Last updated on 2026-03-31 00:50:25 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 2.2.2 | 5.56 | 34.74 | 40.30 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 2.2.2 | 4.42 | 26.23 | 30.65 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 2.2.2 | 8.00 | 49.60 | 57.60 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 2.2.2 | 8.00 | 49.90 | 57.90 | OK | |
| r-devel-macos-arm64 | 2.2.2 | 2.00 | 11.00 | 13.00 | OK | |
| r-devel-windows-x86_64 | 2.2.2 | 8.00 | 52.00 | 60.00 | OK | |
| r-patched-linux-x86_64 | 2.2.2 | 5.12 | 30.64 | 35.76 | OK | |
| r-release-linux-x86_64 | 2.2.2 | 4.95 | 31.27 | 36.22 | OK | |
| r-release-macos-arm64 | 2.2.2 | OK | ||||
| r-release-macos-x86_64 | 2.2.2 | 5.00 | 32.00 | 37.00 | OK | |
| r-release-windows-x86_64 | 2.2.2 | 8.00 | 50.00 | 58.00 | OK | |
| r-oldrel-macos-arm64 | 2.2.2 | OK | ||||
| r-oldrel-macos-x86_64 | 2.2.2 | 5.00 | 25.00 | 30.00 | OK | |
| r-oldrel-windows-x86_64 | 2.2.2 | 7.00 | 52.00 | 59.00 | OK |
Version: 2.2.2
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Jeffrey L. Andrews <jeff.andrews@ubc.ca>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = c("Jeffrey", "L."),
family = "Andrews",
role = c("aut", "cre"),
email = "jeff.andrews@ubc.ca"),
person(given = c("Jaymeson", "R."),
family = "Wickins",
role = "aut"),
person(given = c("Nicholas", "M."),
family = "Boers",
role = "aut"),
person(given = c("Paul", "D."),
family = "McNicholas",
role = "aut"))
as necessary.
Package CITATION file contains call(s) to old-style personList() or
as.personList(). Please use c() on person objects instead.
Package CITATION file contains call(s) to old-style citEntry(). Please
use bibentry() instead.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 2.2.2
Check: examples
Result: ERROR
Running examples in ‘teigen-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: teigen
> ### Title: teigen: Function for Model-Based Clustering and Classification
> ### with the Multivariate t Distribution
> ### Aliases: teigen
>
> ### ** Examples
>
> ###Note that only one model is run for each example
> ###in order to reduce computation time
>
> #Clustering old faithful data with hard random start
> tfaith <- teigen(faithful, models="UUUU", Gs=1:3, init="hard")
<08>Time taken:??? | Approx. remaining:??? | 0% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.2 secs | Approx. remaining: 0.3 secs | 33% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.2 secs | Approx. remaining: 0.1 secs | 67% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.2 secs | Approx. remaining: 0 secs | 100% complete
> plot(tfaith, what = "uncertainty")
> summary(tfaith)
------------- Summary for teigen -------------
------ RESULTS ------
Loglik: -384.389
BIC: -841.6535
ICL: -842.3337
Model: UUUU
# Groups: 2
Clustering Table:
1 2
97 175
>
> #Clustering old faithful with hierarchical starting values
> initial_list <- list()
> clustree <- hclust(dist(faithful))
> for(i in 1:3){
+ initial_list[[i]] <- cutree(clustree,i)
+ }
> tfaith <- teigen(faithful, models="CUCU", Gs=1:3, init=initial_list)
<08>Time taken:??? | Approx. remaining:??? | 0% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.4 secs | Approx. remaining: 0.8 secs | 33% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.4 secs | Approx. remaining: 0.2 secs | 67% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.5 secs | Approx. remaining: 0 secs | 100% complete
> print(tfaith)
BIC and ICL select the same model and groups.
The best model (BIC of -844.52, ICL of -845.1251) is CUCU with G=2
>
> #Classification with the iris data set
> #Introducing NAs is not required; this is to illustrate a `true' classification scenario
> irisknown <- iris[,5]
> irisknown[134:150] <- NA
> triris <- teigen(iris[,-5], models="CUUU", init="uniform", known=irisknown)
<08>Time taken:??? | Approx. remaining:??? | 0% complete<08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08><08>Time taken: 0.1 secs | Approx. remaining: 0 secs | 100% complete
>
> ##Parallel examples:
> ###Note: parallel.cores set to 2 in order to comply
> ###with CRAN submission policies (set to higher
> ###number or TRUE to automatically use all available cores)
>
> #Clustering old faithful data with tEIGEN
> tfaith <- teigen(faithful, models="UUUU",
+ parallel.cores=2, Gs=1:3, init="hard")
> plot(tfaith, what = "contour")
>
> #Classification with the iris data set
> irisknown <- iris[,5]
> irisknown[sample(1:nrow(iris),50)] <- NA
> tiris <- teigen(iris[,-5], parallel.cores=2, models="CUUU",
+ init="uniform", known=irisknown)
Error in serverSocket(port = port) :
creation of server socket failed: port 11687 cannot be opened
Calls: teigen ... <Anonymous> -> makeCluster -> makePSOCKcluster -> serverSocket
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc