CRAN Package Check Results for Package parallelMCMCcombine

Last updated on 2020-02-17 17:52:16 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 1.0 1.87 20.88 22.75 ERROR
r-devel-linux-x86_64-debian-gcc 1.0 1.79 16.92 18.71 ERROR
r-devel-linux-x86_64-fedora-clang 1.0 28.92 ERROR
r-devel-linux-x86_64-fedora-gcc 1.0 27.69 ERROR
r-devel-windows-ix86+x86_64 1.0 5.00 35.00 40.00 NOTE
r-devel-windows-ix86+x86_64-gcc8 1.0 8.00 51.00 59.00 NOTE
r-patched-linux-x86_64 1.0 1.66 22.30 23.96 NOTE
r-patched-solaris-x86 1.0 43.90 NOTE
r-release-linux-x86_64 1.0 1.57 22.01 23.58 NOTE
r-release-windows-ix86+x86_64 1.0 4.00 38.00 42.00 NOTE
r-release-osx-x86_64 1.0 NOTE
r-oldrel-windows-ix86+x86_64 1.0 2.00 42.00 44.00 NOTE
r-oldrel-osx-x86_64 1.0 NOTE

Check Details

Version: 1.0
Check: R code for possible problems
Result: NOTE
    consensusMCcov: no visible global function definition for 'var'
    consensusMCcov: no visible global function definition for 'cov'
    consensusMCindep: no visible global function definition for 'var'
    semiparamDPE: no visible binding for global variable 'var'
    semiparamDPE: no visible global function definition for 'cov'
    semiparamDPE: no visible global function definition for 'runif'
    Undefined global functions or variables:
     cov runif var
    Consider adding
     importFrom("stats", "cov", "runif", "var")
    to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-devel-windows-ix86+x86_64-gcc8, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64

Version: 1.0
Check: examples
Result: ERROR
    Running examples in 'parallelMCMCcombine-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: consensusMCcov
    > ### Title: Consensus Monte Carlo Algorithm (for correlated parameters)
    > ### Aliases: consensusMCcov
    > ### Keywords: combine consensus subposterior posterior parallel
    >
    > ### ** Examples
    > d <- 2 # dimension of the parameter space
    > sampT <- 1000 # number of subset posterior samples
    > M <- 3 # total number of subsets
    >
    > ## simulate Gaussian subposterior samples
    >
    > theta <- array(NA,c(d,sampT,M))
    >
    > norm.mean <- c(1.0, 2.0)
    > norm.sd <- c(0.5, 1.0)
    >
    > for (i in 1:d)
    + for (s in 1:M)
    + theta[i,,s] <- rnorm(sampT, mean=norm.mean[i]+runif(1,-0.01,0.01), sd=norm.sd[i])
    >
    > ## combine samples:
    >
    > full.theta <- consensusMCcov(subchain=theta, shuff=FALSE)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    parallelMCMCcombine
     --- call from context ---
    consensusMCcov(subchain = theta, shuff = FALSE)
     --- call from argument ---
    if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #", k,
     "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
    }
     --- R stacktrace ---
    where 1: consensusMCcov(subchain = theta, shuff = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (subchain, shuff = FALSE)
    {
     ddata = length(dim(subchain))
     if (ddata != 3) {
     stop("The subchain must be an array of dimension c(d,sampletotT,M).")
     }
     d <- dim(subchain)[1]
     sampletotT <- dim(subchain)[2]
     M <- dim(subchain)[3]
     if (M == 1) {
     theta <- array(subchain[, , 1], c(d, sampletotT))
     return(theta)
     }
     if (shuff == TRUE) {
     for (k in 1:M) subchain[, , k] <- subchain[, sample(sampletotT),
     k]
     }
     sigmahatm <- array(NA, c(d, d, M))
     sigmahatM <- matrix(NA, d, d)
     sigmahatM.pre <- matrix(NA, d, d)
     if (d == 1) {
     for (k in 1:M) {
     sigmahatm[1, , k] <- var(subchain[1, , k])
     }
     }
     else {
     for (k in 1:M) {
     sigmahatm[, , k] <- cov(t(subchain[, , k]))
     }
     }
     sigmahatm.inverse <- array(NA, dim = c(d, d, M))
     for (k in 1:M) {
     res <- try(sigmahatm.inverse[, , k] <- solve(sigmahatm[,
     , k]), silent = TRUE)
     if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #",
     k, "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
     }
     }
     sigmahatM <- solve(rowSums(sigmahatm.inverse, dims = 2))
     theta <- matrix(NA, nrow = d, ncol = sampletotT)
     wvec <- array(NA, c(d, 1))
     for (i in 1:sampletotT) {
     wvec <- rep(0, d)
     for (s in 1:M) {
     wvec <- wvec + sigmahatm.inverse[, , s] %*% subchain[,
     i, s]
     }
     theta[, i] <- sigmahatM %*% wvec
     }
     return(theta)
    }
    <bytecode: 0x3f36e20>
    <environment: namespace:parallelMCMCcombine>
     --- function search by body ---
    Function consensusMCcov in namespace parallelMCMCcombine has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(res) == "try-error") { : the condition has length > 1
    Calls: consensusMCcov
    Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘parallelMCMCcombine-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: consensusMCcov
    > ### Title: Consensus Monte Carlo Algorithm (for correlated parameters)
    > ### Aliases: consensusMCcov
    > ### Keywords: combine consensus subposterior posterior parallel
    >
    > ### ** Examples
    > d <- 2 # dimension of the parameter space
    > sampT <- 1000 # number of subset posterior samples
    > M <- 3 # total number of subsets
    >
    > ## simulate Gaussian subposterior samples
    >
    > theta <- array(NA,c(d,sampT,M))
    >
    > norm.mean <- c(1.0, 2.0)
    > norm.sd <- c(0.5, 1.0)
    >
    > for (i in 1:d)
    + for (s in 1:M)
    + theta[i,,s] <- rnorm(sampT, mean=norm.mean[i]+runif(1,-0.01,0.01), sd=norm.sd[i])
    >
    > ## combine samples:
    >
    > full.theta <- consensusMCcov(subchain=theta, shuff=FALSE)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    parallelMCMCcombine
     --- call from context ---
    consensusMCcov(subchain = theta, shuff = FALSE)
     --- call from argument ---
    if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #", k,
     "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
    }
     --- R stacktrace ---
    where 1: consensusMCcov(subchain = theta, shuff = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (subchain, shuff = FALSE)
    {
     ddata = length(dim(subchain))
     if (ddata != 3) {
     stop("The subchain must be an array of dimension c(d,sampletotT,M).")
     }
     d <- dim(subchain)[1]
     sampletotT <- dim(subchain)[2]
     M <- dim(subchain)[3]
     if (M == 1) {
     theta <- array(subchain[, , 1], c(d, sampletotT))
     return(theta)
     }
     if (shuff == TRUE) {
     for (k in 1:M) subchain[, , k] <- subchain[, sample(sampletotT),
     k]
     }
     sigmahatm <- array(NA, c(d, d, M))
     sigmahatM <- matrix(NA, d, d)
     sigmahatM.pre <- matrix(NA, d, d)
     if (d == 1) {
     for (k in 1:M) {
     sigmahatm[1, , k] <- var(subchain[1, , k])
     }
     }
     else {
     for (k in 1:M) {
     sigmahatm[, , k] <- cov(t(subchain[, , k]))
     }
     }
     sigmahatm.inverse <- array(NA, dim = c(d, d, M))
     for (k in 1:M) {
     res <- try(sigmahatm.inverse[, , k] <- solve(sigmahatm[,
     , k]), silent = TRUE)
     if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #",
     k, "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
     }
     }
     sigmahatM <- solve(rowSums(sigmahatm.inverse, dims = 2))
     theta <- matrix(NA, nrow = d, ncol = sampletotT)
     wvec <- array(NA, c(d, 1))
     for (i in 1:sampletotT) {
     wvec <- rep(0, d)
     for (s in 1:M) {
     wvec <- wvec + sigmahatm.inverse[, , s] %*% subchain[,
     i, s]
     }
     theta[, i] <- sigmahatM %*% wvec
     }
     return(theta)
    }
    <bytecode: 0x559085610288>
    <environment: namespace:parallelMCMCcombine>
     --- function search by body ---
    Function consensusMCcov in namespace parallelMCMCcombine has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(res) == "try-error") { : the condition has length > 1
    Calls: consensusMCcov
    Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘parallelMCMCcombine-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: consensusMCcov
    > ### Title: Consensus Monte Carlo Algorithm (for correlated parameters)
    > ### Aliases: consensusMCcov
    > ### Keywords: combine consensus subposterior posterior parallel
    >
    > ### ** Examples
    > d <- 2 # dimension of the parameter space
    > sampT <- 1000 # number of subset posterior samples
    > M <- 3 # total number of subsets
    >
    > ## simulate Gaussian subposterior samples
    >
    > theta <- array(NA,c(d,sampT,M))
    >
    > norm.mean <- c(1.0, 2.0)
    > norm.sd <- c(0.5, 1.0)
    >
    > for (i in 1:d)
    + for (s in 1:M)
    + theta[i,,s] <- rnorm(sampT, mean=norm.mean[i]+runif(1,-0.01,0.01), sd=norm.sd[i])
    >
    > ## combine samples:
    >
    > full.theta <- consensusMCcov(subchain=theta, shuff=FALSE)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    parallelMCMCcombine
     --- call from context ---
    consensusMCcov(subchain = theta, shuff = FALSE)
     --- call from argument ---
    if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #", k,
     "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
    }
     --- R stacktrace ---
    where 1: consensusMCcov(subchain = theta, shuff = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (subchain, shuff = FALSE)
    {
     ddata = length(dim(subchain))
     if (ddata != 3) {
     stop("The subchain must be an array of dimension c(d,sampletotT,M).")
     }
     d <- dim(subchain)[1]
     sampletotT <- dim(subchain)[2]
     M <- dim(subchain)[3]
     if (M == 1) {
     theta <- array(subchain[, , 1], c(d, sampletotT))
     return(theta)
     }
     if (shuff == TRUE) {
     for (k in 1:M) subchain[, , k] <- subchain[, sample(sampletotT),
     k]
     }
     sigmahatm <- array(NA, c(d, d, M))
     sigmahatM <- matrix(NA, d, d)
     sigmahatM.pre <- matrix(NA, d, d)
     if (d == 1) {
     for (k in 1:M) {
     sigmahatm[1, , k] <- var(subchain[1, , k])
     }
     }
     else {
     for (k in 1:M) {
     sigmahatm[, , k] <- cov(t(subchain[, , k]))
     }
     }
     sigmahatm.inverse <- array(NA, dim = c(d, d, M))
     for (k in 1:M) {
     res <- try(sigmahatm.inverse[, , k] <- solve(sigmahatm[,
     , k]), silent = TRUE)
     if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #",
     k, "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
     }
     }
     sigmahatM <- solve(rowSums(sigmahatm.inverse, dims = 2))
     theta <- matrix(NA, nrow = d, ncol = sampletotT)
     wvec <- array(NA, c(d, 1))
     for (i in 1:sampletotT) {
     wvec <- rep(0, d)
     for (s in 1:M) {
     wvec <- wvec + sigmahatm.inverse[, , s] %*% subchain[,
     i, s]
     }
     theta[, i] <- sigmahatM %*% wvec
     }
     return(theta)
    }
    <bytecode: 0x29bae98>
    <environment: namespace:parallelMCMCcombine>
     --- function search by body ---
    Function consensusMCcov in namespace parallelMCMCcombine has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(res) == "try-error") { : the condition has length > 1
    Calls: consensusMCcov
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang

Version: 1.0
Check: examples
Result: ERROR
    Running examples in ‘parallelMCMCcombine-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: consensusMCcov
    > ### Title: Consensus Monte Carlo Algorithm (for correlated parameters)
    > ### Aliases: consensusMCcov
    > ### Keywords: combine consensus subposterior posterior parallel
    >
    > ### ** Examples
    > d <- 2 # dimension of the parameter space
    > sampT <- 1000 # number of subset posterior samples
    > M <- 3 # total number of subsets
    >
    > ## simulate Gaussian subposterior samples
    >
    > theta <- array(NA,c(d,sampT,M))
    >
    > norm.mean <- c(1.0, 2.0)
    > norm.sd <- c(0.5, 1.0)
    >
    > for (i in 1:d)
    + for (s in 1:M)
    + theta[i,,s] <- rnorm(sampT, mean=norm.mean[i]+runif(1,-0.01,0.01), sd=norm.sd[i])
    >
    > ## combine samples:
    >
    > full.theta <- consensusMCcov(subchain=theta, shuff=FALSE)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    parallelMCMCcombine
     --- call from context ---
    consensusMCcov(subchain = theta, shuff = FALSE)
     --- call from argument ---
    if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #", k,
     "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
    }
     --- R stacktrace ---
    where 1: consensusMCcov(subchain = theta, shuff = FALSE)
    
     --- value of length: 2 type: logical ---
    [1] FALSE FALSE
     --- function from context ---
    function (subchain, shuff = FALSE)
    {
     ddata = length(dim(subchain))
     if (ddata != 3) {
     stop("The subchain must be an array of dimension c(d,sampletotT,M).")
     }
     d <- dim(subchain)[1]
     sampletotT <- dim(subchain)[2]
     M <- dim(subchain)[3]
     if (M == 1) {
     theta <- array(subchain[, , 1], c(d, sampletotT))
     return(theta)
     }
     if (shuff == TRUE) {
     for (k in 1:M) subchain[, , k] <- subchain[, sample(sampletotT),
     k]
     }
     sigmahatm <- array(NA, c(d, d, M))
     sigmahatM <- matrix(NA, d, d)
     sigmahatM.pre <- matrix(NA, d, d)
     if (d == 1) {
     for (k in 1:M) {
     sigmahatm[1, , k] <- var(subchain[1, , k])
     }
     }
     else {
     for (k in 1:M) {
     sigmahatm[, , k] <- cov(t(subchain[, , k]))
     }
     }
     sigmahatm.inverse <- array(NA, dim = c(d, d, M))
     for (k in 1:M) {
     res <- try(sigmahatm.inverse[, , k] <- solve(sigmahatm[,
     , k]), silent = TRUE)
     if (class(res) == "try-error") {
     stop(paste("Computation of the inverse of a covariance matrix for",
     "one of the sample vectors in the data subset #",
     k, "is failed.", "Here is the system R error-message:\n",
     attr(res, "condition")))
     }
     }
     sigmahatM <- solve(rowSums(sigmahatm.inverse, dims = 2))
     theta <- matrix(NA, nrow = d, ncol = sampletotT)
     wvec <- array(NA, c(d, 1))
     for (i in 1:sampletotT) {
     wvec <- rep(0, d)
     for (s in 1:M) {
     wvec <- wvec + sigmahatm.inverse[, , s] %*% subchain[,
     i, s]
     }
     theta[, i] <- sigmahatM %*% wvec
     }
     return(theta)
    }
    <bytecode: 0x39bc860>
    <environment: namespace:parallelMCMCcombine>
     --- function search by body ---
    Function consensusMCcov in namespace parallelMCMCcombine has this body.
     ----------- END OF FAILURE REPORT --------------
    Error in if (class(res) == "try-error") { : the condition has length > 1
    Calls: consensusMCcov
    Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc