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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
624fbd66fcdb86938e697ac58ef841e1db5433d0 | 30c71b4abd1160411b3407898da82ed0ada91e2e | /merge_c_m3.R | 054d3c8e0e9b10d59b1a2331b20d6ac17a5e6816 | [] | no_license | rtmill/CMS-to-analytic-csv | c2548edc1ab0d9ff8b39abe35f58cef1753d586b | 7841b4bcdbf5bb1ee31b8b4130027358b3348fcc | refs/heads/master | 2021-01-12T14:07:01.979018 | 2017-05-18T16:47:52 | 2017-05-18T16:47:52 | 70,164,838 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,851 | r | merge_c_m3.R | # Script to merge C-1 w/ m3
# -- assumes you have tables c and m3 loaded
# Add source
# c
df <- data.frame(c[,1])
names(df)[1] <- "RPT_REC_NUM"
df$TYPE <- "INDEPENDENT"
df$SUBPROVIDER <- 0
df$TITLE <- 0
df <- cbind(df, c[,2:8])
#m3
df2 <- data.frame(m3[,1])
names(df2)[1] <- "RPT_REC_NUM"
df2$TYPE <... |
0aafc49eca352a11cbdebc9e3c392cf69f88b14c | 5ba3cc6e6db58697fbbdde318175183c737bc309 | /step3_xIBD/XIBD/R/merge_lists.R | 4448abba1636f3302f95ac89d22bdeac0f61d084 | [] | no_license | RJHFMSTR/PofO_inference | a355711295f737678dd504f58c84c7f7b596535e | f139ece43b19f6e650017774ab4d202d52fd5350 | refs/heads/master | 2023-04-17T18:25:59.448153 | 2022-11-30T15:23:51 | 2022-11-30T15:23:51 | 422,484,656 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 845 | r | merge_lists.R | # Internal Function
#
# Merge Returned Lists from Parallele
#
# \code{merge_lists()} is a function used to merge summary IBD results for multiple pairs when running the IBD analysis on
# multiple cores
#
# @param A List with n objects for one pair.
# @param B List with n objects for another pair. The dimension of each ... |
0cb337aa27e38f5a9ef73ffdcb73a5a43a9a228c | 3f476a051eb22af77130ee485d0cbac40ccde03c | /sensitivity.boxplots.r | 7512762e958f79dce55b67b7895553ec427856aa | [] | no_license | ranalut/Scripts | a62d44f809c460319cdd1e2ad249f32aec9ce4b3 | d863122f53e3a22d23c87a1105c6de46280e2ad0 | refs/heads/master | 2020-12-29T02:38:27.728016 | 2017-01-23T07:00:16 | 2017-01-23T07:00:16 | 8,935,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,097 | r | sensitivity.boxplots.r |
setwd('c:/users/cbwilsey/documents/github/scripts/')
library(maptools)
library(sp)
library(RColorBrewer)
library(fields)
source('data.prep.r')
source('extract.number.r')
# extract.number <- function(x,var.name) { temp <- as.numeric(strsplit(x,split=var.name)[[1]][2]); return(temp) }
sensitive.boxplots <- function(w... |
54a1e79595cf30aa2008c05b60bda61323b62c3b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/modelfree/examples/comploglog_link_private.Rd.R | 54e41b5693c535c86ce8ed2e51e05ee4ae9f8e16 | [] | 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 | 574 | r | comploglog_link_private.Rd.R | library(modelfree)
### Name: comploglog_link_private
### Title: Complementary log-log link function with guessing and lapsing
### rates
### Aliases: comploglog_link_private
### Keywords: nonparametric models regression nonlinear
### ** Examples
data( "01_Miranda" )
x <- example01$x
r <- example01$r
m <- example01... |
a5609ee0e7f54feb9d6b8f3b81db428a2ed0588c | 7eb63399fa00e3c547e5933ffa4f47de515fe2c6 | /man/is.retain.Rd | 5c099afa6e45826843282ecce21a89c0733d49c4 | [] | no_license | bentaylor1/lgcp | a5cda731f413fb30e1c40de1b3360be3a6a53f19 | 2343d88e5d25ecacd6dbe5d6fcc8ace9cae7b136 | refs/heads/master | 2021-01-10T14:11:38.067639 | 2015-11-19T13:22:19 | 2015-11-19T13:22:19 | 45,768,716 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 342 | rd | is.retain.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/mcmcIterator.R
\name{is.retain}
\alias{is.retain}
\title{do we retain this iteration?}
\usage{
is.retain(obj)
}
\arguments{
\item{obj}{an mcmc iterator}
}
\value{
TRUE or FALSE
}
\description{
if this mcmc iteration is one not thinned... |
9c5e6a758f2fc2217102db0a9f0aa5f071df6b68 | 5528c02b97dd00525bd91c884a7a30885e9551db | /plot2.R | eaed3813e814670d0795bbd463e757799defec0a | [] | no_license | Shekeen/ExData_Plotting2 | 17daaad6345b240adfb8c1842e0e71f5b7fdffe0 | 4a77790e583166e2d23a6327a390451c39473658 | refs/heads/master | 2016-09-05T21:55:21.105271 | 2014-07-23T14:31:28 | 2014-07-23T14:31:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,527 | r | plot2.R | emissions_file <- 'summarySCC_PM25.rds'
scc_table_file <- 'Source_Classification_Code.rds'
if (!file.exists(emissions_file) | !file.exists(scc_table_file)) {
tmpfile <- tempfile()
download.file(url='https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip',
destfile=tmpfile)
unzip(tmpfi... |
d0e60bcd1697990408b43c98e189b2f1e0a362df | 8d3a51881b0f757a4bc8eb2f7f87685c77acfab6 | /tests/testthat/test-string.R | 8eafcbcf4d4c2fc046ad41941e6dce4c27576861 | [] | no_license | cran/wakefield | 679968f83c3c889616941569afa303156585d67b | 734a76be93f1df21651312ff2bbf7ba7288bc4f4 | refs/heads/master | 2021-01-15T15:25:42.747684 | 2020-09-13T16:30:02 | 2020-09-13T16:30:02 | 48,091,046 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 66 | r | test-string.R | context("Checking string")
test_that("string ...",{
})
|
437a3802a55433fccdb6325faf40a22c70a84493 | 439ea351c775a7b192bedc1d3f34d618f5c4a186 | /Exe8_Q1_Code.R | a8d4652b0f7fcfc9b1c5363a726472a591df6f4d | [] | no_license | davissage/Intro_Biocomp_ND_317_Tutorial8 | 7901dd9b4c4b8c602f6c9b2fa9de8621a67e98ce | 7f74b152bbbd08af4526402383e37c04eeb01da7 | refs/heads/master | 2021-07-12T06:41:13.330343 | 2017-10-14T02:31:51 | 2017-10-14T02:31:51 | 106,833,497 | 0 | 0 | null | 2017-10-13T14:18:20 | 2017-10-13T14:18:20 | null | UTF-8 | R | false | false | 2,175 | r | Exe8_Q1_Code.R | setwd("c:/Users/DAVIS/Desktop/shell-novice-data/exe8/Intro_Biocomp_ND_317_Tutorial8/")
library(stringr)
Cflorida <- scan(file="Cflorida.vcf",what = character(), skip = 1)
Cflorida2 <- read.table(file="Cflorida.vcf", skip = 1)
colnames(Cflorida2) <- Cflorida[1:90]
Cflorida3 <- as.data.frame(matrix(nrow=999, ncol=90... |
8c9f7e32bd159b52ccaab6155bb3ae472de7d664 | 74d3ccdbeeee691888e89073039b47a9b737d78f | /man/modelCandleFeatures.Rd | 36b21520388609d326b6d44660ce0bf8c7df092c | [
"MIT"
] | permissive | elephann/RQuantTrader | e5f8813eb880ce05cf997f01b9732cfaa57b995f | 067c715c036a5d86596b8589d617ec795a8dc3c1 | refs/heads/master | 2022-03-21T17:35:51.078741 | 2019-09-07T13:18:40 | 2019-09-07T13:18:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 316 | rd | modelCandleFeatures.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FeatureEngineering.R
\name{modelCandleFeatures}
\alias{modelCandleFeatures}
\title{Consecutive Candle Analysis}
\usage{
modelCandleFeatures(data)
}
\arguments{
\item{data}{raw data of prices}
}
\description{
Consecutive Candle Analysis
}
|
9cfea061ed9edaba852454ae24a8e651314af248 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gstat/examples/vv.Rd.R | 0629a4e68f58d7a04d3e91167a1b231ae55f8208 | [] | 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 | 526 | r | vv.Rd.R | library(gstat)
### Name: vv
### Title: Precomputed variogram for PM10 in data set air
### Aliases: vv
### ** Examples
## Not run:
##D # obtained by:
##D library(spacetime)
##D library(gstat)
##D data(air)
##D
##D if (!exists("rural"))
##D rural = STFDF(stations, dates, data.frame(PM10 = as.vector(air)))
##D rr =... |
1c7a92cd5387b715934df439f4cdf249a9610493 | cd901f78760d0856a58e2791d94751b3e3e5c3e8 | /man/batchExonBed.Rd | 133c461ef101547be55cca93239b671b9f28a8ae | [] | no_license | sanadamakomi/exonCNV | 4d6056596d2a17df5e56075400441207bf6eb77f | 92aaeb8ea242aa6965e3910ae5825c68ec30c65b | refs/heads/master | 2022-08-10T09:24:41.165518 | 2022-08-04T07:59:10 | 2022-08-04T07:59:10 | 175,590,331 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 563 | rd | batchExonBed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/batchFunc.R
\name{batchExonBed}
\alias{batchExonBed}
\title{Export exon bed file.}
\usage{
batchExonBed(annoFile, outPath, gene, expand)
}
\arguments{
\item{annoFile}{Path of annotation file.}
\item{outPath}{Path to write to.}
\item{gene}{A... |
34b45e1a6599f66bc2c1ba15d584dae3b73df0db | 7218d2e425fbf03fad09d3d652ead458a3cc6c67 | /inst/doc/v03-outputs.R | d3e62ac9771acf79b8ceb65c2a8d61ce22f4b508 | [] | no_license | cran/basictabler | 5fbe8577fc7c23e313ca6a3cca55015d145ff087 | 0a3b06d307fdd9422a68afc10f86f313534e0b97 | refs/heads/master | 2023-06-04T01:02:39.324085 | 2021-06-26T14:10:02 | 2021-06-26T14:10:02 | 105,392,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,125 | r | v03-outputs.R | ## ---- message=FALSE, warning=FALSE--------------------------------------------
# data for the table
saleIds <- c(5334, 5336, 5338)
items <- c("Apple", "Orange", "Banana")
quantities <- c(5, 8, 6)
prices <- c(0.34452354, 0.4732543, 1.3443243)
# construct the table
library(basictabler)
tbl <- BasicTable$new()
tbl$addD... |
195d561e3eebe82fe4addf3ec7fcad694dd0fe43 | 4d153213c3621e9f313e7a6356328a2f494a7f99 | /otimizacao.R | 9348b29e91d2067c69f4be3cea8276330962e0a1 | [] | no_license | castroantonio/otimizacao-r | 4e09d4a666efb084e7748e69cd91dd13cc1ff78b | 3f9d10cd576273c9b10f31ad3ddf6a62e762c45a | refs/heads/master | 2022-06-25T07:56:37.270101 | 2020-05-06T23:56:01 | 2020-05-06T23:56:01 | 261,903,986 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,414 | r | otimizacao.R | ####****----.... Esemplo de otimizacao com estrutura de dados do R. ...----****####
#### Configuracao do ambiente ####
loadlibrary <- function(x) {
if (!require(x,character.only = TRUE)) {
install.packages(x, repos='http://cran.us.r-project.org', dep=TRUE)
if(!require(x,character.only = TRUE)) stop("Package ... |
2c6d316103f2bf9bfdf1fda165416ee6eacd6127 | fe75287628325776ecbf50e2f5cd656710c02378 | /man/get_parameters.Rd | 164be67aa629b3dda5dca0b0cf39ae7a6d911a83 | [
"MIT"
] | permissive | afcarl/FastRText | e2b9f7bcb6b4a45b4f22ff51b541324dfa417467 | d1da2d42f798f78abd8f5537bc4a265708f69e11 | refs/heads/master | 2020-03-25T19:22:43.949888 | 2017-08-31T18:14:06 | 2017-08-31T18:14:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 511 | rd | get_parameters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/API.R
\name{get_parameters}
\alias{get_parameters}
\title{Export hyper parameters}
\usage{
get_parameters(model)
}
\arguments{
\item{model}{trained fasttext model}
}
\description{
Get hyper paramters used to train the model
}
\examples{
libr... |
eecb6a626aac78da3a72054d66d892f64386809f | cdb360025e038c2d045a397858c707722ba56e88 | /R/readEverything.R | 7ec6bb5e1971e7445b11b2ffefc71e9c3f1ff54c | [] | no_license | ShashankKumbhare/paramonteRold | aa92962e7a8186c5d42d9e793129b2a553f4bc44 | 1bcb5a9e1fcb941134406550585d5b5bfb2acb62 | refs/heads/master | 2023-04-13T11:41:36.960435 | 2021-04-20T09:28:58 | 2021-04-20T09:28:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,174 | r | readEverything.R |
####################################################################################################################################
################################## readEverything ##################################################################################
# >>
readEverything <- function( file, arg2, arg3 = T... |
294273812c41cf93cf66617ed1ca9be60e4719a3 | 6b7c306d52fc6344f6463dcb8847c75f6a99a405 | /Relatorios/RelatorioGerencial/inner_join-hiperlink.R | 00b758e3c76a7a32ceadc4f07cb39ca20c33caf3 | [] | no_license | gbrlla/IpeadataRio_Revisao | 694e13cf998dcc21460f4b6df5b1869aa909e4e5 | 27605c3ef52ffcd5cbe9ec3ef93f1272c3d8a328 | refs/heads/master | 2020-04-29T04:44:06.766273 | 2019-06-07T17:29:04 | 2019-06-07T17:29:04 | 175,856,801 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 139 | r | inner_join-hiperlink.R | planilha<-dplyr::inner_join(coment,link,by="codigo")
setwd("C:\\Users\\b248968182\\Desktop\\SCN10")
write.csv(planilha, file = "scn10.csv") |
e152b75ea15d1f4dc0706835232e123686ed9fef | 93051b30dbf41dcda0f678a8d811c16ac4c9f65d | /man/nomogram.Rd | f904ec839a839935a938b12868faff62b1129966 | [] | no_license | cran/UncertainInterval | 483d0b3db282e31e7413eb10d35cecdb7d031435 | f2465d3b1e06f4ed2b3aa01ebad5329757fc3555 | refs/heads/master | 2021-07-18T06:29:45.578973 | 2021-03-02T15:00:02 | 2021-03-02T15:00:02 | 79,441,666 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,174 | rd | nomogram.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nomogram.R
\name{nomogram}
\alias{nomogram}
\title{Fagan's nomogram to show the relationships between the prior probability, the
likelihood ratios, sensitivity and specificity, and the posterior
probability.}
\usage{
nomogram(
prob.pre.test... |
35d6f05bff0b40fff7df57f25bf85b89c5342658 | 4447836ed0fe92e85edc912d86f8d62860ceac99 | /ui.R | c4403fd00d824a9fbcf3024c95162e68caad9424 | [
"Apache-2.0"
] | permissive | jbpost2/BasicBayes | 90c022f5598a24441ced6f151a887a52705c7f21 | 193c554df50716a99c43c2522c6e38ad581c3532 | refs/heads/master | 2021-07-15T12:46:54.932390 | 2021-03-05T16:15:10 | 2021-03-05T16:15:10 | 84,206,057 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,662 | r | ui.R | ###########################################################################
##R Shiny App to plot different possible posterior distributions from coin example
##Added Gamma/pois
##Justin Post
###########################################################################
#Load package
library(shiny)
library(shiny... |
de3a778a657c7c9400f17cf6eefa79d838cd982e | ad23a4cd57db88a5d6008379abb8d3139f838e24 | /R/print.dyadicc.R | f0f0409ebaf6f74ec01cb0bc89d0752dfd35cc94 | [] | no_license | DLEIVA/nonindependence | 7bb397a35dd300421fffac3f9567009724c4591c | f143aceb6e53aa56f9a772547d63e9e65f5329b1 | refs/heads/master | 2020-03-23T22:09:48.974114 | 2018-07-24T12:47:24 | 2018-07-24T12:47:24 | 142,157,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,069 | r | print.dyadicc.R | print.dyadicc <- function(x,digits=max(4,getOption("digits")-4),...)
{
cat("\n")
cat("Indistinguishable Members: Intraclass Correlation")
cat("\n\n Call: \n")
cat("",deparse(x$call), "\n\n")
cat(" Data", if (length(x$data[,1]) <= 5) ": " else " (5 first rows shown): ", "\n")
print( if (length(x$data... |
62de8907b7f040afe35d2f12e834aa9808cb0f72 | 8bc0b8ed84e03ed0254448aadafce40b3ad27ca7 | /man/tfr.Rd | 4cf54f46b07e404a961228772192ed3cfb342aaa | [] | no_license | grasshoppermouse/globalsmoking | b9402f922f7f88266833c2d7faa55c4c2c1d5a51 | 9f5dc48fe5744a186314dca8ac15128c0054c572 | refs/heads/main | 2023-07-21T14:15:31.431203 | 2020-10-20T15:33:57 | 2020-10-20T15:33:57 | 305,552,478 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,302 | rd | tfr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{tfr}
\alias{tfr}
\title{Total Fertility Rates 1950-2015}
\format{
A data frame with 200 rows and 16 variables:
\describe{
\item{\code{country}}{character. Country name.}
\item{\code{iso3}}{character. ISO Alpha ... |
d45a7ba21257beccde64a327d55f0c2f4ac097c6 | eefcd8a80f3cebe5fc36fff145bea7c985d01d52 | /paper_expansion_custom.R | e807a285e64f1afa721ffe390ae65e3e658b1309 | [] | no_license | bbbales2/ising | 0202f3ecf9cdfe0152c42ecf45f8814afd92e4b7 | be7b24cf13b84db369f82676402ef96460bc276a | refs/heads/master | 2021-01-01T17:08:08.930806 | 2018-05-16T23:32:37 | 2018-05-16T23:32:37 | 98,006,458 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,202 | r | paper_expansion_custom.R | library(reshape2)
library(tidyverse)
library(ggplot2)
library(rstan)
library(parallel)
require(Rcpp)
library(GGally)
require(gtools)
sourceCpp("covariance2.cpp")
p = function(x) {
as.tibble(melt(x)) %>%
ggplot(aes(x = Var1, y = Var2, fill = factor(value))) +
geom_tile() +
xlab("x") +
ylab("y") +
... |
d28d03707260e3814e5ebc661c821d34e89a94c2 | 2bca1f1b682da91997d0547fcf719253bc9ccde0 | /mymain.R | ffb1c8f3dfea0dfbd3b7ea7a7696dff21d9af305 | [] | no_license | shuke1995/Movie-Sentiment-Analysis | 61b02e2400b050c0e531be335d29e3a9194a04df | 2c080b3616968e0b624b6900613122f1c4f1abc6 | refs/heads/master | 2020-09-03T03:21:08.731033 | 2019-11-03T23:27:28 | 2019-11-03T23:27:28 | 219,373,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,525 | r | mymain.R | rm(list = ls())
library(readr)
library(text2vec)
library(data.table)
library(magrittr)
library(RCurl)
library(XML)
library(pROC)
library(glmnet)
library(AUC)
#start.time = proc.time()
#1. read data
all = read.table("data.tsv", stringsAsFactors = F, header = T)
splits = read.table("splits.csv", header = T)
s = 3
Myvo... |
6fb4da312509b6fae4cc2fa24699ad6fce0d2257 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mpt/examples/mptspec.Rd.R | c252dbf6a4dcc309940ac3da426b942ce7b874c1 | [] | 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 | 1,008 | r | mptspec.Rd.R | library(mpt)
### Name: mptspec
### Title: Specify a Multinomial Processing Tree (MPT) Model
### Aliases: mptspec print.mptspec update.mptspec
### Keywords: models
### ** Examples
## Specify storage-retrieval model for pairs
spec1 <- mptspec(
c*r,
(1 - c)*u^2,
2*(1 - c)*u*(1 - u),
c*(1 - r) + (1 - c)*(1 - u)... |
6a0925731c855809bceecd8afe5be17d5777bca2 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.end.user.computing/man/workmail_test_availability_configuration.Rd | 5e10aa7d29b13c1ea2e8910a0269deede6885256 | [
"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 | 1,314 | rd | workmail_test_availability_configuration.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/workmail_operations.R
\name{workmail_test_availability_configuration}
\alias{workmail_test_availability_configuration}
\title{Performs a test on an availability provider to ensure that access is
allowed}
\usage{
workmail_test_availability_con... |
03594164eb97b04c99b0fa60f86104c951cdd413 | 0e155767c1733da56a8e034f254af4aa84da2b67 | /man/fixed_point_computation_function.Rd | 18461b831a0d9054bd5be6e2edd98c6a1318cbde | [
"MIT"
] | permissive | Martin-Summer-1090/syslosseval | 450776ddbfdd7523b734893cf544da3dd3d9d18a | 5c74f0cf7856ef4f8d39890ee349a17d19818d6f | refs/heads/master | 2023-06-12T13:27:21.846827 | 2021-08-10T16:02:37 | 2021-08-10T16:02:37 | 305,985,830 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,936 | rd | fixed_point_computation_function.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fixed_point_computation_function.R
\name{fixed_point_computation_function}
\alias{fixed_point_computation_function}
\title{fixed_point_computation_function}
\usage{
fixed_point_computation_function(
mat,
lb,
data_idx,
data_adv,
base... |
ddc5d5905b356a65ba3aa7c67904428da8ed1f12 | 8c9598a06fb0b1b7a00eb74e63a1ed2cd8329eb5 | /man/MultAdjStrategy.Rd | e7b7ff3e0c9cb85b37ea6c7ebb12ea8714a88757 | [] | no_license | gpaux/Mediana | 1653df59542b80cb3951ce453f8450261b48a752 | e3a7d7f49292f1f3e4b91e831d957353b798df36 | refs/heads/master | 2021-06-08T22:45:10.395261 | 2021-05-29T12:54:24 | 2021-05-29T12:54:24 | 39,732,450 | 22 | 1 | null | null | null | null | UTF-8 | R | false | true | 4,647 | rd | MultAdjStrategy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MultAdjStrategy.R
\name{MultAdjStrategy}
\alias{MultAdjStrategy}
\title{MultAdjStrategy object}
\usage{
MultAdjStrategy(...)
}
\arguments{
\item{\dots}{defines an object of class \code{MultAdjProc}.}
}
\description{
This function creates an o... |
9b3210d47c9db64b1b5d1bea3da63f9612677a51 | 97f8757356dfbcfc5283644a445df3b67034ad0c | /ubuntu/jupyter-kernels/irkernel.R | 6c7e8e9cd72ad07ede64a0499bc47a04a7e2bc34 | [] | no_license | wilsonify/FreshInstall | 3d37e522cb281b2cb4c5200dea4fd4530d7b0abc | 72f0169341bde54d57759d5d3baa093997be60e9 | refs/heads/master | 2023-08-17T05:39:14.358609 | 2023-08-06T13:28:03 | 2023-08-06T13:28:03 | 147,225,996 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,100 | r | irkernel.R | # 1/3) Installing via CRAN
# If you are on linux, go to the Source instructions
# You can install all packages using the following lines in an R console:
install.packages('IRkernel')
# To update the IRkernel package, use update.packages()
# 2/3) Making the kernel available to Jupyter
# warning Important! warning On ... |
b1d42cb15246ed5b1d43853b8b6d0726270449fb | b4deb4525f89b29cdf2f5b2f79274cc9531f39b2 | /R/table2.R | 0196758b34a170a343df3cc4f16a89d68e5628c5 | [] | no_license | cstubben/genomes2 | eda426038a2a7778f81db43f6be0610bfb6b339b | 059661f426231682d3aed4ac1f201a3c049d4470 | refs/heads/master | 2021-01-16T18:42:12.477267 | 2015-12-22T22:31:47 | 2015-12-22T22:31:47 | 9,374,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,121 | r | table2.R | table2 <- function(..., n=10)
{
x1 <- table(...)
top<-n
if(is.na(top) | !top > 0 ) {top <- 1000}
n <- length(dim(x1))
## if table is a vector
if (n == 1) {
# with 1 element or more
if (dim(x1) > 0) {
x1 <- matrix(x1, dimnames=list(names(x1), "Total"))
## remov... |
7b27a157b0e88d90b8e805a6e989423b9e72bd66 | 9cd5fc06ff366ab54395ed4d4b907cfd956444f7 | /Week5/Assignment3.R | 18a7a2fabaec1f8ca5e7bd66729d678c5c1997da | [] | no_license | Abhigpt92/MITx-The-Analytics-Edge | 056fe080b04128415f9747bc004e5447465f1be7 | 7f012fa3d44d7a0f9a3fe0b0117284f270258ee8 | refs/heads/master | 2021-01-19T19:29:42.008986 | 2017-08-23T15:51:15 | 2017-08-23T15:51:15 | 101,196,255 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,595 | r | Assignment3.R | # PROBLEM 1 - LOADING THE DATASET
emails <- read.csv("emails.csv", stringsAsFactors=F)
# How many emails are in the dataset?
nrow(emails)
# How many of the emails are spam?
sum(emails$spam)
# Which word appears at the beginning of every email in the dataset?
head(emails$text)
# Could a spam classifier potentially be... |
b45b8190b82df86ecdc88016050d4a236de99594 | c99d3397fd7e679cf52ac8837049baa75e55e274 | /man/frame_density.Rd | 6fae60f12b0d27bc3561966195f35eea7d562ffb | [] | no_license | rooperc4/TrigCamDensityEstimation | f650328bfc824fa89903c6ca41e938b0fca64011 | 0a107c1e8358eda84164d1568fb223e4654825e2 | refs/heads/master | 2020-04-08T20:53:38.112010 | 2018-11-30T17:46:33 | 2018-11-30T17:46:33 | 159,719,324 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,353 | rd | frame_density.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DensityFunctions.R, R/DensityFunctions2.R
\name{frame_density}
\alias{frame_density}
\title{Absolute density for a frame}
\usage{
frame_density(start_L, start_K, x0, intercept, range_bin, density,
near_limit = 1)
frame_density(start_L, sta... |
2846c516dfd8d3a16b6a67cd085fa0f08817c47e | 5b5cf88112f5239e19302d8cfebff60ca3e2199a | /r/utils-config.R | ca3271588921778961542883314ee47df55942c9 | [] | no_license | eth-mds/bmi | e32ffc57717151c6b79954fb9c64b5c78dc5473e | c45a80e6e4295636364b2d8a4c7860b5cf531465 | refs/heads/main | 2023-08-12T06:33:26.860001 | 2021-10-01T14:47:42 | 2021-10-01T14:47:42 | 371,434,398 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 834 | r | utils-config.R | proj_root <- function() {
rprojroot::find_root(rprojroot::has_file(".gitignore"), path = ".")
}
json_file <- function(name, dir, value = NULL, simplifyVector = TRUE,
simplifyDataFrame = FALSE, simplifyMatrix = FALSE, null = "null", ...) {
assert_that(dir.exists(dir))
file <- paste0(file.path(dir, name), ... |
08df7bcb3e654dfc417cd43c7a6b6235813b9643 | 72d03ec10b4955bcc7daac5f820f63f3e5ed7e75 | /input/gcam-data-system/energy-data/assumptions/A_elec_data.R | b76957918b1338c96a63330bfbf7656b000cfc9d | [
"ECL-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | bgmishra/gcam-core | 54daddc3d037571bf745c4cf0d54c0d7a77f493f | bbfb78aeb0cde4d75f307fc3967526d70157c2f8 | refs/heads/master | 2022-04-17T11:18:25.911460 | 2020-03-17T18:03:21 | 2020-03-17T18:03:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 905 | r | A_elec_data.R |
#Electricity generation fuels whose calibrated quantities in the IEA energy balances are used
electricity_input_fuels <- c( "biomass", "coal", "gas", "refined liquids" )
#Assumed base year heat price, used for calculating adjustment to non-energy costs of electricity technologies with secondary output of heat
heat_pr... |
ed320947e7c9428e51eef505ba504211f1e8156a | f2a982ef2ad5d0a1086830a59f2700bc7e0c668a | /R/mod_formatUI.R | 3f546086e50d4d587614a1458874786184e2cb1f | [] | no_license | jimsforks/cleanser | 6f87363fefd5c0223c17d349ffa19f8d5ff1956c | 1597f2bfcf58a0084c2810fea236e38a51385e43 | refs/heads/master | 2022-03-16T23:49:13.342589 | 2019-09-27T07:43:25 | 2019-09-27T07:43:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,256 | r | mod_formatUI.R | #' mod_format and mod_formatUI function
#' @param id The id of the current element you are entering
#' @description Shiny Module that allows to change the variable types: qualitative, quantitative or date.
#' @export
#' @importFrom DT renderDT DTOutput replaceData
#' @importFrom shinyjs runjs
#' @examples
#' if (inter... |
3a306b7ac71f1a96320fcff59056c43329c0092a | fe17217bf85ed660a1fa3173f6078133c5bc49e0 | /man/L.2x.poisson.strat.Rd | dd27e0fe83d667c6c115ae40ccc7b28dcc44e617 | [] | no_license | rgcstats/ODS | 5a4ba2107328175174b4874e10d8e47733c33397 | 0290071546fdd8dff1c8e9e6d8bc5920d1c04491 | refs/heads/master | 2020-12-10T10:30:36.232624 | 2020-01-13T10:20:29 | 2020-01-13T10:20:29 | 77,803,517 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,053 | rd | L.2x.poisson.strat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/L_2x_poisson_strat.R
\name{L.2x.poisson.strat}
\alias{L.2x.poisson.strat}
\title{Calculates the estimated likelihood for a regression model under a general sample design
assuming one continuous and one binary covariate}
\usage{
L.2x.poisson.s... |
68601dfc6d3b48bd8a7ea77e24eab09231d340ca | bea3b6bb4ce9cc6859db748401ecea7b9e5e8595 | /dc-campaign-finance/contrib-analysis.r | 5d701a621e09cdaeb8801ec7d68316af0638476c | [] | no_license | occupydata/Occupy-data-processing | db45773c3a76d7ed3390a9b496fc140f8013142f | 1960c65ad31799be76a31270ee345ad215ee9afa | refs/heads/master | 2016-09-09T21:07:16.787575 | 2012-06-11T16:12:41 | 2012-06-11T16:12:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,784 | r | contrib-analysis.r | # Copyright (c) 2012 Data Committee of Occupy DC
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, me... |
1d0053404bb73f63419fb9d9f68267c1036c6f38 | b8dda22d68ef15ca86120fe0c670bb4b3165f9dc | /man/getCRUCLdata-package.Rd | 8dbba6b6c4db83b8e101d2e6d1230a74bbd6ff8c | [
"MIT"
] | permissive | hydroinfo-gis/getCRUCLdata | 7ec956049f2bfe26d4e862f0151b7b11d85002e1 | ab866ade0f7621be66a675be24779053590a9b79 | refs/heads/master | 2023-02-07T22:12:57.880407 | 2020-12-16T04:24:19 | 2020-12-16T04:24:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,320 | rd | getCRUCLdata-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getCRUCLdata-package.R
\docType{package}
\name{getCRUCLdata-package}
\alias{getCRUCLdata}
\alias{getCRUCLdata-package}
\title{getCRUCLdata: 'CRU' 'CL' v. 2.0 Climatology Client}
\description{
Provides functions that automate downloading and i... |
b3e09912d48811b4b5341738b45c5aace9ed9a58 | 866899ce28eb6270fde88421319045a17c35c8b9 | /crypto_class.R | b7b3e4342e7c9e11373c4b1058a286262501b3f9 | [] | no_license | martinkabe/crypto-analytics | 11098002f511e09d2f81d63f3c655e7e9266cfde | a0aba28019459b88fdbef7fa6f36ebabcb71fe50 | refs/heads/main | 2023-03-10T23:50:38.484083 | 2021-02-24T22:09:14 | 2021-02-24T22:09:14 | 340,859,788 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,739 | r | crypto_class.R | sapply(c("tidyverse", "rvest", "R6", "dplyr", "prophet", "RSQLS", "bit64"), require, character.only = TRUE)
CleanHtmlData <- R6::R6Class("CleanHtmlData",
private = list(
..path_to_file = as.character(),
..file_name = as.character(),
..data = data.frame(),
..table_name = ... |
399fcfe4dd63f6b0b9eb217ca1eccb9485ca3591 | 12ae74bd0ba9d5494d7301b521b45d1bfa5ff84a | /R/grapes_similar_equals_grapes.R | 21005aee8dbc26b0efe56ca6cdd9a1066f2dc7d1 | [] | no_license | cran/do | 62b609a0f0cc0f0c0cc879adb821b1d9d95b6632 | fa0d7c8f9799326ffa6f0763f490c2873597131b | refs/heads/master | 2021-08-15T11:59:00.793187 | 2021-08-03T10:40:02 | 2021-08-03T10:40:02 | 206,034,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 396 | r | grapes_similar_equals_grapes.R | #' Locate Similarly by grep()
#'
#' @param a vector for matching
#' @param b vector for searching
#'
#' @return A list contains location information.
#' @export
#'
#' @examples
#' 1 %s=% c(1,12,3)
#' c(1,2) %s=% c(1,12,3)
"%s=%" <- function(a,b){
loc=list()
for (i in 1:length(a)) {
loc=c(... |
ce639b6ee847af467626d25bf131ecccc7118da6 | d96211b26845f18d8b744c3466111856f1abce0d | /Marriah_Lewis_HW4.R | 2a18cfd460b8fabbecc9859a5471219fc9e1eff3 | [] | no_license | marriah0024/IST707-MachineLearning-R- | 91ce7e5b013d86cd85c5fb9cdd13a313c72d98d5 | 5803e3a6a31793047a6f0574274d344779eb00c5 | refs/heads/main | 2023-04-22T05:59:43.218626 | 2021-05-21T14:54:37 | 2021-05-21T14:54:37 | 368,610,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,911 | r | Marriah_Lewis_HW4.R | # loading packages
library(ggplot2)
library(factoextra)
library(stringr)
library(tidyr)
library(gridExtra)
library(FunCluster)
library(rpart)
library(caret)
library(rattle)
#loading the data
papers<-read.csv("C:/Users/lewis/Downloads/fedPapers85.csv")
str(papers)
#summary of the authors
summary(papers... |
16972a9b752b6d4b79b33401f40be3ee0fdc4f26 | 61426d4fd3b4edf3e74d229adcebbe9427a11729 | /analysis/model_plots.R | ef0fe83bb96b921e878b546ba568bfa2694d4d03 | [] | no_license | wangdafacai/MXU5MR | e28f71c78c47c8dbe75bd9c87060332d3e8f504a | 6ab9d412245fc925bfd9026f7d05ad2e93eb03ec | refs/heads/master | 2023-03-15T17:55:40.431026 | 2017-05-17T17:32:46 | 2017-05-17T17:32:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,286 | r | model_plots.R | ################################################################################
# Outline for plots to eventually put into a shiny app, right now im thinking
# of putting three options, model, age, and year and plotting a muni map
# that is responsive to all three a state by state age time series,
# and a histogram
#... |
38a4b85be98c6c4aa3bc835a4f3f9f8fde4bec0f | 69f3702c822466f929e77b746c911cb4d580c3fc | /ui/enrich_ui.R | d58f92504b7098febfdb1b8a7bea7d892c100147 | [] | no_license | soulong/shinyHeatmap2Pathway | 73050fd7406a41d49d2f85807d456ee498b381b8 | 79b71a42fafbee44aa6ba58b6748e8b21de58442 | refs/heads/master | 2023-08-19T09:00:23.168083 | 2023-08-03T09:17:50 | 2023-08-03T09:17:50 | 251,056,537 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,411 | r | enrich_ui.R |
fluidRow(
column(width=3,
wellPanel(
style = "overflow-y:scroll; max-height: 1000px",
selectInput("choose_cluster", "select cluster to enrich", 1, 1),
checkboxGroupInput("enrich_db", "choose db", c("go", "kegg", "msigdb", "enrichr"), "go", inline=T),
... |
934a1abb0a7259cc9d213945e4e2c5a071dfb481 | 8699df251aacbb56633c382f0f179136c0a9802c | /ui.R | 54c04640991313edb93e4f1d23b28d44b7dd5230 | [] | no_license | michaelchang64/college_stats | d55470b6ed62bee0d74cc70172276a8ebbbfb9a9 | f59f6803a43e9478f12d86a19634bd1aeb31372b | refs/heads/master | 2021-01-21T19:36:23.019549 | 2017-08-07T17:14:32 | 2017-08-07T17:14:32 | 92,143,304 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,615 | r | ui.R | # ui.R
# Imported shiny packages. ggplot2 is the data visualization package used in app.
library(shiny)
library(ggplot2)
# Creates page that fits dimension of screen
shinyUI(fluidPage(
# This is the title
titlePanel(h1("College Admissions Data Visualizer")),
# This creates the sidebar
sidebarLayout(
... |
a167752029ae36f22ae606173d56821a9b55abe2 | c06ab34b8c9290bb5d42ed3a989cc4aeaa794356 | /man/MT_F1.Rd | 6404e2ea1d692f8a30d3bb6c13ce6865dfc55424 | [
"MIT"
] | permissive | Nowaysis/F1Stats | 86fc1718a7bf33a1322cfa3c0235ce6a6a282fa0 | db24584e41affcf28ad7614e678c23e47c46106c | refs/heads/main | 2023-02-03T03:49:11.213775 | 2020-12-27T19:15:56 | 2020-12-27T19:15:56 | 323,680,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 294 | rd | MT_F1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{MT_F1}
\alias{MT_F1}
\title{Dataset about football}
\format{
An object of class \code{list} of length 71.
}
\usage{
data(MT_F1)
}
\description{
Dataset about football
}
\keyword{datasets}
|
2c218029de1a64e4e8029fe5aa4469b723ccadf4 | 3fdfbd6728f19b4221129cc0d9637e2f1a4e0f3b | /tests/testthat/test-impute_sd.R | b559a79810f175ae96498fa8e80427aa672df7ef | [] | no_license | billdenney/bsd.report | 09e2b5642046ee51956d33199d6e1ae670b424ff | 778f0298da10f6369bd7ffaa1a5c20d3f8f691fa | refs/heads/main | 2023-02-19T22:49:25.273961 | 2023-02-15T21:54:41 | 2023-02-15T21:54:41 | 153,116,819 | 3 | 0 | null | 2023-02-15T22:57:48 | 2018-10-15T13:21:55 | R | UTF-8 | R | false | false | 6,912 | r | test-impute_sd.R | context("impute_sd")
test_that("input checks work", {
# impute_sd does not give an error for NA, so it is pulled out.
expect_error(impute_sd(point=1:2, var1=1, var2=1, n=1, vartype="SD"))
expect_error(impute_sd(point=1, var1=1:2, var2=1, n=1, vartype="SD"))
expect_error(impute_sd(point=1, var1=1, var2=1:2, n=1... |
29a993556cb9d10547d50d3d3fa694c8d059b38f | 4592e2fef5a229a035ea6d1d5158c0f6efe58f9e | /inst/scripts/dashboard-run-locally.R | a1fed0ef205a20f22db739e597c58a611b86ef0b | [
"MIT"
] | permissive | harell/caret.explainer | b199ec3da0fe52bc0bb3c5add6f7e0c7e6d2086b | 39c69d928b6672c7814ab27531abec7ef372808c | refs/heads/master | 2021-02-06T03:09:28.444649 | 2020-06-26T01:37:55 | 2020-06-26T01:37:55 | 243,869,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 682 | r | dashboard-run-locally.R | # Setup -------------------------------------------------------------------
pkgload::load_all(path = ".", helpers = FALSE, quiet = TRUE)
dashboard_source <- getOption("path_dashboard")
dashboard_target <- normalizePath(file.path(tempdir(), "dashboard"))
Dashboard$utils$prepare_app_files(dashboard_source, dashboard_targ... |
219361ef32a5208d9511d1c11be4e8203a0da74d | 1a5e14f3049b4a16a2b3ba69aa750d5abb80c317 | /R/ipwlm.R | 716326764bc312e13d7a0d2903e91c0c97adfc9c | [] | no_license | drmiller1220/attritR | 757d3c5eb14ca422b3f98d4941ae3665873aeeae | 6e32eba77b398e0ac2932a2d0a648b24fecf3b30 | refs/heads/master | 2022-07-21T20:43:42.299490 | 2020-05-17T22:13:18 | 2020-05-17T22:13:18 | 264,313,939 | 0 | 0 | null | 2020-05-19T00:33:56 | 2020-05-15T22:43:14 | R | UTF-8 | R | false | false | 13,881 | r | ipwlm.R | #' Estimating treatment effects given non-random attrition
#'
#' \code{ipwlm} estimates the average treatment effect --- either among
#' respondents (ATE|R = 1) or among the population (ATE) --- under various
#' conditions of non-random attrition (attrition due to treatment alone,
#' attrition due to treatment condi... |
c2e8a78beb69a581646b2df4f9ed90e989618a7a | b35a0c412ea0fb6e214029fd0db3bc41a5a93ed3 | /PCA/get2D3DmatForPCA_script.R | c9f9e46da610f7c5edb8676410d32cb8b8d137aa | [] | no_license | skiamu/StatApp_test | 83bd65300cd0133defb03a1d455698b7aee1b703 | 2ee23ccc8773352709e58421d6fc3b40943b20c5 | refs/heads/master | 2021-01-18T16:13:12.603196 | 2017-06-24T16:13:03 | 2017-06-24T16:13:03 | 86,727,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,473 | r | get2D3DmatForPCA_script.R | # extract 2D and 3D matrix for PCA
# IMPORTANT: changed read_data.R rerun it
# [added a column in Country to match CountryName (TableName was not working),
# added two dataframe to pass from Name to Code and viceversa for ind and cnt]
# 00 preliminars ----
setwd("C:/Users/Leonardo/Desktop/POLIM... |
a46ab9152642b677665443c59ed7bb4176524d53 | 8ef127711f234f6becd9beb6d64fbdaa93879500 | /evaluation/time-synchronization.R | fd36f92803b42950bedd4f6f19f6817b5b216246 | [] | no_license | DSoko2/i3ql-measurements | dca70577aca506741c7b14785a609bf10a5112d1 | 0658d8aeb039e0026520f1f244b038714b7e862c | refs/heads/master | 2022-01-27T16:28:14.640760 | 2019-07-31T15:44:56 | 2019-07-31T15:44:56 | 156,348,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,697 | r | time-synchronization.R | # Calculates the offset as mean of all reference section enter timestamps
evalTimeOffsets <- function(eventData) {
sectionEnterMean <-
filter(eventData, grepl("^section\\..*\\.enter$", event)) %>%
group_by(node, execution) %>%
summarize(timeOffset = mean(time)) %>%
ungroup
return(sectionEnterMean... |
e3d815537f0bb0ff35e544d82f405898d66d40ab | 703d6573ce0d626cf46f1ba9be67517ae52d6665 | /Stat251Project/STAT251_Final_Project.R | 6259ee09801b102453ade360aa511eeb64d4a5cb | [] | no_license | cam4socal/Stat251Project | 5e67a984db3b4cbf41ae21ce8c06517db603056b | 428c6b44cf94eec6717617da630ef49d2f51673e | refs/heads/master | 2020-04-11T05:09:37.272240 | 2018-12-12T20:17:55 | 2018-12-12T20:17:55 | 161,539,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,818 | r | STAT251_Final_Project.R | ##############################
### STAT 251 Final Project ###
####### Dr. Garitt Page ######
#### Fall 2018, Section 1 ####
## A. Hamilton & C. Timpson ##
##############################
library(dplyr)
bakery <- read.csv("C:/Users/cam4s/Documents/STAT251/STAT 251/bakery.csv", header = TRUE)
head(bakery)
tail(bakery)
#... |
754ec7b530b14a3aab625a7043ec38fa6c639fa5 | 19fde05976bb68cb1dd89153956153b546ab7826 | /man/BAMBA.Rd | 6627b1feaca553166aaeca4f35c142ac4d98c2fe | [] | no_license | RGLab/BAMBA | 30f842beb56d93ab304fd7ee6cb759a015d204ab | f26b06f2a44fc987abaaf7e9ce07011b3fa880bb | refs/heads/master | 2021-06-23T04:36:52.121625 | 2019-01-03T20:05:34 | 2019-01-03T20:05:34 | 135,614,518 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,307 | rd | BAMBA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BAMBA.r
\name{BAMBA}
\alias{BAMBA}
\title{Fit the BAMBA model on BAMA or Fc array data}
\usage{
BAMBA(data, dataType = "fc", nChains = 1, nIter = 2000,
outFolder = NULL, outFile = date_filename("BAMBA_stanfit.rds"), ...)
}
\arguments{
\item... |
88353ca9fbb70e29203103dc7faa5cc3759e9f0f | 45967efbed95edfac0fc82e70fb45f2830e1f400 | /plot4.R | 5202f3e4a1b7d41a1a94290cb4db41eb5a8b8979 | [] | no_license | jonpresley/ExData_Plotting1 | 25f33706d20bb0cfcc1379f66095a5d3e451a25b | f67c587fde904f32c872212cb6df26b1800634de | refs/heads/master | 2021-07-06T11:04:40.419312 | 2017-10-04T06:27:17 | 2017-10-04T06:27:17 | 105,479,185 | 0 | 0 | null | 2017-10-01T22:29:31 | 2017-10-01T22:29:31 | null | UTF-8 | R | false | false | 1,146 | r | plot4.R | setwd("/Users/jonpresley/Desktop/R_Working_Directory")
data_all <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?", dec = ".", stringsAsFactors = FALSE)
data <- data_all[data_all$Date %in% c("1/2/2007","2/2/2007") , ]
GAP <- as.numeric(data$Global_active_power)
datetime <- strp... |
046603a12233771eec7874add2fb39ff5eaf8216 | 9c20dfe75c41283db15d80bef6ff60fbf4cb5255 | /Forestplots_APR22.R | 039d4b54ab848281ce7b41f2296d2ec29998b93b | [] | no_license | kristyrobledo/CodingConundrums | c9eadaa313afa5e04e47ecf5e016936b9bee328b | 7713b19a28774c75b9ae0e48b8ff7d648a40d278 | refs/heads/master | 2023-08-04T11:30:56.604221 | 2023-07-27T03:02:00 | 2023-07-27T03:02:00 | 237,126,082 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,749 | r | Forestplots_APR22.R | library(tidyverse)
library(metafor)
library(forestplot)
library(glue)
library(gtsummary)
# meta analysis
glimpse(dat.bcg)
dat <-escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos,di=cneg, data=dat.bcg)
dat$sei <-sqrt(dat$vi)
ma<-rma(yi, sei=sei, data=dat, method="FE")
metafor::forest(ma,
xlab = "Relativ... |
d39b6952a2386d71ef843a72a3e03bf1528478b4 | c315e8d1fdcf23086841a7d9cfb48ceba0b0b357 | /man/MeanListDim.Rd | 43d0e9efe047490e6e453084833ca057b930238c | [] | no_license | cran/s2dverification | c4d5caa8b518356b095c8768a7aadfe6c8da5a64 | a772070454789d66328916463f91d306f1df0a3b | refs/heads/master | 2022-05-22T11:13:13.975865 | 2022-04-20T07:10:06 | 2022-04-20T07:10:06 | 19,931,684 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 869 | rd | MeanListDim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MeanListDim.R
\name{MeanListDim}
\alias{MeanListDim}
\title{Averages An Array Along Multiple Dimensions}
\usage{
MeanListDim(var, dims, narm = TRUE)
}
\arguments{
\item{var}{Input array.}
\item{dims}{List of dimensions to average along.}
\i... |
5f4a4be6bd1cf776d2a2b1b70e495dfe918b2287 | 30d5c568cdd8a4a055387f79c2d76bb2e7b46b35 | /02.Practice_Script/ch5_script.R | aad6e43eed78da1e934502bfdd7d010918400008 | [] | no_license | YBCHEON95/R | d45a57c3a703981ed76259528abc529734eba885 | 60db3aaf5490a95fb1b21abd7344e688b2287859 | refs/heads/master | 2023-05-13T22:33:20.080392 | 2021-06-03T14:34:46 | 2021-06-03T14:34:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,542 | r | ch5_script.R | ### Chapter 5. 상관분석과 회귀분석
## Chapter5-1. 병아리의 성장(체중)에 영향을 미치는 인자는 무엇일까? (상관분석)
# 데이터 불러오기
w <- read.csv("ch5-1.csv", header = TRUE)
head(w)
str(w)
# w 데이터 셋에서 2~5열 데이터만 가져오기(첫열은 factor이므로)
w_n <- w[,2:5]
head(w_n)
# 위와 동일
w_n <- subset(w, select = -c(chick_nm))
head(w_n)
w_cor <- cor(w_n) # w_n 데이터 셋으로 상관분석한 결과... |
6fef3cadb164002248f2a2adc0a726ebeb566f2c | a1657febb6a1dd1810d104f82c3181717e37a96f | /R/screen.R | c666d53fbb8b62e0feec3991bf668dd5e45c1ecd | [] | no_license | oslerinhealth/slamR | 32fe855ffb9d895b48d6c2ba4476263c43194b09 | 50a2461f411b04b924c7cc60399433e89dccbe6d | refs/heads/master | 2021-07-10T17:18:38.449181 | 2021-05-19T21:26:01 | 2021-05-19T21:26:03 | 245,497,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,927 | r | screen.R | # screening
## screen Q using Gibbs (Alternating Gibbs)
##
## @param X N by J binary data matrix
## @param Z_ini N by K initial latent attributes
## @param Q_ini J by K initial Q matrix
## @param max_iter maximum iterations (e.g., 50)
## @param err_prob noise level
##
## @return
## \itemize{
## \item Z_est Estimated l... |
aaeed83f6c69bf6a86fe065bd41e0d4861e6672f | 3922723f5436957ff43d3952aeafe1b055b8b508 | /r/examples/3_histogram.R | ee85e7dfc1a873cf6224da08c7453de8a2d42102 | [] | no_license | sumkincpp/CodeTest | 945d865dafc7f39655bd2d496f2241cf5467d408 | d4e9b47aa8440a36d4fa4251dca23df5f94eb698 | refs/heads/master | 2023-07-16T21:06:05.347753 | 2023-07-07T11:44:12 | 2023-07-07T11:44:12 | 2,765,685 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 300 | r | 3_histogram.R | data <- c(5.28, 14.64, 37.25, 78.9, 44.92, 8.96, 19.22, 34.81, 33.89, 24.28, 6.5, 4.32, 2.77, 17.6, 33.26, 52.78, 5.98, 22.48, 20.11, 65.74, 35.73, 56.95, 30.61, 29.82);
hist(data, breaks=seq(0,80,l=6),
freq=FALSE,col="orange",main="Histogram",
xlab="x",ylab="f(x)",yaxs="i",xaxs="i")
|
5679791e6ef18b50a071abeae5e7e65c23bc1392 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EdSurvey/examples/showCutPoints.Rd.R | 6c843fe8cba1bef02d98090f6cacad26ca0539eb | [] | 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 | 332 | r | showCutPoints.Rd.R | library(EdSurvey)
### Name: showCutPoints
### Title: Retrieve Achievement Level Cutpoints
### Aliases: showCutPoints
### ** Examples
# read in the example data (generated, not real student data)
sdf <- readNAEP(system.file("extdata/data", "M36NT2PM.dat", package="NAEPprimer"))
# show the cut points
showCutPoints(d... |
d46c10b545890f4258abda828d49c0e5f8ac58f4 | 6ef61c4939d90300554cdd4e6b3336146bc08fe1 | /man/congress116.Rd | da042eb1356331920522288f3d30d36e584fcffd | [
"MIT"
] | permissive | r-congress/congress116 | 56b5603e965afcd384a0269e879532b7d5c7b793 | ed02f5b7f0cb9a877f18878b48dd201fea6d5e80 | refs/heads/master | 2020-09-03T22:04:56.019869 | 2019-12-11T21:22:08 | 2019-12-11T21:22:08 | 219,583,888 | 10 | 0 | null | null | null | null | UTF-8 | R | false | true | 753 | rd | congress116.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{congress116}
\alias{congress116}
\title{IDs for members of the 116th U.S. Congress}
\format{A data frame with 544 rows and 3 variables:
\describe{
\item{bioguide}{official congressional ID}
\item{screen_name_of... |
1d82f8a5d2dce98d660e36b6d63b8a3805e8c634 | d86268c2fdd4195208c3fd5aecab31c324af7bca | /omd/man/colfun.Rd | 802f771d9bea33e698ebefbd87b3e7097dbb58b2 | [] | no_license | bio-datascience/omd | 0e2edc61e86c135383b5d4bf29c14c95af026f5f | 5f2f532dfe077388f7911cc7999622c4b6a3f8b8 | refs/heads/master | 2023-08-28T21:44:27.488641 | 2021-11-02T15:25:02 | 2021-11-02T15:25:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 299 | rd | colfun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{colfun}
\alias{colfun}
\title{From a numeric vector between 0 and 1, make Red-Yellow-Blue colors.}
\usage{
colfun(vec)
}
\description{
From a numeric vector between 0 and 1, make Red-Yellow-Blue colors.
}
|
1e970ae88c30f87a69f1bbb499d5c08d19336e2a | 72fc8ccca937ca048ae38ba4d1b9c724577864b9 | /man/buildCondition.Rd | 424df504657639812eda5bd19fb6339623482e98 | [
"MIT"
] | permissive | metadevpro/traitbaser | 42fdd9c8aa7c590cf7e9eab81618e1d59ac5555f | c480bdd7ae088b3e090cd2db8340dd6abe5a64bc | refs/heads/master | 2021-01-12T05:31:20.796821 | 2020-04-20T15:02:56 | 2020-04-20T15:02:56 | 77,943,876 | 3 | 2 | MIT | 2020-04-16T14:33:56 | 2017-01-03T18:50:51 | R | UTF-8 | R | false | true | 1,063 | rd | buildCondition.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/buildCondition.R
\name{buildCondition}
\alias{buildCondition}
\title{Builds a condition}
\usage{
buildCondition(variable, operator, value)
}
\arguments{
\item{variable}{The variable or column name to filter.}
\item{operator}{An operator for ... |
943f56b4cf230e082b9513a79a666ef7a2de3222 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/biogeo/examples/geo2envpca.Rd.R | 7620ee7937e7976ff1304759d0526b28ce02bdfc | [] | 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 | 385 | r | geo2envpca.Rd.R | library(biogeo)
### Name: geo2envpca
### Title: Interactive plot to explore points in geographical and
### environmental space
### Aliases: geo2envpca
### ** Examples
## Not run:
##D plotsetup(6,6)
##D ed<-geo2envpca(edat,"Species U",group1="Species",group2="",
##D world,scaling=1,vars=c("bio1","bio12","bio5","b... |
55e62d2a470425bd8bebd31fb767623a15689a61 | 52f0109765f39fb570a5e944fde6822db845769c | /Session10.R | 4c019b39ff837dab7e1e8700a3cb50fa6b1c96d8 | [] | no_license | cart3ch/bsf_iimtrichy_18-20 | c873a4aa1f746c9ade1518370f8ed250940a3111 | 97c7041357aa647180ae64b9d35af4fe77f353c7 | refs/heads/master | 2020-06-02T20:48:55.653171 | 2019-08-15T17:14:24 | 2019-08-15T17:14:24 | 191,305,275 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 161 | r | Session10.R | ## In-class session
#Time-series analysis
library(readxl)
tsdata<- read_excel("rdata/Tea Production_Classical_Decomposition.xlsx")
#create a time series object
|
e1d09c9f228429907c676e6cee1c74859f59d9fd | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/5616_0/rinput.R | e63c4189665cdbe133c193de50928300e1172c56 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("5616_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="5616_0_unrooted.txt") |
d6bbe7737493a28893eb9b36da07605af49ef8a5 | 29585dff702209dd446c0ab52ceea046c58e384e | /ionr/R/ind_excl_step.R | c4a0ef1c0c51f0ec58cba32d7722cb5f9b82b0c8 | [] | 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 | 4,006 | r | ind_excl_step.R | #' One step in indicator exclusion procedure
#'
#' @description See \code{\link{ind_excl}} for details.
#' @inheritParams ind_excl
#' @param exclude Exclude an item excluded at previous step, e.g., as decided by \code{\link{ind_excl_inc}}
#' @param round Allows rounding of values in returned matrix.
#' @return Pr... |
e5e84eff12d3aaac2dabe71f142f1489cc9c27d2 | d75a1e1e95ae70ce048a0c26fb0f9c283fd5dd70 | /man/EAGLE_1.Rd | 50f8071566467b59fa60101610abc80c4d11ef1d | [] | no_license | Owain-S/kmdata | 49d65b279e7e84e170550f7d1fbdc8573f28784c | 22569373a88f64ef480ea895c8ef7b7b5ced260e | refs/heads/master | 2023-05-25T22:58:06.758825 | 2021-06-01T19:36:49 | 2021-06-01T19:36:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 947 | rd | EAGLE_1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{EAGLE_1}
\alias{EAGLE_1}
\title{EAGLE, figure 1}
\format{
A data frame of 368 observations and 3 variables:
\tabular{lll}{
\tab \code{time} \tab event time (in months) \cr
\tab \code{event} \tab PFS event indicator... |
de6c61334bcc9659ed23eecb6dfb3db64220ff0e | dcf352b37c3b115ec5fab27f1f8b26eb74a06da5 | /R/gps_utilities.R | 690ab85dbd16f741455f80ea9da0e2a2e9072590 | [] | no_license | CraigMohn/bikeCadHr | 6a2242f9f83d9dea7df8819bbb597158e2b0b467 | 699b73db74563d32a3809cf67cbbef7bc409e682 | refs/heads/master | 2021-06-23T20:23:17.317160 | 2020-11-29T22:56:17 | 2020-11-29T22:56:17 | 93,075,658 | 0 | 0 | null | 2017-09-08T16:19:09 | 2017-06-01T16:07:01 | R | UTF-8 | R | false | false | 1,157 | r | gps_utilities.R | ## note that lag_one/lead_one pad the new entry with the first/last value,
## which is different than lag_n/lead_n(,1)
## this gives flexibility with differences, but be careful!
lag_one <- function(vec) {
return(c(vec[1],vec[-length(vec)]))
}
lead_one <- function(vec) {
return(c(vec[-1],vec[length(vec)])... |
7cedca92d1733933093d4c1fdcdad0c0c9207575 | ad46eeffcbee1c270665dacc44f2e057bcd83fa9 | /man/BER.Rd | 91203e8d23091d4db834690a429f72c18cf350da | [] | no_license | gbonte/D2C | ba61ea515785cf881e74cb39b7c905d778edcde3 | ae51f3f24672fd21525d0da69547d46c010941a6 | refs/heads/master | 2022-09-26T05:15:43.442397 | 2022-09-20T08:12:37 | 2022-09-20T08:12:37 | 66,285,031 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 507 | rd | BER.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/util.R
\name{BER}
\alias{BER}
\title{Balanced Error Rate}
\usage{
BER(Ytrue, Yhat)
}
\arguments{
\item{Ytrue}{: binary numeric vector (made of 0 or 1) of real classes}
\item{Yhat}{: binary numeric vector (made of 0 or 1) of predicted classe... |
9013d399475772098d99fa4e116c55cefd8e2bc4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/neat/vignettes/neat.R | 5a815ad417a6def7ad42e724e223fed16ec78668 | [] | 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 | 2,517 | r | neat.R | ## ---- eval=FALSE, echo=TRUE, results='asis'------------------------------
## install.packages('neat')
## ---- eval=TRUE, echo=TRUE, results='asis'-------------------------------
library('neat')
## ---- eval=TRUE, echo=TRUE, results='markup'-----------------------------
data(yeast) # load the data
ls(yeast) # displa... |
35a0bd1c022202c7aed2db94b02312701bda83cc | 39f88826e318b0b351667806602c6957d5ae01d0 | /R/trading_view.R | dfb1f338c894f56b3494cb6e28efb88b06becf1b | [
"MIT"
] | permissive | jngod2011/fundManageR | 9b79169ba3b8157d8ae9ade3bd066c2b5f82c255 | cea3e117a217bb7c770670ddd440822094a728cc | refs/heads/master | 2020-03-28T07:22:15.138769 | 2018-07-29T16:26:53 | 2018-07-29T16:26:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,812 | r | trading_view.R |
get_dictionary_tradeview_types <- function() {
data_frame(
type = c(
"All",
"stock",
"futures",
"forex",
"cfd",
"cryptocurrency",
"Index",
"Economy",
"quandl"
),
slugType = c(
"",
"stocks",
"futures",
"forex",
"cfd",
... |
82e2abc4cd81a941df678ba9701faa1a86a026f8 | 1f3ca2b315f9281595a5680531bef01381454f16 | /R/PATHWAY.r | a3746658d8e1d12c7610a32adac5b19ad98f2374 | [] | no_license | LST512/NGS-script | 3e91c8ca7a9e3c9ec553acbc79ad33a95dac4b6c | 2491e9461879164ef6b918e987d5afbdc38c5cf7 | refs/heads/master | 2020-04-18T03:20:57.374498 | 2019-07-14T09:28:05 | 2019-07-14T09:28:05 | 167,195,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,904 | r | PATHWAY.r | # PATHWAY
rm(list=ls())
setwd("~/data/lst_data/AN3661/raw_data/DEPAC/GO/DAVIDGO")
library(ggplot2)
#------------------------------------
# 柱状图
#------------------------------------
# 设置好工作路径
# 读数据
pathway=read.table("pathway_depac_up.tsv",header=T,sep="\t")
# 初始化数据
pathbar = ggplot(pathway,aes(x=Pathway,y=-1*log10(PVal... |
ad6e0e8f68b52528e8afb3f79e36c5c9eef4f068 | fa8aed0f03a136ea6977b6c2daaa97ee3a9162c2 | /man/expectation.Rd | 8ec13e7164e6879309cfdd6b6d157bea47f57afa | [] | no_license | cran/lestat | d73827e8f7e9b45b85c3cdbf56b711c7c2bf9742 | 421a9c585b26d41c1db4c51bd2f9a73588b15ecf | refs/heads/master | 2021-01-15T19:40:10.190898 | 2018-06-12T17:29:29 | 2018-06-12T17:29:29 | 17,697,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,165 | rd | expectation.Rd | \name{expectation}
\alias{expectation}
\alias{expectation.betabinomial}
\alias{expectation.betadistribution}
\alias{expectation.binomialdistribution}
\alias{expectation.default}
\alias{expectation.discretedistribution}
\alias{expectation.expgamma}
\alias{expectation.fdistribution}
\alias{expectation.gammadistribution}
... |
1a86034e1330a01d826a2da9e87924f02604da09 | 3d353d93d89cd338e60271a1eeb9b22b26cfa17a | /wd-nicks/code/bias lab.r | 34db890aa39ace44b298a65f2c1d2a0d78de9907 | [] | no_license | ceekr/_DataAnalysisRepo | 619735a0cea335b1be470b2dfd8228129cacc145 | 3b7f925e490a781d579c2f2f86e913e078d70bb2 | refs/heads/master | 2020-06-01T13:22:28.711591 | 2013-05-02T05:15:42 | 2013-05-02T05:15:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,464 | r | bias lab.r | #Generate exponential data
#Create 1000 samples of 1000 data points (say, survival times) each
expdata <- array(NA, c(1000,1000)) #Preallocate array
for (i in 1:1000) { #Fill each row
expdata[i,] <- sort(rexp(1000, rate=2), decreasing=TRUE)
}
hist(expdata[1,])
#Generate y variable
y <- 1:1000
plot(expdata[1,], y)
... |
f07f57f098beb21286428fe353dc185d199d8047 | fd91fd81027df91f03e29138b26e2a1b6e31e054 | /R/RandomTreeScore.R | 8458d497da107985cf140e0abc968dce5388ffe5 | [] | no_license | gitter-badger/TreeSearch | 77fa06b36d691f942c8ef578f35f3e005cc2f13e | 5a95195211d980baa6db29260bf929a12c5bf707 | refs/heads/master | 2022-04-20T07:40:33.050434 | 2020-04-16T13:47:57 | 2020-04-16T13:47:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,448 | r | RandomTreeScore.R | #' Parsimony score of random postorder tree
#'
#' @param nTip number of tips (minimum 3)
#' @template morphyObjParam
#'
#' @return the parsimony score of a random tree, for the given Morphy object.
#'
#' @export
RandomTreeScore <- function (nTip, morphyObj) {
if (nTip < 3) {
warning("nTip < 3 not implemented, ... |
3d55f8bbe4c83cd86cb3440bf260da00ccc97aa0 | bb5c3c3af07cbd4230773b1bed5e2f28705077cd | /barcode.R | a4a320043aa0ce4eb7423c9b3675c6404e5f7ea0 | [] | no_license | nchintamaneni/strip_channels | 382fb4cda95a0758a7715670dfc46afd5029fe10 | 196b5d64688fa6b46733b0aed0cd493e2dc838c3 | refs/heads/master | 2020-06-30T03:24:44.722641 | 2019-12-01T19:56:03 | 2019-12-01T19:56:03 | 200,707,374 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,926 | r | barcode.R | # Usage: In Rstudio terminal (or command line if RStudio.exe is added to path), type command
# $ Rscript barcode.R [path/to/folder/of/csv/files]
# Description:
# This Rscript will plot a shaded barcode graphs given a folder of csv files.
# The length / max x value is determined by the longest csv in the folder. File... |
e10284b6fd0c3092e6c4b3ef084a068d5783ddb1 | 0450d0f6e007e4ed64e2efb90b8265e6df697561 | /plot1.R | e4db1e38e941e1f8651e0a136c2a05ef704dcec6 | [] | no_license | vaddss/Exploratory-Data-Analysis | 2b168f1b93273a3d5fe5f89303ee09624268906b | 2a033ff746cb5b5ec857c7772e236dbb13990f40 | refs/heads/master | 2020-05-17T13:46:54.549498 | 2014-07-11T18:08:05 | 2014-07-11T18:08:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 744 | r | plot1.R | # Step 1 (getting the dataset):
temp <- tempfile()
fileUrl <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(fileUrl, temp)
dataset <- read.table(unz(temp,"household_power_consumption.txt"), header=T, na.strings = "?", sep=";")
# Step 2 (subsetting our dataset):
d... |
f8e917bce0d4aa8d6f76893cb08a91244c8947fa | 0d0638f672c3e02683d4d0dde55e351d5a240cee | /ui.R | 5dbbd3fa545cc29ac954ca1bdd6b0c47086481db | [] | no_license | wrightbr/DS9_ShinyApp | 7545a64c8e062be4a43168528763b4e176609b9d | 2c3afc0e1de4da36452b9f0637a6a294c86b3ac0 | refs/heads/master | 2021-01-15T21:45:17.066960 | 2014-12-12T13:37:02 | 2014-12-12T13:37:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,230 | r | ui.R | library(shiny)
# define UI
shinyUI(fluidPage(
# application title
tags$h3("Sample size calculator for estimating a population proportion"),
p("Say you want to estimate the proportion of likely voters that are going to vote for a given candidate (e.g., see this recent poll by ", tags$a(href="http... |
27a1225bc64e7eb9c7e347e46d3b4a9de80077cc | b30e1aa7485835f66523697614a8308a5f202cf7 | /cachematrix.R | aeb8ff1477a7409288632be73e04ca9752888d06 | [] | no_license | BrodyVogel/ProgrammingAssignment2 | 730d6338bf74599349fcbf62fed89bfb45718269 | b55bd0412de4c4e61c46c54d11726cfdf7ce45f9 | refs/heads/master | 2020-04-19T14:18:20.261914 | 2019-01-29T23:03:30 | 2019-01-29T23:03:30 | 168,240,248 | 0 | 0 | null | 2019-01-29T22:34:48 | 2019-01-29T22:34:47 | null | UTF-8 | R | false | false | 1,066 | r | cachematrix.R | ## These functions caches the inverse of a matrix, which could save time with huge matrices
### This function creates a process for caching the inverse of a matrix, if it exists
makeCacheMatrix <- function(x = matrix()) {
mat <- NULL
set <- function(y) {
x <<- y
mat <<- NULL
}
get <- function(... |
73a363172a3c5503b95fb94850f7b600f83f4968 | deca20f404aa14f95dbb266585e59ea264e12691 | /IterativeAlgo/tests/testthat/test-make.matrix.R | 7fd907df719b606bd7b56a1c63533edb7df7235a | [] | no_license | TGuillerme/Parsimony_Inapplicable | 0cea924ffcff59b7cf985260c843553170e3f0c4 | 2710e3c89a9e7d4ee02e8c16b19ca168f99a036c | refs/heads/master | 2021-01-10T15:04:34.638326 | 2016-11-24T16:14:06 | 2016-11-24T16:14:06 | 49,874,989 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,931 | r | test-make.matrix.R | #TEST make.matrix
context("make.matrix")
#Testing sample.distribution
test_that("sample.distribution works", {
#errors
expect_warning(
expect_error(
sample.distribution("a", c(runif,1,2))
)
)
expect_error(
sample.distribution(1, "c(runif,1,2)")
)
... |
c1e4286a1e4ed260df93ed9ead2d8dace5731324 | 6e9f1b8058a2e5a38768c0185351b9a14d69afe2 | /R/Misc.R | 934be86cfd450bee59cfe13ea85d5d27ba516311 | [] | no_license | cran/DLMtool | d4a7cae061a8bcf897d9d01eb913e2584c710a00 | f42d4e74f8eab28738d9305945c7c3ae57ccd160 | refs/heads/master | 2022-07-05T17:08:22.460549 | 2022-06-20T15:20:09 | 2022-06-20T15:20:09 | 24,017,503 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 19 | r | Misc.R |
tiny <- 1E-15
|
e4a29e8612eb59200bff7d063f18289be5a274ea | 492f49a78bea9ab16fc99d159653722113afa125 | /man/separate_header.Rd | ea6c535305d79a2f2b06e8e675c1edda05bc51e7 | [] | no_license | davidgohel/flextable | 48c34514420e435ca70f65354e94aa69786777bc | fc62aaf29c01bbac26fe34ef85240afe4eb201ab | refs/heads/master | 2023-08-23T06:49:13.945566 | 2023-08-20T22:53:39 | 2023-08-20T22:53:39 | 62,127,938 | 502 | 83 | null | 2023-08-20T19:03:11 | 2016-06-28T09:25:11 | R | UTF-8 | R | false | true | 2,419 | rd | separate_header.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/augment_rows.R
\name{separate_header}
\alias{separate_header}
\title{Separate collapsed colnames into multiple rows}
\usage{
separate_header(
x,
opts = c("span-top", "center-hspan", "bottom-vspan", "default-theme"),
split = "[_\\\\.]",
... |
8eba97961f942056fd03549eb2ba275fd5039d1b | 75f2c23f74b501091cbbbf6ac65a2854fdda9075 | /bulb_temp/bulb_temp.R | e2f0ada008a7250eda292bfe217027fa364cb490 | [] | no_license | cortrudolph/preschool_measurement | daf8f984f07166e53a19148a6f7281ccd6657962 | 3ca97755113b1051dff2aa0d2c247d9aed11e942 | refs/heads/master | 2022-07-26T22:50:30.226389 | 2020-05-20T14:50:45 | 2020-05-20T14:50:45 | 263,669,476 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 461 | r | bulb_temp.R | library(tidyverse)
data<-tibble::tribble(
~Time, ~Temperature,
0, 56.3,
0.5, 114,
1, 123,
1.5, 137,
2, 169,
2.5, 164,
3, 147,
3.5, 158,
4, 160,
4.5, 157,
5, 171
... |
66583c3ab4909f14d726b4d1a5171f0f478f8ea4 | e4c4c406e880566f29a56ff4b0899b170563c192 | /triangles_test.R | a0193b1772d748ff1014a0997a16d29a8d6c8ba6 | [] | no_license | will-r-chase/artinpi | cda43cac5f83f176c8ca041473c1e23d6e8f664a | d046b2c453388f489ad29addfde7d1a8456485ba | refs/heads/master | 2020-04-08T18:20:50.942852 | 2019-03-26T14:55:17 | 2019-03-26T14:55:17 | 159,604,403 | 0 | 0 | null | 2018-11-29T03:52:31 | 2018-11-29T03:52:31 | null | UTF-8 | R | false | false | 1,355 | r | triangles_test.R | library(deldir)
library(sf)
library(sp)
library(tidyverse)
#idea sameple pi in groups of 3 digits, each digit is an angle, sum is size (or 1st num is size)
piChar <- read.table("data/PI_10000.txt", stringsAsFactors=F, colClasses = c("character"))[1,1]
piVec <- as.numeric(strsplit(piChar, "")[[1]])
#######... |
7fd0252115e30333bdcdc00b1eae059645a8a61f | 5aaf7a4652ec0f338fc9f595d6c296336f0339f7 | /exercises/04_import_data.R | 06895ebf4d10c9f151ea9f4f3eab37766a311468 | [] | no_license | tertiarycourses/ApacheSparkRTraining | 621a761ab9a9d61936b77cca0b4932f30c086469 | 0422424e53ce46e9198ee5d41a20b0c368815465 | refs/heads/master | 2020-03-27T06:13:36.673348 | 2018-08-29T08:02:00 | 2018-08-29T08:02:00 | 146,090,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,591 | r | 04_import_data.R | ############# copy data to impala ###############
library(DBI)
library(dbplyr)
library(odbc)
library(ggplot2)
library(dplyr)
library(RODBC)
library(readr)
### wait for the hive server to start in your VM > takes about 10 minutes
# $ifconfig
impalacon=dbConnect(drv = odbc::odbc(),
... |
c884e070e9d45a5c36f818858bdc46809ceb4555 | 7da5415ba32fabc012b8b6173ec2e346243f0188 | /consensus_clustering/src/GDAC_CnmfReports/reports/.svn/text-base/CnmfCNReport.R.svn-base | 47f46fe8db42f78b6fb4dc837e78273f6d95de98 | [] | no_license | WANGDI0212/DLBCL_Nat_Med_April_2018 | 472443c164dfda8795e4ccf36cb02ec41ceed36c | 1c5dcd2f7b859f8b7839f4e1d9725e455b14df4d | refs/heads/master | 2022-10-19T17:30:38.563049 | 2020-06-11T20:34:50 | 2020-06-11T20:34:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,169 | CnmfCNReport.R.svn-base | # Filename:
# Authors:
#
# Purpose:
# Command line calling script:
# <R> <libdir>consensusReport_v5.R writeReport -o<expdata> -v<kclus> -s<bestclu> -u<allcluster> -w<markers> -p<cormatrix> -q<markersP> -r<heatmap> -t<heatmapall> -a<file.gif.2> -b<file.gif.3> -c<file.gif.4> -d<file.gif.5> -e<file.gif.6> -f<fil... | |
e58ffacce904db55e326f85d02501d4b8f5efdff | 0f6f92ec1fcc3a27cfe3dc712f0ce92e57dc0c03 | /R/encodepeak.to.granges.r | 38c28ec4195d09ccc6970d029ef5673b48dfbd73 | [] | no_license | tengmx/toolbox | 274ec1b967a9c281a6ac61500f49d05800d0e8cc | e8e55f357bbba93a108feb7d083d62ea469fb9a6 | refs/heads/master | 2021-01-12T16:06:12.905158 | 2016-12-05T16:47:15 | 2016-12-05T16:47:15 | 71,936,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,076 | r | encodepeak.to.granges.r | ### load ENCODE peak file into GRanges
### input: broad/narrow peak file, gzipped status, peak file type
### output: GRanges for all peaks
encodepeak.to.granges <- function(peakfile,gz=TRUE,format=c("broad", "narrow"),
meta=TRUE){
if (!is.logical(gz) || length(gz) != 1L)
st... |
2c1eb74c2516448bcd802b99b51731104b24692a | 515c4a095ef3b6d2879d7943e610fa98441f9afe | /facial_keypoints.R | 76e74d5eb7f5e6a4718605ab94345ec32264bd8d | [] | no_license | KaranvirSinghJ/Facial-Expression-using-R | a3f45c1419b144e7febe0f85e147e068f34b2ab3 | 8c9cc2183db9e27751a4effdafa40f7debfe2fdd | refs/heads/master | 2020-03-20T03:26:28.762526 | 2018-06-13T01:28:27 | 2018-06-13T01:28:27 | 137,146,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,636 | r | facial_keypoints.R | data.dir <- '/home/student/Downloads/'
train.file <- paste0(data.dir, 'training.csv')
test.file <- paste0(data.dir, 'test.csv')
d.train <- read.csv(train.file, stringsAsFactors=F)
im.train <- d.train$Image
d.train$Image <- NULL
head(d.train)
im.train[1]
as.integer(unlist(strsplit(im.train[1], " ")))
install.packages('... |
67f2591abd52a978becb459ef769bdda04dbebc9 | 3e3adfa146c94c393e69dbd1a02767234b1dc173 | /draw_multi_probe_ann.R | af786beea6d9603608e79dc240e26a0b60520f56 | [] | no_license | viirya/fastdict | 1a18ce14ddb18b69531ccaa71bcf2b935d0f777c | 83a2afc2096f57c49da7048b4a21b63a9b36d0ac | refs/heads/master | 2020-05-02T22:01:25.859446 | 2017-02-11T05:38:03 | 2017-02-11T05:38:03 | 15,826,276 | 15 | 4 | null | 2017-02-11T05:38:04 | 2014-01-11T16:52:45 | Python | UTF-8 | R | false | false | 1,878 | r | draw_multi_probe_ann.R |
draw_figure_ggplot <- function(dataname, data_frames) {
library(ggplot2)
print(data_frames)
p <- ggplot(data_frames, aes(x = probe, y = nn, group = distance))
p + geom_line(aes(colour = distance)) + scale_colour_discrete(h = c(0, 360) + 15, c = 100, h.start = 0, direction = 1) + xlab("Number of mul... |
a3b98435ca9d9175c42e3420a5c8de11c20e613a | 372a279bd0a9c0a5c5fa5e4cb932ec41db07ce7e | /man/amce.Rd | 5fd5c21b3602aa00d49e7a76f4172d05e4df563e | [] | no_license | cran/cregg | 07dc7e2d3e94797829b845a0585ab3ba0297c032 | 74636ad832430b994b814a48498ad0e00be51a63 | refs/heads/master | 2021-07-12T16:53:22.169731 | 2020-06-28T20:20:03 | 2020-06-28T20:20:03 | 145,906,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 5,734 | rd | amce.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/amce.R
\name{amce}
\alias{amce}
\alias{amce_by_reference}
\title{Tidy estimation of AMCEs}
\usage{
amce(
data,
formula,
id = ~0,
weights = NULL,
feature_order = NULL,
feature_labels = NULL,
level_order = c("ascending", "descendi... |
191362712c92e67607be19230245aba52da3a205 | 826cc17cd51ccbceeb0b33ee23cab81ccee3932f | /tests/testthat/test-enrichment_depletion_test.R | 258a72e28dd83b943f635c70fb06d2d029d13778 | [
"MIT"
] | permissive | UMCUGenetics/MutationalPatterns | 9b9d38a7ab69d7e29d8900f11fa9fb7ef328cfb9 | ca9caf0d0ba3cd1e13cb909009dc5b3b27b84631 | refs/heads/master | 2023-04-14T23:28:50.852559 | 2022-11-22T11:37:17 | 2022-11-22T11:37:17 | 53,409,261 | 86 | 37 | MIT | 2022-11-22T11:37:18 | 2016-03-08T12:10:11 | R | UTF-8 | R | false | false | 1,830 | r | test-enrichment_depletion_test.R | context("test-enrichment_depletion_test")
# Read distribution data
distr <- readRDS(system.file("states/distr_data.rds",
package = "MutationalPatterns"
))
# Set tissue
tissue <- c(rep("colon", 3), rep("intestine", 3), rep("liver", 3))
## Perform the enrichment/depletion test by tissue type.
output <- enrichment_dep... |
f791f47691c9ed0a5bbeef7e340d13b627270885 | f9d6ff022b97ff2d299c8927cdb8884d51e51701 | /R/count_total_missing_values.R | af21eca89aafd9bdb7c0e18ae0714c21aaccece4 | [] | no_license | antchau/glider | c0571ef7e69c440ca11f99026e0725ed3e126f2b | 9deaafc9aaca9c5f1e9d4fd143d8872169824a7e | refs/heads/master | 2023-01-22T03:05:37.216056 | 2020-11-30T22:26:44 | 2020-11-30T22:26:44 | 286,846,018 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 268 | r | count_total_missing_values.R | #' Count total number of missing values in a data frame.
#'
#' @param df A data frame.
#'
#' @return Count of missing values in the data frame
#' @export
#'
#' @examples count_total_missing_values(mtcars)
count_total_missing_values <- function(df){
sum(is.na(df))
}
|
eb85b22aa6e2fcbf6ee2230e09f5df49a7e000ef | 9b202913ece1e2916e80c913693bc17c0adba768 | /R/processing/process_forsstrom2015.R | 70d787a7693a2df89d7a8c67bd71532bd502699b | [] | no_license | PMassicotte/cdoc | 5217750db3e1fdf699a1a8d5a26a8f1b83d52253 | fef25b848cb1ac1f2c7be171b290ad5d5fef7af7 | refs/heads/master | 2021-09-13T21:36:56.273815 | 2018-05-04T12:59:30 | 2018-05-04T12:59:30 | 80,427,518 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,413 | r | process_forsstrom2015.R | #<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>
# FILE: process_forsstrom2015.R
#
# AUTHOR: Philippe Massicotte
#
# DESCRIPTION: Process raw data from:
#
# Forsström, L., Rautio, M., Cusson, M., Sorvari, S., Albert, R.,
# Kumagai, M., et al. (2015). Dissolved organic matter ... |
60ab07730099b3c90ccca4ac732fadb6138bfebc | 6fb04083c9d4ee38349fc04f499a4bf83f6b32c9 | /tests/next/test_FLPCA.R | 1955d65255c1848112ff324b9e0318653364a684 | [] | no_license | phani-srikar/AdapteR | 39c6995853198f01d17a85ac60f319de47637f89 | 81c481df487f3cbb3d5d8b3787441ba1f8a96580 | refs/heads/master | 2020-08-09T10:33:28.096123 | 2017-09-07T09:39:25 | 2017-09-07T09:39:25 | 214,069,176 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 647 | r | test_FLPCA.R | # DB-Lytix Example.
Renv <- new.env(parent = globalenv())
FLenv <- as.FL(Renv)
Renv$tbl <- iris
Renv$tbl$Species <- as.numeric(Renv$tbl$Species)
FLenv$tbl <- as.FLTable(Renv$tbl,tableName = getOption("TestTempTableName"),
temporary=F, drop = TRUE)
#'
#' fliris <- as.FL(rtbl)
#' flirispca <- prcomp(Species~., dat... |
75699b7690e82c7f26f53a15bdb8ddfafefb7a6f | 30ef24a6c48d15897736f1a1b2bd165344eb6017 | /docs/build.R | 3a9ea2551173179467ac2653d7ceaea9d2a8be33 | [
"MIT"
] | permissive | skhan890/hbgd | 6527d165c103c04e048713bd495d38ae99d611d4 | 6fc084ff17d6b88afc0724273b03e8e822151071 | refs/heads/master | 2021-01-16T20:36:17.479770 | 2016-06-07T19:35:09 | 2016-06-07T19:35:09 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 758 | r | build.R | knitr::opts_knit$set(root.dir = normalizePath("./docs"))
packagedocs::render_docs(
code_path = ".", # location of code directory
docs_path = "./docs", # location of docs directory
package_name = "hbgd", # name of the package
main_toc_collapse = TRUE, # use collapsing toc on main page
rd_t... |
85962396d7e87e094fe0232b23a979e540e5504d | 13750542b2e5406b948059ae7d2bbe9967bb58bc | /initGrid.R | 37293ab7e4d2b70e83fe31253516952f250d2d97 | [] | no_license | jrevenaugh/Riquity | ce65cd3e9f0f30b92b8e773e723b1cb34793e226 | 64ce1eed78467f6a6849744819829f9aa469f4c7 | refs/heads/master | 2020-03-15T01:57:34.876482 | 2018-05-07T21:07:17 | 2018-05-07T21:07:17 | 131,906,279 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 303 | r | initGrid.R | # Initialize a grid with empty hole positions given by vector "hole".
# hole is in key format (characters "1" through "9" and "A" through "F").
source("global.R")
initGrid <- function(hole){
i <- which(key %in% tolower(hole))
grid <- matrix(TRUE, nrow = 15)
grid[i] <- FALSE
return(grid)
}
|
d1b4fccd359041b7e368d78a9a569efd0b00df9c | 332b6802a20847807f84817512df1d681275469e | /server.r | 489fed6d263b45a87bc91b298cff65eb41d8f2c1 | [] | no_license | tischi/shinyHTM | abb45aef3514791df06412602bdf3f7dd4e9de1e | ea35c9f9dce2fb7d079933e76f8f7bd992f92255 | refs/heads/master | 2021-01-09T08:44:31.436833 | 2017-03-14T08:59:14 | 2017-03-14T08:59:14 | 65,203,389 | 0 | 0 | null | 2017-03-13T18:22:09 | 2016-08-08T12:43:49 | R | UTF-8 | R | false | false | 22,708 | r | server.r | # ==========================================================================
# Phylosophy: the 'htm' data.frame is placed in the global enviromnent, where it can be accessed and updated by reactive functions
#
# Heatmaps are built from a reactive data.frame 'htmHM()'
#
# ================================================... |
e36c92f0f0386eccafe19e573c17fe506616a4dd | 2f2fb1e88289f6f2e85b75a6bed98940597ce6cc | /2_phylo_dating/1.4_gene_shop_data_stat.R | bcd880be1aa25ecc0aae808ec38794466d3a903c | [] | no_license | PiraMew/Maya | 744493f3504fae9faf3daea8809ac77e9008e5ed | 30a51ad3bb01243d73cc9cb39d1ad3f7ac5a2a2b | refs/heads/master | 2023-02-28T19:17:05.736971 | 2020-06-16T18:42:41 | 2020-06-16T18:42:41 | 333,092,172 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,893 | r | 1.4_gene_shop_data_stat.R | #!/usr/bin/env Rscript
###########################################################################
# Project: Orania Phylogeny MT
# Script: gene_shop_data_stat.R
# --- Action: Compares gene trees to Astral tree (one direction) in order to
# ------------ select the genes that have the least "good" (BS > 75%) nodes disa... |
d597ccf45f00dc88c514229e122777814338165a | 53d0f71c11b9a2cd600e80d9214c013ec6b8b7d3 | /Functions/scv.bic.R | 5c4c2c8243f80b94b16a6e7057832610b47a648f | [] | no_license | boxinz17/FGM_Neighborhood | af480715f76f5e1cff9c741afc7b879d189ff548 | 7a70cf4a699dd42629686e91d36a73af35711bb1 | refs/heads/main | 2023-08-03T08:39:16.209314 | 2021-09-15T18:57:30 | 2021-09-15T18:57:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,260 | r | scv.bic.R | SCVBIC <- function(X,Y,beta.hat, empty=F){ # beta.hat: pM2 dimensions
n <- nrow(X)
M <- ncol(Y)
p <- ncol(X)/M
if(empty){ # beta.hat is all zero
epsilon.hat <- Y
p.train <- 0
}
else{ # beta.hat is not empty
# Extract Sparsity Pattern
sp.pattern.pM2 <- (beta.hat != 0)
select <- rep(... |
d07e41b45cbd9f54d5e78183ea6f1078ab39914d | 96a399382fb89102ec288e7030e16a8de6079776 | /plot3.R | 76110d7632eba9600148dbac6beefa543c68e27a | [] | no_license | electromel/ExData_Plotting1 | a96a57cf583ead6f28753fd7c9c4712e3910b3a4 | e0c57a77cd90f772401b8e8148c5136d9942072b | refs/heads/master | 2021-01-15T12:49:21.294579 | 2015-04-12T16:39:44 | 2015-04-12T16:39:44 | 33,526,065 | 0 | 0 | null | 2015-04-07T06:26:05 | 2015-04-07T06:26:05 | null | UTF-8 | R | false | false | 806 | r | plot3.R | # setwd("~/OneDrive/Formation/Coursera/Data Science/04 - Exploratory Data Analysis/ExData_Plotting1")
# load data
file <- "./household_power_consumption.txt"
data <- read.table(file, sep=";", header=TRUE, na.strings="?")
data$Date <- as.Date(data$Date,format="%d/%m/%Y")
data <- subset(data, Date > as.Date("2007-01-31"... |
1ee7009fd2cd03a27b9e18bfdf840217de9f4fd7 | c169f200481e291de6229f998c3b74496a11337c | /Titanic.R | 2529d0fc0587439c3bb16a9a34624902eb817b95 | [] | no_license | meetkholia/Titanic_Survival_Analysis | 7d3b44cfeebd3b6adbc773d99181b378faf23f19 | 5d35bd00c23f9f0f0a2c3171ed440fc636c5882c | refs/heads/master | 2016-09-12T09:52:13.127899 | 2016-04-15T21:30:31 | 2016-04-15T21:30:31 | 56,350,890 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 18,053 | r | Titanic.R | # Read the data
train <- read.csv(file="train.csv",header=TRUE, sep=",");
test <- read.csv(file="test.csv",header=TRUE, sep=",");
# to combine both dataset make "surived" variable in test
test.survived <- data.frame(Survived = rep("None",nrow(test)),test[,])
data.combined <- rbind(train,test.survived)
str(data.... |
803747df8f5256f185afb36344a9bda1bac293f3 | a44a64837d1cfc4e43251dab59fbe7c1b0dae823 | /cleaning_data.R | 2f5c9e8a6d08f7340cb69290c48401f597b48f6f | [] | no_license | tanyasarkjain/CaffraMsatAnalysis | e772620c6a9b78f0f168de66bcd7ca568a51b369 | b0838ef4aba2b6e0728acc1d590a2a8c1ac74ac4 | refs/heads/master | 2023-06-09T15:46:13.975807 | 2021-06-29T21:33:30 | 2021-06-29T21:33:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,873 | r | cleaning_data.R | require(gstudio)
require(ggplot2)
require(ggmap)
require(popgraph)
library(igraph)
require(maps)
require(raster)
require(fields)
library(dplyr)
#Reading Coordinate data
SADataCoords<-read.csv("/Users/tanyajain/Desktop/SouthernAfricaResearch/Wogan_etal_Cossypha_caffra_SamplingInfoTanya.csv",header=TRUE)
SADataCoords
#... |
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