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")'.

Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.

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>.

Develops algorithms for fitting, prediction, simulation 
  and initialization of the hidden hybrid Markov/semi-Markov model, 
  introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, 
  which also includes several tools for handling missing data, 
  nonparametric mixture of B-splines emissions (Langrock et al., 2015
  <doi:10.1111/biom.12282>), fitting regime switching regression 
  (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive 
  hidden hybrid Markov/semi-Markov model, spline-based nonparametric 
  estimation of additive state-switching models  
  (Langrock et al., 2018 <doi:10.1111/stan.12133>)
  and many other useful tools 
  (read for more description: Amini et al., 2022 <doi:10.1007/s00180-022-01248-x> and its 
  arxiv version: <doi:10.48550/arXiv.2109.12489>).