Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.

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, and calculation of impulse
  response functions, generalized impulse response functions, and generalized 
  forecast error variance 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 (2024) <doi:10.48550/arXiv.2403.14216>,
  Savi Virolainen (2024) <doi:10.48550/arXiv.2404.19707>.

Functions for performing stochastic search variable selection (SSVS) 
    for binary and continuous outcomes and visualizing the results. 
    SSVS is a Bayesian variable selection method used to estimate the probability 
    that individual predictors should be included in a regression model. 
    Using MCMC estimation, the method samples thousands of regression models 
    in order to characterize the model uncertainty regarding both the predictor 
    set and the regression parameters. For details see Bainter, McCauley, Wager, 
    and Losin (2020) Improving practices for selecting a subset of important 
    predictors in psychology: An application to predicting pain, Advances in 
    Methods and Practices in Psychological Science 3(1), 66-80 
    <DOI:10.1177/2515245919885617>.

Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.