version 0.3.4 fix cran error due to bug in vignette

Author: s3alfisc

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: fwildclusterboot
 Title: Fast Wild Cluster Bootstrap Inference for Linear Regression Models
-Version: 0.3.3
+Version: 0.3.4
 Date: 2021-04-12
 Authors@R: 
     c(

NEWS.md

@@ -1,3 +1,7 @@
+# fwildclusterboot 0.3.4
+
++ Fix CRAN errors caused by a small bug in the vignette
+
 # fwildclusterboot 0.3.3
 
 + implements full enumeration for Rademacher and Mammen Weights if $2^k<B$, where k is the number of clusters and B the number of bootstrap iterations

R/boot_algo2.R

@@ -199,11 +199,35 @@ boot_algo2 <- function(preprocessed_object, boot_iter, point_estimate, impose_nu
         S_diag_XinvXXRu_S_b <- Matrix.utils::aggregate.Matrix(diag_XinvXXRuS_b, clustid[x])
         K_b <- S_diag_XinvXXRu_S_b - tcrossprod(SXinvXXrX_invXX, SuXb)
 
-        C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
-        D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
+        bigstatsr <- FALSE
+        if(bigstatsr == FALSE){
+          C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
+          D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
+        } else if(bigstatr == TRUE){
+          
+          # matrix multiplication via bigstatsr
+          C_fbm <- FBM(dim(K_a)[1], B)
+          D_fbm <- FBM(dim(K_b)[1], B)
+          # C
+          big_apply(C, a.FUN = function(C_fbm, ind, K_a, v) {
+            C_fbm[, ind] <- tcrossprod(K_a, v[ind, ])
+            NULL  ## you don't want to return anything here
+          }, a.combine = 'c', ncores = nthreads, K_a = K_a, v = v)
+          # D
+          big_apply(D, a.FUN = function(D_fbm, ind, K_b, v) {
+            D_fbm[, ind] <- tcrossprod(K_b, v[ind, ])
+            NULL  ## you don't want to return anything here
+          }, a.combine = 'c', ncores = nthreads, K_b = K_b, v = v)
+          
+          C <- C_fbm[]
+          D <- D_fbm[]
+          
+        }
+        
         CC[[x]] <- colSums(C * C)
         DD[[x]] <- colSums(D * D)
         CD[[x]] <- colSums(C * D)
+        
       }
     } else if (!is.null(W)) {
       # project out fe
@@ -225,8 +249,32 @@ boot_algo2 <- function(preprocessed_object, boot_iter, point_estimate, impose_nu
         S_diag_XinvXXRu_S_b <- S_diag_XinvXXRu_S_b - prod_b
         K_b <- S_diag_XinvXXRu_S_b - tcrossprod(SXinvXXrX_invXX, SuXb)
 
-        C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
-        D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
+        bigstatsr <- FALSE
+        if(bigstatsr == FALSE){
+          
+          C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
+          D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
+          
+        } else if(bigstatr == TRUE){
+          
+          # matrix multiplication via bigstatsr
+          C_fbm <- FBM(dim(K_a)[1], B)
+          D_fbm <- FBM(dim(K_b)[1], B)
+          # C
+          big_apply(C, a.FUN = function(C_fbm, ind, K_a, v) {
+            C_fbm[, ind] <- tcrossprod(K_a, v[ind, ])
+            NULL  ## you don't want to return anything here
+          }, a.combine = 'c', ncores = nthreads, K_a = K_a, v = v)
+          # D
+          big_apply(D, a.FUN = function(D_fbm, ind, K_b, v) {
+            D_fbm[, ind] <- tcrossprod(K_b, v[ind, ])
+            NULL  ## you don't want to return anything here
+          }, a.combine = 'c', ncores = nthreads, K_b = K_b, v = v)
+          
+          C <- C_fbm[]
+          D <- D_fbm[]
+          
+        }
         CC[[x]] <- colSums(C * C)
         DD[[x]] <- colSums(D * D)
         CD[[x]] <- colSums(C * D)

README.md

@@ -4,8 +4,7 @@
 # fwildclusterboot
 
 <!-- badges: start -->
-
-<!-- [![packageversion](https://img.shields.io/badge/Package%20version-x86_64-w64-mingw32, x86_64, mingw32, x86_64, mingw32, , 4, 0.4, 2021, 02, 15, 80002, R, R version 4.0.4 (2021-02-15), Lost Library Book-orange.svg?style=flat-square)](commits/master) -->
+<!-- [![packageversion](https://img.shields.io/badge/Package%20version-x86_64-w64-mingw32, x86_64, mingw32, x86_64, mingw32, , 4, 0.5, 2021, 03, 31, 80133, R, R version 4.0.5 (2021-03-31), Shake and Throw-orange.svg?style=flat-square)](commits/master) -->
 
 [![Lifecycle:
 experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
@@ -43,9 +42,7 @@ subcluster bootstrapping for few treated clusters [(MacKinnon & Webb,
 (2018))](https://academic.oup.com/ectj/article-abstract/21/2/114/5078969).
 
 <!-- The following features will be added in the future:  -->
-
 <!-- * support for multivariate hypotheses  -->
-
 <!-- * bootstrap distributions beyond the rademacher distribution -->
 
 ### The `boottest()` function
@@ -73,7 +70,7 @@ summary(feols_boot)
 #>  Number of Clusters: 40
 #> 
 #>        term estimate statistic p.value conf.low conf.high
-#> 1 treatment    0.079     4.123       0    0.039     0.118
+#> 1 treatment    0.079     4.123       0    0.039     0.119
 ```
 
 For a longer introduction to the package’s key function, `boottest()`,