The population proportion using group testing can be estimated by different methods. Four functions including p.mle(), p.gart(), p.burrow() and p.order() are provided to implement four estimating methods including the maximum likelihood estimate, Gart's estimate, Burrow's estimate, and order statistic estimate.

An easy-to-use tool for working with presence/absence tests on pooled or grouped samples. The primary application is for estimating prevalence of a marker in a population based on the results of tests on pooled specimens. This sampling method is often employed in surveillance of rare conditions in humans or animals (e.g. molecular xenomonitoring). The package was initially conceived as an R-based alternative to the molecular xenomonitoring software, PoolScreen <https://sites.uab.edu/statgenetics/software/>. However, it goes further, allowing for estimates of prevalence to be adjusted for hierarchical sampling frames, and perform flexible mixed-effect regression analyses (McLure et al. Environmental Modelling and Software. <DOI:10.1016/j.envsoft.2021.105158>). The package is currently in early stages, however more features are planned or in the works: e.g. adjustments for imperfect test specificity/sensitivity, functions for helping with optimal experimental design, and functions for spatial modelling.

This package implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>, Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022) <doi:10.1093/aje/kwac043>.

This package implements conjugate power priors for efficient Bayesian analysis of normal data. Power priors allow principled incorporation of historical information while controlling the degree of borrowing through a discounting parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This package provides closed-form conjugate representations for both univariate and multivariate normal data using Normal-Inverse-Chi-squared and Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. The conjugate framework builds upon standard Bayesian methods described in Gelman et al. (2013, ISBN:978-1439840955).

This package provides a direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.

The main goal of the psycho package is to provide tools for psychologists, neuropsychologists and neuroscientists, to facilitate and speed up the time spent on data analysis. It aims at supporting best practices and tools to format the output of statistical methods to directly paste them into a manuscript, ensuring statistical reporting standardization and conformity.

This package provides a tool which aims to help evaluate the effect of external borrowing using an integrated approach described in Lewis et al., (2019) <doi:10.1080/19466315.2018.1497533> that combines propensity score and Bayesian dynamic borrowing methods.

Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.

Simulating particle movement in 2D space has many application. The particles package implements a particle simulator based on the ideas behind the d3-force JavaScript library. particles implements all forces defined in d3-force as well as others such as vector fields, traps, and attractors.

Offers an interactive RStudio gadget interface for communicating with OpenAI large language models (e.g., gpt-5', gpt-5-mini', gpt-5-nano') (<https://platform.openai.com/docs/api-reference>). Enables users to conduct multiple chat conversations simultaneously in separate tabs. Supports uploading local files (R, PDF, DOCX) to provide context for the models. Allows per-conversation configuration of system messages (where supported by the model). API interactions via the httr package are performed asynchronously using promises and future to avoid blocking the R console. Useful for tasks like code generation, text summarization, and document analysis directly within the RStudio environment. Requires an OpenAI API key set as an environment variable.

This package provides tools for simplifying the creation and management of data structures suitable for dealing with policy portfolios, that is, two-dimensional spaces of policy instruments and policy targets. The package also allows to generate measures of portfolio characteristics and facilitates their visualization.

Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the arules package by Hahsler et al. (2025)<doi:10.32614/CRAN.package.arules> and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.

Calculate seat apportionment for legislative bodies with various methods. The algorithms include divisor or highest averages methods (e.g. Jefferson, Webster or Adams), largest remainder methods and biproportional apportionment. Gaffke, N. & Pukelsheim, F. (2008) <doi:10.1016/j.mathsocsci.2008.01.004> Oelbermann, K. F. (2016) <doi:10.1016/j.mathsocsci.2016.02.003>.

This package provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) <doi:10.1086/676622> to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984) <doi:10.1007/BF00275224>, bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices. Further details can be found in Torices & Muñoz-Pajares <doi:10.3732/apps.1400104>.

This package provides a suite of non-parametric, visual tools for assessing differences in data structures for two datasets that contain different observations of the same variables. These tools are all based on Principal Component Analysis (PCA) and thus effectively address differences in the structures of the covariance matrices of the two datasets. The PCASDC tools consist of easy-to-use, intuitive plots that each focus on different aspects of the PCA decompositions. The cumulative eigenvalue (CE) plot describes differences in the variance components (eigenvalues) of the deconstructed covariance matrices. The angle plot presents the information loss when moving from the PCA decomposition of one dataset to the PCA decomposition of the other. The chroma plot describes the loading patterns of the two datasets, thereby presenting the relative weighting and importance of the variables from the original dataset.

This package provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

Access the data of the Catalogue of the Timber Forest Species of the Peruvian Amazon Vásquez Martà nez, R., & Rojas Gonzáles, R.D.P.(2022)<doi:10.21704/rfp.v37i3.1956>.

This package provides a simple function to bind a piped object to a placeholder symbol to enable complex function evaluation with the base R |> pipe.

Implementations of several methods for principal component analysis using the L1 norm. The package depends on COIN-OR Clp version >= 1.17.4. The methods implemented are PCA-L1 (Kwak 2008) <DOI:10.1109/TPAMI.2008.114>, L1-PCA (Ke and Kanade 2003, 2005) <DOI:10.1109/CVPR.2005.309>, L1-PCA* (Brooks, Dula, and Boone 2013) <DOI:10.1016/j.csda.2012.11.007>, L1-PCAhp (Visentin, Prestwich and Armagan 2016) <DOI:10.1007/978-3-319-46227-1_37>, wPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, awPCA (Park and Klabjan 2016) <DOI: 10.1109/ICDM.2016.0054>, PCA-Lp (Kwak 2014) <DOI:10.1109/TCYB.2013.2262936>, and SharpEl1-PCA (Brooks and Dula, submitted).

Village potential statistics (PODES) collects various information on village potential and challenges faced by villages in Indonesia. Information related to village potential includes economy, security, health, employment, communication and information, sports, entertainment, development, community empowerment, education, socio-culture, transportation in the village. Information related to challenges includes natural disasters, public health, environmental pollution, social problems and security disturbances that occur in the village.

Tabular data from statistical institutes and agencies are mostly confidential and must be protected prior to publications. The cell-key method is a post-tabular Statistical Disclosure Control perturbation technique that adds random noise to tabular data. The statistical properties of the perturbations are defined by some noise probability distributions - also referred to as perturbation tables. This tool can be used to create the perturbation tables based on a maximum entropy approach as described for example in Giessing (2016) <doi:10.1007/978-3-319-45381-1_18>. The perturbation tables created can finally be used to apply a cell-key method to frequency count or magnitude tables.

This package provides a collection of R functions that are widely used by the Petersen Lab. Included are functions for various purposes, including evaluating the accuracy of judgments and predictions, performing scoring of assessments, generating correlation matrices, conversion of data between various types, data management, psychometric evaluation, extensions related to latent variable modeling, various plotting capabilities, and other miscellaneous useful functions. By making the package available, we hope to make our methods reproducible and replicable by others and to help others perform their data processing and analysis methods more easily and efficiently. The codebase is provided in Petersen (2025) <doi:10.5281/zenodo.7602890> and on CRAN': <doi: 10.32614/CRAN.package.petersenlab>. The package is described in "Principles of Psychological Assessment: With Applied Examples in R" (Petersen, 2024, 2025a) <doi:10.1201/9781003357421>, <doi:10.25820/work.007199>, <doi:10.5281/zenodo.6466589> and in "Fantasy Football Analytics: Statistics, Prediction, and Empiricism Using R" (Petersen, 2025b).

Enables computation of epidemiological statistics, including those where counts or mortality rates of the reference population are used. Currently supported: excess hazard models (Dickman, Sloggett, Hills, and Hakulinen (2012) <doi:10.1002/sim.1597>), rates, mean survival times, relative/net survival (in particular the Ederer II (Ederer and Heise (1959)) and Pohar Perme (Pohar Perme, Stare, and Esteve (2012) <doi:10.1111/j.1541-0420.2011.01640.x>) estimators), and standardized incidence and mortality ratios, all of which can be easily adjusted for by covariates such as age. Fast splitting and aggregation of Lexis objects (from package Epi') and other computations achieved using data.table'.

Hidden Markov Models are useful for modeling sequential data. This package provides several functions implemented in C++ for explaining the algorithms used for Hidden Markov Models (forward, backward, decoding, learning).