adding to docs

docs/source/Multivariate.rst

@@ -17,6 +17,45 @@ Currently, the following multivariate distributions are implemented:
     :file: mvt_table.csv
     :header-rows: 1
 
+PreFitContinuousMultivariate
+----------------------------
+This is the base class for all multivariate distributions. It implements the following methods and attributes:
+
+- logpdf (log of the probability density function)
+- pdf (probability density function)
+- cdf (cumulative density function)
+- mc_cdf (Monte Carlo approximation of the cumulative density function)
+- rvs (random variate generator / sampler)
+- likelihood (likelihood function)
+- loglikelihood (log of the likelihood function)
+- aic (Akaike Information Criterion)
+- bic (Bayesian Information Criterion)
+- marginal_pairplot (pairplot of the marginal distributions)
+- pdf_plot (plot of the probability density function)
+- cdf_plot (plot of the cumulative density function)
+- mc_cdf_plot (plot of the Monte Carlo approximation of the cumulative density function)
+- num_params (The number of parameters in the distribution)
+- num_scalar_params (The number of scalar values across all parameters in the distribution)
+- fit (fitting the distribution to data)
+
+mc_cdf is a numerical approximation of the cumulative density function. This is usually necessary for distributions that do not have a closed form cumulative density function, as the numerical integration alternative is computationally expensive.
+
+num_params is the number of parameter objects in the distribution, i.e. a vector / matrix is counted as 1.
+num_scalar_params counts the number of unique scalar values across all parameter objects.
+
+FittedContinuousMultivariate
+----------------------------
+This class is the fitted version of PreFitContinuousMultivariate's subclasses.
+It implements the same methods as PreFitContinuousMultivariate, but does not require params as an argument.
+It also implements the following additional methods and attributes:
+
+- params (the fitted parameters)
+- num_variables (the number of variables the distribution is fitted too)
+- fitted_num_data_points (the number of observations used to fit the distribution)
+- converged (whether the fitting algorithm converged)
+- summary (a summary of the fitted distribution)
+- save (save the fitted distribution object)
+
 Multivariate Example
 ---------------------
 Here we use the multivariate normal and multivariate symmetric hyperbolic distributions, though all methods and attributes are generalized.::

docs/source/Univariate.rst

@@ -63,7 +63,8 @@ ppf_approx and cdf_approx are approximations of the ppf and cdf functions respec
 FittedUnivariateBase
 ---------------------
 This class is the fitted version of PreFitUnivariateBase's subclasses.
-It implements the same methods as PreFitUnivariateBase, but does not require params as an argument in addition to the following:
+It implements the same methods as PreFitUnivariateBase, but does not require params as an argument.
+It also implements the following additional methods and attributes:
 
 - summary (summary of the distribution fit)
 - params (the fitted parameters)

examples/multivariate_examples/multivariate_example.py

@@ -28,12 +28,12 @@
 fitted_msh = mvt_shyperbolic.fit(rvs_df, method='mle', show_progress=True)
 
 # printing our fitted parameters
-# print(fitted_msh.params.to_dict)
-# print(fitted_msh.params.cov)
+print(fitted_msh.params.to_dict)
+print(fitted_msh.params.cov)
 
 # printing a summary of our fit
-# print(fitted_msh.summary)
-#
+print(fitted_msh.summary)
+
 # # can plot
 fitted_msh.pdf_plot(show=False)
 fitted_msh.mc_cdf_plot(show=False)