• using R Under development (unstable) (2024-12-05 r87423)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-14 (GCC) 14.2.0     GNU Fortran (GCC) 14.2.0
• running under: Fedora Linux 36 (Workstation Edition)
• using session charset: UTF-8
• using option ‘--no-stop-on-test-error’
• checking for file ‘nixmass/DESCRIPTION’ ... OK
• this is package ‘nixmass’ version ‘1.0.2’
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• checking whether package ‘nixmass’ can be installed ... OK See the install log for details.
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• checking Rd files ... NOTE checkRd: (-1) nixmass.Rd:25: Lost braces     25 | \code{nixmass}{ This function is a wrapper for the computation of SWE with different models. The process based model \code{\link[=swe.delta.snow]{delta.snow}} can be chosen, as well as different empirical regression models of \code{\link[=swe.jo09]{Jonas},\link[=swe.pi16]{Pistocchi}, \link[=swe.st10]{Sturm}} and \link[=swe.gu19]{Guyennon}}.        | ^ checkRd: (-1) swe.delta.snow.Rd:31-36: Lost braces     31 | \code{swe.delta.snow}{ computes SWE solely from daily changes of snow depth at an observation site. \cr        | ^ checkRd: (-1) swe.gu19.Rd:23-26: Lost braces     23 | \code{swe.gu19}{ Similar to the model of Pistocchi (2016), this function uses only the day-of-year (DOY) as parameterization for bulk snow density and hence SWE. In contrast to the latter, here, a quadratic term for DOY was added, to reflect non-linearity in the snow bulk density variability. The datums in the input data.frame are converted to DOY as days spent since November 1st. Regression coefficients depend on regions defined in Guyennon et al. (2019), which are \emph{italy} for the Italian Alps, \emph{southwest} for the South-western Italian Alps, \emph{central} for the Central Italian Alpes or \emph{southeast} for the South-western Italian Alps.        | ^ checkRd: (-1) swe.jo09.Rd:20: Lost braces     20 | \code{swe.jo09}{ This model parametrizes bulk snow density using snow depth, season (i.e. month), site altitude and site location. The location is implemented by a density offset according to the region in Switzerland, where the station belongs to. Non computable values are returned as NA.}        | ^ checkRd: (-1) swe.pi16.Rd:20: Lost braces     20 | \code{swe.pi16}{ This function uses only the day-of-year (DOY) as parameterization for bulk snow density and hence SWE. Here, the datums in the input data.frame are converted to DOY as defined in the original reference: negative values between 1.10. and 31.12. DOY=-92 at 1.10. In leap years 31.12. has DOY = 0, in non-leap years 31.12. has DOY = -1 with no day being 0. Non computable values are returned as NA.}        | ^ checkRd: (-1) swe.st10.Rd:18-19: Lost braces     18 | \code{swe.st10}{ This model converts snow depth to SWE using snow depth, day of year and station location (from which a climate class of snow can be inferred. The day of year (DOY) is the day-number of in the season 1.10. - 30.6. The 1.10. refers to DOY = -92. The 1.2. would be DOY = 32, while 15.11. would be DOY = -47. The \emph{snowclass.st10} must be one out of the character strings "alpine","maritime","prairie","tundra" and "taiga". For the Alps probably "alpine" would be the most appropriate climate classification.        | ^
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• checking examples ... [8s/12s] OK
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• DONE

Status: 1 NOTE