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 
  formatting proportions with confidence intervals. This is part of the 
  'R4Epis' project <https://r4epi.github.io/sitrep/>.

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, 39(9): btad545. <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>.

Functions, data sets and shiny apps for "Epidemics: Models and Data in R" by Ottar N. Bjornstad (ISBN 978-3-319-97487-3) <https://www.springer.com/gp/book/9783319974866>. The package contains functions to study the S(E)IR model, spatial and age-structured SIR models; time-series SIR and chain-binomial stochastic models; catalytic disease models; coupled map lattice models of spatial transmission and network models for social spread of infection. The package is also an advanced quantitative companion to the coursera Epidemics Massive Online Open Course <https://www.coursera.org/learn/epidemics>.