Permutation (randomisation) test for single-case phase design data with 
    two phases (e.g., pre- and post-treatment). Correction for dependency of observations 
    is done through stepwise resampling the time series while varying 
    the distance between observations. The required distance 0,1,2,3.. is determined 
    based on repeated dependency testing while stepwise increasing the distance.
    In preparation: Vroegindeweij et al. "A Permutation distancing test 
    for single-case observational AB phase design data: A Monte Carlo simulation study".

The goal of this package is to cover the most common steps in probability of default (PD) rating model development and validation. 
	     The main procedures available are those that refer to univariate, bivariate, multivariate analysis, calibration and validation. 
	     Along with accompanied 'monobin' and 'monobinShiny' packages, 'PDtoolkit' provides functions which are suitable for different 
	     data transformation and modeling tasks such as: 
	     imputations, monotonic binning of numeric risk factors, binning of categorical risk factors, weights of evidence (WoE) and 
	     information value (IV) calculations, WoE coding (replacement of risk factors modalities with WoE values), risk factor clustering, 
	     area under curve (AUC) calculation and others. Additionally, package provides set of validation functions for testing homogeneity, 
	     heterogeneity, discriminatory and predictive power of the model.

Conduct simulation-based customized power calculation 
             for clustered time to event data in a mixed crossed/nested design,
             where a number of cell lines and a number of mice within each cell line are considered to achieve a desired statistical power,
             motivated by Eckel-Passow and colleagues (2021) <doi:10.1093/neuonc/noab137> and Li and colleagues (2024) <doi:10.48550/arXiv.2404.08927>.
             This package provides two commonly used models for powering a design, linear mixed effects and Cox frailty model. Both models account for
             within-subject (cell line) correlation while holding different distributional assumptions about the outcome. 
             Alternatively, the counterparts of fixed effects model are also available, which produces similar estimates of statistical power.