CRAN Package Check Results for Maintainer ‘Peter Solymos <solymos at ualberta.ca>’

Last updated on 2020-02-19 05:48:02 CET.

Package ERROR NOTE OK
bSims 13
dclone 3 10
dcmle 13
detect 13
intrval 13
mefa 13
mefa4 1 12
opticut 13
pbapply 2 11
PVAClone 13
ResourceSelection 13
sharx 13

Package bSims

Current CRAN status: OK: 13

Package dclone

Current CRAN status: NOTE: 3, OK: 10

Version: 2.3-0
Check: package dependencies
Result: NOTE
    Package suggested but not available for checking: ‘BRugs’
Flavors: r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64

Package dcmle

Current CRAN status: OK: 13

Package detect

Current CRAN status: OK: 13

Package intrval

Current CRAN status: OK: 13

Package mefa

Current CRAN status: OK: 13

Package mefa4

Current CRAN status: ERROR: 1, OK: 12

Version: 0.3-6
Check: examples
Result: ERROR
    Running examples in 'mefa4-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: Mefa
    > ### Title: 'Mefa' Class
    > ### Aliases: Mefa Mefa-class MefaDataFrame MefaMatrix mefa stcs
    > ### MefaDataFrame-class MefaMatrix-class mefa-class stcs-class
    > ### dim,Mefa-method dimnames,Mefa-method dimnames<-,Mefa,list-method
    > ### t,Mefa-method show,Mefa-method stack,Mefa-method
    > ### Keywords: manip
    >
    > ### ** Examples
    >
    > x <- data.frame(
    + sample = paste("Sample", c(1,1,2,2,3,4), sep="."),
    + species = c(paste("Species", c(1,1,1,2,3), sep="."), "zero.pseudo"),
    + count = c(1,2,10,3,4,0))
    > samp <- data.frame(samples=levels(x$sample), var1=1:2)
    Error in data.frame(samples = levels(x$sample), var1 = 1:2) :
     arguments imply differing number of rows: 0, 2
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 0.3-6
Check: tests
Result: ERROR
     Running 'mefa4comparisons.R' [11s/13s]
     Running 'mefa4examples.R' [2s/3s]
    Running the tests in 'tests/mefa4examples.R' failed.
    Complete output:
     > #devtools::install_github("psolymos/mefa4")
     > ## examples
     >
     > ## load library
     > library(mefa)
     mefa 3.2-7 2016-01-11
     > library(mefa4)
     Loading required package: Matrix
     mefa4 0.3-6 2019-06-20
    
     Attaching package: 'mefa4'
    
     The following objects are masked from 'package:mefa':
    
     samp, taxa, xtab
    
     >
     > ## run examples with \dontrun sections
     >
     > help_pages <- c("abmibirds",
     + "find_max",
     + "groupSums", "mbind", "Mefa",
     + "Melt",
     + "nameAlnum", "nonDuplicated",
     + "%notin%", "r2rmd",
     + "samp", "Xtab")
     >
     > for (i in help_pages) {
     + cat("\n\n---------- mefa4 example:", i, "----------\n\n")
     + eval(parse(text=paste0("example('", i,
     + "', package = 'mefa4', run.dontrun = TRUE)")))
     + }
    
    
     ---------- mefa4 example: abmibirds ----------
    
    
     abmbrd> data(abmibirds)
    
     abmbrd> str(abmibirds)
     'data.frame': 59341 obs. of 21 variables:
     $ Rotation : Factor w/ 2 levels "Prototype","Rotation 1": 1 1 1 1 1 1 1 1 1 1 ...
     $ ABMI.Site : int 630 630 630 630 630 630 630 630 630 630 ...
     $ Year : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ...
     $ Field.Date : Factor w/ 132 levels "01-Jun-03","01-Jun-04",..: 46 46 46 46 46 46 46 46 46 46 ...
     $ Field.Crew.Members : Factor w/ 67 levels "ABL","ABL/RSW",..: 15 15 15 15 15 15 15 15 15 15 ...
     $ Identification.Date : Factor w/ 135 levels "01-Feb-10","01-Jun-08",..: 135 135 135 135 135 135 135 135 135 135 ...
     $ Identification.Analyst : Factor w/ 13 levels "CF/CS/TH","CLS/MBI",..: 11 11 11 11 11 11 11 11 11 11 ...
     $ Point.Count.Station : int 1 1 1 1 1 1 1 1 2 2 ...
     $ Wind.Conditions : Factor w/ 7 levels "0","1","2","3",..: 4 4 4 4 4 4 4 4 4 4 ...
     $ Precipitation : Factor w/ 5 levels "DNC","Drizzle",..: 4 4 4 4 4 4 4 4 4 4 ...
     $ Start.of.Point.Count : Factor w/ 434 levels "10:00","10:01",..: 152 152 152 152 152 152 152 152 178 178 ...
     $ End.of.Point.Count : Factor w/ 42 levels "10:07","10:21",..: 42 42 42 42 42 42 42 42 42 42 ...
     $ Common.Name : Factor w/ 217 levels "Accipiters","Alder Flycatcher",..: 184 184 142 142 142 150 208 161 208 181 ...
     $ Scientific.Name : Factor w/ 218 levels "Accipiter","Accipiter gentilis",..: 207 207 173 173 173 209 201 149 201 57 ...
     $ Taxonomic.Resolution : Factor w/ 5 levels "Family","Genus",..: 3 3 3 3 3 3 3 3 3 3 ...
     $ Unique.Taxonomic.Identification.Number: Factor w/ 215 levels "174469","174479",..: 130 130 214 214 214 149 122 161 122 195 ...
     $ Time.First.Detected : Factor w/ 129 levels ".1",".2",".3",..: 87 97 112 87 31 40 13 68 31 13 ...
     $ Interval.1 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
     $ Interval.2 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
     $ Interval.3 : Factor w/ 6 levels "","0","1","DNC",..: 5 5 5 5 5 5 5 5 5 5 ...
     $ Behaviour : Factor w/ 21 levels "Alarm Calling",..: 19 19 19 19 19 19 19 19 19 19 ...
    
    
     ---------- mefa4 example: find_max ----------
    
    
     fnd_mx> ## numeric vector
     fnd_mx> compare_sets(1:10, 8:15)
     xlength ylength intersect union xbutnoty ybutnotx
     labels 10 8 3 15 7 5
     unique 10 8 3 15 7 5
    
     fnd_mx> ## factor with 'zombie' labels
     fnd_mx> compare_sets(factor(1:10, levels=1:10), factor(8:15, levels=1:15))
     xlength ylength intersect union xbutnoty ybutnotx
     labels 10 15 10 15 0 5
     unique 10 8 3 15 7 5
    
     fnd_mx> (mat <- matrix(rnorm(10*5), 10, 5))
     [,1] [,2] [,3] [,4] [,5]
     [1,] -2.25115134 1.1380605 0.39014031 -0.2289808 -1.038531623
     [2,] 0.38735324 -0.6412394 0.42222993 -0.1867742 -1.081927051
     [3,] 1.50520350 -0.7076390 -1.26998891 -0.1122049 -0.756342239
     [4,] 0.98119226 -0.3203242 1.66809595 -0.9666750 0.005817471
     [5,] 0.15367711 -1.5652477 -0.40533494 -0.7517001 -0.846028904
     [6,] -0.12810679 -1.1541756 0.26077967 -0.1526124 1.668180015
     [7,] 0.10064873 0.1204347 0.98762268 0.3968991 -1.178891166
     [8,] -0.03572431 -0.0704294 -0.82586354 0.1891311 -1.228069282
     [9,] 1.33189159 -1.3036537 0.06572903 -1.1562431 -0.735217530
     [10,] -1.01367166 -2.1623647 0.61000271 -0.3152456 1.053303056
    
     fnd_mx> (m <- find_max(mat))
     index value
     1 X2 1.1380605
     2 X3 0.4222299
     3 X1 1.5052035
     4 X3 1.6680960
     5 X1 0.1536771
     6 X5 1.6681800
     7 X3 0.9876227
     8 X4 0.1891311
     9 X1 1.3318916
     10 X5 1.0533031
    
     fnd_mx> ## column indices
     fnd_mx> as.integer(m$index)
     [1] 2 3 1 3 1 5 3 4 1 5
    
     fnd_mx> find_min(mat)
     index value
     1 X1 -2.251151
     2 X5 -1.081927
     3 X3 -1.269989
     4 X4 -0.966675
     5 X2 -1.565248
     6 X2 -1.154176
     7 X5 -1.178891
     8 X5 -1.228069
     9 X2 -1.303654
     10 X2 -2.162365
    
     fnd_mx> map <- cbind(c("a","b","c","d","e","f","g"),
     fnd_mx+ c("A","B","B","C","D","D","E"))
    
     fnd_mx> #x <- factor(sample(map[1:6,1], 100, replace=TRUE), levels=map[,1])
     fnd_mx> x <- as.factor(sample(map[1:6,1], 100, replace=TRUE))
    
     fnd_mx> x[2] <- NA
    
     fnd_mx> table(x, reclass(x, map, all = FALSE), useNA="always")
    
     x A B C D <NA>
     a 20 0 0 0 0
     b 0 16 0 0 0
     c 0 12 0 0 0
     d 0 0 12 0 0
     e 0 0 0 21 0
     f 0 0 0 18 0
     <NA> 0 0 0 0 1
    
     fnd_mx> table(x, reclass(x, map, all = TRUE), useNA="always")
    
     x A B C D E <NA>
     a 20 0 0 0 0 0
     b 0 16 0 0 0 0
     c 0 12 0 0 0 0
     d 0 0 12 0 0 0
     e 0 0 0 21 0 0
     f 0 0 0 18 0 0
     <NA> 0 0 0 0 0 1
    
     fnd_mx> map[c(4, 7), 2] <- NA
    
     fnd_mx> table(x, reclass(x, map, all = FALSE, allow_NA = TRUE), useNA="always")
    
     x A B D <NA>
     a 20 0 0 0
     b 0 16 0 0
     c 0 12 0 0
     d 0 0 0 12
     e 0 0 21 0
     f 0 0 18 0
     <NA> 0 0 0 1
    
     fnd_mx> table(x, reclass(x, map, all = TRUE, allow_NA = TRUE), useNA="always")
    
     x A B D <NA>
     a 20 0 0 0
     b 0 16 0 0
     c 0 12 0 0
     d 0 0 0 12
     e 0 0 21 0
     f 0 0 18 0
     <NA> 0 0 0 1
    
     fnd_mx> (mat2 <- exp(mat) / rowSums(exp(mat)))
     [,1] [,2] [,3] [,4] [,5]
     [1,] 0.01798856 0.53322737 0.25240319 0.13589828 0.06048261
     [2,] 0.31384474 0.11220256 0.32498372 0.17675612 0.07221286
     [3,] 0.67827498 0.07419600 0.04228195 0.13457806 0.07066900
     [4,] 0.26459929 0.07200245 0.52590483 0.03772599 0.09976743
     [5,] 0.39628925 0.07103833 0.22658776 0.16025514 0.14582953
     [6,] 0.10165941 0.03643607 0.14998189 0.09919846 0.61272417
     [7,] 0.16472492 0.16801662 0.39991453 0.22152318 0.04582075
     [8,] 0.25155194 0.24297156 0.11414964 0.31497866 0.07634820
     [9,] 0.63971050 0.04585437 0.18034166 0.05313740 0.08095607
     [10,] 0.06134889 0.01945072 0.31114266 0.12334721 0.48471052
    
     fnd_mx> (rmat2 <- redistribute(mat2, source = 1, target = 2:4))
     [,1] [,2] [,3] [,4] [,5]
     [1,] 0 0.54363615 0.2573302 0.13855106 0.06048261
     [2,] 0 0.16956003 0.4911140 0.26711309 0.07221286
     [3,] 0 0.27465044 0.1565146 0.49816597 0.07066900
     [4,] 0 0.10197539 0.7448267 0.05343044 0.09976743
     [5,] 0 0.13252093 0.4226960 0.29895353 0.14582953
     [6,] 0 0.04940476 0.2033649 0.13450615 0.61272417
     [7,] 0 0.20307441 0.4833594 0.26774547 0.04582075
     [8,] 0 0.33391037 0.1568733 0.43286812 0.07634820
     [9,] 0 0.15086693 0.5933479 0.17482908 0.08095607
     [10,] 0 0.02207943 0.3531928 0.14001726 0.48471052
    
     fnd_mx> colMeans(mat2)
     [1] 0.2889992 0.1375396 0.2527692 0.1457399 0.1749521
    
     fnd_mx> colMeans(rmat2)
     [1] 0.0000000 0.1981679 0.3862620 0.2406180 0.1749521
    
     fnd_mx> stopifnot(abs(sum(mat2) - sum(rmat2)) < 10^-6)
    
    
     ---------- mefa4 example: groupSums ----------
    
    
     grpSms> x <- data.frame(
     grpSms+ sample = paste("Sample", c(1,1,2,2,3,4), sep="."),
     grpSms+ species = c(paste("Species", c(1,1,1,2,3), sep="."),
     grpSms+ "zero.pseudo"), count = c(1,2,10,3,4,0))
    
     grpSms> samp <- data.frame(samples=levels(x$sample), var1=1:2)
     Error in data.frame(samples = levels(x$sample), var1 = 1:2) :
     arguments imply differing number of rows: 0, 2
     Calls: eval ... source -> withVisible -> eval -> eval -> data.frame
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Package opticut

Current CRAN status: OK: 13

Package pbapply

Current CRAN status: NOTE: 2, OK: 11

Version: 1.4-2
Check: dependencies in R code
Result: NOTE
    No protocol specified
    No protocol specified
Flavors: r-release-osx-x86_64, r-oldrel-osx-x86_64

Package PVAClone

Current CRAN status: OK: 13

Package ResourceSelection

Current CRAN status: OK: 13

Package sharx

Current CRAN status: OK: 13