matrixStats.benchmarks

---

colAlls() and rowAlls() benchmarks

This report benchmark the performance of colAlls() and rowAlls() against alternative methods.

Alternative methods

• apply() + all()
• colSums() == n or rowSums() == n

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 = "logical")

Results

10x10 matrix

> X <- data[["10x10"]]
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5165349 275.9    8529671 455.6  8529671 455.6
Vcells 9403025  71.8   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5156953 275.5    8529671 455.6  8529671 455.6
Vcells 9375641  71.6   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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	colAlls	0.003025	0.0032665	0.0040817	0.0041145	0.0043715	0.016839
3	colSums==n	0.006548	0.0069500	0.0081327	0.0078040	0.0084610	0.041586
2	apply+all	0.033170	0.0342715	0.0357029	0.0349825	0.0356310	0.084542

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	2.164628	2.12766	1.992471	1.896707	1.935491	2.469624
2	apply+all	10.965289	10.49181	8.747059	8.502248	8.150749	5.020607

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.002932	0.0032580	0.0040104	0.0040765	0.004377	0.015973
3	rowSums==n	0.007202	0.0077930	0.0087189	0.0083520	0.008922	0.036586
2	apply+all	0.032335	0.0341245	0.0354270	0.0346300	0.035332	0.080404

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	2.456344	2.391958	2.174094	2.048816	2.038382	2.290490
2	apply+all	11.028308	10.474064	8.833892	8.495033	8.072196	5.033744

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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
2	rowAlls	2.932	3.2580	4.01035	4.0765	4.3770	15.973
1	colAlls	3.025	3.2665	4.08170	4.1145	4.3715	16.839

	expr	min	lq	mean	median	uq	max
2	rowAlls	1.000000	1.000000	1.000000	1.000000	1.0000000	1.000000
1	colAlls	1.031719	1.002609	1.017791	1.009322	0.9987434	1.054216

Figure: Benchmarking of colAlls() and rowAlls() 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 5155534 275.4    8529671 455.6  8529671 455.6
Vcells 9181870  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5155510 275.4    8529671 455.6  8529671 455.6
Vcells 9186883  70.1   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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	colAlls	0.003316	0.0040280	0.0050496	0.0047005	0.0052810	0.023880
3	colSums==n	0.015972	0.0174195	0.0194266	0.0187190	0.0206390	0.046336
2	apply+all	0.194263	0.1984080	0.2214525	0.2145185	0.2345875	0.329061

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	4.816647	4.324603	3.847191	3.982342	3.908161	1.940368
2	apply+all	58.583534	49.257200	43.855795	45.637379	44.421038	13.779774

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.007901	0.0091755	0.0104472	0.0099110	0.0111075	0.026171
3	rowSums==n	0.045504	0.0479895	0.0529970	0.0502815	0.0571440	0.081814
2	apply+all	0.189569	0.1997645	0.2229071	0.2135790	0.2412965	0.329688

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	5.759271	5.230178	5.072857	5.073302	5.144632	3.126132
2	apply+all	23.993039	21.771511	21.336619	21.549692	21.723745	12.597455

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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	colAlls	3.316	4.0280	5.04956	4.7005	5.2810	23.880
2	rowAlls	7.901	9.1755	10.44716	9.9110	11.1075	26.171

	expr	min	lq	mean	median	uq	max
1	colAlls	1.00000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	2.38269	2.277929	2.068925	2.108499	2.103295	1.095938

Figure: Benchmarking of colAlls() and rowAlls() 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 5156266 275.4    8529671 455.6  8529671 455.6
Vcells 9185359  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5156260 275.4    8529671 455.6  8529671 455.6
Vcells 9190402  70.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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	colAlls	0.002794	0.0034840	0.0042300	0.0041140	0.0046325	0.016881
3	colSums==n	0.017402	0.0195220	0.0204640	0.0201180	0.0213370	0.038035
2	apply+all	0.110305	0.1125515	0.1233028	0.1228605	0.1288965	0.207250

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.00000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	6.228346	5.60333	4.837852	4.890131	4.605936	2.253125
2	apply+all	39.479241	32.30525	29.149800	29.864001	27.824393	12.277116

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.007931	0.0092260	0.0102171	0.0100165	0.0108175	0.023882
2	apply+all	0.096265	0.1006470	0.1124996	0.1095850	0.1262765	0.194379
3	rowSums==n	0.168196	0.1744085	0.1890403	0.1860320	0.2027455	0.232226

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.00000	1.00000	1.00000	1.00000	1.00000	1.000000
2	apply+all	12.13781	10.90906	11.01093	10.94045	11.67335	8.139143
3	rowSums==n	21.20741	18.90402	18.50238	18.57256	18.74236	9.723892

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x10 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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	colAlls	2.794	3.484	4.22997	4.1140	4.6325	16.881
2	rowAlls	7.931	9.226	10.21708	10.0165	10.8175	23.882

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	2.838583	2.648106	2.415402	2.434735	2.335132	1.414727

Figure: Benchmarking of colAlls() and rowAlls() 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 5156499 275.4    8529671 455.6  8529671 455.6
Vcells 9186143  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5156475 275.4    8529671 455.6  8529671 455.6
Vcells 9191156  70.2   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data. The top panel shows times in milliseconds and the bottom panel shows relative times.

	expr	min	lq	mean	median	uq	max
3	colSums==n	0.014091	0.0152290	0.0170517	0.0161795	0.0178900	0.037235
1	colAlls	0.014461	0.0161340	0.0179866	0.0170280	0.0182545	0.039181
2	apply+all	0.861692	0.9203145	1.0139202	0.9529525	1.0794300	1.434210

	expr	min	lq	mean	median	uq	max
3	colSums==n	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	colAlls	1.026258	1.059426	1.054825	1.052443	1.020374	1.052263
2	apply+all	61.151941	60.431709	59.461428	58.898761	60.337060	38.517792

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.018529	0.0234715	0.0265038	0.0254230	0.0273330	0.066533
3	rowSums==n	0.029118	0.0315485	0.0358700	0.0335410	0.0373885	0.069098
2	apply+all	0.873644	0.9240285	1.0197045	0.9694025	1.0901125	1.423477

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	1.571482	1.344119	1.353392	1.319317	1.367889	1.038552
2	apply+all	47.150089	39.368106	38.473955	38.130925	39.882651	21.395052

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 10x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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	colAlls	14.461	16.1340	17.98660	17.028	18.2545	39.181
2	rowAlls	18.529	23.4715	26.50376	25.423	27.3330	66.533

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	1.281308	1.454785	1.473528	1.493011	1.497329	1.698093

Figure: Benchmarking of colAlls() and rowAlls() 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 5156690 275.4    8529671 455.6  8529671 455.6
Vcells 9186633  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5156678 275.4    8529671 455.6  8529671 455.6
Vcells 9236666  70.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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	colAlls	0.016503	0.018137	0.0200962	0.0194905	0.0206635	0.041491
3	colSums==n	0.081474	0.088031	0.0951526	0.0916770	0.0998565	0.153938
2	apply+all	1.367004	1.506773	1.5948512	1.5471875	1.6063595	2.526367

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	colSums==n	4.936921	4.853669	4.734855	4.703676	4.832507	3.710154
2	apply+all	82.833667	83.077301	79.360873	79.381622	77.738984	60.889518

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.042957	0.0492940	0.0539950	0.0518085	0.0559535	0.109683
3	rowSums==n	0.232280	0.2505305	0.2703589	0.2580990	0.2763755	0.399680
2	apply+all	1.364724	1.4836375	1.5768114	1.5557460	1.5938430	2.508230

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
3	rowSums==n	5.407268	5.082373	5.007113	4.981789	4.939378	3.643956
2	apply+all	31.769537	30.097730	29.202932	30.028779	28.485135	22.867992

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 100x1000 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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	colAlls	16.503	18.137	20.09619	19.4905	20.6635	41.491
2	rowAlls	42.957	49.294	53.99497	51.8085	55.9535	109.683

	expr	min	lq	mean	median	uq	max
1	colAlls	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	2.602981	2.717869	2.686826	2.658141	2.707842	2.643537

Figure: Benchmarking of colAlls() and rowAlls() 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 5156896 275.5    8529671 455.6  8529671 455.6
Vcells 9187285  70.1   31876688 243.2 60562128 462.1
> colStats <- microbenchmark(colAlls = colAlls(X), `apply+all` = apply(X, MARGIN = 2L, FUN = all), 
+     `colSums==n` = (colSums(X) == nrow(X)), unit = "ms")
> X <- t(X)
> gc()
          used  (Mb) gc trigger  (Mb) max used  (Mb)
Ncells 5156884 275.5    8529671 455.6  8529671 455.6
Vcells 9237318  70.5   31876688 243.2 60562128 462.1
> rowStats <- microbenchmark(rowAlls = rowAlls(X), `apply+all` = apply(X, MARGIN = 1L, FUN = all), 
+     `rowSums==n` = (rowSums(X) == ncol(X)), unit = "ms")

Table: Benchmarking of colAlls(), apply+all() and colSums==n() 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	colAlls	0.003105	0.004097	0.0055423	0.0049490	0.0060700	0.026122
3	colSums==n	0.084499	0.088823	0.1018254	0.0935520	0.1094800	0.173184
2	apply+all	0.635825	0.671165	0.7549770	0.7020235	0.8062205	1.114587

	expr	min	lq	mean	median	uq	max
1	colAlls	1.00000	1.00000	1.00000	1.00000	1.00000	1.000000
3	colSums==n	27.21385	21.68001	18.37235	18.90321	18.03624	6.629814
2	apply+all	204.77456	163.81865	136.22040	141.85159	132.82051	42.668517

Table: Benchmarking of rowAlls(), apply+all() and rowSums==n() 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	rowAlls	0.042237	0.0455145	0.0514634	0.0478220	0.0536615	0.104833
3	rowSums==n	0.324287	0.3433275	0.3810341	0.3621710	0.4081510	0.542330
2	apply+all	0.636731	0.6672355	0.7344137	0.6960165	0.7531320	1.165268

	expr	min	lq	mean	median	uq	max
1	rowAlls	1.000000	1.000000	1.00000	1.000000	1.00000	1.000000
3	rowSums==n	7.677794	7.543255	7.40398	7.573314	7.60603	5.173276
2	apply+all	15.075195	14.659845	14.27060	14.554316	14.03487	11.115469

Figure: Benchmarking of colAlls(), apply+all() and colSums==n() on 1000x100 data as well as rowAlls(), apply+all() and rowSums==n() on the same data transposed. Outliers are displayed as crosses. Times are in milliseconds.

Table: Benchmarking of colAlls() and rowAlls() 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	colAlls	3.105	4.0970	5.54232	4.949	6.0700	26.122
2	rowAlls	42.237	45.5145	51.46341	47.822	53.6615	104.833

	expr	min	lq	mean	median	uq	max
1	colAlls	1.0000	1.00000	1.000000	1.000000	1.000000	1.000000
2	rowAlls	13.6029	11.10923	9.285536	9.662962	8.840445	4.013207

Figure: Benchmarking of colAlls() and rowAlls() on 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_0.60.1     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] rappdirs_0.3.3          startup_0.15.0          labeling_0.4.2         
[67] bitops_1.0-7            base64enc_0.1-3         boot_1.3-28            
[70] gtable_0.3.0            DBI_1.1.1               markdown_1.1           
[73] R6_2.5.1                lpSolveAPI_5.5.2.0-17.7 rle_0.9.2              
[76] dplyr_1.0.7             fastmap_1.1.0           bit_4.0.4              
[79] utf8_1.2.2              parallel_4.1.1          Rcpp_1.0.7             
[82] vctrs_0.3.8             png_0.1-7               DEoptimR_1.0-9         
[85] tidyselect_1.1.1        xfun_0.25               coda_0.19-4            

Total processing time was 11.79 secs.

Reproducibility

To reproduce this report, do:

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

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Copyright Henrik Bengtsson. Last updated on 2021-08-25 18:49:49 (+0200 UTC). Powered by RSP.