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'
    (<https://kingaa.github.io/pomp/>), 'libbi' (<http://libbi.org>)
    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.
    After users have explored model dynamics with 'idmodelr' they may then
    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.

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, 
	see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) 
	<doi:10.1080/19490976.2022.2106103>.