CRAN Package Check Results for Package mistat

Last updated on 2023-01-30 07:55:12 CET.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	2.0.3	3.87	66.98	70.85	ERROR
r-devel-linux-x86_64-debian-gcc	2.0.3	3.82	51.51	55.33	ERROR
r-devel-linux-x86_64-fedora-clang	2.0.3			90.56	ERROR
r-devel-linux-x86_64-fedora-gcc	2.0.3			88.06	ERROR
r-devel-windows-x86_64	2.0.3	59.00	127.00	186.00	ERROR
r-patched-linux-x86_64	2.0.3	5.86	62.36	68.22	ERROR
r-release-linux-x86_64	2.0.3	4.62	63.35	67.97	ERROR
r-release-macos-arm64	2.0.3			35.00	NOTE
r-release-macos-x86_64	2.0.3			49.00	NOTE
r-release-windows-x86_64	2.0.3	67.00	145.00	212.00	ERROR
r-oldrel-macos-arm64	2.0.3			43.00	NOTE
r-oldrel-macos-x86_64	2.0.3			62.00	NOTE
r-oldrel-windows-ix86+x86_64	2.0.3	9.00	107.00	116.00	ERROR

Check Details

Version: 2.0.3
Check: examples
Result: ERROR
    Running examples in ‘mistat-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: LATHYPPISTON
    > ### Title: Latin Hypercube Design for the Piston Simulator
    > ### Aliases: LATHYPPISTON
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > data(LATHYPPISTON)
    >
    > library(DiceEval)
    Loading required package: DiceKriging
    >
    > Dice <- km(design=LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"],
    + response=LATHYPPISTON[,"seconds"])
    
    optimisation start
    ------------------
    * estimation method : MLE
    * optimisation method : BFGS
    * analytical gradient : used
    * trend model : ~1
    * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 60 0.03 0.016 8000 4e-04 12 40
     - best initial criterion value(s) : -3.903179
    
    N = 7, M = 5 machine precision = 2.22045e-16
    At X0, 0 variables are exactly at the bounds
    At iterate 0 f= 3.9032 |proj g|= 0.1313
    At iterate 1 f = 3.2243 |proj g|= 0.13323
    At iterate 2 f = 2.9684 |proj g|= 0.13187
    At iterate 3 f = 2.4614 |proj g|= 0.11463
    At iterate 4 f = 2.328 |proj g|= 0.14017
    At iterate 5 f = 2.2582 |proj g|= 0.1355
    At iterate 6 f = 2.2508 |proj g|= 0.13208
    At iterate 7 f = 2.2501 |proj g|= 0.1329
    At iterate 8 f = 2.2478 |proj g|= 0.1335
    At iterate 9 f = 2.2375 |proj g|= 0.13508
    At iterate 10 f = 2.2363 |proj g|= 0.13445
    At iterate 11 f = 2.2359 |proj g|= 0.13374
    At iterate 12 f = 2.2358 |proj g|= 0.1338
    At iterate 13 f = 2.235 |proj g|= 0.13421
    At iterate 14 f = 2.2333 |proj g|= 0.13463
    At iterate 15 f = 2.2286 |proj g|= 0.13509
    At iterate 16 f = 2.2171 |proj g|= 0.13507
    At iterate 17 f = 2.1884 |proj g|= 0.13321
    At iterate 18 f = 2.1217 |proj g|= 0.12645
    At iterate 19 f = 1.9776 |proj g|= 0.11007
    At iterate 20 f = 1.8805 |proj g|= 0.10738
    At iterate 21 f = 1.7552 |proj g|= 0.10184
    At iterate 22 f = 1.7501 |proj g|= 0.099237
    At iterate 23 f = 1.7492 |proj g|= 0.10005
    At iterate 24 f = 1.7492 |proj g|= 0.1
    At iterate 25 f = 1.7492 |proj g|= 0.09999
    At iterate 26 f = 1.7492 |proj g|= 0.099975
    At iterate 27 f = 1.7492 |proj g|= 0.099935
    At iterate 28 f = 1.7492 |proj g|= 0.099878
    At iterate 29 f = 1.7491 |proj g|= 0.099776
    At iterate 30 f = 1.7489 |proj g|= 0.099607
    At iterate 31 f = 1.7485 |proj g|= 0.099305
    At iterate 32 f = 1.7474 |proj g|= 0.098743
    At iterate 33 f = 1.7444 |proj g|= 0.097642
    At iterate 34 f = 1.7362 |proj g|= 0.095377
    At iterate 35 f = 1.7138 |proj g|= 0.090587
    At iterate 36 f = 1.6535 |proj g|= 0.08072
    At iterate 37 f = 1.4949 |proj g|= 0.06292
    At iterate 38 f = 1.1119 |proj g|= 0.039191
    At iterate 39 f = 0.54208 |proj g|= 0.021228
    At iterate 40 f = 0.30933 |proj g|= 0.012522
    At iterate 41 f = 0.30681 |proj g|= 0.012472
    At iterate 42 f = 0.30541 |proj g|= 0.012399
    At iterate 43 f = 0.30541 |proj g|= 0.012395
    At iterate 44 f = 0.30541 |proj g|= 0.0057407
    
    iterations 44
    function evaluations 52
    segments explored during Cauchy searches 48
    BFGS updates skipped 0
    active bounds at final generalized Cauchy point 4
    norm of the final projected gradient 0.00574067
    final function value 0.305409
    
    F = 0.305409
    final value 0.305409
    converged
    >
    > library(DiceView)
    
    Attaching package: ‘DiceView’
    
    The following object is masked from ‘package:DiceKriging’:
    
     branin
    
    >
    > sectionview(Dice,
    + center=colMeans(LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"]),
    + conf_lev=c(0.5, 0.9, 0.95),
    + title="", col_sur="darkgrey", lwd=2,
    + Xname=colnames(LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"]))
    Error: cannot allocate vector of size 372529.0 Gb
    Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64

Version: 2.0.3
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘grDevices’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-release-macos-arm64, r-release-macos-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64

Version: 2.0.3
Check: examples
Result: ERROR
    Running examples in ‘mistat-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: LATHYPPISTON
    > ### Title: Latin Hypercube Design for the Piston Simulator
    > ### Aliases: LATHYPPISTON
    > ### Keywords: datasets
    >
    > ### ** Examples
    >
    > data(LATHYPPISTON)
    >
    > library(DiceEval)
    Loading required package: DiceKriging
    >
    > Dice <- km(design=LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"],
    + response=LATHYPPISTON[,"seconds"])
    
    optimisation start
    ------------------
    * estimation method : MLE
    * optimisation method : BFGS
    * analytical gradient : used
    * trend model : ~1
    * covariance model :
     - type : matern5_2
     - nugget : NO
     - parameters lower bounds : 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10 1e-10
     - parameters upper bounds : 60 0.03 0.016 8000 4e-04 12 40
     - best initial criterion value(s) : -3.903179
    
    N = 7, M = 5 machine precision = 2.22045e-16
    At X0, 0 variables are exactly at the bounds
    At iterate 0 f= 3.9032 |proj g|= 0.1313
    At iterate 1 f = 3.2243 |proj g|= 0.13323
    At iterate 2 f = 2.9684 |proj g|= 0.13187
    At iterate 3 f = 2.4614 |proj g|= 0.11463
    At iterate 4 f = 2.328 |proj g|= 0.14017
    At iterate 5 f = 2.2582 |proj g|= 0.1355
    At iterate 6 f = 2.2508 |proj g|= 0.13208
    At iterate 7 f = 2.2501 |proj g|= 0.1329
    At iterate 8 f = 2.2478 |proj g|= 0.1335
    At iterate 9 f = 2.2375 |proj g|= 0.13508
    At iterate 10 f = 2.2363 |proj g|= 0.13445
    At iterate 11 f = 2.2359 |proj g|= 0.13374
    At iterate 12 f = 2.2358 |proj g|= 0.1338
    At iterate 13 f = 2.235 |proj g|= 0.13421
    At iterate 14 f = 2.2333 |proj g|= 0.13463
    At iterate 15 f = 2.2286 |proj g|= 0.13509
    At iterate 16 f = 2.2171 |proj g|= 0.13507
    At iterate 17 f = 2.1884 |proj g|= 0.13321
    At iterate 18 f = 2.1217 |proj g|= 0.12645
    At iterate 19 f = 1.9776 |proj g|= 0.11007
    At iterate 20 f = 1.8805 |proj g|= 0.10738
    At iterate 21 f = 1.7552 |proj g|= 0.10184
    At iterate 22 f = 1.7501 |proj g|= 0.099237
    At iterate 23 f = 1.7492 |proj g|= 0.10005
    At iterate 24 f = 1.7492 |proj g|= 0.1
    At iterate 25 f = 1.7492 |proj g|= 0.09999
    At iterate 26 f = 1.7492 |proj g|= 0.099975
    At iterate 27 f = 1.7492 |proj g|= 0.099935
    At iterate 28 f = 1.7492 |proj g|= 0.099878
    At iterate 29 f = 1.7491 |proj g|= 0.099776
    At iterate 30 f = 1.7489 |proj g|= 0.099607
    At iterate 31 f = 1.7485 |proj g|= 0.099305
    At iterate 32 f = 1.7474 |proj g|= 0.098743
    At iterate 33 f = 1.7444 |proj g|= 0.097642
    At iterate 34 f = 1.7362 |proj g|= 0.095377
    At iterate 35 f = 1.7138 |proj g|= 0.090587
    At iterate 36 f = 1.6535 |proj g|= 0.08072
    At iterate 37 f = 1.4949 |proj g|= 0.06292
    At iterate 38 f = 1.1119 |proj g|= 0.039191
    At iterate 39 f = 0.54208 |proj g|= 0.021228
    At iterate 40 f = 0.30933 |proj g|= 0.012522
    At iterate 41 f = 0.30681 |proj g|= 0.012472
    At iterate 42 f = 0.30541 |proj g|= 0.012399
    At iterate 43 f = 0.30541 |proj g|= 0.012395
    At iterate 44 f = 0.30541 |proj g|= 0.0057407
    
    iterations 44
    function evaluations 52
    segments explored during Cauchy searches 48
    BFGS updates skipped 0
    active bounds at final generalized Cauchy point 4
    norm of the final projected gradient 0.00574067
    final function value 0.305409
    
    F = 0.305409
    final value 0.305409
    converged
    >
    > library(DiceView)
    
    Attaching package: ‘DiceView’
    
    The following object is masked from ‘package:DiceKriging’:
    
     branin
    
    >
    > sectionview(Dice,
    + center=colMeans(LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"]),
    + conf_lev=c(0.5, 0.9, 0.95),
    + title="", col_sur="darkgrey", lwd=2,
    + Xname=colnames(LATHYPPISTON[, !names(LATHYPPISTON) %in% "seconds"]))
    Error: cannot allocate vector of size 372529.0 Gb
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-ix86+x86_64