CODEWITHSUNDEEP
rdevtoolscran
nya
nya

Reputation: 2250

ERROR: unable to collate and parse R files for package - upload to CRAN

Users encounter the same error message while installing packages in R (here or here), and the solutions seem to be to switch site mirror or wait for the maintainers to fix the bugs. However, I have the problem with uploading my package to CRAN, and the package created on a Mac does not pass checks on Windows and the developers version of R.

The problematic upload to CRAN is the sixth version of the package and the first time this error appeared.

* installing *source* package 'packagename' ...
** using staged installation
** R
Error in parse(outFile) : 
  d:/temp/RtmpGW8fFv/R.INSTALL129d81ef3788f/packagename/R/functionname.R:1:1: unexpected '<'
1: <
    ^
ERROR: unable to collate and parse R files for package 'packagename'
* removing 'd:/RCompile/CRANguest/R-devel/lib/packagename'

I tried displaying invisible characters and there is no "<" anywhere, where it is not supposed to be. Next, I normalized line endings, manually deleted characters at the beginnings of lines and nothing helped.

Does anyone know how to fix the above error from the maintainer's perspective?

Edit: Relevant part of the DESCRIPTION file:

Depends: R (>= 3.4.0),
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: limSolve,
    quadprog,
    stats,
    graphics,
    grDevices
RoxygenNote: 6.1.1
Suggests: testthat
BuildVignettes: true

The function uses base commands and limSolve::linp.

#' Fuzzy Linear Regression using the Fuzzy Least Absolute Residual Method
#'
#' The function calculates fuzzy regression coeficients using the fuzzy least absolute
#' residual (FLAR) method proposed by Zeng et al. (2017) 
#' for non-symmetric triangular fuzzy numbers.
#' @param x matrix with the second to last columns representing independent variable
#'    observations. The first column is related to the intercept, so it consists of ones.
#'    Missing values not allowed.
#' @param y matrix of dependent variable observations. The first column contains the 
#'    central tendency, the second column the left spread and the third column the right
#'    spread of non-symmetric triangular fuzzy numbers. Missing values not allowed.
#' @details The FLAR method expects real value input for the explanatory variables, and 
#'    non-symmetric triangular fuzzy numbers for the response variable. The prediction 
#'    returns non-symmetric triangular fuzzy numbers.
#' @note Preferred use is through the \code{\link{fuzzylm}} wrapper function with argument
#'    \code{method = "flar"}.
#' @inherit fuzzylm return
#' @inherit plrls seealso
#' @references Zeng, W., Feng, Q. and Li, J. (2017) Fuzzy least absolute linear regression. 
#'    \emph{Applied Soft Computing} 52: 1009-1019.
#' @keywords fuzzy
#' @export
#' @examples
#'    data(fuzzydat)
#'    fuzzylm(y ~ x, fuzzydat$dia, "flar", , , "yl", "yl")

flar <- function(x, y){
vars <- colnames(x)
n <- nrow(x)
p <- ncol(x)
X <- x

I <- diag(n)
Ir <- diag(p)
Z <- matrix(0, ncol = n, nrow = n)
ZX <- matrix(0, nrow = n, ncol = p)
ZXr <- matrix(0, nrow = p, ncol = p)
Zr <- matrix(0, nrow = p, ncol = n)

f <- c(rep(1, 6*n), rep(0, 3*p))

Req <- cbind(I, -I, Z, Z, Z, Z, X, ZX, ZX)
Req <- rbind(Req, cbind(Z, Z, I, -I, Z, Z, ZX, X, ZX))
Req <- rbind(Req, cbind(Z, Z, Z, Z, I, -I, ZX, ZX, X))

leq <- matrix(c(y))

R <- cbind(-I, Z, Z, Z, Z, Z, ZX, ZX, ZX)
R <- rbind(R, cbind(Z, -I, Z, Z, Z, Z, ZX, ZX, ZX))
R <- rbind(R, cbind(Z, Z, -I, Z, Z, Z, ZX, ZX, ZX))
R <- rbind(R, cbind(Z, Z, Z, -I, Z, Z, ZX, ZX, ZX))
R <- rbind(R, cbind(Z, Z, Z, Z, -I, Z, ZX, ZX, ZX))
R <- rbind(R, cbind(Z, Z, Z, Z, Z, -I, ZX, ZX, ZX))
R <- rbind(R, cbind(Zr, Zr, Zr, Zr, Zr, Zr, ZXr, -Ir, ZXr))
R <- rbind(R, cbind(Zr, Zr, Zr, Zr, Zr, Zr, ZXr, ZXr, -Ir))
R <- rbind(R, cbind(Z, Z, Z, Z, Z, Z, ZX, -X, ZX))
R <- rbind(R, cbind(Z, Z, Z, Z, Z, Z, ZX, ZX, -X))

l <- matrix(rep(0, 8*n + 2*p))

sorig <- limSolve::linp(E = Req, F = leq, G = -R, H = -l, Cost = f, ispos = FALSE)
s <- sorig$X

coefs <- matrix(c(s[(6*n+1):(6*n+p)],
        s[(6*n+p+1):(6*n+2*p)],
        s[(6*n+2*p+1):(6*n+3*p)]), ncol = 3, 
        dimnames = list(vars, c("center", "left.spread", "right.spread")))
lims <- t(apply(x, 2, range))
rownames(lims) <- vars
colnames(lims) <- c("min", "max")
fuzzy <- list(call = NULL, x = x, y = y, lims = lims,
    method = "fls", fuzzynum = "non-symmetric triangular", coef = coefs)
class(fuzzy) <- "fuzzylm"
fuzzy
}

Upvotes: 5

Views: 8577

Answers (1)

nya
nya

Reputation: 2250

It seems that I found the reason for the problem and the solution. The file with the problematic function was created on Windows (RStudio) and edited on a Mac (BBEdit). The Mac tried to keep the Windows line endings during edits, and upon returning the file to Windows, the encoding changed to a rare variation of UTF8. The R compilers on Windows could not process the exotic encoding.

The solution seems to be to consistently force all line endings to LF across platforms and to make sure the file encoding remains stable on different platforms.

Upvotes: 3

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