This package provides templates and functions to simplify the production and maintenance of curriculum vitae.

Identification of Latent Patient Phenotype from Electronic Health Records (EHR) Data using Variational Bayes Gaussian Mixture Model for Latent Class Analysis and Variational Bayes regression for Biomarker level shifts, both implemented by Coordinate Ascent Variational Inference algorithms. Variational methods are used to enable Bayesian analysis of very large Electronic Health Records data. For VB GMM details see Bishop (2006,ISBN:9780-387-31073-2). For Logistic VB see Jaakkola and Jordan (2000) <doi:10.1023/A:1008932416310>. Please see preprint of JSS-submitted paper <doi:10.48550/arXiv.2512.14272>.

Video interactivity within shiny applications using video.js'. Enables the status of the video to be sent from the UI to the server, and allows events such as playing and pausing the video to be triggered from the server.

Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.

Fast algorithms for fitting Bayesian variable selection models and computing Bayes factors, in which the outcome (or response variable) is modeled using a linear regression or a logistic regression. The algorithms are based on the variational approximations described in "Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies" (P. Carbonetto & M. Stephens, 2012, <DOI:10.1214/12-BA703>). This software has been applied to large data sets with over a million variables and thousands of samples.

Calibrates population-level cause-specific mortality fractions (CSMFs) that are derived using computer-coded verbal autopsy (CCVA) algorithms. Leveraging the data collected in the Child Health and Mortality Prevention Surveillance (CHAMPS;<https://champshealth.org/>) project, the package stores misclassification matrix estimates of three CCVA algorithms (EAVA, InSilicoVA, and InterVA) and two age groups (neonates aged 0-27 days, and children aged 1-59 months) across countries (specific estimates for Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa, and a combined estimate for all other countries), enabling global calibration. These estimates are obtained using the framework proposed in Pramanik et al. (2025;<doi:10.1214/24-AOAS2006>) and are analyzed in Pramanik et al. (2026;<doi:10.1136/bmjgh-2025-021747>). Given VA-only data for an age group, CCVA algorithm, and country, the package utilizes the corresponding misclassification matrix estimate in the modular VA-Calibration framework (Pramanik et al.,2025;<doi:10.1214/24-AOAS2006>) and produces calibrated estimates of CSMFs. The package also supports ensemble calibration to accommodate multiple algorithms. More generally, this allows calibration of population-level prevalence derived from single-class predictions of discrete classifiers. For this, users need to provide fixed or uncertainty-quantified misclassification matrices. This work is supported by the Eunice Kennedy Shriver National Institute of Child Health K99 NIH Pathway to Independence Award (1K99HD114884-01A1), the Bill and Melinda Gates Foundation (INV-034842), and the Johns Hopkins Data Science and AI Institute.

Handling of vegetation data from different sources but especially Turboveg 2.0. Taxonomic harmonization (given appropriate taxonomic lists, e.g. GermanSL and EuroSL (Euro+Med extended) see <https://eurosl.infinitenature.org>).

This package provides statistical methods for analytical method comparison and validation studies. Implements Bland-Altman analysis for assessing agreement between measurement methods (Bland & Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>), Passing-Bablok regression for non-parametric method comparison (Passing & Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>), and Deming regression accounting for measurement error in both variables (Linnet (1993) <doi:10.1093/clinchem/39.3.424>). Also includes tools for setting quality goals based on biological variation (Fraser & Petersen (1993) <doi:10.1093/clinchem/39.7.1447>) and calculating Six Sigma metrics, precision experiments with variance component analysis, precision profiles for functional sensitivity estimation (Kroll & Emancipator (1993) <https://pubmed.ncbi.nlm.nih.gov/8448849/>). Commonly used in clinical laboratory method validation. Provides publication-ready plots and comprehensive statistical summaries.

Debugging pipe chains often consists of viewing the output after each step. This package adds RStudio addins and two functions that allow outputing each or select steps in a convenient way.

This package provides tools for the analysis and visualization of animal and plant pedigrees. Analytical methods include equivalent complete generations, generation intervals, effective population size (via inbreeding, coancestry, and demographic approaches), founder and ancestor contributions, partial inbreeding, genetic diversity indices, and additive (A), dominance (D), and epistatic (AA) relationship matrices. Core algorithms â ancestry tracing, topological sorting, inbreeding coefficients, and matrix construction â are implemented in C++ ('Rcpp', RcppArmadillo') and data.table', scaling to pedigrees with over one million individuals. Matrix-free pedigree traversals apply additive relationship matrices or their inverses to vectors and matrices without materializing dense square matrices. Pedigree graphs are rendered via igraph with support for compact full-sib family display; relationship matrices can be visualized as heatmaps. Supports complex mating systems, including selfing and pedigrees in which the same individual can appear as both sire and dam.

Fits Gaussian, Binomial, and Negative-Binomial varying-coefficient mixture-of-experts models with local-linear estimation, explicit label alignment, bandwidth selection, diagnostics, bootstrap inference, analytic-style confidence bands, coefficient-specific analytic generalized likelihood-ratio test (GLRT) diagnostics with optional bootstrap calibration, and local-grid or joint-path expectation-maximization fitting engines.

Tool for easy and efficient discretization of continuous and categorical data. The package calculates the most optimal binning of a given explanatory variable with respect to a user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value to each of the calculated binpoints in order to recode the original variable. The package allows users to impose certain restrictions on the functional form on the resulting binning while maximizing the overall information value in the original data. The package is well suited for logistic scoring models where input variables may be subject to restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring is (Siddiqi 2006, ISBN: 978â 0-471â 75451â 0). The package utilizes the discrete nature of decision trees and Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum information value.

To visualize the probabilities of early termination, fail and success of Simon's two-stage design. To evaluate and visualize the operating characteristics of Simon's two-stage design.

Fits balanced-panel autoregressive models with conditional heteroscedasticity for temporal risk detection. The main estimator combines autoregressive exogenous mean modeling with GARCH-X variance modeling, subject-specific baseline terms, shared population coefficients, and L1 penalization for high-dimensional covariates. The package returns conditional mean and variance estimates, coefficient summaries, simulations, and exceedance-based risk scores defined as estimated conditional threshold-exceedance probabilities. The implementation builds on the lasso of Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, generalized autoregressive conditional heteroscedasticity of Bollerslev (1986) <doi:10.1016/0304-4076(86)90063-1>, and L1-regularized high-dimensional time-series modeling of Medeiros and Mendes (2016) <doi:10.1016/j.jeconom.2015.10.011>.

Simplifies functions to conduct univariate, bivariate, and multivariate statistical techniques. Includes functions designed to replicate plots and tables that would result from similar calls in SPSS', including hst(), box(), qq(), tab(), cormat(), and residplot(). Also includes simplified formulae, such as mode(), scatter(), p.corr(), ow.anova(), and rm.anova().

Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019) <doi:10.1111/2041-210X.13253>. Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A worked sample on the body shape of cichlid fishes is included, based on the dataset from Kerschbaumer et al. (2013) <doi:10.5061/dryad.fc02f>.

Tidy snapshots of the Smithsonian Institution Global Volcanism Program's Volcanoes of the World database. Provides three data frames covering Holocene volcanoes, their eruptions, and the events recorded during those eruptions. The schema follows the layout popularized by the rfordatascience/tidytuesday 2020-05-12 release, refreshed against the current Global Volcanism Program database.

Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.

This package performs 18 omnibus tests yielding a total of 28 distinct methodological variations for testing the composite hypothesis of variance homogeneity.

Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.

Streams and parses variant call format file headers without reading full files. Provides structured metadata, validation, inference, and HTML reporting. For details on the specifications used see Danecek et al. (2021) <doi:10.1093/gigascience/giab008>.

This package provides a port of Inspect', a widely adopted Python framework for large language model evaluation. Specifically aimed at ellmer users who want to measure the effectiveness of their large language model-based products, the package supports prompt engineering, tool usage, multi-turn dialog, and model graded evaluations.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

An interface to the Valhalla routing engineâ s application programming interfaces (APIs) for turn-by-turn routing, isochrones, and origin-destination analyses. Also includes several user-friendly functions for plotting outputs, and strives to follow "tidy" design principles. Please note that this package requires access to a running instance of Valhalla', which is open source and can be downloaded from <https://github.com/valhalla/valhalla>.