> rvector <- function(n, mode = c("logical", "double", "integer"), range = c(-100, +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else {
+         x <- runif(n, min = range[1], max = range[2])
+     }
+     storage.mode(x) <- mode
+     if (na_prob > 0) 
+         x[sample(n, size = na_prob * n)] <- NA
+     x
+ }
> rvectors <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rvector(n = scale * 100, ...)
+     data[[2]] <- rvector(n = scale * 1000, ...)
+     data[[3]] <- rvector(n = scale * 10000, ...)
+     data[[4]] <- rvector(n = scale * 1e+05, ...)
+     data[[5]] <- rvector(n = scale * 1e+06, ...)
+     names(data) <- sprintf("n = %d", sapply(data, FUN = length))
+     data
+ }
> data <- rvectors(mode = "double")
> data <- data[1:3]

> x <- data[["n = 1000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3282833 175.4    5709258 305.0  5709258 305.0
Vcells 6806071  52.0   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE), 
+     `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x, 
+         w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")

> x <- data[["n = 10000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3281145 175.3    5709258 305.0  5709258 305.0
Vcells 6406585  48.9   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE), 
+     `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x, 
+         w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")

> x <- data[["n = 100000"]]
> w <- runif(length(x))
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3281226 175.3    5709258 305.0  5709258 305.0
Vcells 6497151  49.6   26329732 200.9 87357391 666.5
> stats <- microbenchmark(weightedMedian = weightedMedian(x, w = w, ties = "mean", na.rm = FALSE), 
+     `limma::weighted.median` = limma_weighted.median(x, w = w, na.rm = FALSE), `cwhmisc::w.median` = cwhmisc_w.median(x, 
+         w = w), `laeken::weightedMedian` = laeken_weightedMedian(x, w = w), unit = "ms")

R version 3.6.1 Patched (2019-08-27 r77078)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-3-6-branch/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] microbenchmark_1.4-6    matrixStats_0.55.0-9000 ggplot2_3.2.1          
[4] knitr_1.24              R.devices_2.16.0        R.utils_2.9.0          
[7] R.oo_1.22.0             R.methodsS3_1.7.1       history_0.0.0-9002     

loaded via a namespace (and not attached):
 [1] Biobase_2.45.0       bit64_0.9-7          splines_3.6.1       
 [4] network_1.15         assertthat_0.2.1     highr_0.8           
 [7] stats4_3.6.1         blob_1.2.0           robustbase_0.93-5   
[10] pillar_1.4.2         RSQLite_2.1.2        backports_1.1.4     
[13] lattice_0.20-38      limma_3.40.6         glue_1.3.1          
[16] digest_0.6.20        colorspace_1.4-1     sandwich_2.5-1      
[19] Matrix_1.2-17        XML_3.98-1.20        lpSolve_5.6.13.3    
[22] pkgconfig_2.0.2      genefilter_1.66.0    purrr_0.3.2         
[25] ergm_3.10.4          xtable_1.8-4         mvtnorm_1.0-11      
[28] scales_1.0.0         tibble_2.1.3         annotate_1.62.0     
[31] IRanges_2.18.2       TH.data_1.0-10       withr_2.1.2         
[34] BiocGenerics_0.30.0  lazyeval_0.2.2       mime_0.7            
[37] survival_2.44-1.1    magrittr_1.5         crayon_1.3.4        
[40] statnet.common_4.3.0 memoise_1.1.0        laeken_0.5.0        
[43] R.cache_0.13.0       MASS_7.3-51.4        R.rsp_0.43.1        
[46] tools_3.6.1          multcomp_1.4-10      S4Vectors_0.22.1    
[49] trust_0.1-7          munsell_0.5.0        AnnotationDbi_1.46.1
[52] compiler_3.6.1       rlang_0.4.0          grid_3.6.1          
[55] RCurl_1.95-4.12      cwhmisc_6.6          rappdirs_0.3.1      
[58] labeling_0.3         bitops_1.0-6         base64enc_0.1-3     
[61] boot_1.3-23          gtable_0.3.0         codetools_0.2-16    
[64] DBI_1.0.0            markdown_1.1         R6_2.4.0            
[67] zoo_1.8-6            dplyr_0.8.3          bit_1.1-14          
[70] zeallot_0.1.0        parallel_3.6.1       Rcpp_1.0.2          
[73] vctrs_0.2.0          DEoptimR_1.0-8       tidyselect_0.2.5    
[76] xfun_0.9             coda_0.19-3         

html <- matrixStats:::benchmark('weightedMedian')