[bug#44033] [PATCH 1/3] gnu: Add r-decon.

[zimoun <zimon.toutoune@gmail.com>]

Dear,

Thank you for this contribution.


The file gnu/packages/cran.scm has changed between so your patches do
not apply anymore.  Could you rebase on the top?  Please.

(Note that using “git format-patch --base=<hash>” where <hash> is the
hash on which your patch is applying simplifies the process.  IMHO.)


On Fri, 16 Oct 2020 at 18:43, Aniket Patil <aniket112.patil@gmail.com> wrote:
> * gnu/packages/cran.scm (r-decon): New variable.
> ---
>  gnu/packages/cran.scm | 23 +++++++++++++++++++++++
>  1 file changed, 23 insertions(+)
>
> diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm
> index 59a409f8e9..0370cdd993 100644
> --- a/gnu/packages/cran.scm
> +++ b/gnu/packages/cran.scm
> @@ -24596,3 +24596,26 @@ enrichment analysis (GSEA) calculation with or without the absolute filtering.
>    Without filtering, users can perform (original) two-tailed or one-tailed
>  absolute GSEA.")
>      (license license:gpl2)))
> +
> +(define-public r-decon
> +  (package
> +    (name "r-decon")
> +    (version "1.2-4")
> +    (source
> +      (origin
> +        (method url-fetch)
> +        (uri (cran-uri "decon" version))
> +        (sha256
> +          (base32
> +            "1v4l0xq29rm8mks354g40g9jxn0didzlxg3g7z08m0gvj29zdj7s"))))

The indentation does not seem correct.  It should be:

        (sha256
         (base32
          "1v4l0xq29rm8mks354g40g9jxn0didzlxg3g7z08m0gvj29zdj7s"))))

> +    (properties `((upstream-name . "decon")))
> +    (build-system r-build-system)
> +    (native-inputs `(("gfortran" ,gfortran)))

Usually, we use:

    (native-inputs
     `(("gfortran" ,gfortran)))

instead on the in-line.

> +    (home-page
> +      "https://cran.r-project.org/web/packages/decon")
> +    (synopsis
> +      "Deconvolution Estimation in Measurement Error Models")

Usually, the synopsis is on the same line:

    (synopsis "Deconvolution Estimation in Measurement Error Models")


> +    (description
> +      "This package contains a collection of functions to deal with nonparametric measurement error problems using deconvolution kernel methods.  We focus two measurement error models in the package: (1) an additive measurement error model, where the goal is to estimate the density or distribution function from contaminated data; (2) nonparametric regression model with errors-in-variables.  The R functions allow the measurement errors to be either homoscedastic or heteroscedastic.  To make the deconvolution estimators computationally more efficient in R, we adapt the \"Fast Fourier Transform\" (FFT) algorithm for density estimation with error-free data to the deconvolution kernel estimation.  Several methods for the selection of the data-driven smoothing parameter are also provided in the package.  See details in: Wang, X.F.  and Wang, B. (2011).  Deconvolution estimation in measurement error models: The R package decon.  Journal of Statistical Software, 39(10), 1-24.")
> +    (license license:gpl3+)))
> +

The lines should not be longer than 80 characters.  Something like.

    (description
     "This package contains a collection of functions to deal with
     nonparametric measurement error problems using deconvolution
     kernel methods.  We focus two measurement error models in the
     package: (1) an additive measurement error model, where the goal
     is to estimate the density or distribution function from
     contaminated data; (2) nonparametric regression model with
     errors-in-variables.  The R functions allow the measurement errors
     to be either homoscedastic or heteroscedastic.  To make the
     deconvolution estimators computationally more efficient in R, we
     adapt the \"Fast Fourier Transform\" (FFT) algorithm for density
     estimation with error-free data to the deconvolution kernel
     estimation.  Several methods for the selection of the data-driven
     smoothing parameter are also provided in the package.  See details
     in: Wang, X.F.  and Wang, B. (2011).  Deconvolution estimation in
     measurement error models: The R package decon.  Journal of
     Statistical Software, 39(10), 1-24.")


To help you to spot, you can apply the linter with:

  ./pre-inst-env guix lint r-deconv

and then address the warnings, if any.


Could you send a v2?

All the best,
simon