The purpose of this package is to estimate the potential of urban agriculture to contribute to addressing several urban challenges at the city-scale. Within this aim, we selected 8 indicators directly related to one or several urban challenges. Also, a function is provided to compute new scenarios of urban agriculture. Methods are described by Pueyo-Ros, Comas & Corominas (2023) <doi:10.12688/openreseurope.16054.1>.

Perform a Bayesian estimation of the exploratory 
    deterministic input, noisy and gate (EDINA)
    cognitive diagnostic model described by Chen et al. (2018)
    <doi:10.1007/s11336-017-9579-4>.

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.