Version 16.2.0

Fixed

• gnm_solve/gambit-gnm now correctly handles the degenerate case of a game where all
  payoffs are the same (#405), and checks that the perturbation vector specified has at least
  one non-zero component (#194)
• ipa_solve/gambit-ipa ensures the use of a generic perturbation vector; this resolves a
  problem where the method could return non-Nash output (#406)
• gambit-enumpoly could get stuck in an infinite loop, and/or fail to report some equilibria,
  due to floating-point rounding/tolerance issues; this has been fixed on known cases (#198)
• gambit-logit now uses perturbations to attempt to resolve correspondences that have
  bifurcations, and instead tries always to follow a curve that has the same orientation.
  This should eliminate cases in which tracing gets stuck in a loop or reverses itself
  when encountering bifurcations (#3)

Added

• MixedStrategyProfile and MixedBehaviorProfile objects in pygambit can now be iterated in
  various dict-like ways
• gnm_solve/gambit-gnm now exposes several parameters which control the behavior of the
  path-following procedure
• The MixedBehaviorProfile object can now be initialized on creation by a given distribution.
• append_move/append_infoset now resolves either a singleton node reference or any
  iterable set of node references
• Additional regret-related functions added to MixedBehaviorProfile and MixedStrategyProfile
  in both C++ and Python
• Some caching added to payoff/strategy value calculations in MixedStrategyProfile
• gambit-simpdiv now supports expressing output as floating-point with a specified number of
  digits (#296)
• Parameters first_step and max_accel added to gambit_logit for finer control of
  numerical continuation process

Changed

• Gambit now requires a compiler that supports C++17.
• Functions to compute Nash equilibria now return a NashComputationResult object instead of a bare
  list of profiles (#190)
• liap_solve/gambit-liap has been reimplemented to scale payoffs uniformly across games,
  to always take an explicit starting point (in liap_solve), and to specify a regret-based
  acceptance criterion (#330)
• simpdiv_solve/gambit-simpdiv now accepts a regret-based acceptance criterion (#439)
• simpdiv_solve now takes an explicit starting point (#445)
• Converted test suite for mixed behavior profiles to pytest style; added parametrizations for
  test_realiz_prob; added test_martingale_property_of_node_value (#375)
• Improved test suite for mixed strategy profiles (#374)
• Test suite for pygambit moved from src/pygambit/tests/ to tests/
• Improved repr methods in pygambit for game-related classes
• Further extension of test suite for mixed behavior profiles to cover new indexing and profile
  order consistency for payoff-related calculations
• Overhaul of caching in MixedBehaviorProfile to use maps (std::map)
• Creation of random mixed profiles in pygambit is done with new Game.random_strategy_profile and
  Game.random_behavior_profile methods; these accept numpy.random.Generator objects for
  reproducible state.
  Creation of random mixed profiles in C++ is done with new Game::NewRandomStrategyProfile and
  Game::NewRandomBehaviorProfile methods; these accept STL Generator objects for reproducible state.
  The Python implementation is no longer just a wrapper around the C++ one.
• Graphical interface now uses simplicial subdivision as the recommended method for finding some
  equilibria in games with more than two players, instead of Lyapunov function minimisation