CRAN Package Check Results for Package hal9001

Last updated on 2021-10-18 00:48:15 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.4.1 69.22 556.98 626.20 OK
r-devel-linux-x86_64-debian-gcc 0.4.1 53.57 420.92 474.49 OK
r-devel-linux-x86_64-fedora-clang 0.4.1 800.46 OK
r-devel-linux-x86_64-fedora-gcc 0.4.1 774.35 OK
r-devel-windows-x86_64 0.4.1 95.00 636.00 731.00 OK
r-devel-windows-x86_64-gcc10-UCRT 0.4.1 OK
r-patched-linux-x86_64 0.4.1 63.09 557.57 620.66 OK
r-patched-solaris-x86 0.4.1 749.80 ERROR
r-release-linux-x86_64 0.4.1 62.20 550.23 612.43 OK
r-release-macos-arm64 0.4.1 NOTE
r-release-macos-x86_64 0.4.1 OK
r-release-windows-ix86+x86_64 0.4.1 127.00 1167.00 1294.00 OK
r-oldrel-macos-x86_64 0.4.1 OK
r-oldrel-windows-ix86+x86_64 0.4.1 176.00 1207.00 1383.00 OK

Check Details

Version: 0.4.1
Check: tests
Result: ERROR
     Running ‘testthat.R’ [9m/11m]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > library(testthat)
     > library(methods)
     > library(data.table)
     > library(microbenchmark)
     > library(glmnet)
     Loading required package: Matrix
     Loaded glmnet 4.1-2
     > library(SuperLearner)
     Loading required package: nnls
     Loading required package: gam
     Loading required package: splines
     Loading required package: foreach
     Loaded gam 1.20
    
     Super Learner
     Version: 2.0-28
     Package created on 2021-05-04
    
     > library(hal9001)
     Loading required package: Rcpp
     hal9001 v0.4.1: The Scalable Highly Adaptive Lasso
     note: fit_hal defaults have changed. See ?fit_hal for details
     >
     > test_check("hal9001")
     [1] "Inf, 0.773599"
     [1] 0
     [1] -Inf
     [1] "0.773599, 1.526959"
     x2
     0
     [1] "0.773599, 1.011391"
     x2
     -1.242867e-16
     [1] "0.773599, 1.115483"
     x2
     0
     [1] "0.773599, 1.190784"
     x2
     0
     [1] "0.773599, 1.079310"
     x2
     -5.021941e-17
     [1] "0.773599, 1.290604"
     x2
     -4.068237e-17
     [1] "0.773599, 1.206894"
     x2
     -3.382656e-17
     [1] "0.773599, 1.339743"
     x2
     8.610836e-17
     [1] "0.773599, 1.255091"
     x2
     0
     [1] "0.773599, 1.300398"
     x2
     0
     [1] "0.773599, 1.162047"
     x2
     0
     [1] "0.773599, 0.994395"
     x2
     0
     [1] "0.773599, 1.375928"
     x2
     0
     [1] "0.773599, 1.113516"
     x2
     0
     [1] "0.773599, 1.238524"
     x2
     0
     [1] "0.773599, 1.185771"
     x2
     0
     [1] "0.773599, 0.996405"
     x2
     0
     [1] "0.773599, 1.265326"
     x2
     0
     [1] "0.773599, 1.260696"
     x2
     0
     [1] "0.773599, 1.293383"
     x2
     0
     [1] "0.773599, 1.177323"
     x2
     0
     [1] "0.773599, 1.278974"
     x2
     0
     [1] "0.773599, 1.181330"
     x2
     0
     [1] "0.773599, 1.300374"
     x2
     0
     [1] "0.773599, 1.079252"
     x2
     0
     [1] "0.773599, 1.075047"
     x2
     0
     [1] "0.773599, 1.055600"
     x2
     0
     [1] "0.773599, 1.199946"
     x2
     0
     [1] "0.773599, 1.565024"
     x2
     0
     [1] "0.773599, 1.243558"
     x2
     0
     [1] "0.773599, 1.075137"
     x2
     0
     [1] "0.773599, 1.305829"
     x2
     0
     [1] "0.773599, 1.188793"
     x2
     0
     [1] "0.773599, 0.933273"
     x2
     0
     [1] "0.773599, 1.035368"
     x2
     0
     [1] "0.773599, 1.227610"
     x2
     0
     [1] "0.773599, 1.372790"
     x2
     0
     [1] "0.773599, 1.244740"
     x2
     0
     [1] "0.773599, 1.232193"
     x2
     0
     [1] "0.773599, 1.428567"
     x2
     0
     [1] "0.773599, 1.213061"
     x2
     0
     [1] "0.773599, 1.089046"
     x2
     0
     [1] "0.773599, 1.244772"
     x2
     0
     [1] "0.773599, 1.211117"
     x2
     0
     [1] "0.773599, 1.238113"
     x2
     0
     [1] "0.773599, 1.276157"
     x2
     0
     [1] "0.773599, 1.192694"
     x2
     0
     [1] "0.773599, 1.205110"
     x2
     0
     [1] "0.773599, 1.485266"
     x2
     0
     [1] "0.773599, 1.305363"
     x2
     0
     [1] "0.773599, 1.079774"
     x2
     0
     [1] "0.773599, 1.496469"
     x2
     0
     [1] "0.773599, 1.448393"
     x2
     0
     [1] "0.773599, 1.285326"
     x2
     0
     [1] "0.773599, 1.146448"
     x2
     0
     [1] "0.773599, 1.509982"
     x2
     0
     [1] "0.773599, 1.320984"
     x2
     0
     [1] "0.773599, 1.432850"
     x2
     0
     [1] "0.773599, 1.096394"
     x2
     0
     [1] "0.773599, 1.499796"
     x2
     0
     [1] "0.773599, 1.492943"
     x2
     0
     [1] "0.773599, 1.260410"
     x2
     0
     [1] "0.773599, 1.165764"
     x2
     0
     [1] "0.773599, 1.310227"
     x2
     0
     [1] "0.773599, 1.105263"
     x2
     0
     [1] "0.773599, 1.143733"
     x2
     0
     [1] "0.773599, 1.285088"
     x2
     0
     [1] "0.773599, 1.513940"
     x2
     0
     [1] "0.773599, 1.390481"
     x2
     0
     [1] "0.773599, 1.072207"
     x2
     0
     [1] "0.773599, 1.113772"
     x2
     0
     [1] "0.773599, 1.352644"
     x2
     0
     [1] "0.773599, 1.198741"
     x2
     0
     [1] "0.773599, 1.354882"
     x2
     0
     [1] "0.773599, 1.133907"
     x2
     0
     [1] "0.773599, 1.580926"
     x2
     0
     [1] "0.773599, 1.361303"
     x2
     0
     [1] "0.773599, 1.337777"
     x2
     0
     [1] "0.773599, 0.976709"
     x2
     0
     [1] "0.773599, 1.267284"
     x2
     0
     [1] "0.773599, 1.500558"
     x2
     0
     [1] "0.773599, 1.421943"
     x2
     0
     [1] "0.773599, 1.390147"
     x2
     0
     [1] "0.773599, 1.438975"
     x2
     0
     [1] "0.773599, 1.069927"
     x2
     0
     [1] "0.773599, 1.300625"
     x2
     0
     [1] "0.773599, 1.151155"
     x2
     0
     [1] "0.773599, 1.178412"
     x2
     0
     [1] "0.773599, 1.088181"
     x2
     0
     [1] "0.773599, 1.531994"
     x2
     0
     [1] "0.773599, 1.511669"
     x2
     0
     [1] "0.773599, 1.521285"
     x2
     0
     [1] "0.773599, 1.542941"
     x2
     0
     [1] "0.773599, 1.333595"
     x2
     0
     [1] "0.773599, 1.384932"
     x2
     0
     [1] "0.773599, 1.124085"
     x2
     0
     [1] "0.773599, 1.510438"
     x2
     0
     [1] "0.773599, 0.926998"
     x2
     0
     [1] "0.773599, 1.433839"
     x2
     0
     ══ Failed tests ════════════════════════════════════════════════════════════════
     ── Error (test-general_families.R:31:1): (code run outside of `test_that()`) ───
     Error: NA/NaN/Inf in foreign function call (arg 23)
     Backtrace:
     █
     1. ├─base::suppressWarnings(fit_hal(X = x, Y = y, family = binomial())) test-general_families.R:31:0
     2. │ └─base::withCallingHandlers(...)
     3. └─hal9001::fit_hal(X = x, Y = y, family = binomial())
     4. ├─base::do.call(glmnet::cv.glmnet, fit_control)
     5. └─(function (x, y, weights = NULL, offset = NULL, lambda = NULL, ...
     6. └─glmnet:::cv.glmnet.raw(...)
     7. └─glmnet::glmnet(...)
     8. └─glmnet:::glmnet.path(...)
     9. └─glmnet:::glmnet.fit(...)
     10. └─glmnet:::elnet.fit(...)
     ── Error (test-hal_binomial.R:23:1): (code run outside of `test_that()`) ───────
     Error: 'NA' indices are not (yet?) supported for sparse Matrices
     Backtrace:
     █
     1. └─hal9001::fit_hal(X = x, Y = y, family = "binomial", yolo = FALSE) test-hal_binomial.R:23:0
     2. ├─base::do.call(glmnet::cv.glmnet, fit_control)
     3. └─(function (x, y, weights = NULL, offset = NULL, lambda = NULL, ...
     4. └─glmnet:::cv.glmnet.raw(...)
     5. ├─glmnet::buildPredmat(...)
     6. └─glmnet:::buildPredmat.lognetlist(...)
     7. └─glmnet:::buildPredmat.default(...)
     8. ├─stats::predict(...)
     9. ├─glmnet:::predict.lognet(...)
     10. ├─base::NextMethod("predict")
     11. └─glmnet::predict.glmnet(...)
     12. ├─nbeta[, lamlist$left, drop = FALSE]
     13. └─nbeta[, lamlist$left, drop = FALSE]
     14. └─Matrix:::subCsp_cols(x, j, drop = drop)
     15. └─Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
    
     [ FAIL 2 | WARN 7 | SKIP 0 | PASS 62 ]
     Error: Test failures
     Execution halted
Flavor: r-patched-solaris-x86

Version: 0.4.1
Check: installed package size
Result: NOTE
     installed size is 5.5Mb
     sub-directories of 1Mb or more:
     libs 5.0Mb
Flavor: r-release-macos-arm64