code: def get_par_group_contribution(self, include_prior_results=False):
          
          pargrp_dict = {}
          par = self.pst.parameter_data
          groups = par.groupby("pargp").groups
          for grp,idxs in groups.items():
              #pargrp_dict[grp] = list(par.loc[idxs,"parnme"])
              pargrp_dict[grp] = [pname for pname in list(par.loc[idxs,"parnme"])
                                  if pname in self.jco.col_names and pname in self.parcov.row_names]
          return self.get_par_contribution(pargrp_dict,include_prior_results=include_prior_results)
docs: get the forecast uncertainty contribution from each parameter
          group.  Just some sugar for get_contribution_dataframe() - this method
          automatically constructs the parlist_dict argument where the keys are the
          group names and the values are the adjustable parameters in the groups
  
          Parameters
          ----------
          include_prior_results : bool
              flag to return a multi-indexed dataframe with both conditional
              prior and posterior forecast uncertainty estimates.  Default is False
  
  
          Returns
          -------
          pandas.DataFrame : pandas.DataFrame
              a dataframe that summarizes the parameter contribution analysis.
              The dataframe has index (row labels) that are the parameter groups
              and a column labels of forecast names.  The values in the dataframe
              are the posterior variance of the forecast conditional on perfect
              knowledge of the adjustable parameters in each parameter groups
              Varies depending on `include_prior_results`.