docs

README.md

@@ -34,21 +34,12 @@ about their properties (see [DeclareDesign](https://declaredesign.org)).
 ## Installing estimatr
 
 To install the latest stable release of **estimatr**, please ensure that
-you are running version 3.4 or later of R and run the following code:
+you are running version 3.5 or later of R and run the following code:
 
 ``` r
 install.packages("estimatr")
 ```
 
-If you would like to use the latest development release of **estimatr**,
-please ensure that you are running version 3.4 or later of R and run the
-following code:
-
-``` r
-install.packages("estimatr", dependencies = TRUE,
-                 repos = c("http://r.declaredesign.org", "https://cloud.r-project.org"))
-```
-
 ## Easy to use
 
 Once the package is installed, getting appropriate estimates and
@@ -130,12 +121,15 @@ mb <- microbenchmark(
     coeftest(lo, vcov = vcovHC(lo, type = 'HC2'))
   }
 )
+#> Warning in microbenchmark(estimatr = lm_robust(y ~ ., data = dat), `lm +
+#> sandwich` = {: less accurate nanosecond times to avoid potential integer
+#> overflows
 ```
 
 | estimatr      | median run-time (ms) |
 |:--------------|---------------------:|
-| estimatr      |                   21 |
-| lm + sandwich |                   41 |
+| estimatr      |                  7.2 |
+| lm + sandwich |                 24.6 |
 
 ------------------------------------------------------------------------
 

_pkgdown.yml

@@ -1,24 +1,33 @@
+url: https://declaredesign.org/r/estimatr
+
 template:
-  package: ddpkgdowntemplate
+  bootstrap: 5
+  bslib:
+    primary: "#0054AD"
+    border-radius: 0.5rem
+    btn-border-radius: 0.25rem
 
-url: https://declaredesign.org/r/estimatr
+development:
+  mode: auto
 
 navbar:
-  left:
+  right:
   - text: "Software"
     menu:
       - text: DeclareDesign
-        href: http://declaredesign.org/r/declaredesign/
+        href: https://declaredesign.org/r/DeclareDesign/
       - text: randomizr
         href: https://declaredesign.org/r/randomizr/
       - text: fabricatr
         href: https://declaredesign.org/r/fabricatr/
+      - text: estimatr
+        href: https://declaredesign.org/r/estimatr/
       - text: DesignLibrary
         href: https://declaredesign.org/r/designlibrary/
-  - text: "Help"
-    href: http://discuss.declaredesign.org
-  right:
-  - text: Get started
+  - text: declaredesign.org
+    href: https://declaredesign.org
+  left:
+  - text: Getting started
     href: articles/getting-started.html
   - text: Reference
     href: reference/index.html
@@ -44,5 +53,3 @@ navbar:
        href: articles/simulations-ols-variance.html
      - text: How Stata's hat matrix differs with weights
        href: articles/stata-wls-hat.html
-  - text: estimatr home
-    href: https://declaredesign.org/r/estimatr/

vignettes/simulations-debiasing-dim.Rmd

@@ -55,7 +55,7 @@ simp_blocks <- declare_model(
   )
 )
 # Complete random assignment of half of units in each block
-blocked_assign <- declare_assignment(Z = block_ra(blocks = blocks, prob = 0.5), legacy = FALSE)
+blocked_assign <- declare_assignment(Z = block_ra(blocks = blocks, prob = 0.5))
 # Estimand is the ATE
 ate <- declare_inquiry(ATE = mean(Y_Z_1 - Y_Z_0))
 outcomes <- declare_measurement(Y = reveal_outcomes(Y ~ Z))
@@ -65,7 +65,7 @@ Now let's define our two estimators, one that doesn't account for blocking and o
 
 ```{r}
 dim <- declare_estimator(Y ~ Z, inquiry = "ATE", label = "DIM")
-dim_bl <- declare_estimator(Y ~ Z, blocks = blocks, inquiry = "ATE", label = "DIM blocked")
+dim_bl <- declare_estimator(Y ~ Z, .method = difference_in_means, blocks = blocks, inquiry = "ATE", label = "DIM blocked")
 
 # Our design
 simp_bl_des <- simp_blocks +
@@ -136,10 +136,11 @@ corr_blocks <- declare_model(
   ),
   individual = add_level(
     N = indivs_per_block,
-    epsilon = rnorm(N, sd = 3),
-    potential_outcomes(Y ~ Z * 0.5 * block_shock + epsilon)
+    epsilon = rnorm(N, sd = 3)
+    
   )
-)
+) + 
+  declare_model(potential_outcomes(Y ~ Z * 0.5 * block_shock + epsilon))
 # Use same potential outcomes as above, but now treatment probability varies across blocks
 corr_blocked_assign <- declare_assignment(
   Z = block_ra(
@@ -150,7 +151,8 @@ corr_blocked_assign <- declare_assignment(
   ),
   legacy = FALSE
 )
-corr_bl_des <- corr_blocks +
+corr_bl_des <- 
+  corr_blocks +
   corr_blocked_assign +
   ate +
   outcomes +
@@ -295,18 +297,18 @@ diff_size_cls <- declare_model(
   ),
   individual = add_level(
     N = indivs_per_clust,
-    epsilon = rnorm(N, sd = 3),
-    potential_outcomes(Y ~ Z * 0.4 * clust_shock + epsilon)
+    epsilon = rnorm(N, sd = 3)
   )
-)
-clustered_assign <- declare_assignment(Z = cluster_ra(clusters = clusters, prob = 0.5), legacy = FALSE)
+) + 
+  declare_model(potential_outcomes(Y ~ Z * 0.4 * clust_shock + epsilon))
+clustered_assign <- declare_assignment(Z = cluster_ra(clusters = clusters, prob = 0.5))
 
 ht_cl <- declare_estimator(
   formula = Y ~ Z,
   clusters = clusters,
   condition_prs = condition_pr,
   simple = FALSE,
-  model = estimatr::horvitz_thompson,
+  .method = estimatr::horvitz_thompson,
   inquiry = "ATE",
   label = "Horvitz-Thompson Clustered"
 )