Romeb 0.2.0

New features

Added outcome_vars, allowing users to identify longitudinal outcome variables by column names or 1-based column indices.
Added auxiliary_vars, allowing users to identify auxiliary variables by column names or 1-based column indices. Auxiliary variables are used only in the MNAR missingness model.
Added priors, a named-list interface for user-specified prior hyperparameters.
Added inits, allowing users to provide initial values to rjags::jags.model() as a function, a named list for a single chain, or a list of named lists for multiple chains.
Added a structured RomebResult print method that reports model type, selected variables, MCMC settings, parameter mapping, posterior medians, Geweke diagnostics, credible intervals, and HPD intervals.

Replaced the non-exported coda::window() call with stats::window() for post-processing saved MCMC samples.
Added validation that burnIn is smaller than both Niter and the number of saved MCMC iterations.
Added validation for n_adapt, allowing n_adapt = 0 but requiring a non-negative integer.
Added input checks for duplicated variables, overlapping outcome and auxiliary variables, missing outcome values in the complete-data model, unknown prior names, and invalid prior dimensions.

Retained the legacy K argument. For new analyses, outcome_vars and auxiliary_vars are preferred because they do not require a fixed column order in data.
The default prior settings match the original weakly informative implementation.

Updated the reference manual, README, vignette, and tests to document the new variable-selection, prior-specification, initialization, and adaptation interfaces.