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

> ### Name: gMAP
> ### Title: Meta-Analytic-Predictive Analysis for Generalized Linear Models
> ### Aliases: gMAP print.gMAP fitted.gMAP coef.gMAP as.matrix.gMAP
> ###   summary.gMAP
> 
> ### ** Examples
> 
> # Binary data example 1
> 
> # Mean response rate is ~0.25. For binary endpoints
> # a conservative choice for tau is a HalfNormal(0,1) as long as
> # the mean response rate is in the range of 0.2 to 0.8. For
> # very small or large rates consider the n_infinity approach
> # illustrated below.
> # for exact reproducible results, the seed must be set
> set.seed(34563)
> map_AS <- gMAP(cbind(r, n-r) ~ 1 | study,
+                family=binomial,
+                data=AS,
+                tau.dist="HalfNormal", tau.prior=1,
+                beta.prior=2,
+                ## ensure fast example runtime (use defaults)
+                ## please ignore the warning of less than 1000 draws
+                warmup=500, iter=1000, chains=2, thin=1)
Assuming default prior location   for beta: 0
Final MCMC sample equivalent to less than 1000 independent draws.
Please consider increasing the MCMC simulation size.
> print(map_AS)
Generalized Meta Analytic Predictive Prior Analysis

Call:  gMAP(formula = cbind(r, n - r) ~ 1 | study, family = binomial, 
    data = AS, tau.dist = "HalfNormal", tau.prior = 1, beta.prior = 2, 
    iter = 1000, warmup = 500, thin = 1, chains = 2)

Exchangeability tau strata: 1 
Prediction tau stratum    : 1 
Maximal Rhat              : 1.01 

Between-trial heterogeneity of tau prediction stratum
  mean     sd   2.5%    50%  97.5% 
0.3890 0.2110 0.0551 0.3590 0.8980 

MAP Prior MCMC sample
  mean     sd   2.5%    50%  97.5% 
0.2530 0.0881 0.0937 0.2460 0.4600 
> 
> # obtain numerical summaries
> map_sum <- summary(map_AS)
> print(map_sum)
Heterogeneity parameter tau per stratum:
        mean    sd   2.5%   50% 97.5%
tau[1] 0.389 0.211 0.0551 0.359 0.898

Regression coefficients:
             mean    sd  2.5%   50%  97.5%
(Intercept) -1.11 0.178 -1.47 -1.11 -0.751

Mean estimate MCMC sample:
            mean     sd  2.5%   50% 97.5%
theta_resp 0.249 0.0333 0.187 0.247 0.321

MAP Prior MCMC sample:
                 mean     sd   2.5%   50% 97.5%
theta_resp_pred 0.253 0.0881 0.0937 0.246  0.46
> names(map_sum)
[1] "tau"        "beta"       "theta.pred" "theta"     
> # [1] "tau"        "beta"       "theta.pred" "theta"
> map_sum$theta.pred
                    mean         sd       2.5%       50%     97.5%
theta_resp_pred 0.253274 0.08812659 0.09368841 0.2462366 0.4600881
> 
> ## Not run: 
> ##D # graphical model checks (returns list of ggplot2 plots)
> ##D map_checks <- plot(map_AS)
> ##D # forest plot with shrinkage estimates
> ##D map_checks$forest_model
> ##D # density of MAP prior on response scale
> ##D map_checks$densityThetaStar
> ##D # density of MAP prior on link scale
> ##D map_checks$densityThetaStarLink
> ## End(Not run)
> 
> # obtain shrinkage estimates
> fitted(map_AS)
             mean         sd       2.5%       50%     97.5%
Study 1 0.2276509 0.03419302 0.15896580 0.2276818 0.2986743
Study 2 0.2622620 0.04937735 0.17312145 0.2588063 0.3675675
Study 3 0.3147570 0.05599391 0.22006223 0.3111084 0.4333636
Study 4 0.2397609 0.04731476 0.15028614 0.2392842 0.3406891
Study 5 0.2707685 0.03597432 0.20543976 0.2682425 0.3423517
Study 6 0.2656003 0.06385251 0.15333146 0.2587899 0.4119684
Study 7 0.1721853 0.04453192 0.09077959 0.1708423 0.2573369
Study 8 0.2646543 0.05268943 0.17328532 0.2601541 0.3829309
> 
> # regression coefficients
> coef(map_AS)
                 mean       sd      2.5%       50%    97.5%
(Intercept) -1.114313 0.178081 -1.467056 -1.112766 -0.75131
> 
> # finally fit MAP prior with parametric mixture
> map_mix <- mixfit(map_AS, Nc=2)
> plot(map_mix)$mix
Error in inDL(x, as.logical(local), as.logical(now), ...) : 
  ICU init failed: U_FILE_ACCESS_ERROR
Calls: plot ... tryCatch -> tryCatchList -> tryCatchOne -> <Anonymous>
Execution halted