HPDI_from_stanfit       Calculate HPDI for all parameters from a
                        stanfit object Here we use the coda package
HPD_higher_from_column
                        Returns the higher value of the HPD interval
                        for a data frame column
HPD_lower_from_column   Returns the lower value of the HPD interval for
                        a data frame column
bpc                     Bayesian Paired comparison regression models in
                        Stan
bpcs-package            bpcs - A package for Bayesian Paired Comparison
                        analysis with Stan
brasil_soccer_league    This is a dataset with the results matches
                        fromo the first league of the Brazilian soccer
                        championship from 2017-2019. It was reduced and
                        translatedfrom the adaduque/Brasileirao_Dataset
                        repository
check_if_there_are_na   Check for NA in the specfic columns and returns
                        T or F is there is at least 1 NA in those
                        columns
check_if_there_are_ties
                        Check if a data frame column contains ties
check_numeric_predictor_matrix
                        Check if all values in the predictor matrix are
                        numeric and not NA. Note that TRUE will be cast
                        to 1 and FALSE will be cast to 0
check_predictors_df_contains_all_players
                        Check if the predictor df contains all players
                        and only those
check_result_column     Check if a data frame column contains only the
                        values 1 0 and 2. Used to check the format of
                        the results
check_z_column          Check if a data frame column contains only the
                        values 1 or 0. For the z column
compute_scores          Giving a player0 an player1 scores, this
                        functions adds one column to the data frame
                        containing who won (0= player0 1=player1 2=tie)
                        and another if it was a tie. The ties column
                        superseeds the y column. If it was tie the y
                        column does not matter y column: (0= player0
                        1=player1 2=tie) ties column (0=not tie, 1=tie)
compute_ties            Giving a result column we create a new column
                        with ties (0 and 1 if it has)
create_array_of_par_names
                        Create an array with the parameter name and to
                        what player/cluster it refers to in the order
                        stan presents
create_bpc_object       Defines the class bpc and creates the bpc
                        object. To create we need to receive some
                        defined parameters (the arguments from the bpc
                        function), a lookup table and a the stanfit
                        object generated from the rstan sampling
                        procedure
create_cluster_index    Create two columns with the indexes for the
                        names of the players Here we create a new
                        lookup table. Should be used when sampling the
                        parameters
create_cluster_index_with_existing_lookup_table
                        Create two columns with the indexes for the
                        names Here we use an existing lookup table.
                        Should be used in predicting
create_index            Create two columns with the indexes for the
                        names of the players Here we create a new
                        lookup table. Should be used when sampling the
                        parameters
create_index_cluster_lookuptable
                        Create a lookup table of names and indexes Note
                        that the indexes will be created in the order
                        they appear. For string this does not make much
                        difference but for numbers the index might be
                        different than the actual number that appears
                        in names
create_index_lookuptable
                        Create a lookup table of names and indexes Note
                        that the indexes will be created in the order
                        they appear. For string this doesnt make much
                        difference but for numbers the index might be
                        different than the actual number that appears
                        in names
create_index_predictors_with_lookup_table
                        Receives one column with player names and
                        returns a data frame with the relevant index
                        columns based on a given lookup table To be
                        used with the predictors data frame
create_index_with_existing_lookup_table
                        Create two columns with the indexes for the
                        names Here we use an existing lookup table.
                        Should be used in predicting
create_predictor_matrix_with_player_lookup_table
                        Receives a predictor dataframe, a string with
                        the column of the player, a vector of strings
                        with the columns for the predictors and a
                        lookup table and returns an ordered matrix for
                        Stan To be used with the predictors data frame
create_predictors_lookup_table
                        Receives a vector with predictors strings (the
                        column names) and returns a
                        predictor_lookup_table
expand_aggregated_data
                        Expand aggregated data Several datasets for the
                        Bradley-Terry Model aggregate the number of
                        wins for each player in a different column. The
                        models we provide are intended to be used in a
                        long format. A single result for each contest.
                        This function expands datasets that have
                        aggregated data into this long format.
get_hpdi_parameters     Return the mean and the HPDI of the parameters
                        of the model
get_loo                 Tiny wrapper for the PSIS-LOO-CV method from
                        the loo package.
get_model_parameters    Return all the name of parameters in a model
                        from a bpc_object. Here we exclude the log_lik
                        and the lp__ since they are not parameters of
                        the model
get_probabilities       Get the empirical win/draw probabilities based
                        on the ability/strength parameters. Instead of
                        calculating from the probability formula given
                        from the model we create a predictive posterior
                        distribution for all pair combinations and
                        calculate the posterior wins/loose/draw The
                        function returns the mean value of
                        win/loose/draw for the player i. To calculate
                        for player j the probability is 1-p_i
get_rank_of_players     Generate a ranking of the ability based on
                        sampling the posterior distribution of the
                        ranks.
get_sample_posterior    Get the posterior samples for a parameter of
                        the model.
get_stanfit             Retrieve the stanfit object generated by rstan.
get_stanfit_summary     Get stanfit summary table of all parameters
                        excluding log_lik.
get_waic                Tiny wrapper for the WAIC method from the loo
                        package.
inv_logit               Inverse logit function
launch_shinystan        Tiny wrapper to launch a shinystan app to
                        investigate the MCMC.
logit                   Logit function
match_cluster_names_to_cluster_lookup_table
                        Receives a column with cluster names and
                        returns a data frame with the relevant index
                        column based on a given cluster lookup table
match_player_names_to_lookup_table
                        Receives two columns with player names and
                        returns a data frame with the relevant index
                        columns based on a given lookup table
optimization_algorithms
                        Dataset containing an example of the
                        performance of different optimization
                        algorithms against different benchmark
                        functions. This is a reduced version of the
                        dataset presented at the paper: "Statistical
                        Models for the Analysis of Optimization
                        Algorithms with Benchmark Functions.". For
                        details on how the data was collected we refer
                        to the paper.
predict.bpc             Predict results for new data.
print.bpc               Print method for the bpc object.
replace_parameter_index_with_names
                        Replace the name of the parameter from index to
                        name using a lookup_table Receives a data frame
                        and returns a dataframe.
sample_stanfit          Return a data frame by resampling the posterior
                        from a stanfit Here we select a parameter,
                        retrieve the all the posterior from the stanfit
                        and then we resample this posterior n times
summary.bpc             Summary of the model bpc model.
tennis_agresti          This is the expansion of the tennis data from
                        Agresti (2003) p.449 This data refers to
                        matches for several women tennis players during
                        1989 and 1990
