CRAN Package Check Results for Package bartBMA

Last updated on 2020-08-09 18:49:29 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0 241.88 60.42 302.30 OK
r-devel-linux-x86_64-debian-gcc 1.0 165.66 46.62 212.28 OK
r-devel-linux-x86_64-fedora-clang 1.0 431.77 NOTE
r-devel-linux-x86_64-fedora-gcc 1.0 358.90 NOTE
r-devel-windows-ix86+x86_64 1.0 367.00 132.00 499.00 ERROR
r-patched-linux-x86_64 1.0 193.92 60.07 253.99 OK
r-patched-solaris-x86 1.0 364.00 NOTE
r-release-linux-x86_64 1.0 198.33 58.61 256.94 OK
r-release-macos-x86_64 1.0 NOTE
r-release-windows-ix86+x86_64 1.0 457.00 103.00 560.00 ERROR
r-oldrel-macos-x86_64 1.0 NOTE
r-oldrel-windows-ix86+x86_64 1.0 321.00 168.00 489.00 NOTE

Check Details

Version: 1.0
Check: installed package size
Result: NOTE
     installed size is 28.7Mb
     sub-directories of 1Mb or more:
     libs 28.5Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64

Version: 1.0
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘mvnfast’
     All declared Imports should be used.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64

Version: 1.0
Check: running examples for arch ‘i386’
Result: ERROR
    Running examples in 'bartBMA-Ex.R' failed
    The error most likely occurred in:
    
    > ### Name: ITEs_bartBMA
    > ### Title: ITE Predictions (in-sample) using bartBMA and the method
    > ### described by Hill (2011)
    > ### Aliases: ITEs_bartBMA
    >
    > ### ** Examples
    >
    > n <- 250
    > x1 <- rnorm(n)
    > x2 <- rnorm(n)
    > x3 <- rnorm(n)
    > x4 <- rbinom(n,1,0.5)
    > x5 <- as.factor(sample( LETTERS[1:3], n, replace=TRUE))
    >
    > p= 0
    > xnoise = matrix(rnorm(n*p), nrow=n)
    > x5A <- ifelse(x5== 'A',1,0)
    > x5B <- ifelse(x5== 'B',1,0)
    > x5C <- ifelse(x5== 'C',1,0)
    >
    > x_covs_train <- cbind(x1,x2,x3,x4,x5A,x5B,x5C,xnoise)
    >
    > #Treatment effect
    > #tautrain <- 3
    > tautrain <- 1+2*x_covs_train[,2]*x_covs_train[,4]
    >
    > #Prognostic function
    > mutrain <- 1 + 2*x_covs_train[,5] -1*x_covs_train[,6]-4*x_covs_train[,7] +
    + x_covs_train[,1]*x_covs_train[,3]
    > sd_mtrain <- sd(mutrain)
    > utrain <- runif(n)
    > #pitrain <- 0.8*pnorm((3*mutrain/sd_mtrain)-0.5*x_covs_train[,1])+0.05+utrain/10
    > pitrain <- 0.5
    > ztrain <- rbinom(n,1,pitrain)
    > ytrain <- mutrain + tautrain*ztrain
    > #pihattrain <- pbart(x_covs_train,ztrain )$prob.train.mean
    >
    > #set lower and upper quantiles for intervals
    > lbound <- 0.025
    > ubound <- 0.975
    >
    > example_output <- ITEs_bartBMA(x_covariates = x_covs_train,
    + z_train = ztrain,
    + y_train = ytrain)
    >
    >
    >
    > cleanEx()
Flavors: r-devel-windows-ix86+x86_64, r-release-windows-ix86+x86_64