Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.

Implementation of several recent multivariate bias correction
			 methods with a unified interface to facilitate their use. A
			 description and comparison between methods can be found
			 in <doi:10.5194/esd-11-537-2020>.

Offers Bayesian semiparametric density estimation 
             and tail-index estimation for heavy tailed data, by
             using a parametric, tail-respecting transformation
             of the data to the unit interval and then modeling
             the transformed data with a purely nonparametric
             logistic Gaussian process density prior. Based on 
             Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.

An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.

Estimation and inference for parameters in a Gaussian copula model,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. 
        This package also provides a
        semiparametric imputation procedure for missing multivariate
        data.

Computes a simple blinding index for randomized controlled trials introduced in the paper "A simple blinding index for randomized controlled trials" by Petroff, Bacak, Dagres, Dilk and Wachter, which has been submitted for publication.