Running ‘testthat.R’ [10m/12m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # Work-around for bug (https://github.com/hadley/testthat/issues/144)
  > Sys.setenv("R_TESTS" = "")
  > 
  > # library 
  > library(testthat)
  > library(HTSSIP)
  > 
  > # test check
  > test_check('HTSSIP')
  ── 1. Error: Beta diversity from a list of phyloseq objects (parallel) (@test-BD
  write error, closing pipe to the master
  1: physeq_list_betaDiv(physeq_S2D2_l, parallel = TRUE) at testthat/test-BD_ordinations.R:9
  2: plyr::llply(physeq_l, phyloseq::distance, method = method, weighted = weighted, fast = fast, 
         normalized = normalized, parallel = parallel)
  3: structure(lapply(pieces, .fun, ...), dim = dim(pieces))
  4: lapply(pieces, .fun, ...)
  5: FUN(X[[i]], ...)
  6: FUN(X[[i]], ...)
  7: UniFrac(physeq, ...)
  8: UniFrac(physeq, ...)
  9: fastUniFrac(physeq, weighted, normalized, parallel)
  10: foreach(i = spn, .packages = "phyloseq") %dopar% {
         A <- i[1]
         B <- i[2]
         AT <- samplesums[A]
         BT <- samplesums[B]
         if (weighted) {
             wUF_branchweight <- abs(edge_array[, A]/AT - edge_array[, B]/BT)
             numerator <- sum({
                 tree$edge.length * wUF_branchweight
             }, na.rm = TRUE)
             if (!normalized) {
                 return(numerator)
             }
             else {
                 denominator <- sum({
                     tipAges * (OTU[, A]/AT + OTU[, B]/BT)
                 }, na.rm = TRUE)
                 return(numerator/denominator)
             }
         }
         else {
             edge_occ_AB <- edge_occ[, c(A, B)]
             edge_uni_AB_sum <- sum((tree$edge.length * edge_occ_AB)[rowSums(edge_occ_AB, 
                 na.rm = TRUE) < 2, ], na.rm = TRUE)
             uwUFpairdist <- edge_uni_AB_sum/sum(tree$edge.length[rowSums(edge_occ_AB, 
                 na.rm = TRUE) > 0])
             return(uwUFpairdist)
         }
     }
  11: e$fun(obj, substitute(ex), parent.frame(), e$data)
  12: mclapply(argsList, FUN, mc.preschedule = preschedule, mc.set.seed = set.seed, mc.silent = silent, 
         mc.cores = cores)
  13: lapply(seq_len(cores), inner.do)
  14: FUN(X[[i]], ...)
  15: sendMaster(try(lapply(X = S, FUN = FUN, ...), silent = TRUE))
  
  Sparsity threshold: 0.25 
  Density window: 1.71-1.75 
  Sparsity threshold: 0.25 
  Density window: 1.71-1.75 
  Sparsity threshold: 0 
  Density window: 1.7-1.75 
  Sparsity threshold: 0.15 
  Density window: 1.7-1.75 
  Sparsity threshold: 0.3 
  Density window: 1.7-1.75 
  Killed