Running ‘testthat.R’ [38s/39s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(cvwrapr)
  > 
  > test_check("cvwrapr")
  Loading required package: Matrix
  Loaded glmnet 4.1-2
  
  Attaching package: 'glmnet'
  
  The following object is masked from 'package:cvwrapr':
  
      coxnet.deviance
  
  Loading required package: foreach
  Loading required package: iterators
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error (test-kfoldcv-glmnet-family.R:212:3): glmnet multinomial-deviance ─────
  Error in `intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)`: 'NA' indices are not (yet?) supported for sparse Matrices
  Backtrace:
       █
    1. └─glmnet::cv.glmnet(...) test-kfoldcv-glmnet-family.R:212:2
    2.   └─glmnet:::cv.glmnet.raw(...)
    3.     ├─glmnet::buildPredmat(...)
    4.     └─glmnet:::buildPredmat.multnetlist(...)
    5.       └─glmnet:::buildPredmat.array(...)
    6.         ├─stats::predict(...)
    7.         └─glmnet:::predict.multnet(...)
    8.           ├─kbeta[, lamlist$left, drop = FALSE]
    9.           └─kbeta[, lamlist$left, drop = FALSE]
   10.             └─Matrix:::subCsp_cols(x, j, drop = drop)
   11.               └─Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
  ── Error (test-kfoldcv-glmnet-family.R:329:3): glmnet binomial probit-deviance ──
  Error in `elnet.fit(x, z, w, lambda, alpha, intercept, thresh = thresh, 
      maxit = maxit, penalty.factor = vp, exclude = exclude, lower.limits = lower.limits, 
      upper.limits = upper.limits, warm = fit, from.glmnet.fit = TRUE, 
      save.fit = TRUE)`: NA/NaN/Inf in foreign function call (arg 25)
  Backtrace:
      █
   1. └─glmnet::cv.glmnet(...) test-kfoldcv-glmnet-family.R:329:2
   2.   └─glmnet:::cv.glmnet.raw(...)
   3.     └─glmnet::glmnet(...)
   4.       └─glmnet:::glmnet.path(...)
   5.         └─glmnet:::glmnet.fit(...)
   6.           └─glmnet:::elnet.fit(...)
  ── Error (test-kfoldcv-glmnet-family.R:344:3): glmnet binomial probit-mse ──────
  Error in `elnet.fit(x, z, w, lambda, alpha, intercept, thresh = thresh, 
      maxit = maxit, penalty.factor = vp, exclude = exclude, lower.limits = lower.limits, 
      upper.limits = upper.limits, warm = fit, from.glmnet.fit = TRUE, 
      save.fit = TRUE)`: NA/NaN/Inf in foreign function call (arg 25)
  Backtrace:
      █
   1. └─glmnet::cv.glmnet(...) test-kfoldcv-glmnet-family.R:344:2
   2.   └─glmnet:::cv.glmnet.raw(...)
   3.     └─glmnet::glmnet(...)
   4.       └─glmnet:::glmnet.path(...)
   5.         └─glmnet:::glmnet.fit(...)
   6.           └─glmnet:::elnet.fit(...)
  ── Error (test-kfoldcv-glmnet-family.R:361:3): glmnet binomial probit-mae ──────
  Error in `elnet.fit(x, z, w, lambda, alpha, intercept, thresh = thresh, 
      maxit = maxit, penalty.factor = vp, exclude = exclude, lower.limits = lower.limits, 
      upper.limits = upper.limits, warm = fit, from.glmnet.fit = TRUE, 
      save.fit = TRUE)`: NA/NaN/Inf in foreign function call (arg 25)
  Backtrace:
      █
   1. └─glmnet::cv.glmnet(...) test-kfoldcv-glmnet-family.R:361:2
   2.   └─glmnet:::cv.glmnet.raw(...)
   3.     └─glmnet::glmnet(...)
   4.       └─glmnet:::glmnet.path(...)
   5.         └─glmnet:::glmnet.fit(...)
   6.           └─glmnet:::elnet.fit(...)
  ── Error (test-kfoldcv-glmnet.R:12:3): basic glmnet call ───────────────────────
  Error in `intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)`: 'NA' indices are not (yet?) supported for sparse Matrices
  Backtrace:
       █
    1. └─glmnet::cv.glmnet(...) test-kfoldcv-glmnet.R:12:2
    2.   └─glmnet:::cv.glmnet.raw(...)
    3.     ├─glmnet::buildPredmat(...)
    4.     └─glmnet:::buildPredmat.default(...)
    5.       ├─stats::predict(...)
    6.       ├─glmnet:::predict.elnet(...)
    7.       ├─base::NextMethod("predict")
    8.       └─glmnet::predict.glmnet(...)
    9.         ├─nbeta[, lamlist$left, drop = FALSE]
   10.         └─nbeta[, lamlist$left, drop = FALSE]
   11.           └─Matrix:::subCsp_cols(x, j, drop = drop)
   12.             └─Matrix:::intI(j, n = x@Dim[2], dn[[2]], give.dn = FALSE)
  
  [ FAIL 5 | WARN 48 | SKIP 0 | PASS 312 ]
  Error: Test failures
  Execution halted