Package EDISON (Estimation of Directed Interactions from
    Sequences Of Non-homogeneous gene expression) runs an MCMC
    simulation to reconstruct networks from time series data, using
    a non-homogeneous, time-varying dynamic Bayesian network.
    Networks segments and changepoints are inferred concurrently,
    and information sharing priors provide a reduction of the
    inference uncertainty.

The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in 'shiny'. By the use of generic 'dplyr' methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case.

An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame'
    without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. 
    These modules are intended for reuse across applications.

Runs the eDITH (environmental DNA Integrating Transport 
  and Hydrology) model, which implements a mass balance of environmental DNA (eDNA) 
  transport at a river network scale coupled with a species distribution model 
  to obtain maps of species distribution. eDITH can work with both eDNA concentration 
  (e.g., obtained via quantitative polymerase chain reaction) or metabarcoding 
  (read count) data. Parameter estimation can be performed via Bayesian techniques 
  (via the 'BayesianTools' package) or optimization algorithms. An interface to the 
  'DHARMa' package for posterior predictive checks is provided. See Carraro and 
  Altermatt (2024) <doi:10.1111/2041-210X.14317> for a package introduction; 
  Carraro et al. (2018) <doi:10.1073/pnas.1813843115> and Carraro et al. (2020) 
  <doi:10.1038/s41467-020-17337-8> for methodological details.

Please note: active development has moved to packages 'validate'
    and 'errorlocate'. Facilitates reading and manipulating (multivariate) data
    restrictions (edit rules) on numerical and categorical data. Rules can be
    defined with common R syntax and parsed to an internal (matrix-like format).
    Rules can be manipulated with variable elimination and value substitution
    methods, allowing for feasibility checks and more. Data can be tested against
    the rules and erroneous fields can be found based on Fellegi and Holt's
    generalized principle. Rules dependencies can be visualized with using the
    'igraph' package.