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diff --git a/R/logistic.solution.R b/R/logistic.solution.R
index a3bddab3..d9db13d7 100644
--- a/R/logistic.solution.R
+++ b/R/logistic.solution.R
@@ -1,3 +1,58 @@
+#' Logistic kinetics
+#' 
+#' Function describing exponential decline from a defined starting value, with
+#' an increasing rate constant, supposedly caused by microbial growth
+#' 
+#' @param t Time.
+#' @param parent.0 Starting value for the response variable at time zero.
+#' @param kmax Maximum rate constant.
+#' @param k0 Minumum rate constant effective at time zero.
+#' @param r Growth rate of the increase in the rate constant.
+#' @return The value of the response variable at time \code{t}.
+#' @note The solution of the logistic model reduces to the
+#'   \code{\link{SFO.solution}} if \code{k0} is equal to \code{kmax}.
+#' @references FOCUS (2014) \dQuote{Generic guidance for Estimating Persistence
+#'   and Degradation Kinetics from Environmental Fate Studies on Pesticides in
+#'   EU Registration} Report of the FOCUS Work Group on Degradation Kinetics,
+#'   Version 1.1, 18 December 2014
+#'   \url{http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics}
+#' @examples
+#' 
+#'   # Reproduce the plot on page 57 of FOCUS (2014)
+#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.2),
+#'        from = 0, to = 100, ylim = c(0, 100),
+#'        xlab = "Time", ylab = "Residue")
+#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.4),
+#'        from = 0, to = 100, add = TRUE, lty = 2, col = 2)
+#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.0001, 0.8),
+#'        from = 0, to = 100, add = TRUE, lty = 3, col = 3)
+#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.001, 0.2),
+#'        from = 0, to = 100, add = TRUE, lty = 4, col = 4)
+#'   plot(function(x) logistic.solution(x, 100, 0.08, 0.08, 0.2),
+#'        from = 0, to = 100, add = TRUE, lty = 5, col = 5)
+#'   legend("topright", inset = 0.05,
+#'          legend = paste0("k0 = ", c(0.0001, 0.0001, 0.0001, 0.001, 0.08),
+#'                          ", r = ", c(0.2, 0.4, 0.8, 0.2, 0.2)),
+#'          lty = 1:5, col = 1:5)
+#' 
+#'   # Fit with synthetic data
+#'   logistic <- mkinmod(parent = mkinsub("logistic"))
+#' 
+#'   sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#'   parms_logistic <- c(kmax = 0.08, k0 = 0.0001, r = 0.2)
+#'   d_logistic <- mkinpredict(logistic,
+#'     parms_logistic, c(parent = 100),
+#'     sampling_times)
+#'   d_2_1 <- add_err(d_logistic,
+#'     sdfunc = function(x) sigma_twocomp(x, 0.5, 0.07),
+#'     n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]]
+#' 
+#'   m <- mkinfit("logistic", d_2_1, quiet = TRUE)
+#'   plot_sep(m)
+#'   summary(m)$bpar
+#'   endpoints(m)$distimes
+#' 
+#' @export
 logistic.solution <- function(t, parent.0, kmax, k0, r)
 {
 	parent = parent.0 * (kmax / (kmax - k0 + k0 * exp (r * t))) ^(kmax/r)