Provides a streamlined pipeline to simulate household infection 
    dynamics, estimate transmission parameters, and visualize epidemic timelines. 
    Uses a Bayesian approach with 'Stan' that models transmission probability as a
    function of viral load, seasonality and infectivity, multiple infection 
    episodes (reinfections), and waning immunity modeling.
    Li et al. (2026) <doi:10.64898/2026.04.01.26349903>).

A Bayesian household transmission model to estimate household transmission dynamics, with accounting for infection from community and tertiary cases.

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.

Allows users to create high-quality heatmaps from labelled, hierarchical data. Specifically, for data with a two-level hierarchical structure, it will produce a heatmap where each row and column represents a category at the lower level. These rows and columns are then grouped by the higher-level group each category belongs to, with the names for each category and groups shown in the margins. While other packages (e.g. 'dendextend') allow heatmap rows and columns to be arranged by groups only, 'hhmR' also allows the labelling of the data at both the category and group level.

In medical research, supervised heterogeneity analysis has important implications. Assume that there are two types of features. Using both types of features, our goal is to conduct the first supervised heterogeneity analysis that satisfies a hierarchical structure. That is, the first type of features defines a rough structure, and the second type defines a nested and more refined structure. A penalization approach is developed, which has been motivated by but differs significantly from penalized fusion and sparse group penalization. 
             Reference: 
             Ren, M., Zhang, Q., Zhang, S., Zhong, T., Huang, J. & Ma, S. (2022). "Hierarchical cancer heterogeneity analysis based on histopathological imaging features". Biometrics, <doi:10.1111/biom.13426>.