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2855376f043d8d8e1e27ad3bfd48c6b8b4ce64bd | 04a5bb75750cb0da7be0234bb23225e7a6f5f63b | /pheatmap-conting-table.R | 00ab0261eec820fcfa9adb2ac09371843915015b | [] | no_license | BioRRW/Random-R-Scripts-heatmaps-and-clustering | 1b353f76217116cea0d2b8b0f7820be4a9c9cd84 | f6858006e33dd0a73d46e554a3c21be984638495 | refs/heads/main | 2023-01-13T21:30:35.064561 | 2020-11-17T18:09:21 | 2020-11-17T18:09:21 | 313,686,270 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,820 | r | pheatmap-conting-table.R | ### Reed Woyda
# reedwoyda@gmail.com
# 11/17/2020
# Script to make pheatmaps with metadata
library(pheatmap)
library(viridis)
# data must be in the format: rownames = isolate, colnames = genes
# metadata must be in the format: rownames = isolates (*have to be in same order as data rownames), colnames = metadata categ... |
105f566395a6f0d75884ca7f3c2c109ca485952d | 0a4cc2bafe6fb3396ac9c07dc1e382a8a897a2d5 | /misc/SW_WA_2021_2024/bunbury_busselton/analysis/jatwc_to_inundation/make_vrt.R | 0af2f9eb84de03673392f23a1b764dc2c27ab824 | [
"BSD-3-Clause"
] | permissive | GeoscienceAustralia/ptha | 240e360ff9c33cbdfa6033115841035c39e7a85f | 124d0caa76ed143d87fa0dfe51434d9500268a5a | refs/heads/master | 2023-08-31T12:00:57.055692 | 2023-08-31T06:05:18 | 2023-08-31T06:05:18 | 39,749,535 | 26 | 8 | BSD-3-Clause | 2023-08-29T04:13:20 | 2015-07-27T01:44:11 | R | UTF-8 | R | false | false | 248 | r | make_vrt.R | make_vrt<-function(all_files){
temp_file = paste0(tempfile(), '.vrt')
gdal_command = paste0('gdalbuildvrt -resolution highest ', temp_file, ' ', paste(all_files, collapse=" "))
system(gdal_command, intern=TRUE)
return(temp_file)
}
|
ef2ce5c7256092a9c5725cb00a90c37d400705c5 | 56679529fa4da13ada1ae93dd4979ae6dc46f40c | /FIG_2/summary_plot.R | be23368126bd60ccb04d320eb63f0c426f468356 | [] | no_license | marzuf/MANUSCRIPT_FIGURES | da3c1ef2d6b435923e4eae8687ae25dcaa902861 | 80bf0ce87f54c10e3cbd18fd10e9791f34b5174a | refs/heads/master | 2023-05-05T21:09:19.238311 | 2021-05-15T14:45:43 | 2021-05-15T14:45:43 | 259,663,576 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,688 | r | summary_plot.R |
# Rscript summary_plot.R
require(doMC)
require(foreach)
require(ggplot2)
require(ggsci)
require(colorRamps)
require(reshape2)
require(ggpubr)
setDir <- ""
registerDoMC(50)
plotType <- "svg"
source("../../Yuanlong_Cancer_HiC_data_TAD_DA/subtype_cols.R")
source("../settings.R")
settingFolder <- file.path(runFold... |
edf9db1ca4beb7819d3c29ceff2095806b12756f | 1945b47177455e900baae351c1179197e0e4078d | /man/NEONMICROBE_DIR_SOIL.Rd | 75e50c39ef3eb306761552b3dd22b4d81a791a33 | [] | no_license | naithanilab/neonMicrobe | 19711934b281d12adef4fd5ba85b517b3f99e344 | 359c2a3947b9151f370e889785e5bfe4fa207121 | refs/heads/master | 2023-04-12T18:36:26.916925 | 2021-04-30T17:04:49 | 2021-04-30T17:04:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 443 | rd | NEONMICROBE_DIR_SOIL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dirs.R
\name{NEONMICROBE_DIR_SOIL}
\alias{NEONMICROBE_DIR_SOIL}
\title{Dynamic Directory Name for Soil Data}
\usage{
NEONMICROBE_DIR_SOIL()
}
\value{
Directory path (character).
}
\description{
For NEON soil data DP1.10086.001:
"Soil physical... |
e4ffff9c8956d63d4c0b40f2e27324f1821f1aab | c0f4ec09bf6ecb259fb9e26dec331f8804f2237d | /man/plotDrugData.Rd | f65b526e0c4753f7dd6f3babe64a3e8a976d8a11 | [
"MIT"
] | permissive | Lthura/mpnstXenoModeling | bc52ff0455b18870c3a878ca209bc6ac9b208153 | 437ac9e87a3ccadbefd071674e9b246d0f7661ce | refs/heads/master | 2022-11-29T23:42:33.039456 | 2020-08-11T14:23:24 | 2020-08-11T14:23:24 | 285,438,039 | 0 | 0 | MIT | 2020-08-06T00:54:14 | 2020-08-06T00:54:14 | null | UTF-8 | R | false | true | 245 | rd | plotDrugData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotDataSets.R
\name{plotDrugData}
\alias{plotDrugData}
\title{plotHistogram of drug data}
\usage{
plotDrugData(drugData)
}
\description{
plotHistogram of drug data
}
|
4424a7bf65b331c921a71804e650b3f17a71c2d9 | 731be3ddee70944aae00c72748ed837c4c00c82e | /tSNE/tsne.R | 6fbaee20b13f6580e603c599ab77360060d6813f | [] | no_license | beherasan/RScripts | 24c6f6872b0af9b74320600b2feed5b20e2aadac | 87228c8275d3dbf2ab99e4d74927c02ac2a8cf88 | refs/heads/master | 2023-07-05T09:34:39.109622 | 2021-08-26T06:29:55 | 2021-08-26T06:29:55 | 130,580,944 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 810 | r | tsne.R | library('Rtsne')
## Read the comma-separated file with the "label" column for group, here it contains additional label as "Age"
train<- read.csv(file.choose())
Labels<-train$label
train$label<-as.factor(train$label)
colors = rainbow(length(unique(train$label)))
names(colors) = unique(train$label)
## Select perplexity b... |
8bf6bd61c20e1956705758c4e0e57997558dad48 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tadaatoolbox/examples/ord_gamma.Rd.R | d1ac9d675441c058d2873bce9c518c3c0e01ab43 | [] | 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 | 249 | r | ord_gamma.Rd.R | library(tadaatoolbox)
### Name: ord_gamma
### Title: Gamma
### Aliases: ord_gamma
### ** Examples
df <- data.frame(rating = round(runif(50, 1, 5)),
group = sample(c("A", "B", "C"), 50, TRUE))
tbl <- table(df)
ord_gamma(tbl)
|
49690d725640857147b7ea6788224a712f6ed6c0 | 2c05957ccb67c479652453a10965b7b261a2376d | /R/las_read_mnemonics_df.R | 33b8bbd5766442e8da46056d99a2b5094bbb5d3b | [] | no_license | mgmdick/lastools | f9effdc6ea04841a1356da8e270c70c07f10a398 | 54cad63bdda3fb9dab11112b1f0b12426bda2b95 | refs/heads/master | 2020-05-17T05:13:54.114146 | 2019-10-09T10:01:56 | 2019-10-09T10:01:56 | 183,527,836 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 755 | r | las_read_mnemonics_df.R | #' @title Read las curve mnemonics data to data frame
#'
#' @description This function extracts las mnemonics from all las files in a directory
#' @param dir The Target Directory containing the .las files (required)
#' @export
#' @examples
#' get_all_las_mnemonics(dir)
read_las_mnemonics_df <- function(dir)
{
get_... |
4fb64257d8846e6e9fb84cc9b1693c7659bbf24e | 23da05ef70a4cffe9c2ee2a58982378dd575327c | /URA_Ch18.R | 1d289c2919f97f320317b9d9450ca806a9dc64cc | [] | no_license | rmuhumuza/URA-Rcode | 3fd74ee000363743ba8a67dbf886c1f085757609 | 98bf00df9877cee416c28af33b1322becf0d79e5 | refs/heads/master | 2022-04-14T00:59:34.256271 | 2020-04-09T03:11:22 | 2020-04-09T03:11:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,587 | r | URA_Ch18.R |
## Figure 18.1
charity = read.csv("https://raw.githubusercontent.com/andrea2719/
URA-DataSets/master/charitytax.csv")
attach(charity)
Y2 = subset(CHARITY, DEPS ==2)
hist(Y2, breaks=10, freq=F, main="", xlab="Log Charitable Contributions When DEPS = 2")
rug(Y2)
legend("topleft", c(paste("mean =", round(mean(Y2)... |
1058d50decd71ab202edeec948e390ccdb79c91a | 928788e6b9b0247c7a06adcf5e75451c6743e247 | /man/get_lon180.Rd | f8f869a26351a6763051b88b39b5305e33ed063e | [] | no_license | C3S-Data-Rescue-Lot1-WP3/stlocationqc | dade8a33fafb94bd7d2b124bf0f03631435ae452 | ed3b0410e4a179445912df67740b132471677182 | refs/heads/master | 2020-04-10T05:07:21.388641 | 2018-12-28T16:04:51 | 2018-12-28T16:04:51 | 160,818,158 | 0 | 1 | null | 2018-12-28T16:04:52 | 2018-12-07T12:00:08 | R | UTF-8 | R | false | true | 2,314 | rd | get_lon180.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/1_get_lon180.R
\name{get_lon180}
\alias{get_lon180}
\title{Tests the Geographic Coordinates and Transforms the Longitude from (0, 360)
to (-180, +180).}
\usage{
## If there are the coordinates data frame
get_lon180(coords)
##
## If there are ... |
8305819d7efcd8c33df0639751a55f1bee4a315b | 36b14b336e0efdda255fa3f163af65127e88105f | /man/Problem8.31.Rd | b925be8cd44190a0a110fc8f9da3899dced54399 | [] | no_license | ehassler/MontgomeryDAE | 31fcc5b46ae165255446e13beee9540ab51d98b3 | 43a750f092410208b6d1694367633a104726bc83 | refs/heads/master | 2021-06-24T13:46:19.817322 | 2021-03-11T16:36:37 | 2021-03-11T17:13:18 | 199,803,056 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 569 | rd | Problem8.31.Rd | \name{Problem8.31}
\alias{Problem8.31}
\docType{data}
\title{Exercise 8.31}
\usage{data("Problem8.31")}
\format{A data frame with 8 observations on the following variable(s).\describe{
\item{\code{AcidStrength}}{a numeric vector}
\item{\code{ReactionTime}}{a numeric vector}
\item{\code{AmountOfAcid}}{a numeric vector}
... |
1c8ef6175c6e17fcd7a57258e731598b6089f38e | 948b78fc214a1b9981790c83abb6284758dbfa89 | /r-library/R/zzz.R | 54da89b24f5dc6577f7aa54aeef19d167798d9e3 | [
"MIT"
] | permissive | terminological/jepidemic | 4ea81235273649b21cf11108c5e78dd7612fdf6e | f73cc26b0d0c431ecc31fcb03838e83d925bce7a | refs/heads/main | 2023-04-14T10:13:56.372983 | 2022-05-24T22:07:10 | 2022-05-24T22:07:10 | 309,675,032 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 498 | r | zzz.R | # Generated by r6-generator-maven-plugin: do not edit by hand
# This runs when the library is loaded.
# The precise result of this is a little uncertain as it depends on whether rJava has already been
# initialised and what other libraries are using it.
.onLoad <- function(libname, pkgname) {
# add in specific java ... |
e93e95e8847bb3d90dd226f7897b47ee0d4a5a86 | 2035e29e335ae2ffd834832a4b31a0c554006689 | /hws/hw04/resources/MCMC.r | 59cc6b8405c448bcc787aad9cb9596c8c20c337d | [] | no_license | tonyelhabr/isye-6420 | a3941c4c7595f4da8b6b2e081d944c55c8d35299 | ff46637c080c63f4b171d3ec831033771ff766f5 | refs/heads/master | 2022-03-17T09:57:47.559390 | 2019-12-15T18:37:40 | 2019-12-15T18:37:40 | 201,238,357 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 712 | r | MCMC.r |
n <-100
set.seed(2)
y <- 2 * rnorm(100) + 1
#------------------------------------------
NN <- 10000
mus <- c()
taus <- c()
sumdata <- sum(y)
#hyperparameters
mu0 <- 0.5
tau0 <- 1/100
a <- 1/2
b <- 2
# start, initial values
mu <- 0.5
tau <- 0.5
for (i in 1 : NN){
newmu <- rnorm(1, (tau * sumdata+tau0*mu0)/(... |
bf9126fb61122c4ad520cceccc822f4481f644b7 | 8cd2d5cd7bdcf74a72c04e98946189d2146a8ac2 | /ecocrop/jjv.salinity.r | f4d2c2d931a80d76577089c73c83a242406ab96f | [] | no_license | LaurenHodgson/Projects | fe7383c9d054fccb964ee54fc8a495b3de0812db | 8935bc6f81bd173ed679552282cc7b32568c5fea | refs/heads/master | 2016-09-06T02:38:54.899728 | 2012-12-13T05:00:13 | 2012-12-13T05:00:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,065 | r | jjv.salinity.r | library(rgdal); library(SDMTools) #load the library
soil.dir = '/home/22/jc148322/flatdata/soil/'
wd = '/homes/31/jc165798/tmp/soils/'; setwd(wd) #define and set the working directory
load("bil.indata.RData") #bil.data = readGDAL('hwsd.bil') #read in the data
soil.asc = asc.from.sp(bil.data) #convert to an ascii gri... |
4a937a495c02858e63365ac424aa8c218972e51e | ba65d8b42dfce42e1a4594d5a58a815194082112 | /man/readVcfFiles.Rd | bb179fa703d7dc95af7d393c232de30fccbeef16 | [
"MIT"
] | permissive | acc-bioinfo/TMBleR | b4ac594173ecc2ead98fd19696136f0d235065a3 | f3ded88b111b8db0867222aaa8be4bcd9fe8e80d | refs/heads/main | 2023-06-19T22:21:49.508537 | 2021-07-16T16:42:20 | 2021-07-16T16:42:20 | 378,995,779 | 4 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,242 | rd | readVcfFiles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readVcfFiles.R
\name{readVcfFiles}
\alias{readVcfFiles}
\title{Read VCF files}
\usage{
readVcfFiles(vcfFiles, assembly)
}
\arguments{
\item{vcfFiles}{list of one or more \code{character} strings corresponding to
the vcf file names. Each eleme... |
bdb5a8db20d926887a06c69ceaff67c29e0f6408 | ab60dd90b4b0f1ac5b113e9a48373d7a780ebd6e | /testpull.r | c891ae068864cb74619b02b994558a575a50afd6 | [] | no_license | adamsb0713/new-GGPlot- | 7a880346588d279053d1e3b9a870c531d587ca0e | b43821c213b6db1a12af79b2f4f6cc985d64e7ef | refs/heads/master | 2020-03-18T08:12:41.346983 | 2018-06-12T02:39:21 | 2018-06-12T02:39:21 | 134,496,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 44 | r | testpull.r | ##test script
x=1
y=2
a=y/8
l=x*25
b=y*3
|
5be2220510ad82703fb440d39d00d90bd96a4200 | f93b8f713c8d1eb03fd918c556526fdeb097df69 | /Project Script.R | 92a8c0fa1761779fa93dcb0ae978d9fe8b2ffece | [] | no_license | jjrwrenn/Math421Assignment | 6456b27c531f35e7001793c41bc5272b8439fa4d | 66176a32e62b5eb06833a63b62d0fef5c7f38af1 | refs/heads/master | 2020-04-06T08:07:56.768286 | 2018-12-20T06:30:30 | 2018-12-20T06:30:30 | 157,296,189 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 37,116 | r | Project Script.R | # Clear environment
rm(list = ls())
set.seed(12192018)
# Read in data file
data = read.csv(file = "C:\\Users\\student\\Documents\\Applied Data Mining\\Final Project\\default of credit card clients.csv", header = TRUE)
# Remove this NULLs variable, is ID only
data$X<-NULL
# Replace names
names(data) <- c('... |
60f1e194bb8268a3e54988d19063768a5771cd3f | 6da4fd73af6ac5b3ab3ac8c4ed6c4816a4582813 | /output_prep_MATLAB.R | 88f7143e4b0f93d4124350d7467fd309bb6729ff | [] | no_license | yierge/eBass | 13562c8d3641ce368dcef4386b16cd274dfa95b2 | 316d35c537e5a3345fbc74afc12915b9b2af30fb | refs/heads/master | 2023-02-08T16:08:51.440820 | 2020-12-22T08:24:12 | 2020-12-22T08:24:12 | 288,008,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 505 | r | output_prep_MATLAB.R | #inputdata is the result from primarythres.R
PrepeBass<-function(testresult)
{
fdr_rate<-inflate_cluster<-c()
for (j in 1:length(testresult[,2]))
{
finalthres[j]<-testresult[j,6]
fdr_rate[j]<-1-testresult[j,2]
fdr_cluster[j]<-length(which(pdata<=finalthres[j]))*fdr_rate[j]
inflate_cluster[j]<-fdr_cluster[j]*tes... |
f24c245c65bbffdd06296450886d14800e5755aa | 8d67857ba1f7781ca8bb0500f2b4d724a4f46e5a | /R/helpers.R | 8a05501ddaec14f1a402d6391f0b36ca912e1285 | [] | no_license | PhilipPallmann/jocre | b9eae5cc676886e127f969f6317022033dd82dff | 4e07f565a1cb32dc9dcc17730acc781aee33472d | refs/heads/master | 2020-05-29T08:52:09.854986 | 2017-05-12T14:43:52 | 2017-05-12T14:43:52 | 69,365,878 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,526 | r | helpers.R | print.JOC <- function(x, digits=max(3, getOption("digits") - 4), ...){
if(x$method %in% c("expanded", "fix.seq", "tost")){
cat(paste("Parameter estimates and ", 100 * (1 - x$alpha), "% simultaneous confidence intervals:\n\n", sep=""))
}else{
cat(paste("Parameter estimates and projected bou... |
1baffdf9b30213d745f8411600b0fc885b01b86f | 76d33f5007d50fc7cc3a22a1aa7878afde234950 | /aeltere-Versionen/3_suf_wsi-brb-2015_befragungsdaten-aufbereiten.R | 0a9e3dc8f2ffbbd5d91c54464f6c1eec64ad0b6b | [] | no_license | helge-baumann/suf_brb_2016 | 19e235d8479c7227f5fec428672977e4ad37ede2 | ada5645756b17c04d07d1c85ae9b9a43cdf6fc22 | refs/heads/master | 2021-03-30T05:52:40.130402 | 2020-03-19T14:44:22 | 2020-03-19T14:44:22 | 248,023,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,166 | r | 3_suf_wsi-brb-2015_befragungsdaten-aufbereiten.R | # Befragungsdaten aufbereiten
# Gewichte zuspielen------------------------------------------------------------
gew <- daten_input[["gew"]][,c("lfd", "gewbr_k", "gewbr_l")]
dat <- merge(daten_input[["dat"]], gew, by="lfd")
# Variablenlabels und Valuelabels anfügen
dat.varlabels.alle <- c(varlabels[["dat"]], varlabels[... |
297959200dcc0bb4ca65d4de64cc95820070a728 | 7d95ef134deeba5f74fe238fdaa21bfadc915e7d | /plot3.R | 34495eb2b93c40cfb48201602b682c6681483fb3 | [] | no_license | acrung/ExData_Plotting1 | db565d6e392c26f8d3980c0aa9ba57b7f2c071b9 | 5e2ec4c5ab0509d75092e6910d19aa671c5a4d07 | refs/heads/master | 2021-01-20T18:09:49.489887 | 2015-05-06T15:45:00 | 2015-05-06T15:45:00 | 35,055,813 | 0 | 0 | null | 2015-05-04T19:50:07 | 2015-05-04T19:50:07 | null | UTF-8 | R | false | false | 2,039 | r | plot3.R | Sys.setlocale("LC_TIME", "English")
con<-file("household_power_consumption.txt")
open(con)
#Getting only good rows
#2007-02-01 # first lign 66638
#and 2007-02-02 #last lign 69517
# 69517-66638 = 2879
myColNames <- c("date","time","globalactivepower","globalreactivepower", "voltage",
"globalintensity","... |
a99b182d19a9455617748c3b5a5b5476459af751 | daa0947ca5fbad70bdb9b6545b14b44562804407 | /Tasks/Task_03/Patterns_Imports_2019.R | 993672b19d2d16b9856a717e3db53e252ca3568b | [
"MIT"
] | permissive | ankurRangi/Data-Analysis-CEEW | f8d8c9d810f40878523d1187757d5ab50cef7008 | cb654152ba2ae10bdd50729fa48dfdf4c576f01c | refs/heads/main | 2023-04-15T12:24:06.674176 | 2021-05-03T10:20:14 | 2021-05-03T10:20:14 | 361,378,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,963 | r | Patterns_Imports_2019.R |
library(readxl)
library(stringr)
indo_2019 <- read_xlsx("D:/CEEW/Data/Task_03/indonesia.xlsx", 3)
sing_2019 <- read_xlsx("D:/CEEW/Data/Task_03/singapore.xlsx", 3)
mala_2019 <- read_xlsx("D:/CEEW/Data/Task_03/malaysia.xlsx", 3)
thai_2019 <- read_xlsx("D:/CEEW/Data/Task_03/thailand.xlsx", 3)
viet_2019 <- read_x... |
dccb81ac87aba9fb571d81881bd05071c0f0e49f | c7953ff0d490aca333db79d5275f5a339e6bf2ec | /man/meta_tissue.Rd | baafc4f6f5543b040c340ec668f85e138101e4b5 | [] | no_license | brandonjew/mcLMM | 339717e1e2f38315c94218a9cb35567845ad1fad | ca0d16e083fc25be278750df6a2e9096e3b54c1a | refs/heads/master | 2023-07-01T06:53:56.055349 | 2021-07-09T16:59:46 | 2021-07-09T16:59:46 | 253,665,211 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,386 | rd | meta_tissue.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mcLMM.R
\name{meta_tissue}
\alias{meta_tissue}
\title{Ultra-fast meta-tissue algorithm}
\usage{
meta_tissue(
expr,
geno,
covs = NULL,
heuristic = FALSE,
newRE = TRUE,
force.iter = FALSE,
verbose = TRUE
)
}
\arguments{
\item{expr... |
33cc8b164b6685cc9b456ae95fb2cfd36c70c562 | 3b0f4a825fefab15559d472a9b1d69049c5c9ada | /cachematrix.R | bcefd037fe940cb510a3ca75398ad6f00d25af7d | [] | no_license | spillerwill/ProgrammingAssignment2 | 31c984c7f18bf1f109dab44161303740bde39e18 | 942205c427ef616d967f7262fe142594f91e5b76 | refs/heads/master | 2020-04-18T07:19:11.873279 | 2019-01-24T13:26:27 | 2019-01-24T13:26:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,671 | r | cachematrix.R | ## Coursera - Johns Hopkins - R Programming - Week 3 - Assignment 2
## This pair of functions computes the inverse of a matrix and creates an object
## to associate the matrix with its inverse in the cache. The computation
## function checks for a pre-existing computed inverse to avoid unneccessary
## repeated compu... |
3cc0bf56a96249bd9294a4f5b061160fc08ac39f | dd25f95d4444b4a1f7445a5c2fb8209418b000b9 | /inst/examples/shiny_example_01/app.R | c1dddd00ee1b9fd2a9bb6fe6d1b57069952f5b51 | [
"MIT"
] | permissive | harveyl888/barRating | fb276e9ba76b57146fcab680ae6458ceab3e1234 | 45ea330775d6afc433b2e0c346276da88e201056 | refs/heads/master | 2021-01-20T18:24:03.180233 | 2016-07-21T12:43:55 | 2016-07-21T12:43:55 | 60,821,983 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,655 | r | app.R | ## Example
library(shiny)
library(barRating)
server <- function(input, output, session) {
output$bar1 <- renderBarRating(barRating(choices = as.character(seq(1:10)),
theme = 'bars-1to10',
selected = '7',
... |
dac51b9525b7ba1e69b7374a4b1184f46b9f60ab | 3f650f820a147adbc121a3e666dc531fd13f9c8b | /SupFigTable.R | cdfcd501fc2df25ed727852b315c727443cbf3df | [] | no_license | solocell/BiologicalProcessActivity | 42f05cfa13498034fd8d9f06cd7b950d971793d7 | 346c927954f87789cee78e3068e34059f3dd38d6 | refs/heads/master | 2022-02-17T19:03:25.715268 | 2019-09-28T00:02:47 | 2019-09-28T00:02:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,201 | r | SupFigTable.R | system("cp ./Benchmark/batchCorrect.pdf ./SupFig1.pdf")
#SupFig1
load("./Dropout/GTEx/GO.rda")
load("./Immune/Chromium/GO.rda")
load("./Immune/Chromium/Chromium.rda")
load("./Embryo/E-MTAB-3929/GO.rda")
pdf("SupFig2.pdf", width = 9, height = 6)
par(mfcol = c(2, 3))
plot(rowMeans(rpkm_drop[, 1:20]), rowSums(rpkm_drop[,... |
70fab33021a137550d7a1e2175fb3448e8c9e98c | 29585dff702209dd446c0ab52ceea046c58e384e | /coxinterval/R/maximalint.R | 28989aaee558f46ff32025dd429a6b2a8e189516 | [] | 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 | 1,305 | r | maximalint.R | ### maximal intersections from an interval-type Surv object
maximalint <- function(x, eps = 1e-7)
{
if (is.null(nrow(x))) x <- matrix(x, nrow = 1)
if (ncol(x) == 2) x[is.na(x[, 2]), 2] <- Inf
else if (ncol(x) > 2) {
## right-censored
x[x[, 3] == 0, 2] <- Inf
## exact
x[x[, 3] == 1, 2] <- x[x[, 3] ... |
f8a71189dff7a71dc0ae0b89fb266991b5a7596c | b57bd24e098c7a94241dbb96c95b004a4b5fedab | /4-Analysis/seed-production-threshold-simulation-functions.R | 36fc904c8c3424d6d2031ab879d38a9296a930fb | [] | no_license | hnguyen19/matrix-prospective | 40dcc4d3625ddd2dab30d30dca2369fb4d3ef68e | 2d38316ed3f124f5ac5c151ee913077810170cb5 | refs/heads/master | 2023-06-28T19:38:30.795602 | 2023-06-16T16:01:22 | 2023-06-16T16:01:22 | 407,684,146 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,315 | r | seed-production-threshold-simulation-functions.R |
################################################ 2-year rotation ################################################
###### Lambda calculation #######
rot_2year_conv_lambda <- function(vec, prt_C, em_C, sv_C, seed_C, poh_C, ow_C,
prt_S, em_S, sv_S, seed_S, poh_S, ow_S){
seed_C[1,3] <- r... |
7c512aef070fea9af7d15e673386a77c8871a003 | 29585dff702209dd446c0ab52ceea046c58e384e | /lle/R/find_nn_k.R | 3dd31cee20c919f08f3200dcc91bf866a2cda91d | [] | 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 | 838 | r | find_nn_k.R | find_nn_k <-
function(X,k,iLLE=FALSE){
#calculate distance-matrix
nns <- as.matrix(dist(X))
#get ranks of all entries
nns <- t(apply(nns,1,rank))
#choose the k+1 largest entries without the first (the data point itself)
nns <- ( nns<=k+1 & nns>1 )
#optional: improved LLE
if( iLLE ){
N <-... |
f03ae7f1b8ebb883d3ff592c100e17412e434233 | 2f3ea7ef177cbe890cfa48af195d04cc385155ff | /BIEN.R | dd4a6fabc868f6b1394bf0f1a2977fbf0c90d6ab | [] | no_license | stdupas/BIEN | 57bf60b19db89c31c68e73531c66d8b92bffa865 | 7c28725990e85e72daa3d5857b7eae8866686a03 | refs/heads/master | 2020-03-22T20:56:30.392974 | 2019-04-02T20:08:28 | 2019-04-02T20:08:28 | 140,644,320 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 99,438 | r | BIEN.R | setwd("C:/Users/steph/OneDrive/Documents/GitHub/BIEN")
#library(raster)
#edata
#idata
#
# New version 15-12-2018
# ecolearn model
# variables = vector of variale names
# dada = array of ecosystem dynamics including indicator data and interacting ecosystem variables
# in dim 1 is time,
# in dim 2 are ... |
12d730db833bd11b870b217f74942ec62757f669 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query05_query10_1344n/query05_query10_1344n.R | 6ae98a2914ad472dd4bef398f10aaea4e53d7986 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 71 | r | query05_query10_1344n.R | bf968afd3c7687316f4c8214c8ac3f6a query05_query10_1344n.qdimacs 568 1323 |
6374290c2fe382d47e602ace4e0f215935cc4d6d | cf4c963a4f0e25672c2d6b5323810486554fa4e7 | /R/DANB_Coexpression.R | 875205d0c6aa26b481392855cbe634e6e72a18f3 | [] | no_license | tallulandrews/M3Drop | 1f5e96c1c3bd98c88889bae54cbd13e83d4a34bf | 9128921ec4077bb442fdbfb83e7a6c9185483c97 | refs/heads/master | 2023-02-19T04:34:43.763543 | 2023-02-16T23:21:33 | 2023-02-16T23:21:33 | 63,236,849 | 29 | 9 | null | 2018-04-26T21:59:17 | 2016-07-13T10:22:59 | R | UTF-8 | R | false | false | 2,310 | r | DANB_Coexpression.R | NBumiCoexpression <- function(counts, fit, gene_list=NULL, method=c("both", "on", "off")) {
# Set up
if (is.null(gene_list)) {
gene_list <- names(fit$vals$tjs)
}
pd_gene <- matrix(-1, nrow=length(gene_list), ncol=ncol(counts));
name_gene <- rep("", length(gene_list))
for (i in 1:length(gene_list)) {
gid <- wh... |
8230258fbfa385c958b7f8100ba4bae84bacb8a9 | a0269d7359c770f77345c69ef95c160680db75b1 | /man/hugo_read_data.Rd | 8737e97b45a78cde8aed449ff631cd67948ad93e | [] | no_license | hugo4r/hugo | 3898062a8f8a0ef4be3f7965ff299d4663771619 | da660022bb69b819edfdce82e85638637c76165c | refs/heads/master | 2020-03-12T02:34:24.268026 | 2018-06-17T20:56:41 | 2018-06-17T20:56:41 | 130,405,601 | 5 | 22 | null | 2018-06-17T20:56:42 | 2018-04-20T19:31:14 | R | UTF-8 | R | false | true | 3,201 | rd | hugo_read_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hugo_read_data.R
\name{hugo_read_data}
\alias{hugo_read_data}
\title{Reads data to R}
\usage{
hugo_read_data(path, file_extension = NA, header = NA, separator = NA,
decimal = NA, file_name_to_save = NULL)
}
\arguments{
\item{path}{the name ... |
d7546ab90db1bd20249c349d6c9b17cb1c30ba5c | 00c0db9350641b0f3c700836b3d10df5b3f78a65 | /man/diversity.predict.Rd | 3bf40f89dafd5c18653b8c015f1eaaee187d0f93 | [] | no_license | derek-corcoran-barrios/DiversityOccu | 4f50546e7b9f966d735c8b2dde6256cc6540f98f | a9ab7cc477f873e8f01067af020f1de6987265b2 | refs/heads/master | 2020-12-29T02:32:38.350106 | 2019-11-07T23:11:11 | 2019-11-07T23:11:11 | 49,165,821 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 2,558 | rd | diversity.predict.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DiversityOccu.R
\name{diversity.predict}
\alias{diversity.predict}
\title{Makes a spacially explicit prediction of the occupancy of multiple species
and alpha diversity, and select the area where}
\usage{
diversity.predict(model, diverse, new... |
ae1484a42365edd681926234cf3d913b0de767bc | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/GENLIB/man/gen.half.founder.Rd | adde8d9e94c77f08437fa81b6d91543c0c6a1967 | [] | 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 | 807 | rd | gen.half.founder.Rd | \name{gen.half.founder}
\alias{gen.half.founder}
\title{Get half-founder id numbers}
\description{Returns the id numbers of the half-founders. Half-founders are defined as the individuals with only one known parent in the genealogy (i.e., either mother id=0 or father id=0).}
\usage{gen.half.founder( gen, ...)}
\argumen... |
3cbd4ef5f96feae27828a93a75acec6dd00e7e12 | 8a3f753e5af0f96b8b7b8f46bc0e9b7e618f31cf | /run/utils/plot_accuracy.r | c3a3ed4493f3a94141b1c6edd07eb057f472b726 | [
"Apache-2.0"
] | permissive | hxj5/csp-benchmark | 6d02ad16576e359f8d872308a85b22427443fe85 | 5e7c54e1aa86526d14ab24a3b71d176ccce89a63 | refs/heads/master | 2023-05-12T11:16:38.398938 | 2021-06-10T03:17:52 | 2021-06-10T03:17:52 | 348,695,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,204 | r | plot_accuracy.r | #!/usr/bin/env Rscript
#Aim: to visualize the results of comparing coverage matrices of two Apps.
#@abstract Parse mtx file to get mtx info.
#@param fn Name of mtx file [STR]
#@return A list with four elements if success, NULL otherwise [list]
# The four elements are:
# $nrow Number of r... |
a95454945709e9cfef0ef1f6c913996b4e68533c | 08c4885facb8a0a40a995eaf989c6252fb8350b0 | /man/create_utilmod.Rd | 8e0e2c323fbbe7ead20d39d59f935717fd95b220 | [] | no_license | InnovationValueInitiative/IVI-NSCLC | a1aeefb23113fe88b578e1023d21fbf0a6488254 | bafd1faa8b1887b91cd42af00b43cd5b41ee53cf | refs/heads/master | 2020-03-24T08:23:52.095148 | 2019-07-23T04:21:11 | 2019-07-23T04:21:11 | 142,594,493 | 14 | 14 | null | 2019-07-23T04:21:12 | 2018-07-27T15:29:57 | R | UTF-8 | R | false | true | 2,090 | rd | create_utilmod.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilmod.R
\name{create_utilmod}
\alias{create_utilmod}
\title{Create utility model}
\usage{
create_utilmod(n = 100, struct, patients, ae_probs,
params_utility = iviNSCLC::params_utility, ae_duration = c("month",
"progression"))
}
\argumen... |
a3da27f201afae2c10ca5efac0284035400b475a | bb2cfa25c58bf3f4563acfa703c538742c54a17c | /Subset_ca1.R | e5c2b31f071ee48b90aec043b6f1d267b541176c | [] | no_license | hirjus/Galku | 876c24ffc2d665de097b90d0fcf4dc97b620b21d | be94314810c3c171373a7f2d4139728e811b70c1 | refs/heads/master | 2021-01-25T14:39:28.748578 | 2020-11-14T08:14:31 | 2020-11-14T08:14:31 | 123,716,832 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,912 | r | Subset_ca1.R | #Subset_ca1.R 7.9.20
#
# Data G1_4_Calaaj1.Rmd
#
# Data
#
# ISSP2012esim2.dat
# spCAmaaga1 maaga-ca-objekti (täydentävillä maa-pisteillö)
# maagaTab1 taulukko jossa maaga-rivit ja maat täydentävinä pisteinä
maagaTab1
# Koodilohko subsetCA-1
X11()
maagaCA2sub1 <- ca(~maaga + Q1b,ISSP2012esim2.dat, subsetrow... |
3fe76414305ef4e7841a469b2cb0154241906f2f | 04947ec352b9f31ae8b58182a1ee585f23c1ee69 | /Scripts/04_Plots.R | eb5334c0f64913c9607587c6b24abd26da52cd54 | [] | no_license | lessardlab/GlobalPolyMorp | 464e4ae88d5212e651c08cf01e0ee42028a04d02 | 7f7d97c0f03ba347d6e5c6818a10b60a411049db | refs/heads/main | 2023-04-10T20:15:17.693806 | 2021-08-26T22:10:39 | 2021-08-26T22:10:39 | 300,376,034 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,340 | r | 04_Plots.R | ### GPM Figures
## Gabriel Muñoz _ dic 2020.
## Please note that functions to produce plots feed on the "occ" dataset, you need to have that one loaded first.
# source("GM_Scripts/1_PrepData.R") # run this line if you haven't gone through the 1. Script.
#source("4_Functions.R")
occ<-read.csv("Data/occ_withsite... |
5c6d3ada9bb79daf5b0e871ce17aa5354b22836d | 1c3daff070c084ca947dd383378b7b1d05e11b1d | /cachematrix.R | a2940ba69b7ffc917fc9fcd8625995e56e434a72 | [] | no_license | srijan-nayak/ProgrammingAssignment2 | bf1e0d9d6e578c18517c99193fd74b1822d43e92 | 70e34b776489a5f4ebb9b57ea6df846ce5c5ae96 | refs/heads/master | 2022-11-13T06:22:31.081087 | 2020-07-04T07:42:35 | 2020-07-04T07:42:35 | 277,044,854 | 0 | 0 | null | 2020-07-04T07:42:37 | 2020-07-04T05:27:33 | R | UTF-8 | R | false | false | 1,265 | r | cachematrix.R | # Calling makeCacheMatrix on a matrix gives a special cache matrix whose matrix
# and inverse matrix can be set or looked up.
# Calling cacheSolve on a cache matrix returns its cached inverse matrix if
# available, otherwise returns a newly calculated inverse matrix.
# makeCacheMatrix takes a matrix and returns a list... |
a2f76fb7e9f6a7b3a9742d8af1bd4fb1171378b3 | 01088d692f73ceacd86f3bc63b0ae06faf60f75c | /man/bsspline2.Rd | b79ec4d73c2a2057eac01d6dc0a255eb6f72bf61 | [] | no_license | cran/ciuupi2 | fc0c9b303e0a2808be68a9b36262acb6cfdde48b | 192af8b6a6ba3b56f710af973b12afa636b3f7a1 | refs/heads/master | 2023-03-22T06:03:02.642259 | 2021-03-11T14:40:02 | 2021-03-11T14:40:02 | 346,926,442 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,598 | rd | bsspline2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bsspline2.R
\name{bsspline2}
\alias{bsspline2}
\title{Evaluate the functions b and s at x}
\usage{
bsspline2(x, bsvec, alpha, m, natural = 1)
}
\arguments{
\item{x}{A value or vector of values at which the functions b and s are to be
evaluate... |
04f6c6d720b98eb60a19c0a5e93d9df097b1fecc | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/rstudioserver_analysis/BM_all_merged/9-downstream_GLMPCA_BM_KeepMorePlates.R | ebc694af80803572c4af8a39fa143c87b5638af6 | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,016 | r | 9-downstream_GLMPCA_BM_KeepMorePlates.R | # Jake Yeung
# Date of Creation: 2020-02-04
# File: ~/projects/scchic/scripts/rstudioserver_analysis/BM_all_merged/9-downstream_GLMPCA_BM_KeepMorePlates.R
# description
jstart <- Sys.time()
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(glmpca)
library(scchicFuncs)
library... |
707d47249308875afccf608c128228aaac92e830 | 08172e1a0e8ed79c05c1cf704cacc00530732ef8 | /thesis/plots/file_size.R | 38f39020234d7f328e7015a6e1d63207083b10b3 | [] | no_license | olafurpg/thesis | 84d53620d2a51287440e4cff33285d16434c852c | 27672678f858ef1b936cc660cc902e75434eabfa | refs/heads/master | 2020-12-29T02:38:27.231554 | 2017-09-22T13:10:58 | 2017-09-22T13:10:58 | 51,314,768 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 429 | r | file_size.R | # library(stargazer, lib.loc="plots")
library(xtable, lib.loc="plots")
# library(Hmisc, lib.loc="plots")
options(xtable.floating = FALSE)
options(xtable.timestamp = "")
sizes <- read.table("data/file_sizes.dat")
# summary(sizes)
sizes <- quantile(sizes$V1, c(.50, .75, .90, .99))
# append(sizes, 1)
# sizes
# summary(s... |
1a7e8d3aaa62ee2f724aee1b0f317f5bfc3523f2 | 63a8b105407d6d2a25b25df44637a730d63168e9 | /R/utils/85pct_rule.R | b9db51b08ceb12382a298fcde5589365fde31e29 | [] | no_license | jmhewitt/ctds_dives_jabes | 5857b0e37a01fd80e77b099bf46ef5cf63147b36 | 30de1a1d3b825cf2f07261f40db64b3558053976 | refs/heads/main | 2023-01-03T17:27:16.503272 | 2020-10-30T16:27:07 | 2020-10-30T16:27:07 | 308,685,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,343 | r | 85pct_rule.R | # create a vector to label dive stages
stagevec = function (length.out, breaks) {
rep(1:3, c(breaks[1] - 1, breaks[2] - breaks[1], length.out +
1 - breaks[2]))
}
# use 85% max depth rule to determine time in stages
times.stages = function(dives.obs) {
do.call(rbind, lapply(dives.obs, function(d) {
... |
bc8a529c95217185b8fade6a6670ba322b940249 | 9f85f5d13c66e79bd470806b054404242bbe58cc | /man/unzip_process.Rd | 58eef4bd81af016152a0bfc062ca076c6538c205 | [
"CC0-1.0",
"MIT"
] | permissive | r-lib/zip | f7749877eabdf9cf3844dbd0dacf4972c11cbc56 | 70f0b1518304652ed04a7e7a991396e923030bb7 | refs/heads/main | 2023-05-11T15:56:15.575775 | 2023-05-09T19:16:47 | 2023-05-09T19:16:47 | 87,673,925 | 58 | 22 | NOASSERTION | 2023-09-04T15:35:44 | 2017-04-09T01:06:13 | C | UTF-8 | R | false | true | 1,747 | rd | unzip_process.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/process.R
\name{unzip_process}
\alias{unzip_process}
\title{Class for an external unzip process}
\usage{
unzip_process()
}
\value{
An \code{unzip_process} R6 class object, a subclass of
\link[processx:process]{processx::process}.
}
\descripti... |
6f1b126ae922a93851eb9075fe3d8371ae519ea8 | 94c16636d7d4c98c918fde5096cf3a4118c02415 | /tests/testthat/test_dyadr.R | 84cad7eee05604aac4d87cda78e4ab145e687dbb | [] | no_license | RandiLGarcia/dyadr | 66c87d6be3b3eb4e7bf37568dc43f6e037d34961 | 5d317dceb2e278887b9684e172bd79a0c12974af | refs/heads/master | 2021-07-14T20:50:59.289227 | 2021-03-17T13:27:55 | 2021-03-17T13:27:55 | 61,908,363 | 17 | 14 | null | 2020-07-29T15:24:03 | 2016-06-24T19:43:21 | R | UTF-8 | R | false | false | 1,744 | r | test_dyadr.R | context("dyadr")
test_that("var_labels error works", {
expect_error(var_labels(iris), "labels")})
test_that("apim error works", {
expect_message(apim("x"), "please enter a formula")})
test_that("crsp output returns correct value", {
if (require(nlme)) {
apimi = gls(Satisfaction_A ~ Tension_A + SelfPos_P,
... |
b9405d65c95f5d5b46477c66a342f6bc42b76e76 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rorcid/examples/orcid_auth.Rd.R | 484afa46537dbfdd767cd50b973c19f8329c769e | [] | 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 | 263 | r | orcid_auth.Rd.R | library(rorcid)
### Name: orcid_auth
### Title: ORCID authorization
### Aliases: orcid_auth rorcid-auth
### ** Examples
## Not run:
##D x <- orcid_auth()
##D orcid_auth(reauth = TRUE)
##D #orcid_auth(scope = "/read-public", reauth = TRUE)
## End(Not run)
|
8e1b5dcb979780d336db2a87691177019205948d | e8f045b7fb162d00c03f3bf26a9eaa9681c66997 | /scripts/analysis/13_figures_SI.R | ccc54ab68106c53f193abb077eb944f51e9a450e | [] | no_license | CamilleAnna/HamiltonRuleMicrobiome_gitRepos | f648d3f4997947cdd0d6aa9107bfee9325ab1891 | 3a28d0196df93e39dc46cd8e05b0a356e4baf1c0 | refs/heads/master | 2023-08-15T11:01:31.186998 | 2021-10-01T13:32:51 | 2021-10-01T13:32:51 | 260,581,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,919 | r | 13_figures_SI.R | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
# Simonet & McNally 2020 #
# SI file figures #
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~#
#local_project_dir='/path/to/where/repo/is/cloned'
setwd(paste0(local_project_dir, '/HamiltonRuleMicrobiome_gitRepos/'))
source('./scripts... |
466dfa279bd28f5009d5ad1b98321d1bef04db05 | 3fa1b23746232975b3b014db2f525007a3b49991 | /anna_code/simband_local/peak_caller.R | 9b0b433e5f1310553626ad669411121821240cb2 | [] | no_license | AshleyLab/myheartcounts | ba879e10abbde085b5c9550f0c13ab3f730d7d03 | 0f80492f7d3fc53d25bdb2c69f14961326450edf | refs/heads/master | 2021-06-17T05:41:58.405061 | 2021-02-28T05:33:08 | 2021-02-28T05:33:08 | 32,551,526 | 7 | 1 | null | 2020-08-17T22:37:43 | 2015-03-19T23:25:01 | OpenEdge ABL | UTF-8 | R | false | false | 1,766 | r | peak_caller.R | # objective function, spline interpolation of the sample spectrum
f <- function(x, q, d) spline(q, d, xout = x)$y
x <- sp$freq
y <- log(sp$spec)
nb <- 10 # choose number of intervals
iv <- embed(seq(floor(min(x)), ceiling(max(x)), len = nb), 2)[,c(2,1)]
# make overlapping intervals to avoid problems... |
818cb3ec2d11e9db35a8ea88316d743e700fc97e | 1c7c8e7d87b3be88afe606f0d8d3935017658302 | /school-boundary.R | 415a0ec16ccf37308528daf971e8ce43b869f586 | [] | no_license | dobrowski/mapping | df2fa0f931cc29cb54bd1506e70dc5f1fc07fa57 | 183acea0b5c1b0024a90d019804c2e1444431402 | refs/heads/master | 2020-05-01T18:57:24.642538 | 2019-03-25T18:23:52 | 2019-03-25T18:23:52 | 177,635,587 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,684 | r | school-boundary.R |
### Establish local school shapefiles -------
primary <- st_read("SABS_1516/SABS_1516_Primary.shp")
middle <- st_read("SABS_1516/SABS_1516_Middle.shp")
primary.ca <- primary %>%
filter(stAbbrev == "CA") %>%
st_transform(4269)
middle.ca <- middle %>%
filter(stAbbrev == "CA") %>%
s... |
e0f7711e9865a3b68e5401d5730e10c79bd19aad | 9172a35be3d169fe90e7c0b157b835a34f346be3 | /ui.R | c05792ecc5afbe332f2f64e703b868b16ba61d6e | [] | no_license | JagadeeshaKV/NewYorkAirQuality | 1a40384118ec5e08e6ddf65c504112c043314d46 | 95eb0f9389f2d01c2e4bae68d8447af72fd17cf7 | refs/heads/master | 2020-03-31T12:00:50.559303 | 2018-10-09T07:52:48 | 2018-10-09T07:52:48 | 152,200,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,986 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinyjs)
# Define UI for application that draws a histo... |
9a2d706597980dc2eece1e1cd6395f509ed02add | 5a297c4677a198dfc1a30a265368b9fe05c4a3f9 | /CRT_MEP_SICI_anlyzer_sub.R | e4076453536ae1a0568181327f9eb020bfa33205 | [] | no_license | naokit-dev/My-small-scripts | 1c082c6bcb4e5b4a91a9b5a2516ea4ac57af5198 | f89fddf29462c6d6b3e4572db0ed09f1e94f4acc | refs/heads/master | 2022-10-22T20:18:33.827137 | 2020-06-16T02:39:22 | 2020-06-16T02:39:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,603 | r | CRT_MEP_SICI_anlyzer_sub.R | setwd("~/Documents/R/CRT_MEP_SICI")
library(dplyr)
library(ggplot2)
#作図テーマ
theme_set(theme_classic(base_size = 18, base_family = "Helvetica"))
# データの読み込み
x <- read.csv("CRT_MEP_SICI_grand.txt", header = TRUE)
head(x)
#Trialcondをラベル付け
x$Sub_ID <- factor(x$Sub_ID)
x$TrialCond <- factor(x$TrialCond, levels=c(0,1,2), l... |
407f7239924c390ba3a16d266af41471c48bc9f0 | dec6bce6c646cbdaab39de26aa796ac6ec546fd6 | /inst/shiny/ui.R | c52903551d1e00a40dca5ebb580f093c5c145d42 | [] | no_license | cran/soilcarbon | 172c550423a07a83e6dd1d5ae700ef80a4746d6f | 5c6947262cc36c2f56499ea9fe8cff823899c860 | refs/heads/master | 2021-01-20T08:37:38.351226 | 2017-08-04T02:17:31 | 2017-08-04T02:17:31 | 90,169,921 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,840 | r | ui.R |
library(soilcarbon)
library(ggplot2)
shinyUI(fluidPage(
theme = "bootstrap_simplex.css",
# Application title
headerPanel("Powell Center soilcarbon workbench"),
sidebarPanel(
conditionalPanel(condition="input.conditionedPanels==1",
h3("Visualize database"),
fluidRow(column(6... |
8d8908236771acc3f107bdd2646b7dad7a8173c4 | 83223e278bbb92d45c38b7de6648da1a5bb64bf7 | /man/rmNAfromTable.Rd | c712f2154c7c89339b3fc0a8f53ef59ae07b5a89 | [] | no_license | gdario/coxph2table | 804bb1abd6806092217b114e0c76e1f6d27d32b6 | 9edb5b29561911c49ee6f9a99718669820244d2d | refs/heads/master | 2020-05-30T18:33:34.998129 | 2013-04-21T09:08:08 | 2013-04-21T09:08:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 505 | rd | rmNAfromTable.Rd | \name{rmNAfromTable}
\alias{rmNAfromTable}
\title{Remove NAs from an output table}
\usage{
rmNAfromTable(xt)
}
\arguments{
\item{xt}{an output table (data frame or xtable)}
}
\value{
the same table (data frame or xtable) without the rows
where \code{NA} appeared.
}
\description{
Remove NAs from an output tabl... |
8064a4a002b3980e7a9a8b102c430e07bddfa304 | f5e1eb18ef32b847556eed4f3707b1d5a9689247 | /r_modules/trade_prevalidation/R/getAllReportersRaw.R | 5359465b55992266775b8faad9a27d83eca046e5 | [] | no_license | mkao006/sws_r_api | 05cfdd4d9d16ea01f72e01a235614cb0dcaf573d | 040fb7f7b6af05ec35293dd5459ee131b31e5856 | refs/heads/master | 2021-01-10T20:14:54.710851 | 2015-07-06T08:03:59 | 2015-07-06T08:03:59 | 13,303,093 | 2 | 5 | null | 2015-06-30T07:57:41 | 2013-10-03T16:14:13 | R | UTF-8 | R | false | false | 273 | r | getAllReportersRaw.R | getAllReportersRaw <- function(dmn = "trade",
dtset = "ct_raw_tf",
dimen = "reportingCountryM49") {
if(!is.SWSEnvir()) stop("No SWS environment detected.")
faosws::GetCodeList(dmn, dtset, dimen)
} |
6914386222ecba835a6b8198dfebccae692eccd8 | ce8d434798a232380d3a10eaa7279a0b7cda68dc | /Linear Regression/LR_Lasso.R | 37e87d841984c05c58a595f95a5d0527deaf9857 | [] | no_license | HananGit/Prudential-Life-Insurance-Risk-Prediction | 40d08db2502d3e4df71f6ee61ef5838e1f829359 | aa46678a8278ec69dd15c75d007f98909c6dfd84 | refs/heads/master | 2021-02-08T21:07:53.397116 | 2020-03-06T22:15:01 | 2020-03-06T22:15:01 | 244,197,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,019 | r | LR_Lasso.R | #Splitting dataset into train and test datasets
set.seed(2) # we set the seed to make sure that the train and test data will not change every time we divide them by running the sample function
sample_index_2 = sample(1:nrow(Prudential_final_Data_2), nrow(Prudential_final_Data_2)*0.8) #length(sample_index) should be... |
a996255e8b68935837889d319f7a74aacbdb284d | 3ad3ce5f38d636b649abd6e2e8741d482d8f6d72 | /R/cleanabs.R | dd61671cd2a5cb974d169d4eda2f0ad2c750c427 | [] | no_license | cran/pubmed.mineR | 279ce024df5b7913885ec96ad906226aa78633ce | 7612a2d68f503794f8dee8479e8b388949e4b70d | refs/heads/master | 2021-12-09T18:39:44.578225 | 2021-11-26T14:50:03 | 2021-11-26T14:50:03 | 17,919,888 | 5 | 6 | null | null | null | null | UTF-8 | R | false | false | 288 | r | cleanabs.R | setGeneric("cleanabs", function(object) standardGeneric("cleanabs"));
setMethod("cleanabs","Abstracts",function(object){temp1 = which(object@Abstract!="NONE"); temp=new("Abstracts", Journal=object@Journal[temp1], Abstract=object@Abstract[temp1], PMID=object@PMID[temp1]);return(temp)})
|
5402151df21937247123f9f52c0d6a2f66172a85 | 95b8a3279797515372dd46b03d8d47be5b32bbb5 | /HW7/hw7_code.R | 9fe50e4c74672170cefcacad9b90af3e56f2a44b | [
"MIT"
] | permissive | qzyu999/applied-time-series-analysis-winter-19 | 69f66d031b1c36a0e80426cf4c52843a28c8c267 | af5bda8f738c4cdf8ace11ec4ed750790e0a62fc | refs/heads/master | 2022-11-06T03:01:05.705830 | 2020-05-15T03:14:06 | 2020-05-15T03:14:06 | 264,046,759 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,908 | r | hw7_code.R | library(xlsx); library(astsa) # Load libraries
global_temp <- read.xlsx('GlobalTemp_NASA.xlsx', sheetIndex = 1) # Load data
X_t <- diff(global_temp$Temp.Anomaly) # First differenced data
par(mfrow=c(2,2))
# plot periodogram of X_t
p_0 <- spec.pgram(X_t, log = 'no',
main = 'Periodogram of First Diff... |
96f4fae8ed3f7475986da5a78315ea3e6c688a8a | 91bfc625b23246e77b9f05ddbcac30b0b53f8201 | /run_analysis.R | 6f46408b0ffd1f4ea8240fc0423767df13528c44 | [] | no_license | Puranjay2406/getting-and-cleaning-data-project | 3e7bb0dfd5bf51230192540acd2d178c27640ba6 | d05d3bff3284a1969b7541936bfa44d7faa1b35c | refs/heads/master | 2020-03-28T16:59:00.646955 | 2018-09-09T13:47:50 | 2018-09-09T13:47:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,519 | r | run_analysis.R | ################################ download file and unzip it####################
library(dplyr)
if(!file.exists("./data")){dir.create("./data");dir.create("./data/unzipdt")}
dturl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(dturl,"./data/wearabledt.zip... |
1c49f0469167827d2ba7d57978f403ae4e2c416d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmomco/examples/pp.f.Rd.R | 07d80d9247512a163eb62ba5b8dc4989fbb3df29 | [] | 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 | 307 | r | pp.f.Rd.R | library(lmomco)
### Name: pp.f
### Title: Quantile Function of the Ranks of Plotting Positions
### Aliases: pp.f
### Keywords: plotting position rankit
### ** Examples
X <- sort(rexp(10))
PPlo <- pp.f(0.25, X)
PPhi <- pp.f(0.75, X)
plot(c(PPlo,NA,PPhi), c(X,NA,X))
points(pp(X), X) # Weibull i/(n+1)
|
e16f05cb56ce038132c4c81f9eacd9fe92642eee | 230297d590fbc7156d408f0db2e4cb33a2c75fa5 | /ui.R | db718cd73475862d7cf18068599d9b61c46d0f37 | [] | no_license | jadavs/LeagueOfLegends-DataAnalysis | 3bc0acc705271b28c53dee13c688f8502902fcf8 | 146d0bc1590c66cee2705e385768cf6b1441b5c5 | refs/heads/master | 2021-08-24T01:31:28.875776 | 2017-12-07T13:16:23 | 2017-12-07T13:16:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,442 | r | ui.R | library("shiny")
library("plotly")
library("shinythemes")
shinyUI <- fluidPage(title = "LoL Analysis", theme = shinytheme('sandstone'),
tags$head(
tags$link(rel = "stylesheet", type = "text/css", href = "style.css")
),
navbarPage("Leag... |
518f1b46fb58539493f12e3e3190b490511f9cf4 | 319f8ff8b0228260ecfe14dc32964955529c0eb6 | /cpue/dorado_cpue_gam_standardization_month.R | e9bce5bc8bdf6e764ff90fe7c7e30cce054bcf36 | [] | no_license | imarpe/MDB | 0228324b32838d948b9007b11f6bf0034a63bd9d | bad916d26d8647b7bc7894b0878bef02ebb10b05 | refs/heads/master | 2020-04-09T14:59:00.384111 | 2016-06-23T02:27:56 | 2016-06-23T02:27:56 | 41,062,374 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,998 | r | dorado_cpue_gam_standardization_month.R | # Clean:
rm(list = ls(all = TRUE)) #clear all;
graphics.off() # close all;
gc() # Clear memmory (residuals of operations?, cache? Not sure)
require(gam)
require(pgirmess)
source("cpue_standardization_functions.R")
# Data pre-processing -----------------------------------------------------
perico = read.csv("perico.c... |
08fff484f6ed6777dd804def0cf3d18cef47e0bb | 8adbe657f2087fbec42acca33c047ba9926591f7 | /Model Building for Booking cancellations.R | 3994015b27753421e35242b1545b534323625917 | [
"MIT"
] | permissive | anuraglahon16/Final-Project-R | 219b44888c09b10752ca3111ec6e87122cbd13f6 | 52ac47e93eb282fa0feb9534ae42325059de04a8 | refs/heads/master | 2022-06-30T23:56:03.804661 | 2020-05-11T23:01:52 | 2020-05-11T23:01:52 | 258,346,715 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,099 | r | Model Building for Booking cancellations.R | getwd()
setwd('C:/Users/anura/Downloads')
hotels=read.csv("hotel_bookings.csv")
########################## MODEL BUILDING for booking cancellations #########################
install.packages("tidyverse")
install.packages("Hmisc")
install.packages("DataExplorer")
install.packages("ggplot2")
library(funMo... |
654195199a900011c97971556f2e5bb66af65c25 | 790b18693ac1ad05892bc84b9d2704ce02d144e5 | /interpreting-xgboost.R | 2953694373a6453057cabaf8bc0c0a6d9bf5c2aa | [] | no_license | tom-beard/ml-experiments | f97b8cb4d20dbb2d942f17b622a4c73fc6a89215 | 1afe606089e14bafa1641b05104dab5527ed09b0 | refs/heads/master | 2022-09-21T23:30:22.655308 | 2022-09-18T08:38:45 | 2022-09-18T08:38:45 | 240,835,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,142 | r | interpreting-xgboost.R | # from https://bgreenwell.github.io/pdp/articles/pdp-example-xgboost.html
# init --------------------------------------------------------------------
library(tidyverse)
library(xgboost)
library(pdp)
library(vip)
ames <- AmesHousing::make_ames()
# CV -----------------------------------------------------------------... |
f8866c8c0a6332550f2b96d44975a9ffb27d824b | 87829b9b9e8d1d5b89af005578bd7e062cd07971 | /crt_d_cov.R | fe8b49f29870c0ba84895f239d34506a0a0e1a5e | [] | no_license | hedbergec/PowerWorkShopCode | a230010780359db2029e83b151d88edd00b4b368 | e7e4ef347f75208d7945eda8e0503919dece6349 | refs/heads/master | 2020-05-18T04:23:30.862628 | 2019-04-30T01:42:37 | 2019-04-30T01:42:37 | 184,171,693 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 283 | r | crt_d_cov.R |
n <- 20
m <- 5
df <- 2*m-2 -1
alpha <- .05
power <- .8
rho.intra <- .1
R2.unit <- .5
R2.cluster <- .75
D <- 1 + (n-1)*rho.intra - (R2.unit + (n*R2.cluster-R2.unit)*rho.intra)
t.crit <- qt(1-alpha/2, df)
t.beta <- qt(1-power, df)
delta.m <- sqrt((2*D)/(m*n))*(t.crit-t.beta)
delta.m
|
8ba42777f886c2c36e0113b304f7a7fef4be546a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/regclass/examples/confusion_matrix.Rd.R | 2bc8a4936e603ad44dd7ffe062f35e315f7186a2 | [] | 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 | 915 | r | confusion_matrix.Rd.R | library(regclass)
### Name: confusion_matrix
### Title: Confusion matrix for logistic regression models
### Aliases: confusion.matrix confusion_matrix
### ** Examples
#On WINE data as a whole
data(WINE)
M <- glm(Quality~.,data=WINE,family=binomial)
confusion_matrix(M)
#Calculate generalization error u... |
038b36ad23dc68e5d50374b9aa6df50f1eb4d58e | 344614177666758a75a235966d5adbc2232987b6 | /R/cfb_ratings_sp_conference.R | 23f8732cff119e140c76c44be940e289007348b9 | [
"MIT"
] | permissive | saiemgilani/cfbscrapR | 61d677496bac5053c07fac708f34092ce70e141d | 0a33650fb6e7e6768b2cc1318c8960dd291c73f4 | refs/heads/master | 2023-03-31T06:19:47.722785 | 2021-04-03T23:53:23 | 2021-04-03T23:53:23 | 274,903,057 | 28 | 8 | NOASSERTION | 2021-02-20T19:36:07 | 2020-06-25T11:49:29 | R | UTF-8 | R | false | false | 5,247 | r | cfb_ratings_sp_conference.R | #' Get conference-level S&P+ historical rating data
#'
#' @param year (*Integer* optional): Year, 4 digit format (*YYYY*)
#' @param conference (*String* optional): Conference abbreviation - S&P+ information by conference\cr
#' Conference abbreviations P5: ACC, B12, B1G, SEC, PAC\cr
#' Conference abbreviations G5 and FB... |
ad105386d304ba3afe5e4a06805bd146ce9b5d30 | 7454266c05d38cf3286778ba4c8b8a19c378da2b | /rmongo-try.R | 75c1d9e2b7c709571088dc157604794cd67177bd | [] | no_license | mmirolim/analyze-clicks | 0e3d5555a917a8e73b71b73627e54111a55197c8 | 301828e2b43b046b9e15ac945b1b74cbf19d9489 | refs/heads/master | 2016-08-09T18:45:14.777316 | 2015-11-27T16:20:30 | 2015-11-27T16:20:30 | 46,752,445 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 561 | r | rmongo-try.R | library(RMongo)
library(dplyr)
library(parsedate)
today <- as.Date('2015-11-28', format = '%Y-%m-%d')
db <- mongoDbConnect("ltvdb")
output <- dbGetQueryForKeys(db, 'events', '{"regdate": {"$lte":1446249600}, "eventdate": {"$gte": {"$date": "2015-10-31T24:59:37.275Z"}}}', '{"_id":0, "profileid":1, "eventdate":1}', sk... |
7cae39cf0f12765ac3fe1042b4e33edbe046a244 | eac759ea418d8522b239cd420039c5047f34b546 | /R/size.prop.confint.R | 1f6dcf13369758ccf736811c16adf2cfb0c6b1c8 | [] | no_license | cran/OPDOE | 1dac02018283dcbe1ad0cd8b85736baf930718f4 | cc4f73c76e7a3655ddbc7fc8be069d150182f15d | refs/heads/master | 2021-05-15T01:55:50.474710 | 2018-03-17T21:49:18 | 2018-03-17T21:49:18 | 17,681,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 177 | r | size.prop.confint.R | size.prop.confint <- function(p=NULL,delta,alpha)
{
q=qnorm(1-alpha/2)
if (is.null(p)) {n=q^2/(4*delta^2)}
else {n=p*(1-p)*q^2/delta^2}
n=ceiling(n)
return(n)
}
|
5a9b7b9d3bd4839303cc65a4ae17d810ce16abaa | 9f56bef3c268b4d10f86817e15a64bc6ecd93a2f | /R/sim-helpers.R | be44178420d872df21e1df60c95dbe8110359904 | [] | no_license | Qian-Li/HFM | b6707322e41e53c0d61557c11a3da10684a2527f | d047c583095fcd614e05ad616e8207613555fff9 | refs/heads/master | 2021-04-26T22:11:02.759156 | 2018-05-04T06:42:30 | 2018-05-04T06:42:30 | 124,034,587 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,211 | r | sim-helpers.R | ## Simulation Helpers
##
#' Simulation Helper: quantile-based knots
#'
#' @param x A vector of observations
#' @param num.knots An integer, Number of knots.
#' @return A vector of \code{num.knots} length as quantile-based knots for \code{x}
#'
#'
#' @export
default.knots <- function(x,num.knots)
{
# Delete repeated ... |
e08613890cf95c4b73ffe9ddc38b724204b8f4d3 | c570e275bebb8ff8ecb1439d135fc052dfecc47f | /R/modify.R | 8b5fe9e1d73a1297a6d6e4860c6c59d784548e35 | [] | no_license | runehaubo/simr | d0e1ae35f14d3ff94b48c0bebbfb2b0941f5f919 | aec34ad674ffb2f2173ca3da0f507fd6687aee6a | refs/heads/master | 2021-03-19T11:26:32.261723 | 2018-04-20T10:46:22 | 2018-04-20T10:46:22 | 123,607,883 | 0 | 1 | null | 2018-03-02T17:13:19 | 2018-03-02T17:13:17 | R | UTF-8 | R | false | false | 6,642 | r | modify.R | #' Modifying model parameters.
#'
#' These functions can be used to change the size of a model's fixed effects,
#' its random effect variance/covariance matrices, or its residual variance.
#' This gives you more control over simulations from the model.
#'
#' @name modify
#' @rdname modify
#'
#' @param object a fitted m... |
246950343b8433620d7811760b61541290a25f71 | 829efd8da04ee3d78e848bf9926373e0d38caaf9 | /session1.2-Robjects.R | dfbcfa67cb450e1ce93bdd4b9222a3b8722971a6 | [] | no_license | Dr-yang/git | fbab5100eadeb788b3f631bc622e6a12386c18f5 | 2ac3aff48869c73ce74b29b000f3738bf1fce228 | refs/heads/master | 2021-05-27T12:48:09.006450 | 2014-05-13T23:25:02 | 2014-05-13T23:25:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 980 | r | session1.2-Robjects.R | ## session 1.2 R objects
gDat <- read.delim("gapminderDataFiveYear.txt")
str(gDat)
head(gDat)
head(gDat, n=10)
tail(gDat, n=10)
names(gDat)
dim(gDat)
nrow(gDat)
ncol(gDat)
length(gDat)
summary(gDat)
plot(lifeExp ~ year, data=gDat)
plot(lifeExp ~ gdpPercap, data=gDat)
str(gDat)
gDat$lifeExp
summary(gDat$lifeExp)
hist(... |
62bc25e3cc6c50247396fe3b9f4df689dbebb782 | ee1afa85744a7b63f894693b5aff1ed3edef943c | /cachematrix.R | 0a9b362fdbdf5a7256f7567a79905a64836098e9 | [] | no_license | nhhikr/ProgrammingAssignment2 | f2b4f8e440e0af290934ff549fb46c9a732396a9 | 5be3c7da2ec787b46a73a1e459122f6a3e1cb8f5 | refs/heads/master | 2021-01-09T06:27:44.547881 | 2015-01-24T13:04:01 | 2015-01-24T13:04:01 | 29,311,154 | 0 | 0 | null | 2015-01-15T18:18:29 | 2015-01-15T18:18:29 | null | UTF-8 | R | false | false | 1,711 | r | cachematrix.R | ## this pair of functions demonstrate R code
## you must call the first function makeCacheMatrix()
## to set up the data structures to cache a matrix inverse
## then call cacheSolve() to actually compute the inverse
## using the function handle returned by makeCacheMatrix()
## calling makeCacheMatrix(x) creates a fu... |
ecd2cd74b6b4b26a73787ec443e4797f21fb53f3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/qsort/examples/print_cards.Rd.R | 28c848cb2ba8ca2c31307ab841be535234710f33 | [] | 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 | 209 | r | print_cards.Rd.R | library(qsort)
### Name: print_cards
### Title: print_cards
### Aliases: print_cards
### ** Examples
## No test:
print_cards(qset_aqs, desc_col = "description", dir.print = tempdir())
## End(No test)
|
fed789b6ca32ffb765b56a0c55812cf8b99d12b8 | 5885524cbba3f812c1eddc1912ec33304b54c1d4 | /man/as_dummy.Rd | 902df57a2d73427e1be2c3be0feb0540e23fe24a | [
"MIT"
] | permissive | jackobailey/jbmisc | a198ab9855800dfcd8bd2c9d639f6dc70413ddef | 960ce7852fe3144359972500b2858a2976323dff | refs/heads/master | 2021-12-14T19:01:18.073680 | 2021-12-09T20:29:04 | 2021-12-09T20:29:04 | 142,127,918 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 803 | rd | as_dummy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as_dummy.R
\name{as_dummy}
\alias{as_dummy}
\title{Convert Factor Variables to Dummies}
\usage{
as_dummy(x, ..., factor = T, labels = c("Off", "On"))
}
\arguments{
\item{x}{A vector of factor data to convert to a dummy.}
\item{...}{Terms to ... |
da7d2ee6c53a4a340ac224f80142fa1f7313da56 | e0cc4d8b9e96b90e53884c64b82329f6358e7bf3 | /chapter_02.R | 9500999f0d2242fc641ae8bbf01e8c5af84cd6ac | [] | no_license | r-visualization/r-visualization-book | 3912fd814c1644969481065c130d8841a3799cdd | 3cf2764f8760b8b90c0761267cc71502084ac29a | refs/heads/master | 2020-06-02T00:39:22.529009 | 2019-06-09T14:34:07 | 2019-06-09T14:34:07 | 190,981,055 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 27,295 | r | chapter_02.R | # -----------------------------------
# R visualization - 소스코드
# 출판사: 도서출판 인사이트
# 저자: 유충현, 홍성학
# 챕터: 2장
# 파일명: chapter_02.R
# -----------------------------------
# ==================== 소스순번: 001 ====================
# x-좌표를 위한 벡터
x1 <- 1:5
# y-좌표를 위한 벡터
y1 <- x1^2
# 벡터 생성
z1 <- 5:1
# 행렬 생성
(mat1 <- cbind(x... |
37781d63b62788b2e4e855898144f1c93e24cdc5 | b18044c24c29c7a49a54930548a4567dd7cf519e | /man/generate_data.Rd | 04ba815cea2655d1c6ed4f9470636a102c0b2c3d | [] | no_license | ChongWu-Biostat/GLMaSPU | 14bc3ecef928546e91a066479c8f7f1f167aaa6e | 1523be583a355d8d7cb1a796bc8d5ef164ff3cd2 | refs/heads/master | 2021-01-17T17:39:01.712687 | 2017-08-08T18:45:53 | 2017-08-08T18:45:53 | 70,542,503 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,044 | rd | generate_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generate_data.R
\name{generate_data}
\alias{generate_data}
\title{Generate data for generalized linear models in simulation.}
\usage{
generate_data(seed, n, p, beta, alpha)
}
\arguments{
\item{seed}{Random seed.}
\item{n}{Number of samples}
... |
281661d66b7d3dd6075be78488ea82bee85aa8d9 | f44c3ebd7e5af79fc4ba70169612377028418d08 | /ui.R | 90717293c008ee115d49f9031b477e47c978f778 | [] | no_license | mariasu11/DevelopingDataProducts-Course-Project | 6b222bd263c118ec1a49f6c2372204d88cc5a5dd | ab744eb70cdec3ac675a244aff1acb05bf25dfc9 | refs/heads/master | 2021-01-10T07:14:59.838486 | 2015-05-22T15:04:49 | 2015-05-22T15:04:49 | 36,045,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,935 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel("Maryland Home Prices Historical Comparision and Prediction for 2016"),
sidebarLayout(
sidebarPanel(
h1("What is the price of your Maryland home?"),
numericInput(inputId="homePrice", label="Your home price", value= 0,min=0),
submitButto... |
17f4f8cae18d9041a91761e4c7ccadac414dd196 | 77471bc2b7d12997a2702a0fa4719b2f71d46b00 | /man/readhtml.Rd | 706b2bc64bba702d041813e3342fcea832e9c0dd | [] | no_license | songssssss/cleanbot | afeb72d810788176edf5e0b7189841d5f2e23926 | ba7fe5a4745836b3b6da8ff68fbb138c462cbb17 | refs/heads/main | 2023-04-06T23:13:54.673563 | 2021-04-22T04:30:43 | 2021-04-22T04:30:43 | 353,277,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 293 | rd | readhtml.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readhtml.R
\name{readhtml}
\alias{readhtml}
\title{A function}
\usage{
readhtml(file)
}
\arguments{
\item{file}{A string}
\item{expr}{A variable in the dataframe}
}
\value{
A dataframe
}
\description{
A function
}
|
7c3f3d14c0eeab5fc3987e53fffbe1c1c7db29c8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/prabclus/examples/build.ext.nblist.Rd.R | 1fcbf3a7bbda1cb90f0f64b7bd66ce83edc3fc91 | [] | 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 | 340 | r | build.ext.nblist.Rd.R | library(prabclus)
### Name: build.ext.nblist
### Title: Internal: generates neighborhood list for diploid loci
### Aliases: build.ext.nblist
### Keywords: cluster
### ** Examples
data(veronica)
vnb <- coord2dist(coordmatrix=veronica.coord[1:20,], cut=20,
file.format="decimal2",neighbors=TRUE)
build.ext.nb... |
c0324deaf0a50076be09be1a0b9411e63b31c8cf | 9035f2a2fa85a704eed28018dec2667c031781f1 | /FunciónGrafica.R | 8a29401ba1f090ae68b432f1045333fede6070a5 | [] | no_license | AxlRG96/EvidenciaModulo2 | 174ddfb5fc9459fdaac7f4817e81fe938d0f8ca2 | 370754a15c6e7b56ad73cadaa8e00cb4c81d5d88 | refs/heads/main | 2023-04-10T04:58:20.982410 | 2021-04-16T16:09:51 | 2021-04-16T16:09:51 | 358,637,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,294 | r | FunciónGrafica.R | library(httr)
library(jsonlite)
library("dplyr")
library(reshape2)
library(plotly)
urlS <- "http://localhost:4000/api/reporteb/hoja123"
bodyS <- toJSON(list(
list("fecha1"="2021-02-01",
"fecha2"="2021-03-20",
"fechat1"="2021-03-01",
"fechat2... |
25c137f25373ea09a706f5c73f07e456287a6b43 | 432ecdbb1a40e1c5ba7bc1d55d4f068f1e97c2c5 | /gene_pathway_membership_long_to_wide.R | e01cad8eae8b35b242ac64bcf1f47949c7a61871 | [] | no_license | joshuaburkhart/bio | a2b5a2ca638260d8478c7c34d4ee3d1a9039626d | 7288bd3f089fa15dbeffd4a159946e7a899954c7 | refs/heads/master | 2021-01-23T19:38:59.635484 | 2017-10-23T17:20:18 | 2017-10-23T17:20:18 | 6,525,975 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,730 | r | gene_pathway_membership_long_to_wide.R |
# Gene <-> Pathway: Long to Wide R Script
# Author: Joshua Burkhart
# Date: 9/20/2016
# Libraries
library(openxlsx)
library(dplyr)
library(magrittr)
library(reshape2)
# Globals
DATA_DIR <- "/Users/joshuaburkhart/Research/DEET/biting/analysis/"
CONTIG_MAP_FILE <- "DEET_loci_annotation.csv"
CONTIG_MAP_PATH <- paste(DA... |
447b255e990cbb6c7bc48347fa822feb8c946ef0 | f0388bbd23c406ea31b01fe81eb8d03ac3dbbf91 | /R/namespace.R | a879e41fa4db594680fab01fa348a6f403557fef | [
"MIT"
] | permissive | pre-processing-r/chk | 78507709b12c3ec9361ddc87acfb7e748932564b | ddfa8294185c282dc833c7f8170de187f57c0ac0 | refs/heads/master | 2023-08-29T14:56:34.954841 | 2021-10-16T19:41:25 | 2021-10-16T20:03:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 49 | r | namespace.R | #' @import rlang lifecycle
#' @import typed
NULL
|
0bca379113b36bc4aaa06f18bc42915bb937f313 | bf777a173db8979cd63ce88f9be189c2ee29656f | /note_learning_all.r | a3f6dcbe2a1ec2e7609e67eda098fa632f8ea87d | [] | no_license | vimlu/note_code | e1e3c426a06c224597fabb5c5e0980955f33cc42 | 4182cbe4844437935f9464745f4ec7defd09870c | refs/heads/master | 2020-03-28T12:52:55.852472 | 2018-10-26T08:58:00 | 2018-10-26T08:58:00 | 148,343,337 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,774 | r | note_learning_all.r | source("https://bioconductor.org/biocLite.R")
## or source("http://bioconductor.org/biocLite.R")
biocLite("ComplexHeatmap")
biocLite("RTCGA.clinical")
biocLite("ballgown")
install.packages("devtools")
devtools::install_github('alyssafrazee/RSkittleBrewer')
tx<-runif(100)
ty<-rnorm(100)+5*tx
tmodel<-lm(ty~tx)
attribu... |
e92b45c8e1506347bcdbc99cc95d918acd87361f | 8866b741411e2edfa61972369143de26fde5f821 | /man/QueueingModel.i_MMInfKK.Rd | 64ab10737e03aa9f39dc47f0a256faeab82941cb | [] | no_license | cran/queueing | 6232577c0eb67cae7c716ef1432cc54194fb26d4 | 7712782a0d82d73599f128f68e94536b0cf8d4e5 | refs/heads/master | 2020-12-25T17:36:19.513391 | 2019-12-08T21:10:02 | 2019-12-08T21:10:02 | 17,698,913 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 822 | rd | QueueingModel.i_MMInfKK.Rd | % File man/QueueingModel.i_MMInfKK.Rd
\name{QueueingModel.i_MMInfKK}
\alias{QueueingModel.i_MMInfKK}
\title{Builds a M/M/Infinite/K/K queueing model}
\description{
Builds a M/M/Infinite/K/K queueing model
}
\usage{
\method{QueueingModel}{i_MMInfKK}(x, \dots)
}
\arguments{
\item{x}{a object of class i_MMInfKK}
\... |
247dc185bb7cb8b3955057ece45a3310ed46f098 | 4dc66ca0241589deeadafd3c1d656ac08e87beaa | /Scripts_/03_Sensitivities.R | f58aea09f4d6d060d90485007f76cfa41eca51ad | [] | no_license | erwanrh/SSE-model-Longevity | e1cece124531a9f58e71906681bd3a59c68373a1 | 32536e0e662d486a3ef9a2390d44de21715b6421 | refs/heads/master | 2023-02-05T09:27:46.560059 | 2020-12-29T18:58:46 | 2020-12-29T18:58:46 | 263,285,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,945 | r | 03_Sensitivities.R | ######## SCRIPT MODEL SSE Sensitivities Analysis #######-
# Author: Erwan Rahis (erwan.rahis@axa.com), GRM Life, Longevity Team
# Version 1.0
# Last update: 12/05/2020
# Script to fit a Sums Of Smooth Exponential model according to the paper "Sum Of Smooth Exponentials to decompose complex series of counts"
# Camarda... |
6fdfeb5a48a26457db1540db5870c149fb567e41 | 4e6f18e6feb01502f2cd15772b6c0cc2f7c0629d | /man/treatments_by_policy.Rd | 8059289f4095b79911a697dd124dda352441303c | [] | no_license | cancerpolicy/bcimodel | 8f76e9a64c79773c4e7058f1a71eb619a6496d8e | 219df7c778ce8388b661a83de45845b2b0f57815 | refs/heads/master | 2021-01-13T02:55:45.874682 | 2019-06-27T19:50:48 | 2019-06-27T19:50:48 | 77,089,332 | 6 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,375 | rd | treatments_by_policy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treatment.R
\name{treatments_by_policy}
\alias{treatments_by_policy}
\title{Use sim_treatment_by_subgroup to simulate treatments}
\usage{
treatments_by_policy(policies, treat_chars, stagegroups, map, pop_size,
nsim)
}
\arguments{
\item{poli... |
c3b65c65c0028a21dbeb952079a352dbeddc08c5 | 96a3c9ff4b96b5493b1012fa5d60e144270c3952 | /run_analysis.R | 97dca96f8ba1b92b90ab86be948c2de1440980df | [] | no_license | cnemri/DataCleaning | a9c9b18a6939eae93824b5ad012f97be4583c572 | 42d043c4b4bb243e5e53f0622901cadc4dd07236 | refs/heads/master | 2022-02-23T15:30:35.845051 | 2019-10-20T06:32:15 | 2019-10-20T06:32:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,529 | r | run_analysis.R | # Getting and Cleaning Data Course Project
rm(list = ls())
# The script processes Activity Recognition Using Smartphones Dataset
# It has two outputs
# data table X : Dataset as per Step 4 of the Instructions
# data table X_tidy : Tidy Data set as requested in Step 5 of the instructions
if(!file.exists('./data')) {dir.... |
158c405064845817f7514ce3960ecae2559132a7 | 0480eb0ebcc0ffcc5889b8f8674195816c9e57c5 | /Lesson 3 - Functions, loops, ifelse/RNASeqResults.R | 5a8fbed653422bf07c12437b3f934f6aaf3a7bcc | [] | no_license | aharkey/RschoolScripts | d364f9eb2132a8e37378386734b022268d87f0ff | 34bda28b8bbacc111d48db77195926cc7f2f1d37 | refs/heads/master | 2022-11-23T08:20:45.281138 | 2020-07-27T16:01:16 | 2020-07-27T16:01:16 | 268,577,891 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 461 | r | RNASeqResults.R | # Given a range of values, which value has the largest absolute value
maxabs <- function(a)
{ifelse(max(a)>abs(min(a)), max(a), min(a))}
# Given a logFC and pvalue, and cutoffs for both, return a single character
# representing the direction of change (U or D) or no change (n)
upordown <- function(pval, lfc, pcut = 0.... |
b3f0453b06b29598cfdd0e3d1bdde8acb9d74816 | 47522d9918014670e0a78bcb874f98147b27c9c1 | /man/rsd.Rd | 3ef9334ebebefda748bdef2516940142ac1010df | [] | no_license | galangjs/galangjs | b764a043a3b4a78225758cb7a8d583641deb5be2 | 3d7e2e2f609eedd41b2a0d10e68712c776f31ec2 | refs/heads/master | 2021-01-18T13:49:22.298744 | 2015-09-12T03:26:23 | 2015-09-12T03:26:23 | 41,980,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 476 | rd | rsd.Rd | % Generated by roxygen2 (4.1.1.9000): do not edit by hand
% Please edit documentation in R/rsd_et_al.r
\name{rsd}
\alias{rsd}
\title{Relative Standard Deviation}
\usage{
rsd(x, as_pct = FALSE)
}
\arguments{
\item{x}{a numeric vector}
\item{as_pct}{logical stating whether the result should be expressed as a
percentage}... |
34c8778a33048d584bd5ab955b7d6f6aec06cb37 | cfcd465ec5e08de08347c0ddb9b105d5b2ba1013 | /src/prediction/prediction_randomforest.R | 1160a85671a1e1d03a8b21f07a7916af7e7e3345 | [] | no_license | dewaldayres/titanic | a031001bd471ab76964a2e5e3c59892bb08d5df4 | dc349be0030d861bc32c63c354ff52f3f90213a7 | refs/heads/master | 2020-03-11T10:03:03.015295 | 2019-01-09T17:20:47 | 2019-01-09T17:20:47 | 129,926,649 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 581 | r | prediction_randomforest.R |
#
# randomforest prediction
#
set.seed(415)
train <- passengers %>% filter(set=="train")
test <- passengers %>% filter(set=="test")
test <- within(test, rm(survived))
fit <- randomForest(survived ~ passenger_class + gender + age + fare + family_size + title, # 80.38
data=trai... |
87425489843a604ebd357e2be9918eaa51bce589 | 1e8406ba4774f2717acb4d53b26589e5913e951a | /cor_gene_counts.R | 7c4e810c907e37ac23cf5b481b70a8b53bba70fb | [] | no_license | russ-dvm/cchfv-rnaseq | 27e507242bd0c14fdf1545d82625ed088638ebbd | fd052396a6050a5feb4f44f8826a67fae0bd2885 | refs/heads/master | 2021-10-16T14:52:34.235047 | 2019-02-11T16:53:50 | 2019-02-11T16:53:50 | 104,894,232 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,530 | r | cor_gene_counts.R | library(tidyverse)
library(ggpubr)
library(plyr)
hep <- read.csv("~/Dropbox/temp/gene_count_matrix.csv", h = T)
huh <- read.csv("~/Dropbox/temp/huh_gene_counts.csv", h = T)
cor_fnc <- function(x, offset){
cor_list <- list()
for (i in 1:(ncol(x)-1)){
j <- i
i <- i + offset
if (i %% 3 == 0){
lib... |
c78bd8250f5a5e3353e875ba6b5499ab84595e67 | c109be0d330c4a0a06c055a2b1802ffc4b9c73d5 | /Rstem/man/wordStem.Rd | 392ee9efc7341ce3212a01bc9f2d5267301bd58a | [] | no_license | bradleyboehmke/Air-Force-Little-Blue-Book | c228706934cf694a1af29ea916411fd9186cdb3b | 3aa57b80d1add460d4c128f223f3c170bfc6f85f | refs/heads/master | 2021-01-10T14:20:40.904660 | 2016-03-13T18:56:37 | 2016-03-13T18:56:37 | 48,626,590 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,838 | rd | wordStem.Rd | \name{wordStem}
\alias{wordStem}
\title{Get the common root/stem of words}
\description{
This function computes the stems of each of the
given words in the vector.
This reduces a word to its base component,
making it easier to compare words
like win, winning, winner.
See \url{http://snowball.tartarus.org/} ... |
2ab8f6fcf7ea1836b1d68281c45b46e53c015524 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.developer.tools/man/codeartifact_list_repositories.Rd | e39376d66724a1f6178a80495fa333214af27d51 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,175 | rd | codeartifact_list_repositories.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codeartifact_operations.R
\name{codeartifact_list_repositories}
\alias{codeartifact_list_repositories}
\title{Returns a list of RepositorySummary objects}
\usage{
codeartifact_list_repositories(
repositoryPrefix = NULL,
maxResults = NULL,... |
7f8e50394fe6e3285e612513337d6402eaf2076d | 22057bf4f2eb001f739761e53a5578de578e6920 | /scripts_on_file1/compare_tracer_premodeling.R | 1b0d819eef077c764fa296a772a1b62d290816bb | [] | no_license | mrubayet/archived_codes_for_sfa_modeling | 3e26d9732f75d9ea5e87d4d4a01974230e0d61da | f300fe8984d1f1366f32af865e7d8a5b62accb0d | refs/heads/master | 2020-07-01T08:16:17.425365 | 2019-08-02T21:47:18 | 2019-08-02T21:47:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,979 | r | compare_tracer_premodeling.R |
#This file is used for plotting flux averaged concentrations with multiple realizations
rm(list=ls())
#set the path to the files
main_path = '/files2/scratch/chenxy/'
#comp_paths = c('MeanField/IFRC2/120x120x30','IFRC2_Tracer_MeanField_BoundaryTracer/Injected')
#comp_legends = c('Dirichlet','Zero_gradient')
... |
2f11b137420cecf7d9a3f63222540567149ae968 | 4dcbd7bde2c131cb0d3c96990eb20df6e9ea22ed | /man/filter_noise.Rd | 52068006951a390eb92adf5363997beb89bd1af2 | [] | no_license | LindaLuck/VoxR | e3428d0967a2d5b5b3470cdfe85df92d75e7dfb9 | 4ad74f91aa421881120fc387137861d57c92f790 | refs/heads/master | 2022-12-24T02:44:23.030784 | 2020-09-30T07:50:08 | 2020-09-30T07:50:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,723 | rd | filter_noise.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/filter_noise.R
\name{filter_noise}
\alias{filter_noise}
\title{Statistical filtering of a point cloud.}
\usage{
filter_noise(data, k, sigma, store_noise, message)
}
\arguments{
\item{data}{a data.frame or data.table containing the x, y, z, ..... |
82d4f6e593974326668ce63c3d01890d1ff918ee | 72a7ccc09560084e0a7465ff3a7c10c61e06f889 | /man/plotDiffPathways.Rd | a0f7db9fd46a284d05b0312d1d7f62f2f06ec430 | [] | no_license | liuqivandy/scRNABatchQC | e2cbe87ce3be2b87139b0c8f1e5c9f806e99e02a | 32689ef428eeff0a6e97e6089cb9594d1f7c7b3d | refs/heads/master | 2021-06-11T20:26:40.934055 | 2021-03-20T17:39:39 | 2021-03-20T17:39:39 | 119,717,047 | 11 | 6 | null | 2020-09-08T11:54:23 | 2018-01-31T17:04:37 | HTML | UTF-8 | R | false | true | 1,264 | rd | plotDiffPathways.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotFunctions.R
\name{plotDiffPathways}
\alias{plotDiffPathways}
\title{plot pathways enriched in differentially expressed genes}
\usage{
plotDiffPathways(
scesMerge,
margins = c(5, 5),
keysize = 1,
col = colorpanel(75, low = "white",... |
8a909877b4b10af803b94abae1bf3278d4fa39ea | e189d2945876e7b372d3081f4c3b4195cf443982 | /man/resize_max.Rd | 62cf27daf2f3c71108614f083224a0115b4abd9a | [
"Apache-2.0"
] | permissive | Cdk29/fastai | 1f7a50662ed6204846975395927fce750ff65198 | 974677ad9d63fd4fa642a62583a5ae8b1610947b | refs/heads/master | 2023-04-14T09:00:08.682659 | 2021-04-30T12:18:58 | 2021-04-30T12:18:58 | 324,944,638 | 0 | 1 | Apache-2.0 | 2021-04-21T08:59:47 | 2020-12-28T07:38:23 | null | UTF-8 | R | false | true | 450 | rd | resize_max.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vision_core.R
\name{resize_max}
\alias{resize_max}
\title{Resize_max}
\usage{
resize_max(img, resample = 0, max_px = NULL, max_h = NULL, max_w = NULL)
}
\arguments{
\item{img}{image}
\item{resample}{resample value}
\item{max_px}{max px}
\i... |
762e0b44f3b227793358d8540c4d2e3fc39204a4 | 8c2a46a573a0841d60392de018ab642c86682c35 | /inst/tests/test-FLStock_cpp.R | 0965c819c922acc77a739f10139268b556e4c1d0 | [] | no_license | drfinlayscott/FLRcppAdolc | aae91bc53f2998b83ee1e0ebf783d88fc27f2e78 | 9394549750aad754f15a521d57252a9d8c47185f | refs/heads/master | 2021-01-18T18:16:34.118974 | 2014-08-25T11:43:38 | 2014-08-25T11:43:38 | 9,937,693 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,701 | r | test-FLStock_cpp.R | context("CPP implementation of FLStock")
test_that("FLStock SEXP constructor",{
data(ple4)
sn <- test_FLStock_sexp_constructor(ple4)
expect_that(sn, is_identical_to(ple4@stock.n))
})
test_that("FLStock wrap and as",{
data(ple4)
fls <- test_FLStock_wrap(ple4)
expect_that(fls, is_identical_to(pl... |
46281f013ea7abb23d60d4ddab3d2e4bad984e7e | 74bc48ba64859a63855d204f1efd31eca47a223f | /DSM/1003.Prav_CV_CrossValidation.R | 1911177c7afb4377d1a0a32abe7d673f9dfc6dff | [] | no_license | PraveenAdepu/kaggle_competitions | 4c53d71af12a615d5ee5f34e5857cbd0fac7bc3c | ed0111bcecbe5be4529a2a5be2ce4c6912729770 | refs/heads/master | 2020-09-02T15:29:51.885013 | 2020-04-09T01:50:55 | 2020-04-09T01:50:55 | 219,248,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,766 | r | 1003.Prav_CV_CrossValidation.R | ###############################################################################################################################
# Sys.time()
# save.image(file = "DSM2017_03.RData" , safe = TRUE)
Sys.time()
load("DSM2017_03.RData")
Sys.time()
##############################################################################... |
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