Merge branch 'master' of github.com:DoubleML/doubleml-for-r into store-models

Author: MalteKurz

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: DoubleML
 Type: Package
 Title: Double Machine Learning in R
-Version: 0.4.0.9000
+Version: 0.5.0.9000
 Authors@R: c(
     person("Philipp", "Bach", email = "philipp.bach@uni-hamburg.de", role="aut"),
     person("Victor", "Chernozhukov", role="aut"),

R/double_ml.R

@@ -398,7 +398,7 @@ DoubleML = R6Class("DoubleML",
           }
 
           # ml estimation of nuisance models and computation of psi elements
-          res = private$ml_nuisance_and_score_elements(private$get__smpls())
+          res = private$nuisance_est(private$get__smpls())
           private$psi_a_[, private$i_rep, private$i_treat] = res$psi_a
           private$psi_b_[, private$i_rep, private$i_treat] = res$psi_b
           if (store_predictions) {
@@ -837,7 +837,7 @@ DoubleML = R6Class("DoubleML",
         if (tune_on_folds) {
           for (i_rep in 1:self$n_rep) {
             private$i_rep = i_rep
-            param_tuning = private$ml_nuisance_tuning(
+            param_tuning = private$nuisance_tuning(
               private$get__smpls(),
               param_set, tune_settings, tune_on_folds)
             private$tuning_res_[[i_treat]][[i_rep]] = param_tuning
@@ -856,7 +856,7 @@ DoubleML = R6Class("DoubleML",
           }
         } else {
           private$i_rep = 1
-          param_tuning = private$ml_nuisance_tuning(
+          param_tuning = private$nuisance_tuning(
             private$get__smpls(),
             param_set, tune_settings, tune_on_folds)
           private$tuning_res_[[i_treat]] = param_tuning

R/double_ml_iivm.R

@@ -276,7 +276,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
         "ml_r1" = nuisance)
       invisible(self)
     },
-    ml_nuisance_and_score_elements = function(smpls, ...) {
+    nuisance_est = function(smpls, ...) {
 
       if (self$subgroups$always_takers == FALSE &
         self$subgroups$never_takers == FALSE) {
@@ -374,8 +374,10 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
         "ml_r1" = r1_hat$models)
       return(res)
     },
-    score_elements = function(y, z, d, g0_hat, g1_hat, m_hat, r0_hat,
-      r1_hat, smpls) {
+    score_elements = function(y = y, z = z, d = d,
+      g0_hat = g0_hat, g1_hat = g1_hat, m_hat = m_hat,
+      r0_hat = r0_hat, r1_hat = r1_hat,
+      smpls = smpls) {
 
       u0_hat = y - g0_hat
       u1_hat = y - g1_hat
@@ -402,7 +404,7 @@ DoubleMLIIVM = R6Class("DoubleMLIIVM",
       }
       return(psis)
     },
-    ml_nuisance_tuning = function(smpls, param_set, tune_settings,
+    nuisance_tuning = function(smpls, param_set, tune_settings,
       tune_on_folds, ...) {
 
       if (!tune_on_folds) {

R/double_ml_irm.R

@@ -214,7 +214,7 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
         "ml_m" = nuisance)
       invisible(self)
     },
-    ml_nuisance_and_score_elements = function(smpls, ...) {
+    nuisance_est = function(smpls, ...) {
 
       cond_smpls = get_cond_samples(
         smpls,
@@ -303,11 +303,14 @@ DoubleMLIRM = R6Class("DoubleMLIRM",
         }
         psis = list(psi_a = psi_a, psi_b = psi_b)
       } else if (is.function(self$score)) {
-        psis = self$score(y, d, g0_hat, g1_hat, m_hat, smpls)
+        psis = self$score(
+          y = y, d = d,
+          g0_hat = g0_hat, g1_hat = g1_hat, m_hat = m_hat,
+          smpls = smpls)
       }
       return(psis)
     },
-    ml_nuisance_tuning = function(smpls, param_set, tune_settings,
+    nuisance_tuning = function(smpls, param_set, tune_settings,
       tune_on_folds, ...) {
 
       if (!tune_on_folds) {