UpdateExperiments: Separate move of the experiment configuration
In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Updating the experiment configuration separately from the inclusion indicators.

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UpdateExperiments(dta, cov_cols, current_cutoffs, current_coefs,
  current_vars, min_exper_sample = 20, prop_distribution = c("Uniform",
  "Normal"), normal_percent = 1, mu_priorY, Sigma_priorY)

`dta`	Data frame including the covariates as C1, C2, ..., the exposure as X and the outcome as Y.
`cov_cols`	The indices of the columns including the covariates.
`current_cutoffs`	Numeric of length K. The current values for the points in the experiment configuraiton.
`current_coefs`	The current coefficients in an array format, with dimensions corresponding to the exposure/outcome model, the experiments, and the coefficient (intercept, slope, covariates).
`current_vars`	Matrix. Rows correspond to exposure/outcome model, and columns to experiments. Entries are the current variances.
`min_exper_sample`	The minimum number of observations within an experiment. Defaults to 20.
`prop_distribution`	Character vector. Options include 'Uniform' or 'Normal' representing the type of distribution that will be used to propose a move of the cutoffs in the separate update. Defaults to uniform.
`normal_percent`	Numeric. Parameter controling the width of a normal proposal for the experiment configuration. Used only when prop_distribution is set to Normal. Smaller values represent smaller variance of the truncated normal proposal distribution. Defaults to 1.
`mu_priorY`	Vector of length equal to the number of covariates + 2 with entries corresponding to the prior mean of the intercept, slope, coefficient in the outcome model.
`Sigma_priorY`	The normal prior covariance matrix of the parameters in mu_priorY.