CRAN Package Check Results for Package MXM

Last updated on 2019-12-02 06:50:55 CET.

Check Details

Version: 1.4.4
Check: examples
Result: ERROR
    Running examples in 'MXM-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: BIC based forward selection
    > ### Title: Variable selection in regression models with forward selection
    > ### using BIC
    > ### Aliases: bic.fsreg
    > ### Keywords: Markov Blanket Variable Selection
    >
    > ### ** Examples
    >
    > set.seed(123)
    > dataset <- matrix( runif(500 * 20, 1, 100), ncol = 20 )
    > target <- 3 * dataset[, 10] + 2 * dataset[, 15] + 3 * dataset[, 20] + rnorm(500, 0, 5)
    >
    > a1 <- bic.fsreg(target, dataset, tol = 4, ncores = 1, test = "testIndReg" )
    > a3 <- MMPC(target, dataset, ncores = 1)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    MXM
     --- call from context ---
    MMPC(target, dataset, ncores = 1)
     --- call from argument ---
    if (class(dataset) == "matrix") {
     test <- "testIndFisher"
    } else if (class(dataset) == "data.frame") {
     if (length(Rfast::which.is(dataset)) > 0) {
     test <- "testIndReg"
     }
     else test <- "testIndFisher"
    }
     --- R stacktrace ---
    where 1: MMPC(target, dataset, ncores = 1)
    
     --- value of length: 2 type: logical ---
    [1] TRUE FALSE
     --- function from context ---
    function (target, dataset, max_k = 3, threshold = 0.05, test = NULL,
     ini = NULL, wei = NULL, user_test = NULL, hash = FALSE, hashObject = NULL,
     ncores = 1, backward = FALSE)
    {
     runtime <- proc.time()
     stat_hash <- NULL
     pvalue_hash <- NULL
     if (hash) {
     if (is.null(hashObject)) {
     stat_hash <- Rfast::Hash()
     pvalue_hash <- Rfast::Hash()
     }
     else if (class(hashObject) == "list") {
     stat_hash <- hashObject$stat_hash
     pvalue_hash <- hashObject$pvalue_hash
     }
     else stop("hashObject must be a list of two hash objects (stat_hash, pvalue_hash)")
     }
     if (!is.null(dataset)) {
     if (sum(class(target) == "matrix") == 1) {
     if (sum(class(target) == "Surv") == 1)
     stop("Invalid dataset class. For survival analysis provide a dataframe-class dataset")
     }
     }
     if (is.null(dataset) || is.null(target)) {
     stop("invalid dataset or target (class feature) arguments.")
     }
     else target <- target
     if (any(is.na(dataset))) {
     warning("The dataset contains missing values (NA) and they were replaced automatically by the variable (column) median (for numeric) or by the most frequent level (mode) if the variable is factor")
     if (is.matrix(dataset)) {
     dataset <- apply(dataset, 2, function(x) {
     x[which(is.na(x))] = median(x, na.rm = TRUE)
     return(x)
     })
     }
     else {
     poia <- unique(which(is.na(dataset), arr.ind = TRUE)[,
     2])
     for (i in poia) {
     xi <- dataset[, i]
     if (is.numeric(xi)) {
     xi[which(is.na(xi))] <- median(xi, na.rm = TRUE)
     }
     else if (is.factor(xi)) {
     xi[which(is.na(xi))] <- levels(xi)[which.max(as.vector(table(xi)))]
     }
     dataset[, i] <- xi
     }
     }
     }
     targetID <- -1
     if (is.character(target) & length(target) == 1) {
     findingTarget <- target == colnames(dataset)
     if (!sum(findingTarget) == 1) {
     warning("Target name not in colnames or it appears multiple times")
     return(NULL)
     }
     targetID <- which(findingTarget)
     target <- dataset[, targetID]
     }
     if (is.numeric(target) & length(target) == 1) {
     if (targetID > dim(dataset)[2]) {
     warning("Target index larger than the number of variables")
     return(NULL)
     }
     targetID <- target
     target <- dataset[, targetID]
     }
     if (sum(class(target) == "matrix") == 1) {
     if (ncol(target) >= 2 & class(target) != "Surv") {
     if ((is.null(test) || test == "auto") & (is.null(user_test))) {
     test <- "testIndMVreg"
     }
     }
     }
     la <- length(unique(as.numeric(target)))
     if (typeof(user_test) == "closure") {
     test <- user_test
     }
     else {
     if (is.null(test) || test == "auto") {
     if (la == 2)
     target <- as.factor(target)
     if (sum(class(target) == "matrix") == 1)
     test = "testIndMVreg"
     if ("factor" %in% class(target)) {
     if (is.ordered(target) & length(unique(target)) >
     2) {
     test <- "testIndOrdinal"
     }
     else if (!is.ordered(target) & length(unique(target)) >
     2) {
     test <- "testIndMultinom"
     }
     else test <- "testIndLogistic"
     }
     else if ((is.numeric(target) || is.integer(target)) &
     survival::is.Surv(target) == FALSE) {
     if (sum(floor(target) - target) == 0 & la > 2) {
     test <- "testIndQPois"
     }
     else {
     if (class(dataset) == "matrix") {
     test <- "testIndFisher"
     }
     else if (class(dataset) == "data.frame") {
     if (length(Rfast::which.is(dataset)) > 0) {
     test <- "testIndReg"
     }
     else test <- "testIndFisher"
     }
     }
     }
     else if (survival::is.Surv(target)) {
     test <- "censIndCR"
     }
     else stop("Target must be a factor, vector, matrix with at least 2 columns column or a Surv object")
     }
     av_tests <- c("testIndFisher", "testIndSpearman", "testIndReg",
     "testIndRQ", "testIndBeta", "censIndCR", "censIndWR",
     "censIndER", "censIndLLR", "testIndClogit", "testIndLogistic",
     "testIndPois", "testIndNB", "testIndBinom", "gSquare",
     "auto", "testIndZIP", "testIndMVreg", "testIndIGreg",
     "testIndGamma", "testIndNormLog", "testIndTobit",
     "testIndQPois", "testdIndQBinom", "testIndMMReg",
     "testIndMMFisher", "testIndMultinom", "testIndOrdinal",
     "testIndSPML", NULL)
     ci_test <- test
     if (length(test) == 1) {
     test <- match.arg(test, av_tests, TRUE)
     if (test == "testIndFisher") {
     test <- testIndFisher
     }
     else if (test == "testIndMMFisher") {
     test <- testIndMMFisher
     }
     else if (test == "testIndMMReg") {
     test <- testIndMMReg
     }
     else if (test == "testIndSpearman") {
     target <- rank(target)
     dataset <- Rfast::colRanks(dataset)
     test <- testIndSpearman
     }
     else if (test == "testIndReg") {
     test <- testIndReg
     }
     else if (test == "testIndMVreg") {
     if (min(target) > 0 & sd(Rfast::rowsums(target)) ==
     0)
     target = log(target[, -1]/target[, 1])
     test <- testIndMVreg
     }
     else if (test == "testIndBeta") {
     test <- testIndBeta
     }
     else if (test == "testIndRQ") {
     test <- testIndRQ
     }
     else if (test == "testIndIGreg") {
     test <- testIndIGreg
     }
     else if (test == "testIndPois") {
     test <- testIndPois
     }
     else if (test == "testIndNB") {
     test <- testIndNB
     }
     else if (test == "testIndGamma") {
     test <- testIndGamma
     }
     else if (test == "testIndNormLog") {
     test <- testIndNormLog
     }
     else if (test == "testIndZIP") {
     test <- testIndZIP
     }
     else if (test == "testIndTobit") {
     test <- testIndTobit
     }
     else if (test == "censIndCR") {
     test <- censIndCR
     }
     else if (test == "censIndWR") {
     test <- censIndWR
     }
     else if (test == "censIndER") {
     test <- censIndER
     }
     else if (test == "censIndLLR") {
     test <- censIndLLR
     }
     else if (test == "testIndClogit") {
     test <- testIndClogit
     }
     else if (test == "testIndBinom") {
     test <- testIndBinom
     }
     else if (test == "testIndLogistic") {
     test <- testIndLogistic
     }
     else if (test == "testIndMultinom") {
     test <- testIndMultinom
     }
     else if (test == "testIndOrdinal") {
     test <- testIndOrdinal
     }
     else if (test == "testIndQBinom") {
     test <- testIndQBinom
     }
     else if (test == "testIndQPois") {
     test <- testIndQPois
     }
     else if (test == "gSquare") {
     test <- gSquare
     }
     else if (test == "testIndSPML") {
     test <- testIndSPML
     if (!is.matrix(target))
     target <- cbind(cos(target), sin(target))
     }
     }
     else {
     stop("invalid test option")
     }
     }
     max_k <- floor(max_k)
     varsize <- ncol(dataset)
     if ((typeof(max_k) != "double") || max_k < 1)
     stop("invalid max_k option")
     if (max_k > varsize)
     max_k = varsize
     if ((typeof(threshold) != "double") || threshold < 0 || threshold >=
     1)
     stop("invalid threshold option")
     if (!is.null(user_test))
     ci_test <- "user_test"
     if (identical(ci_test, "testIndFisher")) {
     oop <- options(warn = -1)
     on.exit(options(oop))
     if (!is.matrix(dataset))
     dataset <- as.matrix(dataset)
     if (!is.null(hashObject) & length(hashObject) == 0)
     hashObject <- NULL
     if (targetID != -1) {
     a <- as.vector(cor(target, dataset))
     dof <- dim(dataset)[1] - 3
     wa <- 0.5 * log((1 + a)/(1 - a)) * sqrt(dof)
     id <- which(is.na(a))
     if (length(id) > 0)
     wa[id] <- 0
     wa[targetID] <- 0
     ini <- list()
     ini$stat <- wa
     ini$pvalue <- log(2) + pt(abs(wa), dof, lower.tail = FALSE,
     log.p = TRUE)
     }
     results <- Rfast2::mmpc(target, dataset, max_k = max_k,
     alpha = threshold, method = "pearson", ini = ini,
     hash = hash, hashobject = hashObject)
     results$selectedVarsOrder <- results$selected
     results$selectedVars <- sort(results$selected)
     results$selected <- NULL
     lista <- as.list.environment(results$hashobject$pvalue_hash)
     results$hashObject <- list()
     if (length(lista) > 0) {
     results$hashObject$stat_hash <- results$hashobject$stat_hash
     results$hashobject$stat_hash <- NULL
     results$hashObject$pvalue_hash <- results$hashobject$pvalue_hash
     results$hashobject$pvalue_hash <- NULL
     results$hashobject <- NULL
     }
     else {
     results$hashobject <- NULL
     results$hashObject$stat_hash <- NULL
     results$hashObject$pvalue_hash <- NULL
     }
     results$threshold <- results$alpha
     results$alpha <- NULL
     }
     else {
     results <- InternalMMPC(target, dataset, max_k, log(threshold),
     test, ini, wei, user_test, hash, varsize, stat_hash,
     pvalue_hash, targetID, ncores = ncores)
     }
     varsToIterate <- results$selectedVarsOrder
     if (backward & length(varsToIterate) > 0) {
     varsOrder <- results$selectedVarsOrder
     bc <- MXM::mmpcbackphase(target, dataset[, varsToIterate,
     drop = FALSE], test = test, wei = wei, max_k = max_k,
     threshold = threshold)
     met <- bc$met
     results$selectedVars <- varsOrder[met]
     results$selectedVarsOrder <- varsOrder[met]
     results$pvalues[varsToIterate] <- bc$pvalues
     results$n.tests <- results$n.tests + bc$counter
     }
     runtime <- proc.time() - runtime
     MMPCoutput <- new("MMPCoutput", selectedVars = results$selectedVars,
     selectedVarsOrder = results$selectedVarsOrder, hashObject = results$hashObject,
     pvalues = results$pvalues, stats = results$stats, univ = results$univ,
     max_k = results$max_k, threshold = results$threshold,
     n.tests = results$n.tests, runtime = runtime, test = ci_test)
     return(MMPCoutput)
    }
    <bytecode: 0x9f1f1c0>
    <environment: namespace:MXM>
     --- function search by body ---
    Function MMPC in namespace MXM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.4.4
Check: examples
Result: ERROR
    Running examples in ‘MXM-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: BIC based forward selection
    > ### Title: Variable selection in regression models with forward selection
    > ### using BIC
    > ### Aliases: bic.fsreg
    > ### Keywords: Markov Blanket Variable Selection
    >
    > ### ** Examples
    >
    > set.seed(123)
    > dataset <- matrix( runif(500 * 20, 1, 100), ncol = 20 )
    > target <- 3 * dataset[, 10] + 2 * dataset[, 15] + 3 * dataset[, 20] + rnorm(500, 0, 5)
    >
    > a1 <- bic.fsreg(target, dataset, tol = 4, ncores = 1, test = "testIndReg" )
    > a3 <- MMPC(target, dataset, ncores = 1)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    MXM
     --- call from context ---
    MMPC(target, dataset, ncores = 1)
     --- call from argument ---
    if (class(dataset) == "matrix") {
     test <- "testIndFisher"
    } else if (class(dataset) == "data.frame") {
     if (length(Rfast::which.is(dataset)) > 0) {
     test <- "testIndReg"
     }
     else test <- "testIndFisher"
    }
     --- R stacktrace ---
    where 1: MMPC(target, dataset, ncores = 1)
    
     --- value of length: 2 type: logical ---
    [1] TRUE FALSE
     --- function from context ---
    function (target, dataset, max_k = 3, threshold = 0.05, test = NULL,
     ini = NULL, wei = NULL, user_test = NULL, hash = FALSE, hashObject = NULL,
     ncores = 1, backward = FALSE)
    {
     runtime <- proc.time()
     stat_hash <- NULL
     pvalue_hash <- NULL
     if (hash) {
     if (is.null(hashObject)) {
     stat_hash <- Rfast::Hash()
     pvalue_hash <- Rfast::Hash()
     }
     else if (class(hashObject) == "list") {
     stat_hash <- hashObject$stat_hash
     pvalue_hash <- hashObject$pvalue_hash
     }
     else stop("hashObject must be a list of two hash objects (stat_hash, pvalue_hash)")
     }
     if (!is.null(dataset)) {
     if (sum(class(target) == "matrix") == 1) {
     if (sum(class(target) == "Surv") == 1)
     stop("Invalid dataset class. For survival analysis provide a dataframe-class dataset")
     }
     }
     if (is.null(dataset) || is.null(target)) {
     stop("invalid dataset or target (class feature) arguments.")
     }
     else target <- target
     if (any(is.na(dataset))) {
     warning("The dataset contains missing values (NA) and they were replaced automatically by the variable (column) median (for numeric) or by the most frequent level (mode) if the variable is factor")
     if (is.matrix(dataset)) {
     dataset <- apply(dataset, 2, function(x) {
     x[which(is.na(x))] = median(x, na.rm = TRUE)
     return(x)
     })
     }
     else {
     poia <- unique(which(is.na(dataset), arr.ind = TRUE)[,
     2])
     for (i in poia) {
     xi <- dataset[, i]
     if (is.numeric(xi)) {
     xi[which(is.na(xi))] <- median(xi, na.rm = TRUE)
     }
     else if (is.factor(xi)) {
     xi[which(is.na(xi))] <- levels(xi)[which.max(as.vector(table(xi)))]
     }
     dataset[, i] <- xi
     }
     }
     }
     targetID <- -1
     if (is.character(target) & length(target) == 1) {
     findingTarget <- target == colnames(dataset)
     if (!sum(findingTarget) == 1) {
     warning("Target name not in colnames or it appears multiple times")
     return(NULL)
     }
     targetID <- which(findingTarget)
     target <- dataset[, targetID]
     }
     if (is.numeric(target) & length(target) == 1) {
     if (targetID > dim(dataset)[2]) {
     warning("Target index larger than the number of variables")
     return(NULL)
     }
     targetID <- target
     target <- dataset[, targetID]
     }
     if (sum(class(target) == "matrix") == 1) {
     if (ncol(target) >= 2 & class(target) != "Surv") {
     if ((is.null(test) || test == "auto") & (is.null(user_test))) {
     test <- "testIndMVreg"
     }
     }
     }
     la <- length(unique(as.numeric(target)))
     if (typeof(user_test) == "closure") {
     test <- user_test
     }
     else {
     if (is.null(test) || test == "auto") {
     if (la == 2)
     target <- as.factor(target)
     if (sum(class(target) == "matrix") == 1)
     test = "testIndMVreg"
     if ("factor" %in% class(target)) {
     if (is.ordered(target) & length(unique(target)) >
     2) {
     test <- "testIndOrdinal"
     }
     else if (!is.ordered(target) & length(unique(target)) >
     2) {
     test <- "testIndMultinom"
     }
     else test <- "testIndLogistic"
     }
     else if ((is.numeric(target) || is.integer(target)) &
     survival::is.Surv(target) == FALSE) {
     if (sum(floor(target) - target) == 0 & la > 2) {
     test <- "testIndQPois"
     }
     else {
     if (class(dataset) == "matrix") {
     test <- "testIndFisher"
     }
     else if (class(dataset) == "data.frame") {
     if (length(Rfast::which.is(dataset)) > 0) {
     test <- "testIndReg"
     }
     else test <- "testIndFisher"
     }
     }
     }
     else if (survival::is.Surv(target)) {
     test <- "censIndCR"
     }
     else stop("Target must be a factor, vector, matrix with at least 2 columns column or a Surv object")
     }
     av_tests <- c("testIndFisher", "testIndSpearman", "testIndReg",
     "testIndRQ", "testIndBeta", "censIndCR", "censIndWR",
     "censIndER", "censIndLLR", "testIndClogit", "testIndLogistic",
     "testIndPois", "testIndNB", "testIndBinom", "gSquare",
     "auto", "testIndZIP", "testIndMVreg", "testIndIGreg",
     "testIndGamma", "testIndNormLog", "testIndTobit",
     "testIndQPois", "testdIndQBinom", "testIndMMReg",
     "testIndMMFisher", "testIndMultinom", "testIndOrdinal",
     "testIndSPML", NULL)
     ci_test <- test
     if (length(test) == 1) {
     test <- match.arg(test, av_tests, TRUE)
     if (test == "testIndFisher") {
     test <- testIndFisher
     }
     else if (test == "testIndMMFisher") {
     test <- testIndMMFisher
     }
     else if (test == "testIndMMReg") {
     test <- testIndMMReg
     }
     else if (test == "testIndSpearman") {
     target <- rank(target)
     dataset <- Rfast::colRanks(dataset)
     test <- testIndSpearman
     }
     else if (test == "testIndReg") {
     test <- testIndReg
     }
     else if (test == "testIndMVreg") {
     if (min(target) > 0 & sd(Rfast::rowsums(target)) ==
     0)
     target = log(target[, -1]/target[, 1])
     test <- testIndMVreg
     }
     else if (test == "testIndBeta") {
     test <- testIndBeta
     }
     else if (test == "testIndRQ") {
     test <- testIndRQ
     }
     else if (test == "testIndIGreg") {
     test <- testIndIGreg
     }
     else if (test == "testIndPois") {
     test <- testIndPois
     }
     else if (test == "testIndNB") {
     test <- testIndNB
     }
     else if (test == "testIndGamma") {
     test <- testIndGamma
     }
     else if (test == "testIndNormLog") {
     test <- testIndNormLog
     }
     else if (test == "testIndZIP") {
     test <- testIndZIP
     }
     else if (test == "testIndTobit") {
     test <- testIndTobit
     }
     else if (test == "censIndCR") {
     test <- censIndCR
     }
     else if (test == "censIndWR") {
     test <- censIndWR
     }
     else if (test == "censIndER") {
     test <- censIndER
     }
     else if (test == "censIndLLR") {
     test <- censIndLLR
     }
     else if (test == "testIndClogit") {
     test <- testIndClogit
     }
     else if (test == "testIndBinom") {
     test <- testIndBinom
     }
     else if (test == "testIndLogistic") {
     test <- testIndLogistic
     }
     else if (test == "testIndMultinom") {
     test <- testIndMultinom
     }
     else if (test == "testIndOrdinal") {
     test <- testIndOrdinal
     }
     else if (test == "testIndQBinom") {
     test <- testIndQBinom
     }
     else if (test == "testIndQPois") {
     test <- testIndQPois
     }
     else if (test == "gSquare") {
     test <- gSquare
     }
     else if (test == "testIndSPML") {
     test <- testIndSPML
     if (!is.matrix(target))
     target <- cbind(cos(target), sin(target))
     }
     }
     else {
     stop("invalid test option")
     }
     }
     max_k <- floor(max_k)
     varsize <- ncol(dataset)
     if ((typeof(max_k) != "double") || max_k < 1)
     stop("invalid max_k option")
     if (max_k > varsize)
     max_k = varsize
     if ((typeof(threshold) != "double") || threshold < 0 || threshold >=
     1)
     stop("invalid threshold option")
     if (!is.null(user_test))
     ci_test <- "user_test"
     if (identical(ci_test, "testIndFisher")) {
     oop <- options(warn = -1)
     on.exit(options(oop))
     if (!is.matrix(dataset))
     dataset <- as.matrix(dataset)
     if (!is.null(hashObject) & length(hashObject) == 0)
     hashObject <- NULL
     if (targetID != -1) {
     a <- as.vector(cor(target, dataset))
     dof <- dim(dataset)[1] - 3
     wa <- 0.5 * log((1 + a)/(1 - a)) * sqrt(dof)
     id <- which(is.na(a))
     if (length(id) > 0)
     wa[id] <- 0
     wa[targetID] <- 0
     ini <- list()
     ini$stat <- wa
     ini$pvalue <- log(2) + pt(abs(wa), dof, lower.tail = FALSE,
     log.p = TRUE)
     }
     results <- Rfast2::mmpc(target, dataset, max_k = max_k,
     alpha = threshold, method = "pearson", ini = ini,
     hash = hash, hashobject = hashObject)
     results$selectedVarsOrder <- results$selected
     results$selectedVars <- sort(results$selected)
     results$selected <- NULL
     lista <- as.list.environment(results$hashobject$pvalue_hash)
     results$hashObject <- list()
     if (length(lista) > 0) {
     results$hashObject$stat_hash <- results$hashobject$stat_hash
     results$hashobject$stat_hash <- NULL
     results$hashObject$pvalue_hash <- results$hashobject$pvalue_hash
     results$hashobject$pvalue_hash <- NULL
     results$hashobject <- NULL
     }
     else {
     results$hashobject <- NULL
     results$hashObject$stat_hash <- NULL
     results$hashObject$pvalue_hash <- NULL
     }
     results$threshold <- results$alpha
     results$alpha <- NULL
     }
     else {
     results <- InternalMMPC(target, dataset, max_k, log(threshold),
     test, ini, wei, user_test, hash, varsize, stat_hash,
     pvalue_hash, targetID, ncores = ncores)
     }
     varsToIterate <- results$selectedVarsOrder
     if (backward & length(varsToIterate) > 0) {
     varsOrder <- results$selectedVarsOrder
     bc <- MXM::mmpcbackphase(target, dataset[, varsToIterate,
     drop = FALSE], test = test, wei = wei, max_k = max_k,
     threshold = threshold)
     met <- bc$met
     results$selectedVars <- varsOrder[met]
     results$selectedVarsOrder <- varsOrder[met]
     results$pvalues[varsToIterate] <- bc$pvalues
     results$n.tests <- results$n.tests + bc$counter
     }
     runtime <- proc.time() - runtime
     MMPCoutput <- new("MMPCoutput", selectedVars = results$selectedVars,
     selectedVarsOrder = results$selectedVarsOrder, hashObject = results$hashObject,
     pvalues = results$pvalues, stats = results$stats, univ = results$univ,
     max_k = results$max_k, threshold = results$threshold,
     n.tests = results$n.tests, runtime = runtime, test = ci_test)
     return(MMPCoutput)
    }
    <bytecode: 0x5601f0b0b4e8>
    <environment: namespace:MXM>
     --- function search by body ---
    Function MMPC in namespace MXM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.4.4
Check: dependencies in R code
Result: NOTE
    Namespace in Imports field not imported from: ‘knitr’
     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-osx-x86_64, r-oldrel-osx-x86_64

Version: 1.4.4
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘FS_guide.ltx’ using tex
    Error: processing vignette 'FS_guide.ltx' failed with diagnostics:
    Running 'texi2dvi' on 'FS_guide.ltx' failed.
    BibTeX errors:
    The top-level auxiliary file: FS_guide.aux
    I couldn't open style file apa.bst
    ---line 80 of file FS_guide.aux
     : \bibstyle{apa
     : }
    I'm skipping whatever remains of this command
    I found no style file---while reading file FS_guide.aux
    --- failed re-building ‘FS_guide.ltx’
    
    --- re-building ‘article.ltx’ using tex
    --- finished re-building ‘article.ltx’
    
    --- re-building ‘guide.ltx’ using tex
    Error: processing vignette 'guide.ltx' failed with diagnostics:
    Running 'texi2dvi' on 'guide.ltx' failed.
    BibTeX errors:
    The top-level auxiliary file: guide.aux
    I couldn't open style file apa.bst
    ---line 52 of file guide.aux
     : \bibstyle{apa
     : }
    I'm skipping whatever remains of this command
    I found no style file---while reading file guide.aux
    --- failed re-building ‘guide.ltx’
    
    --- re-building ‘FBED_KVerrou_2_5.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘FBED_KVerrou_2_5.Rmd’
    
    --- re-building ‘MMPC_tutorial.Rmd’ using knitr
    --- finished re-building ‘MMPC_tutorial.Rmd’
    
    --- re-building ‘Networks_KVerrou.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘Networks_KVerrou.Rmd’
    
    --- re-building ‘SES_KMVerrou_11_12.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘SES_KMVerrou_11_12.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘FS_guide.ltx’ ‘guide.ltx’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc

Version: 1.4.4
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘FS_guide.ltx’ using tex
    Error: processing vignette 'FS_guide.ltx' failed with diagnostics:
    Running 'texi2dvi' on 'FS_guide.ltx' failed.
    BibTeX errors:
    The top-level auxiliary file: FS_guide.aux
    I couldn't open style file apa.bst
    ---line 80 of file FS_guide.aux
     : \bibstyle{apa
     : }
    I'm skipping whatever remains of this command
    I found no style file---while reading file FS_guide.aux
    --- failed re-building ‘FS_guide.ltx’
    
    --- re-building ‘article.ltx’ using tex
    --- finished re-building ‘article.ltx’
    
    --- re-building ‘guide.ltx’ using tex
    Error: processing vignette 'guide.ltx' failed with diagnostics:
    Running 'texi2dvi' on 'guide.ltx' failed.
    BibTeX errors:
    The top-level auxiliary file: guide.aux
    I couldn't open style file apa.bst
    ---line 52 of file guide.aux
     : \bibstyle{apa
     : }
    I'm skipping whatever remains of this command
    I found no style file---while reading file guide.aux
    --- failed re-building ‘guide.ltx’
    
    --- re-building ‘FBED_KVerrou_2_5.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘FBED_KVerrou_2_5.Rmd’
    
    --- re-building ‘MMPC_tutorial.Rmd’ using knitr
    --- finished re-building ‘MMPC_tutorial.Rmd’
    
    --- re-building ‘Networks_KVerrou.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘Networks_KVerrou.Rmd’
    
    --- re-building ‘SES_KMVerrou_11_12.Rmd’ using knitr
    
    Attaching package: 'dplyr'
    
    The following objects are masked from 'package:stats':
    
     filter, lag
    
    The following objects are masked from 'package:base':
    
     intersect, setdiff, setequal, union
    
    --- finished re-building ‘SES_KMVerrou_11_12.Rmd’
    
    SUMMARY: processing the following files failed:
     ‘FS_guide.ltx’ ‘guide.ltx’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-patched-solaris-x86