Running 'testthat.R' [1s/2s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(IGP)
  
  Initialize Python Version 2.7.16 (default, Apr  6 2019, 01:42:57) 
  [GCC 8.3.0]
  
  > 
  > test_check("IGP")
  [1] "laGP"
  [1] "mlegp"
  -- 1. Error: 1D data works (@test_IGP.R#11)  -----------------------------------
  BLAS/LAPACK routine 'DPPTRS' gave error code -1
  1: IGP(package = package, X = X1, Z = Z1) at testthat/test_IGP.R:11
  2: IGP_mlegp$new(X = X, Z = Z, ...)
  3: .subset2(public_bind_env, "initialize")(...)
  4: self$init(...)
  5: self$.init(...)
  6: capture.output(m <- do.call(mlegp::mlegp, argss))
  7: evalVis(expr)
  8: withVisible(eval(expr, pf))
  9: eval(expr, pf)
  10: eval(expr, pf)
  11: do.call(mlegp::mlegp, argss)
  12: (function (X, Z, constantMean = 1, nugget = NULL, nugget.known = 0, min.nugget = 0, 
         param.names = NULL, gp.names = NULL, PC.UD = NULL, PC.num = NULL, PC.percent = NULL, 
         simplex.ntries = 5, simplex.maxiter = 500, simplex.reltol = 1e-08, BFGS.maxiter = 500, 
         BFGS.tol = 0.01, BFGS.h = 1e-10, seed = 0, verbose = 1, parallel = FALSE) 
     {
         X = as.matrix(X)
         orig.X = X
         Z = as.matrix(Z)
         reps = NULL
         un.sum = NULL
         if (nrow(X) == 1) {
             stop("error: there must be at least two input values")
         }
         if (!is.null(param.names) && length(param.names) != dim(X)[2]) {
             stop("length of param.names must match number of columns of X")
         }
         if (!is.null(PC.percent)) 
             PC.num = numSingularValues(Z, PC.percent)
         if (!is.null(PC.num)) {
             weights = pcweights(Z, weights.num = PC.num)
             Z = t(weights$Vprime)
             if (PC.num == 1) {
                 stop("error: currently not implemented for PC.num = 1 (set PC.num directly)")
             }
             PC.UD = weights$UD
         }
         if (!is.null(dim(Z))) {
             numGPs = dim(Z)[2]
         }
         else {
             numGPs = 1
             Z = matrix(Z)
         }
         if (length(seed) != 1 && length(seed) != ncol(Z)) {
             stop("error: seed length must equal dimensions of Z, or 1")
         }
         if (is.null(gp.names)) {
             gp.names = paste("gp #", 1:numGPs)
         }
         if (length(gp.names) != numGPs) {
             stop("length of gp.names must match number of GPs")
         }
         if (dim(X)[1] != dim(Z)[1]) {
             stop("error: X matrix and output matrix must have same number of rows; \n\tfor PC weights, make sure each column of Z is a computer model run")
         }
         if (min(diag(var(Z))) <= 0) {
             stop("error: Z does not vary for at least one column")
         }
         if (dim(Z)[2] > 1 && !is.null(nugget) && length(nugget) > 1) {
             stop("error: cannot handle nugget matrix with multiple field observations; try fitting 1 gp at a time")
         }
         if (!is.null(nugget) && length(nugget) > dim(Z)[1]) {
             stop("length of nugget matrix must match number of observations")
         }
         if (min(apply(X, 2, max) - apply(X, 2, min)) == 0) {
             stop("error: at least one parameter does not vary in design")
         }
         if (constantMean != 1) {
             ones = rep(1, dim(X)[1])
             dX = cbind(ones, X)
             t = try(solve(t(dX) %*% dX), TRUE)
             if (class(t) == "try-error") {
                 stop("error: design matrix with intercept is not full rank; set constantMean = 1")
             }
         }
         if (nugget.known == 1) {
             if (!is.null(nugget) && length(nugget) > 1 && length(nugget) != nrow(X)) {
                 stop("error: for nugget.known = 1, length of nugget matrix must be nrow(X) or 1")
             }
             un.sum = uniqueSummary(X, Z)
             orig.X = X
             X = un.sum$uniqueX
             Z = un.sum$uniqueMeans
             reps = un.sum$reps
         }
         if (!is.null(nugget) && length(nugget) == 1 && nugget <= 0) {
             if (anyReps(X)) 
                 stop("at least 2 or more inputs are identical...must use nugget!")
         }
         if (is.null(nugget)) {
             if (!anyReps(X)) {
                 if (verbose > 0) 
                     cat("no reps detected - nugget will not be estimated\n")
             }
             else {
                 if (verbose > 0 && nugget.known == 0) 
                     cat("reps detected - nugget will be estimated\n")
             }
         }
         numEstimates = dim(X)[2] + 1
         if (anyReps(X) || !is.null(nugget) || length(nugget) > 1) 
             numEstimates = numEstimates + 1
         if (constantMean == 1) {
             numEstimates = numEstimates + 1
         }
         else {
             numEstimates = numEstimates + dim(X)[2] + 1
         }
         estimates = rep(0, numEstimates)
         if (verbose > 0) 
             cat("\n")
         l = NULL
         Zlist = list()
         if (length(seed) == 1) 
             seed = rep(seed, ncol(Z))
         for (i in 1:ncol(Z)) {
             Zlist[[i]] = list()
             Zlist[[i]]$Z = matrix(Z[, i])
             Zlist[[i]]$i = i
             Zlist[[i]]$seed = seed[i]
         }
         if (!parallel) {
             l = lapply(Zlist, mlegp2, XX = X, orig.XX = orig.X, nugget = nugget, nugget.known = nugget.known, 
                 reps = reps, un.sum = un.sum, numEstimates = numEstimates, constantMean = constantMean, 
                 simplex.ntries = simplex.ntries, simplex.maxiter = simplex.maxiter, simplex.reltol = simplex.reltol, 
                 BFGS.maxiter = BFGS.maxiter, BFGS.tol = BFGS.tol, BFGS.h = BFGS.h, min.nugget = min.nugget, 
                 verbose = verbose, param.names = param.names)
         }
         if (parallel) {
             if (requireNamespace("snowfall", quietly = TRUE)) {
                 cat("fitting ")
                 cat(length(Zlist))
                 cat(" GPs in parallel mode...\n")
                 l = snowfall::sfLapply(Zlist, mlegp2, XX = X, orig.XX = orig.X, nugget = nugget, 
                     nugget.known = nugget.known, reps = reps, un.sum = un.sum, numEstimates = numEstimates, 
                     constantMean = constantMean, simplex.ntries = simplex.ntries, simplex.maxiter = simplex.maxiter, 
                     simplex.reltol = simplex.reltol, BFGS.maxiter = BFGS.maxiter, BFGS.tol = BFGS.tol, 
                     BFGS.h = BFGS.h, min.nugget = min.nugget, verbose = verbose, param.names = param.names)
                 cat("...done\n")
             }
             else {
                 cat("Error: the package \"snowfall\" is required when parallel = TRUE")
             }
         }
         if (numGPs == 1) {
             return(l[[1]])
         }
         return(gp.list(l, UD = PC.UD, param.names = param.names, gp.names = gp.names))
     })(X = structure(c(0.0493469808716327, 0.204218131955713, 0.0249937756452709, 0.607824997510761, 
     0.377712496789172, 0.448181313928217, 0.431415374623612, 0.226294085150585, 0.89749110606499, 
     0.868268524995074, 0.0887824974488467, 0.933639480033889, 0.343735232017934, 0.565296397078782, 
     0.707517720526084, 0.408560898154974, 0.732537311501801, 0.863679846515879, 0.929786564316601, 
     0.83151650428772, 0.332384346984327, 0.274056094232947, 0.952710877871141, 0.123984773177654, 
     0.0207149714697152, 0.996309562586248, 0.233057375764474, 0.256853663362563, 0.627091316506267, 
     0.22465895419009, 0.584328862372786, 0.351134397555143, 0.125352597795427, 0.730858829338104, 
     0.843609892530367, 0.7510468326509, 0.993562728632241, 0.456546175526455, 0.851327106356621, 
     0.122654374223202, 0.859386339085177, 0.89127535931766, 0.86096934392117, 0.354972113389522, 
     0.621088061481714, 0.152416511438787, 0.346461141249165, 0.132591414032504, 0.773797592381015, 
     0.327889699488878, 0.714667939348146, 0.0453208175022155, 0.405684606404975, 0.267501928377897, 
     0.43561292742379, 0.879079302540049, 0.640582490013912, 0.864393045427278, 0.743958909530193, 
     0.161811725702137, 0.547072097891942, 0.581548473797739, 0.495421071536839, 0.462004812667146, 
     0.906639140564948, 0.128052616724744, 0.867540983250365, 0.489737530937418, 0.393068741308525, 
     0.899939069058746, 0.487277671229094, 0.37301719468087, 0.713834477122873, 0.934759834315628, 
     0.776824190514162, 0.756470263237134, 0.743058471009135, 0.397466185269877, 0.442984228488058, 
     0.365536679513752), .Dim = c(40L, 2L)), Z = c(-0.46858156013566, 0.32662716356213, 
     -0.610255009142197, 0.163983123597904, 0.00610678380528773, 1.13885579829093, 1.24008213401915, 
     1.73038979704294, -1.58861382364364, 0.146361700484164, -0.446795635025221, -0.123780104065061, 
     1.39067018545835, 0.595007009597993, -0.571226518111139, -0.14306231226339, -1.76746598541101, 
     -1.50808422842188, -1.42626382039998, -0.0205880541704216, 0.577859752384558, 0.497446753207929, 
     -0.266040455449328, 0.939464988181314, -0.423564609073053, 0.696917688513151, 0.25401298407185, 
     1.06232453830861, -0.0947196675429197, 0.400762437885434, -0.426542509741219, 1.5216610897429, 
     -0.264855620426763, -1.39193894638118, -1.81875537329169, -1.99825665910288, -1.03886520673469, 
     0.870967409652174, -0.454023158612713, 1.44280632628579), verbose = 0, nugget = 1e-08, 
         nugget.known = 1e-08)
  13: lapply(Zlist, mlegp2, XX = X, orig.XX = orig.X, nugget = nugget, nugget.known = nugget.known, 
         reps = reps, un.sum = un.sum, numEstimates = numEstimates, constantMean = constantMean, 
         simplex.ntries = simplex.ntries, simplex.maxiter = simplex.maxiter, simplex.reltol = simplex.reltol, 
         BFGS.maxiter = BFGS.maxiter, BFGS.tol = BFGS.tol, BFGS.h = BFGS.h, min.nugget = min.nugget, 
         verbose = verbose, param.names = param.names)
  14: FUN(X[[i]], ...)
  
  == testthat results  ===========================================================
  OK: 11 SKIPPED: 0 FAILED: 1
  1. Error: 1D data works (@test_IGP.R#11) 
  
  Error: testthat unit tests failed
  Execution halted