An engine for univariate time series forecasting using
    different regression models in an autoregressive way. The engine
    provides an uniform interface for applying the different models. 
    Furthermore, it is extensible so that users can easily apply their
    own regression models to univariate time series forecasting and 
    benefit from all the features of the engine, such as preprocessings
    or estimation of forecast accuracy.

Tools for generating and handling of UUIDs (Universally Unique Identifiers).

Estimate ambient vitamin D-effective or erythemal dose using ultraviolet radiation (UV) data from the 'TEMIS' database, based on date and geographical location.

A tool for checking how much information is disclosed when
    reporting summary statistics.

A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).

An implementation of the Uniform Manifold Approximation and
    Projection dimensionality reduction by McInnes et al. (2018)
    <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and
    to carry out supervised dimensionality reduction. An implementation of
    the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370>
    is also provided. This is a complete re-implementation in R (and C++,
    via the 'Rcpp' package): no Python installation is required. See the
    uwot website (<https://github.com/jlmelville/uwot>) for more
    documentation and examples.

Provides convenience functions for user experience research
    with an emphasis on quantitative user experience testing and reporting.
    The functions are designed to translate statistical approaches to 
    applied user experience research.

An R interface to V8 <https://v8.dev>: Google's open source JavaScript
    and WebAssembly engine. This package can be compiled either with V8 or NodeJS 
    when built as a shared library.

Calibrates cause-specific mortality fractions (CSMF) estimates generated by computer-coded verbal autopsy (CCVA) algorithms from WHO-standardized verbal autopsy (VA) survey data. It leverages data from the multi-country Child Health and Mortality Prevention Surveillance (CHAMPS) project <https://champshealth.org/>, which determines gold standard causes of death via Minimally Invasive Tissue Sampling (MITS). By modeling the CHAMPS data using the misclassification matrix modeling framework proposed in Pramanik et al. (2025, <doi:10.1214/24-AOAS2006>), the package includes an inventory of 48 uncertainty-quantified misclassification matrices for three CCVA algorithms (EAVA, InSilicoVA, InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight "countries" (seven countries in CHAMPS -- Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa -- and an estimate for countries not in CHAMPS). Given a VA-only data for an age group, CCVA algorithm, and country, the package uses the corresponding uncertainty-quantified misclassification matrix estimates as an informative prior, and utilizes the modular VA-calibration to produce calibrated CSMF estimates. It also supports ensemble calibration when VA-only data are provided for multiple algorithms. More generally, the package can be applied to calibrate predictions from a discrete classifier (or ensemble of classifiers) utilizing user-provided fixed or uncertainty-quantified misclassification matrices. This work is supported by the Bill and Melinda Gates Foundation Grant INV-034842.