CRAN Package Check Results for Maintainer ‘Renato Panaro <renatovp at ime.usp.br>’

Last updated on 2024-12-18 19:49:45 CET.

Package	ERROR	NOTE	OK
spsurv	2	9	2

Package spsurv

Current CRAN status: ERROR: 2, NOTE: 9, OK: 2

Additional issues

Intel

Version: 1.0.0
Check: examples
Result: ERROR Running examples in ‘spsurv-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: bppo > ### Title: Bernstein Polynomial Based Proportional Odds Model > ### Aliases: bppo > > ### ** Examples > > > library("spsurv") > data("veteran") Warning in data("veteran") : data set ‘veteran’ not found > > fit <- bppo(Surv(time, status) ~ karno + celltype, + data = veteran) Error in optimHess(theta, fn, gr, control = list(fnscale = -1)) : non-finite value supplied by optim Calls: bppo ... spbp.mle -> <Anonymous> -> <Anonymous> -> .local -> optimHess Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.0
Check: tests
Result: ERROR Running ‘testthat.R’ [28s/35s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(spsurv) Loading required package: survival Loading required package: loo This is loo version 2.8.0 - Online documentation and vignettes at mc-stan.org/loo - As of v2.0.0 loo defaults to 1 core but we recommend using as many as possible. Use the 'cores' argument or set options(mc.cores = NUM_CORES) for an entire session. Attaching package: 'loo' The following object is masked from 'package:testthat': compare Loading required package: coda Loading required package: MASS Attaching package: 'spsurv' The following object is masked from 'package:coda': traceplot The following objects are masked from 'package:stats': coef, confint, vcov The following object is masked from 'package:base': mode > > test_check("spsurv") SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000108 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.08 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 1000 [ 10%] (Warmup) Chain 1: Iteration: 200 / 1000 [ 20%] (Warmup) Chain 1: Iteration: 300 / 1000 [ 30%] (Warmup) Chain 1: Iteration: 400 / 1000 [ 40%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 600 / 1000 [ 60%] (Sampling) Chain 1: Iteration: 700 / 1000 [ 70%] (Sampling) Chain 1: Iteration: 800 / 1000 [ 80%] (Sampling) Chain 1: Iteration: 900 / 1000 [ 90%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 4.853 seconds (Warm-up) Chain 1: 3.682 seconds (Sampling) Chain 1: 8.535 seconds (Total) Chain 1: SAMPLING FOR MODEL 'spbp' NOW (CHAIN 1). Chain 1: Chain 1: Gradient evaluation took 0.000103 seconds Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.03 seconds. Chain 1: Adjust your expectations accordingly! Chain 1: Chain 1: Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) Chain 1: Iteration: 100 / 1000 [ 10%] (Warmup) Chain 1: Iteration: 200 / 1000 [ 20%] (Warmup) Chain 1: Iteration: 300 / 1000 [ 30%] (Warmup) Chain 1: Iteration: 400 / 1000 [ 40%] (Warmup) Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) Chain 1: Iteration: 600 / 1000 [ 60%] (Sampling) Chain 1: Iteration: 700 / 1000 [ 70%] (Sampling) Chain 1: Iteration: 800 / 1000 [ 80%] (Sampling) Chain 1: Iteration: 900 / 1000 [ 90%] (Sampling) Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) Chain 1: Chain 1: Elapsed Time: 6.285 seconds (Warm-up) Chain 1: 4.054 seconds (Sampling) Chain 1: 10.339 seconds (Total) Chain 1: [ FAIL 1 | WARN 10 | SKIP 0 | PASS 0 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test.R:33:1'): (code run outside of `test_that()`) ────────────────── Error in `optimHess(theta, fn, gr, control = list(fnscale = -1))`: non-finite value supplied by optim Backtrace: ▆ 1. └─spsurv::bppo(Surv(time, status) ~ karno + factor(celltype), data = veteran) at test.R:33:1 2. └─spsurv:::spbp.default(...) 3. └─spsurv:::spbp.mle(standata = standata, ...) 4. ├─rstan::optimizing(...) 5. └─rstan::optimizing(...) 6. └─rstan (local) .local(object, ...) 7. └─stats::optimHess(theta, fn, gr, control = list(fnscale = -1)) [ FAIL 1 | WARN 10 | SKIP 0 | PASS 0 ] Error: Test failures Execution halted Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.0
Check: dependencies in R code
Result: NOTE Namespace in Imports field not imported from: ‘rstantools’ All declared Imports should be used. Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 1.0.0
Check: LazyData
Result: NOTE 'LazyData' is specified without a 'data' directory Flavors: r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 1.0.0
Check: for GNU extensions in Makefiles
Result: NOTE GNU make is a SystemRequirements. Flavors: r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 1.0.0
Check: installed package size
Result: NOTE installed size is 90.4Mb sub-directories of 1Mb or more: doc 1.5Mb libs 88.5Mb Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 1.0.0
Check: examples
Result: ERROR Running examples in ‘spsurv-Ex.R’ failed The error most likely occurred in: > ### Name: bppo > ### Title: Bernstein Polynomial Based Proportional Odds Model > ### Aliases: bppo > > ### ** Examples > > > library("spsurv") > data("veteran") Warning in data("veteran") : data set ‘veteran’ not found > > fit <- bppo(Surv(time, status) ~ karno + celltype, + data = veteran) Error in optimHess(theta, fn, gr, control = list(fnscale = -1)) : non-finite value supplied by optim Calls: bppo ... spbp.mle -> <Anonymous> -> <Anonymous> -> .local -> optimHess Execution halted Flavor: r-oldrel-macos-x86_64

Version: 1.0.0
Check: tests
Result: ERROR Running ‘testthat.R’ [17s/31s] Running the tests in ‘tests/testthat.R’ failed. Last 13 lines of output: ── Error ('test.R:33:1'): (code run outside of `test_that()`) ────────────────── Error in `optimHess(theta, fn, gr, control = list(fnscale = -1))`: non-finite value supplied by optim Backtrace: ▆ 1. └─spsurv::bppo(Surv(time, status) ~ karno + factor(celltype), data = veteran) at test.R:33:0 2. └─spsurv:::spbp.default(...) 3. └─spsurv:::spbp.mle(standata = standata, ...) 4. ├─rstan::optimizing(...) 5. └─rstan::optimizing(...) 6. └─rstan (local) .local(object, ...) 7. └─stats::optimHess(theta, fn, gr, control = list(fnscale = -1)) [ FAIL 1 | WARN 7 | SKIP 0 | PASS 0 ] Error: Test failures Execution halted Flavor: r-oldrel-macos-x86_64