#' gauseR: Simple methods for fitting Lotka-Volterra models describing Gause's "Struggle for Existence"
#'
#' A collection of tools and data for analyzing the Gause microcosm experiments, and for fitting
#' Lotka-Volterra models to time series data. Includes methods for fitting single-species logistic
#' growth, and multi-species interaction models, e.g. of competition, predator/prey relationships,
#' or mutualism. See documentation for individual functions for examples. In general, see the
#' lv_optim() function for examples of how to fit parameter values in multi-species systems.
#'
#' @section Authors:
#' Adam Clark, Lina Muehlbauer, and Maximilienne Schulze.
#'
#' @section Applications:
#' Note that the general methods applied here, as well as the form of the differential equations that
#' we use, are described in detail in the Quantitative Ecology textbook by Lehman et al., cited below.
#' Using the default functions, species dynamics therefore follow the form:
#'
#' dni/dt = ni * (ri + aii * ni + sum_j(aij * nj))
#'
#' @docType package
#' @source Clarence Lehman, Shelby Loberg, and Adam T. Clark. (2019). Quantitative Ecology: A New Unified Approach. University of Minnesota Libraries Publishing. University of Minnesota Digital Conservancy, http://hdl.handle.net/11299/204551
#' @source Lina K. Muehlbauer, Maximilienne Schulze, W. Stanley Harpole, and Adam T. Clark. "gauseR: Simple methods for fitting Lotka-Volterra models describing Gause's 'Struggle for Existence'." Ecology and Evolution.
#' @keywords internal
#' "_PACKAGE"
#' @name gauseR
#' @examples
#' #primary wrapper function
#' ?gause_wrapper # automatically runs functions to get starting values and fit
#' # paramter vales using simulated ODE dynamics.
#'
#' #individual functions
#' ?get_lag # generate time-lagged variables for estimating per-capita growth
#' ?percap_growth # generate estimates of per-capita growth rates for species
#' ?get_logistic # logistic growth function
#' ?lv_interaction # function for simulating Lotka-Voterra ODE models
#' ?lv_interaction_log # a version of the lv_interaction computed in log abundance space,
#' # which typically works better for optimization
#' ?lv_optim # methods for fitting complex n-species models
#' ?test_goodness_of_fit # tests goodness of fit of model results, with an R2-like statistic
#' ?ode_prediction # generic function for simulating time series,
#' # to be used with other optimizers
NULL
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