315677>, and Rolf Turner (2008) <doi:10.1016/j.csda.2008.01.029> and the references cited therein.

This package contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.

Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided.

This package provides a tool to run Monte Carlo simulation of catastrophe model event loss tables, using a Poisson frequency and Beta severity distribution.

An R interface to United States Environmental Protection Agency (EPA) Environmental Compliance History Online ('ECHO') Application Program Interface (API). ECHO provides information about EPA permitted facilities, discharges, and other reporting info associated with permitted entities. Data are obtained from <https://echo.epa.gov/>.

Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.

Implementation of the Centre of Gravity method and the Extrapolated Centre of Gravity method. It supports replicated observations. Cameron, D.G., et al (1982) <doi:10.1366/0003702824638610> JCGM (2008) <doi:10.59161/JCGM100-2008E>.

The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable(), protect_linked_tables() or runArgusBatchFile() in package sdcTable is called. Lists of level-hierarchy (parameter dimList') and other required input to these functions are created automatically. The suppression method Gauss (default) is implemented independently of sdcTable'. The function, PTgui(), starts a graphical user interface based on the shiny package.