CRAN Package Check Results for Package hamlet

Last updated on 2024-06-13 06:58:04 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9.6 5.19 64.15 69.34 NOTE
r-devel-linux-x86_64-debian-gcc 0.9.6 3.16 33.86 37.02 ERROR
r-devel-linux-x86_64-fedora-clang 0.9.6 84.78 NOTE
r-devel-linux-x86_64-fedora-gcc 0.9.6 91.92 NOTE
r-devel-windows-x86_64 0.9.6 5.00 80.00 85.00 NOTE
r-patched-linux-x86_64 0.9.6 5.21 61.64 66.85 NOTE
r-release-linux-x86_64 0.9.6 4.05 61.02 65.07 NOTE
r-release-macos-arm64 0.9.6 31.00 NOTE
r-release-macos-x86_64 0.9.6 49.00 NOTE
r-release-windows-x86_64 0.9.6 5.00 81.00 86.00 NOTE
r-oldrel-macos-arm64 0.9.6 33.00 NOTE
r-oldrel-macos-x86_64 0.9.6 56.00 NOTE
r-oldrel-windows-x86_64 0.9.6 6.00 88.00 94.00 NOTE

Check Details

Version: 0.9.6
Check: dependencies in R code
Result: NOTE There are ::: calls to the package's namespace in its code. A package almost never needs to use ::: for its own objects: ‘.ga.breed’ ‘.ga.fitness’ ‘.ga.init’ ‘.ga.mutate’ ‘.ga.step’ ‘.ga.weight’ Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 0.9.6
Check: Rd files
Result: NOTE checkRd: (-1) hmap.Rd:87: Lost braces; missing escapes or markup? 87 | The horizontal limits of the row hierarchical clustering. The horizontal limits of the heatmap are {a=leftlim[1], b=leftlim[2], c=xlim[1], d=xlim[2]} where the 'a' is where the row dendrogram begins, 'b' is where the row dendrogram ends, 'c' is where the heatmap itself begins, and 'd' is where the heatmap itself ends. | ^ checkRd: (-1) hmap.Rd:91: Lost braces; missing escapes or markup? 91 | The vertical limits of the row hierarchical clustering. The horizontal limits of the heatmap are {a=ylim[1], b=ylim[2], c=toplim[1], d=toplim[2]} where the 'a' is where the heatmap begins, 'b' is where the heatmap ends, 'c' is where the column dendrogram begins, and 'd' is where the column dendrogram ends. | ^ checkRd: (-1) mix.binary.Rd:19: Lost braces; missing escapes or markup? 19 | e.g. x = {red, green, blue, green} --> x_new = {{1,0,0}, {0,1,0}, {0,0,1}, {0,1,0}} where the dimensions in x_new are is_red, is_green and is_blue | ^ checkRd: (-1) mix.binary.Rd:19: Lost braces 19 | e.g. x = {red, green, blue, green} --> x_new = {{1,0,0}, {0,1,0}, {0,0,1}, {0,1,0}} where the dimensions in x_new are is_red, is_green and is_blue | ^ checkRd: (-1) mix.binary.Rd:19: Lost braces; missing escapes or markup? 19 | e.g. x = {red, green, blue, green} --> x_new = {{1,0,0}, {0,1,0}, {0,0,1}, {0,1,0}} where the dimensions in x_new are is_red, is_green and is_blue | ^ checkRd: (-1) mix.binary.Rd:19: Lost braces; missing escapes or markup? 19 | e.g. x = {red, green, blue, green} --> x_new = {{1,0,0}, {0,1,0}, {0,0,1}, {0,1,0}} where the dimensions in x_new are is_red, is_green and is_blue | ^ checkRd: (-1) mix.binary.Rd:19: Lost braces; missing escapes or markup? 19 | e.g. x = {red, green, blue, green} --> x_new = {{1,0,0}, {0,1,0}, {0,0,1}, {0,1,0}} where the dimensions in x_new are is_red, is_green and is_blue | ^ Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64

Version: 0.9.6
Check: package dependencies
Result: NOTE Packages suggested but not available for checking: 'lme4', 'nbpMatching', 'lmerTest' Flavor: r-devel-linux-x86_64-debian-gcc

Version: 0.9.6
Check: examples
Result: ERROR Running examples in ‘hamlet-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: hamlet-package > ### Title: Hierarchical Optimal Matching and Machine Learning Toolbox > ### Aliases: hamlet-package hamlet > ### Keywords: package > > ### ** Examples > > > ## > ## Exploring the VCaP dataset provided alongside the 'hamlet' package > ## > > data(vcapwide) > data(vcaplong) > > # VCaP Castration-resistant prostate cancer (CRPC) PSA-measurements (and body weight) in wide-format > mixplot(vcapwide[,c("PSAWeek10", "PSAWeek14", "BWWeek10", "Group")], pch=16) > anv <- aov(PSA ~ Group, data.frame(PSA = vcapwide[,"PSAWeek14"], Group = vcapwide[,"Group"])) > summary(anv) Df Sum Sq Mean Sq F value Pr(>F) Group 2 7038 3519 6.286 0.00416 ** Residuals 41 22950 560 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 1 observation deleted due to missingness > TukeyHSD(anv) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = PSA ~ Group, data = data.frame(PSA = vcapwide[, "PSAWeek14"], Group = vcapwide[, "Group"])) $Group diff lwr upr p adj MDV-ARN 12.85805 -8.521008 34.23710 0.3192860 Vehicle-ARN 30.51267 9.505449 51.51988 0.0029165 Vehicle-MDV 17.65462 -3.724436 39.03367 0.1230428 > summary(aov(BW ~ Group, data.frame(BW = vcapwide[,"BWWeek14"], Group = vcapwide[,"Group"]))) Df Sum Sq Mean Sq F value Pr(>F) Group 2 4.5 2.236 0.289 0.751 Residuals 41 317.6 7.746 1 observation deleted due to missingness > > # VCaP Castration-resistant prostate cancer (CRPC) PSA-measurements (and body weight) in long-format > library(lattice) > xyplot(log2PSA ~ DrugWeek | Group, data = vcaplong, type="l", group=ID, layout=c(3,1)) > xyplot(BW ~ DrugWeek | Group, data = vcaplong, type="l", group=ID, layout=c(3,1)) > > ## > ## Example multigroup (g=3) nbp-matching using the branch and bound algorithm, > ## and subsequent random allocation of submatches to 3 arms > ## > > # Construct an Euclidean distance example distance matrix using 15 observations from the VCaP study > d <- as.matrix(dist(vcapwide[1:15,c("PSAWeek10", "BWWeek10")])) > # Matching using the b&b algorithm to submatches of size 3 > # (which will result in 3 intervention groups) > bb3 <- match.bb(d, g=3) [1] "Performing initial sorting for a good initial guess" [1] "Computing boundaries for minimum distances in possible combinations..." [1] "Starting branch and bound" [1] "Branches: 18" [1] "Bounds: 357" [1] "Ends visited: 4" [1] "Solution cost 73.1793785290756" [1] "Solution: 3,1,3,5,2,4,4,5,1,2,2,5,3,4,1" > str(bb3) List of 6 $ branches: num 18 $ bounds : num 357 $ ends : num 4 $ matrix : num [1:15, 1:15] 0 0 1 0 0 0 0 0 0 0 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : chr [1:15] "ID003" "ID007" "ID008" "ID009" ... .. ..$ : chr [1:15] "ID003" "ID007" "ID008" "ID009" ... $ solution: Named num [1:15] 3 1 3 5 2 4 4 5 1 2 ... ..- attr(*, "names")= chr [1:15] "ID003" "ID007" "ID008" "ID009" ... $ cost : num 73.2 > > solvec <- bb3$solution > # matching vector, where each element indicates to which submatch each observation belongs to > > # Perform an example random allocation of the above submatches, > # these will be randomly allocated to 3 arms based on the submatches > set.seed(1) > groups <- match.allocate(solvec) > > # Illustrate randomization, no baseline differences in these three artificial groups > by(vcapwide[1:15,c("PSAWeek10", "BWWeek10")], INDICES=groups, FUN=function(x) x) groups: Group_A PSAWeek10 BWWeek10 ID003 21.30 35.0 ID007 7.55 31.6 ID016 15.05 39.6 ID025 13.13 33.3 ID031 7.04 36.6 ------------------------------------------------------------ groups: Group_B PSAWeek10 BWWeek10 ID008 23.58 33.6 ID010 9.90 34.1 ID027 9.59 32.0 ID037 13.74 32.4 ID045 14.27 34.8 ------------------------------------------------------------ groups: Group_C PSAWeek10 BWWeek10 ID009 13.17 31.7 ID018 13.53 34.0 ID032 8.49 34.9 ID040 23.62 35.9 ID047 6.57 31.9 > > summary(aov(PSAWeek10 ~ groups, data = data.frame(PSAWeek10 = vcapwide[1:15,"PSAWeek10"], groups))) Df Sum Sq Mean Sq F value Pr(>F) groups 2 5.6 2.78 0.076 0.928 Residuals 12 440.9 36.74 > summary(aov(BWWeek10 ~ groups, data = data.frame(BWWeek10 = vcapwide[1:15,"BWWeek10"], groups))) Df Sum Sq Mean Sq F value Pr(>F) groups 2 9.75 4.873 1.026 0.388 Residuals 12 56.98 4.749 > > ## > ## Example mixed-effects modeling of the longitudinal PSA profiles using > ## the actual experimental groups > ## > > exdat <- vcaplong[vcaplong[,"Group"] %in% c("Vehicle", "ARN"),] > > library(lme4) Error in library(lme4) : there is no package called ‘lme4’ Execution halted Flavor: r-devel-linux-x86_64-debian-gcc