sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6fd8a2e6e569b80a479bf74f6883bccb720af0894e674d5c1f295fb3458ec866 | R | 223 | 10 | context("Test wcs_sql")
library(hBayesDM)
test_that("Test wcs_sql", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(wcs_sql(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
88edeca564f2da3e2a993eea49e491b2c6cc7ed088933a9d36487e0e826a74e9 | R | 223 | 10 | context("Test ts_par7")
library(hBayesDM)
test_that("Test ts_par7", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ts_par7(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
8900346524867f85ad60240c5fd76193e3694cc9c4917a195db5b919e4a6b224 | R | 223 | 10 | context("Test igt_vpp")
library(hBayesDM)
test_that("Test igt_vpp", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(igt_vpp(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a569faf8339f63e329d1531aa06caca2edcbaaf19d2a226ded4f7bd1eaeefa53 | R | 223 | 10 | context("Test cra_exp")
library(hBayesDM)
test_that("Test cra_exp", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(cra_exp(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
b988b7a4937cbab7b83a50d28d2a87dd0d413406e662ee688a7e74cb9794a07b | R | 223 | 10 | context("Test ts_par4")
library(hBayesDM)
test_that("Test ts_par4", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ts_par4(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
98a2276422aee7c78b40e0470ac9510b61b4c369026f5d0e87f55948ceeabfe8 | R | 225 | 14 | # Summer project - evolutionary trends in the elasmobranch chondocranium #
# Load Packages #
library(ape)
library(geomorph)
library(rgl)
library(dispRity)
library(phytools)
library(strap)
library(paleotree)
# Load Data #
|
b7d22ef4ab2de6c44f4a17f973de4693b03e39f9c312a2b0b571baf81005134e | R | 225 | 5 | select<-dplyr::select
learn <- rbind( learn.N, learn.A, learn.B)
transfer <- rbind( cbind(transfer.N, condition="N"),
cbind(transfer.A, condition="A"),
cbind(transfer.B, condition="B"))
|
df06dcd7417fa34cd02a3d6ce92be6694d48019e45f2cfb26ff0cc7844548c0b | R | 225 | 5 | # anticipation/expectation of improvement
fname=file.path("data","raw", "arousal.csv")
arousal<-read.table(fname, header=T, sep=",", as.is = T, na.strings = c("na"))[1:29,1:7]
arousal$Participant=factor(arousal$Participant)
|
2f2ae4ece4d89d13d9107c907abd99f250acd847f477a7779ea149a1c098290d | R | 226 | 10 | context("Test ug_bayes")
library(hBayesDM)
test_that("Test ug_bayes", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ug_bayes(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
688d5746790ba8d777f57007e9a3eafb1462157a80fb5b711466f272ab879ee5 | R | 226 | 10 | context("Test hgf_ibrb")
library(hBayesDM)
test_that("Test hgf_ibrb", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(hgf_ibrb(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
8596f425dbd677564ac06cfc181eb35b0b9b38cf0f746731d73d689416443c95 | R | 226 | 10 | context("Test peer_ocu")
library(hBayesDM)
test_that("Test peer_ocu", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(peer_ocu(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
b6a9fcda3ff77764f83b8337b9d2959a291cca164d79aff195c487ded3e7620f | R | 226 | 10 | context("Test ug_delta")
library(hBayesDM)
test_that("Test ug_delta", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ug_delta(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
2104abf42873bb752a999ef444993a204de305047f5251d09ca61ef749026814 | R | 229 | 10 | context("Test pstRT_ddm")
library(hBayesDM)
test_that("Test pstRT_ddm", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(pstRT_ddm(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
8080d5a52f835da56b528b15857602d4af2a153a2d40617bd8f90644feafb03a | R | 229 | 10 | context("Test bart_par4")
library(hBayesDM)
test_that("Test bart_par4", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bart_par4(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
9b75f3eb75e91b60c67c3cef521c3c7df81b270a9e8bd32a6c61d8efe7d88824 | R | 229 | 10 | context("Test alt_delta")
library(hBayesDM)
test_that("Test alt_delta", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(alt_delta(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
addddd889b89b8c357093c6bf876b6131854688744c5ffecfca738aaa2bd7c97 | R | 229 | 10 | context("Test bart_ewmv")
library(hBayesDM)
test_that("Test bart_ewmv", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bart_ewmv(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
b3a8c0af0b838a3967d0962b73105f9830748b8d9978883bb7e302d09ab06cb5 | R | 229 | 10 | context("Test alt_gamma")
library(hBayesDM)
test_that("Test alt_gamma", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(alt_gamma(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
fbb573d6892d37f127d27afdcfe7e652686f3fe837e720b58328a852bff80783 | R | 232 | 10 | context("Test cra_linear")
library(hBayesDM)
test_that("Test cra_linear", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(cra_linear(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
93b92dab67eea8df113c11f86b7c70e1450e2e2be53faa6286dc015c6b91f9de | R | 235 | 10 | context("Test ra_prospect")
library(hBayesDM)
test_that("Test ra_prospect", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ra_prospect(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
c7d80ff06965bf72750fa16738981b62bfbf882853a8dd21c76ed448eab2d4d2 | R | 236 | 7 | #' MNAR demo data
#'
#' A toy example from Margarita Moreno-Betancur for checking NARFCS.
#'
#' A small dataset with just three columns.
#' @source \url{https://github.com/moreno-betancur/NARFCS/blob/master/datmis.csv}
"mnar_demo_data"
|
7d23f2473e4c3e4c35ec919ebf8959a0b1fc224931cf95ce7140a93055689a3e | R | 238 | 10 | context("Test pstRT_rlddm6")
library(hBayesDM)
test_that("Test pstRT_rlddm6", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(pstRT_rlddm6(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
7d88908654cf3a6c021bb484218e1a5e0005fedb72a2a287386a20c447557bdc | R | 238 | 10 | context("Test pstRT_rlddm1")
library(hBayesDM)
test_that("Test pstRT_rlddm1", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(pstRT_rlddm1(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
886a9c090742c3bebcb83b400c513cbbdfc998ac6a815d4e9da76e60ca97cc14 | R | 238 | 10 | context("Test task2AFC_sdt")
library(hBayesDM)
test_that("Test task2AFC_sdt", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(task2AFC_sdt(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a4d1c9200ce5e0d5fa1811ed27b23d84ce1e81bd3aca662d562d8fce064861a0 | R | 238 | 10 | context("Test choiceRT_ddm")
library(hBayesDM)
test_that("Test choiceRT_ddm", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(choiceRT_ddm(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a72d59e00899fedf4743388bbcbc4194511648aef9ec149ae6ec772c97792326 | R | 238 | 10 | context("Test dd_cs_single")
library(hBayesDM)
test_that("Test dd_cs_single", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dd_cs_single(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
867c15ff4354d2b68b322f4b07b112b3dd881d25a492fca8704559b0ab84c496 | R | 240 | 10 | options(warn=-1)
suppressPackageStartupMessages({
library(tidyverse)
library(testthat)
})
# load functions
source("./workflow/scripts/utils.R")
testthat::test_dir("./tests/testthat/", reporter=c("progress", "minimal", "location")) |
2873e0f1fea54dada4e539e943c016d2049dd9dd5c7f8e47764cc39895e99186 | R | 241 | 10 | context("Test igt_pvl_delta")
library(hBayesDM)
test_that("Test igt_pvl_delta", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(igt_pvl_delta(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
69eb481142efd8aa91ad668fec027187580b50052078b1420eefe53c25789590 | R | 241 | 10 | context("Test rdt_happiness")
library(hBayesDM)
test_that("Test rdt_happiness", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(rdt_happiness(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
995267f08b142e773b0bc4867c1cdad97327d1d12935949169fe35954e683bff | R | 241 | 10 | context("Test igt_pvl_decay")
library(hBayesDM)
test_that("Test igt_pvl_decay", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(igt_pvl_decay(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
c89299a73215f44906a64b466ab02f6ead945eadd6fa30a8491455b17e983d0d | R | 241 | 10 | context("Test dd_hyperbolic")
library(hBayesDM)
test_that("Test dd_hyperbolic", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dd_hyperbolic(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
4d2f8a60bb95f24f84dbe800f23514875f4b095ce9faf399416ce6ef29e355ae | R | 244 | 10 | context("Test pst_gainloss_Q")
library(hBayesDM)
test_that("Test pst_gainloss_Q", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(pst_gainloss_Q(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
7461afa8a9f7dea9878ad3e3b8da16958419a2778e102652dd005b418ca8f1f9 | R | 244 | 10 | context("Test prl_fictitious")
library(hBayesDM)
test_that("Test prl_fictitious", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_fictitious(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a017422443c06565755d2cecdf84ee580ea649f559eacadaeb9062a51c27f8ba | R | 245 | 12 | #@Time : 2023/1/20 14:28
#@Author : Biao Zhang
#@Email : littlebiao@outlook.com
#@File : read_rhesus_2018s.R.r
#@Description: This file is used to ...
# packages
#
load('../brain_rhesus_2018s/Sestan.adultMonkeyNuclei.Psychencode.Rdata')
ls()
|
899664250170cfea5e4a58e3ef7a3872d20c7d4683a346e3d206782a83f9b299 | R | 246 | 8 | blocks1 <- make.blocks(c("bmi", "chl", "hyp", "age"))
test_that("MAKE-PREDICTORMATRIX-001: errors on invalid data arguments", {
expect_error(
make.predictorMatrix(data, blocks = blocks1),
"Data should be a matrix or data frame"
)
})
|
2ac575341f1bbd959be20648fb074031f9f697cb3fff2b006941f6aa724b6b08 | R | 247 | 10 | context("Test hgf_ibrb_single")
library(hBayesDM)
test_that("Test hgf_ibrb_single", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(hgf_ibrb_single(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
730dbc0f4db83606f5993ca75075d15464288300bc745c541a6e47f982955eee | R | 247 | 10 | context("Test banditNarm_4par")
library(hBayesDM)
test_that("Test banditNarm_4par", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_4par(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
907bfe56b4417b76210159faf9460d89f00d5e4d165d95c7cfd935a78098f2fa | R | 247 | 10 | context("Test bandit4arm_4par")
library(hBayesDM)
test_that("Test bandit4arm_4par", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm_4par(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
de1cf49d2fe473a54b288bbd14adba83ae925ca3e1ad2a3752f470f4e479b63c | R | 248 | 7 | # ---- resave the .rds files in data/ with compressed format ----
#
# details:
# The type of compression is chosen automatically by tools
# This command need only be run once and may take several minutes.
tools::resaveRdaFiles(paths = 'data')
|
1710a8c49635e60de79e7ef5de268d995379bb8c30c79469fb98ebda686db076 | R | 250 | 10 | context("Test banditNarm_delta")
library(hBayesDM)
test_that("Test banditNarm_delta", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_delta(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
63620c4db55ac39c5e74cfe3a78d252b97759124b3143be666bbc04c6551fab1 | R | 250 | 10 | context("Test bandit2arm_delta")
library(hBayesDM)
test_that("Test bandit2arm_delta", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit2arm_delta(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a44b2411047f3bd3a1db495684940b4bb2e02011da613e5b57694f142ba5447d | R | 250 | 10 | context("Test prl_rp_multipleB")
library(hBayesDM)
test_that("Test prl_rp_multipleB", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_rp_multipleB(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
ca9c4bd9312181306c9fce11623ec127ad76ff21edae4e214165bb8fec6d8d8c | R | 250 | 10 | context("Test dbdm_prob_weight")
library(hBayesDM)
test_that("Test dbdm_prob_weight", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dbdm_prob_weight(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
d508891e66a793ddcce3b1b81a3b893c6e8416d7e38f94890eed0ec83b8fa769 | R | 250 | 10 | context("Test banditNarm_lapse")
library(hBayesDM)
test_that("Test banditNarm_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
d595dbabc4d5269c4ae21ee6bbb15886a173356a3ed3ea4d5bbba920a222cbf9 | R | 250 | 10 | context("Test bandit4arm_lapse")
library(hBayesDM)
test_that("Test bandit4arm_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
1a2240ee83a97c0fd8f140534dbe42f666fe545b4de304bf20a5b5723ade10a5 | R | 253 | 10 | context("Test prl_fictitious_rp")
library(hBayesDM)
test_that("Test prl_fictitious_rp", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_fictitious_rp(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
3dedf96181abf59a0487d8a0f3f835c841493a570341adec57b9df18b49a9d74 | R | 256 | 10 | context("Test prl_fictitious_woa")
library(hBayesDM)
test_that("Test prl_fictitious_woa", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_fictitious_woa(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
f82ff03541df58c73649423cf300d9fb851e84bb3316c372c5c40423b00f6e8a | R | 256 | 7 | ## Authors: Gerko Vink, Stef van Buuren
rm.whitespace <- function(string, side = "both") {
side <- match.arg(side, c("left", "right", "both"))
pattern <- switch(side, left = "^\\s+", right = "\\s+$", both = "^\\s+|\\s+$")
sub(pattern, "", string)
}
|
5e13bffbb1d8403ed4935d98f60d1fc8f52cbb735880a380f7a450dbc521aaa2 | R | 257 | 7 | vl=fread(file="~/ncnb2_code/metadata/velocyto_libraries.csv")
lib2G= Vectorize(function(x) dayrep.dataset.map[[x]], USE.NAMES=F)
vl2=vl %>% mutate(orig.ident= lib2G(library_name))
fwrite(vl2, file="~/ncnb2_code/metadata/velocyto_libraries_withident.csv") |
43b15bea1fbc60fe29d6891f809f1c2bc5e1a3a9b7afb588ca63c577c8e0ca1b | R | 258 | 9 | #!Rscript
args = commandArgs(trailingOnly=TRUE)
RNA = read.table(args[1],header=TRUE)
RNA = RNA[,c(1,6:7)]
RPK = RNA[,3]/(RNA[,2]/1000)
RPM = sum(RPK)/1000000
TPM = round(RPK/RPM,3)
write.table(TPM,file=args[2],row.names=FALSE,col.names=FALSE,quote=FALSE)
|
7b04ccb6a0dcef33bad3feebccb7b5bcce004769cbd88696c6ad6206d512a5da | R | 259 | 10 | context("Test choiceRT_ddm_single")
library(hBayesDM)
test_that("Test choiceRT_ddm_single", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(choiceRT_ddm_single(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
094ca653ccf0509540356054ecd1935637a93e1f2a4ef3fffb71e32621055840 | R | 262 | 10 | context("Test dd_hyperbolic_single")
library(hBayesDM)
test_that("Test dd_hyperbolic_single", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dd_hyperbolic_single(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
26491889679dbd0b3f171fa88ac283b73c72fb5127c417238911a823558094dc | R | 265 | 10 | context("Test bandit4arm_2par_lapse")
library(hBayesDM)
test_that("Test bandit4arm_2par_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm_2par_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
74000e4fa3df5a61297f3f160d06abdead0f4cfd642f60ec8f5cea7de6ee66c3 | R | 265 | 10 | context("Test prl_fictitious_rp_woa")
library(hBayesDM)
test_that("Test prl_fictitious_rp_woa", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_fictitious_rp_woa(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
b563971c10f05a9c89f641d56639bcc595ca2f5300017e318211d577d05628f3 | R | 265 | 10 | context("Test banditNarm_2par_lapse")
library(hBayesDM)
test_that("Test banditNarm_2par_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_2par_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
6c5fdf773af270e4e028993edaaa2337775437c6993c9cb2d9d2dd64301b3370 | R | 268 | 10 | context("Test bandit4arm_lapse_decay")
library(hBayesDM)
test_that("Test bandit4arm_lapse_decay", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm_lapse_decay(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
f71b653006e6ce9f445ba46d224b8b4b5a5b6e1320aa7eda370599fa1a6459fe | R | 268 | 10 | context("Test banditNarm_lapse_decay")
library(hBayesDM)
test_that("Test banditNarm_lapse_decay", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_lapse_decay(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
07cd301532f820969b125e6a30e142149e32c5cfe0f91a202cea2f126d7c5df5 | R | 274 | 10 | context("Test bandit4arm_singleA_lapse")
library(hBayesDM)
test_that("Test bandit4arm_singleA_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm_singleA_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
8a06f944e648f71cf2669465b57019d7ca48cf45f2be392c133231790f95c1f3 | R | 274 | 10 | context("Test banditNarm_kalman_filter")
library(hBayesDM)
test_that("Test banditNarm_kalman_filter", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_kalman_filter(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
a79bca73dec1d2f314dd881ba23acdcb0fc4e2842224996ff21fc4ce71b8f266 | R | 274 | 10 | context("Test banditNarm_singleA_lapse")
library(hBayesDM)
test_that("Test banditNarm_singleA_lapse", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(banditNarm_singleA_lapse(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
e6344c56d69b824fb8f2efacfdba58f08fe9a8b5ba810966b36bd48be585815e | R | 274 | 10 | context("Test prl_fictitious_multipleB")
library(hBayesDM)
test_that("Test prl_fictitious_multipleB", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_fictitious_multipleB(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
776f215804638f39c7da64af739bba4240c9cb70d6a24b62ea1a8ca22ed5a955 | R | 277 | 10 | context("Test bandit4arm2_kalman_filter")
library(hBayesDM)
test_that("Test bandit4arm2_kalman_filter", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(bandit4arm2_kalman_filter(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
c8764347490ad619fa03ca5c1c04c52f04ed4d88fa2e46d09141c2255d339eee | R | 284 | 29 | #' @export
ggplot2::ggplot_add
#' @export
ggplot2::aes
#' @export
dplyr::`%>%`
#' @export
dplyr::filter
#' @export
dplyr::mutate
#' @export
dplyr::group_by
#' @export
dplyr::ungroup
#' @export
tibble::as_tibble
#' @export
tidygraph::as_tbl_graph
#' @export
igraph::as.igraph
|
139b1dd60e505ca139d2532867912a8220092dcad39dbbf086f68f5dda3a72d1 | R | 312 | 14 | handles.arg <- function(f, a = "data") {
# determine whether function f handles argument a
if (!is.function(f)) {
return(FALSE)
}
a %in% names(formals(f))
}
handles.format <- function(fn) {
# determine whether function fn handles the `format` argument
f <- get(fn)
handles.arg(f, "format")
}
|
15f5ddccfb58a6718a20d06fe188995027507c4d020078c857b17ac98f1514b0 | R | 322 | 10 |
my_ggsave <- function(fname, plot, dpi = 300, ...)
{
ggsave(paste0(fname, ".pdf"), plot = plot, useDingbats = F, bg = "#FFFFFF",
...)
embedFonts(file = paste0(fname, ".pdf"),
outfile = paste0(fname, ".pdf"))
ggsave(paste0(fname, ".png"), plot = plot, dpi = dpi, bg = "#FFFFFF",
...)... |
ef60025a16befeb1fbe6c24aab21398bbae88fb9c9809c9ccb79e53cf859a2ee | R | 324 | 11 | suppressPackageStartupMessages({
requireNamespace("matrixStats")
})
get_top_n_variance <- function(matr, ntop = 500, tol = 1E-9) {
rv <- matrixStats::colVars(matr)
select <- order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))]
# Filter low variance:
select <- select[rv[select] > 1E-9]
matr[, select... |
890748b98c5bf607dce88e91efcb206974540b2eeaf10a2da87130fe7e2c7633 | R | 331 | 11 | library(GDCRNATools)
context("gdcParseMetadata")
test_that("Parse metadata automatically", {
metaMatrix <- gdcParseMetadata(project.id = 'TCGA-CHOL',
data.type = 'RNAseq',
write.meta = FALSE)
expect_equal(unique(metaMatrix$project),"TCGA-... |
0c67b438a14d2465f9632268fa2f305dc371de99460bb792cf2495f6422e85d2 | R | 333 | 11 | update_list <- function(ours, to_update, recursiveness = 0L) {
for (el in names(to_update)) {
element <- to_update[[el]]
if (recursiveness > 0L && !is.null(names(element))) {
ours[[el]] <- update_list(ours[[el]], element, recursiveness = recursiveness - 1)
} else {
ours[[el]] <- element
}
... |
802776c4118e4f47367722016da6f069a71f61f4d5108875b1075782f682720a | R | 337 | 14 | #Code for extracting gene sets from Gemma and GeneWeaver from the Hippocampus and Nucleus Accumbens to create a better .GMT file
#Megan Hagenauer, June 2021
library(plyr)
library(httr)
library(RCurl)
library(jsonlite)
library(devtools)
install_github("PavlidisLab/gemmaAPI.R")
library(gemmaAPI)
?endpointFunctions
?hi... |
67019a44909f6c636fb80613ea8c99f64b9724f6cf1a502bafb830f8f6aed163 | R | 342 | 10 | library(TCGAbiolinks)
query.met <- GDCquery(project = c("TCGA-ESCA", "TCGA-PAAD"),
data.category = "DNA Methylation",
legacy = FALSE,
platform = c("Illumina Human Methylation 450"),
sample.type = "Solid Tissue Normal")
met <- getR... |
cb52be52e787978a7fc50b0d31ebff690383e413601ad69aa303ae206c108841 | R | 346 | 10 | barnard.rubin <- function(m, b, t, dfcom = Inf) {
lambda <- (1 + 1 / m) * b / t
dfold <- (m - 1) / (lambda^2)
if (length(dfcom) == 1L && is.infinite(dfcom)) {
return(dfold)
}
tmp <- (1 - lambda) * (1 + dfcom) * dfcom
df_br <- (m - 1) * tmp / ((dfcom + 3) * (m - 1) + (lambda^2) * tmp)
ifelse(is.infinit... |
05d125b89715af603ed8350764316e2e0bc6f9fdeb7948559b2cc75fdb519e68 | R | 353 | 19 | suppressPackageStartupMessages({
requireNamespace("argparse")
})
get_args <- function() {
# create parser object
parser <- argparse::ArgumentParser()
parser$add_argument(
"--dataset-name",
type = "character",
default = NULL,
help = "dataset name we apply the script to (e.g. sod1)"
)
args... |
20dc1c7ea7ce7850893f3cbee3b3fecabf278cf86e194a0c9d93da0592140ee7 | R | 356 | 12 | cohort_to_factors <- function(cohort, cohort_factors) {
out <- cohort
for (cohort_factor in cohort_factors) {
fac <- cohort_factor[["factor"]]
if ("levels" %in% names(cohort_factor)) {
out[[fac]] <- factor(out[[fac]], levels = as.character(cohort_factor[["levels"]]))
} else {
out[[fac]] <- f... |
4206e1ecdfaee83f6a815da7b8256a40e66f21b81a47df297afb92d7a81cc70d | R | 362 | 16 | log.seq <- function(from,to,length.out) {
exp(seq(log(from),log(to),length.out=length.out))
}
log.sum.exp<- function(x) {
# Computes log(sum(exp(x))
# Uses offset trick to avoid numeric overflow: http://jblevins.org/notes/log-sum-exp
if ( max(abs(x)) > max(x) )
offset <- min(x)
else
offset <- max(x)... |
94415143f357e995dab15babcc0db6855f6658c09c0b3d7f98bdd40def9bc964 | R | 363 | 9 | blocks1 <- name.blocks(list(c("bmi", "chl"), "hyp"))
blots1 <- list(B1 = list(donors = 10), hyp = list(donors = 1))
test_that("BLOTS-001: errors when mixing same global and local argument", {
expect_error(
mice(nhanes, blocks = blocks1, blots = blots1, donors = 7, print = FALSE),
'formal argument "donors" ma... |
7e0abeb01357bc0b95fea092b3b95f249799eebac0d9001307ceccc442bdd01b | R | 367 | 11 | test_that("LOGGEDEVENTS-001: loggedEvents is NULL", {
imp1 <- mice(nhanes, m = 2, print = FALSE)
# copy of data, different names
data2 <- cbind(nhanes, nhanes)
colnames(data2)[5:8] <- c("age2", "bmi2", "hyp2", "chl2")
imp2 <- suppressWarnings(mice(data2, m = 2, print = FALSE))
expect_null(imp1$loggedEvent... |
8ba9756f2402be1e8dde31d622eaa24b4747073ae92a3c159d1ef928857fc1b5 | R | 372 | 17 | d1 <- brandsma[1:200, c("sch", "lpo")]
pred <- make.predictorMatrix(d1)
pred["lpo", "sch"] <- -2
test_that("MICE-IMPUTE-2L-NORM-001: mice::mice.impute.2l.norm() runs empty model", {
expect_silent(
imp <- mice(
d1,
method = "2l.norm",
print = FALSE,
pred = pred,
m = 1,
maxit = ... |
96edd436f157b9ed584b23fb1e7e47da59b5c128a8de4e525365bfc918838078 | R | 379 | 16 | #' @noRd
get_function <- function(pkg, fun) {
if(!requireNamespace(pkg, quietly = TRUE)) {
stop(pkg, " package has not been installed", call. = FALSE)
}
eval(parse(text = paste0(pkg, "::", fun)))
}
#' @importFrom ggnewscale new_scale
#' @noRd
new_scales <- function(...) {
scales <- list(...)
if(length(sc... |
6611dac464a33d0e5fb205626d2b05f837b1ee111132541488ce0c335423c729 | R | 382 | 19 | validate.arguments <- function(
y,
ry,
x,
wy,
allow.x.NULL = FALSE,
allow.x.NA = FALSE
) {
# validate standard arguments of mice.impute functions
if (!allow.x.NULL && is.null(x)) {
stop("Cannot handle NULL value for `x`")
}
if (!allow.x.NA && anyNA(x)) {
stop("Cannot handle NA in `x`")
}
... |
f296ca04a255294e97783a62fad8d6cec1f2437345066d050982be463e40cb18 | R | 384 | 16 | #Brain.GMT
#This project was created with the goal of making a better .gmt file for analyzing our HC and STR results.
#Megan Hagenauer
#Begun 2021-03-26
################
#Working Directory
#Old:
#setwd("~/Documents/Microarray Gen/Angela_HRLR_EE_Stress/NAcc_Remove2outliers/11_FGSEA/UpdatedGMT_20210325")
#New:
#Now... |
0b5ef59d7c5ca60f3b0dc0b4dc93189d48d1447ebcb35a353f4fa7051a29c986 | R | 388 | 18 | #' Title
#'
#' @param libname
#' @param pkgname
#'
#' @return
#' @export
#'
#' @examples
.onAttach <- function(libname, pkgname) {
start_message <- c( "\t\t\t Welcome to FramebyFrame\
\t\t\t Need help? francois@kroll.be\n
\t\t\t If you use FramebyFrame in your work, please cite:\
\t\t\t https://doi.org/10.7554/eLife.... |
ad787ea40df86f16f42b4e798640c0341c9c00cfa7e6a8a455f70d3496b07288 | R | 390 | 22 | #' @export
df.residual.mira <- function(object, ...) {
fit <- object$analyses[[1]]
df.residual(fit)
}
#' @export
df.residual.lme <- function(object, ...) {
object$fixDF[["X"]][1]
}
#' @export
df.residual.mer <- function(object, ...) {
sum(object@dims[2:4] * c(1, -1, -1)) + 1
}
#' @export
df.residual.multin... |
f27e6d0e7e538ab1c6acecb687ee7076485acec425b44883c08f3112caa3aaa8 | R | 410 | 8 | library("Rsmlx")
initializeMlxConnectors(software = "monolix")
project <- "loglogistic.mlxtran"
covariateSearch(project, paramToUse = c("Te"), covToTest = c("tTrt", "mu", "alpha", "beta",
"CD31", "CTC", "gMDSC", "mMDSC",
"ki67", "ki67p_cd3... |
9dc917f8c5485c90f052bd3eedc1e4d46e3603de56be81818e083e42de088053 | R | 412 | 14 | #' Function to estimate mode of MCMC samples
#'
#' Based on codes from 'http://stackoverflow.com/questions/2547402/is-there-a-built-in-function-for-finding-the-mode'
#' see the comment by Rasmus Baath
#'
#' @param x MCMC samples or some numeric or array values.
#' @importFrom stats density
#' @export
#'
estimate_m... |
e52fee6d70181b86211aa9360546f5a9bf88958ed92f44afed127cbf28897f71 | R | 414 | 12 | ## This script extracts results from mash model
library(mashr)
library(dplyr)
load("mashr_meta_results.RData")
m2$result$lfsr |> as.data.frame() |>
tibble::rownames_to_column("effect") |>
data.table::fwrite("lfsr.strong_signals.tsv", sep='\t')
m2$result$PosteriorMean |> as.data.frame() |>
tibble::rownames... |
80b89e0a579ae860da048eb7d1ee7a054a2bacee7c75acd7766d9b0e58f245cb | R | 416 | 16 | if(!exists("argv")) argv <- commandArgs(trailingOnly = T)
argc <- length(argv)
dat <- read.table(argv[1])
out <- argv[2]
name <- if(argc >= 3) argv[3] else "data"
col <- if(argc >= 4) as.integer(argv[4]) else 1
df <- data.frame(dat[[col]])
colnames(df) <- c(name)
summary(df)
cat(sprintf("Std.dev.:%.4g",sd(df[[name]]... |
5dea3aeaf6ca44d62ee847547eb6eb56e22a2027b2f6cfd37ed093be9821e8c1 | R | 428 | 11 | # Install GitHub-hosted R packages required by this pipeline.
# Run after activating the conda environment:
# conda activate DEA
# Rscript install_packages.R
if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("junjunlab/ClusterGVis")
remotes::install_github("junj... |
427db15b7d9d1c793483d873262dd50fcee2c13530c05e2a0d48f59a6406780f | R | 431 | 16 | #TODO: Check or refine this. Maybe change name
select.factR <- function(object, ...){
samples <- object@colData
samples$old.names <- rownames(samples)
samples <- as.data.frame(t(samples))
samples <- tryCatch(
samples[,...],
error = function(e)
dplyr::select(samples, ...))
... |
b7116a80d3a13c669185940c0e68ea54791069772a80b5730ea515eb9f1b06ca | R | 433 | 12 | test_that("POOL-VECTOR-001: pool(fit)$pooled and pool.vector(summary(fit)) are identical", {
# conventional flow
imp <- mice(nhanes2, m = 2, maxit = 2, seed = 1, print = FALSE)
fit <- with(imp, lm(chl ~ age + bmi + hyp))
est1 <- pool(fit)$pooled
# using pool.vector on tidy parameters
par <- summary(fit)[, ... |
57be3244766a21a1f5f7de7f939fc0b6a3ac4c663039d933c040d6b543452a78 | R | 442 | 7 | # from https://github.com/MareesAT/GWA_tutorial
args <- commandArgs(trailingOnly=TRUE)
het <- read.table(args[1], head=TRUE)
het$HET_RATE = (het$"N.NM." - het$"O.HOM.")/het$"N.NM."
het_fail = subset(het, (het$HET_RATE < mean(het$HET_RATE)-3*sd(het$HET_RATE)) | (het$HET_RATE > mean(het$HET_RATE)+3*sd(het$HET_RATE)));
he... |
a301cdf7db46a8435b6c0ac18573df16d4ef3e768e74765a2f8b831d493ad332 | R | 474 | 16 | #' Echoes the package version number
#'
#' @param pkg A character vector with the package name.
#' @return A character vector containing the package name, version number and
#' installed directory.
#' @author Stef van Buuren, Oct 2010
#' @keywords misc
#' @examples
#' version()
#' version("base")
#' @export
version <- ... |
aec8f61b2c799a876c72d83c6a5ea84e0a29fbbabbf89ac6f249b6c12adf5803 | R | 497 | 14 | data(nhanes)
imp <- mice::mice(nhanes, maxit = 2, m = 2, seed = 10, print = FALSE)
fit_mira <- with(data = imp, exp = lm(chl ~ age + bmi))
fit_mipo <- mice::pool(fit_mira)
test_that("POOL-R-SQUARED-001: pool.r.squared mira", {
result <- as.vector(pool.r.squared(fit_mira, adjusted = FALSE)[1, ])
expect_equal(length... |
a0406868bce8d372802efc21563f3ff39f5798ec4abc0bdc6b7b02b6fdc0e033 | R | 501 | 12 | ## Download the supplementary data of Chen et al., Nature (2023) and convert to bed file
library(tidyverse)
d0 = data.table::fread('~/Dropbox/Resources/Papers_suppl/Chen_constraint_2022/constraint_z_genome_1kb.qc.download.txt.gz')
class(d0$z)
d1 = d0 %>% filter(z >= 4)
d2 = d1 %>% dplyr::select(chrom, start, end, z)... |
1a5e10439ea8f99fc891443575bda4efdd1aede23e1aec0b5e458eba9425f86e | R | 509 | 14 | library(readr)
library(dplyr)
raw_transitome_PanCancer = read_csv('data_PanCancer/1_original_data/feature_alignment_modB_trs.csv')
raw_transitome_PanCancer[raw_transitome_PanCancer$Intensity %in% 0,"Intensity"]<-NA
raw_transitome_PanCancer = raw_transitome_PanCancer %>%
mutate(transition = paste(FragmentIon, Pro... |
a18c567f60b5981cb0dbe6e6b3aaca9b057b3468cae104a48110e3ec1c4ea1e9 | R | 509 | 21 | ###DTS script to pool raw data traves in various ways for each group
##pool all data by period
pooled.data<-list()
for(i in 1:NofPeriods)
{
period.data<-data.frame()
for (l in 1:length(xml_list))
{
period.data <- rbind(period.data, grouped.data[[l]][[i]])
}
pooled.data[[i]] <- period.data
}
##create a... |
8c9f9f437bab22c74bb10b591f16c0a2e6ced1f80639866caafd168a074f597e | R | 510 | 18 | install.on.demand <- function(pkg, quiet = FALSE, ...) {
# internal function that checks whether package pkg is
# in the library. If not found, it asks the user permission
# to install from CRAN.
if (requireNamespace(pkg, quietly = TRUE)) {
return()
}
if (interactive()) {
answer <- askYesNo(paste("P... |
111ec85977321685779c467abb03d233f30f50d02aa2c24a62a8d2eea1564edc | R | 511 | 23 | ---
title: "**Quality by cycle**"
---
The following page displays the quality by cycle plots generated during the pipeline.
```{r Quality_by_cycle, results='asis', echo=FALSE}
setwd("..")
plots <- Sys.glob("Picard_stats.dir/*.picard_stats.quality_by_cycle.pdf")
wd <- getwd()
for (i in plots){
filename <- file.p... |
4ab096c3ef6dd168e0c5f78d930eb708ce0697e51e7aba98d400fdc6cf513379 | R | 516 | 24 | ---
title: "**Quality distribution**"
---
The following page displays the quality by cycle plots generated during the pipeline.
```{r Quality_dist, results='asis', echo=FALSE}
setwd("..")
plots <- Sys.glob("Picard_stats.dir/*.picard_stats.quality_distribution.pdf")
wd <- getwd()
for (i in plots){
filename <- f... |
0a200c082bbb5948dec50077b14ba6efb368ebaf1a73e679810fae9d340495ef | R | 517 | 23 | ---
title: "Base distribution"
---
This page describes the base distribution of each of the samples. This is ran as part of picard metrics.
```{r Base_dist, results='asis', echo=FALSE}
setwd("..")
plots <- Sys.glob("Picard_stats.dir/*.base_distribution_by_cycle.pdf")
wd <- getwd()
for (i in plots){
filename <- ... |
87abd79d759aeb4bbb1706ec1a7cc2a415f63a458ae9b20ea162e4427a6325f4 | R | 522 | 18 | ---
title: "README"
output: github_document
date: "`r Sys.Date()`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Data Directory Structure
Files in this directory are either generated using code from this project or downloaded from sources specified in our scripts. The contents of this dir... |
713d1db0d44d96771af95831c4a54d063ba84b0c03a10b142a52e554674e7db8 | R | 526 | 19 | ---
title: "README"
output: github_document
date: "`r Sys.Date()`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Data Directory Structure
Files in this directory are either generated using code from this project or downloaded from sources specified in our scripts. The contents of this di... |
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