Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector with an optional standardization speedup, and 'C++' helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) &lt;<a href="https://doi.org/10.7490%2Ff1000research.1119552.1" target="_top">doi:10.7490/f1000research.1119552.1</a>&gt;.

An algorithm for fitting interpretable additive neural networks for identifiable and visualizable feature effects using post hoc orthogonalization. Fit custom neural networks intuitively using established 'R' 'formula' notation, including interaction effects of arbitrary order while preserving identifiability to enable a functional decomposition of the prediction function. For more details see Koehler et al. (2025) &lt;<a href="https://doi.org/10.1038%2Fs44387-025-00033-7" target="_top">doi:10.1038/s44387-025-00033-7</a>&gt;.

Implement Bayesian multilevel modelling for compositional data. Compute multilevel compositional data and perform log-ratio transforms at between and within-person levels, fit Bayesian multilevel models for compositional predictors and outcomes, and run post-hoc analyses such as isotemporal substitution models. References: Le, Stanford, Dumuid, and Wiley (2025) &lt;<a href="https://doi.org/10.1037%2Fmet0000750" target="_top">doi:10.1037/met0000750</a>&gt;, Le, Dumuid, Stanford, and Wiley (2025) &lt;<a href="https://doi.org/10.1080%2F00273171.2025.2565598" target="_top">doi:10.1080/00273171.2025.2565598</a>&gt;.

A set of all-cause and cause-specific life expectancy sensitivity and decomposition methods, including Arriaga (1984) &lt;<a href="https://doi.org/10.2307%2F2061029" target="_top">doi:10.2307/2061029</a>&gt;, others documented by Ponnapalli (2005) &lt;<a href="https://doi.org/10.4054%2FDemRes.2005.12.7" target="_top">doi:10.4054/DemRes.2005.12.7</a>&gt;, lifetable, numerical, and other algorithmic approaches such as Horiuchi et al (2008) &lt;<a href="https://doi.org/10.1353%2Fdem.0.0033" target="_top">doi:10.1353/dem.0.0033</a>&gt;, or Andreev et al (2002) &lt;<a href="https://doi.org/10.4054%2FDemRes.2002.7.14" target="_top">doi:10.4054/DemRes.2002.7.14</a>&gt;.

Introductory statistics methods to accompany "Investigating Statistical Concepts, Applications, and Methods" (ISCAM) by Beth Chance &amp; Allan Rossman (2024) &lt;<a href="https://rossmanchance.com/iscam4/" target="_top">https://rossmanchance.com/iscam4/</a>&gt;. Tools to introduce statistical concepts with a focus on simulation approaches. Functions are verbose, designed to provide ample output for students to understand what each function does. Additionally, most functions are accompanied with plots. The package is designed to be used in an educational setting alongside the ISCAM textbook.

This R package has been developed with a focus on air pollution and noise but can applied to other exposures. The initial development has been funded by the European Union project BEST-COST. Disclaimer: It is work in progress and the developers are not liable for any calculation errors or inaccuracies resulting from the use of this package. References (in chronological order): WHO (2003a) "Assessing the environmental burden of disease at national and local levels" &lt;<a href="https://www.who.int/publications/i/item/9241546204" target="_top">https://www.who.int/publications/i/item/9241546204</a>&gt; (accessed October 2025); WHO (2003b) "Comparative quantification of health risks: Conceptual framework and methodological issues" &lt;<a href="https://doi.org/10.1186%2F1478-7954-1-1" target="_top">doi:10.1186/1478-7954-1-1</a>&gt; (accessed October 2025); Miller &amp; Hurley (2003) "Life table methods for quantitative impact assessments in chronic mortality" &lt;<a href="https://doi.org/10.1136%2Fjech.57.3.200" target="_top">doi:10.1136/jech.57.3.200</a>&gt; (accessed October 2025); Steenland &amp; Armstrong (2006) "An Overview of Methods for Calculating the Burden of Disease Due to Specific Risk Factors" &lt;<a href="https://doi.org/10.1097%2F01.ede.0000229155.05644.43" target="_top">doi:10.1097/01.ede.0000229155.05644.43</a>&gt; (accessed October 2025); Miller (2010) "Report on estimation of mortality impacts of particulate air pollution in London" &lt;<a href="https://cleanair.london/app/uploads/CAL-098-Mayors-health-study-report-June-2010-1.pdf" target="_top">https://cleanair.london/app/uploads/CAL-098-Mayors-health-study-report-June-2010-1.pdf</a>&gt; (accessed October 2025); WHO (2011) "Burden of disease from environmental noise" &lt;<a href="https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27" target="_top">https://iris.who.int/items/723ab97c-5c33-4e3b-8df1-744aa5bc1c27</a>&gt; (accessed October 2025); Jerrett et al. (2013) "Spatial Analysis of Air Pollution and Mortality in California" &lt;<a href="https://doi.org/10.1164%2Frccm.201303-0609OC" target="_top">doi:10.1164/rccm.201303-0609OC</a>&gt; (accessed October 2025); GBD 2019 Risk Factors Collaborators (2020) "Global burden of 87 risk factors in 204 countries and territories, 1990–2019" &lt;<a href="https://doi.org/10.1016%2FS0140-6736%2820%2930752-2" target="_top">doi:10.1016/S0140-6736(20)30752-2</a>&gt; (accessed October 2025); VanderWeele (2019) "Optimal Approximate Conversions of Odds Ratios and Hazard Ratios to Risk Ratios" &lt;<a href="https://doi.org/10.1111%2Fbiom.13197" target="_top">doi:10.1111/biom.13197</a>&gt; (accessed October 2025); WHO (2020) "Health impact assessment of air pollution: AirQ+ life table manual" &lt;<a href="https://iris.who.int/bitstream/handle/10665/337683/WHO-EURO-2020-1559-41310-56212-eng.pdf?sequence=1" target="_top">https://iris.who.int/bitstream/handle/10665/337683/WHO-EURO-2020-1559-41310-56212-eng.pdf?sequence=1</a>&gt; (accessed October 2025); ETC HE (2022) "Health risk assessment of air pollution and the impact of the new WHO guidelines" &lt;<a href="https://www.eionet.europa.eu/etcs/all-etc-reports" target="_top">https://www.eionet.europa.eu/etcs/all-etc-reports</a>&gt; (accessed October 2025); Kim et al. (2022) "DALY Estimation Approaches: Understanding and Using the Incidence-based Approach and the Prevalence-based Approach" &lt;<a href="https://doi.org/10.3961%2Fjpmph.21.597" target="_top">doi:10.3961/jpmph.21.597</a>&gt; (accessed October 2025); Pozzer et al. (2022) "Mortality Attributable to Ambient Air Pollution: A Review of Global Estimates" &lt;<a href="https://doi.org/10.1029%2F2022GH000711" target="_top">doi:10.1029/2022GH000711</a>&gt; (accessed October 2025); Teaching group in EBM (2022) "Evidence-based medicine research helper" &lt;<a href="https://ebm-helper.cn/en/Conv/HR_RR.html" target="_top">https://ebm-helper.cn/en/Conv/HR_RR.html</a>&gt; (accessed October 2025).

Support the extraction and seamless integration of species ecological traits or preferences from the www.freshwaterecology.info into several ecological model workflows. During data extraction, different taxonomic levels are acceptable, including species, genus, and family, based on the availability of data in the database. The data is cached after the first search and can be accessed during and after online interactions. Only scientific names are acceptable in the search; local or English names are not allowed. A user API key is required to start using the package.

Provides a wrapper for the python module 'FIORA' as well as a 'shiny'-App to facilitate data processing and visualization. 'FIORA' allows to predict Mass-Spectra based on the SMILES code of chemical compounds. It is described in the Nature Communications article by Nowatzky (2025) &lt;<a href="https://doi.org/10.1038%2Fs41467-025-57422-4" target="_top">doi:10.1038/s41467-025-57422-4</a>&gt;.

The confusion matrix (CM) is used to get a classifier's evaluation measure in order to select a method among many. A stochastic matrix and its transformation are computed from the CM. The eigenvalues of the transformed symmetric matrix are used to get an entropy which appears to be a good evaluation measure. Many other measures, commonly used, are provided for comparison purpose.

Curated datasets and intuitive data management functions to streamline epidemiological data workflows. It is designed to support researchers in quickly accessing clean, structured data and applying essential cleaning, summarizing, visualization, and export operations with minimal effort. Whether you're preparing a cohort for analysis or creating reports, 'DIVINE' makes the process more efficient, transparent, and reproducible.

A comprehensive reproducibility framework designed for R and bioinformatics workflows. Automatically captures the entire analysis environment including R session info, package versions, external tool versions ('Samtools', 'STAR', 'BWA', etc.), 'conda' environments, reference genomes, data provenance with smart checksumming for large files, parameter choices, random seeds, and hardware specifications. Generates executable scripts with 'Docker', 'Singularity', and 'renv' configurations. Integrates with workflow managers ('Nextflow', 'Snakemake', 'WDL', 'CWL') to ensure complete reproducibility of computational research workflows.

Download and search data from the 'World Bank Indicators API', which provides access to nearly 16,000 time series indicators. See &lt;<a href="https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation" target="_top">https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation</a>&gt; for further details about the API.

Census and administrative data in South Korea are a basic source of quantitative and mixed-methods research for social and urban scientists. This package provides a 'sf' (Pebesma et al., 2024 &lt;<a href="https://doi.org/10.32614%2FCRAN.package.sf" target="_top">doi:10.32614/CRAN.package.sf</a>&gt;) based standardized workflow based on direct open API access to the major census and administrative data sources and pre-generated files in South Korea.

Set of functions to import COVID-19 pandemic data into R. The Brazilian COVID-19 data, obtained from the official Brazilian repository at &lt;<a href="https://covid.saude.gov.br/" target="_top">https://covid.saude.gov.br/</a>&gt;, is available at the country, region, state, and city levels. The package also downloads world-level COVID-19 data from Johns Hopkins University's repository. COVID-19 data is available from the start of follow-up until to May 5, 2023, when the World Health Organization (WHO) declared an end to the Public Health Emergency of International Concern (PHEIC) for COVID-19.