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gnu: Fix whitespace issues in R package descriptions.

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This mainly addresses `double-space after sentence end period' and `trailing
white space' issues.

* gnu/packages/cran.scm (r-hapassoc, r-brms, r-lpme): Fix description.

Change-Id: I9da669a415d5a62de785d69ce91c1d8eb1a859e5
Signed-off-by: Vagrant Cascadian <vagrant@debian.org>
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Gabriel Wicki 2025-01-05 22:23:38 +01:00 • committed by Vagrant Cascadian
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1 changed files with 17 additions and 17 deletions
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gnu/packages/cran.scm
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@ -27826,8 +27826,8 @@ variance components, using the likelihood-ratio statistics G.")
(synopsis "Inference of trait associations with SNP haplotypes")
(description
"Hapassoc performs likelihood inference of trait associations with
haplotypes and other covariates in @dfn{generalized linear models} (GLMs). The
functions are developed primarily for data collected in cohort or
haplotypes and other covariates in @dfn{generalized linear models} (GLMs).
The functions are developed primarily for data collected in cohort or
cross-sectional studies. They can accommodate uncertain haplotype phase and
handle missing genotypes at some SNPs.")
(license license:gpl2)))
@ -37884,17 +37884,18 @@ inference diagnostics.
"Bayesian Regression Models using 'Stan'")
(description
"Fit Bayesian generalized (non-)linear multivariate multilevel models
using 'Stan' for full Bayesian inference. A wide range of distributions and
link functions are supported, allowing users to fit -- among others -- linear,
robust linear, count data, survival, response times, ordinal, zero-inflated,
hurdle, and even self-defined mixture models all in a multilevel context.
Further modeling options include non-linear and smooth terms, auto-correlation
structures, censored data, meta-analytic standard errors, and quite a few
more. In addition, all parameters of the response distribution can be
predicted in order to perform distributional regression. Prior specifications
are flexible and explicitly encourage users to apply prior distributions that
actually reflect their beliefs. Model fit can easily be assessed and compared
with posterior predictive checks and leave-one-out cross-validation.")
using @emph{Stan} for full Bayesian inference. A wide range of distributions
and link functions are supported, allowing users to fit -- among others --
linear, robust linear, count data, survival, response times, ordinal,
zero-inflated, hurdle, and even self-defined mixture models all in a
multilevel context. Further modeling options include non-linear and smooth
terms, auto-correlation structures, censored data, meta-analytic standard
errors, and quite a few more. In addition, all parameters of the response
distribution can be predicted in order to perform distributional
regression. Prior specifications are flexible and explicitly encourage users
to apply prior distributions that actually reflect their beliefs. Model fit
can easily be assessed and compared with posterior predictive checks and
leave-one-out cross-validation.")
(license license:gpl2)))
(define-public r-mstate
@ -41462,10 +41463,9 @@ kernel estimators.")
"https://cran.r-project.org/web/packages/lpme/")
(synopsis "Nonparametric Estimation of Measurement Error Models")
(description
"Provide nonparametric methods for mean regression model,
modal regression and conditional density estimation in the
presence/absence of measurement error. Bandwidth selection is
also provided for each method.")
"Provide nonparametric methods for mean regression model, modal
regression and conditional density estimation in the presence/absence of
measurement error. Bandwidth selection is also provided for each method.")
(license license:gpl2+)))
(define-public r-aws-signature