rddtools: features list

rddtools: main features

  • Simple visualisation of the data using binned-plot: plot()

  • Bandwidth selection:
  • Estimation:
    • RDD parametric estimation: rdd_reg_lm() This includes specifying the polynomial order, including covariates with various specifications as advocated in Imbens and Lemieux 2008.
    • RDD local non-parametric estimation: rdd_reg_np(). Can also include covariates, and allows different types of inference (fully non-parametric, or parametric approximation).
    • RDD generalised estimation: allows to use custom estimating functions to get the RDD coefficient. Could allow for example a probit RDD, or quantile regression.
  • Post-Estimation tools:
    • Various tools, to obtain predictions at given covariate values ( rdd_pred() ), or to convert to other classes, to lm ( as.lm() ), or to the package np ( as.npreg() ).
    • Function to do inference with clustered data: clusterInf() either using a cluster covariance matrix ( vcovCluster() ) or by a degrees of freedom correction (as in Cameron et al. 2008).
  • Regression sensitivity analysis:
    • Plot the sensitivity of the coefficient with respect to the bandwith: plotSensi()
    • Placebo plot using different cutpoints: plotPlacebo()
  • Design sensitivity analysis:
    • McCrary test of manipulation of the forcing variable: wrapper dens_test() to the function DCdensity() from package rdd.
    • Test of equal means of covariates: covarTest_mean()
    • Test of equal density of covariates: covarTest_dens()
  • Datasets