Provides tools for simulating from discrete-time individual level models for infectious disease data analysis. This epidemic model class contains spatial and contact-network based models with two disease types: Susceptible-Infectious (SI) and Susceptible-Infectious-Removed (SIR).

Provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.

Using variational techniques we address some epidemiological
  problems as the incidence curve decomposition by inverting the renewal 
  equation as described in Alvarez et al. (2021) <doi:10.1073/pnas.2105112118> 
  and Alvarez et al. (2022) <doi:10.3390/biology11040540> or the estimation of 
  the functional relationship between epidemiological indicators. We also 
  propose a learning method for the short time forecast of the trend 
  incidence curve  as described in 
  Morel et al. (2022) <doi:10.1101/2022.11.05.22281904>.

Contains tools for formatting inline code, renaming redundant
  columns, aggregating age categories, adding survey weights, finding the earliest
  date of an event, plotting z-curves, generating population counts and 
  calculating proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epis.netlify.app/>.

The 'epilogi' variable selection algorithm is implemented for the case of continuous response and predictor variables. The relevant paper is: Lakiotaki K., Papadovasilakis Z., Lagani V., Fafalios S., Charonyktakis P., Tsagris M. and Tsamardinos I. (2023). "Automated machine learning for Genome Wide Association Studies". Bioinformatics. <doi:10.1093/bioinformatics/btad545>.

Estimation of epidemiological parameters with 
 Laplacian-P-splines following the methodology of Gressani et al. (2022)
 <doi:10.1371/journal.pcbi.1010618>.