version 1.4.8

DESCRIPTION

@@ -2,7 +2,7 @@ Package: gMOIP
 Type: Package
 Title: Tools for 2D and 3D Plots of Single and Multi-Objective
         Linear/Integer Programming Models
-Version: 1.4.7
+Version: 1.4.8
 Authors@R: person("Lars", "Relund Nielsen", email = "lars@relund.dk", role = c("aut", "cre"), 
     comment = c(ORCID = "0000-0002-4802-3071"))
 URL: https://relund.github.io/gMOIP/, https://github.com/relund/gMOIP/
@@ -16,20 +16,19 @@ Description: Make 2D and 3D plots of linear programming (LP),
 License: GPL (>= 3.3.2)
 Language: en-US
 Encoding: UTF-8
-RoxygenNote: 7.1.1
+RoxygenNote: 7.2.3
 Depends: R (>= 3.5.0)
 Imports: ggrepel, geometry, ggplot2, rgl, MASS, Matrix, grDevices,
         stats, Rfast, plyr, tidyselect, tidyr, tibble, purrr, dplyr,
         rlang, png, sp
 Suggests: tikzDevice, grid, gridExtra, knitr, rmarkdown, roxygen2,
         ggsci, tidyverse, magrittr, scales, pdftools, testthat (>=
         2.1.0), webshot2
-Additional_repositories: https://dmurdoch.github.io/drat
 VignetteBuilder: knitr
 NeedsCompilation: no
-Packaged: 2021-08-23 11:19:02 UTC; au15463
+Packaged: 2023-01-26 12:13:47 UTC; au15463
 Author: Lars Relund Nielsen [aut, cre]
     (<https://orcid.org/0000-0002-4802-3071>)
 Maintainer: Lars Relund Nielsen <lars@relund.dk>
 Repository: CRAN
-Date/Publication: 2021-08-23 14:20:02 UTC
+Date/Publication: 2023-01-26 16:30:02 UTC

MD5

@@ -1,88 +1,89 @@
-cd7eef1adcb7e9b5b1edd96986e14c3f *DESCRIPTION
+21a044616e05b4dcbb2ac4f9ebb93278 *DESCRIPTION
 1447549699eeb9e263f215b5c6c1c07e *NAMESPACE
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 13d0b61a12126c3420a47c2353677e05 *inst/doc/bi-objective_3x_ex1.R
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 1e29f80c945bac41f3a89d0aabf4129c *vignettes/bi-objective_2x.Rmd
 55050a779be09b23e15db7cd38f683d1 *vignettes/bi-objective_3x_ex1.Rmd
 1da2459f0797116847dfce0784b002b6 *vignettes/intro.Rmd
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 8230331a3182286fc0fb0e401d132abf *vignettes/polytope_3d_ex1.Rmd
-f59d47b4da705eb82b404ac9d4b9305f *vignettes/tri-objective.Rmd
+a9008920632419b8d1fa76c9278f218e *vignettes/tri-objective.Rmd

NEWS.md

@@ -1,6 +1,11 @@
+# gMOIP 1.4.8
+
+* Change `rgl.*` to `*3d` functions.
+* Fixed bugs due to dependent package updates.
+
 # gMOIP 1.4.7
 
-*Fixed errors about pandoc missing and added webshot2.
+* Fixed errors about pandoc missing and added `webshot2`.
 
 # gMOIP 1.4.6
 
@@ -30,7 +35,7 @@
 # gMOIP 1.3.0
 
 * Added support for 3D plots using RGL.
-* Added highlevel functions for plotting.
+* Added high level functions for plotting.
 * Can plot the criterion space for bi-objective models.
 
 # gMOIP 1.2.0

R/functions.R

@@ -78,7 +78,7 @@ cornerPoints <- function (A, b, type = rep("c", ncol(A)), nonneg = rep(TRUE, nco
    p <- slices(A, b, type, nonneg)
    p <- do.call(rbind, p)
    tri <- t(geometry::convhulln(p))
-   #rgl.triangles(p[tri,1],p[tri,2],p[tri,3],col="gold2",alpha=.6)
+   #triangles3d(p[tri,1],p[tri,2],p[tri,3],col="gold2",alpha=.6)
    idx <- unique(as.vector(tri))
    p <- p[idx,]
    #points3d(p, col="blue", size = 15)

R/hull.R

@@ -60,7 +60,7 @@ dimFace<-function(pts, dim = NULL) {
 #'   many points to test, it is most efficient to call this function once with the entire set.
 #' @param vertices A \eqn{mxp} array of vertices of the convex hull. May contain redundant
 #'   (non-vertex) points.
-#' @param hull Tessellation (or triangulation) generated by convhulln (only works if the dimension
+#' @param hull Tessellation (or triangulation) generated by `convhulln` (only works if the dimension
 #'   of the hull is \eqn{p}). If hull is \code{NULL}, then it will be generated.
 #' @param tol Tolerance on the tests for inclusion in the convex hull. You can think of `tol` as the
 #'   difference a point value may be different from the values of the hull, and still be perceived
@@ -77,7 +77,7 @@ dimFace<-function(pts, dim = NULL) {
 #'   warning will be given).
 #'
 #' @return An integer vector of length \eqn{n} with values 1 (inside hull), -1 (outside hull) or 0
-#'   (on hull to precision indicated by tol).
+#'   (on hull to precision indicated by `tol`).
 #'
 #' @author Lars Relund \email{lars@@relund.dk}
 #' @export
@@ -146,7 +146,7 @@ inHull <- function(pts, vertices, hull=NULL,
    p <- ncol(vertices)   # dimension of points
    cx <- nrow(pts)   # points to test
    d <- dimFace(vertices, dim = p)
-   if (d == 0) return(-as.integer(apply(pts, 1, FUN = function(x) abs(x - vertices[1,]) > tol)))
+   if (d == 0) return(-as.integer(apply(pts, 1, FUN = function(x) any(abs(x - vertices[1, ]) > tol))))
    if (p == 1) { #1D space - check if between max and min
       m <- min(vertices)
       M <- max(vertices)
@@ -254,20 +254,20 @@ inHull <- function(pts, vertices, hull=NULL,
 
 #' Find segments (lines) of a face.
 #'
-#' @param vertices A mxp array of vertices of the convex hull, as used by
-#'   convhulln.
-#' @param hull Tessellation (or triangulation) generated by convhulln If hull is
+#' @param vertices A `m x p` array of vertices of the convex hull, as used by
+#'   [geometry::convhulln()].
+#' @param hull Tessellation (or triangulation) generated by [geometry::convhulln()] If hull is
 #'   left empty or not supplied, then it will be generated.
 #' @param tol Tolerance on the tests for inclusion in the convex hull. You can
-#'   think of tol as the distance a point may possibly lie outside the hull, and
+#'   think of `tol` as the distance a point may possibly lie outside the hull, and
 #'   still be perceived as on the surface of the hull. Because of numerical slop
 #'   nothing can ever be done exactly here. I might guess a semi-intelligent
-#'   value of tol to be
+#'   value of `tol` to be
 #'
-#'   tol = 1.e-13*mean(abs(vertices(:)))
+#'   `tol = 1.e-13*mean(abs(vertices(:)))`
 #'
 #'   In higher dimensions, the numerical issues of floating point arithmetic
-#'   will probably suggest a larger value of tol.
+#'   will probably suggest a larger value of `tol`.
 #'
 #' @return A matrix with segments.
 #' @author Lars Relund \email{lars@@relund.dk}
@@ -326,9 +326,9 @@ hullSegment <- function(vertices, hull=geometry::convhulln(vertices),
 #'   plus a value greater than on equal zero. If negative, consider the i'th column of the `pts`
 #'   minus a value greater than on equal zero.
 #'
-#' @note Assume that `pts` has been checked using [.checkPts].
+#' @note Assume that `pts` has been checked using [.checkPts()].
 #' @return The points merged with the points on the bounding box. The column `pt` equals 1 if
-#'   points from pts and zero otherwise.
+#'   points from `pts` and zero otherwise.
 #' @export
 #'
 #' @examples
@@ -370,15 +370,14 @@ addRays <- function(pts, m = apply(pts,2,min)-5, M = apply(pts,2,max)+5, directi
 #' @param direction Ray direction. If i'th entry is positive, consider the i'th column of `pts`
 #'   plus a value greater than on equal zero (minimize objective $i$). If negative, consider the
 #'   i'th column of `pts` minus a value greater than on equal zero (maximize objective $i$).
-#' @param tol Tolerance on std. dev. if using PCA.
+#' @param tol Tolerance on standard deviation if using PCA.
 #' @param m Minimum values of the bounding box.
 #' @param M Maximum values of the bounding box.
 #'
 #' @return A list with \code{hull} equal a matrix with row indices of the vertices defining each
 #'   facet in the hull and \code{pts} equal the input points (and dummy points) and columns:
 #'   \code{pt}, true if a point in the original input; false if a dummy point (a point on a ray).
 #'   \code{vtx}, TRUE if a vertex in the hull.
-#' @export
 #'
 #' @examples
 #' ## 1D
@@ -427,6 +426,9 @@ addRays <- function(pts, m = apply(pts,2,min)-5, M = apply(pts,2,max)+5, directi
 #' plotHull3D(pts, addRays = TRUE)
 #' convexHull(pts, addRays = TRUE)
 #' finalize3D()
+#'
+#' @importFrom rlang .data
+#' @export
 convexHull <- function(pts, addRays = FALSE, useRGLBBox = FALSE, direction = 1,
                        tol = mean(mean(abs(pts)))*sqrt(.Machine$double.eps)*2,
                        m = apply(pts,2,min)-5, M = apply(pts,2,max)+5) {
@@ -435,7 +437,7 @@ convexHull <- function(pts, addRays = FALSE, useRGLBBox = FALSE, direction = 1,
    if (length(direction) != p) direction = rep(direction[1],p)
    # print(set)
    if (addRays) {
-      if (rgl::rgl.cur() > 0 & useRGLBBox) {
+      if (rgl::cur3d() > 0 & useRGLBBox) {
          limits <- rgl::par3d()$bbox
          for (i in 1:dim(pts)[1]) {
             pt <- as.vector(pts[i,])
@@ -518,10 +520,10 @@ convexHull <- function(pts, addRays = FALSE, useRGLBBox = FALSE, direction = 1,
    tmp <- dplyr::filter(set, .data$vtx | .data$pt, .preserve = TRUE)
    if (nrow(set) != nrow(tmp)) {
       tmp$newId <- 1:nrow(tmp)
-      mch <- dplyr::left_join(set, tmp, by = 'oldId') %>% dplyr::select(.data$newId)
+      mch <- dplyr::left_join(set, tmp, by = 'oldId') %>% dplyr::select("newId")
       hull <- apply(hull, c(1,2), function(id) {if (is.na(id)) return(NA) else return(mch[id,1])} )
-      set <- dplyr::select(tmp, -.data$oldId, -.data$newId)
-   } else set <- dplyr::select(tmp, -.data$oldId)
+      set <- dplyr::select(tmp, -"oldId", -"newId")
+   } else set <- dplyr::select(tmp, -"oldId")
    return(list(hull = hull, pts = set))
    stop("Cannot find the vertices!")
 }

R/ndset.R

@@ -6,15 +6,15 @@
 #' supported, non-extreme supported, non-supported.
 #'
 #' @param pts A data frame with a column for each variable in the solution
-#'   space (can also be a rangePoints).
+#'   space (can also be a `rangePoints`).
 #' @param obj A p x n matrix(one row for each criterion).
-#' @param crit Either max or min.
+#' @param crit Either `max` or `min`.
 #' @param labels If \code{NULL} or "n" don't add any labels (empty string). If
-#'   'coord' labels are the solution space coordinates. Otherwise number all
+#'   equals `coord`, labels are the solution space coordinates. Otherwise number all
 #'   points from one based on the solution space points.
 #'
-#' @return A data frame with columns x1, ..., xn, z1, ..., zp, lbl (label), nD
-#'   (non-dominated), ext (extreme), nonExt (non-extreme supported).
+#' @return A data frame with columns `x1, ..., xn, z1, ..., zp, lbl (label), nD
+#'   (non-dominated), ext (extreme), nonExt (non-extreme supported)`.
 #' @author Lars Relund \email{lars@@relund.dk}
 #' @export
 #' @examples
@@ -160,15 +160,15 @@ criterionPoints<-function(pts, obj, crit, labels = "coord") {
 #'
 #' @param pts A data frame with points to add (a column for each objective).
 #' @param nDSet A data frame with current non-dominated set (NULL is none yet). Column names of the
-#'   p objectives must be z1, ..., zp.
+#'   p objectives must be `z1, ..., zp`.
 #' @param crit A max or min vector. If length one assume all objectives are optimized in the same
 #'   direction.
 #' @param keepDom Keep dominated points in output.
 #' @param dubND Duplicated non-dominated points are classified as non-dominated.
-#' @param classify Non-dominated points are classified into supported extreme (se), supported
-#'   non-extreme (sne) and unsupported (us)
+#' @param classify Non-dominated points are classified into supported extreme (`se`), supported
+#'   non-extreme (`sne`) and unsupported (`us`)
 #'
-#' @return A data frame with a column for each objective (`z` columns) and nd (non-dominated).
+#' @return A data frame with a column for each objective (`z` columns) and `nd` (non-dominated).
 #'   Moreover if `classify` then columns `se`, `sne`, `us` and `cls`.
 #'
 #' @author Lars Relund \email{lars@@relund.dk}
@@ -422,10 +422,11 @@ addNDSet<-function(pts, nDSet = NULL, crit = "max", keepDom = FALSE, dubND = FAL
 #' @param crit Either max or min.
 #' @param keepDom Keep dominated points.
 #'
-#' @return A data frame with columns z1 and z2, nD (non-dominated),
-#'         ext (extreme), nonExt (non-extreme supported).
+#' @return A data frame with columns z1 and z2, `nD` (non-dominated),
+#'         `ext` (extreme), `nonExt` (non-extreme supported).
 #' @author Lars Relund \email{lars@@relund.dk}
 #' @export
+#' @keywords internal
 #'
 #' @examples
 #' nDSet <- data.frame(z1=c(12,14,16,18), z2=c(18,16,12,4))
@@ -971,6 +972,7 @@ genNDSet <-
 #' @note It is assumed that `pts` are nondominated.
 #'
 #' @return The ND set with classification columns.
+#' @importFrom rlang .data
 #' @export
 #'
 #' @examples
@@ -1053,8 +1055,8 @@ classifyNDSet <- function(pts, direction = 1) {
    set1 <- set %>% left_join(x = set, y = pts[idx,], by = paste0("z", 1:p)) # match id of duplicates
    set1 <- set1 %>%
       dplyr::filter(!is.na(.data$id.y)) %>%
-      dplyr::mutate(id.x = .data$id.y) %>% dplyr::select(.data$z1:.data$id.x)
-   set <- bind_rows(set, pts[idx,]) %>% arrange(id) %>% select(-id)
+      dplyr::mutate(id.x = .data$id.y) %>% dplyr::select("z1":"id.x")
+   set <- dplyr::bind_rows(set, pts[idx,]) %>% dplyr::arrange(id) %>% dplyr::select(-id)
    if (nrow(set1) > 0) for (i in 1:nrow(set1)) set[set1$id.x[i],] <- set1[i, 1:(p+4)]
    return(set)
 }