Migrate documentation of QRE tools.

Closes #336.

doc/api.rst

@@ -27,3 +27,6 @@ Computation of Nash equilibria
 .. automodule:: pygambit.nash
    :members:
 
+.. automodule:: pygambit.qre
+   :members:
+

doc/index.rst

@@ -52,6 +52,7 @@ Table of Contents
    gui
    tools
    pyapi
+   api
    samples
    ideas
    build

doc/pyapi.rst

@@ -440,69 +440,3 @@ For example, taking again the game :file:`e02.efg` as an example::
 
 
 
-API documentation
------------------
-
-
-
-Analysis of quantal response equilibria
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~---------
-
-.. py:module:: pygambit.qre
-
-.. py:function:: fit_fixedpoint(data)
-
-   .. versionadded:: 16.1.0
-
-   Use maximum likelihood estimation to find the point on the
-   principal branch of strategic form game closest to `data`.
-   The `data` are expressed as an instance of a mixed strategy
-   profile on the game.  The `data` should typically be expressed
-   as the counts of observations of each strategy.
-
-   :param data: The observed data to use in fitting.
-   :type data: :py:class:`MixedStrategyProfile`
-   :rtype: :py:class:`LogitQREMixedStrategyFitResult`
-
-.. py:function:: fit_empirical(data)
-
-   .. versionadded:: 16.1.0
-
-   Use maximum likelihood estimation to estimate a quantal
-   response equilibrium using the empirical payoff method.
-   The `data` are expressed as an instance of a mixed strategy
-   profile on the game.  The `data` should typically be expressed
-   as the counts of observations of each strategy.
-
-   :param data: The observed data to use in fitting.
-   :type data: :py:class:`MixedStrategyProfile`
-   :rtype: :py:class:`LogitQREMixedStrategyFitResult`
-
-.. py:class:: LogitQREMixedStrategyFitResult
-
-   .. versionadded:: 16.1.0
-
-   The result of estimating a quantal response equilibrium
-   using given data on a game.
-
-   .. py:attribute:: method
-
-   A text string indicating the estimation method used.
-   This can be "fixedpoint" or "empirical".
-
-   .. py:attribute:: profile
-
-   The estimated :py:class:`MixedStrategyProfile`.
-
-   .. py:attribute:: lam
-
-   The estimated value of the precision parameter lambda.
-
-   .. py:attribute:: data
-
-   The data used in the estimation, represented as a
-   :py:class:`MixedStrategyProfile`.
-
-   .. py:attribute:: log_like
-
-   The log of the likelihood function at the estimated profile.

src/pygambit/qre.py

@@ -364,7 +364,14 @@ def __repr__(self):
 def fit_fixedpoint(
         data: gambit.MixedStrategyProfileDouble
 ) -> LogitQREMixedStrategyFitResult:
-    """Estimate a QRE using the fixed-point method."""
+    """Use maximum likelihood estimation to find the point on the
+    principal branch of strategic form game closest to `data`.
+    The `data` are expressed as an instance of a mixed strategy
+    profile on the game.  The `data` should typically be expressed
+    as the counts of observations of each strategy.
+
+    .. versionadded:: 16.1.0
+    """
     res = gambit.logit_estimate(data)
     return LogitQREMixedStrategyFitResult(
         data, "fixedpoint", res.lam, list(res.profile), res.log_like
@@ -374,7 +381,14 @@ def fit_fixedpoint(
 def fit_empirical(
         data: gambit.MixedStrategyProfileDouble
 ) -> LogitQREMixedStrategyFitResult:
-    """Estimate a QRE using the empirical payoff method."""
+    """Use maximum likelihood estimation to estimate a quantal
+    response equilibrium using the empirical payoff method.
+    The `data` are expressed as an instance of a mixed strategy
+    profile on the game.  The `data` should typically be expressed
+    as the counts of observations of each strategy.
+
+    .. versionadded:: 16.1.0
+    """
     def do_logit(lam: float):
         logit_probs = [[math.exp(lam*v) for v in player] for player in values]
         sums = [sum(v) for v in logit_probs]