Running examples in 'growfunctions-Ex.R' failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: MSPE
> ### Title: Compute normalized mean squared prediction error based on
> ###   accuracy to impute missing data values
> ### Aliases: MSPE
> 
> ### ** Examples
> 
> {
+ library(growfunctions)
+ 
+ ## load the monthly employment count data for a collection of 
+ ## U.S. states from the Current 
+ ## Population Survey (cps)
+ data(cps)
+ ## subselect the columns of N x T, y, associated with 
+ ## the years 2009 - 2013
+ ## to examine the state level employment 
+ ## levels during the "great recession"
+ y_short   <- cps$y[,(cps$yr_label %in% c(2009:2013))]
+ 
+ ## dimensions
+ T         <- ncol(y_short)  ## time points per unit
+ N         <- nrow(y_short)  ## number of units
+ 
+ ## Demonstrate estimation of intermittent missing data 
+ ## from posterior predictive distribution by randomly
+ ## selecting 10 percent of entries in y_short and 
+ ## setting them to NA.
+ 
+ ## randomly assign missing positions in y.
+ ## assume every unit has equal number of 
+ ## missing positions
+ ## randomly select number of missing 
+ ## observations for each unit
+ m_factor  <- .1
+ M         <- floor(m_factor*N*T)
+ m_vec     <- rep(floor(M/N),N)
+ if( sum(m_vec) < M )
+ {
+     m_left              <- M - sum(m_vec)
+     pos_i               <- sample(1:N, m_left, 
+                                 replace = FALSE)
+      m_vec[pos_i]        <- m_vec[pos_i] + 1
+ } # end conditional statement on whether all 
+   # missing cells allocated
+   # randomly select missing 
+   # positions for each unit
+ pos       <- matrix(0,N,T)
+ for( i in 1:N )
+ {
+     sel_ij              <- sample(3:(T-3), m_vec[i], 
+                            replace = FALSE) 
+                            ## avoid edge effects
+     pos[i,sel_ij]       <- 1
+ }
+ 
+ ## configure N x T matrix, y_obs, with 10 percent missing 
+ ## observations (filled with NA)
+ ## to use for sampling.  Entries in y_short 
+ ## that are set to missing (NA) are
+ ## determined by entries of "1" in the 
+ ## N x T matrix, pos.
+ y_obs               <- y_short
+ y_obs[pos == 1]     <- NA       
+ 
+ ## Conduct dependent GP model estimation under 
+ ## missing observations in y_obs.
+ ## We illustrate the ability to have multiple 
+ ## function or covariance terms
+ ## by seting gp_cov = c("se","sn"), which indicates 
+ ## the first term is a
+ ## squared exponential ("se") trend covariance term 
+ ## and the "sn" is a seasonality
+ ## term.  The setting, sn_order = 4, indicates the 
+ ## length scale of the seasonality
+ ## term is 4 month.  The season term is actually 
+ ## "quasi" seasonal, in that the
+ ## seasonal covariance kernel is multiplied by a 
+ ## squared exponential, which allows
+ ## the pattern of seasonality to evolve over time.
+ res_gp_2               <- gpdpgrow(y = y_obs, 
+                                   gp_cov=c("se","sn"), 
+                                   sn_order = 4, 
+                                   n.iter = 10, 
+                                   n.burn = 4, 
+                                   n.thin = 1, 
+                                   n.tune = 0) 
+ 
+ ## 2 plots of estimated functions: 1. faceted by cluster and fit;
+ ## 2.  data for experimental units.
+ ## for a group of randomly-selected functions
+ fit_plots_gp_2        <- cluster_plot( object = res_gp_2,  
+                                       units_name = "state", 
+                                       units_label = cps$st, 
+                                       single_unit = TRUE, 
+                                       credible = TRUE )
+                                       
+ ## Compute out-of-sample fit statistic, normalized mean-square 
+ ## prediction error (MSPE)
+ ## The normalized MSPE will take the predicted values 
+ ## for the entries in y_short purposefully
+ ## set to NA and will subtract them from the known true 
+ ## values in y_short (and square them).
+ ## This MSE on predicted (test) data is then 
+ ## divided by the variance of the test observations
+ ## to output something akin to a percent error.
+ ( nmspe_gp  <- MSPE(res_gp_2, y_short, pos)$nMSPE )    
+ }
Production Interation: 0
Error in gpdpbPost(y, ipr, Omega_t, Omega_s, gp_mod, jitter, b_move, gp_shape,  : 
  BLAS/LAPACK routine 'DTRTI2' gave error code -1
Calls: gpdpgrow -> gpdpgrow.default -> gpdpbPost
Execution halted