Performs sensitivity analysis for publication bias in meta-analyses
    (per Mathur & VanderWeele, 2020 [<doi:10.31219/osf.io/s9dp6>]). These analyses
    enable statements such as: "For publication bias to shift the observed point
    estimate to the null, 'significant' results would need to be at least
    30-fold more likely to be published than negative or 'nonsignificant'
    results." Comparable statements can be made regarding shifting to a chosen
    non-null value or shifting the confidence interval. Provides a worst-case
    meta-analytic point estimate under maximal publication bias obtained simply
    by conducting a standard meta-analysis of only the negative and
    "nonsignificant" studies.

Facilitates the retrieval and analysis of financial data related to public works in Italy, 
    focusing on soil defense investments. It extracts data from 'OpenCoesione', 'OpenBDAP', 
    and the 'ReNDiS' database, eliminating the need for direct access to these platforms.
    The package boasts a user-friendly design, featuring real time updates and a set of functions 
    tailored for data retrieval and visualization.
    See the webpages for further information
    <http://www.rendis.isprambiente.it/rendisweb/>,
    <https://opencoesione.gov.it/en/>, and    
    <https://bdap-opendata.rgs.mef.gov.it/>.

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.