Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) <doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) <doi:10.3390/ijerph18179166>.

Provides functions to estimate, predict and interpolate areal
        data. For estimation and prediction we assume areal data is an average
        of an underlying continuous spatial process as in Moraga et
        al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
        <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
        <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
        (mostly) based on Goodchild and Lam (1980, ISSN:01652273).

Implements a general algorithm for estimating Sparse Multiple Index
    (SMI) models for nonparametric forecasting and prediction. Estimation of SMI
    models requires the Gurobi mixed integer programming (MIP) solver via the 
    gurobi R package. To use this functionality, the Gurobi Optimizer must be 
    installed, and a valid license obtained and activated from 
    <https://www.gurobi.com>. The gurobi R package must then be installed and 
    configured following the instructions at 
    <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. 
    The package also includes functions for fitting nonparametric additive 
    models with backward elimination, group-wise additive index models, and 
    projection pursuit regression models as benchmark comparison methods. In 
    addition, it provides tools for generating prediction intervals to quantify 
    uncertainty in point forecasts produced by the SMI model and benchmark 
    models, using the classical block bootstrap and a new method called 
    conformal bootstrap, which integrates block bootstrap with split conformal 
    prediction.