CRAN Package Check Results for Package TANDEM

Last updated on 2019-11-25 05:47:38 CET.

Check Details

Version: 1.0.2
Check: dependencies in R code
Result: NOTE
    Missing or unexported objects:
     'glmnet::coef.cv.glmnet' 'glmnet::predict.cv.glmnet'
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

Version: 1.0.2
Check: examples
Result: ERROR
    Running examples in 'TANDEM-Ex.R' failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: coef.tandem
    > ### Title: Returns the regression coefficients from a TANDEM fit
    > ### Aliases: coef.tandem
    >
    > ### ** Examples
    >
    > # unpack example data
    > x = example_data$x
    > y = example_data$y
    > upstream = example_data$upstream
    >
    > # fit a tandem model, determine the coefficients and create a prediction
    > fit = tandem(x, y, upstream, alpha=0.5)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    TANDEM
     --- call from context ---
    tandem(x, y, upstream, alpha = 0.5)
     --- call from argument ---
    if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer")) stop("x needs to be a numeric matrix")
     --- R stacktrace ---
    where 1: tandem(x, y, upstream, alpha = 0.5)
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (x, y, upstream, family = "gaussian", nfolds = 10, foldid = NULL,
     lambda_upstream = "lambda.1se", lambda_downstream = "lambda.1se",
     ...)
    {
     if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer"))
     stop("x needs to be a numeric matrix")
     if (!class(y) %in% c("numeric", "integer"))
     stop("y needs to be a numeric vector")
     if (class(upstream) != "logical")
     stop("upstream needs to a logical index vector, integer index vectors are currently not supported")
     if (nrow(x) != length(y))
     stop("Number of samples in x and y don't match")
     if (ncol(x) != length(upstream))
     stop("Number of features in x and upstream don't match")
     if (family != "gaussian")
     stop("Currently only the glmnet family='guassian' is supported")
     if (any(is.na(x)))
     stop("NAs in x are not allowed")
     if (any(is.na(y)))
     stop("NAs in y are not allowed")
     if (nrow(x) < nfolds)
     stop("nfolds should be smaller than the number of samples")
     if (!lambda_upstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_upstream should be either lambda.1se or lambda.min")
     if (!lambda_downstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_downstream should be either lambda.1se or lambda.min")
     if (is.null(foldid)) {
     n = length(y)
     foldid = ceiling(sample(1:n)/n * nfolds)
     }
     fit1 = glmnet::cv.glmnet(x[, upstream], y, foldid = foldid,
     ...)
     residuals = y - glmnet::predict.cv.glmnet(fit1, newx = x[,
     upstream], s = lambda_upstream)
     fit2 = glmnet::cv.glmnet(x[, !upstream], residuals, foldid = foldid,
     ...)
     beta0 = glmnet::coef.cv.glmnet(fit1, s = lambda_upstream)[1] +
     glmnet::coef.cv.glmnet(fit2, s = lambda_downstream)[1]
     beta = matrix(NA, ncol(x), 1)
     beta[upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit1,
     s = lambda_upstream)[-1, , drop = F])
     beta[!upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit2,
     s = lambda_downstream)[-1, , drop = F])
     rownames(beta) = colnames(x)
     beta = Matrix::Matrix(beta)
     fit = list(beta0 = beta0, beta = beta)
     class(fit) = "tandem"
     return(fit)
    }
    <bytecode: 0x194fe60>
    <environment: namespace:TANDEM>
     --- function search by body ---
    Function tandem in namespace TANDEM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building 'my-vignette.Rmd' using rmarkdown
    Loading required package: Matrix
    Loaded glmnet 3.0-1
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    TANDEM
     --- call from context ---
    tandem(x, y, upstream, alpha = 0.5)
     --- call from argument ---
    if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer")) stop("x needs to be a numeric matrix")
     --- R stacktrace ---
    where 1: tandem(x, y, upstream, alpha = 0.5)
    where 2: eval(expr, envir, enclos)
    where 3: eval(expr, envir, enclos)
    where 4: withVisible(eval(expr, envir, enclos))
    where 5: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
     error = eHandler, message = mHandler)
    where 6: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
     enclos)), warning = wHandler, error = eHandler, message = mHandler))
    where 7: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
    where 8: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
     debug = debug, last = i == length(out), use_try = stop_on_error !=
     2L, keep_warning = keep_warning, keep_message = keep_message,
     output_handler = output_handler, include_timing = include_timing)
    where 9: evaluate::evaluate(...)
    where 10: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
     keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
     options$include) 0L else 2L, output_handler = knit_handlers(options$render,
     options))
    where 11: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
     stop_on_error = if (options$error && options$include) 0L else 2L,
     output_handler = knit_handlers(options$render, options)))
    where 12: block_exec(params)
    where 13: call_block(x)
    where 14: process_group.block(group)
    where 15: process_group(group)
    where 16: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
     error = function(e) {
     setwd(wd)
     cat(res, sep = "\n", file = output %n% "")
     message("Quitting from lines ", paste(current_lines(i),
     collapse = "-"), " (", knit_concord$get("infile"),
     ") ")
     })
    where 17: process_file(text, output)
    where 18: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet,
     encoding = encoding)
    where 19: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
     ...)
    where 20: vweave_rmarkdown(...)
    where 21: engine$weave(file, quiet = quiet, encoding = enc)
    where 22: doTryCatch(return(expr), name, parentenv, handler)
    where 23: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 24: tryCatchList(expr, classes, parentenv, handlers)
    where 25: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 26: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-clang/Work/PKGS/TANDEM.Rcheck/vign_test/TANDEM",
     ser_elibs = "/tmp/RtmppXkB4c/file192325fff72.rds")
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (x, y, upstream, family = "gaussian", nfolds = 10, foldid = NULL,
     lambda_upstream = "lambda.1se", lambda_downstream = "lambda.1se",
     ...)
    {
     if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer"))
     stop("x needs to be a numeric matrix")
     if (!class(y) %in% c("numeric", "integer"))
     stop("y needs to be a numeric vector")
     if (class(upstream) != "logical")
     stop("upstream needs to a logical index vector, integer index vectors are currently not supported")
     if (nrow(x) != length(y))
     stop("Number of samples in x and y don't match")
     if (ncol(x) != length(upstream))
     stop("Number of features in x and upstream don't match")
     if (family != "gaussian")
     stop("Currently only the glmnet family='guassian' is supported")
     if (any(is.na(x)))
     stop("NAs in x are not allowed")
     if (any(is.na(y)))
     stop("NAs in y are not allowed")
     if (nrow(x) < nfolds)
     stop("nfolds should be smaller than the number of samples")
     if (!lambda_upstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_upstream should be either lambda.1se or lambda.min")
     if (!lambda_downstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_downstream should be either lambda.1se or lambda.min")
     if (is.null(foldid)) {
     n = length(y)
     foldid = ceiling(sample(1:n)/n * nfolds)
     }
     fit1 = glmnet::cv.glmnet(x[, upstream], y, foldid = foldid,
     ...)
     residuals = y - glmnet::predict.cv.glmnet(fit1, newx = x[,
     upstream], s = lambda_upstream)
     fit2 = glmnet::cv.glmnet(x[, !upstream], residuals, foldid = foldid,
     ...)
     beta0 = glmnet::coef.cv.glmnet(fit1, s = lambda_upstream)[1] +
     glmnet::coef.cv.glmnet(fit2, s = lambda_downstream)[1]
     beta = matrix(NA, ncol(x), 1)
     beta[upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit1,
     s = lambda_upstream)[-1, , drop = F])
     beta[!upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit2,
     s = lambda_downstream)[-1, , drop = F])
     rownames(beta) = colnames(x)
     beta = Matrix::Matrix(beta)
     fit = list(beta0 = beta0, beta = beta)
     class(fit) = "tandem"
     return(fit)
    }
    <bytecode: 0xd872770>
    <environment: namespace:TANDEM>
     --- function search by body ---
    Function tandem in namespace TANDEM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-clang

Version: 1.0.2
Check: examples
Result: ERROR
    Running examples in ‘TANDEM-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: coef.tandem
    > ### Title: Returns the regression coefficients from a TANDEM fit
    > ### Aliases: coef.tandem
    >
    > ### ** Examples
    >
    > # unpack example data
    > x = example_data$x
    > y = example_data$y
    > upstream = example_data$upstream
    >
    > # fit a tandem model, determine the coefficients and create a prediction
    > fit = tandem(x, y, upstream, alpha=0.5)
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    TANDEM
     --- call from context ---
    tandem(x, y, upstream, alpha = 0.5)
     --- call from argument ---
    if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer")) stop("x needs to be a numeric matrix")
     --- R stacktrace ---
    where 1: tandem(x, y, upstream, alpha = 0.5)
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (x, y, upstream, family = "gaussian", nfolds = 10, foldid = NULL,
     lambda_upstream = "lambda.1se", lambda_downstream = "lambda.1se",
     ...)
    {
     if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer"))
     stop("x needs to be a numeric matrix")
     if (!class(y) %in% c("numeric", "integer"))
     stop("y needs to be a numeric vector")
     if (class(upstream) != "logical")
     stop("upstream needs to a logical index vector, integer index vectors are currently not supported")
     if (nrow(x) != length(y))
     stop("Number of samples in x and y don't match")
     if (ncol(x) != length(upstream))
     stop("Number of features in x and upstream don't match")
     if (family != "gaussian")
     stop("Currently only the glmnet family='guassian' is supported")
     if (any(is.na(x)))
     stop("NAs in x are not allowed")
     if (any(is.na(y)))
     stop("NAs in y are not allowed")
     if (nrow(x) < nfolds)
     stop("nfolds should be smaller than the number of samples")
     if (!lambda_upstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_upstream should be either lambda.1se or lambda.min")
     if (!lambda_downstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_downstream should be either lambda.1se or lambda.min")
     if (is.null(foldid)) {
     n = length(y)
     foldid = ceiling(sample(1:n)/n * nfolds)
     }
     fit1 = glmnet::cv.glmnet(x[, upstream], y, foldid = foldid,
     ...)
     residuals = y - glmnet::predict.cv.glmnet(fit1, newx = x[,
     upstream], s = lambda_upstream)
     fit2 = glmnet::cv.glmnet(x[, !upstream], residuals, foldid = foldid,
     ...)
     beta0 = glmnet::coef.cv.glmnet(fit1, s = lambda_upstream)[1] +
     glmnet::coef.cv.glmnet(fit2, s = lambda_downstream)[1]
     beta = matrix(NA, ncol(x), 1)
     beta[upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit1,
     s = lambda_upstream)[-1, , drop = F])
     beta[!upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit2,
     s = lambda_downstream)[-1, , drop = F])
     rownames(beta) = colnames(x)
     beta = Matrix::Matrix(beta)
     fit = list(beta0 = beta0, beta = beta)
     class(fit) = "tandem"
     return(fit)
    }
    <bytecode: 0x5570124bed28>
    <environment: namespace:TANDEM>
     --- function search by body ---
    Function tandem in namespace TANDEM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘my-vignette.Rmd’ using rmarkdown
    Loading required package: Matrix
    Loaded glmnet 3.0-1
    
     ----------- FAILURE REPORT --------------
     --- failure: the condition has length > 1 ---
     --- srcref ---
    :
     --- package (from environment) ---
    TANDEM
     --- call from context ---
    tandem(x, y, upstream, alpha = 0.5)
     --- call from argument ---
    if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer")) stop("x needs to be a numeric matrix")
     --- R stacktrace ---
    where 1: tandem(x, y, upstream, alpha = 0.5)
    where 2: eval(expr, envir, enclos)
    where 3: eval(expr, envir, enclos)
    where 4: withVisible(eval(expr, envir, enclos))
    where 5: withCallingHandlers(withVisible(eval(expr, envir, enclos)), warning = wHandler,
     error = eHandler, message = mHandler)
    where 6: handle(ev <- withCallingHandlers(withVisible(eval(expr, envir,
     enclos)), warning = wHandler, error = eHandler, message = mHandler))
    where 7: timing_fn(handle(ev <- withCallingHandlers(withVisible(eval(expr,
     envir, enclos)), warning = wHandler, error = eHandler, message = mHandler)))
    where 8: evaluate_call(expr, parsed$src[[i]], envir = envir, enclos = enclos,
     debug = debug, last = i == length(out), use_try = stop_on_error !=
     2L, keep_warning = keep_warning, keep_message = keep_message,
     output_handler = output_handler, include_timing = include_timing)
    where 9: evaluate::evaluate(...)
    where 10: evaluate(code, envir = env, new_device = FALSE, keep_warning = !isFALSE(options$warning),
     keep_message = !isFALSE(options$message), stop_on_error = if (options$error &&
     options$include) 0L else 2L, output_handler = knit_handlers(options$render,
     options))
    where 11: in_dir(input_dir(), evaluate(code, envir = env, new_device = FALSE,
     keep_warning = !isFALSE(options$warning), keep_message = !isFALSE(options$message),
     stop_on_error = if (options$error && options$include) 0L else 2L,
     output_handler = knit_handlers(options$render, options)))
    where 12: block_exec(params)
    where 13: call_block(x)
    where 14: process_group.block(group)
    where 15: process_group(group)
    where 16: withCallingHandlers(if (tangle) process_tangle(group) else process_group(group),
     error = function(e) {
     setwd(wd)
     cat(res, sep = "\n", file = output %n% "")
     message("Quitting from lines ", paste(current_lines(i),
     collapse = "-"), " (", knit_concord$get("infile"),
     ") ")
     })
    where 17: process_file(text, output)
    where 18: knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet,
     encoding = encoding)
    where 19: rmarkdown::render(file, encoding = encoding, quiet = quiet, envir = globalenv(),
     ...)
    where 20: vweave_rmarkdown(...)
    where 21: engine$weave(file, quiet = quiet, encoding = enc)
    where 22: doTryCatch(return(expr), name, parentenv, handler)
    where 23: tryCatchOne(expr, names, parentenv, handlers[[1L]])
    where 24: tryCatchList(expr, classes, parentenv, handlers)
    where 25: tryCatch({
     engine$weave(file, quiet = quiet, encoding = enc)
     setwd(startdir)
     output <- find_vignette_product(name, by = "weave", engine = engine)
     if (!have.makefile && vignette_is_tex(output)) {
     texi2pdf(file = output, clean = FALSE, quiet = quiet)
     output <- find_vignette_product(name, by = "texi2pdf",
     engine = engine)
     }
     outputs <- c(outputs, output)
    }, error = function(e) {
     thisOK <<- FALSE
     fails <<- c(fails, file)
     message(gettextf("Error: processing vignette '%s' failed with diagnostics:\n%s",
     file, conditionMessage(e)))
    })
    where 26: tools:::buildVignettes(dir = "/home/hornik/tmp/R.check/r-devel-gcc/Work/PKGS/TANDEM.Rcheck/vign_test/TANDEM",
     ser_elibs = "/home/hornik/tmp/scratch/RtmpRCq3sT/file649e7c8f1412.rds")
    
     --- value of length: 2 type: logical ---
    [1] FALSE TRUE
     --- function from context ---
    function (x, y, upstream, family = "gaussian", nfolds = 10, foldid = NULL,
     lambda_upstream = "lambda.1se", lambda_downstream = "lambda.1se",
     ...)
    {
     if (class(x) != "matrix" | !class(x[1, 1]) %in% c("numeric",
     "integer"))
     stop("x needs to be a numeric matrix")
     if (!class(y) %in% c("numeric", "integer"))
     stop("y needs to be a numeric vector")
     if (class(upstream) != "logical")
     stop("upstream needs to a logical index vector, integer index vectors are currently not supported")
     if (nrow(x) != length(y))
     stop("Number of samples in x and y don't match")
     if (ncol(x) != length(upstream))
     stop("Number of features in x and upstream don't match")
     if (family != "gaussian")
     stop("Currently only the glmnet family='guassian' is supported")
     if (any(is.na(x)))
     stop("NAs in x are not allowed")
     if (any(is.na(y)))
     stop("NAs in y are not allowed")
     if (nrow(x) < nfolds)
     stop("nfolds should be smaller than the number of samples")
     if (!lambda_upstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_upstream should be either lambda.1se or lambda.min")
     if (!lambda_downstream %in% c("lambda.1se", "lambda.min"))
     stop("lambda_downstream should be either lambda.1se or lambda.min")
     if (is.null(foldid)) {
     n = length(y)
     foldid = ceiling(sample(1:n)/n * nfolds)
     }
     fit1 = glmnet::cv.glmnet(x[, upstream], y, foldid = foldid,
     ...)
     residuals = y - glmnet::predict.cv.glmnet(fit1, newx = x[,
     upstream], s = lambda_upstream)
     fit2 = glmnet::cv.glmnet(x[, !upstream], residuals, foldid = foldid,
     ...)
     beta0 = glmnet::coef.cv.glmnet(fit1, s = lambda_upstream)[1] +
     glmnet::coef.cv.glmnet(fit2, s = lambda_downstream)[1]
     beta = matrix(NA, ncol(x), 1)
     beta[upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit1,
     s = lambda_upstream)[-1, , drop = F])
     beta[!upstream, ] = as.matrix(glmnet::coef.cv.glmnet(fit2,
     s = lambda_downstream)[-1, , drop = F])
     rownames(beta) = colnames(x)
     beta = Matrix::Matrix(beta)
     fit = list(beta0 = beta0, beta = beta)
     class(fit) = "tandem"
     return(fit)
    }
    <bytecode: 0x55e690a8ac20>
    <environment: namespace:TANDEM>
     --- function search by body ---
    Function tandem in namespace TANDEM has this body.
     ----------- END OF FAILURE REPORT --------------
    Fatal error: the condition has length > 1
Flavor: r-devel-linux-x86_64-debian-gcc

Version: 1.0.2
Check: examples
Result: ERROR
    Running examples in ‘TANDEM-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: coef.tandem
    > ### Title: Returns the regression coefficients from a TANDEM fit
    > ### Aliases: coef.tandem
    >
    > ### ** Examples
    >
    > # unpack example data
    > x = example_data$x
    > y = example_data$y
    > upstream = example_data$upstream
    >
    > # fit a tandem model, determine the coefficients and create a prediction
    > fit = tandem(x, y, upstream, alpha=0.5)
    Error: 'predict.cv.glmnet' is not an exported object from 'namespace:glmnet'
    Execution halted
Flavors: 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-solaris-x86, r-release-windows-ix86+x86_64

Version: 1.0.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
    --- re-building ‘my-vignette.Rmd’ using rmarkdown
    Loading required package: Matrix
    Loaded glmnet 3.0-1
    
    Quitting from lines 27-31 (my-vignette.Rmd)
    Error: processing vignette 'my-vignette.Rmd' failed with diagnostics:
    'predict.cv.glmnet' is not an exported object from 'namespace:glmnet'
    --- failed re-building ‘my-vignette.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘my-vignette.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: 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-release-windows-ix86+x86_64

Version: 1.0.2
Check: examples
Result: ERROR
    Running examples in ‘TANDEM-Ex.R’ failed
    The error most likely occurred in:
    
    > base::assign(".ptime", proc.time(), pos = "CheckExEnv")
    > ### Name: coef.tandem
    > ### Title: Returns the regression coefficients from a TANDEM fit
    > ### Aliases: coef.tandem
    >
    > ### ** Examples
    >
    > # unpack example data
    > x = example_data$x
    > y = example_data$y
    > upstream = example_data$upstream
    >
    > # fit a tandem model, determine the coefficients and create a prediction
    > fit = tandem(x, y, upstream, alpha=0.5)
    Error: 'predict.cv.glmnet' is not an exported object from 'namespace:glmnet'
    Execution halted
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.0.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘my-vignette.Rmd’ using rmarkdown
    Loading required package: Matrix
    Loaded glmnet 3.0-1
    
    Quitting from lines 27-31 (my-vignette.Rmd)
    Error: processing vignette 'my-vignette.Rmd' failed with diagnostics:
    'predict.cv.glmnet' is not an exported object from 'namespace:glmnet'
    --- failed re-building ‘my-vignette.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘my-vignette.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavors: r-patched-linux-x86_64, r-release-linux-x86_64

Version: 1.0.2
Check: re-building of vignette outputs
Result: WARN
    Error(s) in re-building vignettes:
     ...
    --- re-building ‘my-vignette.Rmd’ using rmarkdown
    Warning in engine$weave(file, quiet = quiet, encoding = enc) :
     Pandoc (>= 1.12.3) and/or pandoc-citeproc not available. Falling back to R Markdown v1.
    Loading required package: Matrix
    Loaded glmnet 3.0-1
    
    Quitting from lines 27-31 (my-vignette.Rmd)
    Error: processing vignette 'my-vignette.Rmd' failed with diagnostics:
    'predict.cv.glmnet' is not an exported object from 'namespace:glmnet'
    --- failed re-building ‘my-vignette.Rmd’
    
    SUMMARY: processing the following file failed:
     ‘my-vignette.Rmd’
    
    Error: Vignette re-building failed.
    Execution halted
Flavor: r-patched-solaris-x86