Prepare for CRAN resubmission

SiMRiv/DESCRIPTION

@@ -1,6 +1,6 @@
 Package: SiMRiv
 Version: 1.0.1
-Date: 2017-07-24
+Date: 2017-08-17
 Title: Individual-Based, Spatially-Explicit Simulation and Analysis of Multi-State Movements in River Networks, Homogeneous, and Heterogeneous Landscapes
 Authors@R: c(person("Lorenzo", "Quaglietta", role = c("aut"), email = "[email protected]")
 		,person("Miguel", "Porto", role = c("aut", "cre"), email = "[email protected]")
@@ -19,10 +19,11 @@ Description: Provides functions to generate and analyze individual-based spatial
   mainly along rivers but also using the matrix), or even in highly contrasted landscapes
   (e.g. fish in a river network). The algorithm and its input parameters are the same for all
   cases, so that results are comparable. Simulated trajectories can then be used as mechanistic
-  null models to test e.g. for species site fidelity and other 'Movement Ecology' hypotheses, or
-  for other predictive purposes. The package should thus be relevant to explore a broad spectrum
-  of ecological phenomena, such as those at the interface of animal behaviour, landscape, spatial
-  and movement ecology, disease and invasive species spread, and population dynamics.
+  null models to test e.g. for species site fidelity and other 'Movement Ecology' hypotheses
+  (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), or for other predictive purposes.
+  The package should thus be relevant to explore a broad spectrum of ecological phenomena, such as those at the interface
+  of animal behaviour, landscape, spatial and movement ecology, disease and invasive species spread, and population dynamics.
 License: GPL (>= 2)
 URL: https://www.r-project.org, https://github.com/miguel-porto/SiMRiv
 BugReports: https://github.com/miguel-porto/SiMRiv
+

SiMRiv/man/adjustModel.Rd

@@ -46,7 +46,7 @@
   This function finds possible parameters for the simulation (solutions), so that the resulting movements are as similar as possible
   to the given real trajectory, in terms of their intrinsic properties measured by step lengths and variation in turning angles. The input parameter
   approximations are found using a multiobjective genetic algorithm (NSGA-II, \cite{Deb et al. 2002}). The algorithm minimizes a vector
-  of N objectives (\code{N=sum(nbins.hist)} if \code{aggregate.obj.hist == FALSE}, \code{N=sum(nbins.hist > 0)} otherwise)
+  of N objectives (N=sum(nbins.hist) if aggregate.obj.hist == FALSE, N=sum(nbins.hist > 0) otherwise)
   whose values are computed by the absolute differences between each pair of bins (real and simulated) of two histograms:
   an histogram of the step lengths and an histogram of either the turning angles themselves (if \code{TA.variation == FALSE}), or of the
   standard deviation in turning angles computed in a moving time window along each trajectory (if \code{TA.variation == TRUE}).
@@ -127,15 +127,17 @@ species.model <- speciesModel("CRW.CRW.sl", steplength = max.step.length)
 
 ## now run optimization
 
-sol <- adjustModel(real.data, species.model, resol = 20, window = 10, nbins.hist = c(3, 3)
-	, step.hist.log = TRUE)
+sol <- adjustModel(real.data, species.model, resol = 20, window = 10
+	, nbins.hist = c(3, 3), step.hist.log = TRUE)
 
-## After finishing, we can extract the input parameters of the optimized solutions
-## (100 by default) in the last generation (generation 1000 by default):
+## After finishing, we can extract the input parameters of the optimized
+## solutions (100 by default) in the last generation (generation 1000
+## by default):
 
 pars <- sol[[length(sol)]]$par
 
-## now we can take the optimized solutions and reconstruct species based on them:
+## now we can take the optimized solutions and reconstruct species
+## based on them:
 
 optimized.species <- apply(pars, 1, species.model)
 

SiMRiv/man/generationPlot.Rd

@@ -5,8 +5,9 @@
   Plots the evolution of the optimized solutions (sets of input parameters) along the \code{\link{adjustModel}} algorithm's generations.
 }
 \usage{
-  generationPlot(solutions, species.model, plot.quantiles = c(0.10, 0.5, 0.90)
-    , only.pareto = FALSE, show.legend = TRUE, lwd = 1.5, mar = c(2.3, 2.3, 0.2, 2.3)
+  generationPlot(solutions, species.model
+    , plot.quantiles = c(0.10, 0.5, 0.90), only.pareto = FALSE
+    , show.legend = TRUE, lwd = 1.5, mar = c(2.3, 2.3, 0.2, 2.3)
     , mgp = c(1.2, 0.2, 0), tcl = -0.25, ...)
 }
 \arguments{

SiMRiv/man/species.Rd

@@ -5,7 +5,8 @@
   Creates a species, characterized by one or more behavioral states, to be simulated with function \code{\link{simulate}}.
 }
 \usage{
-  species(states, trans = transitionMatrix(), name = "<unnamed>", resistanceMap = NULL)
+  species(states, trans = transitionMatrix(), name = "<unnamed>"
+    , resistanceMap = NULL)
 }
 \arguments{
   \item{states}{a list of \code{state}s characterizing the behavior of the species, or a single \code{state}, for simple movements}

SiMRiv/man/speciesModel.Rd

@@ -12,7 +12,8 @@
 \arguments{
   \item{type}{the type of movement to "fit". One of \code{CRW}, \code{RW.CRW}, \code{CRW.CRW}, \code{RW.CRW.sl}, \code{CRW.CRW.sl}}
   \item{perceptual.range}{the perceptual range for all states.}
-  \item{steplength}{the fixed step length for fixed step length types \code{CRW}, \code{RW.CRW}, \code{CRW.CRW} or the maximum allowed value for variable step length types \code{RW.CRW.sl}, \code{CRW.CRW.sl}.}
+  \item{steplength}{the fixed step length for fixed step length types \code{CRW}, \code{RW.CRW}, \code{CRW.CRW} or the maximum allowed value for variable step length types \code{RW.CRW.sl}
+    , \code{CRW.CRW.sl}, \code{CRW.CRW.CRW.sl} and \code{CRW.RW.Rest.sl}.}
   \item{prob.upperbound}{the maximum allowed value for the state switching probabilities. The default is 0.5 because very high state switching probabilities don't make much sense from a biological point of view.}
   \item{max.concentration}{the maximum allowed value for the turning angle concentration parameter for CRWs [0, 1]. By default it is set to 0.99 because values higher than this (for technical reasons) result in straight line paths, which is a technical artifact.}
 }

SiMRiv/src/simulate.c

@@ -1,5 +1,5 @@
 // FIXME: some bug with OMP in i386 architecture...
-#define USEOPENMP
+//#define USEOPENMP
 #ifdef USEOPENMP
 #include <omp.h>
 #endif