sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8aecf4cfd34e65873f33de55142bd1c37dd216728209f34f97df1fc4095b427f | R | 291 | 8 | library(matlib)
load("/home/clausb/R_image/allnetworks.Rdata", verbose = FALSE)
load("/home/clausb/R_image/remainingmodels.Rdata")
detvalues <- list()
for (i in 1:length(remainingmodels)) {
detvalues[[i]] <- det(bensface[[remainingmodels[[i]]]])
}
save(detvalues, file = "detvalues.Rdata")
|
c2051d0ea78cfdee3fdf513268f802aa57eae43e8adc21a17d436fafd92825e9 | R | 304 | 9 | suppressMessages(library(uwot))
args = commandArgs(trailingOnly=TRUE)
datarray <- read.table(args[1], sep="\t")
datarray <- as.matrix(datarray)
embedding <- umap(datarray, n_threads=max(1, RcppParallel::defaultNumThreads()))
write.table(embedding, file="", sep="\t", row.names=FALSE, col.names=FALSE)
|
dfe000a06acdac4ac6cbe89be48e94eb826f9aefa75544ff6e392224fa07d54b | R | 305 | 10 | load("/home/clausb/scripts/objects/tmat.Rdata")
nameslist <- list()
length(nameslist) <- length(unlist(as.list(unique(tmat['Trace']))))
names(nameslist) <- unlist(as.list(unique(tmat['Trace'])))
for (i in 0:8){
nameslist[[i+1]] <- tmat[tmat['Trace'] == i,]
}
save(nameslist, file = "nameslist.Rdata")
|
b299d2c1ab1e8595afdcfa5cd1a719c5531221a33b02dc56147d59cdee1394e0 | R | 308 | 8 | ## Travis CI specific: Explicit garbage collection because it
## looks like Travis CI might run out of memory during 'covr'
## testing and we now have so many tests. /HB 2017-01-11
if ("covr" %in% loadedNamespaces()) {
res <- gc()
testme <- as.environment("testme")
if (testme[["debug"]]) print(res)
}
|
53a40d0e44f52831b781b4de7e40f86df711c9a8b0b8341081201db2608ecb7e | R | 315 | 13 |
load("/home/clausb/R_image/allnetworks.Rdata", verbose = FALSE)
load("/home/clausb/R_image/remainingmodels.Rdata")
eigenvalues <- list()
for (i in 1:length(remainingmodels)) {
e <- eigen(bensface[[remainingmodels[[i]]]])
v <- e$values
eigenvalues[[i]] <- v
}
save(eigenvalues, file = "eigenvalues.Rdata")
|
679d445825964df49e9a1b60daecf108cfd4252dcb0676466cde08c786004f57 | R | 329 | 17 | slow_fcn <- function(x) {
Sys.sleep(x / 10)
sqrt(x)
}
plan(multisession, workers = 2)
js <- capture_journals({
fs <- lapply(3:1, FUN = function(x) future(slow_fcn(x)))
value(fs)
})
## Summarize all journals
js_all <- Reduce(rbind, js)
print(summary(js_all), digits = 2L)
## Shut down parallel workers
plan(seq... |
b5044e59ffe2721162e7d649f581656495deda092a3ddfceeedcd5c354ad52f3 | R | 333 | 7 | morpheus <- function(reference.res.df, comparison.res.df) {
reference <- reference.res.df[,c(1,3)] %>% dplyr::rename("L2FC.ref" = 2)
comparison <- comparison.res.df[,c(1,3)] %>% dplyr::rename("L2FC.comparison" = 2)
final <- left_join(reference, comparison, by = "genes") %>% arrange(desc(L2FC.ref))
retur... |
8dcd9b0ea595a511af88ef51b94bba3eb3574c036352b7d30336628a3ec8e5e2 | R | 335 | 9 | #' strawr
#'
#' API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).
#'
#' @docType package
#' @import Rcpp
#' @useDynLib strawr
#' @name... |
6c6b61c7920ab31f383d526fa3e135d0e8ca1a29f7469b1b58c7366baf915a10 | R | 339 | 8 | test_that("skip_extended works", {
current_value <- Sys.getenv("GALAMM_EXTENDED_TESTS")
Sys.setenv(GALAMM_EXTENDED_TESTS = "true")
expect_condition(skip_extended(), NA, class = "skip")
Sys.setenv(GALAMM_EXTENDED_TESTS = "")
expect_condition(skip_extended(), class = "skip")
Sys.setenv(GALAMM_EXTENDED_TESTS =... |
4f53b1634591bebc3120fb87561df23e8ac050d88031354ea9226d3b8cc99348 | R | 347 | 30 | a %<-% { 1 }
f <- futureOf(a)
print(f)
b %<-% { 2 }
f <- futureOf(b)
print(f)
## All futures
fs <- futureOf()
print(fs)
## Futures part of environment
env <- new.env()
env$c %<-% { 3 }
f <- futureOf(env$c)
print(f)
f2 <- futureOf(c, envir = env)
print(f2)
f3 <- futureOf("c", envir = env)
print(f3)
fs <- futur... |
a5fae6c42f399bfb5036669f2aefd7890319cb44349723cef55d0c1c5f8e3223 | R | 350 | 9 | #' Package GRNOpt
#'
#' Gene Regulatory Network Inference Using Optimization
#'
#' \code{GRNOpt} infers Gene Regulatory Network (GRN) from booleanized transcriptomic data and Prior Knowledge Network (PKN) using optmization methods. The package also includes methods for simulation and visualization.
#'
#' @name GRNOpt
#... |
b58500a09113f747517ecc9eabe7942b3cee7ad7f86068180b6edd23ce93e6d0 | R | 353 | 8 | RRHO_math <- function(deseqDF) {
#this part does the math to get the rankValue used for RRHO
rrho_ranks <- deseqDF[, c(1,3,6)] %>% mutate(rankValue = -1 * log10(pvalue) * sign(log2FoldChange)) %>% arrange(desc(rankValue))
#make dfs that only have gene name and rankValue
rrho_output_df <- rrho_ranks[,c(1... |
50e9295af423c4e2267afc569b4dcb9373a7299bc3a2416f78a771ed1c76fe26 | R | 361 | 22 | ## Use multicore futures
plan(multicore)
## A global variable
a <- 0
## Create future (explicitly)
f <- future({
b <- 3
c <- 2
a * b * c
})
## A multicore future is evaluated in a separate forked
## process. Changing the value of a global variable
## will not affect the result of the future.
a <- 7
print(a)
... |
1c22c6a07da17f370f210d47ddcb81c9ddc6ca409935c8adb520f7dea4745335 | R | 363 | 22 | #' Function to calculate number of solutions
#'
#' @param grn_df
#' @param pruned
#' @param bool_data
#'
#'
# g = grn_df
#
# g = grnAndBooldata(g, bool_data)
#
# pruned = grnAndBooldata(pruned, bool_data)
# x = anti_join(g,pruned)
# library(plyr)
# y = count (x, 'target')
#
# join to have direction
#
# count that dire... |
39739a0793613427c0b61cee1a78910cb23d89960806427b789419325df63d1c | R | 376 | 14 | #' Pipe operator
#'
#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' NOT EXPORTED @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
#' @param lhs A value or the magrittr placeholder.
#' @param rhs A function call using the magrittr seman... |
2602d987fb0cc587e41dac9234e88d2c04caf85e4a6c3d91532d8f30cfb564f2 | R | 378 | 14 | ## Enable journaling of futures
oopts <- options(future.journal = TRUE)
plan(multisession, workers = 2L)
t_start <- Sys.time()
fs <- lapply(1:3, FUN = function(x) future({ Sys.sleep(x); sqrt(x) }))
vs <- value(fs)
js <- lapply(fs, FUN = journal, baseline = t_start)
print(js)
## Stop parallel workers and disable jour... |
989f4641a077c4eb38bc06affb489618323f9ec72a3e4640ed12be7a0ee7398c | R | 382 | 9 | filtered_GOterms <- function(GO.plot.up.ref, rows, GO.plot.up) {
ref_upreg <- GO.plot.up.ref[c(rows), ]
ref_upreg_terms <- as.data.frame(ref_upreg$ID) %>% rename("terms" = 1)
select_upreg_df <- GO.plot.up@result %>% filter(ID %in% ref_upreg_terms$terms)
forM_upreg_df <- select_upreg_df %>% select(c(2,6)) %>% m... |
fdf4e46dbc2e54352ce0d0ebbb5e5790503c87c41d593b95c94334fdc467b81b | R | 383 | 23 | library(ggplot2)
library(tidyverse)
library(rstan)
library(broom)
library(broom.mixed)
library(zoo)
library(dplyr)
library(purrr)
library(xlsx)
library(stringr)
library(cmprsk)
library(survival)
library(survminer)
library(readxl)
library(data.table)
library(magrittr)
library(Greg)
library(timereg)
library(ggsci)
librar... |
dba854ef10990a03d3e90547ebb71255aacb8c2ebe6f5a8b0f86f25163eb7755 | R | 387 | 15 | # -- CRAN packages
install.packages("data.table")
install.packages("plyr")
install.packages("glmnet")
install.packages("WGCNA")
install.packages("reshape2")
install.packages("broom")
install.packages("rtracklayer")
install.packages("RNOmni")
install.packages("tensorflow")
install.packages("keras")
# -- bioconductor pa... |
957696a99471b5199bd260a9fe0fcbff6d7071e70c18da0d7e1999c07a93fd6e | R | 389 | 13 | #' @tags demo fibonacci
#' @tags sequential
library(future)
plan(sequential)
options(future.debug = FALSE)
message("*** Fibonacci demo of the 'future' package ...")
# Temporarily protect against non-default R_FUTURE_PLAN
oopts <- options(future.plan = NULL)
demo("fibonacci", package = "future", ask = FALSE)
options(o... |
ae272d8a54711b3c352d63d97fe36a5561318146d136e1f41cb20ea0143cf79d | R | 395 | 27 | #' @import dplyr
#' @import tidyr
#' @import ggplot2
#' @importFrom data.table as.data.table
#' @importFrom mgcv gam
#' @importFrom stats IQR qt quantile sd
#' @importFrom utils read.table
#' @importFrom broom glance
#' @keywords internal
"_PACKAGE"
# allowing for the use of the dot when piping
utils::globalVariable... |
de737141dd9f566234055b0a3a900fd0bf872f71dea2ffe3bd74d689b9f06023 | R | 406 | 14 | #' Extract the model frame from a galamm object
#'
#' @param formula An object of type `galamm` as described in [galammObject].
#' @param ... Other arguments. Currently not used.
#'
#' @returns A data frame.
#' @export
#' @family details of model fit
model.frame.galamm <- function(formula, ...) {
if (!is.null(formula... |
e06e2d48554bec5552f03519e0449f4beb74d0060ef6362cbb0efcc960d4d62e | R | 408 | 10 | ## This runs 'testme' test inst/testme/test-<name>.R scripts
## Don't edit - it was autogenerated by inst/testme/deploy.R
testme <- function(name) {
path <- system.file(package = 'future', 'testme', mustWork = TRUE)
Sys.setenv(R_TESTME_PATH = path)
Sys.setenv(R_TESTME_PACKAGE = 'future')
Sys.setenv(R_TESTME_NAM... |
08c5d2b47584a2d4e721db1d43e5722e6e8d4f4f3cbdaff0a2c17a8b0e35e82a | R | 410 | 17 | library(ggplot2)
library(galamm)
library(hexSticker)
dat <- subset(cognition, domain == 3 & item == "31")
p <- ggplot(dat, aes(x = x, y = y, group = id)) +
geom_point(size = .1) +
geom_line(linewidth = .1) +
theme_void() +
theme_transparent()
sticker(p,
package="galamm", p_size=20, s_x=1, s_y=1.1, s... |
3b772b97fc3b4870c095d66d93e0a234132770da4104c62f2f5b86b8d2479d48 | R | 412 | 19 | # Evaluate a future using the 'multisession' plan
with(plan(multisession, workers = 2), {
f <- future(Sys.getpid())
w_pid <- value(f)
})
print(c(main = Sys.getpid(), worker = w_pid))
# Evaluate a future locally using the 'multisession' plan
local({
with(plan(multisession, workers = 2), local = TRUE)
f <- fu... |
269e535d9231c428259ddbdf32ed3899499c2cc83fcd3a9d116b6472a57c221f | R | 424 | 21 | if (getRversion() >= "4.4.0") {
library(future)
library(parallel)
plan(sequential)
if (getRversion() >= "4.5.0") {
cl <- makeCluster(1L, type = future::FUTURE)
} else {
cl <- makeClusterFuture(1L)
}
message("clusterCall(cl, identity, quote(y)) ...")
arg <- quote(y)
res <- clusterCall(cl, ... |
9f648ccec7269b0e334b980b4193be26108b3129a426aca3daca8f449f6b3eb4 | R | 425 | 8 | ##-------------------------------------------------
## Set up an object to hold the data,
##
initScClass <- function(){
setClass("scObject", representation(clust="list", densities="list", dimensions="numeric",
marginalClust="list", sampleNames="character", vafs.1d="list",
... |
bead099dbcba63da376dd9b2abd6874a3171efb89b3035d9be9859ac27a161e4 | R | 425 | 9 | library(data.table)
custom_read = function(filename, header = T, colClasses = NULL, fill = F){
if (grepl("gz$", filename)){
return(fread(paste('zcat ', filename, sep=""), header = header, sep="\t", stringsAsFactors=F, showProgress=F, colClasses = colClasses, fill = fill))
} else {
return(fread(filename, he... |
78c0f31cc820971aad21328b9e58d42bc377ca539f2e46373157adf921bc7c0a | R | 426 | 9 | # Build against static libraries from rwinlib
VERSION <- commandArgs(TRUE)
if(!file.exists(sprintf("../windows/libcurl-%s/include/curl/curl.h", VERSION))){
if(getRversion() < "3.3.0") setInternet2()
download.file(sprintf("https://github.com/rwinlib/libcurl/archive/v%s.zip", VERSION), "lib.zip", quiet = TRUE)
dir.... |
d3b6d799e2858fdb9a3cb2c5182c9e5c28a650b95fa9962bf1fcac34d9dff491 | R | 429 | 23 | #' read96wells
#'
#' reads an array in 96-well plate format
#'
#' @param mat matrix with 8 rows and 12 columns
#' @export
read96wells<-function(mat){
v<-NULL
for(i in 1:12){
v<-c(v,mat[,i])
}
return(data.frame(well=paste0(
rep(c("A","B","C","D","E","F","G","H"),12),
sapply(c("01","02","03","04"... |
d0ec435e4d2d0ec0648a83c5ec60e7052bc11b602f4e0ff7ab49e528600a18ab | R | 430 | 23 | ## Use sequential futures
plan(sequential)
## A global variable
a <- 0
## Create a sequential future
f <- future({
b <- 3
c <- 2
a * b * c
})
## Since 'a' is a global variable in future 'f' which
## is eagerly resolved (default), this global has already
## been resolved / incorporated, and any changes to 'a'
#... |
752173f126a269c4eb401d289a7c9fa13896ef980b6cc32f26c41a65d851054b | R | 455 | 28 | \donttest{
## Use multisession futures
plan(multisession)
## A global variable
a <- 0
## Create future (explicitly)
f <- future({
b <- 3
c <- 2
a * b * c
})
## A multisession future is evaluated in a separate R session.
## Changing the value of a global variable will not affect
## the result of the future.
a ... |
1751b6679e5c053e1786b0eb55cde04ebeda125cdd0ce8fdfb5f020bea9fb756 | R | 466 | 18 | #!/usr/bin/env Rscript
# Load TDA package
if(require(TDA)==0) install.packages("TDA"); library(TDA)
# Load Rips filtration
rips_betti <- function(matriz, mdim){
# Compute distance matrix
D=1-matriz
# Take number of nodes
l=dim(D)[1]
# Maximum value of the rips filtration
mscale <- 1
... |
084b4791a0a6f0eded508da0b596d5fea291c3ea99594caaeab7c12be0def96a | R | 467 | 6 | #' @title Data file containing the topology of all 60212 non-redundant four-node gene circuits
#' @description This data contains the topology of all 60212 non-redundant four-node gene circuits.
#' @format A list of 60212 circuits, each element is a matrix of adjacency matrix.
#' Four columns/rows represent genes "A",... |
ad8fdf1d5f2db5cb6cc881dc11a1da58ad485969fa43add9a1a471288ede9eed | R | 467 | 17 | ---
vignette: >
%\VignetteIndexEntry{Link to book}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, echo=FALSE}
# Some compileable code, apparently needed for the engine
# to recognize this as Rmarkdown.
link <- BiocStyle::Biocbook("SingleRBook", label="link")
URL <- sub(".*\\((.+))", ... |
026be16424194c4bbd02b5775d65f771af230149c096beec25c2114e95985b21 | R | 468 | 12 | # Simple function to plot UMAP and dev.stage
clust.stage.plot <- function(data, cluster.col = "seurat_clusters"){
clust.plot <- DimPlot(data, group.by = cluster.col) +
ggtitle(paste("Clusters")) +
theme(plot.title = element_text(hjust = 0.5))
stage.plot <- DimPlot(data, group.by = "orig.ident") +
... |
558da1444f2d293fae04ef8bb72d54a49bc572cddb359160c5ad0165f82f03c1 | R | 471 | 30 | \donttest{
## Use cluster futures
cl <- parallel::makeCluster(2, timeout = 60)
plan(cluster, workers = cl)
## A global variable
a <- 0
## Create future (explicitly)
f <- future({
b <- 3
c <- 2
a * b * c
})
## A cluster future is evaluated in a separate process.
## Regardless, changing the value of a global va... |
a2ce77a519b1341edeb7b891ace9db316992b79fd084b061ab583324498b4852 | R | 478 | 33 | A <- matrix(c(1,2,3,4,5,6), ncol = 2)
pca <- prcomp(A)
str(pca)
View(pca)
C <- t(t(A) - apply(t(A), 1, mean))
V <- cov(C)
eigenRes <- eigen(V)
t(t(eigenRes$vectors) %*% t(C))
pca$x
setwd("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\genetica-libraries\\src\\test\\resources\\")
testMatrix <- read.d... |
bf51e7798bb3bd805af7975a9a4a7b7a3ece5c35293f0c8010a97776734a119f | R | 482 | 20 | ## Like mean(), but fails 90% of the time
shaky_mean <- function(x) {
if (as.double(Sys.time()) %% 1 < 0.90) stop("boom")
mean(x)
}
x <- rnorm(100)
## Calculate the mean of 'x' with a risk of failing randomly
f <- future({ shaky_mean(x) })
## Relaunch until success
repeat({
v <- tryCatch(value(f), error = iden... |
07e499329a98e3e63352c7854fa968abe46a9a9233f43f73f2f4739076d3dee9 | R | 490 | 15 | ```{r}
setwd("coregulation_clustering")
source("../downstreamer_functions.r")
data <- read.downstreamer.batch("../data/final_paper/", potential_traits = c("GenePrioritization"))
bonf.sig.genes <- unique(unlist(sapply(data, function(x){
x <- x$GenePrioritization
return( rownames(x)[x$Bonferroni.significant & x$En... |
3610a9873f6e4b2da25dbbeb3a6de1bee17f29f65e21ccc436c83ccd26741580 | R | 500 | 26 | library(tidyverse)
set.seed(11)
n <- 200
hsced <- tibble(
id = 1:n,
b = rnorm(n)
) %>%
uncount(3, .id = "tp") %>%
uncount(2, .id = "item") %>%
mutate(
x = runif(nrow(.)),
winv = if_else(item == 1, 1, 2),
y = x + b + rnorm(nrow(.), sd = sqrt(winv))
) %>%
select(-b, -winv) %>%
as.data.frame()... |
70e70271e767517fa6b22b3b0566dc3ce351c6c83ec731bdf60060e53d6526f9 | R | 511 | 24 | #' @tags FutureError
#' @tags sequential
library(future)
message("*** FutureError class ...")
## Minimal
ex <- FutureError(message = "Woops")
print(ex)
cond <- tryCatch(message("Woops", appendLF = FALSE), message = identity)
ex2 <- FutureError(message = cond)
print(ex2)
stopifnot(conditionMessage(ex2) == conditionM... |
a843559123c8a9c384771f7b9db2d95af8d8e4140c93e5110690231ea1b1471c | R | 515 | 21 | #' @keywords internal
#' @aliases galamm-package NULL
#'
#' @references
#'
#' \insertRef{sorensenLongitudinalModelingAgeDependent2023}{galamm}
#'
"_PACKAGE"
## usethis namespace: start
#' @useDynLib galamm, .registration = TRUE
## usethis namespace: end
NULL
## usethis namespace: start
#' @importFrom Rcpp sourceCpp
#... |
509e840917152367fa79f6bbf536087497e997e29ae98afdfaba5ccdd4e6689c | R | 521 | 15 | library(data.table)
library(optparse)
option_list = list(
make_option(c("-i", "--infile"),
help="gzipped expression file"),
make_option(c("-o", "--outfile"),
help="outfile (not gzipped)")
)
opt = parse_args(OptionParser(option_list=option_list))
expression <- data.frame(fread(paste0('... |
913ab676f4f9ca64e77f2eccf4004b9909c612a7e666be4f8d3cf980534db62d | R | 525 | 11 | custGO_barplot <- function(enrichGO.plot) {
plot <- ggplot(enrichGO.plot) +
geom_bar(stat = "identity", aes(fill = p.adjust, x = Count, y = reorder(Description, -p.adjust)), color = "black") +
scale_fill_gradient(low = "#005F73", high = "whitesmoke", na.value = NA, guide = guide_colorbar(reverse = TRUE)) ... |
0aecdc5365883daa7021facf185d8bba4a26e2c52e30f984448051eb656bb66c | R | 526 | 19 | library(R.matlab)
library(circular)
radiandata<-readMat("/media/user/4TB/facial/download/Kiki2/Thetas.mat")
radiandata<-radiandata$theta
data <- list(
group1 = circular(c(radiandata[,1]),
units="radians", template="geographics"),
group2 = circular(c(radiandata[,2]),
units="r... |
c591a1a90faa20e96ae08643accc3078a7d9f4970d3936e923d38d7a1967196b | R | 526 | 25 | library(comprehenr)
df <- read.csv('data/ZC0.csv')
fnames = c('reversal','punish')
cnames = c('pfc','pmc')
stat = c()
pval = c()
rname = c()
for ( fname in fnames ) {
for (ctx in cnames) {
xn = paste(fname,ctx,sep='_')
x <- df[[xn]]
yn = paste(fname,'prat',ctx,sep='_')
y <- df[[yn]]
w = wi... |
6f2c05d470879a35b7e44f7f523bffe74592c7cd7399aee37e8d8ef0e2352b92 | R | 527 | 17 | sortData <- function(dataset, output_file_name = "")
{
if (!file.exists(dataset)) {
message("ERROR: Specified Input File does not Exist")
return(FALSE)
}
if( output_file_name == "" ||
substr(output_file_name,
nchar(output_file_name), nchar(output_file_name)) == "/" ){
message("ERROR: You... |
089d3586ca45f44ece706edd01a96a9b8c94ae0920f42f1dda9826e08c17e23f | R | 528 | 21 | #' Title
#'
#' @param par
#'
#' @return
#' NOT EXPORTED @export
#' @keywords internal
#'
#' @examples
slurm_singleTrait_likelihood = function(par){
theta=unlist(par)
test = optim(theta, singleTrait_likelihood,
betX=betX, pi1=pi1, sig1=sig1, w8s=w8s,
m0=m0, M=M, nX=nX, bn=2^7, bins=10,
... |
e75be0701d866b687b2ca8966eb2e964011d71225d7711fc86f3b93e4b3df7c7 | R | 531 | 25 | #' Title
#'
#' @param slr_job
#'
#' @return
#' @keywords internal
#' NOT EXPORTED @export
#'
#' @examples
get_job_status_lhc <- function (slr_job)
{
if (!(class(slr_job) == "slurm_job"))
stop("input must be a slurm_job")
stat <- suppressWarnings(system(paste("squeue -n", slr_job$jobname),
... |
cf0868415d3855ac7d1042ef48f5b476cedd4847e6ecb6f9951bbaacc9070d89 | R | 533 | 22 | #' Set random seed for future assignment
#'
#' @usage fassignment \%seed\% seed
#'
#' @inheritParams future
#'
#' @aliases %seed%
#' @rdname futureAssign
#'
#' @export
`%seed%` <- function(fassignment, seed) {
fassignment <- substitute(fassignment)
envir <- parent.frame(1)
## Temporarily set 'seed' argument
ar... |
c4ab52ee8bf4c70c95dcaec6626d8e8ebcfe24d210f24666d05e2da381724c85 | R | 543 | 22 | #' Specify label for a future assignment
#'
#' @usage fassignment \%label\% label
#'
#' @inheritParams future
#'
#' @aliases %label%
#' @rdname futureAssign
#'
#' @export
`%label%` <- function(fassignment, label) {
fassignment <- substitute(fassignment)
envir <- parent.frame(1)
## Temporarily set 'label' argumen... |
462c8fff9216cc5de440ba102d8991c940a0ea4fb25bdc9d4be4543e1a4c5f05 | R | 551 | 22 | #' Control lazy / eager evaluation for a future assignment
#'
#' @usage fassignment \%lazy\% lazy
#'
#' @inheritParams future
#'
#' @aliases %lazy%
#' @rdname futureAssign
#'
#' @export
`%lazy%` <- function(fassignment, lazy) {
fassignment <- substitute(fassignment)
envir <- parent.frame(1)
## Temporarily set 'l... |
a2608c1de1b84b439502e687124cd9c766709bf873c92e1dd263e45b5bff2297 | R | 551 | 21 | load("/home/clausb/scripts/objects/strace.Rdata")
for (i in 1:355983){
j <- i+1
if (strace[[i,1]] == strace[[j,1]]) {
if (strace[[i,2]] == strace[[j,2]]) {
if (strace[[i,3]] == strace[[j,3]]) {
if (strace[[i,4]] == strace[[j,4]]) {
if (strace[[i,5]] == strace[[j,5]]) {
if (st... |
8394c13c36fafe97d79c67ed42617e443aa565d9293688a695516665d3c2a526 | R | 553 | 15 | bensface <- list()
variabless <- 1:4
danya <- as.matrix(expand.grid(lapply(numeric(length(variabless)), function(x) c(0,1,2))), ncol=length(variabless))
tricep <- as.matrix(expand.grid(lapply(numeric(length(variabless)), function(x) c(1:81))), ncol=length(variabless))
bensface <- list()
for (i in 1:nrow(tricep)){
te... |
74a38ea92132208c9b3ea2bd5519b4af13546dc2d8bfe2c5241de935ff8a53a9 | R | 561 | 22 | library(data.table)
DT <-
fread(
file.path(
'figure-2',
'inputs',
paste0('post_predictive_analysis_all_models_combined_imaging_non_',
'imaging_age_at_assessment.csv')))
eid_val <- unique(DT[evaluation == "Validation"]$eid)
eid_gen <- unique(DT[evaluation == "Generalization"]$ei... |
a54879b01b2186ac426fe709f7cec5b392bee1ba6ec01617b686fd0c3e994702 | R | 561 | 20 | #' @importFrom parallelly makeClusterPSOCK makeNodePSOCK
makeClusterPSOCK_args <- local({
.args <- NULL
function() {
if (is.null(.args)) {
## Arguments meant for makeClusterPSOCK() and makeNodePSOCK()
args <- character(0L)
for (name in c("makeClusterPSOCK", "makeNodePSOCK")) {
if (!... |
1d0cfc29fd2ea3aadfa1efc4d1b8189ef55fc6fd1d89bdb158c7bc4fc7d5ed82 | R | 575 | 29 | #' @tags sequential
library(future)
message("*** sequential() ...")
message("- sequential() w/ required packages ...")
f <- future(median(1:3), lazy = TRUE)
print(f)
## Doesn't work if covr that depends on stats is loaded
try(unloadNamespace("stats"))
v <- value(f)
print(v)
stopifnot(identical(v, 2L))
stopifnot(... |
d633b43e9104f536d1eb4c9a1656d435e857140a04e265dfa8eca759e8a80e80 | R | 591 | 17 | args=commandArgs(TRUE)
fn=args[1]
print("Reading in file")
data=read.delim(fn, header=FALSE, nrow=5)
nc = ncol(data)
data=read.delim(fn, header=FALSE, colClasses=c(rep("character",nc-2),rep("numeric",2)))
x1 = log10(data[,(nc-1)]+1)
x2 = log10(data[,nc]+1)
r.pear=cor(x1, x2)
r.spe=cor(x1, x2, method="spe")
... |
4e6e1b8cd4c6711b4d904444f8b1f33b1f3046b50d5b08baa2186f061cb1e223 | R | 592 | 16 | test_that("gratia functions work", {
dat <- subset(cognition, domain == 1 & item == "11" & id < 10)
dat$y <- dat$y[, 1]
mod <- galamm(y ~ s(x) + (1 | id), data = dat)
expect_s3_class(draw(mod), "ggplot")
expect_s3_class(draw(derivatives(mod)), "ggplot")
expect_s3_class(appraise(mod), "ggplot")
expect_s3_... |
6b6cdc53ab0cbfe5243e2feb19710ba07a2ff4c8e1b79838f1f7fe9ce8346b91 | R | 592 | 29 | #' @tags warn2
#' @tags error
#' @tags future
#' @tags sequential multisession multicore
library(future)
message("*** options(warn = 2) relays an error ...")
for (strategy in supportedStrategies()) {
message("Type of future: ", strategy)
plan(strategy)
f <- future({
stopifnot(getOption("warn") == 0)
o... |
db149908b1b3438d5660b8220c06bc67649f1da6cef61915230624900aa2bd25 | R | 595 | 22 | library(tidyverse)
diet0 <- read.delim("data-raw/diet.dat", header = TRUE, sep = "\t")
diet0$fiber2[diet0$fiber2 == -99] <- NA_real_
diet <- diet0 %>%
pivot_longer(
cols = c(fiber1, fiber2, chd),
values_to = "y", names_to = "item",
values_drop_na = TRUE
) %>%
mutate(
chd = as.integer(item == "chd... |
70a1e5abc01c1d0ff1f7de2146e18ef9b41df5fbb432d27a0970fbe142faf99b | R | 598 | 39 | #' @tags FutureGlobals
library(future)
message("*** FutureGlobals() ...")
fg1 <- FutureGlobals()
print(fg1)
fg2 <- FutureGlobals(fg1)
print(fg2)
g <- globals::as.Globals(list(a = 1, b = 1:3))
print(g)
fg3 <- FutureGlobals(g)
print(fg3)
fg4 <- as.FutureGlobals(g)
print(fg4)
print(resolved(fg3))
fg <- fg4
fg_un... |
cf28b1908aac4064b6f764b507dec76ff7f776f9df352a9d18646bb56e9d8733 | R | 599 | 32 | #' @tags futureOf
#' @tags listenv environment
#' @tags sequential
library(future)
library(listenv)
message("*** futureOf() ...")
a %<-% { 1 } %lazy% TRUE
f1 <- futureOf("a")
print(f1)
f2 <- futureOf(a)
print(f2)
stopifnot(identical(f2, f1))
## Get all futures in the current environment
fs <- futureOf(drop = TRUE)... |
9700128a6e88f04f42bfa984a8bb4c167ebf37d3b2c55b1cce541b7b07c83bc5 | R | 611 | 23 | ## Use sequential futures by default
oplan <- local({
oopts <- options(future.debug = FALSE)
on.exit(options(oopts))
future::plan(future::sequential)
})
supportedStrategies <- function(cores = NA_integer_, excl = "cluster", ...) {
strategies <- future:::supportedStrategies(...)
strategies <- setdiff(strategi... |
88c1c5deb519deb33a00e490c48dfd0457540e7d9d29fac45c6957d3a019b66a | R | 615 | 31 | .main_data <- function(discovery) {
UseMethod(".main_data")
}
.main_data.genomescore_discovery <- function(discovery) {
as.data.frame(discovery[[1]])
}
.main_data.domainogram_discovery <- function(discovery) {
as.data.frame(discovery)
}
.metric_name <- function(discovery) {
UseMethod(".metric_name")
}
.metr... |
1a8af8fe2daa3f36eb588723430ae22a13b73a491eb9aac1668580ad04a4ddc2 | R | 624 | 19 | library(sRACIPE)
library(igraph)
library(combinat)
print("ass")
for(i in 1:10){
if (i == 1){
load("/work/lulab/Ben/distmat/NEWMAN_SUBSAMPLING/inception/split/Iterative_Subsampling/ASSOCIATION/output/ass.mat")
ass.mat.total <- ass.mat
print(i - 1)
} else {
load(paste0("/work/lulab/Ben/distmat/NEWMA... |
e9f5c4e55a58fb80badca5c977ef1be267b2bf2e53fe9915a95e86ea248a7795 | R | 625 | 22 | #' Control whether standard output should be captured or not
#'
#' @usage fassignment \%stdout\% capture
#'
#' @param capture If TRUE, the standard output will be captured, otherwise not.
#'
#' @aliases %stdout%
#' @rdname futureAssign
#'
#' @export
`%stdout%` <- function(fassignment, capture) {
fassignment <- substi... |
96af8d6aeda1b9774b13410b5cca578bb949e29fd659ca786d4a98cf09dc1a5c | R | 627 | 26 | library(ggplot2)
tryCatch(options(stringsAsFactors = F),
error = function(x) print('could not set option'))
my_theme <- theme_minimal() + theme(
text = element_text(family = 'Helvetica', size = 20),
legend.text = element_text(size = 18),
legend.title = element_text(size = 16),
axis.text = element_... |
3634808daa6f42286380103cd3cc2322b241a01f18cb1b2ccb3163a2e1f174fe | R | 628 | 26 | library(ggplot2)
tryCatch(options(stringsAsFactors = F),
error = function(x) print('could not set option'))
my_theme <- theme_minimal() + theme(
text = element_text(family = 'Helvetica', size = 20),
legend.text = element_text(size = 18),
legend.title = element_text(size = 16),
axis.text = element_... |
1d9b67440220c9b01ba7f54510d580d3143ff828fea0abc0dc0ecc532305a182 | R | 630 | 7 | mod_sep_PPTs_huddles <- function(sep_df, timepoint) {
sep_timepoint <- sep_df %>% filter(Timepoint == timepoint)
sep_timepoint_huddles <- sep_timepoint %>% select(c(1:7))
sep_timepoint_huddles <- pivot_longer(sep_timepoint_huddles, cols = c("abspHuddle", "absnHuddle"), names_to = "stimulus", values_to = "raw_h... |
bcb18e388a721013b2cd5dc01800ebfa601f1f595419ccdad7723977d4cbded5 | R | 631 | 24 |
hpo.matrix <- data.frame()
input.path <- "~/Desktop/depict2/hpo_predictions/"
input.files <- list.files(input.path, pattern="height*")
for (file in input.files) {
cat(file, "\n")
data <- read.table(paste0(input.path, file), header=T, row.names =1)
if (ncol(hpo.matrix) == 0) {
hpo.matrix <- data
} else {
... |
77387f67cdfc40867434e6232b741aacc3140bef6a0d1c9d983629bde05abc19 | R | 633 | 33 | if (getRversion() >= "4.4.0") {
library(future)
options(future.debug = FALSE)
message("plan(cluster, workers = parallelly::makeClusterSequential()) ...")
cl <- parallelly::makeClusterSequential()
print(cl)
plan(cluster, workers = cl)
print(plan())
message("future(42)")
f <- future(42)
v <- value... |
f78199dc2b0c1b36ae0b96e1bc0642813cfe64981e13e103ea26b8a9fe39f7aa | R | 637 | 17 | library(h2o)
h2o.init(nthreads = -1) # This means nthreads = num available cores
train_file <- "https://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/mnist/train.csv.gz"
test_file <- "https://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/mnist/test.csv.gz"
train <- h2o.importFile(train_file)
test <- ... |
b784cbae05fdfc496709a3d4ed05ee55851a9358c9782333e78b62b12022cd7a | R | 638 | 21 | #' Control whether standard output should be captured or not
#'
#' @usage fassignment \%conditions\% capture
#'
#' @param capture If TRUE, the standard output will be captured, otherwise not.
#'
#' @aliases %conditions%
#' @rdname futureAssign
#' @export
`%conditions%` <- function(fassignment, capture) {
fassignment ... |
509ff3e46d8bea04e5ec16d4800ad5616cff0a48afa0c9b59901c5405a2b3aac | R | 645 | 18 | load("/work/lulab/Ben/distmat/NEWMAN_SUBSAMPLING/inception/input/ks.dist.mat.all.complete")
load("/work/lulab/Ben/distmat/NEWMAN_SUBSAMPLING/inception/split/Iterative_Subsampling/ASSOCIATION/clean/input/clusters.g10.100k")
for(i in 1:length(clusters.g10.100k)){
tmp.cluster <- clusters.g10.100k[[i]]
tmp <- ks.dist... |
9f96c12d113b6047af664f520f5644665df344dad93ce6a4c7a186622217295b | R | 645 | 24 |
# example for colon (one month) shown. Uses processed data from step 1
# resulting top tables are in FinalProcessedData
library(Biobase)
library(limma)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
grp="dc1m"
eset<-readRDS(file.path("../C_QCandNormalization",grp,"data/CorrectedEset.rds"))
G... |
c79704bd0581c3afb5a6634e5015738497130ba3252960730eae7e61e9580125 | R | 648 | 7 | mod_sep_PPTs_huddles_p1vp2 <- function(sep_df, timepoint) {
sep_timepoint <- sep_df %>% filter(Timepoint == timepoint)
sep_timepoint_huddles <- sep_timepoint %>% select(c(1:7))
sep_timepoint_huddles <- pivot_longer(sep_timepoint_huddles, cols = c("abspHuddle", "absnHuddle"), names_to = "stimulus", values_to = ... |
a34c8c37b047a5092c08b2ca9e28f2b582c7cfc88352f309dcf90cc9e6164e6d | R | 649 | 27 |
args <- commandArgs(trailingOnly = TRUE)
print(args)
st.file = args[[1]]
meta.file = args[[2]]
k=as.numeric(args[[3]])
cut=as.numeric(args[[4]])
database = args[[5]]
method = args[[6]]
dir0=args[[7]]
st.exp <- read.csv(file=st.file,stringsAsFactors=F,check.names=F,row.names=1)
st.meta <- read.csv(file=meta.file,str... |
0ea4b6926a95b63c3b2de919573264812a18a3f00fe154875d1ccdeb707dc000 | R | 655 | 15 | # library(testthat); library(SingleR); source("test-utils.R")
test_that("intersection works as expected for edge cases", {
out <- SingleR:::.create_intersection(c("A", "B", "C", "a"), LETTERS)
expect_identical(out$test, 1:3)
expect_identical(out$reference, 1:3)
out <- SingleR:::.create_intersection(c(... |
efe440cf07374e152bb574516e432dad1da24edbf36a2d37cf9fd0da40dfaa21 | R | 660 | 24 |
library(data.table)
library(optparse)
option_list <- list(
make_option(c("-i", "--infile"),
help="Infile with rows genes and columns PC scores"),
make_option(c("-o", "--outfile"),
help="Outfile with first column scaled and then row scaled PC values")
)
opt <- parse_args(OptionParser(option_l... |
65474c9a805b1bbd1de794dc000905017f891b5fcd675e87e02be72c6ea39c80 | R | 662 | 22 | #
# test.mappings.R
#
# Copyright (c) 2013-2013 GBIC: Danny Arends, Harm-Jan Westra, Lude Franke and Ritsert C. Jansen
# last modified Jul, 2013
# first written Jun, 2013
#
setwd("~/Github/systemsgenetics/eqtl-mapping-pipeline/src/main/scripts")
source("helper.functions.R")
setwd("~/Github/Juha/")
# Load the Illumina... |
d35c9b206513891617a961e6020403329cc99952da32aa82d1058283e1d12f00 | R | 664 | 18 |
#### box plot for meta data stats ####
box.plot <- function(dat, y_col, group_by, y_lab, x_lab){
ggplot(dat, aes(x = dat[[group_by]], y = dat[[y_col]], fill = dat[[group_by]])) +
geom_boxplot() +
xlab(x_lab) +
ylab(y_lab) +
theme(
legend.position = "none",
panel.grid.major = element_blank... |
85fa06e6e3b27261fe501f85ff2b7a19f4a951598d4f1fb6247bfcab20f485cc | R | 666 | 35 | #' @tags mpi
#' @tags cluster
#' @tags skip_on_cran
library(future)
stopCluster <- parallel::stopCluster
makeClusterMPI <- parallelly::makeClusterMPI
message("*** MPI ...")
pkg <- "Rmpi"
if (requireNamespace(pkg, quietly = TRUE)) {
cl <- makeClusterMPI(availableCores())
str(cl)
plan(cluster, workers = cl)
... |
0e017f639ce2057794d5add702aa0ce63a3d82440c82667b123a93a56e5a9b8f | R | 669 | 21 | #' Convert Edge List to Adjacency Matrix
#'
#' \code{el2adj} converts a standard edge list (e.g. geneA Activation geneB)
#' to an adjacencey matrix (of class matrix)
#'
#' @param el Edge List
#' @return Adjacency Matrix
#' @family network conversion
el2adj <- function(el){
library("igraph")
colnames(el) <-... |
6959f578e5eb2e63a9258b47242abb19cfacdd46100b100a989b76cd02754b30 | R | 680 | 32 | qvalue <-function(pval) {
lambda=seq(0,0.90,0.05);
pi0 <- rep(0,length(lambda))
for(i in 1:length(lambda)) {
pi0[i] <- mean(pval >= lambda[i])/(1-lambda[i]);
}
spi0 <- smooth.spline(lambda,pi0,df=3);
pi0 <- predict(spi0,x=max(lambda))$y;
pi0 <- min(pi0,1);
m <- length(pval);
u <- orde... |
393d08a014ea9b27fc3d23c4781fc83eb2f64ad01f51b2d394358a29a2929951 | R | 688 | 15 | library(gplots)
lineage_matrix <- read.csv("/Volumes/GoogleDrive/My\ Drive/python_code/connectome_tools/lineage_analysis/lineage_matrix.csv")
palette <- colorRampPalette(c("white", "orange", "brown4","brown4","brown4","brown4","brown4","brown4"))(n = 128)
width <- 10
height <- 10
pointsize <- 4
#setEPS()
#postscript(... |
2082f3fe88372b6e5d767b28173af1d6f8c139d51fa70809d7271223e089e9cb | R | 692 | 10 | # plot quality metrics for each cluster
QC.plot <- function(seurat_obj, group_by = "seurat_clusters", y_elements = c("nCount_RNA", "nFeature_RNA", "percent.mt") ,
y_lab = c("UMI Count", "Gene Count", "% MT"), x_lab = "Cluster ID"){
plots <- list()
plots$a = box.plot(dat = seurat_obj@meta.data, y... |
617d876cd6b4c8d843f3cfbc02d52b316d68bfba505628e56d7c592cfa81553d | R | 703 | 33 | #' @tags utils-internal session_uuid
library(future)
session_uuid <- future:::session_uuid
message("*** session_uuid() ...")
id0 <- session_uuid()
print(id0)
## Reset session UUID (hack)
environment(session_uuid)$uuids <- list()
id <- session_uuid()
print(id)
stopifnot(id != id0)
## Assert that forked child proce... |
17615db7e44b9a469a74a071db9138a5b4b2f6a2955954d4b6ba604288b144bd | R | 715 | 33 | # See http://www.biostat.jhsph.edu/~iruczins/teaching/jf/ch5.pdf
data(longley)
g <- lm(Employed~GNP+Population,data=longley)
summary(g, cor=T)
# GLS
x <- model.matrix(g)
# Construct sigma matrix
Sigma <- diag(16)
#Sigma <- 0.31041^abs(row(Sigma)-col(Sigma))
Sigma <- 0.3662414^abs(row(Sigma)-col(Sigma))
Sigi <... |
95ab34ef3e2315b98bc2f9a86f2f3ce5bc3f3390cfa9410d8b08a1f3968d3e32 | R | 716 | 28 |
setwd("/groups/umcg-fg/tmp01/projects/downstreamer/depict2_bundle/summary_statistics/plaintext/pricePaper")
files <- list.files(pattern = ".gz")
file <- files[1]
for(file in files){
summstats <- read.delim(gzfile(file))
summstats$P <- 2*pnorm(-abs(summstats$Z))
write.table(summstats, file = gzfile(paste0("... |
a0fc66307a668fb5cf8162d93367840235bfb2d61fe0cfe185bae14ce49f8194 | R | 724 | 22 | library(data.table)
split_files <- function(fname, by = c("target", "variable"))
{
dt <- fread(file.path('inputs',fname))
dt_split <- split(dt, by = by)
split_names <- gsub(" ", "", names(dt_split))
split_names <- gsub(".", "_", split_names, fixed = T)
for (ii in seq_along(dt_split))
{
... |
6e4121380a579d5f7bc5c3b8000462c2c97145f7a368f38618cfcfa2f48906c8 | R | 735 | 35 | #' @tags startup onAttach
library(future)
options(future.debug = FALSE)
plan("default")
message("*** .onAttach() ...")
pkgname <- "future"
message("- .onAttach() w/ option future.startup.loadScript ...")
for (value in list(NULL, FALSE, TRUE)) {
options(future.startup.loadScript = value)
.onAttach(pkgname, pk... |
f7e7606c5257fc1be02a41cf68c18060168380f01fbc99e655af6dc928f6703b | R | 744 | 19 | #' Shows slots of ACTIONetExperiment (ACE) object
#'
#' @param object ACTIONetExperiment object
#'
#' @export
#' @importMethodsFrom SummarizedExperiment show
setMethod("show", "ACTIONetExperiment", function(object) {
validObject(object)
callNextMethod()
cat(
"rowMaps(", (length(rowMaps(object, all ... |
3b598f49f9bdf8309c320631f6fe976117069066c63cd115501b721f1b4b4514 | R | 747 | 7 | huddle_make_tidy <- function(wide_df, timepoint) {
df <- wide_df %>% filter(Timepoint == timepoint)
df_huddles <- df %>% select(Animal, Cohort, PairingType, Timepoint, Condition, abspHuddle, absnHuddle) #selects for all animal information and also the huddle columns
df_huddles <- pivot_longer(df_huddles, cols ... |
cd0ffd02062f9f6693084909e91494883cd41696492530fe17515239c12449fa | R | 748 | 36 | if (getRversion() >= "4.4.0") {
library(future)
library(parallel)
a <- 42
FUN <- function(x) {
message("Process ID: ", Sys.getpid())
list(a = a, mean = mean(rnorm(n = x)))
}
message("makeCluster():")
plan(multisession)
cl <- makeCluster(2)
set.seed(42)
clusterSetRNGStream(cl)
clusterE... |
3d3208276267670358a56313049d32365206571c39f9811d3bd6529785957770 | R | 764 | 21 | ---
title: "clustering lineage matrix"
output: html_notebook
---
```{r}
library(gplots)
lineage_matrix <- read.csv("/Volumes/GoogleDrive/My\ Drive/python_code/connectome_tools/lineage_analysis/lineage_matrix.csv")
palette <- colorRampPalette(c("white", "orange", "brown4","brown4","brown4","brown4","brown4","brown4"))... |
5ca7d668b57fc128da9fa23519e45f5bca62cdf2e3f7e95115aa8f5f94db79b8 | R | 775 | 24 | library(readr)
library(stringr)
ff <- read_lines("inst/include/autodiff/common/eigen.hpp")
ind1 <- str_which(ff, "#include <Eigen/Core>")
ff <- c(ff[1:ind1], "#include <Eigen/SparseCore>",
ff[(ind1 + 1):length(ff)])
ind2 <- min(str_which(ff, "EIGEN_VERSION_AT_LEAST")) - 1
ff <- c(ff[1:ind2],
"templ... |
d253326116c06ca3055368de3b039746417e0a866f41c2373510f3329a623b5f | R | 785 | 10 | rrho_quad_genes <- function(rrhoPlot, newname) {
newname.uu <- as.data.frame(stri_list2matrix(rrhoPlot[["genelist_uu"]])) %>% dplyr::rename("list1_uu" = 1, "list2_uu" = 2, "overlap_uu" = 3)
newname.dd <- as.data.frame(stri_list2matrix(rrhoPlot[["genelist_dd"]])) %>% dplyr::rename("list1_dd" = 1, "list2_dd" = 2, "... |
7902412d774d439264035ff6fa35a9b0795500de0a14c6b9ad470a9dd98f6db2 | R | 789 | 22 | knit_vignettes <- function() {
old_wd <- getwd()
do.call(on.exit, list(bquote(setwd(.(old_wd))), add = TRUE))
setwd("vignettes-raw/")
knitr::opts_chunk$set(error = FALSE)
files <- c("lmm_factor", "glmm_factor", "mixed_response",
"lmm_heteroscedastic", "semiparametric", "optimization",
... |
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