Add covr

.Rbuildignore

@@ -7,3 +7,4 @@
 readme.md
 notes.org
 .lintr
+^codecov\.yml$

.travis.yml

@@ -7,3 +7,7 @@ addons:
   apt:
     packages:
       - ghostscript
+r_packages:
+  - covr
+after_success:
+  - Rscript -e 'covr::codecov()'

codecov.yml

@@ -0,0 +1,12 @@
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+    patch:
+      default:
+        target: auto
+        threshold: 1%

doc/RDHonest.R

@@ -0,0 +1,87 @@
+## ---- include=FALSE, cache=FALSE-----------------------------------------
+library("knitr")
+knitr::opts_knit$set(self.contained = FALSE)
+knitr::opts_chunk$set(tidy = TRUE, collapse=TRUE, comment = "#>",
+                      tidy.opts=list(blank=FALSE, width.cutoff=55))
+
+## ------------------------------------------------------------------------
+library("RDHonest")
+## Assumes first column in the data frame corresponds to outcome,
+## and second to running variable
+dl <- RDData(lee08, cutoff = 0)
+
+## Transform earnings to log earnings
+do <- RDData(data.frame(logearn=log(cghs$earnings),
+                        year14=cghs$yearat14), cutoff = 1947)
+
+## ---- fig.width=4.5, fig.height=3.5, fig.cap="Lee (2008) data"-----------
+## plot 25-bin averages in for observations 50 at most points away from the cutoff.
+## See Figure 1
+plot_RDscatter(dl, avg=25, window = 50, xlab="Margin of victory",
+    ylab="Vote share in next election")
+
+## ---- fig.width=4.5, fig.height=3.5, fig.cap="Oreopoulos (2006) data"----
+## see Figure 2
+f2 <- plot_RDscatter(do, avg=Inf, xlab="Year aged 14", ylab="Log earnings",
+    propdotsize=TRUE)
+## Adjust size of dots if they are too big
+f2 + ggplot2::scale_size_area(max_size = 4)
+
+## ------------------------------------------------------------------------
+## Usual critical value
+CVb(0, alpha=0.05) # returns a list
+CVb(1/2, alpha=0.05)$cv # extract critical value
+
+## Tabulate critical values for different significance levels
+## when bias-sd ratio equals 1/4
+knitr::kable(CVb(1/4, alpha=c(0.01, 0.05, 0.1)), caption="Critical values")
+
+## ------------------------------------------------------------------------
+RDHonest(voteshare ~ margin, data=lee08, kern="uniform", M=0.1, hp=10, sclass="T")
+RDHonest(voteshare ~ margin, data=lee08, kern="uniform", M=0.1, hp=10, sclass="H")
+
+## ------------------------------------------------------------------------
+RDHonest(voteshare ~ margin, data=lee08, kern="triangular",
+    M=0.1, opt.criterion="MSE", sclass="H")
+## Choose bws optimal for length of CI, allowing for different bws
+## on either side of cutoff
+RDHonest(voteshare ~ margin, data=lee08, kern="triangular", M=0.1,
+    opt.criterion="FLCI", sclass="H", bw.equal=FALSE)
+
+## ------------------------------------------------------------------------
+RDOptBW(voteshare ~ margin, data=lee08, kern="triangular",
+    M=0.1, opt.criterion="MSE", sclass="H")
+
+## ------------------------------------------------------------------------
+## Replicate Table 2, column (10)
+RDHonest(log(earnings) ~ yearat14, cutoff=1947,
+    data=cghs, kern="uniform", M=0.04, opt.criterion="FLCI", sclass="H")
+## Triangular kernel generally gives tigher CIs
+RDHonest(log(earnings) ~ yearat14, cutoff=1947,
+    data=cghs, kern="triangular", M=0.04, opt.criterion="FLCI", sclass="H")
+
+## ------------------------------------------------------------------------
+## Replicate Table 2, column (6), run local linear regression (order=1)
+## with a uniform kernel (other kernels are not yet implemented)
+RDHonestBME(log(earnings) ~ yearat14, cutoff=1947,
+    data=cghs, hp=3, order=1)
+
+## ------------------------------------------------------------------------
+2*RDHonest(voteshare ~ margin, data=lee08, kern="optimal", M=0.1, opt.criterion="FLCI", se.initial="Silverman", se.method="nn")$hl
+
+2*RDHonest(voteshare ~ margin, data=lee08, kern="triangular", M=0.1, opt.criterion="FLCI", se.initial="Silverman", se.method="nn", sclass="T")$hl
+
+
+## ------------------------------------------------------------------------
+## Add variance estimate to the lee data so that the RDSmoothnessBound
+## function doesn't have to compute them each time
+dl <- RDHonest::RDPrelimVar(dl, se.initial="NN")
+
+### Only use three point-average for averages of a 100 points closest to cutoff,
+### and report results separately for points above and below cutoff
+RDSmoothnessBound(dl, s=100, separate=TRUE, multiple=FALSE, sclass="T")
+
+### Pool estimates based on observations below and above cutoff, and
+### use three-point averages over the entire support of the running variable
+RDSmoothnessBound(dl, s=100, separate=FALSE, multiple=TRUE, sclass="H")
+

readme.md

@@ -10,9 +10,9 @@ using procedures from [Armstrong and Kolesár
 (2018)](https://doi.org/10.1257/aer.20160945)
 ([preprint](https://arxiv.org/abs/1606.04086))
 
-See vignette `RDHonest.pdf` (stored under `doc/`) for description of the
-package, and `manual.pdf` (also stored under `doc/`) for documentation of the
-package functions.
+See vignette [RDHonest](doc/RDHonest.pdf) for description of the package
+(available through `vignette("RDHonest")` once package is installed), and the
+package [manual](doc/manual.pdf) for documentation of the package functions.
 
 This software package is based upon work supported by the National Science
 Foundation under grant numbers SES-1628939 (Armstrong) and SES-1628878