* using log directory 'd:/Rcompile/CRANpkg/local/2.6/stochasticGEM.Rcheck'
* using R version 2.6.2 RC (2008-02-04 r44320)
* checking for file 'stochasticGEM/DESCRIPTION' ... OK
* this is package 'stochasticGEM' version '0.0-1'
* checking package name space information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking whether package 'stochasticGEM' can be installed ... OK
* checking package directory ... OK
* checking for portable file names ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the name space can be loaded with stated dependencies ... OK
* checking for unstated dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking data for non-ASCII characters ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking for portable use of $BLAS_LIBS ... OK
* creating stochasticGEM-Ex.R ... OK
* checking examples ... ERROR
Running examples in 'stochasticGEM-Ex.R' failed.
The error most likely occurred in:

> ### * SIRMH
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SIR.MH
> ### Title: Fit a partially observed Markovian/nonMarkovian SIR general
> ###   epidemic model
> ### Aliases: SIR.MH
> ### Keywords: models optimize
> 
> ### ** Examples
> 
>     data(smallpox)
>     priors <- list(infectionRate = c(0.0,0.0), removalRate = c(0.0,0.0), theta = 0) 
>     temp <- SIR.MH(N=119,removalTimes=smallpox, priorValues=priors,
+         bayesReps=1000,burnIn=500,bayesThin=1,infectious.density = "exponential")
>     summary(temp$infRateSIR)

Iterations = 501:1000
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

          Mean             SD       Naive SE Time-series SE 
     7.872e-04      1.506e-04      6.734e-06      9.773e-06 

2. Quantiles for each variable:

     2.5%       25%       50%       75%     97.5% 
0.0004958 0.0006873 0.0007832 0.0008885 0.0011024 

>     summary(temp$remRateSIR)

Iterations = 501:1000
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

          Mean             SD       Naive SE Time-series SE 
     0.0836526      0.0156035      0.0006978      0.0008821 

2. Quantiles for each variable:

   2.5%     25%     50%     75%   97.5% 
0.05677 0.07314 0.08315 0.09359 0.11750 

>     summary(temp$infectiousPeriod)

Iterations = 501:1000
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

          Mean             SD       Naive SE Time-series SE 
       12.3880         2.4031         0.1075         0.1355 

2. Quantiles for each variable:

 2.5%   25%   50%   75% 97.5% 
 8.51 10.69 12.03 13.67 17.61 

>     summary(temp$reproductionNumber)

Iterations = 501:1000
Thinning interval = 1 
Number of chains = 1 
Sample size per chain = 500 

1. Empirical mean and standard deviation for each variable,
   plus standard error of the mean:

          Mean             SD       Naive SE Time-series SE 
       1.15385        0.28995        0.01297        0.01295 

2. Quantiles for each variable:

  2.5%    25%    50%    75%  97.5% 
0.6574 0.9630 1.1174 1.3321 1.7715 

>     #
>     #
>     priors <- list(infectionRate = c(0.0,0.0), 
+         infectiousScale = c(0.0,0.0), infectiousShape = c(0.0,0.0),
+         theta = c(1,0))
>     temp <- SIR.MH(N=119,removalTimes=smallpox, priorValues=priors,
+         bayesReps=1000,burnIn=500,bayesThin=1,infectious.density = "gamma")
>