Regression Discontinuity Designs as Local Randomized Experiments

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Documentation for package ‘LRErdd’ version 0.1.0

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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
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
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
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
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
RegressionDiscontinuityClass Class providing object with RegressionDiscontinuityClass
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