package pre-release

DESCRIPTION

@@ -2,8 +2,8 @@ Package: moms
 Title: Measuring Occupancy in Multidimensional Spaces
 Author: Thomas Guillerme [aut, cre, cph]
 Maintainer: Thomas Guillerme <[email protected]>
-Version: 0.3.4
-Date: 2019-09-16
+Version: 0.3.5
+Date: 2019-10-01
 Description: A shiny app for exploring measurements of multidimensional space occupancy (disparity).
 Depends:
 	R (>= 3.5.2),

NAMESPACE

@@ -2,7 +2,7 @@ import(dispRity)
 import(stats)
 import(grDevices)
 import(graphics)
-#importFrom("ellipse", "plotcorr")
+# importFrom("shinyMatrix", "matrixInput", "updateMatrixInput")
 
 ## Functions
 export(reduce.space)
@@ -11,6 +11,9 @@ export(vdp.dispRity)
 export(vdp.plot)
 export(vdp.check.table)
 
+## Shiny utilities (moms)
+# export(shinyMatrix.alias)
+
 ## Simulations (paper)
 export(space.simulation)
 export(shift.group.simulation)
@@ -35,4 +38,4 @@ export(s.test)
 export(extract.results)
 export(summarise.metrics)
 export(save.results)
-export(load.results)
+export(load.results)

NEWS.md

@@ -21,7 +21,7 @@ dispRity 0.2.0 (2016-04-01)
 
   * Blabla
  -->
- moms v0.3 (2019-09-16)
+ moms v0.3 (2019-10-01)
 =========================
 
 ### NEW FEATURES
@@ -31,6 +31,7 @@ dispRity 0.2.0 (2016-04-01)
 ### MINOR IMPROVEMENTS
 
   * the functions to generate the paper are now properly documented with examples
+  * pre-release cleaning
 
 moms v0.2 (2019-05-09)
 =========================

R/extract.results.R

@@ -6,13 +6,8 @@
 #' @param metric which metric to extract
 #' @param element which element to extract (e.g. number of dimensions, type of space, etc.) can be "all"
 #' @param transfo which transformation to extract (e.g. random, density, etc.) can be "all"
-#'
-#' @examples
-#'
-#' @seealso
 #' 
 #' @author Thomas Guillerme
-#' @export
 
 extract.results <- function(all_results, metric, element = "all", transfo = "all") {
 

R/fast.disparity.R

@@ -17,14 +17,11 @@
 #' ## A group of four observations
 #' group <- c(TRUE, TRUE, TRUE, TRUE, FALSE)
 #' 
-#' ## The disparity
+#' ## The disparity
 #' fast.disparity(group, space, metric, rare.dim = 5)
 #'
-#' @seealso
 #' 
 #' @author Thomas Guillerme
-#' @export
-#' 
 
 fast.disparity <- function(group, space, metric, rare.dim) {
     ## Setting up the default args

R/metrics.simulation.R

@@ -25,7 +25,6 @@
 #' @seealso space.simulation shift.group.simulation
 #' 
 #' @author Thomas Guillerme
-#' @export
 
 metrics.simulation <- function(space, reduction, metrics, rare.dim = NULL, verbose = TRUE) {
 

R/moms-package.R

@@ -1,6 +1,6 @@
-#' Measuring Occupancy of Multidimensional Spaces
+#' @title Measuring Occupancy of Multidimensional Spaces
 #' 
-#' A shiny app for measuring occupancy of multidimensional spaces
+#' @description A shiny app for measuring occupancy of multidimensional spaces
 #' 
 #' @name moms-package
 #'
@@ -11,21 +11,27 @@
 
 NULL
 
-#' Demo datasets
+#' @title Demo datasets
 #'
-#' A set six trait spaces with different groups and different dimensions
+#' @description A set six trait spaces with different groups and different dimensions
 #'
+#' @details
 #' \itemize{
-#'   \item \code{Beck2014} @@@
-#'   \item \code{Wright2017} @@@
-#'   \item \code{Marcy2016} @@@
-#'   \item \code{@@@} @@@
-#'   \item \code{Jones2015} @@@
-#'   \item \code{@@@} @@@
+#'   \item \code{Beck2014} A palaeobiology study of mammals. The data is a 105 dimensions ordination (PCO) of the distances between 106 mammals based on discrete morphological characters.
+#'   \item \code{Wright2017} A palaeobiology study of crinoids. The data is a 41 dimensions ordination (PCO) of the distances between 42 crinoids based on discrete morphological characters.
+#'   \item \code{Marcy2016} A geometric morphometric study of gophers (rodents). The data is a 134 dimensions ordination (PCA) the Procrustes superimposition of landmarks from 454 gopher skulls.
+#'   \item \code{Hopkins2016} A geometric morphometric study of trilobites. The data is a 134 dimensions ordination (PCA) the Procrustes superimposition of landmarks from 46 trilobites cephala.
+#'   \item \code{Jones2015} An ecological landscape study. The data is a 47 dimensions ordination (PCO) of the Jaccard distances between 48 field sites based on species composition.
+#'   \item \code{Healy2019} A life history analysis of the pace of life in animals. The data is a 6 dimensions ordination (PCA) of 6 life history traits from 285 animal species.
 #' }
-#'
-#' @format @@@
-#' @source @@@
-#' @references @@@
+#' @references Beck, R. M., & Lee, M. S. (2014). Ancient dates or accelerated rates? Morphological clocks and the antiquity of placental mammals. Proceedings of the Royal Society B: Biological Sciences, 281(1793), 20141278.
+#' @references Wright, D. F. (2017). Bayesian estimation of fossil phylogenies and the evolution of early to middle Paleozoic crinoids (Echinodermata). Journal of Paleontology, 91(4), 799-814.
+#' @references Marcy, A. E., Hadly, E. A., Sherratt, E., Garland, K., & Weisbecker, V. (2016). Getting a head in hard soils: convergent skull evolution and divergent allometric patterns explain shape variation in a highly diverse genus of pocket gophers (Thomomys). BMC evolutionary biology, 16(1), 207.
+#' @references Hopkins, M.J. and Pearson, J.K., 2016. Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics. Palaeontologia Electronica, 19(3), pp.1-54.
+#' @references Jones, N. T., Germain, R. M., Grainger, T. N., Hall, A. M., Baldwin, L., & Gilbert, B. (2015). Dispersal mode mediates the effect of patch size and patch connectivity on metacommunity diversity. Journal of Ecology, 103(4), 935-944.
+#' @references Healy, K., Ezard, T.H., Jones, O.R., Salguero-Gomez, R. and Buckley, Y.M., 2019. Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nature ecology & evolution, p.1.
+#' 
 #' @name Demo_data
+#' 
 NULL
+

R/reduce.space.R

@@ -7,7 +7,7 @@
 #' @param remove the proportion of elements to be removed (in probability)
 #' @param parameters the parameter(s) for removal selection (see details). If left empty, the \code{parameters} is estimated to reach the amount set by \code{remove}.
 #' @param tuning Optinal parameters for tuning the parameter estimations (if remove is required and parameters is missing) a list of three parameters: "max" for the maximum of operations, "tol" for the tuning (e.g. 0.1 close), "inc.steps" for the initial increment value during optimisation (default = 2 - the bigger the value, the slower the increment).
-#' @param verbose
+#' @param verbose wether to be verbose or not
 #' @param return.optim logical, whether to also return the optimal value.
 #' 
 #' @details
@@ -39,8 +39,6 @@
 #' plot(space, pch = 19, col = c("grey", "black")[as.factor(density)],
 #'      main = "Density removal")
 #' 
-#'
-#' @seealso
 #' 
 #' @author Thomas Guillerme
 

R/reduce.space_fun.R

@@ -8,7 +8,6 @@
 # ' @param remove the proportion of elements to be removed (in probability)
 # ' @param verbose
 # ' @param space
-#' 
 ## Optimisation function
 optimise.parameter <- function(fun, args, criterion, tuning, verbose) {
 

R/results.fable.R

@@ -9,12 +9,8 @@
 #' @param overlap whether to add the Bhattacharyya Coefficients or not
 #' @param plot.param the plot parameters
 #'
-#' @examples
-#'
-#' @seealso
 #' 
 #' @author Thomas Guillerme
-#' @export
 
 generate.fable.plot <- function(data, metric, what, scale = TRUE, overlap = FALSE, plot.param) {
 

R/shift.group.simulation.R

@@ -17,7 +17,7 @@
 #' @seealso space.simulation metric.simulation
 #' 
 #' @author Thomas Guillerme
-#' @export
+
 shift.group.simulation <- function(space, remove, verbose = TRUE) {
 
     ## Double check for full TRUE or full FALSE

R/shinyMatrix.alias.R

@@ -0,0 +1,17 @@
+# #' @title shinyMatrix utilities
+# #'
+# #' @description Alias functions for shinyMatrix
+# #'
+# #' @param x an input
+# #' @param type either \code{"input"} or \code{"update"}
+# #' 
+# #' @author Thomas Guillerme
+
+# shinyMatrix.alias <- function(x, type = "input") {
+#     if(type == "input") {
+#         out <- shinyMatrix::matrixInput(x)
+#     } else {
+#         out <- shinyMatrix::updateMatrixInput(x)
+#     }
+#     return(out)
+# }