Update types to make optional types explicit

This release fixes the failing linting tests by update types to make optional types explicit.

Improve macid to efg transformation

This release significantly improves the efficiency of the MACID to EFG transformation for the get_spe() function.

Mixed Nash Equilibria in N-agent games

This release adds methods for computing mixed Nash equilibria and mixed subgame perfect equilibria (for an arbitrary number of players) using the pygambit library.

Backwards compatibility Notes:
Note that this breaks backwards compatibility by removing the get_all_pure_ne, get_all_pure_ne_in_sg, and get_all_pure_spe class methods from the MACID class. This is because pure variants of NE and SPE can be found by simply selecting the "enumpure" solver as an argument in the new get_ne, get_ne_in_sg, and get_spe MACID class methods.

Exposes layout as a keyword argument in Causal Bayesian Network's draw method

This version:

• exposes layout as a keyword argument in Causal Bayesian Network's draw method. This allows the user to try alternative layout algorithms (compared with the Kamada-Kawai default).
• fixes various setup and PyPI bugs

add sampling method

add the method CausalBayesianNetwork.sample()

CPD arguments must now match case of parent names

This release breaks backwards compatibility by forcing the arguments for CPDs to match the case of the names of the parent variables. Previously, the lowercase version of the parent names were used, which lead to less intuitive model specification and more complex and brittle code.

Force pgmpy to be version 0.1.13

Later versions of pgmpy create bugs in PyCID

• until these are resolved, PyCID will use pgmpy version 0.1.13

Add random (MA)CIDs

v0.2.7

update doc string

Performance improvements and random_dag

v0.2.6

various minor

Add quantitative VOC function and Causal Bayesian Network Class

In this release, we provide:

• a function that computes the quantitative value of control.
• a CausalBayesianNetwork class for defining causal Bayesian networks (many of the methods previously housed in the MACIDBase class now sit within CausalBayesianNetwork and MACIDBase inherits from CausalBayesianNetwork)