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values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f36d73c9dbe8a8b1892b90ca838aa40d4fe7b658 | bd0bb262fda22a09140f77b8eb6db5b5b951d895 | /fig01b.R | 5ad3d9497f3147ea337a8ae0a4642946968e5c25 | [] | no_license | erikbsolbu/ecy22 | 470ae9a26be9f5bf70e92475a484e1d95274c9ce | 871f18869f7538841ab9da4a1f12cc749cdbfaff | refs/heads/main | 2023-04-08T10:37:06.115137 | 2022-03-24T12:03:04 | 2022-03-24T12:03:04 | 456,653,719 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,162 | r | fig01b.R | # Required packages
library(tidyverse)
# Create variables
var1 <- factor(rep(paste(rep(paste("X_", 1:2, sep = ""),
each = 5),
"(",
rep(paste("t_", 1:5, sep = ""),
2),
")", sep =... |
35c07fe6b1e2bc72f1a6861b5a887c0435fcf04f | 6168dfdfd024f7d5bb03b1faea633f0e0690e093 | /swc-r/tran.R | 82ccd957bca0e98975b45c045a6ccef18ccfe1aa | [] | no_license | star1327p/IMSM2015 | 01ebddc0b3d495369f5df9f7c801366371e0dbaa | 9abd35d414fae1c382549843ef068dc8843698fc | refs/heads/master | 2021-10-29T13:20:09.443478 | 2021-10-10T07:55:10 | 2021-10-10T07:55:10 | 39,018,937 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,433 | r | tran.R | speeds <- read.table('car-speeds.csv', sep =",", header = TRUE)
speeds_UT <- subset(speeds, State == 'Utah')
moments <- function(vec){
mm <- mean(vec)
ll <- sd(vec)
n <- length(vec)
ret <- data.frame(mean = mm, sd = ll, vec_length = n)
return(ret)
}
tapply(speeds_UT$Speed, speeds_UT$Color, moments)
tappl... |
eefc734e426a4c126d78dfb616692e20d40b6d6d | 6fd75a5fff8a0da2c0f5fa95567b14fe77dc58a5 | /man/chisq_test2.Rd | c6acfa180adf46bf117104ba77e6b09004b990d8 | [] | no_license | zhangmanustc/StatComp18024 | 47a30f2dbf0c5cfcad9f130b2c9e4a1e433d04f7 | 873a7f6ca3dfafec8430e402261802d5dbc22c8d | refs/heads/master | 2020-04-15T20:05:31.419177 | 2019-01-18T12:35:16 | 2019-01-18T12:35:16 | 164,978,725 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 570 | rd | chisq_test2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chisq_test2.R
\name{chisq_test2}
\alias{chisq_test2}
\title{Make a faster version of chisq.test()}
\usage{
chisq_test2(x, y)
}
\arguments{
\item{x}{vector x}
\item{y}{vector y}
}
\value{
compute the chi-square test statistic to... |
9142790d1dfbf89e23cff68e4f7693e9ab0d87e9 | d7eb7c0c62a7236b3fc25db9a6dd9d289c937775 | /1-data/scripts/cesm_maps_ttests.R | 38969a4eada6fd6f4039054274c3f8f6eae3f351 | [] | no_license | kvcalvin/iesm-45v85 | f455554127193ded9026ad91f91cc602dfbe2142 | a69537e8ae96ee3704e3bd8bb019ac6f2ade51cc | refs/heads/master | 2020-05-29T17:17:57.254766 | 2020-03-31T16:46:11 | 2020-03-31T16:46:11 | 189,271,084 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,413 | r | cesm_maps_ttests.R | source( "header.R" )
print( "SET PATHS" )
case1_ref_path <- "/pic/projects/iESM/rcp85_results/diagnostics/b.e11.BRCP85C5BPRP.f09_g16.iESM_uncoupled.001_2006-2090"
case1_impacts_path <- "/pic/projects/iESM/rcp85_results/diagnostics/b.e11.BRCP85C5BPRP.f09_g16.iESM_coupled.001_2006-2088"
case2_ref_path <- "/pic/projects... |
c6831bae20d814cc9cec2181780ba68fc822f1c4 | 89d219d3dfa744543c6ba1c1b3a99e4dcabb1442 | /R/trimean.R | ae69f54c2acbcbccacdcce3d3e7e0679ed9621bc | [] | no_license | pteetor/tutils | e2eb5d2fba238cbfe37bf3c16b90df9fa76004bb | fe9b936d8981f5cb9b275850908ef08adeffef4e | refs/heads/master | 2022-06-17T11:34:30.590173 | 2022-06-14T02:02:15 | 2022-06-14T02:02:15 | 77,761,145 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 386 | r | trimean.R | #'
#' Trimean - robust location statistic
#'
#' The trimean is defined as (Q1 + 2*Q2 + Q3) / 4.
#'
#' @param x Vector of numeric values
#' @param na.rm If TRUE, remove NA values from x
#'
#' @return Trimean value
#'
#' @export
#'
trimean = function(x, na.rm=FALSE) {
q = quantile(x, probs=c(0.25, 0.50, 0.75),
... |
575465e3b342d60ba1e476446c981355264e2281 | c76086b45c23c48b250515e0bb158c6ca84da6e4 | /tests/testthat.R | cff8729e6397a35ddff1a90ad7603f61229a27da | [] | no_license | Ironholds/hail | 7b821830395b1a6cc1971d4acd29f39fa033bd4c | ad4d7922edd102ee662d4913362b6e7a14370014 | refs/heads/master | 2021-01-10T17:54:59.278987 | 2017-01-04T22:53:26 | 2017-01-04T22:53:26 | 48,045,952 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 52 | r | testthat.R | library(testthat)
library(hail)
test_check("hail")
|
7fae00a1d5f55224ceb11e8b489f09f7066ac1fa | 5e85f011e52d2c5b51d833100e9e38677d047f6e | /Functions/Useful WebBuilder functions.R | 39788a6ec9103c6904e28b27b8d999debff3dcb2 | [
"MIT"
] | permissive | Jpomz/honestly-you-eat-this | 26fce41c4f068ea31a5703999864c05ef878f156 | fa1fe624d73494769bf402f998a87096fb2e145e | refs/heads/master | 2021-06-16T00:55:20.503870 | 2019-05-21T19:35:03 | 2019-05-21T19:35:03 | 115,657,416 | 1 | 1 | null | 2018-02-07T05:29:39 | 2017-12-28T20:31:48 | R | UTF-8 | R | false | false | 6,868 | r | Useful WebBuilder functions.R | ######################################################
# this script was copied exactly from the supplementary material of Gray et al 2015, Food Webs. http://dx.doi.org/10.1016/j.fooweb.2015.09.001.
# if using, please cite the original publication
# J Pomeranz 27 Nov 2018
# original script below this line
##########... |
9a08f39cf9582ead7071624e7e99286d13ef500c | 689635789d25e30767a562933f39fcba1cebecf1 | /Alpha Modelling/QuantStrat/trash/algo1.R | 1a47efea0d5aa0c9a411f90dff5195328616e0b7 | [] | no_license | Bakeforfun/Quant | 3bd41e6080d6e2eb5e70654432c4f2d9ebb5596c | f2874c66bfe18d7ec2e6f2701796fb59ff1a0ac8 | refs/heads/master | 2021-01-10T18:23:23.304878 | 2015-08-05T12:26:30 | 2015-08-05T12:26:30 | 40,109,179 | 5 | 0 | null | 2015-08-05T12:12:09 | 2015-08-03T06:43:12 | R | UTF-8 | R | false | false | 269 | r | algo1.R | # https://quantstrattrader.wordpress.com/2014/11/02/its-amazing-how-well-dumb-things-get-marketed/
# It's Amazing How Well Dumb Things [Get Marketed]
# Posted on November 2, 2014 by Ilya Kipnis
# Posted in Asset Allocation, ETFs, Portfolio Management, R, SeekingAlpha
|
62df83b4a53de8121cd69de05c19475fc5f1516b | f41df8c9eed5eaae611f9df1690d822b1c5798b0 | /tests/testthat.R | c7ff8162829d25ab13ff4e3e425039879ac03d29 | [
"CC0-1.0"
] | permissive | jakoever/histmaps | a0870aac42156bed8934b840029d63a0afa18cec | b414574c7daf6a42c8706ee58ad13ac3a7612a93 | refs/heads/master | 2022-01-17T05:37:26.780466 | 2018-10-19T08:30:19 | 2018-10-19T08:30:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(histmaps)
test_check("histmaps")
|
550164f84fd704e23fe39bf532e14a4239107ece | 59584c1a407817efec7315d47af9c02a89e7fff4 | /plot6.R | 2d39ee4427b5f8a18845ff264f8d12cd3983a9de | [] | no_license | cmukhtmu/Course4Week4 | 5f20c475cbadf0f297171f9d71524c40c728f707 | b4fbad4c376009d45fcb51e9aa2cc053d793b8f2 | refs/heads/master | 2020-04-24T20:17:02.022867 | 2019-02-23T16:55:53 | 2019-02-23T16:55:53 | 172,238,375 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,584 | r | plot6.R | library(data.table)
library(ggplot2)
library(gridExtra)
library(ggpubr)
plot6 <- function()
{
# downloading the zip file
if(!file.exists("./exdata_data_NEI_data.zip"))
{
print("Downloading zip file, please wait...")
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fda... |
edaf0e69e0b195d45049f37d658f5210a9c89cee | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BLCOP/examples/mvdistribution-class.Rd.R | 2c28f303cd292fbd64bc33612f86e282e143d779 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 193 | r | mvdistribution-class.Rd.R | library(BLCOP)
### Name: mvdistribution-class
### Title: Class "mvdistribution"
### Aliases: mvdistribution-class
### Keywords: classes
### ** Examples
showClass("mvdistribution")
|
95837032982f87a58b5bb20773514d638d72bab7 | a4021a064ad356d5b261ae116eec8d96e74eefb9 | /tests/testthat/test-iucn_getname.R | b1bc3a079f6ae06149539b33e295545ee7055cb9 | [
"CC0-1.0"
] | permissive | dlebauer/taxize_ | dc6e809e2d3ba00a52b140310b5d0f416f24b9fd | 9e831c964f40910dccc49e629922d240b987d033 | refs/heads/master | 2020-12-24T23:28:49.722694 | 2014-04-01T18:22:17 | 2014-04-01T18:22:17 | 9,173,897 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 387 | r | test-iucn_getname.R | context("iucn_getname")
temp <- iucn_getname(name = "Cyanistes caeruleus", verbose=FALSE)
test_that("iucn_summary returns the correct value", {
expect_that(temp, equals("Parus caeruleus"))
})
test_that("iucn_getname returns the correct class", {
expect_that(temp, is_a("character"))
})
test_that("iucn_getname gets... |
22e2c82798538b508e41f9965e7abdfe0f31d6a4 | 9975d797c0cb2d44ffad2d7cbeab31159b56855f | /tmp/realignBwaParser.R | 19c120b848189f9c36fee20b4dc270cdb0327dbf | [] | no_license | msubirana/DNAseqPipeline | 16654870ba0a0797f9ca82fdf63613c34af4ceef | 1a102eec826507e3a5b520519ab267f4243f9307 | refs/heads/main | 2023-09-03T05:29:22.782578 | 2021-11-15T15:56:46 | 2021-11-15T15:56:46 | 335,334,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | realignBwaParser.R | #load package
devtools::load_all('/gpfs42/robbyfs/scratch/lab_lpasquali/msubirana/marc/repos/DNAseqPipeline')
#parse arguments
args <- base::commandArgs(trailingOnly = TRUE)
#define variables
bam <- args[1]
ref <- args[2]
out_dir <- args[3]
threads <- parallel::detectCores()
#realign BAMs using DNAseqPipeline::real... |
a81115269a546fbf308215a43b3378fba0ef5e3e | 968617612e55205cb56fef91c826d59e46acd23f | /Data Science/Coursera/Data Science/Coursework/Assignment1/corr.R | 6c95f0373097b5190184f1bbff3eb46013e212a9 | [] | no_license | B4PJS/Assignment1 | b1e59a7cabb4a82def6eaf224dfce5031bfaf62f | 2a48539e44e112baa4fc46cc0833505a637a1356 | refs/heads/master | 2016-09-01T08:38:58.944850 | 2016-02-08T21:04:49 | 2016-02-08T21:04:49 | 51,327,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | r | corr.R | corr <- function(directory,threshold = 0){
pols <- data.frame()
cors <- vector()
for(i in 1:332){
wkfile <- paste("./",directory,"/",sprintf("%03i",i),".csv",sep = "")
x<- read.csv(wkfile)
y<-x[!is.na(x[,2])&!is.na(x[,3]),]
if(length(y$sulfate)>threshold){
retval <- cor(x = y$sulfate, y = y$... |
a556793f18df131ed19017c6098c675790d00379 | 331234b7eabbe4daf51ee8143c6dcbc768da0595 | /King et al. - 2016 - Placoderm R functions/geoplot.epoch.rates.R | 32f80422033f96f3b011092bc53e1e6517ca0a87 | [] | no_license | IanGBrennan/Convenient_Scripts | 6e3959ad69e594b21a5b4b2ca3dd6b30d63170be | 71f0e64af8c08754e9e3d85fe3fb4770a12ec2d4 | refs/heads/master | 2021-05-12T03:57:32.339140 | 2020-01-30T01:58:19 | 2020-01-30T01:58:19 | 117,630,023 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 816 | r | geoplot.epoch.rates.R | geoplot.epoch.rates <- function(rate.sample, epochs, line.col="black", plot.mean=T, mean.col="red", plot.bounds=T, ...){
data.frame(binmin=epochs[2:length(epochs)], binmax=epochs[1:((length(epochs))-1)]) -> epochbounds
apply(data.frame(binmin=epochs[2:length(epochs)], binmax=epochs[1:length(epochs)-1]), 1, mean) -> m... |
779ce97c9ec40621223cda8575d3d001d16b70f5 | c76918157a67d76509c46650e42eeef2714c455b | /R/profPlot.R | 17088bf8eec050844504904f085298f2bfa1184f | [] | no_license | butterflyology/ordiBreadth | 55b8817ae0e4c8db365f7c6930d92c5b37990548 | 41d3a543b6c5eaae3f909cf66ed10a02e32032f2 | refs/heads/master | 2021-01-10T12:58:18.289553 | 2016-04-28T03:17:24 | 2016-04-28T03:17:24 | 53,803,191 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 547 | r | profPlot.R | profPlot <- function(specialization, id, col = c("black", "red")){
#specialization output from ordi.breadthvX
cols <- rep(col[1], length(specialization$distances[[id]]))
cols[which(specialization$group.vectors[id, ] == TRUE)] <- col[2]
specdist <- specialization$distances[[id]]#[which(specialization$group.vect... |
77a0010d72807494425c6616ec32e1bcad4c17d3 | 7e7ba3eea4e4cfce0f5de796ac8b14cf457258bd | /plot1.R | fba6fc1ffd02bfb4a31f589e51862c4b7a78788e | [] | no_license | Keervar/ExData_Plotting1 | cf1856fac1055019ca9b92b9564f5ccd82521059 | 0e111c003f11ce7397ca35523b71001e9465534b | refs/heads/master | 2021-01-01T05:57:02.549536 | 2017-07-16T16:06:53 | 2017-07-16T16:06:53 | 97,316,352 | 0 | 0 | null | 2017-07-15T13:04:00 | 2017-07-15T13:04:00 | null | UTF-8 | R | false | false | 420 | r | plot1.R | df <- read.table("household_power_consumption.txt", na.strings = "?", header = TRUE, sep = ";")
df$Date <- as.Date(df$Date , format ="%d/%m/%Y")
df1 <- subset(df, df$Date == "2007-02-01"| df$Date=="2007-02-02")
hist(df1$Global_active_power,col = "red", xlab = "Global Active Power (kilowatts)", ylab = "Frequency",
... |
64891f85b8b9a1e8fee6e389c83aa833c92101e3 | 2882750a43ae5fa188ec0805953831010a205b1b | /R/bisexual_flag.R | e76b76e2a74125fe6d451d6e12e30d8119ae15a6 | [
"CC-BY-4.0",
"MIT"
] | permissive | Shornone/rosemary | ab0a0039006e7c3d82a7eea639d21243c3572216 | 807386719d366dc98ab3a0dc76e4c43a3c6b9650 | refs/heads/master | 2020-12-01T12:11:17.149352 | 2020-03-31T13:02:11 | 2020-03-31T13:02:11 | 230,621,467 | 0 | 0 | CC-BY-4.0 | 2020-03-31T13:02:12 | 2019-12-28T14:34:26 | null | UTF-8 | R | false | false | 857 | r | bisexual_flag.R | # URL: https://www.flickr.com/photos/stringrbelle/49258653886
#' @rdname rosemary
#' @export
bisexual_flag <- function(dir = NULL, ...) {
dir <- check_dir(dir)
file <- file.path(dir, "bisexual_flag.png")
nrow <- 100
ncol <- 150
jasmines::use_seed(43) %>%
jasmines::scene_rows(n = nrow, grain = ncol) %>... |
bc0375f21712e9a49b354e55b710c22ee2f92997 | a3ac16582493f7eb5011c5eab0093c4b5ec2a2f1 | /Paper/JournalManuscript/subscripts/sanity-checks-figs.R | 3b4f3dfae220d9d2a2bbe83e564e0cc8488e25d0 | [] | no_license | marisacasillas/NorthAmericanChildren-ADSvsCDS | ba38e98fc789589817e82fd0f5011270385932ab | a2a155a81f5c716923857d3d9584bbe193d54781 | refs/heads/master | 2018-09-20T07:09:46.461428 | 2018-07-10T22:11:14 | 2018-07-10T22:11:14 | 111,093,867 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 25,885 | r | sanity-checks-figs.R | ################################################################################
# BLOCK DUR SUMMARIES PRIOR TO EXCLUSIONS ####
blk.dur.plot <- ggplot(blockdata, aes(x=(blk_dur))) +
geom_histogram(binwidth = 0.5) +
xlab("block duration (min)") +
geom_vline(xintercept = (mean(blockdata$blk_dur) +
... |
044d45a205d6c99882ec4a45d7c74531d9ff7352 | bdb1db73498d4366556a34fd03bd105296396ad2 | /R/data.R | 90b7fdff3549fd2c894d94536b8a6376d16bf026 | [] | no_license | cran/diffusionMap | 3b9b634f5dffdae28215003920e7d16694a41f06 | 0fb2aae0da72fa97de3479ac689952de61a09eb5 | refs/heads/master | 2020-06-08T18:23:38.788875 | 2019-09-10T21:50:18 | 2019-09-10T21:50:18 | 17,695,503 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 815 | r | data.R |
#' Annulus toy data set
#'
#' The annulus data frame has 1000 rows and 2 columns. 500 data points are
#' from the noisy annulus and 500 data points reside within the annulus.
#'
#' @format Data are in two dimensions.
#' @keywords data
#'
#' @docType data
"annulus"
#' Chainlink toy clustering data set
#'
#' The Chain... |
eb2ab6218b7e0ef88cf958c32423413c1d891832 | 129c835434be005639193cfdaa2e9daf30f85da1 | /man/gencooccur.Rd | fc2a0770bee1bef6ee6c8b529b77d2d7ae92a1a3 | [] | no_license | cran/cooccurNet | 67aae6e4a003cb32fae98aafdc11bd101a5f3df9 | e0d16e980b9b919c1656f0995b3fd68bb4b9fbdc | refs/heads/master | 2020-09-26T08:08:19.805303 | 2017-01-27T09:38:24 | 2017-01-27T09:38:24 | 66,298,993 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,289 | rd | gencooccur.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cooccurrence.R
\name{gencooccur}
\alias{gencooccur}
\title{gencooccur}
\usage{
gencooccur(data = list(), cooccurFilter = NULL,
networkFile = "cooccurNetwork", module = FALSE,
moduleFile = "cooccurNetworkModule", property = FALSE,
... |
7992a957b5e48eabc33b83ea330be9a668bae81a | e5f84c07e4b229502c67fff9ced2fa29420d5777 | /R.old.code/zPoisProcAnalisis_FS.R | d01c9400ffa080cb3d8bc66ecc3475ced494a717 | [] | no_license | paurabassa/storm-generation | cd648f4ef868f312f48fb909b4aa4971a4760804 | a7ace284d5be9dbf7830f1d984e5492c25042f0c | refs/heads/master | 2021-01-22T17:22:51.416127 | 2015-04-20T18:28:37 | 2015-04-20T18:28:37 | 31,430,642 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,894 | r | zPoisProcAnalisis_FS.R | ##
##
## Analisis of interarrival time of storms via a non-homogenuous
## Poisson Process. Amended by Francesco to include modelling via
## GAM models
##
##
####################
# Part 1: Data fit #
####################
#set random seed
set.seed(1234)
# Read data
waves <- read.csv("./Clean.Data/waves... |
eb36f339be41c4a2a3e76e11a70965feb34645ba | 5d84ca384c45b6280004fced1e2e11a1d43e8ff9 | /scripts/MarinhoCatunda_etal_2021_Figure1a.c_soil_perharvest.R | 44a5c5aa0f3fc4ac7c60a67443fa1efd470abdc7 | [] | no_license | kjfuller06/PACE | 90cb0d55dd28a4264756a594ca5faf821a117e06 | 158e57be90abeb99d93c42648a126abf4cef9d49 | refs/heads/master | 2022-04-26T21:01:34.076401 | 2022-04-22T21:55:41 | 2022-04-22T21:55:41 | 192,671,350 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,299 | r | MarinhoCatunda_etal_2021_Figure1a.c_soil_perharvest.R | Baug = as.POSIXct("2019-08-20 08:00:00")
Boct = as.POSIXct("2019-09-30 08:00:00")
Faug_13 = as.POSIXct("2019-08-20 08:00:00")
Faug_46 = as.POSIXct("2019-08-21 08:00:00")
Foct = as.POSIXct("2019-10-08 08:00:00")
Fnov = as.POSIXct("2019-11-18 08:00:00")
Laug = as.POSIXct("2019-08-16 08:00:00")
Loct = as.POSIXct("2019-10-... |
a511316d70719989653c4585f9879e090f8a1fd8 | d9a3434bc86c670a563a09c248b2ba142842634d | /cachematrix.R | 9dee097c2e84ea34c0fd16c7c547f0f72086399d | [] | no_license | Aladroite/ProgrammingAssignment2 | cc00f859c799e9969b7b3f7b463b3d1495409851 | 53bdc61bc2d51015d4e80e4d31aa81fa9450ec2b | refs/heads/master | 2020-03-25T21:49:39.923635 | 2018-08-09T21:41:16 | 2018-08-09T21:41:16 | 144,191,333 | 0 | 0 | null | 2018-08-09T18:47:34 | 2018-08-09T18:47:33 | null | UTF-8 | R | false | false | 1,224 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do: Alison Lewis, makeCacheMatrix and cacheSolve Assignments
## The makeCacheMatrix function: creates a special 'matrix' object
## that can cache its reverse. The special 'matrix' object is a list of
## functions that can: 1. Set the value... |
0d742add927e9d5ad4f14112da0d8c95067b03a2 | 80c6b6739dab6bbfde951f8ff3fa1bb3b8768563 | /pgmm.methods.R | d02c8a807ea6664a70ffe683fe03a85e2e0f6523 | [] | no_license | Eligijus112/bachelor | 18d6a38502d4f92f2e596b6db4de9cd46cec2fbe | 10433c7753a60abaa330f60c37512410b56594e7 | refs/heads/master | 2021-01-13T15:48:18.113949 | 2017-05-21T11:18:36 | 2017-05-21T11:18:36 | 79,720,111 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,134 | r | pgmm.methods.R | coef.pgmm <- function(object,...){
model <- describe(object, "model")
if(model == "onestep") coefficients <- object$coefficients
else coefficients <- object$coefficients[[2]]
coefficients
}
summary.pgmm <- function(object, robust = TRUE, time.dummies = FALSE, ...){
# model <- describe(object, "model")
# ef... |
fd759daa5ba5a248224b814477028b0faa67df3f | da8baef13311f0a710b1c33d279caf8550be0665 | /R/CounterBind.R | aff063e8d193b8ff118205cb607479a23cb5260a | [] | no_license | colinpmillar/FishCounter | 51c9e5abe846fd6b36822bef698cf8afc25f91e0 | 10e389a6a690d59a58f19ca18f8e8f7866a04c12 | refs/heads/master | 2021-01-21T08:43:51.879886 | 2015-04-02T20:29:16 | 2015-04-02T20:29:16 | 33,184,834 | 0 | 0 | null | 2015-03-31T12:44:32 | 2015-03-31T12:44:32 | null | UTF-8 | R | false | false | 3,417 | r | CounterBind.R | #' bind_counter_data
#' bind individual data files, clean up data, and return a master data file.
#' A function to bind and process Logie counter data
#' This function allows you to bind together mulitple counter data files, remove errors and produce a master datafile.
#' @param path_to_folder This is the file path for... |
79af205049b6a6d36b281ff1fd7b45134cb8bc76 | bc42c76a961ef56d4d08a714c0eaabb4366a36a1 | /R/DepCPSPKenv.R | 0ba7b35d1c2cd750652b4a060e928ad90713308f | [] | no_license | cran/IndTestPP | 593ab1dc0ddb6addd008e80aed948d88058a240c | a628d5be9c314513541656d6e2ea28dd9bc91cee | refs/heads/master | 2021-06-28T21:12:36.085070 | 2020-08-28T18:00:03 | 2020-08-28T18:00:03 | 64,703,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 488 | r | DepCPSPKenv.R | DepCPSPKenv <-
function(i,lambdaix, lambdaiy, lambdaixy,lambdax, lambday, r=NULL, T=NULL,typeEst, fixed.seed=NULL)
{
if (!is.null(fixed.seed)) fixed.seed2<-fixed.seed+i
auxpos<-DepNHCPSP(lambdaiM=cbind(lambdaix, lambdaiy, lambdaixy), d=2 ,dplot=FALSE,
fixed.seed=fixed.seed2)$posNH
Kaux<-NHKaux(lambdaC=lam... |
d4695debdcd2c182c86ee2ec67ffda640a5ab5d0 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/E_Penman/AFL_E_Penman/E_Penman_valgrind_files/1615918467-test.R | fe6bd4d682300e03bf44566f5e02beb2f94bf90d | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,277 | r | 1615918467-test.R | testlist <- list(Rext = numeric(0), Rs = numeric(0), Z = numeric(0), alpha = numeric(0), atmp = numeric(0), relh = c(-1.84445564980858e-242, 5.97935653522269e-39, 1.0286791867366e-307, 5.93388628067419e+252, -6.10798253418121e-263, -3.96270958130688, 6.07090976184692e+178, 1.97234986880642e-128, -9.... |
c2b89b279048f0c99a56ad2e105f21a43a86481d | f8766acedba45c6ab57de62edfd26a53338d976a | /Prewhitening.R | 9de4c84467c3ff65ddf34f89dd6267cfb7362c59 | [] | no_license | jabranham/brazil | 1521aedf46ea7633cbaa9bb442800897b4e57bb3 | 4d62221e319265cdc2a0f77f7f89970ecfc6250e | refs/heads/master | 2021-01-15T10:19:04.560501 | 2016-06-14T14:09:38 | 2016-06-14T14:09:38 | 56,794,522 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 581 | r | Prewhitening.R | #Prewhitening
library(forecast)
source("run-first.R")
preW.R <- auto.arima(ts(w.list$Proportion.Rousseff), D=0, seasonal=F, ic="bic")
res.R <- residuals(preW.R)
preW.N <- auto.arima(ts(w.list$Proportion.Neves), D=0, seasonal=F, ic="bic")
res.N <- residuals(preW.N)
preW.psb <- auto.arima(ts(w.list$Proportion.PSB), D=0,... |
dc84266b1688a5ae2ade8ca62fef0ff5b9980f94 | f7f5260f4a084a39bdb30149d1b39ff961bfd55f | /private/backend/get_product_sales_sum_by_day.R | 124403f6620efc38791d2ccb6a7dd7280e3198d8 | [] | no_license | naroarnold/grocery | 32764967496f041f7522421415f574b6e52d9a0d | feb6cafe9e498baa9ec7d813a2d3bd081ddf5b25 | refs/heads/master | 2021-01-15T13:52:25.480811 | 2015-12-02T17:27:20 | 2015-12-02T17:27:20 | 46,244,526 | 0 | 0 | null | 2015-11-16T01:25:51 | 2015-11-16T01:25:50 | null | UTF-8 | R | false | false | 405 | r | get_product_sales_sum_by_day.R | get_p_daily_sales_avg <- function(product_id,con)
{
#find overall sales of a given product
query <- paste("SELECT store_sales,day_of_month,month_of_year FROM sales_fact_dec_1998_p,time_by_day WHERE product_id = ",product_id,"AND sales_fact_dec_1998_p.time_id = time_by_day.time_id;")
data <- dbGetQuery(con,quer... |
f980e47dfa3a49b9ac658d4a04f16090353ea461 | a4aa93fe9fae6d6218d96188bda72ea793f222f3 | /plumber.R | 3ba49458b7cda12c693abe4eac2bff82e70c37dc | [] | no_license | ratnexa/plumber-test | d4cf6829ba57ed1d72c94c5b59556da007a2143a | 34eafa595d36c66501bdfd216ed05c530e6141da | refs/heads/master | 2022-12-08T04:35:05.557154 | 2020-09-03T19:13:08 | 2020-09-03T19:13:08 | 292,612,564 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 900 | r | plumber.R | library(caret)
library(httr)
library(magrittr)
library(plumber)
library(tidyverse)
# plumber.R
# Read model data.
# model.list <- readRDS(file = 'model_list.rds')
#* @param wt
#* @param qsec
#* @param am
#* @post /predict
# CalculatePrediction <- function(wt, qsec, am){
# wt %<>% as.numeric
# qsec %<>% as.numeri... |
cc57fde2c86e26ffc97a0cab012a9cc2ac1337f8 | 13c6a1a108ffd9edc98bbbd0287c503b484db2f3 | /man/qqANOVA.Rd | 7a76906610a83d2828e91535a017199ff6b47f8e | [] | no_license | cran/MPV | 81f894f2ecca63c391037ea8dc6033dbc7b57d98 | b757e5149d3b29c0a8f20cc4315acec17a27693b | refs/heads/master | 2023-03-03T14:14:08.549709 | 2023-02-26T02:40:02 | 2023-02-26T02:40:02 | 17,680,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 783 | rd | qqANOVA.Rd | \name{qqANOVA}
\alias{qqANOVA}
\title{
QQ Plot for Analysis of Variance
}
\description{ This function is used to display the weight of the
evidence against null main effects in data coming from a 1 factor
design, using a QQ plot. In practice this method is often called via
the function GANOVA.
}
\usage{
qqANOVA(x, ... |
7e3116e2dff9a91758e8320071406ccfac5f6f94 | 17205acfbd00aeb7edf976966f251698c5ae2458 | /GpOutput2D.Rcheck/00_pkg_src/GpOutput2D/man/Inverse2D.OrthoNormalBsplines2D.Rd | 1fe579fe6bcd0bcf70cc439accae12191a93ca87 | [] | no_license | tranvivielodie/GpOutput2D | 148be512b5ecd99fba7d105fa3171d8c86d531fd | 9f779d33fb86f8bff8b3da3db4845c37b3045f75 | refs/heads/main | 2023-03-04T19:28:27.694988 | 2021-02-18T10:11:00 | 2021-02-18T10:11:00 | 316,458,801 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 828 | rd | Inverse2D.OrthoNormalBsplines2D.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OrthoNormalBsplines2D_utils.R
\name{Inverse2D.OrthoNormalBsplines2D}
\alias{Inverse2D.OrthoNormalBsplines2D}
\title{Inverse transform of control points of two-dimensional B-splines basis}
\usage{
\method{Inverse2D}{OrthoNormalBsplines2D}(obje... |
8c40caaf703420657ad2e1365207a9ac78f36f68 | 7e78610eba99aab8b7f7abb7e8d2520c681aa0e5 | /medidas_resumo.R | 6eb7e1a03efca41a99b044e5ea333f91556fe14f | [] | no_license | joaohmorais/garu-estatistica | 69eed5db648c66e84f97aff0d40e61a5b85f6b2f | c125c6baffc625499f8087838de033980084608e | refs/heads/master | 2020-05-25T20:09:30.926790 | 2019-07-25T21:26:39 | 2019-07-25T21:26:39 | 187,968,563 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,358 | r | medidas_resumo.R | library(shinyjs)
medidas_resumo <- fluidPage(
title = "Medidas Resumo",
useShinyjs(),
withMathJax(),
fluidRow(
column(6,
h4(strong("Gerador de Elementos")),
p("Gere uma amostra de elementos (números) através dos parâmetros e do botão 'Gerar Elementos'. Depois
acompanh... |
9a76d27265abc0b6c491e803b2eed412ab3a1553 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/tokenizers/tests/testthat/test-encoding.R | 643f99dc21dcad08dc716691574744edc924d1b0 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 667 | r | test-encoding.R | context("Encodings")
test_that("Encodings work on Windows", {
input <- "César Moreira Nuñez"
reference <- c("césar", "moreira", "nuñez")
reference_enc <- c("UTF-8", "unknown", "UTF-8")
output_n1 <- tokenize_ngrams(input, n = 1, simplify = TRUE)
output_words <- tokenize_words(input, simplify = TRUE)
output_... |
957abeadf9f99e966723e088eed04948d60f0c10 | fea71570dc6786eb56eb1b3faf0c282b908f96fc | /server.R | e3aaf82e185198292fd0738527b8fe37ec483d73 | [] | no_license | turgeonmaxime/tavi_demand | 51c6b820362d79de909440a10cef6955e8af3624 | a5a2512ba6e1238ab6b160e4b870312b9a7be3a5 | refs/heads/master | 2020-03-22T22:15:46.987367 | 2018-07-12T17:05:41 | 2018-07-12T17:05:41 | 140,742,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,570 | r | server.R | # Define server logic required to draw a histogram
library(tidyverse)
n_years <- 25
baseline_pop <- 36065
server <- function(input, output) {
y_lim <- reactive({
if (input$sts > 45 &&
input$symp > 75 &&
input$savs > 5) c(0, 1500) else if (input$savs > 5) c(0, 1000) else c(0, 800)
})
datase... |
edc9f80946790d811a4808c20e1e6fe63698ee8b | 648c3c15a9d15da278c1d17c27d34e487353400c | /a_Archive/for Beisit.R | 05ba8bb4439db215bf423d51ff7ca91c63d89b8d | [] | no_license | OxfordEcosystemsLab/GEMcarbon.R | c50c485ef27664b8b2b0d5cd4364b082630e70f3 | bddd26fcd3e9909bbb9ca730838541d48d3da5d0 | refs/heads/master | 2020-04-07T05:39:38.683573 | 2018-12-12T13:01:29 | 2018-12-12T13:01:29 | 18,173,751 | 5 | 6 | null | null | null | null | UTF-8 | R | false | false | 37,465 | r | for Beisit.R | > setwd("/Users/cecile/Dropbox/GEMcarbondb/db_csv/db_csv_2015/readyforupload_db/acj_pan_2015")
> census_ACJ01 <- read.table("ACJcensus_clean_date.csv", header=TRUE, sep=",", na.strings=c("NA", "NaN", ""), dec=".", strip.white=TRUE)
> head(census_ACJ01)
X.1 Tag.No Old.No T1 T2 X Y Family genus species Old... |
8ff0d33d87b491acc8b9e43a5405db584bd767b6 | 2643a442e25b937a853f4576db73d7a23835b1c6 | /R/define_variables.R | 0350fa756208f843ae125307c50fce92b768450d | [
"MIT"
] | permissive | mperezrocha83/ecocomDP | 0e768bd754389dc617bbbd7c6dd383c254dbf2ed | e8165540722b0641471e7f4315e241848b847cd9 | refs/heads/master | 2020-09-04T17:59:12.234472 | 2019-10-14T23:01:23 | 2019-10-14T23:01:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,175 | r | define_variables.R | #' Define categorical variables of ecocomDP tables
#'
#' @description
#' Get variable definitions and units from parent EML.
#'
#' @usage define_variables(data.path, parent.pkg.id)
#'
#' @param data.path
#' (character) Path to ecocomDP tables.
#' @param parent.pkg.id
#' (character) Parent data package ID ... |
00ece5ff086a6937f19277402b1e958e356ed6b8 | 1b15d76ac488aaeadeb6b4e7cc4344dfff815266 | /movie_boxoffie_forecast.R | 44e9b63283d7802a65d55bb4eb59481aef3b6b8e | [] | no_license | maujy/imdbR | 29ff885b33ad0dc8d5e0c3834d8c559f0657cbf8 | 83842601372d9a89306e2d945b1a5d6a9b2ec0a1 | refs/heads/master | 2021-09-03T21:25:00.077177 | 2018-01-12T04:31:59 | 2018-01-12T04:31:59 | 115,768,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,199 | r | movie_boxoffie_forecast.R | movie_title_list = read.csv("movie_title_list_v1.00.csv", header=TRUE)
str(movie_title_list)
View(movie_title_list)
movie_title_data.frame = movie_title_list[, c(-1, -2, -3, -4, -6, -10 )]
str(movie_title_data.frame)
View(movie_title_data.frame)
par(mfrow = c(1,2))
plot(main='Movie Box Office')
plot(movie_title_data.... |
a9bb7675ead13e5b54e23e2aee7a78c7e1783d28 | 33a08ca3c15a5fc678d78777b53d8868a7bc7897 | /man/print.FSchr.Rd | 81e56b3d668dcc27b3cb734adaab29542d110e1b | [] | no_license | hjanime/DCS | 09f10d37d549eae3837b1c15d65d319f8c45e8ea | 33570353b7812fc0d1fb2744cc34d6e92375b90f | refs/heads/master | 2020-03-23T02:44:05.383267 | 2018-07-15T01:18:57 | 2018-07-15T01:18:57 | 140,989,262 | 0 | 0 | null | 2018-07-15T01:17:32 | 2018-07-15T01:17:32 | null | UTF-8 | R | false | true | 344 | rd | print.FSchr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ClassDefinitions.R
\name{print.FSchr}
\alias{print.FSchr}
\title{print.FSchr}
\usage{
\method{print}{FSchr}(x, ..., n = 6)
}
\arguments{
\item{x}{An \code{FSchr} object.}
\item{...}{Unused.}
\item{n}{Number of observations to print.}
}
\des... |
b07164b9ce85b93fe002be01f2767123b6501d81 | 35db3db7776101724d8ab20e4dfcf3f9e7b361a1 | /ui.r | 9cd57cc618db498a09a1561995563d54dd1cd660 | [] | no_license | mamonu/LDAShiny | dce38b1bad84021fbd150175645ad118ec2fa04d | 045c23a39130656a247feec623f41240838fb092 | refs/heads/master | 2021-01-10T07:12:02.627342 | 2016-03-04T17:45:25 | 2016-03-04T17:45:25 | 53,063,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,280 | r | ui.r | library(shiny)
library(formattable)
fluidPage(
# Application title
titlePanel("Interactive LDA"),
sidebarLayout(
# Sidebar with a slider and selection inputs
sidebarPanel(
fileInput('file1', 'Choose file to upload',
accept = c(
'text/csv',
'text... |
55b7ca941dc5a81a3281b7170c52a3fd762f7a7b | f40c36525935715889897ad377597a7cf0cc32ef | /Brachy4g24315DNA.R | 87d24f89535d08ea8f02a6fce6baa2cb3fb1d8b6 | [] | no_license | KimNewell/2018-07-10_AyliffeProteinAlign | c80c5fa8e7fa20244fef3c43331c9e404d2f2c11 | c5e343e5c72a4481a5b811bbbd27e71b86c27f8e | refs/heads/master | 2020-03-22T16:57:06.394372 | 2018-07-17T13:02:40 | 2018-07-17T13:02:40 | 140,362,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,385 | r | Brachy4g24315DNA.R | library(msa)
library(tools)
library(tidyverse)
library(dplyr)
Bradi4g24315DNASeq <- readDNAStringSet("C:/Users/new163/Desktop/2018-07-10_AyliffeProteinAlign/data/Bradi4g24315DNA.txt")
Bradi4g24315DNASeq
Bradi4g24315DNASeqAlign <- msa(Bradi4g24315DNASeq)
# To make a PDF file first have to make a 'tex' file th... |
c2e26229d94d17ff1c9e5db93fe34cab6e40f9d8 | 44a3fad6338a63ac5417b1e52e47420c0e013f45 | /man/dExtDep.Rd | 29d48f982fd64a695191497e442a462f632da120 | [] | no_license | cran/ExtremalDep | 4faac60ce0040262a98410edc6488ddf939ad9bd | 18238416ddb6567610c4457dc332316272dbd16e | refs/heads/master | 2023-03-06T18:03:59.304908 | 2023-02-26T14:40:02 | 2023-02-26T14:40:02 | 236,595,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,844 | rd | dExtDep.Rd | \name{dExtDep}
\alias{dExtDep}
\title{Parametric and non-parametric density of Extremal Dependence}
\description{This function calculates the density of parametric multivariate extreme distributions and corresponding angular density, or the non-parametric angular density represented through Bernstein polynomials.}
\u... |
accd701dc805fa94995540654978e26734d80ca0 | 29585dff702209dd446c0ab52ceea046c58e384e | /SPmlficmcm/R/Spmlficmcm.R | de7051530fdc3a5ce0e2118cba98e50767d2bff8 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,487 | r | Spmlficmcm.R | Spmlficmcm <-
function(fl,N,gmname,gcname,DatfE,typ,start,p=NULL){
# ============================================================
# Etape 1 estimation des valeurs initiales
# Valeurs initiales des parametre du modele
# baterie des tests
... |
b46b06f061077df934018748c4a55a219c8b3fbc | 73dcbcc208f33f1536c7fcf9a628b78363fec583 | /man/kp_teamstats.Rd | b3bbdcc28f3bb50cd843c88d22c500b157ac6bfb | [
"MIT"
] | permissive | sportsanalytics-world/hoopR | 2a9ca3bd13ba02515900559dc801cbbdf4b5adde | 4ddbfaf4e608dc6b2a543ea367c948891b651b0a | refs/heads/master | 2023-04-19T19:20:20.433838 | 2021-05-21T10:23:42 | 2021-05-21T10:23:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 549 | rd | kp_teamstats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kp_ratings_tables.R
\name{kp_teamstats}
\alias{kp_teamstats}
\title{Get Team Stats}
\usage{
kp_teamstats(min_year, max_year, defense = FALSE)
}
\arguments{
\item{min_year}{First year of data to pull}
\item{max_year}{Last year of data to pull... |
048020a7713eb90f6c1a6411f2dd0636902e6a8b | 4f48793191dba5555eb5a4c92489e4e5b8e268d9 | /movielens_project.R | 3ded3eb33eedfc7fb76d24b73cdc58955c92b0e8 | [] | no_license | ZRNaum/movielens | a24eb2d7773f13acb8944c2f627a97ce7d868c0d | fbb839b7a23cbaa242dcd14ed330cc35b219449f | refs/heads/main | 2023-05-08T17:38:53.048742 | 2021-05-28T15:45:56 | 2021-05-28T15:45:56 | 355,951,969 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,876 | r | movielens_project.R | ##########################################################
# Create edx set, validation set (final hold-out test set)
##########################################################
# Note: this process could take a couple of minutes
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.o... |
20b2df456408f5f7975fe523e5753e7a2638dbf3 | 08ce90eef085080a6c7e2028cccce91e576d1b77 | /plot3.R | 2e3eeb6217cf39f0e5da4735b7c566d0162b6368 | [] | no_license | gustavolobo/ExData_Plotting1 | 16ef04e356a630b47959e9ad817617ed91f1fa23 | c607ac18a0ec1ff5e7db2ec5a4ec9faa571da125 | refs/heads/master | 2021-01-22T20:36:04.483202 | 2014-09-06T01:45:53 | 2014-09-06T01:45:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 733 | r | plot3.R | source("./loadData.R")
## Open PNG device and create 'plot3.png' in my working directory
png(file = "plot3.png")
data$Date <- as.Date(data$Date,format="%d/%m/%Y")
data$posix_date <- as.POSIXct(paste(data$Date, data$Time))
plot(
data$posix_date,
data$Sub_metering_1,
type = "l",
xaxt = "n",
xlab="",
ylab="E... |
872d03bbf047b43e9e5873bb77a5881bb00c272f | 99e7f60f7cb80ff1f80b3dfd4937210a8552a95f | /analysis.R | c2f7071a8a38530dc525d2842b302050fe8dc657 | [] | no_license | YingzhiGou/GameOfGoals | 3141755b7f700ad4726ceb94b6f2eec43f3b7bc0 | 406652f9348ff1717487ea31a4af81c950a6b51a | refs/heads/master | 2020-09-21T23:19:54.626431 | 2019-12-05T12:22:36 | 2019-12-05T12:22:36 | 183,752,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,465 | r | analysis.R | data.files <- list.files(path="output",
recursive=T,
pattern="*.csv"
,full.names=T)
data = lapply(data.files, read.csv)
library(data.table)
data <- rbindlist(data)
library(grid)
library(gridExtra)
library(ggplot2)
library(ggpubr)
library(Rmi... |
39e6d1467246ce0f001602cb79430179c2aa4053 | d63cddc3eae16b16b0e67d671eacf23cb249af45 | /R/load-maps.R | f808078cb26430980239db948549465063119a5a | [
"MIT"
] | permissive | tjmckinley/arear | acfb63bd4b2a842600cd95768dfb67659bc39760 | 99523d47021dc88aa21c0b0aeb10395f2f5a3054 | refs/heads/main | 2023-06-04T22:34:30.671092 | 2021-06-30T13:45:15 | 2021-06-30T13:45:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,024 | r | load-maps.R |
#' Gets maps for which the metadata is known
#'
#' @param mapId - a name of a map
#' @param sources - a list of map sources - see arear::mapsources
#' @param codeType - defaults to mapId, the codeType of the map
#' @param wd - optional working directory
#'
#' @return a standard sf map
#' @export
#'
#' @examples
#' \do... |
b976bd8c15f3cc853e55cbd997b22adcfa4b88b2 | 9ea74e7e088c6351ac4d29522529c0e79b51dcc4 | /man/parse.formula.Rd | d10366391ec7284baa59d72de5bd2400ced84a84 | [] | no_license | imbs-hl/ranger | 0ccd500db3cd97e5fda5d3b150517c71ed55ade6 | 5a04d9355a1a96315973d88492160bc00a91e399 | refs/heads/master | 2023-08-17T01:09:34.455853 | 2023-08-16T09:26:19 | 2023-08-16T09:26:19 | 41,377,351 | 739 | 245 | null | 2023-09-12T20:10:46 | 2015-08-25T17:18:39 | C++ | UTF-8 | R | false | true | 732 | rd | parse.formula.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/formula.R
\name{parse.formula}
\alias{parse.formula}
\title{Parse formula}
\usage{
parse.formula(formula, data, env = parent.frame())
}
\arguments{
\item{formula}{Object of class \code{formula} or \code{character} describing the model to fit.... |
f6f10fa9cfac3d46246a2a22a5544fd76097d73a | f26781b86f2dea0394809d1951bad4550d82ba3c | /module/_module_summaryTab.R | a9a30f7dbab3105826631a5dd5f2ea3c4d96b302 | [] | no_license | fyang72/handbook | 0ac0d616f033747347bce3fe72219223a2553ab8 | 89abb7b557b83d9b651821780b92410623aaa9a2 | refs/heads/master | 2022-09-30T10:36:14.303860 | 2019-12-16T19:32:13 | 2019-12-16T19:32:13 | 171,066,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,808 | r | _module_summaryTab.R |
################################################################################
################################################################################
# summaryTab
################################################################################
############################################################... |
5a92036064f50da1a54ad2b67fdfba786407cc63 | bccaf9ca75d67fef6bec733e784c582149a32ed1 | /plagiat/R/jpdf2imp.f.R | fca845e05a18272d9d9c6d2a06aafc6b4d269928 | [] | no_license | brooksambrose/pack-dev | 9cd89c134bcc80711d67db33c789d916ebcafac2 | af1308111a36753bff9dc00aa3739ac88094f967 | refs/heads/master | 2023-05-10T17:22:37.820713 | 2023-05-01T18:42:08 | 2023-05-01T18:42:08 | 43,087,209 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,398 | r | jpdf2imp.f.R | #' JSTOR PDF to imp
#'
#' @param jpdf Vector of full paths to every pdf you with to import.
#' @param ocr
#' @param depth
#' @param ncores
#' @param logdir
#'
#' @import data.table stringr pdftools stringdist igraph progress
#'
#' @return
#' @export
#'
#' @examples
jpdf2imp.f<-function(jpdf,ocr=F,depth=5,ncores,logdir)... |
99af36cf7586d53009a742a6035e061a77936c27 | 1cb8211da96b773389dec14a5bee30a8cc98f9df | /R/connectivity_score-methods.R | e3edca33a0f2fe7a760501f0b246779b922a6353 | [] | no_license | albert-ying/gCMAP_mod | 2843b38ad18447c7984684ee76cf11976388781b | 99b600ced1dba9a0fd1c388a1081b8ca14101e1a | refs/heads/master | 2023-02-15T05:26:22.017484 | 2021-01-05T02:04:09 | 2021-01-05T02:04:09 | 326,838,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,956 | r | connectivity_score-methods.R |
setMethod(
"connectivity_score",
signature(experiment = "eSet", query = "CMAPCollection"),
function(experiment, query, element = "z", keep.scores = FALSE) {
if (!(element %in% assayDataElementNames(experiment))) {
stop("Requested element name not found in data.")
}
if (any(signed(query) == FAL... |
55f6644fdbb02fc1b6f2db94cb886284bb9d5378 | 2d00505c7940f1bd1dcff122aa1e8bbd5d2edea2 | /R/bindings.R | 487d835f797351f59abd202d456ecfaa2d64ad72 | [] | no_license | kashenfelter/RGCCTranslationUnit | cb647e6c57e78656bb196e68834d60fd664e66cd | 1bd45f5589516334afa57a75e936d2a36ff943b6 | refs/heads/master | 2020-03-23T00:32:22.815755 | 2013-04-21T21:38:45 | 2013-04-21T21:38:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 69,435 | r | bindings.R | # Deal with structures, not just pointers to them, as arguments
# and also generate get and set methods for them.
# Default constructor if none present.
# or perhaps, just anyway.
# What is vfld? virtual?
# Yep.
# Get overloaded methods across classes,
# perhaps simply if they are virtual methods.
# map 'long ... |
2bd554d92847d478004a95c9328092007d893241 | d24c36a4eeff0f3911179a6747f8fbc9cdcadacf | /eqtl/TIMBR_eqtl.R | b3a72d8415af03fbbdc673b060328defd69e092e | [] | no_license | wesleycrouse/TIMBR_data | 9ab2d4a119b0bf1392737e489401a3fd239d4fbd | 9a822473158c676d18657c051f5a711feb09d5d0 | refs/heads/master | 2022-12-28T06:55:56.593654 | 2020-10-16T17:35:07 | 2020-10-16T17:35:07 | 267,590,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,668 | r | TIMBR_eqtl.R | command.line <- commandArgs(trailingOnly = TRUE)
phenotype.var <- as.character(command.line[1])
locus <- as.character(command.line[2])
#phenotype.var <- "X1212612"
#locus <- "M17.345"
#drop \r that appears when running from command line
locus <- unlist(strsplit(locus, "[^[:print:]]"))
####################
library(... |
944f61b02a8523882210332519be29f2ca4a0429 | 0ed1b94c9712f4a4e1bf3bebcd0946baed15cfb3 | /man/localrank.Rd | 3a0322ffc537c86f939ba6455bb103bfe8c2652e | [
"BSD-2-Clause"
] | permissive | RBigData/localrank | 0df97f1e9c7bd798c7ff8d37d323fc381d87eef5 | 5510e641eb5d5a935adc77970d93da63b6ab3089 | refs/heads/master | 2020-05-07T14:49:22.653782 | 2019-04-10T15:23:54 | 2019-04-10T15:23:54 | 180,610,032 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 240 | rd | localrank.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/localrank.r
\name{localrank}
\alias{localrank}
\title{localrank}
\usage{
localrank()
}
\description{
Get the node-local MPI rank from the default communicator.
}
|
fdb1bbcd24254e5d3873d7c152620fe0330240a9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DACF/examples/lw.t.test.Rd.R | 997dd68cd5b5a2de0623ad732b921149bf440997 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 341 | r | lw.t.test.Rd.R | library(DACF)
### Name: lw.t.test
### Title: lw.t.test
### Aliases: lw.t.test
### ** Examples
x1.c=induce.cfe(0,.3,rnorm(1000,20,5)) #group 1 scores with 30% ceiling data
x2.c=induce.cfe(.15,0,rnorm(1000,30,5)) #group 2 scores with 15% floor data
lw.t.test(x1.c,x2.c,"a") #using truncated n
lw.t.test(x1.c,x2.c,"b") ... |
fe5a989db173345e71495ab15688dc9f239b2cf8 | 23fba2056e355417694f0f2eaac36076f20fb643 | /R/config_hello.R | 9ab4206d767ccc3666bcb04c2f18896ac4d6b305 | [
"MIT"
] | permissive | gcinar/covr-config-issue | 1585688505345bf030b71d4f6724b28967976012 | f59190e455b937bd2ee661bda4a9ad54a66a1c0c | refs/heads/master | 2020-08-26T20:58:09.618707 | 2019-11-06T02:21:24 | 2019-11-06T02:21:24 | 217,146,714 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 218 | r | config_hello.R | library(config)
config_hello <- function() {
msg <- config::get("some_config", file = system.file("config.yml", package = "covrconfig"))
return(msg)
}
hello <- function() {
msg <- "hi"
return(msg)
}
|
51116ce8ae5a7c3fe69e376c79c2703782a5a6b9 | ca89150de15796fe9d6046dc10170b4c6f905e48 | /Project 2 - Outlier Detection Benchmark Datasets/generate_silhouette_scores.R | 381a8d9670a674985fbf577570103b9ff1ff231b | [] | no_license | shouyang/NSERC2019 | b716d2c580c6c433cafa85434e873715751fb426 | 67649e46ade9800b6475c2077e1a2ec8e49b4c63 | refs/heads/master | 2020-07-15T13:19:07.757327 | 2019-08-31T21:36:02 | 2019-08-31T21:36:02 | 205,570,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 548 | r | generate_silhouette_scores.R | library(foreign)
library(glue)
library(NbClust)
#
DATASET_NAME <- "WBC_withoutdupl_v10"
FILE_PATH <- "datasets/literature/WBC/WBC_withoutdupl_norm_v10.arff"
df <- read.arff(FILE_PATH)
df.inliers <- df[df$outlier == "no", ]
df.outliers <- df[df$outlier == "yes", ]
attribute_names <- names(df)[!names(df) %in% c... |
65751c91ec7e89dbd757a273f236c83521b6ad60 | 55926613ee7503229c9f4a5560a205e30b2956cf | /secondpart.R | d822f2fd11f31f4696db77da6bcc8c1f2ad946eb | [] | no_license | biofelip/Nitrates-and-Valvometry | 67fd8f819052fbc7805760b5678bd0641e885488 | c050e206bf734b2b808c521bf23d6f62fb5e30ca | refs/heads/master | 2023-03-11T17:59:50.792302 | 2021-02-22T13:05:44 | 2021-02-22T13:05:44 | 275,123,057 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 5,960 | r | secondpart.R | ##este script busca extraer los datos de valvometria y analsisis de videos
##para todos los videos de Control, 250 y 500 mg para los 5 primeros minutos
##posteriores a la adicion de los nitratos
setwd("C:/Users/felip/Documents/PhD/Douda/experiments joanna/Second approach/analisis videos completo")
library(tidyverse)... |
826a3bb2ac71a02be5567856fe78f9ebb8bb2102 | c8c8e105b1d397477aef2b1f8408dbe9ea74ebc7 | /R/internal_functions.r | a809a60149e3b19ef79d6acef67b8d1005e4883d | [] | no_license | Arothron/deformetrics | d251b80231afc185d0ad0a6f60ee7037fb522fd8 | 1b21cffe153d5139f0db41e7198e0e2a527d8edf | refs/heads/master | 2021-01-21T23:13:18.349232 | 2017-04-11T17:14:01 | 2017-04-11T17:14:01 | 95,209,056 | 1 | 0 | null | 2017-06-23T10:11:30 | 2017-06-23T10:11:30 | null | UTF-8 | R | false | false | 108,984 | r | internal_functions.r | #' Internal_functions
#'
#' Here are reported a collection of internal functions
#' @author Paolo Piras
#' @export
plot2dhull<-function(matrix,group,scalevariable=1,asp=1,extl=F,legend=T,xl=NULL,yl=NULL,posl=c("topright"),labels=NULL,clabel=0,pch=19,lwd=0.5,colhull=NULL,col=as.numeric(group),xlab=NULL,ylab... |
10057b62679fb07f590922fb9421225ed561d236 | 0f71eeb60ff8502daccd2e6b145363c7d540e36b | /R-code/antonio/svm-no-caret.R | d1181a385732749eaaa7cbe357d474ec2f36d968 | [] | no_license | analytics-ufcg/recogme | 47e16f54b9a2792f33722ceb50a546bb6749a68d | e3e7dd1de1b94cb8fd8d03214d85e9d108220de5 | refs/heads/master | 2021-01-12T21:10:32.836690 | 2015-12-16T18:18:16 | 2015-12-16T18:18:16 | 43,513,165 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 733 | r | svm-no-caret.R | library( 'e1071' )
dados.train <- read.delim("~/Documents/recogme/recogme-R-antonio/dados-train.psv")
dados.train <- dados.train[complete.cases(dados.train),]
dados.train <- dados.train %>% filter(V1.x != "arthur.senaufcg@gmail.com", V1.x != "orion.lima@ccc.ufcg.edu.br") %>% droplevels()
levels(dados.train$V1.x)
str... |
ebc783a2ee2bb679929c2a4a487c2d4f39275cd8 | 4b68fab7e23c59ffafe18ec2018c5362033aa7e2 | /man/p1.given.p0.Rd | dbbe108626cae8fd196b6265b9bc3554abf00b13 | [] | no_license | cran/MChtest | def7ef61bd0b6bf7833d57a9622b9be46ba822db | 4b1c22dee02a6a6cb47e62ba67d5c6e548c2b034 | refs/heads/master | 2020-05-18T00:28:50.363675 | 2019-05-16T12:10:03 | 2019-05-16T12:10:03 | 17,680,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,410 | rd | p1.given.p0.Rd | \name{p1.given.p0}
\alias{p1.given.p0}
\alias{p0.given.p1}
\title{Find p0 (or p1) associated with p1 (or p0) that gives minimax tsprt}
\description{ Consider the SPRT for testing Ho:p=p0 vs H1:p=p1 where p1\eqn{<} Alpha \eqn{<} p0.
For Monte Carlo tests, we want to reject and conclude that p\eqn{<}Alpha. In term... |
d2c090d09b4acf8f37eb9590a36885a8742f3b23 | 5bb2c8ca2457acd0c22775175a2722c3857a8a16 | /tests/testthat/test-zelig.R | 1219c5b3244a8546d7af5890cbfc19e8cd1969e9 | [] | no_license | IQSS/Zelig | d65dc2a72329e472df3ca255c503b2e1df737d79 | 4774793b54b61b30cc6cfc94a7548879a78700b2 | refs/heads/master | 2023-02-07T10:39:43.638288 | 2023-01-25T20:41:12 | 2023-01-25T20:41:12 | 14,958,190 | 115 | 52 | null | 2023-01-25T20:41:13 | 2013-12-05T15:57:10 | R | UTF-8 | R | false | false | 11,702 | r | test-zelig.R | #### Integration tests for the Zelig estimate, set, sim, plot workflow ####
# FAIL TEST sim workflow -------------------------------------------------------
test_that('FAIL TEST sim method warning if insufficient inputs', {
z5 <- zls$new()
expect_output(z5$zelig(Fertility ~ Education, model="ls", data = swis... |
1d0ea848a7196da5b2ec953baf8190dfbac720ae | 609ea58450b0f00105e7fcb81e9c336ababbe041 | /Belgium_done.R | 6e8156235b43c3294a3fd91329cf4992c2a843da | [] | no_license | pabbarros/TheImpactOfDemocratisationEpisodesOnEconomicGrowth | 865ed59554b30f240e725960f63e82739386d223 | 083b5cd8fd67b00ee8828d915c73ef7238a3d516 | refs/heads/main | 2023-08-26T14:10:45.926162 | 2021-09-22T10:18:16 | 2021-09-22T10:18:16 | 408,583,660 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 36,842 | r | Belgium_done.R | rm(list = ls())
#### DATA - ALL DATABASES ######
library(readr)
library(readxl)
library(tidyverse)
library(dplyr)
library(SciViews)
library(reshape2)
library(Synth)
library(data.table)
library(xtable)
ERT <- read.csv("ERT.csv")
dem <- read_excel("dem.xlsx")
ep_study <- read_excel("ep_study.xlsx")
colnam... |
14cd11e9a7c8dd54eef1bc5047a8a61bf36b571a | a5d5f059a2cf7e8a1512f77ab518b467eba21fa6 | /sup/OpenMORDM Supplemental Material Rev2/OpenMORDM/man/nsample.Rd | c13f097990e420f4090028047ef64435f8ad2c79 | [] | no_license | pedroliman/ie-mordm | 450c6c77e4a40f47fb42d5490bbdd8bcb04877d2 | 2c66696a3d6b4353ad7d58e7f4a4b4e364c64250 | refs/heads/master | 2020-04-13T12:39:36.198755 | 2018-12-27T18:52:04 | 2018-12-27T18:52:04 | 163,208,793 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 547 | rd | nsample.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{nsample}
\alias{nsample}
\title{Generate normally distributed random inputs.}
\usage{
nsample(mean, sd, nsamples, problem)
}
\arguments{
\item{mean}{scalar or vector specifying the mean value for each decision
variable}
\item{sd}{scalar or vector sp... |
9833c216399c6080b2d10e8d372607c98a55aba3 | d3ed21ee5797fbf7e00629e4a9490add83556484 | /man/lambda-package.Rd | fc8278fc4b7b4ac6d5185b9a0b8d3f0b12e66b0e | [] | no_license | isglobal-brge/lambda | b01f19feb69f750c63aecd35a2fea3ff231a42f6 | 37c2a3796ae771a9e0e7b6ed9c353599133eb292 | refs/heads/master | 2021-09-17T18:28:13.340573 | 2018-07-04T09:56:29 | 2018-07-04T09:56:29 | 86,354,557 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 712 | rd | lambda-package.Rd | \name{lambda-package}
\alias{lambda-package}
\alias{lambda}
\docType{package}
\title{
Ageement measure lambda
~~ package title ~~
}
\description{
Computes the agreement measure between two experiments across numerous conditions
}
\details{
\tabular{ll}{
Package: \tab lambda\cr
Type: \tab Package\cr
Version: \tab 1.0\c... |
68a17ef8a7a64b427d069fffb701e755aab1148c | 8f0e229a51c03cb0c1c8a43f6f3ef8c863872376 | /cachematrix.R | b9a442636dc6ee15dcd669e7269bf9f098125e10 | [] | no_license | patriciacrain/ProgrammingAssignment2 | 42d8dbba7edf6d77365ea004b89e1fdc67d7fa52 | 2798ec35776092bca68b0ebdc6ab1a8c8e52fa14 | refs/heads/master | 2021-01-17T04:52:25.233730 | 2014-04-25T17:27:03 | 2014-04-25T17:27:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,293 | r | cachematrix.R | ## These functions create a special kind of matrix which can be
## used to cache the inverse of a matrix rather than calculating it repeatedly.
## The makeCacheMatrix function creates a list of functions which cache the inverse of the matrix.
makeCacheMatrix <- function(x = matrix()) {
## Creates the inverse, init... |
45d7a617fd2f4d351f39a0c189ed77537466a98d | bb453317a3e4aae6f20ddfdcf3fd4b691bc12198 | /man/plot_FV_post_norm_kmom.Rd | 5f6640e1327dd73787e8c6be29e81762f3ce869c | [] | no_license | fabriziomaturo/AnnuityRIR | 252a81f85a1011dbb63e06b639c0d04798772082 | 6c15c17a2b9aabeb9a233cc7325a45b7d26b4f75 | refs/heads/master | 2021-08-14T19:05:01.087113 | 2017-11-16T14:49:05 | 2017-11-16T14:49:05 | 110,983,531 | 1 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,141 | rd | plot_FV_post_norm_kmom.Rd | \name{plot_FV_post_norm_kmom}
\encoding{WINDOWS-1252}
\alias{plot_FV_post_norm_kmom}
\title{Plot the final expected
value of an \eqn{n}-payment annuity, with payments of 1 unit each made
at the end of every year (annuity-immediate), valued at the rate
\eqn{X},
using the estimated moments of the normal distributi... |
480de8efea5370498f859ff65446e6ade080b863 | c66c6c157b77a75465d66f7fcf4451e298f18089 | /tests/testthat/test_format.R | 6f79d55828b01414a0a91230f75f1f00d64181ad | [
"MIT"
] | permissive | ebenmichael/augsynth | 0bad8bddc88b4004eab717006906cbaf797b849d | 0db0db2fbbda899e1a8b729b85af76fb4cb148ae | refs/heads/master | 2023-08-17T03:32:47.387205 | 2023-08-02T16:00:12 | 2023-08-02T16:00:12 | 155,753,448 | 109 | 53 | MIT | 2023-08-25T01:27:06 | 2018-11-01T17:45:11 | R | UTF-8 | R | false | false | 3,110 | r | test_format.R | context("Test data formatting")
library(Synth)
data(basque)
basque <- basque %>% mutate(trt = case_when(year < 1975 ~ 0,
regionno != 17 ~0,
regionno == 17 ~ 1)) %>%
filter(regionno != 1)
test_that("... |
3679195f2f769ce7ae006b9d39a98369f5a190d5 | f46b9e716ea5f2a47a4759e7f0116bc9c88129de | /R/methylation_scores.R | 8d5e4fd51fad70f04e24b5d879d52a2f18abde6a | [
"Unlicense"
] | permissive | hhhh5/ewastools | e18c59ee1a9678f1a87ee3101f555e0aab601e1b | f7646cacd73266708479b3fea5d625054d179f95 | refs/heads/master | 2023-09-01T13:03:01.281328 | 2023-02-14T06:00:19 | 2023-02-14T06:00:19 | 116,701,843 | 29 | 11 | Unlicense | 2023-02-09T07:08:01 | 2018-01-08T16:39:21 | R | UTF-8 | R | false | false | 2,455 | r | methylation_scores.R | #' Various methylation scores
#'
#'
#' @rdname MethylationScores
#' @param beta matrix of beta values
#' @param model Name of the model, i.e., one of `maternal_smoking`, `gestational_age`, or `horvath_clock`
#'
methylation_score = function(beta,model){
if(model=="cord_blood:maternal_smoking"){
### model based on... |
2644744048cce434ef9d8c2bc42b856c32b5a8e0 | 2baed5a8a605c42d1e8ba05a341ac1ef63bcfeb2 | /scripts/01-datasets/01-real/helpers-download_from_sources/dataset_planaria-plass.R | 6f0870d1a6dc578c80579bfd60975e1b081dc0bd | [
"MIT"
] | permissive | dynverse/dynbenchmark | 0c58ef1a9909b589b1cd8b0a0e69c2fa222aeb2b | 8dca4eb841b8ce6edfb62d37ff6cfd4c974af339 | refs/heads/master | 2022-12-08T04:01:13.746186 | 2022-11-25T08:25:50 | 2022-11-25T08:25:50 | 102,579,801 | 171 | 40 | null | 2019-04-07T20:59:59 | 2017-09-06T07:53:11 | R | UTF-8 | R | false | false | 1,858 | r | dataset_planaria-plass.R | library(dynbenchmark)
library(tidyverse)
dataset_preprocessing("real/silver/whole-schmidtea-mediterranea_plass")
# get settings
source(paste0(dynbenchmark::get_dynbenchmark_folder(), "/scripts/01-datasets/01-real/helpers-download_from_sources/helper_planaria-plass.R"))
# counts
counts_file <- download_dataset_source... |
b264d171b50947a170d594367e341e08053963cb | 4e838c6d530ce49fb79f37eba659ec15f0efce4b | /butterfat/butterfat.R | 552bcf9ddc01f809f8a5d4c6055c68d91a31d2e9 | [] | no_license | amromeo/learnR | 5d2fe303f2857f62388b1245c91a0185a6c302af | 8af7c861c71a1c8baa13e4cead4585bbadbcb70d | refs/heads/master | 2020-12-30T09:58:52.396605 | 2017-08-15T00:50:59 | 2017-08-15T00:50:59 | 99,250,020 | 0 | 1 | null | 2017-08-03T15:51:52 | 2017-08-03T15:51:51 | null | UTF-8 | R | false | false | 392 | r | butterfat.R | bwplot(Butterfat ~ Age, data=butterfat)
lm1 = lm(Butterfat ~ Age, data=butterfat)
coef(lm1)
bwplot(Butterfat ~ Breed, data=butterfat)
lm2 = lm(Butterfat ~ Breed, data=butterfat)
coef(lm2)
lm3 = lm(Butterfat ~ Breed + Age, data=butterfat)
coef(lm3)
lm4 = lm(Butterfat ~ Breed + Age + Breed:Age, data=butterfat)
coef(... |
cd4bb9b4e866785479687273dda2cd53c70b78c8 | ab9315145932e1d0edcb1b52df1cc3ed078a317c | /tests/testthat.R | e60123a6ea4ee29849654028dd0c4b67f4412248 | [] | no_license | johnchower/flashreport | cfcbefc1afe5d77da6857ae21323d48ed3a6db96 | c23dca38331dc9632c251983a2db59cf95dae664 | refs/heads/master | 2020-07-28T12:31:42.252620 | 2017-05-03T16:39:10 | 2017-05-03T16:39:10 | 73,411,648 | 0 | 0 | null | 2017-05-03T20:53:11 | 2016-11-10T18:54:18 | R | UTF-8 | R | false | false | 66 | r | testthat.R | library(testthat)
library(flashreport)
test_check("flashreport")
|
414230cbc1c82d5aab086dc176b2d614fa0f75e9 | 9e6884df4d8f0e26355026761393e3bb47c4b7e8 | /mcri/scThyroidcluster.R | c13f832094bcae77540cc6fbc45a7dac3aa04c07 | [] | no_license | Shicheng-Guo/GscRbasement | f965ecc6e191175f371aa1edeae81e5c545f1759 | 8d599bed0dd8b61b455f97965c0f4bed5aae2e96 | refs/heads/master | 2022-05-01T09:20:23.072449 | 2022-03-13T18:46:04 | 2022-03-13T18:46:04 | 62,922,944 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,651 | r | scThyroidcluster.R | setwd("~/hpc/methylation/Pancancer/RNA-seq")
source("https://raw.githubusercontent.com/Shicheng-Guo/GscRbasement/master/GscTools.R")
library("metafor")
library("meta")
library("metacor")
load("~/hpc/methylation/Pancancer/RNA-seq/rnaseqdata.pancancer.RData")
Symbol2ENSG<-function(Symbol){
db<-read.table("... |
ef911c4eee4eb8fb49d27dcdd48efb0d7e0393ae | 37805035a6f36ac225ebf20026209b50184f8b4d | /processSurveys.R | 45038e5d4714a5f0083f623d10f12e020be17b57 | [
"MIT"
] | permissive | dwulff/dwulff.github.io | fbabcac6173898ae5a9d2d1e077094e325043953 | 089804b3df53ca3499e92cb92d8e60ca6f6419f9 | refs/heads/master | 2021-01-18T12:12:32.532553 | 2018-11-13T08:27:23 | 2018-11-13T08:27:23 | 68,724,311 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 645 | r | processSurveys.R | options(stringsAsFactors = F)
require(googlesheets)
setwd('~/Dropbox (2.0)/Work/Software/dwulff.github.io/')
goodchoices = read.table('_Goodchoices/GoodchoicesSurveys.txt',header=F,sep='\n')[,1]
networks = read.table('_Networks/NetworksSurveys.txt',header=F,sep='\n')[,1]
#tab = paste0(networks[10],' (Antworten)')... |
3d88be484f645cbf46a8d4091d76f047fe910f0a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FSTpackage/examples/FST.prelim.Rd.R | 7bc4843e37fe876cdda64845c769b073a78f7631 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | r | FST.prelim.Rd.R | library(FSTpackage)
### Name: FST.prelim
### Title: The preliminary data management for FST (functional score tests)
### Aliases: FST.prelim
### Keywords: preliminary work
### ** Examples
library(FSTpackage)
# Load data example
# Y: outcomes, n by 1 matrix where n is the total number of observations
# X: covariate... |
99153ae39ecfa797d633abf20e3083875f513418 | e4119d40fe082fce021731457061ec399f158d8e | /ui.R | 987f548d9e96a618ddb336d5d3f42b8d15514759 | [] | no_license | musarder/shiny-myapp | 4bd422fb956c785049eb773beebc1a9c4d5a519d | f62fb052a78fe014e7ab724a77b0e153f5f38cde | refs/heads/master | 2021-01-10T06:51:56.103455 | 2016-02-28T15:07:00 | 2016-02-28T15:07:00 | 52,726,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 478 | r | ui.R | library(shiny)
# Define UI for dataset viewer application
shinyUI(pageWithSidebar(
# Application title
headerPanel("Air Pollution in China"),
# Sidebar with controls to select a city
sidebarPanel(
selectInput("city", "Choose a city:",
choices = c("Beijing", "Shanghai", "Ti... |
aa65e0789d5f6f74b45cf12d79ff68beec18ecb4 | 9f2a64b71a9888f6a1e7d7bdd3c941131f6a5005 | /man/plotCRM.Rd | d8318e33ee2f4d1af2e5754d18b412ba57ab32a6 | [] | no_license | cran/EstCRM | b2ae3dd49a1a30540a8bfb23d7fc6bfa0881dc7d | c46090d13106c3840fdd076ae66274caa04bd8da | refs/heads/master | 2022-09-25T16:16:35.107516 | 2022-09-16T20:46:10 | 2022-09-16T20:46:10 | 17,679,047 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,208 | rd | plotCRM.Rd | \name{plotCRM}
\alias{plotCRM}
\title{
Draw Three-Dimensional Item Category Response Curves
}
\description{
This function draws the 3D item category response curves given the item parameter matrix and the maximum and minimum possible values that examinees can get as a score for the item. This 3D representation is... |
320977b3a9c445ac01a0024ae16810f4689559fd | 1a8a95fc74b655288b46c9ee16b77ff9e6e48bfa | /Capstone Data Wrangling.R | 5f4f51d2acb9acf17fc1ea07c99d102028d03ec4 | [] | no_license | colemanGH319/Springboard-Capstone-Project-Foundations- | 535ce3e869dc881b910428a732beb4e27f419d54 | a9b98e843ccf51ca010389659c4f0b2fc8d0efcb | refs/heads/master | 2021-01-01T16:09:44.953205 | 2017-09-14T17:28:51 | 2017-09-14T17:28:51 | 97,783,189 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,621 | r | Capstone Data Wrangling.R | #1.Loading data and packages
library(dplyr)
library(tidyr)
library(ggplot2)
library(caTools)
library(caret)
loans14 <- read.csv("LoanStats3c.csv")
loans15 <- read.csv("LoanStats3d.csv")
#Lending Club offers a table with definitions for each column header from
#the loan dataset. This will be useful for re... |
48119af67a9e2a124030ca4cd4742f165524d704 | acd892099150ca5482399db8e88a0f5dbb731d75 | /R/print.ggedit.R | 4fff20b37090bef3ff34135f38ef53077f030e3b | [] | no_license | DataXujing/ggedit | a30ecb4f47b2d4f9131b5cd0983b7d6504d60445 | 4a329cfe4ff3e099b2b389d84042bda64d61671b | refs/heads/master | 2021-07-14T01:44:51.790528 | 2017-10-17T20:23:52 | 2017-10-17T20:23:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,797 | r | print.ggedit.R | #' @title Print ggedit objects
#' @description Plots lists of ggplot2 plot objects
#' layout functionality.
#' @param x list of ggplot2 plot objects
#' @param ... list which defines the location of each plot within
#' the viewport layout. (see details)
#' @details If ... is NULL then a default layout is used filling i... |
ec588d4192fd88c94caf7af9d3a025ee106e2fbc | 81b587b4ba6c0e5cc7da4b73bb7d5104ff5112e0 | /R/explore_metadata.R | a563abc3cd2430061861ac79b7735ebedb6ed7ef | [
"MIT"
] | permissive | gzahn/syringodium_bacteria | 09bf99d1987e22407f79bf442a54adfffd3776c0 | 4bc89d345e5c4df2c37088ff5d216af4de29d822 | refs/heads/main | 2023-05-02T09:26:57.728802 | 2023-04-25T14:52:39 | 2023-04-25T14:52:39 | 584,835,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 674 | r | explore_metadata.R | # load packages ####
library(tidyverse)
library(readxl)
source("./R/theme.R")
# read and clean metadata ####
meta <- read_xlsx("./data/SI_Indo_Metadata_CorrectGPS.xlsx") %>%
janitor::clean_names() %>%
rename("east_west" = "east_or_west_of_wallace_line") %>%
separate(gps,into = c("lat","lon"),sep=" ") %>%
... |
47d9f4d8b93991a52c6296df96b6ac450c1ed179 | 19c54dba01519328e58e21e348eae3638930d803 | /man/phyloFromCtree.Rd | dc22020f2f70f44216ddb3ef0da6a29e60d4e621 | [] | no_license | jtmccr1/TransPhylo | 724961ddcc1af0ccd0843ef99d92e722cac63681 | 181f975316286645e5f6a5179e1e925f1249f246 | refs/heads/master | 2020-05-20T12:34:55.313194 | 2019-05-30T10:14:33 | 2019-05-30T10:14:33 | 185,575,975 | 0 | 1 | null | 2019-05-08T09:35:26 | 2019-05-08T09:35:26 | null | UTF-8 | R | false | true | 502 | rd | phyloFromCtree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phyloFromCtree.R
\name{phyloFromCtree}
\alias{phyloFromCtree}
\title{Converts a phylogenetic tree into an ape phylo object with node.comments identifying the host of each node}
\usage{
phyloFromCtree(ctree)
}
\arguments{
\item{ctree}{combined... |
63087a769545ec74d20c909d990a41b1653da46f | c750c1991c8d0ed18b174dc72f3014fd35e5bd8c | /pkgs/bayesm/man/rbprobitGibbs.Rd | 10d7a02342b6f1b290a1c948010c49c58068f80e | [] | no_license | vaguiar/EDAV_Project_2017 | 4b190e66fe7a6b4078cfe1b875bccd9b5a594b25 | 288ffaeec1cfdd873fe7439c0fa0c46a90a16a4f | refs/heads/base | 2021-01-23T02:39:36.272851 | 2017-05-01T23:21:03 | 2017-05-01T23:21:03 | 86,010,131 | 1 | 0 | null | 2017-05-01T23:43:04 | 2017-03-24T00:21:20 | HTML | UTF-8 | R | false | false | 2,136 | rd | rbprobitGibbs.Rd | \name{rbprobitGibbs}
\alias{rbprobitGibbs}
\concept{bayes}
\concept{MCMC}
\concept{probit}
\concept{Gibbs Sampling}
\title{ Gibbs Sampler (Albert and Chib) for Binary Probit }
\description{
\code{rbprobitGibbs} implements the Albert and Chib Gibbs Sampler for the binary probit model.
}
\usage{
rbprobit... |
2196dcc8220a0415ed6c5f9b01d64a2cba8599d9 | 2c170474a479f0582d1685a8df22ca98dd157798 | /R/helper.R | 02e8cf4e84dcc1fa887f8d1a76523bfc0286a352 | [] | no_license | wcmbishop/gogoplot | 80574a1161a44222265f9478d891ac6d4a696033 | 1857b750305c15a9bb90dfdb12b96230c14a0ff8 | refs/heads/master | 2021-03-27T17:13:16.813628 | 2018-03-30T18:08:07 | 2018-03-30T18:08:07 | 106,642,044 | 3 | 2 | null | 2018-03-30T18:08:08 | 2017-10-12T03:53:19 | R | UTF-8 | R | false | false | 253 | r | helper.R |
select_choices <- function(.data) {
# name choices with: "name (type)"
var_types <- vapply(.data, tibble::type_sum, character(1))
x <- c(CONST_NONE, names(var_types))
names(x) <- c("none", sprintf("%s (%s)", names(var_types), var_types))
x
}
|
358d99ccf8a1fe5b53dae5c7e41e50e999c970d2 | 3d3f11d6002a505483003a59e0a94264ddd86757 | /R/lqa.update2.R | f13fba36e16d1299dba33a49815a23a053a4ae18 | [] | no_license | cran/lqa | 75a867677d54e2e1b84dbe90822628adf42e6238 | fb0b5a6a7e8ce9028ac5f48adee8ffa8b3434d0b | refs/heads/master | 2020-06-03T14:21:37.296678 | 2010-07-12T00:00:00 | 2010-07-12T00:00:00 | 17,697,200 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,123 | r | lqa.update2.R | lqa.update2 <-
function (x, y, family = NULL, penalty = NULL, intercept = TRUE, weights = rep (1, nobs), control = lqa.control (), initial.beta, mustart, eta.new, gamma1 = 1, ...)
{
gamma <- gamma1
if (is.null (family))
stop ("lqa.update: family not specified")
if (is.null (penalty))
stop ("lqa.... |
a18ea8debf937a586d72540741ec3ecd9a7b74fd | b6f0c3c44642b22a7338f8f45baf704ca2bcaf81 | /run_analysis.R | 1902fb21f1033b4aa8c9f257eaa2b2b3892d5e8f | [] | no_license | LiuyinC/GettingClearingDataAssignment | 0bd5027e1f92fefab90720e6d20ea01fa64f82a5 | 95aa8d82468ab408875cb439e03613dda24071d8 | refs/heads/master | 2021-01-10T22:10:13.753229 | 2014-05-23T22:05:05 | 2014-05-23T22:05:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,723 | r | run_analysis.R | ## Read Training data with Y label as the first column, subject lable as second column
Training <- cbind(read.table("./UCI HAR Dataset/train/y_train.txt"),
read.table("./UCI HAR Dataset/train/subject_train.txt"),
read.table("./UCI HAR Dataset/train/X_train.txt"))
## Read Testing data... |
90da350475640803d1633e70d595802e0a8c1662 | 550b5147909aa269b50af580c55f9e59f9c91d5e | /man/sfWideCsvToCorpus.Rd | 4e41d992a15916011fe392e96de005f0fafcd911 | [] | no_license | clbustos/SemanticFields | b96ca71308428e1afc740dc527e4acdbfca8356a | 9ebfaa0044db74a2f3e349be0e30f8d832179c73 | refs/heads/master | 2021-01-15T11:50:18.353144 | 2015-10-16T18:43:39 | 2015-10-16T18:43:39 | 34,122,591 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 657 | rd | sfWideCsvToCorpus.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/sf_wide_csv_to_corpus.r
\name{sfWideCsvToCorpus}
\alias{sfWideCsvToCorpus}
\title{Transform wide representation (each line a set of responses)
on a csv file to corpus object representation}
\usage{
sfWideCsvToCorpus(filename, center_o... |
0c0d9e8f16e69a49c22e7eb58ed7e103bf6741aa | 73e26f49080d5b49902636b7099e89b38dc48ed6 | /scripts/2_LOAD_DATA/create_empty_data_frame.R | 7d81966fefd8ff6588121d64a3188bc215c7767e | [] | no_license | bteinstein/SLA | 14f67ff687eb30cc9fcc73552b25f93ce1f8d55e | e416d6bc6f8d95f38779dfe264768bc9c1d3c9ae | refs/heads/master | 2023-07-22T17:04:26.216051 | 2021-09-07T19:31:27 | 2021-09-07T19:31:27 | 403,389,954 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 942 | r | create_empty_data_frame.R |
create_TBL_Attempt_TABLE <- function(nr){
suppressWarnings(
data.table::data.table(
# data_frame(
AWB = character(),
# Expected
First_Attempt_Time = as.POSIXct(character()),
# First_Attempt_Site = character(nr),
First_Attempt_Site = character(length = nr),
# Optional
Last_Attempt_Time = as.POSIX... |
311cd68c46460f06d108d3bec0e50f575933ecec | 451013306c7697810789dd0610f03fa1e81e9bbc | /plot4.R | b95c8dc75fac34fc93cd33c8cd0a8e50ebee359a | [] | no_license | justgo129/ExData_Plotting1 | 17369111f94502f12d54959c846ea6b051df6101 | 885de76b4fe53b3c780a6a8ea73689313d3b30d3 | refs/heads/master | 2020-12-02T16:36:17.469837 | 2017-07-13T02:34:02 | 2017-07-13T02:34:02 | 96,559,084 | 0 | 0 | null | 2017-07-07T17:02:49 | 2017-07-07T17:02:48 | null | UTF-8 | R | false | false | 2,336 | r | plot4.R | #Plot 4.R
#Read in Data to R - needed for all plots
par(mar = c(5.1, 4.1, 4.1, 2.1))
powerc<-read.csv ("household_power_consumption.txt", header=TRUE, sep=";")
powerc[powerc == "?"]<-NA # replace all "?" fields with "NA"
library(lubridate); library(dplyr); library(tidyr); library(graphics)
powerc<-filter(... |
c32944bc762a2f96a6de96c5fa3da1f03f3c499b | 5d555b24611a2967fd7942cb69dbea15b713c9a2 | /scripts/figures/venn.r | 44e8d2041c71a876cd890081febb1c19422518bc | [] | no_license | utsw-medical-center-banaszynski-lab/Martire-et-al-2019-Nature-Genetics | a6429413ed87ab6365e64c8f977acd4f0b2477a7 | 8b8fb2de7e1310de7ab3257a44bc9a7802a22cde | refs/heads/master | 2020-05-14T21:40:29.161485 | 2019-07-08T22:58:47 | 2019-07-08T22:58:47 | 181,966,915 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,120 | r | venn.r | #########Using VennDiagram package in R ##########
#Install the package
install.packages("gdata")
install.packages("VennDiagram")
#Load the package
library(gdata)
require("VennDiagram")
library(gplots)
geneList <- read.xls("GeneList.xlsx", sheet=1, stringsAsFactors=FALSE, header=FALSE)
head(geneList)
# Notice there ... |
8cafa761fb59be3878434070e5b25e1ad2a56e2e | 81354954be61599eae884b0fa9f6ce4bccdd06b0 | /man/ctd_BlueLake2018_VisualCortexOnly.Rd | b287e7c4f4780e0f31ae513973af4a7bdfe1fb22 | [] | no_license | obada-alzoubi/MAGMA_Celltyping | 272631794f31733c24c6384fd2f181a0972eca9c | f931877dedcde103723f14242d5242fdca7b3af6 | refs/heads/master | 2023-07-10T15:53:22.307531 | 2021-08-20T09:19:22 | 2021-08-20T09:19:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 546 | rd | ctd_BlueLake2018_VisualCortexOnly.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.r
\docType{data}
\name{ctd_BlueLake2018_VisualCortexOnly}
\alias{ctd_BlueLake2018_VisualCortexOnly}
\title{Celltype data (Blue Lake 2018 Visual Cortex)}
\format{
An object of class \code{list} of length 2.
}
\source{
# Generated on Natha... |
ec5557f0657f5d6c63aa07fe89896e337c7e92e0 | ed1920915c1f7070c7cec39de8ca82672be18cc5 | /source/mhealth/downtime.R | 27fcc17b6ae25e2e37b1ce8000ba6f6d5c319c70 | [] | no_license | sthallor/miscRscripts | d28f7a9cdbc53fc7c7994c6000d1753b3679236d | c3a5a206c35cdbbb15f07a4ea9250ff861b2e7f1 | refs/heads/master | 2022-11-06T03:39:03.951539 | 2020-06-21T23:21:47 | 2020-06-21T23:21:47 | 273,998,540 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,056 | r | downtime.R | #######################################################################################
# downtime.R - looking at EDR rig downtime data
# Ensign Energy Services Inc. retains all rights to this software
# FHS, May 6, 2016
#######################################################################################
dirName <-... |
2c8fa94b08c6c9d38e6a97726d2ca408b9e003bd | de8b069ae217e9d0c14c3b9b2c9bb8afb43baad4 | /inst/unitTests/test_compartments.R | 07a783bf66c5864866cc8e23e98d16ba84d961d0 | [] | no_license | aleiyishi/minfi | 833723ffbb2af19318d23fc44c22a15eff7a50ba | 52832f5f5f71f38a371effab01eba6957f577264 | refs/heads/master | 2020-12-25T22:19:56.790474 | 2016-07-27T00:42:27 | 2016-07-27T00:42:27 | 64,294,127 | 1 | 0 | null | 2016-07-27T09:09:49 | 2016-07-27T09:09:49 | null | UTF-8 | R | false | false | 528 | r | test_compartments.R | test_compartments <- function() {
stopifnot(require(minfiData))
stopifnot(require(digest))
load(file.path(path.package("minfi"), "unitTests", "digest_compartments.rda"))
GMsetEx <- mapToGenome(MsetEx)
gr.cor <- createCorMatrix(GMsetEx, res=500*1000)
checkEquals(digest_compartments$cor.matrix,
... |
726533fe37e1eb085ed29a79f444a546226b3c03 | 8a2ef8564dac1551f9a389c3cc04837453c933d6 | /5_ScriptConsensusClustering/7_ObtencionGrupos_NMF_Luad_Lusc.R | 41b32155525da9449162392b644616e16a198551 | [] | no_license | anemartinezlarrinaga2898/TFM_ANE_MARTINEZ | 0a311e96ccad34f331d798b1f3c459539614e1c7 | 8e41157613fff0cfa4f5eb27e6984d02d000b9ef | refs/heads/main | 2023-07-27T21:41:44.218050 | 2021-08-27T10:15:35 | 2021-08-27T10:15:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,912 | r | 7_ObtencionGrupos_NMF_Luad_Lusc.R | # TFM, Master en Metodos Computacionales UNAV
#Autor: Ane Martinez Larrinaga
#Tutores: Silvestre Vicent Cambra y Mikel Hernaez
#Fecha: 17-04-2021
#SCRIPT para obtener los grupos de NMF:
###################################################################################################################################... |
4f44a754ea7d26e6505b2985b7db59824669ec19 | 19f7fc6594fcbce0c4e1c76ae455887976eb6859 | /R/FLLogRegr.R | eab8890e329d41fea00a7f4c5dcbcd33338e0aa0 | [] | no_license | AnanyaT/AdapteR | 696d16ebed0df716614c1d90c7f6b1092953cb69 | 3842d3e662f8e4fdf5709df41deada875aff9a2e | refs/heads/master | 2021-01-18T17:10:47.836244 | 2016-06-03T09:57:24 | 2016-06-03T09:57:24 | 59,019,034 | 0 | 0 | null | 2016-05-17T12:08:53 | 2016-05-17T12:08:52 | null | UTF-8 | R | false | false | 11,306 | r | FLLogRegr.R | #' @include utilities.R
#' @include data_prep.R
#' @include FLTable.R
NULL
#' An S4 class to represent FLLinRegr
#'
#' @slot formula an object of class 'formula': Model Formula
#' @slot deeptable A character vector containing
#' the deeptable on conversion from a widetable
#' @slot AnalysisID An output character ID fr... |
409f32ec4d3bdb1925581e9c36b55811fd022df5 | a2295ee6ec97b25e993259b4161673caec545943 | /basic_assignment/assignment_1_task1/combine data.R | 50fb663db95b941bf79710b3e1b0f7d2593a7270 | [] | no_license | ccfelius/DataMining | f9f7dc27e8f9e8cd7511e6720b9b3dab75cc260c | be381fd57454cbe6ba86f31725cc06cf3375605d | refs/heads/main | 2023-05-11T22:47:51.694508 | 2021-05-28T11:56:51 | 2021-05-28T11:56:51 | 352,127,800 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,065 | r | combine data.R | # combine data
# load packages
library(tidyverse)
library(rstudioapi)
library(lubridate)
# set working directory
setwd(dirname(getActiveDocumentContext()$path))
# load the cat data
cat_data <- read_delim("data/data_fin.csv", delim = ";")
program_data <- read_csv("data/cleaned_programs.csv")
# join data
data <- cat_... |
6ca2427d4643ccfad9faf169eadd994866a523dd | c7e42d6a5fef98d887fafb1d4e0b0e4265fa8a5e | /abc/ououcirabc.r | 9b7d6a725d0f3692184dc936ebd0dbcefe729c93 | [] | no_license | ChihPingWang/ououcir | 97a8353f9cae07be4e09c5d7a54068f8003642cc | 5ea14ad175d1bcc1991bbc00fff50747318f59b5 | refs/heads/master | 2020-05-18T17:13:05.538999 | 2019-05-02T08:29:56 | 2019-05-02T08:29:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,189 | r | ououcirabc.r | rm(list=ls())
#install.packages("TreeSim")
library(TreeSim)
library(EasyABC)
library(coda)
#oubmbmintegrand<-function(s, alpha.y=alpha.y){
# alpha.y*exp(alpha.y*s)*rnorm(n=1,mean=0,sd=s)
# }
n<-20
numbsim<-1;lambda<-2.0;mu<-0.5;frac<-0.6;age<-2
tree<-sim.bd.taxa.age(n=n,numbsim=1,lambda=lambda,mu=mu,frac=frac,ag... |
1f6ff464a1236ef87778bd03f73624853033430d | a0d9649db543993b406a1a221483f07542662a2a | /2017-06-21/entries-vs-discovery.R | 32f60dc280abe48018f2281cf287386e996a9672 | [] | no_license | chrokh/rndsim-stats | 01aee26576bac78903b90a7b8d263d36aaa305a4 | b8f2eed3de395da9f977ad2b3829b94e89a6dd03 | refs/heads/master | 2021-01-20T16:24:33.543641 | 2017-08-30T13:28:36 | 2017-08-30T13:28:36 | 90,836,674 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,664 | r | entries-vs-discovery.R | source('shared.R')
library(RColorBrewer)
# ARGUMENTS
# ==================
args <-
parseArgs(list(
make_option(
c('-i', '--input'),
default =NULL,
help ='dataset file path',
metavar... |
3c887cc6d69514176d3cd486c06c0efc1fc1c6b1 | 1930041c603bc542e02b921c02daf5e43ec11f7c | /R/get_privateHoldings.R | 22dda9be23b92b9e47761237687d8117b6874a20 | [] | no_license | bplloyd/Core | 826069468e1da5fdf0e2c631101ce5be38e21b9f | f7731b36c222f1c097a94d872ac1ac3f78f25ca2 | refs/heads/master | 2021-01-21T17:32:13.584931 | 2017-05-09T21:55:21 | 2017-05-09T21:55:21 | 81,851,307 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 324 | r | get_privateHoldings.R | #' @include executeSP.R
#' @include abbToStrategy.R
#' @include make_paramString.R
get_privateHoldings = function(id=NA, strategy=NA, vintage=NA, active = NA)
{
paramString = make_paramString(id, strategy, vintage, active)
procString = "usp_get_PrivateHoldings"
executeSP(procString, paramString, schema = "Core")... |
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