The first goal of this package is to provide a multitude of tree models,
    i.e., functions that generate rooted binary trees with a given number of leaves.
    Second, the package allows for an easy evaluation and comparison of tree shape
    statistics by estimating their power to differentiate between different tree models.
    Please note that this R package was developed alongside the manuscript
    "Tree balance in phylogenetic models" by
    S. J. Kersting, K. Wicke, and M. Fischer (2024) <doi:10.48550/arXiv.2406.05185>,
    which provides further background and the respective mathematical definitions.
    This project was supported by the project ArtIGROW, which is a part of the
    WIR!-Alliance ArtIFARM –  Artificial Intelligence in Farming funded by the
    German Federal Ministry of Education and Research (No. 03WIR4805).

Makes it easy to push data to 'Power BI' using R and the 'Power BI 
    REST APIs' (see <https://docs.microsoft.com/en-us/rest/api/power-bi/>). 
    A set of functions for turning data frames into 'Power BI' datasets and 
    refreshing these datasets are provided. Administrative tasks such as 
    monitoring refresh statuses and pulling metadata about workspaces and users
    are also supported.

Provides tools for Bayesian power analysis and assurance calculations using the statistical frameworks of 'brms' and 'INLA'. Includes simulation-based approaches, support for multiple decision rules (direction, threshold, ROPE), sequential designs, and visualisation helpers. Methods are based on Kruschke (2014, ISBN:9780124058880) "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan", O'Hagan & Stevens (2001) <doi:10.1177/0272989X0102100307> "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness", Kruschke (2018) <doi:10.1177/2515245918771304> "Rejecting or Accepting Parameter Values in Bayesian Estimation", Rue et al. (2009) <doi:10.1111/j.1467-9868.2008.00700.x> "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations", and Bürkner (2017) <doi:10.18637/jss.v080.i01> "brms: An R Package for Bayesian Multilevel Models using Stan".