micInt: Find microbial interactions

#' @import methods
#'
#' @details
#'
#' \subsection{Common}{
#' The starting point for this packages is an OTU table. See description of its format under \code{\link{runAnalysis}}. Note the functions
#' \code{\link{OTU_stats}} and \code{\link{refine_data}} which are used internally by many of the functions, but
#' can be useful in user-written code as well.
#' }
#'
#' \subsection{CoNet}{
#' For the CoNet approach, the major function is \code{\link{runAnalysis}} which provides a more or less
#' complete pipeline. This is a wrapper around the package \code{\link{ccrepe}}. For more advanced users, the lower level
#' API using \code{\link{ccrepe_analysis}} might be preferable. In addition, there are many auxillary functions
#' such as generating interaction tables (\code{\link{create_interaction_table}}), writing them to file (\code{\link{write.interaction_table}})
#' and making diagnostic plots of the tables (\code{\link{autoplot.interaction_table}}).
#' }
#'
#' \subsection{Lotka-Volterra}{
#' This approach is based on
#' The Lotka-Volterra systems are generated by \code{\link{integralSystem}}
#' For the time being, \code{\link{ridge_fit}} is the only function available to solve
#' the systems (works even if they are over- or underdetermined) by ridge regularization.
#' The regularization parameters have to be determined somehow, this is where \code{\link{cv.LV}} comes
#' into play. It performs cross-validation in order to pick the best regularization parameters.
#' Finally, the estimated coefficients can be put into the differential equations again in order
#' to predict future dynamics of a microbial community using \code{\link{predict.LV}}
#' }
#'
#'
#' @references
#'
#'
#' Faust K and Raes J. CoNet app: inference of biological association networks using Cytoscape \emph{F1000Research 2016}, 5:1519
#' (doi: \url{https://doi.org/10.12688/f1000research.9050.2})
#'
#' Karoline Faust et al. "Microbial Co-occurrence Relationships in the Human Microbiome".
#' In: \emph{PLoS Comput. Biol.} 8.7 (July 2012). issn: 1553-734X. doi: \url{https://doi.org/10.1371/journal.pcbi.
#' 1002606}.
#'
#'
#' Richard R. Stein et al. ``Ecological Modeling from Time-Series Inference: Insight into Dy-
#' namics and Stability of Intestinal Microbiota.'' In: \emph{PLoS Comput. Biol. 9.12 (desember
#' 2013)}. issn: 1553-7358. doi: \url{https://doi.org/10.1371/journal.pcbi.1003388}.
#'
#' P. H. Kloppers and J. C. Greeff. ``Lotka-Volterra model parameter
#' estimation using experiential data''. In: \emph{Appl. Math. Comput. 224}
#' (Nov. 2013), pp. 817–825. ISSN: 0096-3003. DOI: \url{https://doi.org/10.1016/j.amc.2013.08.093}
#'
#' @seealso
#'
#' \code{\link{refine_data}} Convert an OTU table into a refined table (required for some functions in the package)
#'
#' \code{\link{OTU_stats}} Get statistics on OTUs
#'
#' \code{\link{runAnalysis}} High level CoNet wrapper
#'
#' \code{\link{ccrepe_analysis}} Low level CoNet wrapper
#'
#' \code{\link{create_interaction_table}} Make CoNet interaction table
#'
#' \code{\link{integralSystem}} Make equations for estimating the Lotka-Volterra coeffcients
#'
#' \code{\link{ridge_fit}} Solve Lotka-Volterra systems by ridge regularization
#'
#' \code{\link{cv.LV}} Find optimal regularization parameters by cross-validation
#'
#' \code{\link{predict.LV}} Predict the development of a microbial community by
#' estimated Lotka-Volterra coeffcients
#'
#'
#'
#' @keywords internal
"_PACKAGE"
# Ensures that R CMD check does not complain about magrittr piping dots
globalVariables(".")