Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.

Package for data exploration and result presentation. Full 'epicalc' package with data management functions is available at '<https://medipe.psu.ac.th/epicalc/>'.

Contains utilities and functions for the cleaning, processing and
    management of patient level public health data for surveillance
    and analysis held by the UK Health Security Agency, UKHSA.

Mathematical models of infectious diseases in humans and animals.
    Both, deterministic and stochastic models can be simulated and plotted.

Tools to quantify transmissibility throughout
    an epidemic from the analysis of time series of incidence as described in
    Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
    <doi:10.1093/aje/kwh255>.

Analysis and visualization of plant disease progress curve data. Functions for
    fitting two-parameter population dynamics models (exponential, monomolecular, logistic
    and Gompertz) to proportion data for single or multiple epidemics using either linear
    or no-linear regression.  Statistical and visual outputs are provided to aid in model
    selection. Synthetic curves can be simulated for any of the models given the parameters.
    See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058>
    for further information on the methods.