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 40106bb0, 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(18, 10.4, 19.9, 20.7, 19.9, 23.9, 19.9, 8.7, 22.6, 21.2, 20.1, 23.3, 22.7, 10.9, 23.6), X = list(crim = c(0.32543, 25.9406, 4.34879, 0.09065, 3.1636, 0.05059, 3.83684, 15.1772, 0.06724, 0.05646, 0.1396, 0.0456, 2.24236, 15.8744, 0.0566), zn = c(0, 0, 0, 20, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), indus = c(21.89, 18.1, 18.1, 6.96, 18.1, 4.49, 18.1, 18.1, 3.24, 12.83, 8.56, 13.89, 19.58, 18.1, 3.41), chas = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L), nox = c(0.624, 0.679, 0.58, 0.464, 0.655, 0.449, 0.77, 0.74, 0.46, 0.437, 0.52, 0.55, 0.605, 0.671, 0.489), rm = c(6.431, 5.304, 6.167, 5.92, 5.759, 6.389, 6.251, 6.152, 6.333, 6.232, 6.167, 5.888, 5.854, 6.545, 7.007), age = c(98.8, 89.1, 84, 61.5, 48.2, 48, 91.1, 100, 17.2, 53.7, 90, 56, 91.8, 99.1, 86.3), dis = c(1.8125, 1.6475, 3.0334, 3.9175, 3.0665, 4.7794, 2.2955, 1.9142, 5.2146, 5.0141, 2.421, 3.1121, 2.422, 1.5192, 3.4217), rad = c(4L, 24L, 24L, 3L, 24L, 3L, 24L, 24L, 4L, 5L, 5L, 5L, 5L, 24L, 2L), tax = c(437, 666, 666, 223, 666, 247, 666, 666, 430, 398, 384, 276, 403, 666, 270), ptratio = c(21.2, 20.2, 20.2, 18.6, 20.2, 18.5, 20.2, 20.2, 16.9, 18.7, 20.9, 16.4, 14.7, 20.2, 17.8), black = c(396.9, 127.36, 396.9, 391.34, 334.4, 396.9, 350.65, 9.32, 375.21, 386.4, 392.69, 392.8, 395.11, 396.9, 396.9), lstat = c(15.39, 26.64, 16.29, 13.65, 14.13, 9.62, 14.19, 26.45, 7.34, 12.34, 12.33, 13.51, 11.64, 21.08, 5.5)), newX = list(    crim = c(5.82401, 0.10959, 2.01019, 0.05602, 4.55587, 0.04297,     0.09744, 0.06617, 0.06466, 0.52014, 0.10469, 0.28392, 0.07503,     0.10574, 0.21719), zn = c(0, 0, 0, 0, 0, 52.5, 0, 0, 70,     20, 40, 0, 33, 0, 0), indus = c(18.1, 11.93, 19.58, 2.46,     18.1, 5.32, 5.96, 3.24, 2.24, 3.97, 6.41, 7.38, 2.18, 27.74,     10.59), chas = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,     1L, 0L, 0L, 0L, 1L), nox = c(0.532, 0.573, 0.605, 0.488,     0.718, 0.405, 0.499, 0.46, 0.4, 0.647, 0.447, 0.493, 0.472,     0.609, 0.489), rm = c(6.242, 6.794, 7.929, 7.831, 3.561,     6.565, 5.841, 5.868, 6.345, 8.398, 7.267, 5.708, 7.42, 5.983,     5.807), age = c(64.7, 89.3, 96.2, 53.6, 87.9, 22.9, 61.4,     25.8, 20.1, 91.5, 49, 74.3, 71.9, 98.8, 53.8), dis = c(3.4242,     2.3889, 2.0459, 3.1992, 1.6132, 7.3172, 3.3779, 5.2146, 7.8278,     2.2885, 4.7872, 4.7211, 3.0992, 1.8681, 3.6526), rad = c(24L,     1L, 5L, 3L, 24L, 6L, 5L, 4L, 5L, 5L, 4L, 5L, 7L, 4L, 4L),     tax = c(666, 273, 403, 193, 666, 293, 279, 430, 358, 264,     254, 287, 222, 711, 277), ptratio = c(20.2, 21, 14.7, 17.8,     20.2, 16.6, 19.2, 16.9, 14.8, 13, 17.6, 19.6, 18.4, 20.1,     18.6), black = c(396.9, 393.45, 369.3, 392.63, 354.7, 371.72,     377.56, 382.44, 368.24, 386.86, 389.25, 391.13, 396.9, 390.11,     390.94), lstat = c(10.74, 6.48, 3.7, 4.45, 7.12, 9.51, 11.41,     9.97, 4.97, 5.91, 6.05, 11.74, 6.47, 18.07, 16.03)), 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$link ==                 "log" && any(y <= 0))))             stop("cannot find valid starting values: please specify some")        mustart <- y    }), validmu = function (mu)     TRUE, valideta = function (eta)     TRUE), id = c(4L, 5L, 6L, 8L, 9L, 13L, 15L, 16L, 17L, 20L, 23L, 24L, 26L, 29L, 30L), obsWeights = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1))
 5: do.call(pred_fn, list(Y = tempOutcome, X = subset(tempLearn,     select = tempWhichScreen[library$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 ...