Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.

Compute and tune some positive definite and sparse covariance
  estimators.

Interactive shiny application for working with Probability Distributions. Calculations and  Graphs are provided.

An implementation of data analysis tools for samples of symmetric or 
  Hermitian positive definite matrices, such as collections of covariance matrices 
  or spectral density matrices. The tools in this package can be used to perform: (i) 
  intrinsic wavelet transforms for curves (1D) or surfaces (2D) of Hermitian positive 
  definite matrices with applications to dimension reduction, denoising and clustering in the 
  space of Hermitian positive definite matrices; and (ii) exploratory data analysis and inference 
  for samples of positive definite matrices by means of intrinsic data depth functions and 
  rank-based hypothesis tests in the space of Hermitian positive definite matrices.

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.