person("Quentin F.", "Gronau", email="quentingronau@web.de", role = c("ctb")))
Description: Computes the posterior model probabilities for standard meta-analysis models
a wide range of non-informative or informative priors for the mean effect size
and the heterogeneity coefficient. Moreover, using pre-compiled Stan models,
meta-analysis with continuous and discrete moderators with Jeffreys-Zellner-Siow (JZS)
priors can be fitted and tested. This allows to compute Bayes factors and
perform Bayesian model averaging across random- and fixed-effects meta-analysis
Imports: mvtnorm, logspline, coda, LaplacesDemon, rstan (>= 2.18.1),

Authors@R: c(person("Daniel W.", "Heck", email="heck@uni-mannheim.de", role=c("aut","cre"),

with and without moderators. For a primer on Bayesian model-averaged meta-analysis,
see Gronau, Heck, Berkhout, Haaf, & Wagenmakers (2020, <doi:10.31234/osf.io/97qup>).

Imports: bridgesampling, coda, LaplacesDemon, logspline, mvtnorm,

Authors@R: c(person(given = "Daniel W.",

Authors@R: c(person("Dipro", "Sinha", role = c("aut", "cre"),email = "diprosinha@gmail.com"),
person("Sayanti", "Guha Majumdar", role = "aut", email = "sayanti23gm@gmail.com"),
person("Anu", "Sharma", role = "aut", email = "anu9.dixit@gmail.com"),
person(as.person("Dwijesh Chandra Mishra"), role = "aut", email = "dwij.mishra@gmail.com")
Description: Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
Imports: factoextra, cluster, dbscan, dplyr, seqinr, Biostrings

Title: Extract and Summarise Data from Published Figures
Authors@R: c(person("Joel", "Pick", email = "joel.l.pick@gmail.com", role = c("aut")), person("Shinichi", "Nakagawa", email = "s.nakagawa@unsw.edu.au", role = c("aut")), person("Daniel", "Noble", email = "daniel.wa.noble@gmail.com", role = c("aut", "cre")))
Description: High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Imports: magick, stats, graphics, utils, purrr
Suggests: mockery, testthat, knitr, rmarkdown
Packaged: 2018-07-23 01:11:06 UTC; danielnoble