**rddtools** is an R package designed to offer a set of
tools to run all the steps required for a Regression Discontinuity
Design (RDD) Analysis, from primary data visualisation to discontinuity
estimation, sensitivity and placebo testing.

This github website hosts the source code. One of the easiest ways to
install the package from github is by using the R package
**devtools**:

```
if (!require('remotes')) install.packages('remotes')
::install_github('bquast/rddtools') remotes
```

Note however the latest version of rddtools only works with R 3.0, and that you might need to install Rtools if on Windows.

The (preliminary) documentation is available in the help files
directly, as well as in the *vignettes*. The vignettes can be
accessed from R.

`vignette('rddtools')`

Simple visualisation of the data using binned-plot:

`plot()`

Bandwidth selection:

- MSE-RDD bandwidth procedure of Imbens
and Kalyanaraman 2012:
`rdd_bw_ik()`

- MSE global bandwidth procedure of Ruppert
et al 1995:
`rdd_bw_rsw()`

- MSE-RDD bandwidth procedure of Imbens
and Kalyanaraman 2012:
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.

- RDD parametric estimation:
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).

- Various tools, to obtain predictions at given covariate values (
Regression sensitivity analysis:

- Plot the sensitivity of the coefficient with respect to the
bandwith:
`plotSensi()`

*Placebo plot*using different cutpoints:`plotPlacebo()`

- Plot the sensitivity of the coefficient with respect to the
bandwith:
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()`

- McCrary test of manipulation of the forcing variable: wrapper
Datasets

- Contains the seminal dataset of Lee
2008:
`house`

- Contains functions to replicate the Monte-Carlo simulations of Imbens
and Kalyanaraman 2012:
`gen_mc_ik()`

- Contains the seminal dataset of Lee
2008: