Analysis of large datasets of fixed coupon bonds, allowing for irregular first and last coupon periods and various day count conventions. With this package you can compute the yield to maturity, the modified and MacAulay durations and the convexity of fixed-rate bonds. It provides the function AnnivDates, which can be used to evaluate the quality of the data and return time-invariant properties and temporal structure of a bond.

Bone Profiler is a scientific method and a software used to model 
    bone section for paleontological and ecological studies. See Girondot and Laurin 
    (2003) <https://www.researchgate.net/publication/280021178_Bone_profiler_A_tool_to_quantify_model_and_statistically_compare_bone-section_compactness_profiles>
    and Gônet, Laurin and Girondot (2022) <https://palaeo-electronica.org/content/2022/3590-bone-section-compactness-model>.

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.