Suggests: testthat (>= 2.2.1), rmarkdown (>= 1.15), knitr (>= 1.24),

and 'EpiModel' (<http://www.epimodel.org>), 'idmodelr' serves primarily as an educational
Suggests: testthat (>= 2.1.1), rmarkdown (>= 1.13), knitr (>= 1.23),

Description: Explore a range of infectious disease models in a consistent
framework. The primary aim of 'idmodelr' is to provide a library of
infectious disease models for researchers, students, and other interested
individuals. These models can be used to understand the underlying dynamics
and as a reference point when developing models for research. 'idmodelr'
also provides a range of utilities. These include: plotting functionality;
a simulation wrapper; scenario analysis tooling; an interactive dashboard;
tools for handling mult-dimensional models; and both model and parameter
look up tables. Unlike other modelling packages such as 'pomp'
and 'EpiModel' (<http://www.epimodel.org>), 'idmodelr' serves primarily as
an educational resource. It is most comparable to epirecipes
(<http://epirecip.es/epicookbook/chapters/simple>) but provides a more
consistent framework, an R based workflow, and additional utility tooling.
implement their model using one of these packages in order to utilise the
model fitting tools they provide.  For newer modellers, this package
reduces the barrier to entry by containing multiple infectious disease
models, providing a consistent framework for simulation and visualisation,
and signposting towards other, more research focussed, resources.

Title: A Cartographic Tool to Chart Spatial Microbial Interaction
Description: Most existing approaches for network reconstruction can only infer an overall network
and, also, fail to capture a complete set of network properties. To address these issues,
a new model has been developed, which converts static data into their 'dynamic' form.
'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic,
omnidirectional and personalized networks. For more information on functional
clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>,
Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model,
Imports: grDevices, stats, mvtnorm, orthopolynom, parallel, deSolve,
ggplot2, reshape2, glmnet, igraph, scales, patchwork