diff options
| -rw-r--r-- | gnu/packages/cran.scm | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/gnu/packages/cran.scm b/gnu/packages/cran.scm index 3cfdf5ffbb..98597e3bbe 100644 --- a/gnu/packages/cran.scm +++ b/gnu/packages/cran.scm @@ -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 |