Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).

Provides an interface to 'MetaPost' (Hobby, 1998) 
    <http://www.tug.org/docs/metapost/mpman.pdf>.
    There are functions to generate an R description of a 'MetaPost'
    curve, functions to generate 'MetaPost' code from an R description,
    functions to process 'MetaPost' code, and functions to read
    solved 'MetaPost' paths back into R.

A simple and effective tool for computing and visualizing statistical power for meta-analysis,
    including power analysis of main effects (Jackson & Turner, 2017)<doi:10.1002/jrsm.1240>, 
    test of homogeneity (Pigott, 2012)<doi:10.1007/978-1-4614-2278-5>, subgroup analysis, 
    and categorical moderator analysis (Hedges & Pigott, 2004)<doi:10.1037/1082-989X.9.4.426>.

The meta-analysis is performed to increase the statistical power by integrating the results from several experiments. The p-values are often combined in meta-analysis when the effect sizes are not available. The 'metapro' R package provides not only traditional methods (Becker BJ (1994, ISBN:0-87154-226-9), Mosteller, F. & Bush, R.R. (1954, ISBN:0201048523) and Lancaster HO (1949, ISSN:00063444)), but also new method named weighted Fisher’s method we developed. While the (weighted) Z-method is suitable for finding features effective in most experiments, (weighted) Fisher’s method is useful for detecting partially associated features. Thus, the users can choose the function based on their purpose. Yoon et al. (2021) "Powerful p-value combination methods to detect incomplete association" <doi:10.1038/s41598-021-86465-y>.

Set of tools for descriptive analysis of metaproteomics data generated from high-throughput 
    mass spectrometry instruments. These tools allow to cluster peptides and proteins abundance, 
    expressed as spectral counts, and to manipulate them in groups of metaproteins. This information 
    can be represented using multiple visualization functions to portray the global metaproteome 
    landscape and to differentiate samples or conditions, in terms of abundance of metaproteins, 
    taxonomic levels and/or functional annotation. The provided tools allow to implement flexible 
    analytical pipelines that can be easily applied to studies interested in metaproteomics analysis.