chains argument in redist_flip().Improves SMC performance by pre-allocating some memory while drawing spanning trees.
Replaces SMC label-counting adjustments (exact and importance-sampling-based) with a new backward kernel that eliminates approximation error and requires far less computation
4.2.0 introduced some regressions in redist_shortburst() along with the new features. The following issues are fixed:
Add summary() support for plans sampled with the flip algorithm. This does not replace the full flip diagnostic suite, but provides an easy way to compute r-hats.
redistmetrics package.redist_shortburst().
With multiple scorers, the algorithm will stochastically explore to try to
find the largest Pareto frontier for the scores. The frontier can be accessed with
attr(<plans obj>, "pareto_score").redist.mcmc(), which was replaced by redist.flip() a few years ago, and finally redist_flip().redist_ci interface for confidence interval calculationredist.plot.distr_qtys() for custom geometry types.redist_constr() and ?constraints). For the first time,
user-defined custom constraints are supported and integrated within all three
algorithms.summary.redist_plans()redistmetrics package
This will speed up compilation time and also provides a cleaner, more extensible
interface for the implementation of additional metrics.doRNGmatch_numbers() using the Hungarian methodmin_move_parity() calculates how much population needs to be moved between
districts in order to completely balance a redistricting plan.cli errors and
warnings throughout the packageredist.splits()color_graph()redist_mergesplit_parallel()rbind() generic for redist_plans objectsredist.smc() in favor of redist_smc() and redist.mergesplit() in favor of redist_mergesplit().redist_map and redist_plans objectsredist_mergesplit()redist_shortburst() along
with scoring functions (?scorers)compare_plans() and classify_plans()iowa dataset and cleaned-up package dataredist.subset allows for easy subsetting of an adjacency graphNEWS.md file to track changes to the package