Tools for Bayesian estimation of meta-analysis models that account
    for publications bias or p-hacking. For publication bias, this package
    implements a variant of the p-value based selection model of Hedges (1992)
    <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also
    implements the mixture of truncated normals model for p-hacking described
    in Moss and De Bin (2019) <arXiv:1911.12445>.

A bunch of convenience functions that transform the results of some basic statistical analyses
       into table format nearly ready for publication. This includes descriptive tables, tables of
       logistic regression and Cox regression results as well as forest plots.

Text mining of PubMed Abstracts (text and XML) from <https://pubmed.ncbi.nlm.nih.gov/>.

Easy function for text-mining the 'PubMed' repository based on defined sets of terms.
    The relationship between fix-terms (related to your research topic) and pub-terms (terms which pivot around your research focus)
    is calculated using the pointwise mutual information algorithm ('PMI'). Church, Kenneth Ward and Hanks, Patrick (1990) <https://www.aclweb.org/anthology/J90-1003/>
    A text file is generated with the 'PMI'-scores for each fix-term. Then for each collocation pairs (a fix-term + a pub-term),
    a text file is generated with related article titles and publishing years. Additional Author section will follow in the next version updates.

A set of tools to extract bibliographic content from 'PubMed' database using 'NCBI' REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>.

Provides various functions for retrieving and
    interpreting information from 'Pubmed' via the API,
    <https://www.ncbi.nlm.nih.gov/home/develop/api/>.

Create a word cloud using the abstract of publications from 'Pubmed'.

Provides functions to create high-quality, publication-ready plots for numeric and categorical data, including bar plots, violin plots, boxplots, line plots, error bars, correlation plots, linear model plots, odds ratio plots, and normality plots.