v3.1.0

This is a minor update to VoteKit that includes some new election analysis tools, some refactored visualization code for bar plots, and a minor tweak to how scores are computed from ranked ballots to allow for different averaging conventions for ties.

Added

• added support for three types of averaging conventions within score_profile_from_rankings: average,
  low, and high.
• r-representation scores: compute how "satisfied" voters are with a given winners set.
• matrices: create three kinds of matrices based on profiles: boost, mentions, and average distance. Accompanying heatmap code that plots them.
• support for Python 3.12, 3.13
• contributing guidelines and community resources to our docs.

Changed

• changed the default Borda scoring to use low averaging, where tied rankings receive the lowest possible
  points
• changed the sampling method for boosted_random_dictator and random_dictator to more clearly
  use the first place votes distribution
• changed the structure of plot_summary_stats. Is now split into many functions, all called
  profile_STAT_plot or multi_profile_STAT_plot, where STAT can be fpv, borda, mentions,
  and ballot_lengths. Built on top of more general bar_plot and multi_bar_plot functions.
• removed support for Python 3.9.

New Contributors

• @wkirby made their first contribution in #153

Full Changelog: v3.0.0...v3.1.0