Implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.

Easy to use functions to visualize marker data
    from biparental populations. Useful for both analyzing and
    presenting genotypes in the ABH format.

Combine multidimensional arrays into a single array.
  This is a generalization of 'cbind' and 'rbind'.  Works with
  vectors, matrices, and higher-dimensional arrays.  Also
  provides functions 'adrop', 'asub', and 'afill' for manipulating,
  extracting and replacing data in arrays.

The Brazilian Jurimetrics Association (ABJ in
    Portuguese, see <https://abj.org.br/> for more information) is
    a non-profit organization which aims to investigate and promote the
    use of statistics and probability in the study of Law and its
    institutions. This package has a set of datasets commonly used in
    our book.

The Brazilian Jurimetrics Association (ABJ in Portuguese, see
    <https://abj.org.br/> for more information) is a non-profit
    organization which aims to investigate and promote the use of
    statistics and probability in the study of Law and its institutions.
    This package implements general purpose tools used by ABJ, such as
    functions for sampling and basic manipulation of Brazilian lawsuits
    identification number. It also implements functions for text cleaning,
    such as accentuation removal.

A high-performance, flexible and extensible framework to
    develop continuous-time agent based models. Its high performance
    allows it to simulate millions of agents efficiently. Agents are
    defined by their states (arbitrary R lists). The events are handled in
    chronological order. This avoids the multi-event interaction problem
    in a time step of discrete-time simulations, and gives precise
    outcomes. The states are modified by provided or user-defined events.
    The framework provides a flexible and customizable implementation of
    state transitions (either spontaneous or caused by agent
    interactions), making the framework suitable to apply to epidemiology
    and ecology, e.g., to model life history stages, competition and
    cooperation, and disease and information spread. The agent
    interactions are flexible and extensible. The framework provides
    random mixing and network interactions, and supports multi-level
    mixing patterns.  It can be easily extended to other interactions such
    as inter- and intra-households (or workplaces and schools) by
    subclassing an R6 class. It can be used to study the effect of
    age-specific, group-specific, and contact- specific intervention
    strategies, and complex interactions between individual behavior and
    population dynamics. This modeling concept can also be used in
    business, economical and political models. As a generic event based
    framework, it can be applied to many other fields. More information
    about the implementation and examples can be found at
    <https://github.com/junlingm/ABM>.