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;.

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;.

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.

https://cran.r-project.org/package=healthiar

https://cran.r-project.org/package=fwtraits

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.

https://cran.r-project.org/package=DIVINE

https://cran.r-project.org/package=Capsule