Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.

The purpose of this package is to share a collection of functions the author wrote during weekends for managing
    kitchen and garden tasks, e.g. making plant growth charts or Thanksgiving kitchen schedule charts, etc. 
    Functions might include but not limited to:
    (1) aiding summarizing time related data; 
    (2) generating axis transformation from data; and
    (3) aiding Markdown (with html output) and Shiny file editing.

Implements Mander & Thompson's (2010) 
    <doi:10.1016/j.cct.2010.07.008> methods for two-stage designs
    optimal under the alternative hypothesis for phase II [cancer] trials.  Also
    provides an implementation of Simon's (1989) 
    <doi:10.1016/0197-2456(89)90015-9> original methodology and 
    allows exploration of the operating characteristics of sub-optimal designs.

The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.

Several robust estimators for linear regression and variable selection are provided. 
              Included are Maximum tangent likelihood estimator by Qin, et al., (2017), arXiv preprint <doi:10.48550/arXiv.1708.05439>, 
              least absolute deviance estimator and Huber regression. The penalized version of each of these 
              estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to 
              produce consistent estimates for both fixed and high-dimensional settings.

Functions for detecting multicollinearity. This test gives statistical support to two of the most famous methods for detecting multicollinearity in applied work: Klein’s rule and Variance Inflation Factor (VIF). See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2015) <doi:10.33333/rp.vol51n2.05>.