Provides a comprehensive and curated collection of datasets related to the lungs, respiratory system, and associated diseases. 
    This package includes epidemiological, clinical, experimental, and simulated datasets on conditions such as lung cancer, asthma, 
    Chronic Obstructive Pulmonary Disease (COPD), tuberculosis, whooping cough, pneumonia, influenza, and other respiratory illnesses. 
    It is designed to support data exploration, statistical modeling, teaching, and research in pulmonary medicine, public health, 
    environmental epidemiology, and respiratory disease surveillance.

A method of clustering functional data using
    subregion information of the curves. It is intended to supplement the
    'fda' and 'fda.usc' packages in functional data object clustering. It
    also facilitates the printing and plotting of the results in a tree
    format and limits the partitioning candidates into a specific set of
    subregions.

Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS). Christian L. Müller, Richard Bonneau, Zachary Kurtz (2016) <doi:10.48550/arXiv.1605.07072>.

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.

Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.