E.step.bin              Expectation step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for binary outcomes
E.step.bin2             Expectation step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for binary outcomes
E.step.gauss            Expectation step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for Gaussian outcomes
E.step.gauss2           Expectation step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for Gaussian outcomes
EM.bin                  Maximum Likelihood Estimate (MLE) of the
                        Complier Average Causal Effect (CACE) for
                        binary outcomes
EM.bin2                 Maximum Likelihood Estimate (MLE) of the
                        Complier Average Causal Effect (CACE) for
                        binary outcomes
EM.gauss                Maximum Likelihood Estimate (MLE) of the
                        Complier Average Causal Effect (CACE) for
                        Gaussian outcomes
EM.gauss2               Maximum Likelihood Estimate (MLE) of the
                        Complier Average Causal Effect (CACE) for
                        Gaussian outcomes
M.step.bin              Maximization step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for binary outcomes
M.step.bin2             Maximization step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for binary outcomes
M.step.gauss            Maximization step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for Gaussian outcomes
M.step.gauss2           Maximization step of the
                        Expectation-Maximization (EM) algorithm to
                        calculate the MLE of the Complier Average
                        Causal Effect (CACE) for Gaussian outcomes
RegressionDiscontinuityClass
                        Class providing object with
                        RegressionDiscontinuityClass
all_binom2num           From binomial to numerical
cutting_range           Selection of the cutting range
da.bin                  Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for binary outcomes
da.bin.h0               Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for a binary outcome under the
                        sharp null hypothesis of no effect
da.bin.h02              Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for a binary outcome under the
                        sharp null hypothesis of no effect
da.bin2                 Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for binary outcomes
da.gauss                Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for Gaussian outcomes
da.gauss.h0             Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for a Gaussian outcome under the
                        sharp null hypothesis of no effect
da.gauss.h02            Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for a Gaussian outcome under the
                        sharp null hypothesis of no effect
da.gauss2               Data Augmentation (DA) step of Markov Chain
                        Monte Carlo (MCMC) algorithm to derive the
                        posterior median of the Complier Average Causal
                        Effect (CACE) for Gaussian outcomes
fuzzy_fep1sided         Fuzzy - FEP approach for binary one sided
fuzzy_fep2sided         Fuzzy - FEP binary two sided
fuzzy_fep_bw            Bandwidth selection for Fuzzy - FEP bandwidth
fuzzy_fep_numeric1sided
                        Fuzzy - FEP 1 sided numerical
fuzzy_fep_numeric2sided
                        Fuzzy - FEP 2 sided numerical
fuzzy_neyman            Fuzzy - neyman approach
fuzzy_neyman_bw         Bandwidth selection for Fuzzy - neyman
                        bandwidth
grants                  Italian university grants and student dropout
mcmc.bin                Posterior median of the Complier Average Causal
                        Effect (CACE) for binary outcomes
mcmc.bin.h0             Posterior distributions of the parameter vector
                        and the compliance status of each unit for a
                        binary outcome under the sharp null hypothesis
                        of no effect
mcmc.bin.h02            Posterior distributions of the parameter vector
                        and the compliance status of each unit for a
                        binary outcome under the sharp null hypothesis
                        of no effect
mcmc.bin2               Posterior median of the Complier Average Causal
                        Effect (CACE) for binary outcomes
mcmc.gauss              Posterior median of the Complier Average Causal
                        Effect (CACE) for Gaussian outcomes
mcmc.gauss.h0           Posterior distributions of the parameter vector
                        and the compliance status of each unit for a
                        Gaussian outcome under the sharp null
                        hypothesis of no effect
mcmc.gauss.h02          Posterior distributions of the parameter vector
                        and the compliance status of each unit for a
                        Gaussian outcome under the sharp null
                        hypothesis of no effect
mcmc.gauss2             Posterior median of the Complier Average Causal
                        Effect (CACE) for Gaussian outcomes
open_LRErdd_framework   Example of 'LRErdd' in shiny version
rand_pajd               Randomization-based tests adjusted for multiple
                        testing
rand_pajd_bw            Bandwidth selection for Randomization-based
                        tests adjusted for multiple testing
sharp_fep               Calculate Fisher Exact p-value
sharp_fep_bw            Bandwidth selection for Fisher Exact p-value
sharp_neyman            SHARP RDD: RANDOMIZATION-BASED INFERENCE -
                        NEYMAN APPROACH
sharp_neyman_bw         Bandwidth selection for Sharp - neyman approach
