An improved multiple testing procedure for controlling false discovery rates which is developed based on the Bonferroni procedure with integrated estimates from the Benjamini-Hochberg procedure and the Storey's q-value procedure. It controls false discovery rates through controlling the expected number of false discoveries.

Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.

Bindings for additional tree-based model engines for use with
    the 'parsnip' package. Models include gradient boosted decision trees
    with 'LightGBM' (Ke et al, 2017.),
    conditional inference trees and conditional random forests with
    'partykit' (Hothorn and Zeileis, 2015. and
    Hothorn et al, 2006. <doi:10.1198/106186006X133933>), and accelerated
    oblique random forests with 'aorsf' (Jaeger et al, 2022 
    <doi:10.5281/zenodo.7116854>).

Subgroup analyses are routinely performed in clinical trial
    analyses. From a methodological perspective, two key issues of
    subgroup analyses are multiplicity (even if only predefined subgroups
    are investigated) and the low sample sizes of subgroups which lead to
    highly variable estimates, see e.g. Yusuf et al (1991)
    <doi:10.1001/jama.1991.03470010097038>.
    This package implements subgroup estimates
    based on Bayesian shrinkage priors, see Carvalho et al (2019)
    <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates 
    based on penalized likelihood inference are available, based on Simon et al (2011) 
    <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots
    address the aforementioned issues and can complement standard forest plots 
    in practical clinical trial analyses.

Make some distributions from the 'C++' library 'Boost'
    available in 'R'. In addition, the normal-inverse Gaussian distribution
    and the generalized inverse Gaussian distribution are provided. The 
    distributions are represented by 'R6' classes. The method to sample from 
    the generalized inverse Gaussian distribution is the one given in 
    "Random variate generation for the generalized inverse Gaussian 
    distribution" Luc Devroye (2012) <doi:10.1007/s11222-012-9367-z>.