Running examples in ‘SuperLearner-Ex.R’ failed
The error most likely occurred in:

> ### Name: SL.dbarts
> ### Title: Discrete bayesian additive regression tree sampler
> ### Aliases: SL.dbarts
> 
> ### ** Examples
> 
> 
> data(Boston, package = "MASS")
> Y = Boston$medv
> # Remove outcome from covariate dataframe.
> X = Boston[, -14]
> 
> set.seed(1)
> 
> # Sample rows to speed up example.
> row_subset = sample(nrow(X), 30)
> 
> sl = SuperLearner(Y[row_subset], X[row_subset, ], family = gaussian(),
+ cvControl = list(V = 2), SL.library = c("SL.mean", "SL.dbarts"))
Loading required package: dbarts

 *** caught segfault ***
address 3ff0c692, cause 'memory not mapped'

Traceback:
 1: .Call(C_dbarts_run, ptr, as.integer(numBurnIn), as.integer(numSamples))
 2: sampler$run(0L, control@n.burn, FALSE)
 3: dbarts::bart(x.train = X, y.train = Y, x.test = newX, sigest = sigest,     sigdf = sigdf, sigquant = sigquant, k = k, power = power,     base = base, binaryOffset = binaryOffset, weights = obsWeights,     ntree = ntree, ndpost = ndpost, nskip = nskip, printevery = printevery,     keepevery = keepevery, keeptrainfits = keeptrainfits, usequants = usequants,     numcut = numcut, printcutoffs = printcutoffs, nthread = nthread,     keepcall = keepcall, verbose = verbose)
 4: (function (Y, X, newX, family, obsWeights, id, sigest = NA, sigdf = 3,     sigquant = 0.9, k = 2, power = 2, base = 0.95, binaryOffset = 0,     ntree = 200, ndpost = 1000, nskip = 100, printevery = 100,     keepevery = 1, keeptrainfits = TRUE, usequants = FALSE, numcut = 100,     printcutoffs = 0, nthread = 1, keepcall = TRUE, verbose = FALSE,     ...) {    .SL.require("dbarts")    model = dbarts::bart(x.train = X, y.train = Y, x.test = newX,         sigest = sigest, sigdf = sigdf, sigquant = sigquant,         k = k, power = power, base = base, binaryOffset = binaryOffset,         weights = obsWeights, ntree = ntree, ndpost = ndpost,         nskip = nskip, printevery = printevery, keepevery = keepevery,         keeptrainfits = keeptrainfits, usequants = usequants,         numcut = numcut, printcutoffs = printcutoffs, nthread = nthread,         keepcall = keepcall, verbose = verbose)    if (family$family == "gaussian") {        pred = model$yhat.test.mean    }    if (family$family == "binomial") {        pred = colMeans(stats::pnorm(model$yhat.test))    }    fit = list(object = model)    class(fit) = c("SL.dbarts")    out = list(pred = pred, fit = fit)    return(out)})(Y = c(32, 22.3, 13.4, 23.2, 22.6, 21.6, 12.7, 18.6, 22.2, 19, 24.1, 21.2, 18.7, 22.9, 7.2), X = list(crim = c(0.07875, 0.0459, 6.71772, 3.56868, 0.06724, 0.26938, 1.13081, 0.22876, 0.07151, 0.05497, 0.0795, 3.67367, 0.14932, 0.08829, 18.0846),     zn = c(45, 52.5, 0, 0, 0, 0, 0, 0, 0, 0, 60, 0, 25, 12.5,     0), indus = c(3.44, 5.32, 18.1, 18.1, 3.24, 9.9, 8.14, 8.56,     4.49, 5.19, 1.69, 18.1, 5.13, 7.87, 18.1), chas = c(0L, 0L,     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), nox = c(0.437,     0.405, 0.713, 0.58, 0.46, 0.544, 0.538, 0.52, 0.449, 0.515,     0.411, 0.583, 0.453, 0.524, 0.679), rm = c(6.782, 6.315,     6.749, 6.437, 6.333, 6.266, 5.713, 6.405, 6.121, 5.985, 6.579,     6.312, 5.741, 6.012, 6.434), age = c(41.1, 45.6, 92.6, 75,     17.2, 82.8, 94.1, 85.4, 56.8, 45.4, 35.9, 51.9, 66.2, 66.6,     100), dis = c(3.7886, 7.3172, 2.3236, 2.8965, 5.2146, 3.2628,     4.233, 2.7147, 3.7476, 4.8122, 10.7103, 3.9917, 7.2254, 5.5605,     1.8347), rad = c(5L, 6L, 24L, 24L, 4L, 4L, 4L, 5L, 3L, 5L,     4L, 24L, 8L, 5L, 24L), tax = c(398, 293, 666, 666, 430, 304,     307, 384, 247, 224, 411, 666, 284, 311, 666), ptratio = c(15.2,     16.6, 20.2, 20.2, 16.9, 18.4, 21, 20.9, 18.5, 20.2, 18.3,     20.2, 19.7, 15.2, 20.2), black = c(393.87, 396.9, 0.32, 393.37,     375.21, 393.39, 360.17, 70.8, 395.15, 396.9, 370.78, 388.62,     395.11, 395.6, 27.25), lstat = c(6.68, 7.6, 17.44, 14.36,     7.34, 7.9, 22.6, 10.63, 8.44, 9.74, 5.49, 10.58, 13.15, 12.43,     29.05)), newX = list(crim = c(23.6482, 0.0837, 0.17899, 0.10574, 0.20608, 0.32543, 1.83377, 9.51363, 0.97617, 4.83567, 0.11432, 4.66883, 0.10008, 0.06047, 0.25356), zn = c(0, 45, 0, 0, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), indus = c(18.1, 3.44, 9.69, 27.74, 5.86, 21.89, 19.58, 18.1, 21.89, 18.1, 8.56, 18.1, 2.46, 2.46, 9.9), chas = c(0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), nox = c(0.671, 0.437, 0.585, 0.609, 0.431, 0.624, 0.605, 0.713, 0.624, 0.583, 0.52, 0.713, 0.488, 0.488, 0.544),     rm = c(6.38, 7.185, 5.67, 5.983, 5.593, 6.431, 7.802, 6.728,     5.757, 5.905, 6.781, 5.976, 6.563, 6.153, 5.705), age = c(96.2,     38.9, 28.8, 98.8, 76.5, 98.8, 98.2, 94.1, 98.4, 53.2, 71.3,     87.9, 95.6, 68.8, 77.7), dis = c(1.3861, 4.5667, 2.7986,     1.8681, 7.9549, 1.8125, 2.0407, 2.4961, 2.346, 3.1523, 2.8561,     2.5806, 2.847, 3.2797, 3.945), rad = c(24L, 5L, 6L, 4L, 7L,     4L, 5L, 24L, 4L, 24L, 5L, 24L, 3L, 3L, 4L), tax = c(666,     398, 391, 711, 330, 437, 403, 666, 437, 666, 384, 666, 193,     193, 304), ptratio = c(20.2, 15.2, 19.2, 20.1, 19.1, 21.2,     14.7, 20.2, 21.2, 20.2, 20.9, 20.2, 17.8, 17.8, 18.4), black = c(396.9,     396.9, 393.29, 390.11, 372.49, 396.9, 389.61, 6.68, 262.76,     388.22, 395.58, 10.48, 396.9, 387.11, 396.42), lstat = c(23.69,     5.39, 17.6, 18.07, 12.5, 15.39, 1.92, 18.71, 17.31, 11.45,     7.67, 19.01, 5.68, 13.15, 11.5)), family = list(family = "gaussian",     link = "identity", linkfun = function (mu)     mu, linkinv = function (eta)     eta, variance = function (mu)     rep.int(1, length(mu)), dev.resids = function (y, mu, wt)     wt * ((y - mu)^2), aic = function (y, n, mu, wt, dev)     {        nobs <- length(y)        nobs * (log(dev/nobs * 2 * pi) + 1) + 2 - sum(log(wt))    }, mu.eta = function (eta)     rep.int(1, length(eta)), initialize = expression({        n <- rep.int(1, nobs)        if (is.null(etastart) && is.null(start) && is.null(mustart) &&             ((family$link == "inverse" && any(y == 0)) || (family$link ==                 "log" && any(y <= 0))))             stop("cannot find valid starting values: please specify some")        mustart <- y    }), validmu = function (mu)     TRUE, valideta = function (eta)     TRUE), id = c(2L, 3L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 17L, 18L, 24L, 27L, 29L), obsWeights = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
 5: do.call(pred_fn, list(Y = tempOutcome, X = subset(tempLearn,     select = tempWhichScreen[library$library$rowScreen[s], ],     drop = FALSE), newX = subset(tempValid, select = tempWhichScreen[library$library$rowScreen[s],     ], drop = FALSE), family = family, id = tempId, obsWeights = tempObsWeights))
 6: doTryCatch(return(expr), name, parentenv, handler)
 7: tryCatchOne(expr, names, parentenv, handlers[[1L]])
 8: tryCatchList(expr, classes, parentenv, handlers)
 9: tryCatch(expr, error = function(e) {    call <- conditionCall(e)    if (!is.null(call)) {        if (identical(call[[1L]], quote(doTryCatch)))             call <- sys.call(-4L)        dcall <- deparse(call)[1L]        prefix <- paste("Error in", dcall, ": ")        LONG <- 75L        sm <- strsplit(conditionMessage(e), "\n")[[1L]]        w <- 14L + nchar(dcall, type = "w") + nchar(sm[1L], type = "w")        if (is.na(w))             w <- 14L + nchar(dcall, type = "b") + nchar(sm[1L],                 type = "b")        if (w > LONG)             prefix <- paste0(prefix, "\n  ")    }    else prefix <- "Error : "    msg <- paste0(prefix, conditionMessage(e), "\n")    .Internal(seterrmessage(msg[1L]))    if (!silent && isTRUE(getOption("show.error.messages"))) {        cat(msg, file = outFile)        .Internal(printDeferredWarnings())    }    invisible(structure(msg, class = "try-error", condition = e))})
10: try(do.call(pred_fn, list(Y = tempOutcome, X = subset(tempLearn,     select = tempWhichScreen[library$library$rowScreen[s], ],     drop = FALSE), newX = subset(tempValid, select = tempWhichScreen[library$library$rowScreen[s],     ], drop = FALSE), family = family, id = tempId, obsWeights = tempObsWeights)))
11: FUN(X[[i]], ...)
12: lapply(validRows, FUN = .crossValFUN, Y = Y, dataX = X, id = id,     obsWeights = obsWeights, library = library, kScreen = kScreen,     k = k, p = p, libraryNames = libraryNames)
13: do.call("rbind", lapply(validRows, FUN = .crossValFUN, Y = Y,     dataX = X, id = id, obsWeights = obsWeights, library = library,     kScreen = kScreen, k = k, p = p, libraryNames = libraryNames))
14: SuperLearner(Y[row_subset], X[row_subset, ], family = gaussian(),     cvControl = list(V = 2), SL.library = c("SL.mean", "SL.dbarts"))
An irrecoverable exception occurred. R is aborting now ...