NEWS.md
This release is created to assist in replicating previous journal articles that used this software. The API of maxcovr is likely to change from here.
max_coverage_relocation now works for different data, thanks to a bug fix.glpk, and gurobi, in addition to lpSolve. Testing still to be conducted.coverage added as a one liner to take two dataframes and from the coverage of one dataframe on another. Documentation and tests still need worksummary_mc_cv_relocate added for relocation methods. This will eventially be an S3 method, I think?max_coverage, replacing them with key functions. This will make things easier to debug and extend in the future. These functions start with extract_. There will likely be some updates to this in the futuremax_coverage functions to work with vectors, this fule is called maxcovr-refactor-vectors. This provides some more efficient computation of preparation of matrices for optimisation, and will provide substantial speedups for larger N, and for when multiple n_added’s are needed.max_coverage now returns the entire solution from lpSolve, except for the constraints, because they are too large (can easily be over 1Gb)new function, max_coverage_relocation, takes a arguments for total cost, installation cost, and relocation costs and then works out how many facilities it can place, and potentially remove and replace to obtain optimum coverage. The function is currently under development. In the future it will be absorted into max_coverage.
added a results extraction method for max_coverage_relocation, extract_mc_result_relocation. Eventually this will be be absorbed into the extract_mc_result function, through some kind of S3 method.
added print and summary S3 method for max_coverage and max_coverage_relocation, and an is.maxcovr_relocation and is.maxcovr, which should be handy for testing.
nearest to find the nearest lat/long points from one dataframe to another and then calculate the distance between the two. This is at least 10 times faster than the previous method using joins and dplyr.mc_model$summary.summarise_coverage() function, which calculates coverage based upon a distance dataframe created from facility_user_dist().york_crime data containing crime locations around the city of york.spherical_distance and distance_matrix
max_coverage to include the extract_mc_result inside the function, rather than needing a separate function call. This was to save space, as the A matrix and friends can be rather large, especially if you are running the coverage multiple times under different conditions.facility_user_distance - coverage_distance, which allows you to specify the coverage you are interested in, and nearest, where you specify nearest to be “facility” if you want the nearest facility to each user, “user” if you want the nearest user to each facility, and NULL if you just want the complete pairwise distances.NEWS.md file to track changes to the package.<!–NEW FEATURES, MINOR IMPROVEMENTS, BUG FIXES, DEPRECATED AND DEFUNCT – >