This package implements methods for analyzing latent variable models with measurement error correction, including Item Response Theory (IRT) models. Provides tools for various correction methods such as Bayesian Markov Chain Monte Carlo (MCMC), over-imputation, bootstrapping for robust standard errors, Ordinary Least Squares (OLS), and Instrumental Variables (IV) based approaches. Supports flexible specification of observable indicators and groupings for latent variable analyses in social sciences and other fields. Methods are described in a working paper (2025) <doi:10.48550/arXiv.2507.22218>.

This package produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical analyses and visualisation of the new functional geography.

This package contains a set of functions to create data libraries, generate data dictionaries, and simulate a data step. The libname() function will load a directory of data into a library in one line of code. The dictionary() function will generate data dictionaries for individual data frames or an entire library. And the datestep() function will perform row-by-row data processing.

Helps to render interlinear glossed linguistic examples in html rmarkdown documents and then semi-automatically compiles the list of glosses at the end of the document. It also provides a database of linguistic glosses.

Generate concentration-time profiles from linear pharmacokinetic (PK) systems, possibly with first-order absorption or zero-order infusion, possibly with one or more peripheral compartments, and possibly under steady-state conditions. Single or multiple doses may be specified. Secondary (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are computed.

Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.

An approach to analyzing Likert response items, with an emphasis on visualizations. The stacked bar plot is the preferred method for presenting Likert results. Tabular results are also implemented along with density plots to assist researchers in determining whether Likert responses can be used quantitatively instead of qualitatively. See the likert(), summary.likert(), and plot.likert() functions to get started.

This package provides functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.

Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.

An implementation of a method of extending a logistic regression model beyond linear effects of the co-variates. The extension in is constructed by first equating the logistic regression model to a naive Bayes model where all the margins are specified to follow natural exponential distributions conditional on Y, that is, a model for Y given X that is specified through the distribution of X given Y, where the columns of X are assumed to be mutually independent conditional on Y. Subsequently, the model is expanded by adding vine - copulas to relax the assumption of mutual independence, where pair-copulas are added in a stage-wise, forward selection manner. Some heuristics are employed during the process of selecting edges, as well as the families of pair-copula models. After each component is added, the parameters are updated by a (smaller) number of gradient steps to maximise the likelihood. When the algorithm has stopped adding edges, based the criterion that a new edge should improve the likelihood more than k times the number new parameters, the parameters are updated with a larger number of gradient steps, or until convergence.

Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is logConDens that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.

This package provides a robust collection of functions tailored for microbial ecology analysis, encompassing both data analysis and visualization. It introduces an encapsulation feature that streamlines the process into a summary object. With the initial configuration of this summary object, users can execute a wide range of analyses with a single line of code, requiring only two essential parameters for setup. The package delivers comprehensive outputs including analysis objects, statistical outcomes, and visualization-ready data, enhancing the efficiency of research workflows. Designed with user-friendliness in mind, it caters to both novices and seasoned researchers, offering an intuitive interface coupled with adaptable customization options to meet diverse analytical needs.

Additional appenders for the logging package lgr that support logging to Elasticsearch', Dynatrace', AWSCloudWatchLog', databases, syslog', email- and push notifications, and more.

This package provides tools to authenticate with LOBSTER (Limit Order Book System - The Efficient Reconstruction, <https://app.lobsterdata.com/>), request, download, and process high-frequency limit order book data. Streamlines the end-to-end workflow from data request to analysis-ready datasets. For advanced high-frequency econometric analysis, see the highfrequency package.

This package provides a class that links matrix-like objects (nodes) by rows or by columns while behaving similarly to a base R matrix. Very large matrices are supported if the nodes are file-backed matrices.

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>.

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.

This package provides extensions to the leaflet package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps.

Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.

Handling, processing, and analyzing geographic data on species distributions and environmental variables. Read Vilela & Villalobos (2015) <doi:10.1111/2041-210X.12401> for details.

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

Interfaces R with LSD simulation models. Reads object-oriented data in results files (.res[.gz]) produced by LSD and creates appropriate multi-dimensional arrays in R'. Supports multiple core parallel threads of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente and Marcelo C. Pereira (documentation and downloads available at <https://www.labsimdev.org/>).

Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.

This package provides a unified interface to large language models across multiple providers. Supports text generation, tidy data workflows, structured output with optional JSON Schema validation, XML-like tag extraction, and embeddings. Includes chat sessions, consistent error handling, and parallel batch tools.