weightedMean_subset
matrixStats: Benchmark report
This report benchmark the performance of weightedMean() on subsetted computation.
> 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 = mode)
> data <- data[1:4]> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3254530 173.9 5709258 305.0 5709258 305.0
Vcells 7938294 60.6 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.001531 | 0.0015845 | 0.0016795 | 0.0016275 | 0.0016715 | 0.004561 |
| 2 | weightedMean(x, w, idxs) | 0.002270 | 0.0023210 | 0.0032812 | 0.0023775 | 0.0024460 | 0.089417 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.004627 | 0.0048040 | 0.0049241 | 0.0048655 | 0.0049630 | 0.008610 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 1.482691 | 1.464815 | 1.953624 | 1.460830 | 1.463356 | 19.604692 |
| 3 | weightedMean(x[idxs], w[idxs]) | 3.022208 | 3.031871 | 2.931873 | 2.989555 | 2.969189 | 1.887744 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3250877 173.7 5709258 305.0 5709258 305.0
Vcells 6820531 52.1 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.008241 | 0.0084615 | 0.0088564 | 0.008524 | 0.0086355 | 0.036244 |
| 2 | weightedMean(x, w, idxs) | 0.016593 | 0.0168810 | 0.0173057 | 0.017060 | 0.0172010 | 0.035846 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.033846 | 0.0345210 | 0.0354220 | 0.034843 | 0.0353440 | 0.047289 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.0000000 |
| 2 | weightedMean(x, w, idxs) | 2.013469 | 1.995036 | 1.954019 | 2.001408 | 1.991894 | 0.9890189 |
| 3 | weightedMean(x[idxs], w[idxs]) | 4.107026 | 4.079773 | 3.999569 | 4.087635 | 4.092872 | 1.3047401 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3250949 173.7 5709258 305.0 5709258 305.0
Vcells 7037091 53.7 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.073563 | 0.0756315 | 0.0773616 | 0.0776105 | 0.0782045 | 0.093444 |
| 2 | weightedMean(x, w, idxs) | 0.251108 | 0.2545820 | 0.2622681 | 0.2653170 | 0.2655925 | 0.286673 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.410592 | 0.4208010 | 0.4347360 | 0.4344995 | 0.4377520 | 0.728485 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 3.413509 | 3.366084 | 3.390159 | 3.418571 | 3.396128 | 3.067859 |
| 3 | weightedMean(x[idxs], w[idxs]) | 5.581502 | 5.563833 | 5.619533 | 5.598463 | 5.597530 | 7.795953 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3251021 173.7 5709258 305.0 5709258 305.0
Vcells 9197140 70.2 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.043374 | 1.214168 | 1.295131 | 1.264495 | 1.360686 | 1.683094 |
| 2 | weightedMean(x, w, idxs) | 8.374396 | 9.454982 | 9.922480 | 9.753282 | 10.199404 | 13.196994 |
| 3 | weightedMean(x[idxs], w[idxs]) | 9.828954 | 14.754568 | 15.383674 | 15.084395 | 15.431275 | 26.039318 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.00000 | 1.00000 | 1.000000 | 1.00000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 8.026265 | 7.78721 | 7.66137 | 7.713187 | 7.49578 | 7.840913 |
| 3 | weightedMean(x[idxs], w[idxs]) | 9.420355 | 12.15200 | 11.87808 | 11.929189 | 11.34081 | 15.471101 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on integer+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
> 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 = mode)
> data <- data[1:4]> x <- data[["n = 1000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3251099 173.7 5709258 305.0 5709258 305.0
Vcells 7356020 56.2 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.00153 | 0.0016075 | 0.0017469 | 0.0016625 | 0.0017975 | 0.004865 |
| 2 | weightedMean(x, w, idxs) | 0.00224 | 0.0022870 | 0.0026696 | 0.0023330 | 0.0024010 | 0.032507 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.00457 | 0.0049725 | 0.0053566 | 0.0051435 | 0.0055415 | 0.011743 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 1.464052 | 1.422706 | 1.528164 | 1.403308 | 1.335744 | 6.681809 |
| 3 | weightedMean(x[idxs], w[idxs]) | 2.986928 | 3.093313 | 3.066363 | 3.093835 | 3.082893 | 2.413772 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 10000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3251165 173.7 5709258 305.0 5709258 305.0
Vcells 7381126 56.4 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 10000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.008132 | 0.0084440 | 0.0090100 | 0.0086185 | 0.0088180 | 0.023303 |
| 2 | weightedMean(x, w, idxs) | 0.016094 | 0.0166165 | 0.0174300 | 0.0169310 | 0.0171770 | 0.040581 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.034302 | 0.0361780 | 0.0380877 | 0.0370390 | 0.0384335 | 0.060759 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 1.979095 | 1.967847 | 1.934503 | 1.964495 | 1.947947 | 1.741450 |
| 3 | weightedMean(x[idxs], w[idxs]) | 4.218151 | 4.284462 | 4.227254 | 4.297616 | 4.358528 | 2.607347 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 10000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 100000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3251237 173.7 5709258 305.0 5709258 305.0
Vcells 7628674 58.3 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 100000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 0.073528 | 0.0770400 | 0.0802106 | 0.0786250 | 0.0803380 | 0.102687 |
| 2 | weightedMean(x, w, idxs) | 0.234819 | 0.2360200 | 0.2477158 | 0.2485910 | 0.2556555 | 0.272487 |
| 3 | weightedMean(x[idxs], w[idxs]) | 0.428035 | 0.4349115 | 0.4582200 | 0.4528735 | 0.4658515 | 0.843115 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 3.193600 | 3.063603 | 3.088319 | 3.161730 | 3.182249 | 2.653569 |
| 3 | weightedMean(x[idxs], w[idxs]) | 5.821388 | 5.645269 | 5.712714 | 5.759917 | 5.798644 | 8.210533 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 100000 data. Outliers are displayed as crosses. Times are in milliseconds.
> x <- data[["n = 1000000"]]
> idxs <- sample.int(length(x), size = length(x) * 0.7)
> x_S <- x[idxs]
> w <- runif(length(x))
> w_S <- w[idxs]
> gc()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3251309 173.7 5709258 305.0 5709258 305.0
Vcells 10104133 77.1 25448368 194.2 87357391 666.5
> stats <- microbenchmark(weightedMean_x_w_S = weightedMean(x_S, w = w_S, na.rm = FALSE), `weightedMean(x, w, idxs)` = weightedMean(x,
+ w = w, idxs = idxs, na.rm = FALSE), `weightedMean(x[idxs], w[idxs])` = weightedMean(x[idxs],
+ w = w[idxs], na.rm = FALSE), unit = "ms")Table: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.222968 | 1.523889 | 1.611831 | 1.609341 | 1.700403 | 1.983512 |
| 2 | weightedMean(x, w, idxs) | 10.783339 | 13.336184 | 13.620122 | 13.594388 | 14.025677 | 14.598417 |
| 3 | weightedMean(x[idxs], w[idxs]) | 12.677596 | 15.040961 | 16.662921 | 15.482764 | 16.042821 | 29.830299 |
| expr | min | lq | mean | median | uq | max | |
|---|---|---|---|---|---|---|---|
| 1 | weightedMean_x_w_S | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 | 1.000000 |
| 2 | weightedMean(x, w, idxs) | 8.817352 | 8.751414 | 8.450094 | 8.447177 | 8.248443 | 7.359883 |
| 3 | weightedMean(x[idxs], w[idxs]) | 10.366253 | 9.870116 | 10.337884 | 9.620561 | 9.434717 | 15.039132 |
Figure: Benchmarking of weightedMean_x_w_S(), weightedMean(x, w, idxs)() and weightedMean(x[idxs], w[idxs])() on double+n = 1000000 data. Outliers are displayed as crosses. Times are in milliseconds.
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 14.03 secs.
To reproduce this report, do:
html <- matrixStats:::benchmark('weightedMean_subset')Copyright Dongcan Jiang. Last updated on 2019-09-10 21:14:28 (-0700 UTC). Powered by RSP.
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