An implementation of the sandwich smoother proposed in Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>.    A hero is a specific type of sandwich.  Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes. Also implements the spatio-temporal sandwich smoother of French and Kokoszka (2021) <doi:10.1016/j.spasta.2020.100413>.

A modular and computationally efficient R package for  
  parameterizing, simulating, and analyzing health economic simulation 
  models. The package supports cohort discrete time state transition models 
  (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
  N-state partitioned survival models (Glasziou et al. 1990)
  <doi:10.1002/sim.4780091106>, and individual-level continuous 
  time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
  encompassing both Markov (time-homogeneous and time-inhomogeneous) and 
  semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is 
  quantified with standard graphical and tabular summaries of a probabilistic 
  sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, 
  <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
  make individual-patient simulation, probabilistic sensitivity analysis, 
  and incorporation of patient heterogeneity fast.

Provides tools for estimating sample sizes primarily based on heritability, while also considering additional parameters such as statistical power and fold change. The package normalizes heritability values according to trait-specific heritability and classification to enhance accuracy in sample size estimation.

Unsupervised multivariate filter feature selection using the UFS-rHCM or UFS-cHCM algorithms based on the heterogeneous correlation matrix (HCM). The HCM consists of Pearson's correlations between numerical features, polyserial correlations between numerical and ordinal features,  and polychoric correlations between ordinal features. Tortora C., Madhvani S., Punzo A. (2025). "Designing unsupervised mixed-type feature selection techniques using the heterogeneous correlation matrix." International Statistical Review <doi:10.1111/insr.70016>. This work was supported by the National Science foundation NSF Grant N 2209974 (Tortora) and by the Italian Ministry of University and Research (MUR) under the PRIN 2022 grant number 2022XRHT8R (CUP: E53D23005950006), as part of ‘The SMILE Project: Statistical Modelling and Inference to Live the Environment’, funded by the European Union – Next Generation EU (Punzo).