This is a port of the WTC MATLAB package written by Aslak Grinsted
    and the wavelet program written by Christopher Torrence and Gibert P.
    Compo. This package can be used to perform univariate and bivariate
    (cross-wavelet, wavelet coherence, wavelet clustering) analyses.

Compute multivariate location, scale, and correlation
        estimates based on Tukey's biweight M-estimator.

Business days calculations based on a list of holidays and
    nonworking weekdays. Quite useful for fixed income and derivatives pricing.

Maximum likelihood estimation of copula-based zero-inflated 
    (and non-inflated) Poisson and negative binomial count models, based on the 
    article <doi:10.18637/jss.v109.i01>. Supports Frank and Gaussian copulas. 
    Allows for mixed margins (e.g., one margin Poisson, the other zero-inflated 
    negative binomial), and several marginal link functions. Built-in methods for 
    publication-quality tables using 'texreg', post-estimation diagnostics using 
    'DHARMa', and testing for marginal zero-modification via <doi:10.1177/0962280217749991>. 
    For information on copula regression for count data, see Genest and Nešlehová (2007) 
    <doi:10.1017/S0515036100014963> as well as Nikoloulopoulos (2013) <doi:10.1007/978-3-642-35407-6_11>. 
    For information on zero-inflated count regression generally, see Lambert (1992) 
    <https://www.jstor.org/stable/1269547?origin=crossref>. The author acknowledges 
    support by NSF DMS-1925119 and DMS-212324.

Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.

Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.