This package provides a unified framework for finite-mixture latent variable models, including latent class analysis (LCA), latent profile analysis (LPA), latent class/profile analysis with covariates, and latent transition analysis (LTA), within one consistent interface. Estimation methods include the expectation-maximization (EM) algorithm; neural network estimation, which requires Python and its dependent libraries; integration with Mplus', which requires an installed copy of Mplus'; and stochastic EM (SEM) through the optional flexmix', Rmixmod', and RMixtComp backends. flexmix and the default Rmixmod path perform configurable warm-up trajectories and promote the best candidates to full SEM replications. Rmixmod additionally exposes its native strategy interface, including chained SEM-to-EM estimation, whereas RMixtComp exposes its native SEM and Gibbs controls without the external warm-up stage. Model assessment includes the Akaike information criterion (AIC), Bayesian information criterion (BIC), Schwarz information criterion (SIC), consistent AIC (CAIC), approximate weight of evidence (AWE), sample-size-adjusted BIC (SABIC), entropy, and average posterior probabilities. Model-comparison procedures include the ordinary likelihood-ratio test, the Mplus TECH11 Vuong-Lo-Mendell-Rubin and adjusted Lo-Mendell-Rubin tests, and fixed-replicate or sequential parametric bootstrap likelihood-ratio tests. Standard errors can be estimated by nonparametric bootstrap, numerical observed information, or analytic observed information based on Louis identity. Classification-error-adjusted maximum-likelihood and Bolck-Croon-Hagenaars three-step methods support covariates predicting latent membership, initial-status and transition regressions, and latent classes or states predicting continuous and categorical external observed dependent variables. Simulation, posterior-probability, classification-error, extraction, summary, plotting, model-adjustment, and update utilities are also provided for reproducible workflows.

This package provides methods for assessing agreement between repeated measurements obtained by two or more methods using the longitudinal concordance correlation coefficient (LCC). Polynomial mixed-effects models (via nlme') describe how concordance, Pearson correlation and accuracy evolve over time. Functions are provided for model fitting, diagnostic plots, extraction of summaries, and non-parametric bootstrap confidence intervals (including parallel computation), following Oliveira et al. (2018) <doi:10.1007/s13253-018-0321-1>.

Routines for fitting Logic Regression models. Logic Regression is described in Ruczinski, Kooperberg, and LeBlanc (2003) <DOI:10.1198/1061860032238>. Monte Carlo Logic Regression is described in and Kooperberg and Ruczinski (2005) <DOI:10.1002/gepi.20042>.

Compute and visualize using the visNetwork package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).

This package provides missing data imputation through two complementary engines: a large language model engine that communicates with the Anthropic Claude application programming interface for context-aware semantic imputation, and a fully self-contained offline engine implementing nineteen statistical and machine learning algorithms entirely in base R with no additional package dependencies. Offline methods include mean, median, mode, last observation carried forward, next observation carried backward, hot-deck, predictive mean matching, k-nearest neighbours, ordinary least-squares regression, Lasso with coordinate descent, Ridge with closed-form solution, Bayesian Ridge regression with evidence approximation following MacKay (1992), support vector regression with a radial basis function kernel, classification and regression trees, random forests, gradient boosting, iterative random forest imputation, principal component analysis imputation via iterative singular value decomposition, and nuclear-norm minimisation via singular value thresholding. When no API key is available the package automatically falls back to the offline engine, ensuring full operation in environments without internet access. Every imputed value is accompanied by a confidence score and a plain-language reasoning string, producing reproducible audit trails. The automatic method selector chooses the best algorithm per column based on data type, skewness, missingness rate, and inter-column correlations.

Computes comorbidity indices and combined frailty scores for multiple ICD coding systems, including ICD-10-CA, ICD-10-CM, and ICD-11. The package provides tools to preprocess episode data, map diagnosis codes to chronic categories, propagate conditions across episodes, and generate comorbidity and frailty measures. The methodology is described in Nikiema, Bayani, and Bally (2026), "A Semantic-Based Carry-Forward Approach: Uncovering Chronic Disease Burden in Real-World Data Analysis", International Journal of Medical Informatics, article 106709 <doi:10.1016/j.ijmedinf.2026.106709>.

This package provides a framework for integrating Large Language Models (LLMs) with R programming through workflow automation. Built on the ReAct (Reasoning and Acting) architecture, enables bi-directional communication between LLMs and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.

This package provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.

Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D). The input variables of the datasets can be quantitative, qualitative/categorical or mixed. The output variable of the datasets is a scalar (quantitative). The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM". The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors" by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <doi:10.48550/arXiv.1806.07504>. The package is developed in IDEAL of Northwestern University.

This package implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

This package contains a suite of shiny applications meant to explore linear model inference feature through simulation and games.

Utilities for querying plain text accounting files from Ledger', HLedger', and Beancount'.

Efficient implementation of Friedman's boosting algorithm with l2-loss function and coordinate direction (design matrix columns) basis functions.

This package implements a local indicator of stratified power to analyze local spatial stratified association and demonstrate how spatial stratified association changes spatially and in local regions, as outlined in Hu et al. (2024) <doi:10.1080/13658816.2024.2437811>.

This package performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.

Generates the Langa-Weir classification of cognitive function for the 2022 Health and Retirement Study (HRS) cognition data. It is particularly useful for researchers studying cognitive aging who wish to work with the most recent release of HRS data. The package provides user-friendly functions for data preprocessing, scoring, and classification allowing users to easily apply the Langa-Weir classification system. For details regarding the; HRS <https://hrsdata.isr.umich.edu/> and Langa-Weir classifications <https://hrsdata.isr.umich.edu/data-products/langa-weir-classification-cognitive-function-1995-2022>.

This package provides functionality to train and evaluate algorithm selection models for portfolios.

This package provides likelihood functions as defined by Fisher (1922) <doi:10.1098/rsta.1922.0009> and a function that creates likelihood functions from density functions. The functions are meant to aid in education of likelihood based methods.

Provide access to the lz-string <http://pieroxy.net/blog/pages/lz-string/index.html> C++ library for Lempel-Ziv (LZ) based compression and decompression of strings.

This package provides a function for classifying a landscape into different categories based on the Topographic Position Index (TPI) and slope. It offers two types of classifications: Slope Position Classification, and Landform Classification. The function internally calculates the TPI for the given landscape and then uses it along with the slope to perform the classification. Optionally, descriptive statistics for every class are calculated and plotted. The classifications are useful for identifying the position of a location on a slope and for identifying broader landform types.

The goal of LCMSQA is to make it easy to check the quality of liquid chromatograph/mass spectrometry (LC/MS) experiments using a shiny application. This package provides interactive data visualizations for quality control (QC) samples, including total ion current chromatogram (TIC), base peak chromatogram (BPC), mass spectrum, extracted ion chromatogram (XIC), and feature detection results from internal standards or known metabolites.

Simplifies the whole process of creating stacked tilted maps, that are often used in scientific publications to show different environmental layers for a geographical region. Tilting maps and layering them allows to easily draw visual correlations between these environmental layers.

This package provides tools to generate and analyze 3-level, 4-level and 5-level linear factorial block designs, including complete factorial layouts, fractional factorial layouts, confounded factorial layouts, and design-characteristic summaries. The package includes utilities for recursive construction, defining-contrast identification, alias and confounding summaries, incidence matrix construction, and selected design-characteristic diagnostics. The methodological framework follows foundational work on factorial block designs, including Gupta (1983) <doi:10.1111/j.2517-6161.1983.tb01253.x>.

This package provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.