Running 'testthat.R' [1s/1s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(IGP)
  
  Initialize Python Version 2.7.16 (default, Mar  4 2019, 15:29:09) 
  [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(0.562558584148064, 0.826923598535359, 0.368686373811215, 0.522082675714046, 
     0.763247546274215, 0.697018235689029, 0.683638561051339, 0.0252575092017651, 0.905316422460601, 
     0.861853967420757, 0.376890513347462, 0.185746256494895, 0.2232168270275, 0.0723978392779827, 
     0.396962468512356, 0.133261104812846, 0.199544237460941, 0.9201361075975, 0.416983964154497, 
     0.223486674716696, 0.355039312737063, 0.776617591036484, 0.974633161444217, 0.730629947502166, 
     0.571938514243811, 0.272302769823, 0.501317376503721, 0.0676638383883983, 0.998603504383937, 
     0.0874265220481902, 0.514400691492483, 0.379041153239086, 0.0347908686380833, 0.1025670315139, 
     0.942253751214594, 0.213994537247345, 0.887887472985312, 0.17587272496894, 0.644008219009265, 
     0.112329512136057, 0.393411507830024, 0.568998066941276, 0.837309651542455, 0.981773208128288, 
     0.676479051588103, 0.0832768341060728, 0.0373296753969043, 0.743510082596913, 0.540505723794922, 
     0.235277489526197, 0.701765018049628, 0.156714863609523, 0.115807247813791, 0.6176393404603, 
     0.67276063375175, 0.850885015213862, 0.690973581280559, 0.333114786772057, 0.752931568073109, 
     0.0760560792405158, 0.745851460378617, 0.185160201974213, 0.0167775070294738, 0.658437201753259, 
     0.266949730925262, 0.734145314898342, 0.243776821531355, 0.433107279241085, 0.746900690020993, 
     0.0523770335130394, 0.676309118978679, 0.146624129032716, 0.54137856210582, 0.728699146769941, 
     0.157849267823622, 0.415468555176631, 0.789605481550097, 0.327555865282193, 0.348788533359766, 
     0.197491612751037), .Dim = c(40L, 2L)), Z = c(0.238257362504885, -1.305381711841, 
     -0.118297413254255, -0.252928976918676, -1.8919920338008, -0.446823895770821, -0.681807662351431, 
     -0.84183686235352, -0.811123185621884, 0.232247311234526, -0.254594513870981, 1.7541207242631, 
     1.65108281644503, -0.233939337807461, -0.282965010868273, -0.0633491569388699, 0.0183783945125264, 
     0.385643284252857, -0.502488233810051, 1.44711384066481, -0.20984951146669, -0.0669590625060115, 
     -0.0519597083316893, -1.83183743177067, 0.556629786714411, -0.00402249259803289, 
     0.988600005532876, 0.821628619984026, -1.00911879197013, 0.844308940397288, -0.983753219108312, 
     1.48425509800879, -0.0408353328460849, -0.391271626935968, 0.484386780608989, 1.4786366961201, 
     -1.61605481062669, 1.77533025721428, 0.0275138865383324, 1.59510843080472), 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