UpdateAlphas: Update the inclusion indicators from their full conditional.
In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Use Gibbs sampling to update the inclusion indicator of all covariates in the exposure and outcome models.

1 2	UpdateAlphas(dta, cov_cols, current_cutoffs, current_alphaY, current_coefs, current_vars, mu_priorX, Sigma_priorX, mu_priorY, Sigma_priorY, omega)

`dta`	A data set including a column of the exposure of interest as X, the outcome of interest as Y, and all potential confounders as C1, C2, ...
`cov_cols`	The indices of the columns in dta corresponding to the potential confounders.
`current_cutoffs`	Numeric of length K. The current values for the points in the experiment configuraiton.
`current_alphaY`	Matrix of 0/1 entries. Rows correspond to experiments, and columns to covariate. Entries 0/1 represent exclusion/inclusion of the covaraite in the corresponding outcome model.
`current_coefs`	The current coefficients of the MCMC. Three dimensional array with dimensions corresponding to exposure/outcome model, experiment, and coefficients (intercept, slope, covariates).
`current_vars`	Matrix. Rows correspond to exposure/outcome model, and columns to experiments. Entries are the current variances.
`mu_priorX`	The mean of the normal prior on the coefficients of the exposure model. Numeric vector of length equal to the number of potential confounders + 1 with the first entry corresponding to the intercept.
`Sigma_priorX`	Covariance matrix of the normal prior on the regression coefficients of the exposure model.
`mu_priorY`	The mean of the normal prior on the coefficients of the outcome model. Numeric vector with entries corresponding to intercept, slope of exposure, and potential covariates.
`Sigma_priorY`	The covariance matrix of the normal prior on the regression coefficients of the outcome model.
`omega`	The omega of the BAC prior.