Estimation, model selection and other aspects of
    statistical inference in Graphical Gaussian models with edge and
    vertex symmetries (Graphical Gaussian models with colours).
    Documentation about 'gRc' is provided in the paper by Hojsgaard and
    Lauritzen (2007, <doi:10.18637/jss.v023.i06>) and the paper by
    Hojsgaard and Lauritzen (2008, <doi:10.1111/j.1467-9868.2008.00666.x>).

We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.

When the number of treatments is large with limited experimental resources then Row-Column(RC) designs with multiple units per cell can be used. These designs are called Generalized Row-Column (GRC) designs and are defined as designs with v treatments in p rows and q columns such that the intersection of each row and column (cell) consists of k experimental units. For example (Bailey & Monod (2001)<doi:10.1111/1467-9469.00235>), to conduct an experiment for comparing 4 treatments using 4 plants with leaves at 2 different heights row-column design with two units per cell can be used. A GRC design is said to be structurally complete if corresponding to the intersection of each row and column, there appears at least two treatments. A GRC design is said to be structurally incomplete if corresponding to the intersection of any row and column, there is at least one cell which does not contain any treatment.

Implement maximum likelihood estimation for Poisson generalized
  linear models with grouped and right-censored count data. Intended to be used
  for analyzing grouped and right-censored data, which is widely applied in
  many branches of social sciences. The algorithm implemented is described
  in Fu et al., (2021) <doi:10.1111/rssa.12678>.