matrixStats.benchmarks

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colMads() and rowMads() benchmarks

This report benchmark the performance of colMads() and rowMads() against alternative methods.

Alternative methods

• apply() + mad()
• colMads2() and rowMads2()

where rowMads2() and colMads2() are:

> rowMads2 <- function(x, const = 1.4826, na.rm = FALSE) {
+     mu <- rowMedians(x, na.rm = na.rm)
+     x <- abs(x - mu)
+     mad <- rowMedians(x, na.rm = FALSE)
+     const * mad
+ }
> colMads2 <- function(x, const = 1.4826, na.rm = FALSE) {
+     mu <- colMedians(x, na.rm = na.rm)
+     x <- abs(x - mu)
+     mad <- colMedians(x, na.rm = FALSE)
+     const * mad
+ }

Data type “integer”

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 = mode)

Results

10x10 integer matrix

> X <- data[["10x10"]]
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5250188 280.4    7916910 422.9  7916910 422.9
Vcells 10132068  77.4   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
           used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  5236357 279.7    7916910 422.9  7916910 422.9
Vcells 10086015  77.0   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.006008	0.0089390	0.0128773	0.011302	0.0151575	0.035319
2	colMads2	0.007256	0.0098135	0.0163289	0.012464	0.0175690	0.159177
3	apply+mad	0.578038	0.6448000	0.7424238	0.685349	0.7585050	1.542347

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.207723	1.09783	1.268039	1.102814	1.159096	4.506838
3	apply+mad	96.211385	72.13335	57.653772	60.639621	50.041564	43.669045

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	0.005267	0.0078910	0.0111999	0.010449	0.0135275	0.033022
2	rowMads2	0.007037	0.0095090	0.0152600	0.012227	0.0169130	0.171203
3	apply+mad	0.526914	0.5933545	0.6972633	0.663766	0.7822795	1.119822

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.00000	1.00000	1.000000	1.000000
2	rowMads2	1.336055	1.205044	1.36251	1.17016	1.250268	5.184513
3	apply+mad	100.040630	75.193828	62.25621	63.52436	57.828830	33.911392

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+10x10 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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
2	rowMads	5.267	7.891	11.19990	10.449	13.5275	33.022
1	colMads	6.008	8.939	12.87728	11.302	15.1575	35.319

	expr	min	lq	mean	median	uq	max
2	rowMads	1.000000	1.00000	1.000000	1.000000	1.000000	1.00000
1	colMads	1.140687	1.13281	1.149767	1.081635	1.120495	1.06956

Figure: Benchmarking of colMads() and rowMads() on integer+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 integer matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5235065 279.6    7916910 422.9  7916910 422.9
Vcells 9702982  74.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5235053 279.6    7916910 422.9  7916910 422.9
Vcells 9708015  74.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.288204	0.3042840	0.3219376	0.3093615	0.324000	0.540244
2	colMads2	0.329864	0.3517865	0.3745369	0.3581860	0.373225	0.643909
3	apply+mad	5.485353	5.9617085	6.5075370	6.1003425	6.417079	15.308379

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.14455	1.156112	1.163384	1.157823	1.151929	1.191885
3	apply+mad	19.03288	19.592580	20.213663	19.719139	19.805801	28.336046

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	0.277340	0.3005590	0.3248627	0.3104560	0.3224795	0.550608
2	rowMads2	0.324027	0.3519075	0.3828661	0.3659225	0.3937625	0.660055
3	apply+mad	5.529593	5.9854355	6.5428063	6.1587030	6.5604375	16.253279

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.168338	1.170843	1.178547	1.178661	1.221047	1.198775
3	apply+mad	19.937957	19.914345	20.140221	19.837603	20.343735	29.518785

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+100x100 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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	colMads	288.204	304.284	321.9375	309.3615	324.0000	540.244
2	rowMads	277.340	300.559	324.8627	310.4560	322.4795	550.608

	expr	min	lq	mean	median	uq	max
1	colMads	1.0000000	1.0000000	1.000000	1.000000	1.0000000	1.000000
2	rowMads	0.9623045	0.9877581	1.009086	1.003538	0.9953071	1.019184

Figure: Benchmarking of colMads() and rowMads() on integer+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 integer matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5235818 279.7    7916910 422.9  7916910 422.9
Vcells 9706758  74.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5235806 279.7    7916910 422.9  7916910 422.9
Vcells 9711791  74.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.205421	0.2096165	0.2285328	0.215315	0.2275780	0.348785
2	colMads2	0.292945	0.2973145	0.3207022	0.304180	0.3163755	0.497397
3	apply+mad	0.945397	0.9828475	1.0577727	1.003020	1.0608850	1.626488

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.426071	1.418374	1.403309	1.412721	1.390185	1.426085
3	apply+mad	4.602241	4.688789	4.628538	4.658384	4.661632	4.663297

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	0.205531	0.214717	0.2469517	0.2315225	0.2665785	0.396427
2	rowMads2	0.300974	0.320105	0.3565909	0.3386340	0.3806480	0.662962
3	apply+mad	0.936455	1.011062	1.1561816	1.0873200	1.2314520	1.721523

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowMads2	1.464373	1.490823	1.443970	1.46264	1.427902	1.672343
3	apply+mad	4.556271	4.708814	4.681813	4.69639	4.619472	4.342598

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+1000x10 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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	colMads	205.421	209.6165	228.5328	215.3150	227.5780	348.785
2	rowMads	205.531	214.7170	246.9517	231.5225	266.5785	396.427

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads	1.000536	1.024333	1.080596	1.075273	1.171372	1.136594

Figure: Benchmarking of colMads() and rowMads() on integer+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 integer matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236031 279.7    7916910 422.9  7916910 422.9
Vcells 9707203  74.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236019 279.7    7916910 422.9  7916910 422.9
Vcells 9712236  74.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.300013	0.3129680	0.3362636	0.3262210	0.3472170	0.464604
2	colMads2	0.386706	0.4063285	0.4363596	0.4227135	0.4488505	0.608235
3	apply+mad	53.390469	55.6037720	58.9248073	57.1347870	61.3852145	79.644082

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.288964	1.298307	1.297671	1.295789	1.292709	1.309147
3	apply+mad	177.960518	177.665998	175.233965	175.141352	176.792077	171.423582

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	0.293525	0.3190915	0.3424545	0.338164	0.349548	0.548934
2	rowMads2	0.365048	0.3952690	0.4366536	0.421001	0.447570	0.682582
3	apply+mad	52.077990	55.4750085	62.1849491	57.019343	61.826714	441.039492

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowMads2	1.243669	1.238733	1.27507	1.244961	1.280425	1.243468
3	apply+mad	177.422673	173.852981	181.58603	168.614467	176.876177	803.447212

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+10x1000 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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	colMads	300.013	312.9680	336.2636	326.221	347.217	464.604
2	rowMads	293.525	319.0915	342.4545	338.164	349.548	548.934

	expr	min	lq	mean	median	uq	max
1	colMads	1.0000000	1.000000	1.000000	1.00000	1.000000	1.000000
2	rowMads	0.9783743	1.019566	1.018411	1.03661	1.006713	1.181509

Figure: Benchmarking of colMads() and rowMads() on integer+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 integer matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236235 279.7    7916910 422.9  7916910 422.9
Vcells 9708251  74.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236223 279.7    7916910 422.9  7916910 422.9
Vcells 9758284  74.5   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	2.732366	2.958270	3.042362	3.000695	3.091835	3.745999
2	colMads2	3.190511	3.452091	3.658987	3.510760	3.724194	5.686655
3	apply+mad	57.593631	60.101595	65.938331	62.828356	71.874273	89.755145

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	colMads2	1.167673	1.166929	1.20268	1.169982	1.204526	1.518061
3	apply+mad	21.078300	20.316467	21.67340	20.937931	23.246481	23.960269

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	2.736589	2.893521	3.100275	2.993682	3.089872	4.526965
2	rowMads2	3.185507	3.393479	3.567830	3.511339	3.602651	4.824806
3	apply+mad	54.651441	58.901973	63.772764	61.090222	68.626045	98.092594

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.164043	1.172786	1.150811	1.172916	1.165955	1.065793
3	apply+mad	19.970643	20.356508	20.570033	20.406383	22.209992	21.668512

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+100x1000 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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
2	rowMads	2.736589	2.893521	3.100275	2.993682	3.089872	4.526965
1	colMads	2.732366	2.958270	3.042362	3.000695	3.091835	3.745999

	expr	min	lq	mean	median	uq	max
2	rowMads	1.0000000	1.000000	1.0000000	1.000000	1.000000	1.0000000
1	colMads	0.9984568	1.022377	0.9813199	1.002343	1.000635	0.8274857

Figure: Benchmarking of colMads() and rowMads() on integer+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 integer matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236441 279.7    7916910 422.9  7916910 422.9
Vcells 9708929  74.1   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236429 279.7    7916910 422.9  7916910 422.9
Vcells 9758962  74.5   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on integer+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	2.149437	2.268655	2.403680	2.297431	2.368051	3.833008
2	colMads2	2.853136	3.018869	3.098946	3.065358	3.130867	3.554206
3	apply+mad	9.178282	9.702927	10.523006	9.886722	10.321673	19.899442

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	colMads2	1.327388	1.330687	1.289251	1.334254	1.322128	0.9272629
3	apply+mad	4.270086	4.276952	4.377874	4.303382	4.358720	5.1915994

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on integer+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	rowMads	2.167019	2.293264	2.378061	2.332356	2.394051	3.319538
2	rowMads2	2.914743	3.069144	3.148932	3.105767	3.151811	4.700630
3	apply+mad	9.493770	9.820789	10.446945	9.976705	10.187645	20.374530

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.345047	1.338330	1.324159	1.331601	1.316518	1.416049
3	apply+mad	4.381028	4.282449	4.393052	4.277521	4.255400	6.137761

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on integer+1000x100 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on integer+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	colMads	2.149437	2.268655	2.403680	2.297431	2.368051	3.833008
2	rowMads	2.167019	2.293264	2.378061	2.332356	2.394051	3.319538

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowMads	1.00818	1.010848	0.989342	1.015202	1.010979	0.8660399

Figure: Benchmarking of colMads() and rowMads() on integer+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Data type “double”

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 = mode)

Results

10x10 double matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236649 279.7    7916910 422.9  7916910 422.9
Vcells 9824775  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236634 279.7    7916910 422.9  7916910 422.9
Vcells 9824903  75.0   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+10x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.007301	0.0091490	0.0125283	0.0107825	0.0141795	0.035670
2	colMads2	0.008105	0.0098190	0.0141751	0.0122500	0.0156610	0.055392
3	apply+mad	0.552625	0.6012315	0.6747279	0.6417020	0.7154615	1.131534

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.00000	1.00000	1.000000	1.000000
2	colMads2	1.110122	1.073232	1.13145	1.13610	1.104482	1.552902
3	apply+mad	75.691686	65.715543	53.85639	59.51329	50.457456	31.722288

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+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	rowMads	0.006446	0.0086830	0.0108839	0.0107285	0.0122325	0.030743
2	rowMads2	0.007483	0.0100635	0.0131113	0.0125020	0.0151265	0.030781
3	apply+mad	0.520657	0.5641715	0.6454382	0.6239045	0.6974975	1.018438

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowMads2	1.160875	1.158989	1.20465	1.165307	1.236583	1.001236
3	apply+mad	80.772107	64.974260	59.30199	58.153936	57.020029	33.127476

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+10x10 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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
2	rowMads	6.446	8.683	10.88392	10.7285	12.2325	30.743
1	colMads	7.301	9.149	12.52828	10.7825	14.1795	35.670

	expr	min	lq	mean	median	uq	max
2	rowMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colMads	1.13264	1.053668	1.151082	1.005033	1.159166	1.160264

Figure: Benchmarking of colMads() and rowMads() on double+10x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x100 double matrix

> X <- data[["100x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236851 279.7    7916910 422.9  7916910 422.9
Vcells 9825697  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5236839 279.7    7916910 422.9  7916910 422.9
Vcells 9835730  75.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+100x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.359134	0.3713445	0.3974377	0.3823495	0.4008460	0.714462
2	colMads2	0.382480	0.3948385	0.4205244	0.4058825	0.4208245	0.801874
3	apply+mad	5.465700	5.7846365	6.3019386	5.9572075	6.2228885	15.371729

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.065006	1.063267	1.058089	1.061548	1.049841	1.122347
3	apply+mad	15.219110	15.577547	15.856420	15.580529	15.524387	21.515111

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+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	rowMads	0.359058	0.3785015	0.4075618	0.388182	0.424616	0.557033
2	rowMads2	0.378043	0.4006405	0.4309033	0.414079	0.450724	0.573165
3	apply+mad	5.478529	5.8585485	6.4845089	6.101722	6.381883	15.598265

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.052874	1.058491	1.057271	1.066714	1.061486	1.028961
3	apply+mad	15.258061	15.478270	15.910493	15.718713	15.029776	28.002407

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+100x100 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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	colMads	359.134	371.3445	397.4377	382.3495	400.846	714.462
2	rowMads	359.058	378.5015	407.5618	388.1820	424.616	557.033

	expr	min	lq	mean	median	uq	max
1	colMads	1.0000000	1.000000	1.000000	1.000000	1.0000	1.0000000
2	rowMads	0.9997884	1.019273	1.025473	1.015254	1.0593	0.7796538

Figure: Benchmarking of colMads() and rowMads() on double+100x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x10 double matrix

> X <- data[["1000x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237060 279.7    7916910 422.9  7916910 422.9
Vcells 9825837  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237048 279.7    7916910 422.9  7916910 422.9
Vcells 9835870  75.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+1000x10 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.323200	0.3264030	0.3657205	0.3290165	0.4122505	0.613896
2	colMads2	0.350441	0.3527365	0.3882815	0.3565655	0.3868775	0.647756
3	apply+mad	0.982257	1.0118440	1.1458180	1.0289080	1.2622370	2.460177

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
2	colMads2	1.084285	1.080678	1.061689	1.083731	0.9384525	1.055156
3	apply+mad	3.039162	3.099984	3.133043	3.127223	3.0618204	4.007482

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+1000x10 data (transposed). The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
2	rowMads2	0.356836	0.3630025	0.4414221	0.370424	0.4699085	0.715253
1	rowMads	0.325514	0.3309335	0.4164100	0.372599	0.5018485	0.665517
3	apply+mad	0.987841	1.0165795	1.2594425	1.049528	1.3445800	2.578281

	expr	min	lq	mean	median	uq	max
2	rowMads2	1.000000	1.0000000	1.0000000	1.000000	1.000000	1.0000000
1	rowMads	0.912223	0.9116563	0.9433375	1.005872	1.067971	0.9304638
3	apply+mad	2.768333	2.8004752	2.8531480	2.833315	2.861366	3.6047119

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+1000x10 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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	colMads	323.200	326.4030	365.7205	329.0165	412.2505	613.896
2	rowMads	325.514	330.9335	416.4100	372.5990	501.8485	665.517

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowMads	1.00716	1.01388	1.138602	1.132463	1.217339	1.084088

Figure: Benchmarking of colMads() and rowMads() on double+1000x10 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

10x1000 double matrix

> X <- data[["10x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237273 279.8    7916910 422.9  7916910 422.9
Vcells 9826923  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237261 279.7    7916910 422.9  7916910 422.9
Vcells 9836956  75.1   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	0.354982	0.3693505	0.4034630	0.3840110	0.4020530	0.600879
2	colMads2	0.406891	0.4221795	0.4625114	0.4384475	0.4643095	0.701584
3	apply+mad	49.588505	51.9684120	55.2081224	53.0631895	59.1562110	69.601091

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.14623	1.143032	1.146354	1.141758	1.154847	1.167596
3	apply+mad	139.69301	140.702157	136.835634	138.181431	147.135355	115.832124

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+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	rowMads	0.352994	0.3769265	0.4116897	0.3906765	0.421354	0.629198
2	rowMads2	0.406476	0.4240100	0.4570114	0.4440850	0.461010	0.771423
3	apply+mad	50.049250	51.8938395	56.0030932	53.8218575	60.201223	76.673614

	expr	min	lq	mean	median	uq	max
1	rowMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.15151	1.124914	1.110087	1.136708	1.094116	1.226042
3	apply+mad	141.78499	137.676283	136.032298	137.765792	142.875641	121.859278

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+10x1000 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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	colMads	354.982	369.3505	403.4631	384.0110	402.053	600.879
2	rowMads	352.994	376.9265	411.6897	390.6765	421.354	629.198

	expr	min	lq	mean	median	uq	max
1	colMads	1.0000000	1.000000	1.00000	1.000000	1.000000	1.000000
2	rowMads	0.9943997	1.020512	1.02039	1.017358	1.048006	1.047129

Figure: Benchmarking of colMads() and rowMads() on double+10x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

100x1000 double matrix

> X <- data[["100x1000"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237477 279.8    7916910 422.9  7916910 422.9
Vcells 9827058  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237465 279.8    7916910 422.9  7916910 422.9
Vcells 9927091  75.8   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+100x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	3.610391	3.679383	3.805536	3.746309	3.851326	4.708104
2	colMads2	3.730725	3.843780	4.041137	3.895529	3.998488	6.323009
3	apply+mad	54.997643	56.781560	64.271396	57.845460	62.692104	424.452660

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.000000	1.00000	1.000000	1.000000	1.000000
2	colMads2	1.03333	1.044681	1.06191	1.039831	1.038211	1.343005
3	apply+mad	15.23315	15.432361	16.88892	15.440654	16.278057	90.153629

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+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	rowMads	3.620537	3.712763	3.877254	3.762095	3.876806	6.061784
2	rowMads2	3.774482	3.874991	4.053704	3.962638	4.078225	5.099599
3	apply+mad	54.672733	56.408330	60.612307	57.967467	61.591422	78.752876

	expr	min	lq	mean	median	uq	max
1	rowMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.0000000
2	rowMads2	1.04252	1.043695	1.045509	1.053306	1.051955	0.8412703
3	apply+mad	15.10072	15.193089	15.632791	15.408294	15.887158	12.9916995

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+100x1000 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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	colMads	3.610391	3.679383	3.805536	3.746309	3.851326	4.708104
2	rowMads	3.620537	3.712763	3.877254	3.762095	3.876806	6.061784

	expr	min	lq	mean	median	uq	max
1	colMads	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads	1.00281	1.009072	1.018846	1.004214	1.006616	1.287521

Figure: Benchmarking of colMads() and rowMads() on double+100x1000 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

1000x100 double matrix

> X <- data[["1000x100"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237683 279.8    7916910 422.9  7916910 422.9
Vcells 9828355  75.0   33191153 253.3 53339345 407.0
> colStats <- microbenchmark(colMads = colMads(X, na.rm = FALSE), colMads2 = colMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 2L, FUN = mad, na.rm = FALSE), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5237671 279.8    7916910 422.9  7916910 422.9
Vcells 9928388  75.8   33191153 253.3 53339345 407.0
> rowStats <- microbenchmark(rowMads = rowMads(X, na.rm = FALSE), rowMads2 = rowMads2(X, na.rm = FALSE), 
+     `apply+mad` = apply(X, MARGIN = 1L, FUN = mad, na.rm = FALSE), unit = "ms")

Table: Benchmarking of colMads(), colMads2() and apply+mad() on double+1000x100 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
1	colMads	3.309192	3.340129	3.531283	3.382344	3.474675	5.444749
2	colMads2	3.501391	3.537879	3.748482	3.609486	3.705232	11.490747
3	apply+mad	9.639740	9.765279	10.340336	9.896778	10.194869	19.671879

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	colMads2	1.058080	1.059205	1.061507	1.067155	1.066353	2.110427
3	apply+mad	2.913019	2.923624	2.928209	2.926011	2.934050	3.613000

Table: Benchmarking of rowMads(), rowMads2() and apply+mad() on double+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	rowMads	3.341375	3.368112	3.530423	3.420777	3.482632	5.431077
2	rowMads2	3.499420	3.533547	3.658017	3.572109	3.621186	5.883676
3	apply+mad	9.667425	9.780869	10.423393	9.901760	10.096395	19.759984

	expr	min	lq	mean	median	uq	max
1	rowMads	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowMads2	1.047299	1.049118	1.036141	1.044239	1.039784	1.083335
3	apply+mad	2.893248	2.903962	2.952449	2.894594	2.899071	3.638318

Figure: Benchmarking of colMads(), colMads2() and apply+mad() on double+1000x100 data as well as rowMads(), rowMads2() and apply+mad() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colMads() and rowMads() on double+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	colMads	3.309192	3.340129	3.531283	3.382344	3.474675	5.444749
2	rowMads	3.341375	3.368112	3.530423	3.420777	3.482632	5.431077

	expr	min	lq	mean	median	uq	max
1	colMads	1.000000	1.000000	1.0000000	1.000000	1.00000	1.000000
2	rowMads	1.009725	1.008378	0.9997565	1.011363	1.00229	0.997489

Figure: Benchmarking of colMads() and rowMads() on double+1000x100 data (original and transposed). Outliers are displayed as crosses. Times are in milliseconds.

Appendix

Session information

R version 4.1.1 Patched (2021-08-10 r80727)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS

Matrix products: default
BLAS:   /home/hb/software/R-devel/R-4-1-branch/lib/R/lib/libRblas.so
LAPACK: /home/hb/software/R-devel/R-4-1-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_1.4-7   matrixStats_0.60.0     ggplot2_3.3.5         
[4] knitr_1.33             R.devices_2.17.0       R.utils_2.10.1        
[7] R.oo_1.24.0            R.methodsS3_1.8.1-9001 history_0.0.1-9000    

loaded via a namespace (and not attached):
 [1] Biobase_2.52.0          httr_1.4.2              splines_4.1.1          
 [4] bit64_4.0.5             network_1.17.1          assertthat_0.2.1       
 [7] highr_0.9               stats4_4.1.1            blob_1.2.2             
[10] GenomeInfoDbData_1.2.6  robustbase_0.93-8       pillar_1.6.2           
[13] RSQLite_2.2.8           lattice_0.20-44         glue_1.4.2             
[16] digest_0.6.27           XVector_0.32.0          colorspace_2.0-2       
[19] Matrix_1.3-4            XML_3.99-0.7            pkgconfig_2.0.3        
[22] zlibbioc_1.38.0         genefilter_1.74.0       purrr_0.3.4            
[25] ergm_4.1.2              xtable_1.8-4            scales_1.1.1           
[28] tibble_3.1.4            annotate_1.70.0         KEGGREST_1.32.0        
[31] farver_2.1.0            generics_0.1.0          IRanges_2.26.0         
[34] ellipsis_0.3.2          cachem_1.0.6            withr_2.4.2            
[37] BiocGenerics_0.38.0     mime_0.11               survival_3.2-13        
[40] magrittr_2.0.1          crayon_1.4.1            statnet.common_4.5.0   
[43] memoise_2.0.0           laeken_0.5.1            fansi_0.5.0            
[46] R.cache_0.15.0          MASS_7.3-54             R.rsp_0.44.0           
[49] progressr_0.8.0         tools_4.1.1             lifecycle_1.0.0        
[52] S4Vectors_0.30.0        trust_0.1-8             munsell_0.5.0          
[55] tabby_0.0.1-9001        AnnotationDbi_1.54.1    Biostrings_2.60.2      
[58] compiler_4.1.1          GenomeInfoDb_1.28.1     rlang_0.4.11           
[61] grid_4.1.1              RCurl_1.98-1.4          cwhmisc_6.6            
[64] rstudioapi_0.13         rappdirs_0.3.3          startup_0.15.0         
[67] labeling_0.4.2          bitops_1.0-7            base64enc_0.1-3        
[70] boot_1.3-28             gtable_0.3.0            DBI_1.1.1              
[73] markdown_1.1            R6_2.5.1                lpSolveAPI_5.5.2.0-17.7
[76] rle_0.9.2               dplyr_1.0.7             fastmap_1.1.0          
[79] bit_4.0.4               utf8_1.2.2              parallel_4.1.1         
[82] Rcpp_1.0.7              vctrs_0.3.8             png_0.1-7              
[85] DEoptimR_1.0-9          tidyselect_1.1.1        xfun_0.25              
[88] coda_0.19-4            

Total processing time was 1.43 mins.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 22:18:42 (+0200 UTC). Powered by RSP.