colRowQuantiles - HenrikBengtsson/matrixStats GitHub Wiki

matrixStats: Benchmark report

colQuantiles() and rowQuantiles() benchmarks

This report benchmark the performance of colQuantiles() and rowQuantiles() against alternative methods.

Alternative methods

  • apply() + quantile()

Data

> rmatrix <- function(nrow, ncol, mode = c("logical", "double", "integer", "index"), range = c(-100, 
+     +100), na_prob = 0) {
+     mode <- match.arg(mode)
+     n <- nrow * ncol
+     if (mode == "logical") {
+         x <- sample(c(FALSE, TRUE), size = n, replace = TRUE)
+     }     else if (mode == "index") {
+         x <- seq_len(n)
+         mode <- "integer"
+     }     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
+     dim(x) <- c(nrow, ncol)
+     x
+ }
> rmatrices <- function(scale = 10, seed = 1, ...) {
+     set.seed(seed)
+     data <- list()
+     data[[1]] <- rmatrix(nrow = scale * 1, ncol = scale * 1, ...)
+     data[[2]] <- rmatrix(nrow = scale * 10, ncol = scale * 10, ...)
+     data[[3]] <- rmatrix(nrow = scale * 100, ncol = scale * 1, ...)
+     data[[4]] <- t(data[[3]])
+     data[[5]] <- rmatrix(nrow = scale * 10, ncol = scale * 100, ...)
+     data[[6]] <- t(data[[5]])
+     names(data) <- sapply(data, FUN = function(x) paste(dim(x), collapse = "x"))
+     data
+ }
> data <- rmatrices(mode = "double")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3179358 169.8    5709258 305.0  5709258 305.0
Vcells 6475075  49.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177679 169.8    5709258 305.0  5709258 305.0
Vcells 6470221  49.4   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 0.204771 0.2105605 0.2178498 0.215639 0.2237735 0.277387
2 apply+quantile 0.749683 0.7618105 0.7855426 0.778345 0.8060455 1.075560
expr min lq mean median uq max
1 colQuantiles 1.00000 1.000000 1.000000 1.000000 1.00000 1.000000
2 apply+quantile 3.66108 3.618012 3.605891 3.609482 3.60206 3.877471

Table: Benchmarking of rowQuantiles() and apply+quantile() on 10x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 0.206766 0.2157140 0.2280534 0.224474 0.2295620 0.284570
2 apply+quantile 0.752545 0.7676515 0.8118481 0.801476 0.8111235 1.064999
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 3.639597 3.558654 3.559904 3.570463 3.533353 3.742485

Figure: Benchmarking of colQuantiles() and apply+quantile() on 10x10 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 10x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 204.771 210.5605 217.8498 215.639 223.7735 277.387
2 rowQuantiles 206.766 215.7140 228.0534 224.474 229.5620 284.570
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles 1.009743 1.024475 1.046838 1.040971 1.025868 1.025895

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3176235 169.7    5709258 305.0  5709258 305.0
Vcells 6086658  46.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3176226 169.7    5709258 305.0  5709258 305.0
Vcells 6096696  46.6   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 1.667526 1.717488 1.805961 1.755473 1.828817 2.532032
2 apply+quantile 7.725793 7.869764 8.623150 7.984455 8.368327 24.770606
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 4.633087 4.582135 4.774825 4.548321 4.575813 9.782896

Table: Benchmarking of rowQuantiles() and apply+quantile() on 100x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 1.703577 1.743685 1.818584 1.790552 1.831886 2.262452
2 apply+quantile 7.699512 7.821835 8.453948 7.928989 8.204308 17.136500
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 4.519615 4.485806 4.648644 4.428238 4.478612 7.574304

Figure: Benchmarking of colQuantiles() and apply+quantile() on 100x100 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 100x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 1.667526 1.717488 1.805961 1.755473 1.828817 2.532032
2 rowQuantiles 1.703577 1.743685 1.818584 1.790552 1.831886 2.262452
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.0000000
2 rowQuantiles 1.021619 1.015253 1.006989 1.019982 1.001678 0.8935322

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3176965 169.7    5709258 305.0  5709258 305.0
Vcells 6090169  46.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3176956 169.7    5709258 305.0  5709258 305.0
Vcells 6100207  46.6   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 0.569989 0.5843265 0.5998415 0.5927725 0.598683 0.944226
2 apply+quantile 1.240029 1.2566525 1.2884913 1.2720650 1.284201 1.816026
expr min lq mean median uq max
1 colQuantiles 1.000000 1.0000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 2.175531 2.1506 2.148053 2.145958 2.145043 1.923296

Table: Benchmarking of rowQuantiles() and apply+quantile() on 1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 0.579148 0.591894 0.6044141 0.6020005 0.607459 0.844155
2 apply+quantile 1.189937 1.215024 1.2409754 1.2348835 1.244353 1.698761
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.000000 1.000000 1.0000 1.000000 1.00000
2 apply+quantile 2.054634 2.052772 2.053187 2.0513 2.048456 2.01238

Figure: Benchmarking of colQuantiles() and apply+quantile() on 1000x10 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 1000x10 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 569.989 584.3265 599.8415 592.7725 598.683 944.226
2 rowQuantiles 579.148 591.8940 604.4141 602.0005 607.459 844.155
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles 1.016069 1.012951 1.007623 1.015567 1.014659 0.894018

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177153 169.7    5709258 305.0  5709258 305.0
Vcells 6090883  46.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177144 169.7    5709258 305.0  5709258 305.0
Vcells 6100921  46.6   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 11.71571 12.18752 13.55159 12.58262 13.51043 25.37621
2 apply+quantile 71.34555 73.34382 79.28271 75.91291 86.14064 111.41644
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 6.089731 6.017944 5.850437 6.033157 6.375863 4.390586

Table: Benchmarking of rowQuantiles() and apply+quantile() on 10x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 11.76357 12.18355 15.75459 12.62987 12.87938 266.1704
2 apply+quantile 70.98810 72.54362 77.77507 74.25125 84.08314 110.3797
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.000000 1.000000 1.00000 1.00000 1.0000000
2 apply+quantile 6.034569 5.954227 4.936662 5.87902 6.52851 0.4146957

Figure: Benchmarking of colQuantiles() and apply+quantile() on 10x1000 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 10x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 11.71571 12.18752 13.55159 12.58262 13.51043 25.37621
2 rowQuantiles 11.76357 12.18355 15.75459 12.62987 12.87938 266.17038
expr min lq mean median uq max
1 colQuantiles 1.000000 1.0000000 1.000000 1.000000 1.0000000 1.00000
2 rowQuantiles 1.004085 0.9996741 1.162564 1.003755 0.9532915 10.48897

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177337 169.7    5709258 305.0  5709258 305.0
Vcells 6091379  46.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177328 169.7    5709258 305.0  5709258 305.0
Vcells 6191417  47.3   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 15.94949 16.39826 18.22907 17.03291 17.93682 37.48265
2 apply+quantile 78.03791 80.35847 86.19026 81.81766 91.94407 122.29199
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 4.892816 4.900427 4.728176 4.803505 5.125998 3.262629

Table: Benchmarking of rowQuantiles() and apply+quantile() on 100x1000 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 16.22328 16.82461 19.30184 17.49396 19.63542 46.58731
2 apply+quantile 77.73885 80.14350 88.41864 82.86826 94.35297 134.76324
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 4.791808 4.763469 4.580839 4.736965 4.805243 2.892703

Figure: Benchmarking of colQuantiles() and apply+quantile() on 100x1000 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 100x1000 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 15.94949 16.39826 18.22907 17.03291 17.93682 37.48265
2 rowQuantiles 16.22328 16.82461 19.30184 17.49396 19.63542 46.58731
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000 1.000000 1.000000 1.000000 1.000000
2 rowQuantiles 1.017166 1.026 1.058849 1.027068 1.094699 1.242903

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177529 169.7    5709258 305.0  5709258 305.0
Vcells 6091993  46.5   22343563 170.5 56666022 432.4
> probs <- seq(from = 0, to = 1, by = 0.25)
> colStats <- microbenchmark(colQuantiles = colQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 2L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 3177520 169.7    5709258 305.0  5709258 305.0
Vcells 6192031  47.3   22343563 170.5 56666022 432.4
> rowStats <- microbenchmark(rowQuantiles = rowQuantiles(X, probs = probs, na.rm = FALSE), `apply+quantile` = apply(X, 
+     MARGIN = 1L, FUN = quantile, probs = probs, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colQuantiles() and apply+quantile() on 1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 4.794218 4.948212 5.233887 4.99597 5.095167 13.85089
2 apply+quantile 11.677002 11.793934 12.435548 11.91788 12.195339 22.84092
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000
2 apply+quantile 2.435643 2.383474 2.375968 2.385499 2.393511 1.649059

Table: Benchmarking of rowQuantiles() and apply+quantile() on 1000x100 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 rowQuantiles 5.167341 5.26751 5.53447 5.32830 5.388846 13.79203
2 apply+quantile 11.684250 11.81924 12.29935 11.91869 12.181646 22.81130
expr min lq mean median uq max
1 rowQuantiles 1.000000 1.0000 1.000000 1.000000 1.00000 1.000000
2 apply+quantile 2.261173 2.2438 2.222318 2.236865 2.26053 1.653948

Figure: Benchmarking of colQuantiles() and apply+quantile() on 1000x100 data as well as rowQuantiles() and apply+quantile() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colQuantiles() and rowQuantiles() on 1000x100 data (original and transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

expr min lq mean median uq max
1 colQuantiles 4.794218 4.948212 5.233887 4.99597 5.095167 13.85089
2 rowQuantiles 5.167341 5.267510 5.534470 5.32830 5.388846 13.79203
expr min lq mean median uq max
1 colQuantiles 1.000000 1.000000 1.00000 1.00000 1.000000 1.0000000
2 rowQuantiles 1.077828 1.064528 1.05743 1.06652 1.057639 0.9957505

Figure: Benchmarking of colQuantiles() and rowQuantiles() on 1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

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      glue_1.3.1           digest_0.6.20       
[16] colorspace_1.4-1     sandwich_2.5-1       Matrix_1.2-17       
[19] XML_3.98-1.20        lpSolve_5.6.13.3     pkgconfig_2.0.2     
[22] genefilter_1.66.0    purrr_0.3.2          ergm_3.10.4         
[25] xtable_1.8-4         mvtnorm_1.0-11       scales_1.0.0        
[28] tibble_2.1.3         annotate_1.62.0      IRanges_2.18.2      
[31] TH.data_1.0-10       withr_2.1.2          BiocGenerics_0.30.0 
[34] lazyeval_0.2.2       mime_0.7             survival_2.44-1.1   
[37] magrittr_1.5         crayon_1.3.4         statnet.common_4.3.0
[40] memoise_1.1.0        laeken_0.5.0         R.cache_0.13.0      
[43] MASS_7.3-51.4        R.rsp_0.43.1         tools_3.6.1         
[46] multcomp_1.4-10      S4Vectors_0.22.1     trust_0.1-7         
[49] munsell_0.5.0        AnnotationDbi_1.46.1 compiler_3.6.1      
[52] rlang_0.4.0          grid_3.6.1           RCurl_1.95-4.12     
[55] cwhmisc_6.6          rappdirs_0.3.1       labeling_0.3        
[58] bitops_1.0-6         base64enc_0.1-3      boot_1.3-23         
[61] gtable_0.3.0         codetools_0.2-16     DBI_1.0.0           
[64] markdown_1.1         R6_2.4.0             zoo_1.8-6           
[67] dplyr_0.8.3          bit_1.1-14           zeallot_0.1.0       
[70] parallel_3.6.1       Rcpp_1.0.2           vctrs_0.2.0         
[73] DEoptimR_1.0-8       tidyselect_0.2.5     xfun_0.9            
[76] coda_0.19-3

Total processing time was 57.27 secs.

Reproducibility

To reproduce this report, do:

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

Copyright Henrik Bengtsson. Last updated on 2019-09-10 20:50:39 (-0700 UTC). Powered by RSP.

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