This is a collection of assorted functions and examples collected from various projects. Currently we have functionalities for simplifying overlapping time intervals, Charlson comorbidity score constructors for Danish data, getting frequency for multiple variables, getting standardized output from logistic and log-linear regressions, sibling design linear regression functionalities a method for calculating the confidence intervals for functions of parameters from a GLM, Bayes equivalent for hypothesis testing with asymptotic Bayes factor, and several help functions for generalized random forest analysis using grf'.

Create causal models for use in epidemiological studies, including sufficient-component cause models as introduced by Rothman (1976) <doi:10.1093/oxfordjournals.aje.a112335>.

This package provides a shiny gadget to create ggplot2 figures interactively with drag-and-drop to map your variables to different aesthetics. You can quickly visualize your data accordingly to their type, export in various formats, and retrieve the code to reproduce the plot.

This package implements the approximate Bayesian method for bandwidth selection in multivariate kernel density estimation (KDE) proposed in Filippone & Sanguinetti (2011) <doi:10.1016/j.csda.2011.05.023>. The method uses the Expectation Propagation (EP) algorithm to approximate the posterior distribution of the inverse bandwidth (precision matrix) under a leave-one-out cross-validated likelihood. Three covariance structures are supported: isotropic (scalar precision), diagonal, and full precision matrix. Online Bayesian updating is supported for the isotropic case. The approximate posterior can be used for bandwidth selection, model comparison (via the model evidence / Bayes factor), and online learning.

Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) <doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A., Storvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>, and Hubin, A., Heinze, G., & De Bin, R. (2023) <doi:10.3390/fractalfract7090641>, and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) <doi:10.48550/arXiv.2110.05316>, which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models.

The EvoPER, Evolutionary Parameter Estimation for Individual-based Models is an extensible package providing optimization driven parameter estimation methods using metaheuristics and evolutionary computation techniques (Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization for continuous domains, Tabu Search, Evolutionary Strategies, ...) which could be more efficient and require, in some cases, fewer model evaluations than alternatives relying on experimental design. Currently there are built in support for models developed with Repast Simphony Agent-Based framework (<https://repast.github.io/>) and with NetLogo (<https://www.netlogo.org/>) which are the most used frameworks for Agent-based modeling.

The algorithm of semi-supervised learning based on finite Gaussian mixture models with a missing-data mechanism is designed for a fitting g-class Gaussian mixture model via maximum likelihood (ML). It is proposed to treat the labels of the unclassified features as missing-data and to introduce a framework for their missing as in the pioneering work of Rubin (1976) for missing in incomplete data analysis. This dependency in the missingness pattern can be leveraged to provide additional information about the optimal classifier as specified by Bayesâ rule.

An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.

This package provides tools to compute directly standardised rates using the 2013 European Standard Population for age and deprivation-standardised rates using a 10% per decile assumption. Sex standardisation uses an assumed equal proportion of both males and females. The package Includes variance estimation and 95% confidence intervals for population health applications. Functions support flexible grouping variables and age bands, enabling reproducible and automated analyses.

This package provides a wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. This package provides standard matrix and graph output that can be used seamlessly with other packages. See <doi:10.21105/joss.01866> for a summary of these methods and its evolution in literature.

Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.

This package provides functions for economic evaluation, including Cost-Benefit Analysis, Benefit-Cost Ratio, Net Present Value, Internal Rate of Return, Partial Budgeting, Budget Impact Analysis, Cost-Effectiveness Analysis, Decision Tree Analysis, One-Way, Two-Way, Multi-Way, and Probabilistic Sensitivity Analyses, Expected Value of Perfect Information, and Expected Value of Partial Perfect Information. The implemented methods are based on established approaches in economic evaluation and decision analysis; see Drummond et al. (2015, ISBN:9780199665884), Briggs et al. (2006, ISBN:9780198526629), Boardman et al. (2018, ISBN:9781108415996), and van Hout et al. (1994) <doi:10.1002/hec.4730030505>. The package produces summaries, graphical displays, and reproducible workflows for applications in veterinary science, agriculture, public health, epidemiology, health economics, and related fields.

The EXPOS model uses a digital elevation model (DEM) to estimate exposed and protected areas for a given hurricane wind direction and inflection angle. The resulting topographic exposure maps can be combined with output from the HURRECON model to estimate hurricane wind damage across a region. For details on the original version of the EXPOS model written in Borland Pascal', see: Boose, Foster, and Fluet (1994) <doi:10.2307/2937142>, Boose, Chamberlin, and Foster (2001) <doi:10.1890/0012-9615(2001)071[0027:LARIOH]2.0.CO;2>, and Boose, Serrano, and Foster (2004) <doi:10.1890/02-4057>.

This package provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models representing linear dynamical systems. Currently, two such algorithms (one offline and one online) are implemented for the single-output cumulative structural equation model with an additive-noise output measurement equation and assumptions of normality and independence. The corresponding scientific papers are referenced in the descriptions of the functions implementing these algorithms.

This package provides tools for quantifying Traditional Ecological Knowledge (TEK), modeling TEK in decision frameworks, and designing structured decision-framing exercises in conservation and development contexts. The package implements quantitative ethnobotany indices (Use Value, Relative Frequency of Citation, etc.) but positions them within a larger framework of Bayesian modeling and participatory decision analysis. Includes critical assessment of indices limitations and case studies of participatory workshops.

This package provides functions to numericise R objects (coerce to numeric objects), summarise MCMC (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as R translations of some BUGS (Bayesian Using Gibbs Sampling), JAGS (Just Another Gibbs Sampler), STAN and TMB (Template Model Builder) functions.

Computes the top-k singular triplets or eigenpairs of large sparse and structured matrices: the computation behind principal component analysis on big sparse data, spectral embeddings, and low-rank approximation. Every result carries a numerical certificate with residuals, a backward-error bound, orthogonality loss, and a pass/fail flag, and bounds that can only be estimated are reported as such rather than passed. Centered, scaled, and composed operators are solved through native C++ kernels without forming dense matrices. Drop-in replacements for the RSpectra interface are included.

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages eRm', psychotools or pairwise can be used.

This package provides statistical methods for estimating bivariate dependency (correlation) from marginal summary statistics across multiple studies. The package supports three modules of bivariate joint distribution estimated from marginal summary data: (1) two binary, (2) two continuous, (3) one binary and one continuous These methods enable privacy-preserving joint estimation when individual-level data are unavailable. The approaches are detailed in Shang, Tsao, and Zhang (2025a) <doi:10.48550/arXiv.2505.03995> and Shang, Tsao, and Zhang (2025b) <doi:10.48550/arXiv.2508.02057>.

Implementation of method for estimating excess mortality and other health related outcomes from weekly or daily count data described in Acosta and Irizarry (2021) "A Flexible Statistical Framework for Estimating Excess Mortality".

R shiny web apps for epidemiological Agent-Based Models. It provides a user-friendly interface to the Agent-Based Modeling (ABM) R package epiworldR (Meyer et al., 2023) <DOI:10.21105/joss.05781>. Some of the main features of the package include the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Recovered (SIR), and Susceptible-Exposed-Infected-Recovered (SEIR) models. epiworldRShiny provides a web-based user interface for running various epidemiological ABMs, simulating interventions, and visualizing results interactively.

Automatic Generation of Exams in R for Sakai'. Question templates in the form of the exams package (see <https://www.r-exams.org/>) are transformed into XML format required by Sakai'.

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.