Diff of /src/modules/glm/samplers/BinaryFactory.h [01a998] .. [081fbe] Maximize Restore

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--- a/src/modules/glm/samplers/BinaryFactory.h
+++ b/src/modules/glm/samplers/BinaryFactory.h
@@ -7,17 +7,44 @@
 
     class BinaryGLM;
 
+    /**
+     * @short Base class for Binary GLM factories
+     */
     class BinaryFactory : public GLMFactory
     {
 	bool _gibbs;
     public:
 	BinaryFactory(std::string const &name, bool gibbs);
+	/**
+	 * Checks that the outcome variable is a Bernoulli random
+	 * variable or a binomial variable with size fixed to 1, and
+	 * the link function is logit or probit.  Linear models with
+	 * identity link are also permitted.
+	 */
 	bool checkOutcome(StochasticNode const *snode,
 			  LinkNode const *lnode) const;
+	/**
+	 * Returns a newly allocated object of class BinaryGLM, unless
+	 * we have a pure linear model with normal outcome variables.
+	 * In this case, an object of class Linear is returned, as 
+	 * the overhead of the BinaryGLM class is not required.
+	 *
+	 * This function is called by GLMFactory#makeSampler
+	 */
 	GLMMethod *newMethod(GraphView const *view,
 			     std::vector<GraphView const *> const &sub_views,
 			     unsigned int chain) const;
+	/**
+	 * If the factory was constructed with parameter "gibbs" set
+	 * to true, then snode must be scalar. Otherwise, snode must
+	 * be unbounded.
+	 *
+	 * @param snode Candidate node for sampling by BinaryGLM
+	 */
 	bool canSample(StochasticNode const *snode) const;
+	/**
+	 * Returns a newly allocated BinaryGLM object.
+	 */
 	virtual BinaryGLM *newBinary(GraphView const *view,
 				     std::vector<GraphView const *> const 
 				     &sub_views,