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CHECK report for spotSegmentation on tokay2

This page was generated on 2020-10-17 11:57:51 -0400 (Sat, 17 Oct 2020).

TO THE DEVELOPERS/MAINTAINERS OF THE spotSegmentation PACKAGE: Please make sure to use the following settings in order to reproduce any error or warning you see on this page.
Package 1722/1905HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
spotSegmentation 1.62.0
Chris Fraley
Snapshot Date: 2020-10-16 14:40:19 -0400 (Fri, 16 Oct 2020)
URL: https://git.bioconductor.org/packages/spotSegmentation
Branch: RELEASE_3_11
Last Commit: 317a05d
Last Changed Date: 2020-04-27 14:10:59 -0400 (Mon, 27 Apr 2020)
malbec2 Linux (Ubuntu 18.04.4 LTS) / x86_64  OK  OK  ERROR 
tokay2 Windows Server 2012 R2 Standard / x64  OK  OK [ ERROR ] NA 
machv2 macOS 10.14.6 Mojave / x86_64  OK  OK  ERROR  OK 

Summary

Package: spotSegmentation
Version: 1.62.0
Command: C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:spotSegmentation.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings spotSegmentation_1.62.0.tar.gz
StartedAt: 2020-10-17 08:33:26 -0400 (Sat, 17 Oct 2020)
EndedAt: 2020-10-17 08:33:59 -0400 (Sat, 17 Oct 2020)
EllapsedTime: 32.8 seconds
RetCode: 1
Status:  ERROR  
CheckDir: spotSegmentation.Rcheck
Warnings: NA

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:spotSegmentation.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings spotSegmentation_1.62.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/spotSegmentation.Rcheck'
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* using option '--no-vignettes'
* checking for file 'spotSegmentation/DESCRIPTION' ... OK
* this is package 'spotSegmentation' version '1.62.0'
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking whether package 'spotSegmentation' can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* loading checks for arch 'i386'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* loading checks for arch 'x64'
** checking whether the package can be loaded ... OK
** checking whether the package can be loaded with stated dependencies ... OK
** checking whether the package can be unloaded cleanly ... OK
** checking whether the namespace can be loaded with stated dependencies ... OK
** checking whether the namespace can be unloaded cleanly ... OK
* checking dependencies in R code ... NOTE
Package in Depends field not imported from: 'mclust'
  These packages need to be imported from (in the NAMESPACE file)
  for when this namespace is loaded but not attached.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
plot.spotseg : plotSpotImage: no visible global function definition for
  'par'
plot.spotseg : plotSpotImage: no visible global function definition for
  'image'
plot.spotseg: no visible global function definition for 'postscript'
plotBlockImage: no visible global function definition for 'par'
plotBlockImage: no visible global function definition for 'image'
plotBlockImage: no visible global function definition for 'gray'
spotgrid : spotgridPeaks: no visible global function definition for
  'runif'
spotgrid : spotgridPeaks: no visible global function definition for
  'embed'
spotgrid: no visible global function definition for 'contour'
spotseg : spotseg1 : plotSpotImage: no visible global function
  definition for 'par'
spotseg : spotseg1 : plotSpotImage: no visible global function
  definition for 'image'
spotseg : spotseg1: no visible global function definition for
  'mclustBIC'
spotseg : spotseg1: no visible global function definition for 'hcE'
spotseg : spotseg1: no visible global function definition for 'frame'
spotseg: no visible global function definition for 'par'
spotseg: no visible global function definition for 'median'
Undefined global functions or variables:
  contour embed frame gray hcE image mclustBIC median par postscript
  runif
Consider adding
  importFrom("grDevices", "gray", "postscript")
  importFrom("graphics", "contour", "frame", "image", "par")
  importFrom("stats", "embed", "median", "runif")
to your NAMESPACE file.
* checking Rd files ... NOTE
prepare_Rd: plot.spotseg.Rd:23: Dropping empty section \author
prepare_Rd: plotBlockImage.Rd:20: Dropping empty section \author
prepare_Rd: spotgrid.Rd:21: Dropping empty section \details
prepare_Rd: spotgrid.Rd:33: Dropping empty section \note
prepare_Rd: spotgrid.Rd:34: Dropping empty section \author
prepare_Rd: spotseg.Rd:45: Dropping empty section \author
prepare_Rd: summary.spotseg.Rd:27: Dropping empty section \author
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of 'data' directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in 'vignettes' ... WARNING
Files in the 'vignettes' directory but no files in 'inst/doc':
  'spotsegdoc.pdf'
Package has no Sweave vignette sources and no VignetteBuilder field.
* checking examples ...
** running examples for arch 'i386' ... ERROR
Running examples in 'spotSegmentation-Ex.R' failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.spotseg
> ### Title: Microarray Spot Segmentation Plot
> ### Aliases: plot.spotseg
> ### Keywords: methods
> 
> ### ** Examples
> 
> data(spotSegTest)
> 
> # columns of spotSegTest:
> #  1 intensities from the Cy3 (green) channel
> #  2 intensities from the Cy5 (red) channel
> 
> dataTransformation <- function(x) (256*256-1-x)^2*4.71542407E-05 
> 
> chan1 <- matrix(dataTransformation(spotSegTest[,1]), 144, 199)
> chan2 <- matrix(dataTransformation(spotSegTest[,2]), 144, 199)
> 
> hivGrid <- spotgrid( chan1, chan2, rows = 4, cols = 6, show = TRUE)
 ----------- FAILURE REPORT -------------- 
 --- failure: length > 1 in coercion to logical ---
 --- srcref --- 
: 
 --- package (from environment) --- 
spotSegmentation
 --- call from context --- 
spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
 --- call from argument --- 
show && !is.na(rowcut)
 --- R stacktrace ---
where 1: spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)

 --- value of length: 5 type: logical ---
[1] TRUE TRUE TRUE TRUE TRUE
 --- function from context --- 
function (chan1, chan2, rows = NULL, cols = NULL, span = NULL, 
    show = FALSE) 
{
    signal <- chan1 + chan2
    s <- min(signal)
    r <- min(signal[signal > 0])
    if (s <= 0) {
        signal <- signal - s + 1
    }
    else if (r < 1) {
        signal <- signal + 1
    }
    signal <- log(signal)
    spotgridPeaks <- function(series, span = 3, seed = 0) {
        if (!(span%%2)) 
            span <- span + 1
        d <- sort(diff(sort(series)))
        if (!d[1]) {
            d <- d[as.logical(d)][1]
            set.seed(seed)
            noise <- runif(length(series), min = -d/2, max = d/2)
            series <- series + noise
        }
        zmaxcol <- apply(embed(series, span)[, span:1], 1, function(x) {
            if (length(y <- seq(along = x)[x == max(x)]) == 1) 
                y
            else 0
        })
        halfspan <- (span - 1)/2
        c(rep(FALSE, halfspan), zmaxcol == 1 + halfspan)
    }
    isum <- colSums(signal)
    gridcomp <- function(isum, nspots, span) {
        N <- length(isum)
        if (!(span%%2)) 
            span <- span + 1
        Peaks <- spotgridPeaks(isum, span)
        Vales <- spotgridPeaks(-isum, span)
        iPeaks <- (1:N)[Peaks]
        iVales <- (1:N)[Vales]
        nPeaks <- length(iPeaks)
        nVales <- length(iVales)
        if (FALSE) {
            plot(1:N, isum, type = "l", xlab = "", ylab = "")
            points(iPeaks, isum[iPeaks], pch = "P")
            points(iVales, isum[iVales], pch = "V")
        }
        V <- rep(0, nPeaks - 1)
        for (i in 2:nPeaks) {
            K <- iPeaks[i - 1]:iPeaks[i]
            I <- isum[K]
            J <- K[I == min(I)]
            if (length(J) > 1) {
                M <- match(J, iVales, nomatch = 0)
                J <- if (any(M)) 
                  (iVales[M])[1]
                else J[1]
            }
            V[i - 1] <- J
        }
        P <- rep(0, nVales - 1)
        for (i in 2:nVales) {
            K <- iVales[i - 1]:iVales[i]
            I <- isum[K]
            J <- K[I == max(I)]
            if (length(J) > 1) {
                M <- match(J, iPeaks, nomatch = 0)
                J <- if (any(M)) 
                  (iPeaks[M])[1]
                else J[1]
            }
            P[i - 1] <- J
        }
        iPeaks <- unique(sort(c(iPeaks, P)))
        iVales <- unique(sort(c(iVales, V)))
        nPeaks <- length(iPeaks)
        nVales <- length(iVales)
        if (nPeaks < nspots) {
            nspots <- nPeaks
        }
        peakvals <- rep(0, nPeaks)
        for (i in 1:nPeaks) {
            m <- iPeaks[i]
            J <- iVales[iVales < m]
            K <- iVales[iVales > m]
            lSum <- rSum <- 0
            d <- 0
            if (length(J)) {
                lSum <- isum[J[length(J)]]
                d <- d + 1
            }
            if (length(K)) {
                rSum <- isum[K[1]]
                d <- d + 1
            }
            peakvals[i] <- isum[m] - (lSum + rSum)/d
        }
        i <- 0
        smax <- sum(sort(peakvals))
        k <- nPeaks - nspots
        if (k) {
            smax <- s <- smax - sum(peakvals[-(1:nspots)])
            for (j in 1:k) {
                d <- (peakvals[j + nspots] - peakvals[j])
                s <- s + d
                if (s > smax) {
                  smax <- s
                  i <- j
                }
            }
        }
        span <- max(span, max(diff(iPeaks[i + 1:nspots])))
        j <- 1
        while (TRUE) {
            if (iVales[j] > iPeaks[i + 1] && iVales[j] < iPeaks[i + 
                2]) 
                break
            j <- j + 1
        }
        index <- rep(0, nspots + 1)
        index[2:nspots] <- iVales[j:(j + nspots - 2)]
        halfspan <- floor(span/2)
        index[1] <- iPeaks[i + 1] - halfspan
        index[nspots + 1] <- iPeaks[nspots + i] + halfspan
        if (FALSE) {
            par(ask = T)
            plot(1:N, isum, type = "l", xlab = "", ylab = "")
            points(iPeaks, isum[iPeaks], pch = "P")
            points(iVales, isum[iVales], pch = "V")
            abline(v = index[1], col = "red")
            abline(v = index[length(index)], col = "red")
            for (i in 1:length(index)) {
                abline(v = index[i], col = "red")
            }
        }
        index
    }
    if (show) {
        plotBlockImage(signal)
    }
    rowcut <- colcut <- NA
    if (!is.null(rows) && rows > 0) {
        if (is.null(span)) {
            span <- floor(nrow(signal)/rows)
            if (!(span%%2)) 
                span <- span - 1
        }
        rowcut <- gridcomp(rowSums(signal), rows, if (length(span) == 
            2) 
            span[1]
        else span)
        if (length(rowcut) < rows + 1) {
            span <- min(diff(rowcut))
            rowcut <- gridcomp(rowSums(signal), rows, span)
        }
        if (length(rowcut) < rows + 1) 
            warning("fewer peaks than spots")
        if (show && (is.null(cols) || cols <= 0)) {
            chan1[] <- 0
            chan1[rowcut, ] <- 1
            contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1, 
                levels = 1, drawlabels = FALSE, col = "red", 
                add = TRUE)
        }
    }
    if (!is.null(cols) && cols > 0) {
        if (is.null(span)) {
            span <- floor(ncol(signal)/cols)
            if (!(span%%2)) 
                span <- span - 1
        }
        colcut <- gridcomp(colSums(signal), cols, if (length(span) == 
            2) 
            span[2]
        else span)
        if (length(colcut) < cols + 1) {
            span <- min(diff(colcut))
            colcut <- gridcomp(colSums(signal), cols, span)
        }
        if (length(colcut) < cols + 1) 
            warning("fewer peaks than spots")
        if (show && (is.null(rows) || rows <= 0)) {
            chan1[] <- 0
            chan1[, colcut] <- 1
            contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1, 
                levels = 1, drawlabels = FALSE, col = "red", 
                add = TRUE)
        }
    }
    if (show && !is.na(rowcut) && !is.na(colcut)) {
        chan1[] <- 0
        chan1[rowcut, colcut[1]:colcut[length(colcut)]] <- 1
        chan1[rowcut[1]:rowcut[length(rowcut)], colcut] <- 1
        contour(z = t(chan1[nrow(chan1):1, ]), nlevels = 1, levels = 1, 
            drawlabels = FALSE, col = "red", add = TRUE)
    }
    list(rowcut = rowcut, colcut = colcut)
}
<bytecode: 0x036560d8>
<environment: namespace:spotSegmentation>
 --- function search by body ---
Function spotgrid in namespace spotSegmentation has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: length > 1 in coercion to logical

** running examples for arch 'x64' ... ERROR
Running examples in 'spotSegmentation-Ex.R' failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: plot.spotseg
> ### Title: Microarray Spot Segmentation Plot
> ### Aliases: plot.spotseg
> ### Keywords: methods
> 
> ### ** Examples
> 
> data(spotSegTest)
> 
> # columns of spotSegTest:
> #  1 intensities from the Cy3 (green) channel
> #  2 intensities from the Cy5 (red) channel
> 
> dataTransformation <- function(x) (256*256-1-x)^2*4.71542407E-05 
> 
> chan1 <- matrix(dataTransformation(spotSegTest[,1]), 144, 199)
> chan2 <- matrix(dataTransformation(spotSegTest[,2]), 144, 199)
> 
> hivGrid <- spotgrid( chan1, chan2, rows = 4, cols = 6, show = TRUE)
 ----------- FAILURE REPORT -------------- 
 --- failure: length > 1 in coercion to logical ---
 --- srcref --- 
: 
 --- package (from environment) --- 
spotSegmentation
 --- call from context --- 
spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)
 --- call from argument --- 
show && !is.na(rowcut)
 --- R stacktrace ---
where 1: spotgrid(chan1, chan2, rows = 4, cols = 6, show = TRUE)

 --- value of length: 5 type: logical ---
[1] TRUE TRUE TRUE TRUE TRUE
 --- function from context --- 
function (chan1, chan2, rows = NULL, cols = NULL, span = NULL, 
    show = FALSE) 
{
    signal <- chan1 + chan2
    s <- min(signal)
    r <- min(signal[signal > 0])
    if (s <= 0) {
        signal <- signal - s + 1
    }
    else if (r < 1) {
        signal <- signal + 1
    }
    signal <- log(signal)
    spotgridPeaks <- function(series, span = 3, seed = 0) {
        if (!(span%%2)) 
            span <- span + 1
        d <- sort(diff(sort(series)))
        if (!d[1]) {
            d <- d[as.logical(d)][1]
            set.seed(seed)
            noise <- runif(length(series), min = -d/2, max = d/2)
            series <- series + noise
        }
        zmaxcol <- apply(embed(series, span)[, span:1], 1, function(x) {
            if (length(y <- seq(along = x)[x == max(x)]) == 1) 
                y
            else 0
        })
        halfspan <- (span - 1)/2
        c(rep(FALSE, halfspan), zmaxcol == 1 + halfspan)
    }
    isum <- colSums(signal)
    gridcomp <- function(isum, nspots, span) {
        N <- length(isum)
        if (!(span%%2)) 
            span <- span + 1
        Peaks <- spotgridPeaks(isum, span)
        Vales <- spotgridPeaks(-isum, span)
        iPeaks <- (1:N)[Peaks]
        iVales <- (1:N)[Vales]
        nPeaks <- length(iPeaks)
        nVales <- length(iVales)
        if (FALSE) {
            plot(1:N, isum, type = "l", xlab = "", ylab = "")
            points(iPeaks, isum[iPeaks], pch = "P")
            points(iVales, isum[iVales], pch = "V")
        }
        V <- rep(0, nPeaks - 1)
        for (i in 2:nPeaks) {
            K <- iPeaks[i - 1]:iPeaks[i]
            I <- isum[K]
            J <- K[I == min(I)]
            if (length(J) > 1) {
                M <- match(J, iVales, nomatch = 0)
                J <- if (any(M)) 
                  (iVales[M])[1]
                else J[1]
            }
            V[i - 1] <- J
        }
        P <- rep(0, nVales - 1)
        for (i in 2:nVales) {
            K <- iVales[i - 1]:iVales[i]
            I <- isum[K]
            J <- K[I == max(I)]
            if (length(J) > 1) {
                M <- match(J, iPeaks, nomatch = 0)
                J <- if (any(M)) 
                  (iPeaks[M])[1]
                else J[1]
            }
            P[i - 1] <- J
        }
        iPeaks <- unique(sort(c(iPeaks, P)))
        iVales <- unique(sort(c(iVales, V)))
        nPeaks <- length(iPeaks)
        nVales <- length(iVales)
        if (nPeaks < nspots) {
            nspots <- nPeaks
        }
        peakvals <- rep(0, nPeaks)
        for (i in 1:nPeaks) {
            m <- iPeaks[i]
            J <- iVales[iVales < m]
            K <- iVales[iVales > m]
            lSum <- rSum <- 0
            d <- 0
            if (length(J)) {
                lSum <- isum[J[length(J)]]
                d <- d + 1
            }
            if (length(K)) {
                rSum <- isum[K[1]]
                d <- d + 1
            }
            peakvals[i] <- isum[m] - (lSum + rSum)/d
        }
        i <- 0
        smax <- sum(sort(peakvals))
        k <- nPeaks - nspots
        if (k) {
            smax <- s <- smax - sum(peakvals[-(1:nspots)])
            for (j in 1:k) {
                d <- (peakvals[j + nspots] - peakvals[j])
                s <- s + d
                if (s > smax) {
                  smax <- s
                  i <- j
                }
            }
        }
        span <- max(span, max(diff(iPeaks[i + 1:nspots])))
        j <- 1
        while (TRUE) {
            if (iVales[j] > iPeaks[i + 1] && iVales[j] < iPeaks[i + 
                2]) 
                break
            j <- j + 1
        }
        index <- rep(0, nspots + 1)
        index[2:nspots] <- iVales[j:(j + nspots - 2)]
        halfspan <- floor(span/2)
        index[1] <- iPeaks[i + 1] - halfspan
        index[nspots + 1] <- iPeaks[nspots + i] + halfspan
        if (FALSE) {
            par(ask = T)
            plot(1:N, isum, type = "l", xlab = "", ylab = "")
            points(iPeaks, isum[iPeaks], pch = "P")
            points(iVales, isum[iVales], pch = "V")
            abline(v = index[1], col = "red")
            abline(v = index[length(index)], col = "red")
            for (i in 1:length(index)) {
                abline(v = index[i], col = "red")
            }
        }
        index
    }
    if (show) {
        plotBlockImage(signal)
    }
    rowcut <- colcut <- NA
    if (!is.null(rows) && rows > 0) {
        if (is.null(span)) {
            span <- floor(nrow(signal)/rows)
            if (!(span%%2)) 
                span <- span - 1
        }
        rowcut <- gridcomp(rowSums(signal), rows, if (length(span) == 
            2) 
            span[1]
        else span)
        if (length(rowcut) < rows + 1) {
            span <- min(diff(rowcut))
            rowcut <- gridcomp(rowSums(signal), rows, span)
        }
        if (length(rowcut) < rows + 1) 
            warning("fewer peaks than spots")
        if (show && (is.null(cols) || cols <= 0)) {
            chan1[] <- 0
            chan1[rowcut, ] <- 1
            contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1, 
                levels = 1, drawlabels = FALSE, col = "red", 
                add = TRUE)
        }
    }
    if (!is.null(cols) && cols > 0) {
        if (is.null(span)) {
            span <- floor(ncol(signal)/cols)
            if (!(span%%2)) 
                span <- span - 1
        }
        colcut <- gridcomp(colSums(signal), cols, if (length(span) == 
            2) 
            span[2]
        else span)
        if (length(colcut) < cols + 1) {
            span <- min(diff(colcut))
            colcut <- gridcomp(colSums(signal), cols, span)
        }
        if (length(colcut) < cols + 1) 
            warning("fewer peaks than spots")
        if (show && (is.null(rows) || rows <= 0)) {
            chan1[] <- 0
            chan1[, colcut] <- 1
            contour(z = t(chan1[nrow(signal):1, ]), nlevels = 1, 
                levels = 1, drawlabels = FALSE, col = "red", 
                add = TRUE)
        }
    }
    if (show && !is.na(rowcut) && !is.na(colcut)) {
        chan1[] <- 0
        chan1[rowcut, colcut[1]:colcut[length(colcut)]] <- 1
        chan1[rowcut[1]:rowcut[length(rowcut)], colcut] <- 1
        contour(z = t(chan1[nrow(chan1):1, ]), nlevels = 1, levels = 1, 
            drawlabels = FALSE, col = "red", add = TRUE)
    }
    list(rowcut = rowcut, colcut = colcut)
}
<bytecode: 0x00000000075e4688>
<environment: namespace:spotSegmentation>
 --- function search by body ---
Function spotgrid in namespace spotSegmentation has this body.
 ----------- END OF FAILURE REPORT -------------- 
Fatal error: length > 1 in coercion to logical

* checking PDF version of manual ... OK
* DONE

Status: 2 ERRORs, 1 WARNING, 3 NOTEs
See
  'C:/Users/biocbuild/bbs-3.11-bioc/meat/spotSegmentation.Rcheck/00check.log'
for details.


Installation output

spotSegmentation.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.11/bioc/src/contrib/spotSegmentation_1.62.0.tar.gz && rm -rf spotSegmentation.buildbin-libdir && mkdir spotSegmentation.buildbin-libdir && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=spotSegmentation.buildbin-libdir spotSegmentation_1.62.0.tar.gz && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL spotSegmentation_1.62.0.zip && rm spotSegmentation_1.62.0.tar.gz spotSegmentation_1.62.0.zip
###
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  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100  539k  100  539k    0     0  2531k      0 --:--:-- --:--:-- --:--:-- 2605k

install for i386

* installing *source* package 'spotSegmentation' ...
** using staged installation
** R
** data
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
  converting help for package 'spotSegmentation'
    finding HTML links ... done
    plot.spotseg                            html  
    plotBlockImage                          html  
    spotSegTest                             html  
    spotgrid                                html  
    spotseg                                 html  
    summary.spotseg                         html  
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path

install for x64

* installing *source* package 'spotSegmentation' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'spotSegmentation' as spotSegmentation_1.62.0.zip
* DONE (spotSegmentation)
* installing to library 'C:/Users/biocbuild/bbs-3.11-bioc/R/library'
package 'spotSegmentation' successfully unpacked and MD5 sums checked

Tests output


Example timings

spotSegmentation.Rcheck/examples_i386/spotSegmentation-Ex.timings

nameusersystemelapsed

spotSegmentation.Rcheck/examples_x64/spotSegmentation-Ex.timings

nameusersystemelapsed