Implements the Self-Similarity Test for Normality (SSTN), a new 
    statistical test designed to assess whether a given sample originates from 
    a normal distribution. The procedure is based on iteratively estimating the 
    characteristic function of the sum of standardized i.i.d. random variables and 
    comparing it to the characteristic function of the standard normal distribution. 
    A Monte Carlo procedure is used to determine the empirical distribution of the 
    test statistic under the null hypothesis. Details of the methodology are 
    described in Anarat and Schwender (2025), "A normality test based on self-similarity" 
    (Submitted).

Penalized and non-penalized maximum likelihood estimation of smooth
  transition vector autoregressive models with various types of transition weight
  functions, conditional distributions, and identification methods. Constrained
  estimation with various types of constraints is available. Residual based
  model diagnostics, forecasting, simulations, counterfactual analysis, and
  computation of impulse response functions, generalized impulse response functions,
  generalized forecast error variance decompositions, as well as historical
  decompositions. See
  Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
  Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
  Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>,
  Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Functions for sample size estimation and simulation in clinical
  trials. Includes methods for selecting the best group using the
  Indifference-zone approach, as well as designs for non-inferiority,
  equivalence, and negative binomial models. For the sample size calculation
  for non-inferiority of vaccines, the approach is based on Fleming, Powers,
  and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone
  approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and
  Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).