c(
person("Maria", "Llambrich", , "maria.llambrich@urv.cat", role = c("aut", "cre"),
comment = c(ORCID = "0000-0001-8418-0982")),
person("Eudald", "Correig", , "eudald.correig@urv.cat", role = "aut"),
person("Raquel", "Cumeras", , "raquel.cumeras@urv.cat", role = "aut")
)
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.

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. Methodology is described in the
Llambrich et al. (2021) <doi:10.1093/bioinformatics/btab591>.

Alternating Manifold Proximal Gradient Method for Sparse PCA
c(
person("Justin", "Huang", role = "aut"),
person("Benjamin","Jochem", role = "aut"),
person("Shiqian", "Ma", role = "aut"),
person("Lingzhou", "Xue", email = "lzxue@psu.edu", role = c("cre","aut")))
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.
Chen, S., Xue, L., & Zou, H. (2020) <doi:10.1287/ijoo.2019.0032>.
Zou, H., Hastie, T., & Tibshirani, R. (2006) <doi:10.1198/106186006X113430>.
Zou, H., & Xue, L. (2018) <doi:10.1109/JPROC.2018.2846588>.
Justin Huang [aut],
Benjamin Jochem [aut],
Shiqian Ma [aut],
Lingzhou Xue [cre, aut]