MakeArrays: Create arrays to save MCMC posterior samples
In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Create arrays to save MCMC posterior samples

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MakeArrays(X = NULL, chains, Nsims, num_exper, num_conf, omega, minX,
  maxX, starting_cutoffs, starting_alphas, starting_coefs, starting_vars,
  min_exper_sample = 0)

`X`	Numeric vector. Observed exposure values. Can be left NULL if min_exper_sample is set to 0.
`chains`	The number of separate MCMC chains.
`Nsims`	The number of posterior samples per chain.
`num_exper`	The number of experiments we are allowing.
`num_conf`	The number of potential confounders.
`omega`	The omega of the BAC prior on inclusion indicators.
`minX`	The minimum observed exposure value.
`maxX`	The maximum observed exposure value.
`starting_alphas`	Array with dimensions corresponding to the model (exposure / outcome), the experiment, and the potential confounders. Entries 0/1 represent exclusion/inclusion of the covariate in the corresponding model.
`starting_coefs`	Array with the starting values of all coefficients. Dimensions are: Exposure/Outcome model, chains, experiments, and covariate (intercept, coefficient of exposure, covariates). The coefficient of exposure should be NA for the exposure model.
`starting_vars`	Array including the starting values for the residual variances. Dimensions correspond to: Exposure/Outcome model, chains, and experiment.
`min_exper_sample`	The minimum number of observations within an experiment. It will be used to ensure that starting cutoffs are allowed under the prior specification.
`starting`	cutoffs Matrix with rows corresponding to different chains. Each row includes K ordered values of MCMC starting cutoffs. If left NULL, random started values are used.