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>.

Provides an R interface for 'SSW' (Striped Smith-Waterman)
    via its 'Python' binding 'ssw-py'. 'SSW' is a fast 'C' and 'C++'
    implementation of the Smith-Waterman algorithm for pairwise sequence
    alignment using Single-Instruction-Multiple-Data (SIMD) instructions.
    'SSW' enhances the standard algorithm by efficiently returning alignment
    information and suboptimal alignment scores.
    The core 'SSW' library offers performance improvements for various
    bioinformatics tasks, including protein database searches,
    short-read alignments, primary and split-read mapping,
    structural variant detection, and read-overlap graph generation.
    These features make 'SSW' particularly useful for genomic applications.
    Zhao et al. (2013) <doi:10.1371/journal.pone.0082138> developed the
    original 'C' and 'C++' implementation.

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.