person(given = "Kristine J.", family = "Rosenberger", role = c("ctb")),
- measures of inconsistency degrees of freedom in Bayesian network meta-analysis
Packaged: 2021-03-14 19:00:30 UTC; Lifeng Lin

metafor (>= 3.0-2), methods, stats, utils
- bivariate generalized linear mixed models for synthesizing odds ratios, relative risks,
- models for multivariate meta-analysis, measures of inconsistency degrees of freedom
in Bayesian network meta-analysis, and predictive P-score

role = c("aut", "cre"), email = "lifenglin@arizona.edu",
person(given = "Yipeng", family = "Wang", role = c("ctb")),
- heterogeneity tests and measures and penalization methods that are robust to outliers

Title: Meta-Analysis for Non-Integral Data
Description: Combination of results for meta-analysis using significance and effect size only. P-values and fold-change are combined to obtain a global significance on each metabolite. Produces a volcano plot summarising the relevant results from meta-analysis. Vote-counting reports for metabolites. And explore plot to detect discrepancies between studies at a first glance.
Imports: magrittr (>= 1.5), tibble (>= 3.0.0), dplyr (>= 1.0.0), readr
Author: Maria Llambrich [aut, cre] (<https://orcid.org/0000-0001-8418-0982>),

person("Maria", "Llambrich", , "maria.llambrich@urv.cat", role = c("aut", "cre"),
person("Eudald", "Correig", , "eudald.correig@urv.cat", role = "aut"),
person("Raquel", "Cumeras", , "raquel.cumeras@urv.cat", role = "aut")
Description: Combination of results for meta-analysis using significance
and effect size only. P-values and fold-change are combined to obtain
a global significance on each metabolite. Produces a volcano plot
summarising the relevant results from meta-analysis. Vote-counting
reports for metabolites. And explore plot to detect discrepancies
Suggests: markdown, metaboliteIDmapping, testthat, vdiffr

between studies at a first glance. Methodology is described in the
Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.

Title: Alternating Manifold Proximal Gradient Method for Sparse PCA
person("Justin", "Huang", role = "aut"),
person("Lingzhou", "Xue", email = "lzxue@psu.edu", role = c("cre","aut")))
Description: Alternating Manifold Proximal Gradient Method for Sparse PCA uses the Alternating Manifold Proximal
Gradient (AManPG) method to find sparse principal components from a data or covariance matrix. Provides
a novel algorithm for solving the sparse principal component analysis problem which provides
advantages over existing methods in terms of efficiency and convergence guarantees.
Packaged: 2021-07-24 13:10:34 UTC; benjochem