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

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: evaluation
> ### Title: Observed sequence evaluation given a model
> ### Aliases: evaluation
> ### Keywords: methods
> 
> ### ** Examples
> 
> ## Values for a hidden Markov model with categorical observations
> # Set the model parameters
> n <- c("First","Second")
> m <- c("A","T","C","G")
> A <- matrix(c(0.8,0.2,
+               0.1,0.9),
+             nrow = 2,
+             byrow = TRUE)
> 
> B <- matrix(c(0.2, 0.2, 0.3, 0.3,
+               0.4, 0.4, 0.1, 0.1),
+             nrow = 2,
+             byrow = TRUE)
> 
> Pi <- c(0.5, 0.5)
> 
> params <- list( "Model" = "HMM",
+                 "StateNames" = n,
+                 "ObservationNames" = m,
+                 "A" = A,
+                 "B" = B,
+                 "Pi" = Pi)
> 
> HMM <- verifyModel(params)
> 
> # Data simulation
> set.seed(100)
> length <- 100
> observationSequence <- generateObservations(HMM, length)
> 
> #Sequence evaluation
> 
> # It assumes that it will be evaluated using the forward algorithm
> evaluation(HMM, observationSequence$Y)  
[1] -131.7363
> 
> # The user sets the backward algorithm to evaluate the algorithm
> evaluation(HMM, observationSequence$Y, "b") 
[1] -131.7363
> 
> ## Values for a hidden Markov model with discrete observations
> 
> n <- c("Low","Normal","High")
> 
> A <- matrix(c(0.5, 0.3,0.2,
+               0.2, 0.6, 0.2,
+               0.1, 0.3, 0.6),
+             ncol=length(n), byrow=TRUE)
> 
> B <- c(2600,  # First distribution with mean 2600
+        2700,  # Second distribution with mean 2700
+        2800)  # Third distribution with mean 2800
> 
> Pi <- rep(1/length(n), length(n))
> 
> HMM.discrete <- verifyModel(list("Model"="PHMM", "StateNames" = n, "A" = A, "B" = B, "Pi" = Pi))
> 
> # Data simulation
> set.seed(100)
> length <- 100
> observationSequence <- generateObservations(HMM.discrete, length)
> 
> #Sequence evaluation
> 
> # It assumes that it will be evaluated using the forward algorithm
> evaluation(HMM.discrete, observationSequence$Y)  
[1] -580.9131
> 
> # The user sets the backward algorithm to evaluate the algorithm
> evaluation(HMM.discrete, observationSequence$Y, "b") 
[1] -580.9131
> 
> ## Values for a hidden Markov model with continuous observations                          
> # Number of hidden states = 3
> # Univariate gaussian mixture model
> 
> N = c("Low","Normal", "High")
> A <- matrix(c(0.5, 0.3,0.2,
+               0.2, 0.6, 0.2,
+               0.1, 0.3, 0.6),
+             ncol= length(N), byrow = TRUE)
> 
> Mu <- matrix(c(0, 50, 100), ncol = length(N))
> Sigma <- array(c(144, 400, 100), dim = c(1,1,length(N)))
> Pi <- rep(1/length(N), length(N))
> 
> HMM.cont.univariate <- verifyModel(list( "Model"="GHMM", 
+                                          "StateNames" = N,
+                                          "A" = A, 
+                                          "Mu" = Mu, 
+                                          "Sigma" = Sigma, 
+                                          "Pi" = Pi))
> 
> # Data simulation
> set.seed(100)
> length <- 100
> observationSequence <- generateObservations(HMM.cont.univariate, length)
> 
> #Sequence evaluation
> 
> # It assumes that it will be evaluated using the forward algorithm
> evaluation(HMM.cont.univariate, observationSequence$Y)  
Error in evaluation(HMM.cont.univariate, observationSequence$Y) : 
  BLAS/LAPACK routine 'DTRTI2' gave error code -1
Execution halted