Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. This package provides functions to standardize infusion and bolus dose data while linking it to drug level or concentration data.

Programmatic access to microdata from Colombia's Gran Encuesta Integrada de Hogares ('GEIH'), published by DANE'. Provides a tidy interface to download, parse, and harmonize labor market surveys from 2007 to present. R companion to the pulso-co Python package.

Model-implied simulation-based power estimation (MSPE) for nonlinear (and linear) SEM, path analysis and regression analysis. A theoretical framework is used to approximate the relation between power and sample size for given type I error rates and effect sizes. The package offers an adaptive search algorithm to find the optimal N for given effect sizes and type I error rates. Plots can be used to visualize the power relation to N for different parameters of interest (POI). Theoretical justifications are given in Irmer et al. (2024a) <doi:10.31219/osf.io/pe5bj> and detailed description are given in Irmer et al. (2024b) <doi:10.3758/s13428-024-02476-3>.

Use Prime Factorization for simplifying computations, for instance for ratios of large factorials.

This package provides a collection of utilities and ggplot2 extensions to assist with visualisations in genomic epidemiology. This includes the phylepic chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included ggplot2 extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.

This package provides a lightweight message system relying purely on base R. Comes with built-in and pre styled message types and provides an easy way to create custom messages. Supports individually styled and colored text as well as timing information. Designed to make console output more informative and visually organized.

Currently incorporate the generalized odds-rate model (a type of linear transformation model) for interval-censored data based on penalized monotonic B-Spline. More methods under other semiparametric models such as cure model or additive model will be included in future versions. For more details see Lu, M., Liu, Y., Li, C. and Sun, J. (2019) <arXiv:1912.11703>.

Engineered features and "helper" functions ancillary to the public.ctn0094data package, extending this package for ease of use (see <https://CRAN.R-project.org/package=public.ctn0094data>). This public.ctn0094data package contains harmonized datasets from some of the National Institute of Drug Abuse's Clinical Trials Network (NIDA's CTN) projects. Specifically, the CTN-0094 project is to harmonize and de-identify clinical trials data from the CTN-0027, CTN-0030, and CTN-51 studies for opioid use disorder. This current version is built from public.ctn0094data v. 1.0.6.

Eco-phylogenetic and community phylogenetic analyses. Keeps community ecological and phylogenetic data matched up and comparable using comparative.comm objects. Wrappers for common community phylogenetic indices ('pez.shape', pez.evenness', pez.dispersion', and pez.dissimilarity metrics). Implementation of Cavender-Bares (2004) correlation of phylogenetic and ecological matrices ('fingerprint.regression'). Phylogenetic Generalised Linear Mixed Models (PGLMMs; pglmm') following Ives & Helmus (2011) and Rafferty & Ives (2013). Simulation of null assemblages, traits, and phylogenies ('scape', sim.meta.comm').

Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...

This package provides publicationâ quality and interactive plots for exploring the posterior output of Latent Space Item Response Models, including Posterior Interaction Profiles, radar charts, 2â D latent maps, and itemâ similarity heat maps. The methods implemented in this package are based on work by Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>.

This package provides tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Utilizes the lme4 and optimx packages (previously the optim() function from stats') to estimate (generalized) linear mixed models (GLMM) with factor structures using a profile likelihood approach, as outlined in Jeon and Rabe-Hesketh (2012) <doi:10.3102/1076998611417628> and Rockwood and Jeon (2019) <doi:10.1080/00273171.2018.1516541>. Factor analysis and item response models can be extended to allow for an arbitrary number of nested and crossed random effects, making it useful for multilevel and cross-classified models.

Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.

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).

Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to Garcà a-Portugués and Meilán-Vila (2025) <doi:10.1080/01621459.2025.2521898> and Garcà a-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

Create an interactive pizza chart visualizing a specific player's statistics across various attributes in a sports dataset. The chart is constructed based on input parameters: data', a dataframe containing player data for any sports; player_stats_col', a vector specifying the names of the columns from the dataframe that will be used to create slices in the pizza chart, with statistics ranging between 0 and 100; name_col', specifying the name of the column in the dataframe that contains the player names; and player_name', representing the specific player whose statistics will be visualized in the chart, serving as the chart title.

Builds potentiometric surface products from groundwater monitoring data. The package prepares groundwater observations from direct water-level measurements or depth-to-water data paired with land-surface elevations, interpolates thin-plate spline surfaces by default, supports alternative and user-supplied interpolation methods, exports raster and contour products, and derives hydraulic-gradient flow arrows. Raster operations use methods from Hijmans (2025) <doi:10.32614/CRAN.package.terra>, thin-plate spline interpolation uses methods from Nychka et al. (2021) <doi:10.5065/D6W957CT>, and geostatistical interpolation uses methods from Pebesma (2004) <doi:10.1016/j.cageo.2004.03.012>.

This package provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).

Fits the Poisson-Tweedie generalized linear mixed model described in Signorelli et al. (2021, <doi:10.1177/1471082X20936017>). Likelihood approximation based on adaptive Gauss Hermite quadrature rule.

This package provides functions and graphics for projecting daily incidence based on past incidence, and estimates of the serial interval and reproduction number. Projections are based on a branching process using a Poisson-distributed number of new cases per day, similar to the model used for estimating R in EpiEstim or in earlyR', and described by Nouvellet et al. (2017) <doi:10.1016/j.epidem.2017.02.012>. The package provides the S3 class projections which extends matrix', with accessors and additional helpers for handling, subsetting, merging, or adding these objects, as well as dedicated printing and plotting methods.

This package provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

Computation of robust standard errors of Poisson fixed effects models, following Wooldridge (1999).

Includes functions for keyword search of pdf files. There is also a wrapper that includes searching of all files within a single directory.