JumpOver: MCMC jump over move
In gpapadog/LERCA: Local Exposure-Response Confounding Adjustment

Description Usage Arguments

Simultaneous update of the experiment configuration, inclusion indicators, and slopes that reorder to points in the experiment configuration.

JumpOver(dta, current_cutoffs, current_alphas, current_coefs,
  approx_likelihood = TRUE, cov_cols, omega = 5000, mu_priorY,
  Sigma_priorY, comb_probs = c(0.01, 0.5, 0.99), split_probs = c(0.2,
  0.95), min_exper_sample = 20, jump_slope_tune = 0.05)

`dta`	Data frame including the covariates as C1, C2, ..., the exposure as X and the outcome as Y.
`current_cutoffs`	Numeric of length K. The current values for the points in the experiment configuraiton.
`current_alphas`	Array of dimensions that correspond to the exposure or outcome model, the experiment, and potential confounding. Entries are 0/1 corresponding to exclusion/inclusion of the covaraite in the corresponding model of the experiment.
`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).
`approx_likelihood`	Logical. If set to true the BIC will be used to calculate the marginal likelihood. FALSE not supported yet.
`cov_cols`	The indices of the columns including the covariates.
`omega`	The parameter of the BAC prior on inclusion indicators.
`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.
`comb_probs`	When two experiments are combined, comb_probs represents the probability of alpha = 1 when 0, 1, and 2 corresponding alphas are equal to 1. Vector of length 3.
`split_probs`	When one experiment is split, split_probs describes the probability that the alpha of a new experiment is equal to 1, when the alpha of the current experiment is 0, and when it is 1. Vector of length 2.
`min_exper_sample`	The minimum number of observations within an experiment. Defaults to 20.
`jump_slope_tune`	The standard deviation of the proposal on the slopes.