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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fcec51de69d94721b28fd4123def77e873676169 | 431d385c76325212a14a52570c9c2c8aecdd7a27 | /plotSentiments.r | 10127d2fa703bc422e2369a7d6736dddf16def26 | [] | no_license | mattravenhall/TweetSentiment | 2e5d5f20195ceb3eeae4f7d73559ac8d36c35172 | 3fb92a43d522f1534e7c3075a0146346b5795713 | refs/heads/master | 2021-03-31T01:42:33.344348 | 2018-03-08T17:16:42 | 2018-03-08T17:16:42 | 124,422,683 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,130 | r | plotSentiments.r | library(data.table)
# Clean out tweets from .csv
system("cut -d',' -f1-3 sentiments.txt > sentiments2.txt")
# Read in .csv
dat <- fread('sentiments2.txt')
dat$TimeClean <- as.POSIXct(dat$Time, format="%Y-%m-%d %H:%M:%S")
dat <- subset(dat, nTweets > 5)
#dat <- dat[!is.na(dat$Sentiment),]
# Plot .csv to file
png('sen... |
986e6b3be960d274a25a043e18f19ad72af5598e | f9f39737fc94196ff48525acd49f6828c7719eac | /functions.R | b529619e4ca1247d8f1498b5e817d86a3e12b917 | [
"MIT"
] | permissive | KatharinaGruber/BrazilWindpower_biascorr | cbac02514c0d4423cff84358356f2d0a1db53032 | 6849dda96c2999157194c7d4d2aa80e2cda45c98 | refs/heads/master | 2021-06-16T18:07:59.508895 | 2021-03-23T07:37:19 | 2021-03-23T07:37:19 | 175,448,056 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 58,213 | r | functions.R | # this file contains functions for the simulation of wind power generation from MERRA-2 data
# and also for performing different types of bias correction
# function to calculate power generated in one location without using correction factors
# method: interpolation method to use (1:NN,2:BLI,4:IDW, no BCI becau... |
48f69697baee385eb87b1b142e34fe829339568e | bbe5ae041a1f8bde6fe00fa6c53b02d9f329983d | /R-Deep-Task1-CLEAN.R | 64cf84fb107ec8a2fa73c77622f5fcc35f3c9b75 | [] | no_license | ChristianTorrens/DeepAnalysis1 | bac8c0a06f691de1b080e202c6680ff5112b3b75 | 88f8382aa61ebac7ef1213b34353cd3c226ddbdb | refs/heads/master | 2020-03-29T21:42:39.568032 | 2018-10-03T08:00:51 | 2018-10-03T08:00:51 | 150,382,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,677 | r | R-Deep-Task1-CLEAN.R | #https://en.selectra.info/energy-france/guides/electricity-cost#
#Mutate etc# #https://jules32.github.io/2016-07-12-Oxford/dplyr_tidyr/#
#### Installing and Calling libraries####
install.packages("chron")
source("https://raw.githubusercontent.com/iascchen/VisHealth/master/R/calendarHeat.R")
devtools::install_github(... |
9595537b9aee8686236eed47932e68217d9db984 | 0d811a56cdbb88d97f48438e2715d62c31400e8b | /1.7 Loops.R | 4175a916843f9cda16b7cd2782142b440ef485ab | [] | no_license | cbibinski/Exercises | 2bd473d76cfed40bfb4b30ebd873ece9f00b8445 | 06118f2db2a7519e4c1c2d777d5c3d86c7132476 | refs/heads/main | 2023-01-06T07:55:00.679127 | 2020-11-09T05:57:17 | 2020-11-09T05:57:17 | 309,395,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 487 | r | 1.7 Loops.R | library(TurtleGraphics)
turtle_init()
## turtle makes a square
for (side in 1:4){
turtle_forward(distance = 10)
turtle_left(angle = 90)
}
## turtle makes a triangle
for (side in 1:3){
turtle_forward(distance = 10)
turtle_left(angle = 120)
}
## print the telegram message until "STOP" appears, using a while loop... |
3176dbf434b2942b1e7137b80d4a3c20fe79a809 | 514864ae56784472819818e1cb58f998c344dfa1 | /R/CDA_function.R | 082d49b02ec554a7487d20130779abdb4c42fee1 | [] | no_license | cran/factas | ce20bf1f6cfc3bfac1b292557b3652e6338a08c8 | 6294d79925874da143a3ff1719c88da182f16d8e | refs/heads/master | 2016-09-06T17:50:42.099668 | 2014-01-23T00:00:00 | 2014-01-23T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,602 | r | CDA_function.R |
CDA<-function(data,groups,stream=TRUE,nb_fact,principal_factors=TRUE,principal_axes=FALSE,eigenvalues=FALSE,corr=FALSE,graphics=FALSE,data_init,exec_time,print_step)
{
data<-preprocess_CDA(data)
p<-dim_y <- ncol(data)
dim_r<-groups[1]
dim_s<-groups[2]
X <- A <- Y <- list()
for (i in 1:nb_fact)
{X[[i]]<-A[[i]]<-Y[... |
144f74195c81c8548e344d92d88ba1f7462b292f | 1f33d96b17045eb81c4e509aa98bf946f390bef4 | /histo.R | 56c36fefa689aeb22b4cf7928b1a7658fc215fa3 | [] | no_license | georgeredinger/DressageHorsePriceDistribution | 392eaad5da8e98af61b160d1d8f6cd0c5394373b | ce587d1116d688575d27a477fec06e4fffdff00d | refs/heads/master | 2020-12-24T14:17:46.430771 | 2013-11-12T19:57:37 | 2013-11-12T19:57:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 109 | r | histo.R | data <- read.csv("prices.dat")
print(data[,1])
png(filename="histo.png")
hist(data[,1],breaks=20)
dev.off()
|
02a3eeb29ec13e1821df1a1d63c0d275daa9c092 | c0995410addadff0b5ec94ff06c61a2d33568ce4 | /Code/table1.R | 6737979102ce05ed458fa7ba15b04fbd62d13d08 | [
"CC-BY-4.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | kotrinak/sociality | 4124fc617b2957393bd071fd6523500236413431 | 244ec15bef50114e7dafe038ca9f767d3e85571c | refs/heads/master | 2023-04-12T17:59:29.068654 | 2022-04-11T22:12:33 | 2022-04-11T22:12:33 | 281,337,915 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,260 | r | table1.R | # Kotrina Kajokaite
# This script makes a table 1 for Kajokaite et al. 2022
library(xtable)
library(rethinking)
#load data
sla <- read.csv("Data/survival_dataset_all.csv", stringsAsFactors = F)
slf <- read.csv("Data/survival_dataset_ff.csv", stringsAsFactors = F)
slm <- read.csv("Data/survival_dataset_fm.csv", string... |
a6889c4aeccb769caed2b4e395ec5fb1d423510d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/decon/examples/DeconCdf.Rd.R | 295c64745a3c541931223cfe86da3b4d59644b30 | [] | 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 | 1,072 | r | DeconCdf.Rd.R | library(decon)
### Name: DeconCdf
### Title: Estimating cumulative distribution function from data with
### measurement error
### Aliases: DeconCdf
### Keywords: nonparametric smooth measurement error
### ** Examples
#####################
## the R function to estimate the smooth distribution function
SDF <- func... |
b27fc55715f57061956dc55565b15caa82fa066a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rsdmx/examples/addSDMXServiceProvider.Rd.R | 2e7f5705214d1128d50efb88b2bd7417045a37b4 | [] | 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 | 704 | r | addSDMXServiceProvider.Rd.R | library(rsdmx)
### Name: addSDMXServiceProvider
### Title: addSDMXServiceProvider
### Aliases: addSDMXServiceProvider
### ** Examples
#create a provider
myBuilder <- SDMXREST20RequestBuilder(regUrl = "http://www.myorg.org/registry",
repoUrl = "http://www.myorg.org/repository"... |
4cd9a795bef9614543e1d29db25d798501c2014d | 4c7b4c60c3268c1115a70a3adc7c5cc45dbc75c2 | /scripts/moby.R | 430031d425350ea1788be45888027614df5678b2 | [] | no_license | nazabic/data_analysis | cc6d9c7519590a90fbe14b6b71f10ac32e2715aa | 8738f5a3882132c300de0bf6db8e8bc2274b6cb3 | refs/heads/master | 2021-01-16T21:03:32.350206 | 2016-12-01T17:19:16 | 2016-12-01T17:19:16 | 64,401,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 261 | r | moby.R | library("poweRlaw")
data("moby")
m_pl= displ$new(moby)
est=estimate_xmin(m_pl)
m_pl$setXmin(est)
plot(m_pl)
lines(m_pl, col=2)
m_ln =dislnorm$new(moby)
est=estimate_xmin(m_ln)
m_ln$setXmin(est)
lines(m_ln, col=3)
bs=bootstrap(m_pl,no_of_sims=5000,threads=2)
|
ca07f7357aaf75a7f7cc961654fc77838f89a39a | e876e495beb59578d18d086e93c06d7af92e2231 | /plot2.R | 53d487c63047a8bde8751cb0b0ee3ad361ded56f | [] | no_license | sjavaad/ExData_Plotting1 | 56d063b7073abe2591fdfbdb82a5c75c032d1444 | 5e556ef43d4f9764a779636a1ec92cf879059f1c | refs/heads/master | 2020-12-26T03:34:42.962460 | 2014-05-09T10:48:15 | 2014-05-09T10:48:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 570 | r | plot2.R | ##Plot 2.R
## for this code to run, we need to install the package sqldf using
## the command "install.packages("sqldf")" [if not already installed]
require("sqldf")
mySql <- "SELECT * from file WHERE Date = '1/2/2007' OR Date = '2/2/2007'"
myTab <- read.csv.sql("household_power_consumption.txt",sql=mySql,sep=";")... |
c33d609623671811a7566d9acdfbd240f0d843cf | 9b11f370ad33a7bde061eb07ed40b521456c8405 | /R/SearchConsole.R | c4ddfea39711fac6e7323d987bc4af1281fd99c6 | [] | no_license | ceaksan/PageContentAnalysis | 919b1b2404947c2bc73f08c2035247fc1266411c | d67c42aaed3e4801b5c3507561ed187fe4009980 | refs/heads/main | 2023-07-04T16:03:31.387143 | 2021-08-18T05:44:54 | 2021-08-18T05:57:36 | 352,784,265 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,049 | r | SearchConsole.R | library(googleAuthR)
library(searchConsoleR)
email <- "user@gmail.com"
JSONfile <- '<client-id>.apps.googleusercontent.com.json'
gar_set_client(JSONfile)
gar_auth(email = email,
scopes = 'https://www.googleapis.com/auth/webmasters.readonly')
SC_sites <- list_websites()
site <- SC_sites[which(grepl('domain... |
9fb3f5339da30b7791f377048ea6de551cc673a8 | 71ab6249e974d0a9bb16ce3fbdc958b7dbb39d3c | /man/ch.getPhitModel.Rd | ea7405f363b792349a25ef4754fb51ec3bd8b220 | [] | no_license | ccpluncw/ccpl_R_chutils | bbfe2aa8186dee5f7c4a1afd6fc78021a76674c0 | 3e55658c38d7d10c129755711bb7b02d0591fb3c | refs/heads/master | 2023-04-27T00:37:23.466391 | 2023-04-16T00:33:44 | 2023-04-16T00:33:44 | 136,065,945 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 876 | rd | ch.getPhitModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ch.getPhitModel.r
\name{ch.getPhitModel}
\alias{ch.getPhitModel}
\title{a function to return a p(Hit) model that can be evaluated (i.e, an expression) from an nls object}
\usage{
ch.getPhitModel(
pHitFit,
yLab = "p(Hit)",
xLab = express... |
6d9753e5b7d75a2585b1031376f1e6a7036513f3 | 2b8daa92a672b457b9a1dc51a1e5a77b087be724 | /man/swslm.Rd | e9d641efc1791b66e84565378780f2873968b062 | [] | no_license | bjb40/apcwin | 1ea79ab2430c21c2e2fae9e7f3010f11e5f32baf | 59d47f3dcc3c4424abd1fb58b6883d96ed8362f7 | refs/heads/master | 2021-04-26T22:45:54.235820 | 2018-06-23T18:55:24 | 2018-06-23T18:55:24 | 124,143,822 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 382 | rd | swslm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/samplingfuns.R
\name{swslm}
\alias{swslm}
\title{A wrapper that samples from model space and draws from posterior.}
\usage{
swslm(formula, ...)
}
\arguments{
\item{formula}{a formula.}
}
\value{
An object of apceffects.
}
\description{
A wrap... |
8c1cbc0c3e3bab69af659a62bb5ddf93e4446213 | 520c59eb08d90cb65c994c8121782c42617a5558 | /005_review_data.R | c583880e4ac6ae9d0f6568e691f92db05594e8d4 | [
"MIT"
] | permissive | ASanchez-Tojar/animal_personality_terminology | 0269adb487fb0e402c42c8e90609683b17786856 | 1877ab5f6538016aff8055f52848059f21ce04ee | refs/heads/main | 2023-04-07T12:15:27.689782 | 2022-01-14T09:18:29 | 2022-01-14T09:18:29 | 359,851,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,601 | r | 005_review_data.R | ################################################################################
# Authors:
# Alfredo Sanchez-Tojar (alfredo.tojar@gmail.com)
# Affiliation: Dept. Evolutionary Biology, Bielefeld University, Germany
# Profile: https://scholar.google.de/citations?user=Sh-Rjq8AAAAJ&hl=de
# Script first created on th... |
69eb9cf7210cec304ffcd74a27dc42399a1a802a | df472aa1f924985294ee7ead8fa2535b7f861044 | /NUCOMBog/tests/testthat/testBasicFunctions.R | d59356485c0fac476a7d55f2da378ae9c57fcedd | [] | no_license | COST-FP1304-PROFOUND/NUCOMBog | 9574e90a9390bb92a9f8a57720930f04b367d93e | be3b07478221bd3896ea621f8e5f2b10d9a12a45 | refs/heads/master | 2021-01-20T05:43:33.174840 | 2017-04-29T17:21:08 | 2017-04-29T17:21:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | testBasicFunctions.R | context("Test basic functions")
set.seed(1)
library(NUCOMBog)
test_that("Basic functions work",{
skip_on_cran()
# Add functions that can be run without the model here
}
)
|
69a963727848ad6a04eaad470c0afb57475e8288 | c5f44a778993b372be8006af73e8568597553ef3 | /scripts/network_graph.R | 16eebbfbaedf6b2cda308008968d7c2dbd730fb7 | [] | no_license | conorotompkins/spotify | ee8bd5c86b9f20deeb09ad262cab72f5b2bc842e | 20d051055168d6908ca5b64963b81c52c42a8969 | refs/heads/master | 2022-11-24T13:56:40.987340 | 2020-07-29T18:19:25 | 2020-07-29T18:19:25 | 283,020,703 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,092 | r | network_graph.R | library(tidyverse)
library(geniusr)
#edit r environment file here
#usethis::edit_r_environ()
geniusr::genius_token()
geniusr::search_artist("Queens of the Stone Age")
#geniusr::search_artist("Desert Sessions")
qotsa <- geniusr::get_artist(artist_id = 25320)
qotsa_songs <- geniusr::get_artist_songs_df(25320)
qots... |
bfaf3c3637eeb2accf8ab2fb9f952747f0628684 | 03da8319e0dc8f3d378e10526eb4e87f4884c121 | /R/data.R | 8e4d3f72d957b71c261b6d48e0ce3f38f8f17079 | [] | no_license | cran/GPP | 99fee8d8826a40a7a19c9432e94c18c4ffccf7ec | f21ba020791544afe3d98dc21375a2ae945990f6 | refs/heads/master | 2023-01-21T10:59:32.093557 | 2020-11-27T09:20:06 | 2020-11-27T09:20:06 | 317,816,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 787 | r | data.R | #' 1960-2003 GDP dataset
#'
#' An example dataset for using \code{\link{GPP}} to estimate the counterfactual GDP of West Germany assuming no reunification.
#'
#' @format A data frame with 748 rows and 14 columns. For detailed explanations of the exact measures, see \url{https://www.dropbox.com/s/n1bvqb54xrw8vyj/GPSynth... |
434827fd75bfe4178a03d8606391602c0efca48c | 29585dff702209dd446c0ab52ceea046c58e384e | /BSquare/R/qreg_spline.R | fe62b0ea55b123e3447dffd5bf7e721310d2359a | [] | 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 | 13,159 | r | qreg_spline.R | qreg_spline <-
function(X,Y=NULL,Y_low=NULL,Y_high=NULL,status = NULL,
knots_inter = c(.1,.5,.9),
Pareto = TRUE,
varying_effect=NULL,
tau= seq(0.05,0.95,0.05),
burn=10000, iters=50000,
q_low = 0.0... |
1c965aaf090da4e737b6df741ba2ee1ca9164759 | 2099a2b0f63f250e09f7cd7350ca45d212e2d364 | /AI-Dataset/Summary_rnd/S0004370215000776.xml.A.R | cfdd5381ec4b9943c95ee23a4729c1c2ee892194 | [] | no_license | Angela7126/SLNSumEval | 3548301645264f9656b67dc807aec93b636778ef | b9e7157a735555861d2baf6c182e807e732a9dd6 | refs/heads/master | 2023-04-20T06:41:01.728968 | 2021-05-12T03:40:11 | 2021-05-12T03:40:11 | 366,429,744 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 981 | r | S0004370215000776.xml.A.R | <html>
<head>
<meta name="TextLength" content="SENT_NUM:6, WORD_NUM:92">
</head>
<body bgcolor="white">
<a href="#0" id="0">The main property of this encoding is that it correctly captures consistent fixpoints of the approximate operator.</a>
<a href="#1" id="1">Let {a mathematical formula}E⊆Zu¯ be directed and {a math... |
f1c7d1cb67b6516f7031fbf1e8dc157c83865abd | 9d606b309626fe67b171a16175717fbaab2e5bb5 | /Functions/DWstats.R | 20685e5837a7bfbf17a6f9c237106bd43304784f | [] | no_license | yuxidchen/LupronCode | b7c8fa6885b08156841bbff6a8d6c24a82ca13b2 | ab593f313a1250c6318dc3028cf83da80ccfa16c | refs/heads/master | 2020-12-24T19:28:13.030188 | 2016-05-19T23:29:12 | 2016-05-19T23:29:12 | 59,250,020 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 356 | r | DWstats.R | DWstat <- function(residuals) {
LagResiduals = MyLag(residuals, 1)
# DWData$numerator <- ifelse(DWData$Index>1 & !is.na(DWData$Residuals),(DWData$Residuals - DWData$LagResiduals)^2,0)
numerator = (residuals - LagResiduals)^2
result = sum(numerator)/sum(residuals^2)
# return(as.numeric(sum(DWData$numerator... |
9228461856eefa6242354e0547579cdbb19a17b6 | e2bf42ce43945365e26a34567083ce0f0f0946f9 | /utility/apply_pbem_scaling_factor_on_oncotab.R | 1a9092e0e81f1a1c770832a75446d2b3d4a69294 | [] | no_license | ncasiraghi/abemus_raw_code | 0a665916a8f64c8d65719300a0473862de288790 | 65eda0d883c74d03761d352fea7c46e6be7e2467 | refs/heads/master | 2020-03-27T09:06:55.383957 | 2019-02-28T11:24:30 | 2019-02-28T11:24:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | apply_pbem_scaling_factor_on_oncotab.R | #!/usr/bin/env Rscript
args <- commandArgs(trailingOnly = TRUE)
if(length(args)!=2){
message("\nERROR:\t2 argument required")
message("\nUSAGE:\tRscript apply_pbem_scaling_factor_on_oncotab.R <Results_folder_abemus> <Pbem scaling factor>\n")
cat(paste("\t[1] path\tResults folder generated by abemus","\n"))
cat... |
2dcabc6ac48d1ccc941b9f7abe97fafc1ea04c10 | 9fb76e69e684b8c7af76feb3ab303375a3b19b85 | /clustering/code_clustering.r | 1e2ad56ed36a39709e23613b2b2767d70fb8e058 | [] | no_license | JayTheJay/Text-Mining_Depression_Dataset | 638c8a00d47233ecbef9ef2d7602198f96b4d51f | 858829d89aacbad6ad7b8c8c339614a9ad6d66cd | refs/heads/master | 2022-12-06T14:33:01.744642 | 2020-08-30T16:39:24 | 2020-08-30T16:39:24 | 291,509,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,439 | r | code_clustering.r | setwd("C:\\Users\\Maciek\\Desktop\\TextMiningProject")
library(jsonlite)
library(tm)
library(SnowballC)
library(tidytext)
library(dplyr)
library(wordcloud)
library(cluster)
library(fpc)
library(ggplot2)
data <- fromJSON(txt = "data\\texts.txt")
#preparing data
cleaned_txt <- Corpus(VectorSource(data))
clean... |
77e317ce98dbd70f2c014e632bc7c2accaff88b0 | e6131689004ff6d8309da84cb3cd1032b26beaa9 | /inst/doc/pseudoprime/man/fermat.test.Rd | bde041b1dbe08137e3d208d01265f124755b7ed9 | [] | no_license | cran/roxygen | b2ae93413d3a0f4d0a40d98b2bbe642e6df0382d | 561faff04171237ec8720f2e2c5cb423c802d30d | refs/heads/master | 2016-09-06T10:02:45.937969 | 2011-12-23T00:00:00 | 2011-12-23T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 799 | rd | fermat.test.Rd | \name{fermat.test}
\alias{fermat.test}
\title{Test an integer for primality with Fermat's little theorem.}
\usage{fermat.test(n)}
\description{Test an integer for primality with Fermat's little theorem.}
\details{Fermat's little theorem states that if \eqn{n} is a prime
number and \eqn{a} is any positive integer less t... |
e03c454854f9599718a82dc3641f3cb3d66e5adf | 94dcbff4ef2072f5a5ecbb95af1f259f31ad3b20 | /man/int.est.pl.Rd | 9096fbd34003202bdb8bee3c08be376e3bf66f81 | [] | no_license | DistanceDevelopment/WiSP | bf51406076ded020098f4973003eafc05a45d437 | e0e2665d6b3b49ba634944b4bb7303be41620e5a | refs/heads/master | 2021-06-05T02:54:08.957306 | 2020-09-14T20:03:59 | 2020-09-14T20:03:59 | 9,773,511 | 0 | 1 | null | 2020-09-14T09:30:06 | 2013-04-30T15:05:50 | R | ISO-8859-16 | R | false | false | 2,900 | rd | int.est.pl.Rd | \name{int.est.pl}
\alias{int.est.pl}
\title{Plot Sampling Method Abundance Estimation: Interval Estimate}
\description{
This function calculates confidence intervals for group abundance for the plot sampling method.
}
\usage{
int.est.pl(samp, HT=FALSE, vlevels = c(0.025, 0.975), ci.type = "boot.n... |
b417a709740756b54ff0d3c4216f3186ac8dcd46 | 735eab5da1eee942ec4e490a56cc8122e9aabe51 | /Project 1 Chess - Jill Anderson.R | 4fb413dad2ca34650d523071e0f6ea29eade2a4a | [] | no_license | jillenergy/Project-1-Chess-Tournament | ce496a9617638ab1702459d9c028e0a870056cb0 | 7f2b920def49087b2c8e05c8bb8344d695b910ae | refs/heads/master | 2021-07-02T09:59:32.302320 | 2017-09-21T02:26:50 | 2017-09-21T02:26:50 | 104,290,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,021 | r | Project 1 Chess - Jill Anderson.R | ## Assignment: Read in the raw data of a chess tournament.
## Create a table comprised of the following attributes: Player’s Name, Player’s State, Total Points, Player’s Pre-Rating, and Average Pre-Rating Rating of Opponents
## For the first player, the information would be: Gary Hua, ON, 6.0, 1794, 1605
## Load libr... |
802d56164faa42664f24f29f38aaf100aa0af132 | 68563acf6c42af465caa02612f9b536c366ab4c1 | /SEEerver.r | 895976c5d0022eaeea2890344dbb13c08ccda307 | [] | no_license | beekash222/ETL_ML_Xgboost | 6bf37e1dcb467368aae781456e7544c3b11f89d1 | 1ff7e8fd4aa190acd06dd2a96220c54bf71df3b0 | refs/heads/master | 2020-03-17T00:58:02.923194 | 2020-01-23T19:22:50 | 2020-01-23T19:22:50 | 133,135,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,718 | r | SEEerver.r | server <- function(input, output,session) {
observeEvent(input$rpart, {
output$contents <- renderTable({
req(input$file1)
inFile <- input$file1
df <- read_excel(inFile$datapath, 1)
})
})
###########Spec Column transformation#############
observe... |
5a6c399528e5c67bc57e9ac6cb8221dcebc0bf9a | 58554949cc0ed4d1d940136c496072f167a1b485 | /r/R/col.R | 9e2a5aaadcc0d85ce84dde556d12dffbab2ebf54 | [
"Apache-2.0"
] | permissive | wbeck32/libcore | 284568484288a4cee100e172f250f5ee7c1da411 | 8d01b38f16ab3f27c88a3cfcc1344f39963aa435 | refs/heads/master | 2021-08-11T16:21:28.463209 | 2017-11-13T22:27:05 | 2017-11-13T22:27:05 | 110,302,874 | 0 | 0 | null | 2017-11-10T23:40:30 | 2017-11-10T23:40:30 | null | UTF-8 | R | false | false | 580 | r | col.R | col <- function(table, column) {
assert(is_table(table), "`table` parameter must be a table")
if (is_integer(column)) {
if (column < 0) {
stop("`column` parameter must be greater than zero")
}
if (column > ncol(table)) {
stop("`column` parameter is greater than the number of columns in table... |
5475b971f90b999be36ea5ff026e037eca75bb62 | 310b4062d798583be0f76d173e1b369a2c8e4a20 | /R/plotPhiSeries.R | 23d9b83f0ac8aefec0d2c776b95c3438e3fda5c8 | [] | no_license | stcolema/mdiHelpR | d5600d7259ddcf8cdd1402820a553c2df63b1462 | cbbed03ab2ba71b84cdf3e3da119ccd239c75630 | refs/heads/master | 2023-06-09T16:14:08.491622 | 2021-06-30T12:16:41 | 2021-06-30T12:16:41 | 225,420,253 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,543 | r | plotPhiSeries.R | #!/usr/bin/env Rscript
# Function to save plot of phi parameter from MDI from each iteration of MCMC
#' @importFrom dplyr select contains
#' @importFrom ggplot2 ggplot aes geom_point labs ggsave
#' @importFrom magrittr set_colnames set_rownames
#' @importFrom pheatmap pheatmap
#' @export
plotPhiSeries <- function(mcm... |
1f4d9905a4cc3f0ce6ea21e7ae8f0666e8aa3815 | 52792b52803988179e18a52ca43fc7684abd3df2 | /soma_temporary_operations_public.R | 99e82cdafb465b2eacc7cc34ad5aed42b8fd8d68 | [] | no_license | bsmiller25/db-management | 3e22f2317ce28f2629f506c1ef7eb55aaac12ef2 | 27979ffc32bc3922b9073ab286ef6d6c64756cca | refs/heads/master | 2021-01-10T12:54:01.969676 | 2016-01-20T20:20:10 | 2016-01-20T20:20:10 | 50,057,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,390 | r | soma_temporary_operations_public.R |
### Collect Public SOMA Temporary Operations Data
soma_temporary_operations_public <- function(task = c("update","create")){
require("DBI")
require("RPostgreSQL")
require("XML")
require("gdata")
require("tis")
task <- match.arg(task)
# parameters to change if run elsewhere
setwd("/mma/prod/MBS/MB... |
d8ccfd5c059bd763864e142d0b53359a83ae5907 | d45320d2fc526a13c54c6e2f172286cc17cac96a | /Illinois_GM_Score.R | 95a8924572176bcab1bebc3dc331346bcb245944 | [] | no_license | emryskaya/MathOfGerrymandering | bd55e39e5b9cee437504a0feebaf4fdf2eeb80db | 0937ddc4d01c58571cedff080b5bf63b712b1419 | refs/heads/master | 2023-01-01T10:37:43.353431 | 2020-10-26T05:04:35 | 2020-10-26T05:04:35 | 291,190,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,816 | r | Illinois_GM_Score.R | library(Matrix)
library(tigris)
library(sp)
library(sf)
library(dplyr)
library(tidyverse)
library(rgeos)
library(parallel)
library(leaflet)
##Tract Level
IL_tracts <- tracts(state = 'IL')
IL_tracts <- IL_tracts[order(IL_tracts$TRACTCE),]
sf_IL_tracts <- st_as_sf(IL_tracts)
IL_tract_adjacency_list <- st_intersects(sf_I... |
1db7d533ab1a2668cbc67c58ef5014a9a28791b4 | 179e15f315e5b6936a9873cc7c20f8f45e6bd0e8 | /run_analysis.R | 53088687ab9b62596b04c79c0e5b0e6f9a967b08 | [] | no_license | mehadesai/gettingandcleaningdata | 610c1432445ce3b7bc11b28c7a1aef3bc3ebee23 | 5a80c5b2e701217bf1d98a7249aaf91d30ebe85c | refs/heads/master | 2016-09-05T15:18:06.739207 | 2014-11-23T21:29:48 | 2014-11-23T21:29:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,970 | r | run_analysis.R | # install.packages(c('data.table', 'reshape2', 'plyr'))
library('data.table')
library('plyr')
library('reshape2')
## PART 1
base_dir <- paste(getwd(), '/UCI\ HAR\ Dataset/', sep = '', collapse = NULL)
# read data from test set.
test_set_file_path <- paste(base_dir, 'test/X_test.txt', sep = '', collapse = NULL)
test_... |
5979d677c231795c9dab591c86318f30acdc06ca | 2a2d3489886a0e4bd5b76ca726adc3b7f44386cb | /standalone/scores_ml_standalone.R | a76f3ce4678a6347c8e6c42fbdc87761fb810102 | [
"MIT"
] | permissive | liufan-creat/magic | 68d51fdf847dda49500f5a963d4fce74198c9462 | a672b94c9262335cbec68e6817cd4de8eb701c65 | refs/heads/master | 2021-10-23T16:11:02.362069 | 2019-03-18T18:29:45 | 2019-03-18T18:29:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,346 | r | scores_ml_standalone.R | #!/usr/bin/env Rscript
# Generates classifiers for scores data output from predict_mae.R
######
### LIBRARIES
######
# Loads or installs all required packages
load_libraries <- function() {
get_package("caret", dependencies = TRUE)
get_package("doMC", repos = "http://R-Forge.R-project.org")
get_package("pROC"... |
672e9d4930f7ba48748a27a5d2e3ee63a6049c90 | 0f172b6f94115e34fab3994a4c95a047294e36fa | /R/Read_docs.R | bf9aea3fa9a4ba81daf7b079dea600a8297b341f | [] | no_license | jcval94/DataMiningTools | 866932e4df4f1e2e645a14bc966921395737d6b4 | fb4e7995b2f5acee742492e52fd2ad982d7b4d59 | refs/heads/master | 2020-07-07T03:42:10.047077 | 2020-01-07T05:38:26 | 2020-01-07T05:38:26 | 203,234,373 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,272 | r | Read_docs.R | library(purrr)
library(assertthat)
library(readr)
library(textreadr)
Read_docs <- function(dir = getwd(), text_, deep = 2, word_pdf_omit = T) {
if (missing(text_)) {
warning("text_ must have a value")
return(invisible())
}
lt <- list.files(dir)
SPL <- do.call(c, map(lt, ~strsplit(.x, "."... |
a049f8613bb93c2161c68405ea77da86d1c6416a | 7374303c14e64c42bed64be1c8aff78e9aefa3d8 | /R/kdr.R | a36bb08a3c3426728eac375d0002f5a0999b9fcd | [] | no_license | cran/ks | cd7d27f9a0d865f577c0bc4e857dbeca09ed55a6 | f571ffa28e9dbc5ab649b4f6ac30879cf8fad43c | refs/heads/master | 2022-11-30T03:47:41.411752 | 2022-11-24T02:40:02 | 2022-11-24T02:40:02 | 17,696,943 | 6 | 6 | null | null | null | null | UTF-8 | R | false | false | 14,010 | r | kdr.R | ######################################################################
## Kernel density ridge estimation for 2D/3D data
#####################################################################
kdr <- function(x, y, H, p=1, max.iter=400, tol.iter, segment=TRUE, k, kmax, min.seg.size, keep.path=FALSE, gridsize, xmin, xmax... |
b7746954597c92f16383ad3228b176b35f10a47c | 1de8c2a4fb90df3295cc0678d9d449a6f1bb9b48 | /generate_phylANOVA_label.R | a9b3de223e689491a2a4ccb0531327a5b46dbc2d | [] | no_license | jrosaceae/comparative_methods_misc | 946970ca2950d84d59a4547a38ff0307e932b2ab | 780bfb67c0037021c30245101b9bccb217073db0 | refs/heads/master | 2022-09-18T05:03:13.451758 | 2020-05-18T17:08:53 | 2020-05-18T17:08:53 | 265,007,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 737 | r | generate_phylANOVA_label.R | `generate_phylANOVA_label` <-
function(phyNOVA)
{
# All that is needed is the output from phytools::phylANOVA
# Extract p-values and assign groups from phyloANOVA post-hoc table
df.prep<-phyNOVA$Pt
df.prep[lower.tri(df.prep,diag = T)] <- NA
df.prep<-na.omit(melt(df.prep))
df.prep[,1]<-paste(df.prep[,1],"-"... |
f6b13b1bc833ab06ca1c3025b8d01c68e0e4b94b | c8b5d303995efaf03fa2972306c966d097e7e452 | /pokemon_radar_plot.R | aee789c910de4fcd58b3a305f1f795028c60d72e | [] | no_license | northernned/pokemon-radar-plot | 1befe0c51e35da4bc62b4b1fd77f583c8944455f | 9bfe189051274c0d5a0066f5f47e890329bbd271 | refs/heads/master | 2020-03-19T01:54:12.225430 | 2018-06-03T11:50:27 | 2018-06-03T11:50:27 | 135,581,299 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,165 | r | pokemon_radar_plot.R | # import libraries
library(ggplot2)
library(palettetown)
library(png)
# create the data with the stats for chosen pokemon
Articuno <- c(HP = 90, Attack = 85,
Defense = 100, Sp.Atk = 95,
Sp.Def = 125, Speed = 85)
Moltres <- c(HP = 90, Attack = 100,
Defense = 90, Sp.Atk = 125... |
871cfcec27bc217b132df474ccb656a76af95c80 | 4674c7ff9404adde68b1b21478eafbff3a2a5843 | /BIOSurvey2/BIOSurvey2/R/summary.post.stratify.R | da249aa897caa9b7806e1ce8586b7588a5e986b8 | [] | no_license | risdell/VA_Oyster_Survey | cfed9bf6d1be1e6aef32005a20ea059afc3f4a58 | 7de643aa33b6d91fac3283fa830d356a285c8432 | refs/heads/master | 2023-01-25T01:30:40.208883 | 2023-01-20T16:14:05 | 2023-01-20T16:14:05 | 189,088,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 176 | r | summary.post.stratify.R | summary.post.stratify <-
function(object,...)
{
list(object,ystr.mean=sum(object$Nh*object$ybpos)/sum(object$Nh),V.mean=sum(object$Nh^2*object$Vst.ybpos)/sum(object$Nh)^2)
}
|
8ee6024e27bba48b4422021179013d04a1b46e8c | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610051326-test.R | 88df6bba95979a49ed427b6dd4e1a9e02896bfc9 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 556 | r | 1610051326-test.R | testlist <- list(rates = numeric(0), thresholds = numeric(0), x = c(-1.26836459270732e-30, NaN, 1.00891829346114e-309, -2.37619995226551e-289, -1.26836459123889e-30, 9.37339630957792e-312, -5.78534238436574e-34, -1.26836459270829e-30, -1.26836459122741e-30, 9.37339630957792e-312, 1.70257006040729e-313, -3.975994022... |
1dce1276f2168a0fb40934e551f8e1abd9630f45 | 2195aa79fbd3cf2f048ad5a9ee3a1ef948ff6601 | /docs/SyncFilterDialog.rd | cbd0263e54e4157dea3d14ff3beb143349279f2e | [
"MIT"
] | permissive | snakamura/q3 | d3601503df4ebb08f051332a9669cd71dc5256b2 | 6ab405b61deec8bb3fc0f35057dd880efd96b87f | refs/heads/master | 2016-09-02T00:33:43.224628 | 2014-07-22T23:38:22 | 2014-07-22T23:38:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,641 | rd | SyncFilterDialog.rd | =begin
=[同期フィルタ]ダイアログ
[同期フィルタ]では個々の同期フィルタについて、その同期フィルタが使われる条件とその動作を編集します。
((<[同期フィルタ]ダイアログ|"IMG:images/SyncFilterDialog.png">))
+[条件]
同期フィルタの条件を((<マクロ|URL:Macro.html>))で指定します。対象のメッセージに対して指定されたマクロを評価した結果がTrueになると、この同期フィルタで設定した動作が実行されます。
+[編集]
((<[条件]ダイアログ|URL:ConditionsDialog.html>))を開いて条件を編集します。
... |
f7bca9c6fbf9c1627007db21552a3caa0fdb137b | 691927e840f0d8f32057add34ec071adbc3231b6 | /cachematrix.R | 8f74a7ec176d168921664de3e260bc8cf599a752 | [] | no_license | aemon12/ProgrammingAssignment2 | f663288bb25c9eb6b3f67659760db58b30278792 | a4d82dd51b55eecd4bc2888ea33356c0fc6d16e0 | refs/heads/master | 2021-01-16T21:17:16.600212 | 2015-06-19T21:27:21 | 2015-06-19T21:27:21 | 37,617,193 | 0 | 0 | null | 2015-06-17T19:48:42 | 2015-06-17T19:48:42 | null | UTF-8 | R | false | false | 1,153 | r | cachematrix.R | ## This set of functions can be used to store the result of matrix inversion calculation
## in order not to repeat this lengthy operation.
## If any data within the solved matrix is changed, the inverse result is erased.
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) ... |
3f87c9c1a1d795997b399358e80bf2e02d380b3b | 07ae778ef3a85a9ce3f2ff76a970c6e61d9ef83e | /rprogramming/lab1/pollutantmean.R | 8d99309d130b1fae17fb5f4af63bd3eb238820c1 | [] | no_license | jiyoochang95/datasciencecoursera | 3f2700ccb2c13f50c03d8ee59aa77edf45798e7a | b786827c8d36a04b0857ecd55ae8eecfadb2e895 | refs/heads/master | 2021-01-16T18:26:19.857191 | 2017-08-25T23:56:50 | 2017-08-25T23:56:50 | 100,079,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 721 | r | pollutantmean.R |
#Calculates the mean of a pollutant
pollutantmean<- function(directory, pollutant, id = 1:332){
#figure out the csv file name for the id
bind<-data.frame()
for (i in id){
if (i<10){
newid <- paste("00", i, ".csv", sep="")
}
else if (i>=10 && i<100){
newid <... |
423c46107369e40ec97f96902e9aa04f277516d9 | 3194aa9fe7bbc3ede88d02d554bd339f6bd04fc4 | /man/toy.Rd | 3dd5262d33535685826ef440ceae2f77505c759d | [] | no_license | RobinHankin/emulator | e99b3a0997c9be1bc96220da96c30a0e694b6a8f | cebc2ea3d9d12ee6a349fa51c469d6fd092b78be | refs/heads/master | 2023-02-04T21:16:13.656391 | 2023-01-29T19:51:39 | 2023-01-29T19:51:39 | 126,391,589 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 883 | rd | toy.Rd | \name{toy}
\alias{toy}
\docType{data}
\title{A toy dataset}
\description{
A matrix consisting of 10 rows and 6 columns corresponding to 10 points in
a six-dimensional space.}
\usage{data(toy)}
\examples{
data(toy)
real.relation <- function(x){sum( (1:6)*x )}
d <- apply(toy, 1, real.relation)
# S... |
009c47ff72307e755365a83cc54690b83b1342f1 | 3fedf9cce68666e98dfd9acc079bc0af73d42165 | /scripts/main.R | 972f839a24765933e83d5dac27c4889b44be2023 | [] | no_license | woldemarg/lightit_test | caae7082ac071dd4853c2b9359e1595f3e88d58c | b3d9bd57415513aebb5c9b1733303f48a1b4d573 | refs/heads/master | 2021-02-13T22:29:33.028262 | 2020-04-27T23:45:53 | 2020-04-27T23:45:53 | 244,739,765 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,591 | r | main.R | library(tidyverse)
library(lubridate) #parse dates
library(geosphere) #calculate distance to event location
library(randomForest)
library(glmnet)
library(fastDummies) #one-hot-encoding for ridge and xgb
library(gbm)
library(xgboost)
#all files were previously encoded to utf-8
description <- read_csv("data/enco... |
5935957f35ac93fe257d6b0ab2d1b1e46db33188 | 7975cb4f2e83c574a4412e65f9f804399e1eb5a0 | /man/contributing_basins_at_geom.Rd | 398d21ed66f0f9392f46862b0fe39330a76996ae | [] | no_license | jmigueldelgado/assimReservoirs | 7ff70516b18e5eb60f1d29ebaf900eed61af8794 | 7218f36e07b6f1705d2b289dcd3bf7c0da80c5d2 | refs/heads/master | 2022-12-03T11:58:59.176677 | 2020-04-06T19:26:07 | 2020-04-06T19:26:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 623 | rd | contributing_basins_at_geom.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ident_contributing_basins_gauges.R
\name{contributing_basins_at_geom}
\alias{contributing_basins_at_geom}
\title{Identify contributing basins - sf}
\usage{
contributing_basins_at_geom(geom = res_max[res_max$id_jrc == 25283, ])
}
\arguments{
\... |
7b1ff3176658098046a45f756e136a04267efe26 | 070bc33923e734dae0b4be22e1802c73f96e715c | /ifelse.R | 74ac95f5fcfc4981b0e478fbb338abae699677ef | [] | no_license | siddarthansaravanan/R-Basics | 94235aabb425cf7552cb7fc916c79a8b0c95f619 | 3d0866e4ed884d122d42a8cdcd7232d8b1584a7a | refs/heads/master | 2021-01-21T20:06:56.943468 | 2017-06-12T08:24:42 | 2017-06-12T08:24:42 | 92,189,008 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 37 | r | ifelse.R | x$a<-ifelse(x$b>a$c,"TRUE","FALSE")
|
701f9b29350a3f91eb13aa87b6db5d8156ddc124 | efd73fe53d598844d6e549912d5ba1f2fe602e34 | /Parameters and distributions.R | f90f9c355876be6219051726a456da9fa933859e | [
"CC0-1.0"
] | permissive | yhjung1231/Laundry-QMRAproject-2022 | ef0611cf2e6395ad672bf217c798f3f7fddc847b | adf2cc09d2e38b96382563662146bfb8dc9362aa | refs/heads/main | 2023-07-23T00:19:21.669558 | 2023-07-08T00:32:17 | 2023-07-08T00:32:17 | 386,435,027 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,632 | r | Parameters and distributions.R | #File 1 Parameters and distributions
library (truncdist)
library(triangle)
iterations <- 50000
set.seed(100)
#Total hand area (cm^2)
T.handarea<-runif(iterations, min=445, max=535)
#Surface area of laundry
Surface.area.laundry<-runif(iterations, min=4.9*10^3, max=1.8*10^4)
Item.laundry<-runif(iterat... |
3e1960ddbb75ad6d9a87050ac2a6ab8ceef0b63d | 3877ee02e7deec476c64901c474a24ad56dcd431 | /man/listMetaGenomes.Rd | 668cbc9fbfd6cc7c8e527665c329bb420e303790 | [] | no_license | ropensci/biomartr | 282d15b64b1d984e3ff8d7d0e4c32b981349f8ca | e82db6541f4132d28de11add75c61624644f6aa1 | refs/heads/master | 2023-09-04T09:40:15.481115 | 2023-08-28T15:56:25 | 2023-08-28T15:56:25 | 22,648,899 | 171 | 34 | null | 2023-09-14T12:28:02 | 2014-08-05T15:34:55 | R | UTF-8 | R | false | true | 1,062 | rd | listMetaGenomes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/listMetaGenomes.R
\name{listMetaGenomes}
\alias{listMetaGenomes}
\title{List available metagenomes on NCBI Genbank}
\usage{
listMetaGenomes(details = FALSE)
}
\arguments{
\item{details}{a boolean value specifying whether only the scientific n... |
675175004f006922566807d6866e7853584fd66d | e7a321655bd46d1d2b73d38f3e69e161d6d4e186 | /man/approx.hessian.vector.product.Rd | ff9709997e42710718d4684c1c848cc68a1fa138 | [] | no_license | mattdneal/GPLVM | 0594aca5174ed7e0f0ff7f6c17cced54dce1cd14 | bd46d9e563c7bdd482837d75442af2d0d23385d8 | refs/heads/master | 2021-01-21T10:46:26.478446 | 2019-01-19T09:22:51 | 2019-01-19T09:22:51 | 101,985,787 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 278 | rd | approx.hessian.vector.product.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LSA-BCSGPLVM.R
\name{approx.hessian.vector.product}
\alias{approx.hessian.vector.product}
\title{Title}
\usage{
approx.hessian.vector.product(g, x, v)
}
\arguments{
\item{r}{}
}
\description{
Title
}
|
b36dfab4438848cbb46611c41fb169dd2b33e0fe | 3280b33a933df0b9630ccf2899b11ab9cced792f | /man/get.decode.Rd | 0a91d5d82d9ea832a40d94fab7c993bbb208d9fc | [] | no_license | cran/us.census.geoheader | 92918a2764fdd8d55d9a650746d3e0a80c67619d | 7f1e4f43cc7721890a1e417eb59ed362468fbc48 | refs/heads/master | 2022-11-12T09:47:56.846318 | 2020-06-25T09:20:02 | 2020-06-25T09:20:02 | 276,713,911 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 267 | rd | get.decode.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/uscgh.R
\name{get.decode}
\alias{get.decode}
\title{(internal) Return the Decode Table}
\usage{
get.decode()
}
\value{
the decode table
}
\description{
(internal) Return the Decode Table
}
|
2f59c7766d8b544b1de12255feb7e0fae1326044 | 074aa4a68f3ef87710eb48bc1fc904e1aba41da1 | /R/helpers.R | 51463c16c45598fbb8e597a197fba4aac2dea5fb | [] | no_license | AuckeBos/keywords-extraction-patient-reviews | bf771ed97e9ac9cfea25cf9720d1946d49d6e71c | aad9330c95333265191b18769938127ca8f167e4 | refs/heads/main | 2023-06-12T06:33:05.618847 | 2021-07-05T11:54:34 | 2021-07-05T11:54:34 | 383,117,833 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,605 | r | helpers.R | # ==================================================================================================================== #
# FUNCTIONALITY:
# Provide helper functions used in other scripts.
# - write_log writes a message to stdout, including timestamp
# - start_eta and eta are used for timing long-running functions
# ===... |
e2b5a9d52b8a52fbe509d2d46b564fc9cef84bb5 | 03e0d13444bff1042ade6d03f6e5e8a35cff353f | /Archive/Main_Sections/Fermentables/fermentablesWeights.R | c2fe070dbc008163fdf9f3d4e376b587620a45f6 | [] | no_license | BenjaminBearce/BK_Brew | 02ef7bfb7abd8cdbe0a5c250bbbea149b959b1da | f83029854b6d4f78b6001d51bf32e9e91d841ddb | refs/heads/master | 2021-01-17T07:10:17.746306 | 2016-12-11T19:18:53 | 2016-12-11T19:18:53 | 45,363,903 | 0 | 6 | null | 2015-12-31T01:19:29 | 2015-11-02T00:41:15 | R | UTF-8 | R | false | false | 913 | r | fermentablesWeights.R | #-----------------------------------------------------------#
#---------------------- Grain Weights ----------------------#
#-----------------------------------------------------------#
fermentablesWeights <- function(grains = FALSE){
if(grains == FALSE){
cat("No beer selected")
}else{
OG <- b... |
714f47951cd4c576c830cc43cb5ffad67691e208 | 26039c7524e788d6ca0e1ae1e219dd22606fd6bc | /backends/mxnet/test/gen_r_json.R | 91a84d3a1b55c99efc0747e52cc36c2629147f5d | [] | no_license | tomz/deepwater | 9976521e5b7560b396aa7bebaf20db75e46a04cb | 05b649da1b67fe0c2d01bb5554f6f6015253a98f | refs/heads/master | 2020-04-06T06:18:40.843151 | 2016-08-25T20:14:02 | 2016-08-25T20:14:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 495 | r | gen_r_json.R | library(mxnet)
#name = "alexnet"
#name = "googlenet"
#name = "inception-bn"
name = "inception-v3"
#name = "lenet"
#name = "mlp"
#name = "resnet"
#name = "vgg"
source(paste("symbol_", name, ".R", sep = ''))
network <- get_symbol(10)
cat(network$as.json(), file = paste("symbol_", name, "-R.json", sep = ''), sep = "")... |
7de8c93c46183e3ca3e70bd61c6e9ce0f4d00d90 | 871378e379c6e31796ef961d1f70de2ad454939b | /R/msg.R | dfbb4ef39d44f61bb0a22bb6ed1569a54ccd6635 | [] | no_license | cran/ESPRESSO | 3cc192f231b3c3c98894714a25366b7eacd1e890 | ef9c47486ce493980faa9662fd141a7e1f73f009 | refs/heads/master | 2021-01-23T03:53:31.983741 | 2011-04-01T00:00:00 | 2011-04-01T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 383 | r | msg.R | .onAttach = function(libname, pkgname){
packageStartupMessage("\nWE ARE AWARE OF A BUG ON THE LAST VERSION OF THE TOOL AND ARE WORKING TO SORT IT OUT")
packageStartupMessage("A NEW VERSION WILL BE AVAILABLE BY THE END OF DECEMBER 2013")
packageStartupMessage("WE ARE ALSO WORKING ON A WEB-BASED VERSION THAT WIL... |
bbbf537bc1f7a2e720237cb1bef9d20bbee95e54 | f4e63cf535679b0240a0e05b2b3dcab9496c7f8c | /Machine Learning/machineLearning-Decision tree learning.R | 99343577d2dac8b14842d22f7636da9ed8ed8402 | [] | no_license | Philip-Abraham/MachineLearning_R | 27e77f7c368a316ab43b800251b71a323c66936c | e931955c87a48a87485a25d0ffb59bf3d7b96641 | refs/heads/master | 2021-07-06T15:59:47.774905 | 2017-09-27T22:07:26 | 2017-09-27T22:07:26 | 105,074,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,251 | r | machineLearning-Decision tree learning.R | library(titanic)
data("titanic_train")
titanic <- titanic_train[,c(2,3,5,6)]
titanic <- titanic[complete.cases(titanic),]
# First, you'll want to split(70/30) the dataset into train and test sets. You'll notice
# that the titanic dataset is sorted on titanic$Survived , so you'll need to first
# shuffle the... |
5184ad2755a341dd974fe47eb0677c8ca4fa0103 | d97273424f84121eaf3a57479b00c8fc3de6bf31 | /analyzeCodes/plotTradeRe.R | 11148ef416961128bce49bbc95ef7b86953c3c7b | [] | no_license | dvaruas/stock-market-prediction | f05a3d32fd2ad6731d1734a81b3f44b6b4b49522 | b7ad99aeb2d9fab6cb9bc3bbf6d9bebd35e2c1f5 | refs/heads/master | 2022-08-15T06:09:51.644096 | 2020-05-16T08:57:04 | 2020-05-16T08:57:04 | 64,368,402 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,287 | r | plotTradeRe.R | getPlotTrade <- function()
{
ourMethodData <- read.csv('../stock_quotes/ourMethodReturn.csv')
baseData <- read.csv('../stock_quotes/baseReturn.csv')
dateValues <- as.Date(ourMethodData$date, format = '%d-%m-%y')
ourValues <- as.numeric(ourMethodData$returnValue)
baseValues <- as.numeric(baseData$returnValue)
p... |
4f970be49051bbc8766b7cb8d6c4c930841e626d | 1482c0c2e994197d04c2149eb19ce2f313cd7a45 | /R/clustering.R | f78b1f46466a35c455d96d2c05e6cbdb9415f164 | [
"MIT"
] | permissive | alexloboda/SVDFunctions | 4adffe4b7e101a68b5cf756d8fefee45610303c5 | 666dbc820f81a3ab03e706fea380deaeb1d6f4f5 | refs/heads/master | 2023-05-11T13:44:28.623205 | 2023-03-28T15:12:38 | 2023-03-28T15:12:38 | 153,036,232 | 6 | 1 | null | 2019-05-14T17:17:20 | 2018-10-15T01:28:35 | C++ | UTF-8 | R | false | false | 6,396 | r | clustering.R | checkTree <- function(t, cs) {
if (is.list(t)) {
if (length(t) != 2) {
stop("Tree must be binary")
}
cs <- checkTree(t[[1]], cs)
checkTree(t[[2]], cs)
} else {
if (is.null(t) || is.na(t)) {
stop("Node must not be null nor NA")
}
if (!(t %in% cs)) {
stop(paste("Cluster i... |
cb8fdb606e9400e1076c1881021cae979759f480 | a8751ed8f4113510037204fb0f03964235fa2250 | /man/AgeTrans.Rd | 01bbcf0ac04bb7788e14771e54328797d9725f15 | [] | no_license | al00014/Biograph | 83ed141195adfec30634576e6a99c4ed54cbf0a3 | 15b46e3416f83964aab1baeaa58d1d92fa4e7e2b | refs/heads/master | 2023-04-23T19:58:23.499789 | 2016-03-31T17:50:43 | 2016-03-31T17:50:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 748 | rd | AgeTrans.Rd | \name{AgeTrans}
\alias{AgeTrans}
\title{Ages at transition}
\description{Converts dates at transition to ages at transition}
\usage{AgeTrans(Bdata)}
\arguments{
\item{Bdata}{Biograph object: data in Biograph format}
}
\value{
\item{ages}{ages at transition}
\item{ageentry}{ages at entry into observation}
\item{... |
d7670ec7bf3db2fab14df1bf5414e3b5e20b0180 | 00bf0bbb222c10aae4625b0ed5046d4b8b0e7c37 | /refm/api/src/forwardable.rd | 80e64eff400316ab28cb7d8080ed2fa4fe7a9657 | [] | no_license | foomin10/doctree | fe6a7097d544104fe71678121e6764d36a4b717a | a95789a60802c8f932c0a3e9ea21a4fc2058beb8 | refs/heads/master | 2023-02-18T20:00:32.001583 | 2023-02-05T00:49:18 | 2023-02-05T00:49:18 | 32,222,138 | 1 | 0 | null | 2015-03-14T16:54:52 | 2015-03-14T16:54:51 | null | UTF-8 | R | false | false | 7,131 | rd | forwardable.rd | category DesignPattern
クラスやオブジェクトに、メソッドの委譲機能を追加するためのライブラリです。
#@#以下のモジュールが定義されます。
#@# * [[c:Forwardable]]
#@# * [[c:SingleForwardable]]
#@#詳細は [[unknown:"ruby-src:doc/forwardable.rd.ja"]] を参照してください。
=== 参考
* Rubyist Magazine 0012 号 標準添付ライブラリ紹介【第 6 回】委譲 ([[url:https://magazine.rubyist.net/articles/0012/0012-Bun... |
e6a26c8639d8935b0ad36f2676d9dfaf11d47b1c | 431860954259d02f7768dd02e6554badbf6faacc | /man/getindexcat.Rd | 8bd713957bd6e6737c1a9af2b048df3c15dc998b | [] | no_license | nicolas-robette/GDAtools | 5e6a7d4454d5edac3fab9bfa202f96ceddadfc66 | 4708925717cb4d0cd957faa46fd813dfcd860c41 | refs/heads/master | 2023-07-07T02:54:15.110104 | 2023-06-29T18:58:32 | 2023-06-29T18:58:32 | 214,293,710 | 5 | 3 | null | 2021-06-11T08:41:34 | 2019-10-10T22:04:12 | R | UTF-8 | R | false | false | 794 | rd | getindexcat.Rd | \name{getindexcat}
\alias{getindexcat}
\title{Names of the categories in a data frame}
\description{Returns a vector of names corresponding the the categories in a data frame exclusively composed of categorical variables.}
\usage{getindexcat(data)}
\arguments{
\item{data}{data frame of categorical variables}
}
... |
d272b514c99774e124da82f6e84e7720324bd9c6 | 5702c21b14d615d637b7d75ee696d55e5034be43 | /man/layout_1d_graphics.Rd | 3c03b172504d0a27533965fe3a3b7484c07c4d42 | [] | no_license | great-northern-diver/zenplots | 90219a66099df5bd7c934d36cd202d250801bdc5 | 2f5431dc2ba3318e8b23349804996757ea81910a | refs/heads/master | 2023-09-05T04:09:22.049308 | 2023-08-25T17:12:01 | 2023-08-25T17:12:01 | 198,439,000 | 3 | 2 | null | 2023-08-25T17:12:03 | 2019-07-23T13:43:12 | R | UTF-8 | R | false | true | 459 | rd | layout_1d_graphics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot1dgraphics.R
\name{layout_1d_graphics}
\alias{layout_1d_graphics}
\title{Layout plot in 1d}
\usage{
layout_1d_graphics(zargs, ...)
}
\arguments{
\item{zargs}{argument list as passed from \code{\link{zenplot}()}}
\item{...}{additional arg... |
09252cff48df8ee06694fffa50c75424bfa46266 | 1eeb158bacf26f51928087935110bff8148a88fb | /man/HTestimator.Rd | def6b5774dbe66ee5534ff54598f1ef166321ae1 | [] | no_license | cran/sampling | 5a97b7b5b14a7c27c7fd64bf68c72531ccb50128 | 2f974546de2b26dc5d71b44e3c56f8cc4e6bd696 | refs/heads/master | 2021-06-05T21:02:00.219517 | 2021-01-13T10:50:05 | 2021-01-13T10:50:05 | 17,699,455 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 829 | rd | HTestimator.Rd | \name{HTestimator}
\alias{HTestimator}
\title{The Horvitz-Thompson estimator}
\description{Computes the Horvitz-Thompson estimator of the population total.}
\usage{HTestimator(y,pik)}
\arguments{
\item{y}{vector of the variable of interest; its length is equal to n, the sample size.}
\item{pik}{vector of the fir... |
cfb2474d30a29f7b42290a6fdb031336bf1e4d5b | aa4b646ee65be2bc6a5903b4cbc31f99dd0ea18f | /R/03_Plotting_Results.R | 2ccb552dfba44a558c4aafab566617519e1803fd | [] | no_license | aaronweinstock/mls-preseason18-simulation | 2793429c7a233eb37a8f7e6ab77021ac5f48542a | 10ec389c50cbc871302b886c5fc288f5ff414c5a | refs/heads/master | 2020-04-10T21:28:02.921337 | 2018-12-11T09:07:40 | 2018-12-11T09:07:40 | 161,297,204 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,232 | r | 03_Plotting_Results.R | # install.packages("ggplot2")
# install.packages("gridExtra")
library(ggplot2)
library(grid)
library(gridExtra)
# Read in simulation probabilities
prob = readRDS("Data/R_Data/Example_Probabilities.rds")
# Plot function
plot_sim_results = function(prob){
# Reorganize simulated probabilities for plotting using ggplot... |
756364fd1e79231cdef25a1dd777a288ae5b0f5e | 59fb03eed32d0fec98930e6f937263e5ce696d9b | /R/checkargs.R | dc914ee13d869c441d3f555b3c4db63bf7fdbb89 | [] | no_license | vathymut/forestError | 8a66469e65509f867e6993a18c7ca11ff046dc27 | 5d9c4b2d01e2ad65bdedf1bdd499ba342e27be8d | refs/heads/master | 2023-07-11T13:59:58.474788 | 2021-08-10T19:22:23 | 2021-08-10T19:22:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,263 | r | checkargs.R | # check forest argument for problems
checkForest <- function(forest) {
if (typeof(forest) != "list") {
stop("'forest' is not of the correct type")
} else if (!any(c("randomForest", "ranger", "rfsrc", "quantregForest") %in% class(forest))) {
stop("'forest' is not of the correct class")
} else if (is.null(f... |
b58514fdd16e1d37f4ec8e6d2c79965a304e52ee | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed_and_cleaned/10055_0/rinput.R | a7b5fc7101ec55dc6678c08848845dc377db5a67 | [] | 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 | 137 | r | rinput.R | library(ape)
testtree <- read.tree("10055_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10055_0_unrooted.txt") |
ad7f57bea3e442c91fecb4160190e8c0402f93a3 | 7212c1fea0fd1e286ca10e978d24c9cc4ef05af7 | /TLC.R | aa9320e4082417233f13a292369b7d7163590a52 | [] | no_license | maps16/Estadistica | 92635e96f254946572dd4b8c0d82dcb4c028bd3a | c1bfd6c4123378903fc8cb8e83824c85e906f1e2 | refs/heads/master | 2021-01-10T07:11:55.266656 | 2016-04-04T17:02:46 | 2016-04-04T17:02:46 | 52,023,966 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | r | TLC.R | #Escenario de Simulacion: Normal
n = 30
media0 = 10
sigma0 = 1
M = 20000
#Calculo de la frecuencia de Cobertura
z = c()
for(i in 1:M){
y = rnorm(n, media0, sigma0)
z[i] = (sqrt(n)*(mean(y)-media0))/sigma0
}
x = z
hist(x, freq = FALSE, col = blues9)
curve(dnorm(x,0,1),col=2, add=TRUE)
#Escenario de Simulaci... |
e055e32f77978525d47d42faca676222c21aa356 | 8740aaffc97232135206f322898ac0295af678f9 | /4. Data Manipulation with dplyr/arrange1.R | e7b7c70c1bbd187490aba4738c40c9df4376294f | [] | no_license | ThanitsornMsr/Datacamp | 9b79f56a04eb4aaa30ee8e846b7d52a26d2bbcb4 | fa22e61edebf12b6a8b47df95f1b221fc9a693f1 | refs/heads/master | 2022-08-03T08:32:51.554392 | 2020-05-22T11:35:19 | 2020-05-22T11:35:19 | 266,045,917 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 224 | r | arrange1.R | counties_selected <- counties %>%
select(state, county, population, private_work, public_work, self_employed)
# Add a verb to sort in descending order of public_work
counties_selected %>%
arrange(desc(public_work)) |
274439d2fdda3980bc3515a686dbce596a468e06 | 95d4670a6eee58e1530f38bd430f2f1c77d5660b | /plot4.R | b6074f2c583c60a966996419330ab5d6a684f38b | [] | no_license | Kostasstam/ExData_Plotting1 | 797c948e7ecb30018d9b71812d82f5d14474db2a | 14c95f4fa200f1ca7a3a84b956b023b81649d27a | refs/heads/master | 2020-05-23T11:14:51.233412 | 2017-01-29T20:49:54 | 2017-01-29T20:49:54 | 80,368,653 | 0 | 0 | null | 2017-01-29T19:57:39 | 2017-01-29T19:57:39 | null | UTF-8 | R | false | false | 1,348 | r | plot4.R | #Read table
library(dplyr)
fulldata<-read.table("household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".", as.is = TRUE)
str(fulldata)
data<-filter(fulldata, Date=="1/2/2007" | Date=="2/2/2007")
dim(data)
head(data)
str(data)
data$Global_active_power<-as.numeric(data$Global_active... |
1e0cf149c5524926a23769992c6830157c7e5af9 | 023f423499a21441ba85e2a7f646229b0592ead8 | /analysis/runScript.R | 93a52165337e4ee019935cf9481a18e346dd12a4 | [] | no_license | YanqiangLi/lincp21 | a7c25782423e7575525d499fc41a434263efb23b | 0c020fd8a433b86a7b5f9b5ad7fb6ea58c6ea3dd | refs/heads/master | 2021-01-02T22:36:54.650584 | 2016-06-30T18:15:50 | 2016-06-30T18:15:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 592 | r | runScript.R |
dat<-read.csv("autoanalysisInfo.csv",header=TRUE,stringsAsFactors=FALSE)
library(knitr)
i<-as.numeric((commandArgs(TRUE)[1]))
filename<-dat$filename[i]
print(filename)
print(dat$strain[i])
dir.create(filename)
setwd(filename)
strain<-dat$strain[i]
timepoint<-dat$sample[i]
tissue<-dat$sample[i]
alpha<-0.05
dir<-dat$dir... |
e791929514a7db3aed9a3104dfe1c311e97632c5 | d075e77fbe941830535e4df7121694305b42938a | /evaluation.R | 7b1c0e1388cc2f7e9df209160a956d29c3c290fc | [] | no_license | skyler120/MSGL | c4493ea8cc62e0d8d9464cc8ec036b07f5ecec31 | e39432f80424802b932429b4b0876b1d1e81a4f8 | refs/heads/master | 2021-01-22T04:14:25.441575 | 2017-05-26T06:03:48 | 2017-05-26T06:03:48 | 92,442,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 278 | r | evaluation.R | library(pROC)
evaluate_beta <- function(beta, beta_true,y_hat,y,tol=1e-6){
beta_close = sum(abs(beta-beta_true)<tol)/length(beta)
myROC = roc(y, y_hat)
return(list(prop = beta_close, sens = myROC$sensitivities,
spec = myROC$specificities,auc = myROC$auc))
} |
54d2794c74fd88148fec767dde17464d991422c9 | a9a9af4f010a883720f70391d2af66f437cb15c3 | /man/retrieve_abc_experiment_for_plotting.Rd | 82bc3fb3815c6a5073495e456b9eb9efc782e215 | [] | no_license | kalden/spartanDB | ad4162c78ef54170c21c08a8a7a822fafc457636 | bc698715cdce55f593e806ac0c537c3f2d59ac7a | refs/heads/master | 2020-03-26T23:32:14.724243 | 2019-02-20T11:05:17 | 2019-02-20T11:05:17 | 145,549,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 934 | rd | retrieve_abc_experiment_for_plotting.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/emulated_abc_to_db.R
\name{retrieve_abc_experiment_for_plotting}
\alias{retrieve_abc_experiment_for_plotting}
\title{Retrieve posteriors from database and produce density plots}
\usage{
retrieve_abc_experiment_for_plotting(dblink, parameters,... |
545d45959cd63efbe2fe48deca9ec907ae433bf7 | aa4105b401fbe639e71dbf5093615317dfe47139 | /RProgramming/cacheMatrix.R | 2aa46a0cc5494f30cf73295e0d5ce7250975097b | [] | no_license | cchmusso/datasciencecoursera | 0d02b6d0c8f4c1f8b3475ef3cb644ab4275ac7ae | 8787faf3a5cda90b6c73a191fded7ab4084aeee0 | refs/heads/gh-pages | 2021-01-10T13:25:55.420461 | 2018-01-21T18:33:57 | 2018-01-21T18:33:57 | 46,266,815 | 0 | 0 | null | 2018-01-21T18:33:58 | 2015-11-16T10:07:38 | HTML | UTF-8 | R | false | false | 1,682 | r | cacheMatrix.R | # Matrix inversion is computationally expensive. In order to eliminate
# redundant evaluation, the functions in this module will allow the user to
# create a matrix that, upon calculation, caches its inverse and returns that
# inverse on subsequent queries until the original matrix changes (invalidating
# the cached m... |
c8ef9d75550cce401b2fa464358b2c115698037a | a702380ea7f842b78885777855dada88ac329f0d | /man/PLS4jack.Rd | cbb1863729d60a2bb418192b820dfc1edea69053 | [] | no_license | HerveAbdi/data4PCCAR | bc077569605bca5119814bc7ca8b155c3fcc4141 | 78478d1ad5b3b1eeb88a3448d417694129bc70d8 | refs/heads/master | 2022-09-14T05:05:24.686219 | 2022-09-04T21:24:43 | 2022-09-04T21:24:43 | 129,653,026 | 8 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,948 | rd | PLS4jack.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PLS_jack_svds_HA.R
\name{PLS4jack}
\alias{PLS4jack}
\title{in PLS regression (PLSR) compute a
supplementary projection for a jackknifed estimation of
one supplementary element.
The prediction is performed
for 1 to \code{nfactor} latent variab... |
ca0e41ef93839618815b33d7faae4c51d3a832be | 6f32382cf98a130b7e7f518f0d6e760a7006e09d | /Paper1/Figure5/1.visualize_PCAs.R | 2f0aeb6265674367626917ac4e968bc4ec4bc16a | [] | no_license | DanChitwood/PassifloraLeaves | 0df07ca52193932aa4b107a4e4cfaa7a9fe9e8e9 | 493c28d04f0a43bb4d7f3d0b90dfcab6ec83622c | refs/heads/master | 2021-01-01T03:44:51.938914 | 2017-12-03T19:12:32 | 2017-12-03T19:12:32 | 56,823,168 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,729 | r | 1.visualize_PCAs.R | #Read in ggplot2
library(ggplot2)
#Read in data
data <- read.table("./0.classes_and_heteroblasty.txt", header=TRUE)
#Visualize landmark PCA by species class
p <- ggplot(data=data, aes(land_pc1, land_pc2, colour=class))
p + geom_point(size=3, alpha=0.6) + theme_bw() + scale_colour_brewer(type="qual", palette=2)
p <-... |
17f31d96a744adfc39479bc16c19a948c966b9cd | d75b7bc015b47d94254bcc9334ba15972d3ec9a1 | /1. FIRST YEAR/Introduction to Computing/Exercices_Laura/exercici82.R | 4c1406c79c362b95a78a94be3b2bbd776b16358a | [] | no_license | laurajuliamelis/BachelorDegree_Statistics | a0dcfec518ef70d4510936685672933c54dcee80 | 2294e3f417833a4f3cdc60141b549b50098d2cb1 | refs/heads/master | 2022-04-22T23:55:29.102206 | 2020-04-22T14:14:23 | 2020-04-22T14:14:23 | 257,890,534 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 232 | r | exercici82.R | SumaVectors <- function (v1,v2){
s1 <- 0
s2 <- 0
for (i in 1:length(v1)){
s1 <- s1 + v1[i]
}
for (i in 1:length(v2)){
s2 <- s2 + v2[i]
}
return(s1+s2)
}
v1 <- c(1,1,1,1)
v2 <- c(1,1,1,1,5,6)
|
c6f26490f4e15ef2dbedf8e3699c0ccd8523ae4c | 8da9024b102ccfde5f2bbc999114adb82fbc39f1 | /man/makeMovie.Rd | e6c779489774b22663ee268c4978c0441b0d392a | [] | no_license | pmur002/director | 4747d2a823cdaa56472f4692db2d5823819d9b7b | 59202fe01cfe7c2677e5160614716e31a81f314f | refs/heads/master | 2021-06-23T21:25:25.459912 | 2021-02-17T02:08:01 | 2021-02-17T02:08:01 | 75,572,092 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,033 | rd | makeMovie.Rd | \name{makeMovie}
\alias{makeMovie}
\title{
Make a Movie
}
\description{
Make a movie from an XML script file.
}
\usage{
makeMovie(filename,
wd=paste0(gsub("[.]xml$", "", filename), "-movie"),
TTS=espeakTTS(),
world=realWorld,
validate=TRUE,
clean=FALSE)
}
\argument... |
ff36010222582f92f40e5e4317644b5986c58ba4 | 5d0ae5bb914a6c9d05d0fcb3b8ebb28f84d81851 | /R/custom_model.R | 7d8cbaee11ee8ba70b1c6684b1e2d70ae7d53960 | [] | no_license | yangxhcaf/Master_Thesis | 57678f3b347d35bab021345a9b97dbdedcba155e | e14ee6cbe4c74c13aa1faf4f5cdb4ff380e6498c | refs/heads/master | 2022-06-21T14:16:57.963992 | 2020-05-12T11:14:48 | 2020-05-12T11:14:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,919 | r | custom_model.R | #' Custom Model
#'
#' Build a customized, vgg16 and unet based model
#'
#' @source Partially based on the work of Christian Knoth at https://github.com/DaChro/cannons_at_marmots
#'
#' @param input_shape Dimensions of input. Standard: 128*128 resolution, 1 channel greyscale; for RGB use 3 channels
#' @param num_classes ... |
33e35c72587296a32e2d9549008adef43c255ed4 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.machine.learning/man/polly_get_speech_synthesis_task.Rd | cf4a101336d5aaab09c082c9f6b1e74e7cbb6203 | [
"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 | 782 | rd | polly_get_speech_synthesis_task.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/polly_operations.R
\name{polly_get_speech_synthesis_task}
\alias{polly_get_speech_synthesis_task}
\title{Retrieves a specific SpeechSynthesisTask object based on its TaskID}
\usage{
polly_get_speech_synthesis_task(TaskId)
}
\arguments{
\item{... |
ac4af16e5a89d04c7af302cd55b783a4d19068bd | 2b3d3b4f510d250b196607ec7c78095711bd2aef | /devel/alligator.R | 6ad818f0cdd6bd4154012ad6a1ff5a8c65ccd7fc | [
"MIT"
] | permissive | cjgeyer/glmbb | df80553cc60d3fb9a636530f89668843d7a331b5 | 34d7ee54ce5ae6476319020c41d233cb060e9da5 | refs/heads/master | 2021-08-06T18:12:04.643391 | 2021-01-26T22:43:48 | 2021-01-26T22:43:48 | 55,895,366 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,190 | r | alligator.R |
library(Matrix)
library(CatDataAnalysis)
data(table_8.1)
d <- transform(table_8.1,
lake = factor(lake,
labels = c("Hancock", "Oklawaha", "Trafford", "George")),
gender = factor(gender, labels = c("Male", "Female")),
size = factor(size, labels = c("<=2.3", ">2.3")),
food = factor(food,
... |
0a49c26cba2b0b64c81864f89121463442d38e3e | 2ca5918b1a1f74b8e59fe034af4e9f3a917a5454 | /code/2020/2020_34_PlantsInDanger.R | bc38be58e86787c69fa37f3faaab62d1247ea597 | [] | no_license | bonschorno/TidyTuesday | 9300738ccf845baaf152a668ff30fcd23a5ee09a | e485dc9f8915084848abd9bfdd8c5970942bbb81 | refs/heads/master | 2023-02-18T22:53:31.666381 | 2021-01-20T07:58:45 | 2021-01-20T07:58:45 | 286,958,495 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,880 | r | 2020_34_PlantsInDanger.R | #Week 34: Plants in Danger
library(tidyverse)
library(ggalluvial)
library(hrbrthemes)
library(ggsci)
plants <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-08-18/plants.csv')
#data wrangling
plants_clean <- plants %>%
filter(group == "Flowering Plant",
... |
448232206eb13c2e35161f2dc52735ff258e81e8 | 839de161296bcbb4593fe20f282518607866b346 | /R/loplot_v1.R | ae767ce963fe76a697ba80a5f9113f883a436d44 | [] | no_license | cran/statTarget | 1a37156aabc3b664674fedf4a225bab2cf9eb828 | befb9ebe688584ccdab61941c635356dc3fbb828 | refs/heads/master | 2021-01-20T18:33:26.720214 | 2016-07-20T09:46:08 | 2016-07-20T09:46:08 | 63,609,082 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,552 | r | loplot_v1.R | #' loplot provide the visible figure of QC-RLS correction.
#' @param x the file before QC-RLS correction.
#' @param z the file after QC-RLS correction.
#' @param i a index for the name of variable.
#' @export
loplot <- function(x,z,i){
# x is the loess
cn <- colnames(x)
qcid <- grep("QC",cn)
RSD30_CV=paste... |
69790ef6673ac594a162915babd1bccde4bbd727 | 630a70ffc25834bfe3aa9623708f47e0245a461f | /RExamples/api_examples.R | a17f383baab0a6604a41460b75d0c8bd62357411 | [] | no_license | mjiapalucci/MC | be2214ebdc3842f63025f5498219061d39ec819e | 58f0f2d2f5f9352c721385f5593f5d03747cc1b6 | refs/heads/main | 2023-04-05T10:56:11.335397 | 2021-03-31T21:19:23 | 2021-03-31T21:19:23 | 303,212,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 412 | r | api_examples.R | library(httr)
library(jsonlite)
library(lubridate)
library(tidyverse)
r <- GET("https://data.montgomerycountymd.gov/resource/xhwt-7h2h")
glimpse(r)
content(r, "text")
jsonRespText <- content(r, as="text")
jsonRespParsed <- content(r, as="parsed")
jsonRespParsed
df <- fromJSON(jsonRespText)
head(df)
class(df)
df1 <... |
3820d35b352c3b465da6ed0b32946aeabceceaad | 13895420920703501ab66c28a3927089a2de042e | /R/simGene.R | 6c1ad2774001e83bfcf22219b2bd1cbaba078183 | [] | no_license | cran/psych | 3349b3d562221bb8284c45a3cdd239f54c0348a7 | ee72f0cc2aa7c85a844e3ef63c8629096f22c35d | refs/heads/master | 2023-07-06T08:33:13.414758 | 2023-06-21T15:50:02 | 2023-06-21T15:50:02 | 17,698,795 | 43 | 42 | null | 2023-06-29T05:31:57 | 2014-03-13T05:54:20 | R | UTF-8 | R | false | false | 1,036 | r | simGene.R | "simGene" <- function(ng=10,traits=1,n.obs=1000,dom=TRUE) {
X <- array(sample(2,ng*n.obs*traits*3,replace=TRUE),dim=c(n.obs,ng,traits,3))
MZ <- DZ <- array(NA,dim=c(n.obs,ng,traits))
MZt <- DZt <- matrix(NA,n.obs,traits)
for(t in 1:traits) {
if(dom) { MZ[,1:ng,t] <- X[,1:ng,t,1] * X[,1:ng,t,2] #the allele values ar... |
3d0c6ec490c4180fde020a862b3ae79975dba5ed | 0e0c93c587aedbcb71c8aeae3427a7b17c6422b0 | /AND2.R | e2f1cb860ba9017c56b43882b9caf65a902d350c | [] | no_license | sekersse/QIT | fb5ae76871274020ab5d9076bcd8be616352db20 | 1becaf3286dfdb2456f88b44097a859427dd6eb4 | refs/heads/master | 2020-05-31T11:00:36.345434 | 2019-06-04T18:04:07 | 2019-06-04T18:04:07 | 190,253,206 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,407 | r | AND2.R | library(neuralnet)
set.seed(7896129)
########################
### Ergebnisvektor
########################
AND <- c(0,0,0,1)
##### Input und Ergebnis als data.frame
data.frame(Var1=c(0,1,0,1), Var2=c(0,0,0,1), AND)
and.data <- data.frame(expand.grid(c(0,1), c(0,1)), AND)
and.data
##### Neuronales Netz berechnen
##... |
735d23ddaeb1272cd23d11129df9bb2a8f1a5411 | 8f460306738b0e454489b44e25970f52f1b236d3 | /classification.R | 5f95141cbc4c68c9dcb32688676ebb7d0af1fc2c | [] | no_license | mattymo18/TCGA_Gene_Expression | 1343a6ec48dcc8b8c3b161fa01736bbced200d3f | 6eae8f1ff59d79536f1fc5877e31304c4973ecbe | refs/heads/master | 2023-03-24T09:29:42.205171 | 2021-03-23T00:58:30 | 2021-03-23T00:58:30 | 342,007,463 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,200 | r | classification.R | # data and libs
library(tidyverse)
library(class)
library(knitr)
library(kableExtra)
webshot::install_phantomjs()
DF.Center <- read.csv("derived_data/TCGA.centered.csv") %>%
select(-X)
#set seed
set.seed(315)
# Break into train and test sets.
#Sample the rows for the training set
index <-sample(1:nrow(DF.Center),... |
969359d1f797394cde42aed15ed6d2fdc396ecbf | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Elementary_Number_Theory_by_David_M._Burton/CH10/EX10.8/Ex10_8.R | d624315341452dbd96592a91f31b5afa3eb9b1c5 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 250 | r | Ex10_8.R | #page 215
p <- 113
r <- 3
k <- 37
two <- (r ^ 2) %% p
four <- (two ^ 2) %% p
eight <- (four ^ 2) %% p
sixteen <- (eight ^ 2) %% p
thirty_two <- (sixteen ^ 2) %% p
a <- (r * four * thirty_two) %% p
public_key <- c(p, r, a)
print(public_key) |
60e9dba832247241cd73ed41d3bfd206ce42b717 | 38c84e91ec840f606b0c8a4b8d1a45a6144a091a | /man/SedimentWater.Rd | 57305d341747c985197d9a4d69b4ae57c7cd6ab2 | [] | no_license | zhenglei-gao/SedimentWater | d0ee6d9e5304bee5aa29f1cc87a3fc6f76995cfd | ab5b84e617dad9ffced2111a92dfd156c0d3aedf | refs/heads/master | 2021-01-10T01:49:05.716956 | 2015-09-29T12:35:44 | 2015-09-29T12:35:44 | 43,310,233 | 0 | 0 | null | 2015-09-29T12:35:44 | 2015-09-28T15:44:28 | R | UTF-8 | R | false | false | 256 | rd | SedimentWater.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/SedimentWater-package.r
\docType{package}
\name{SedimentWater}
\alias{SedimentWater}
\alias{SedimentWater-package}
\title{SedimentWater.}
\description{
SedimentWater.
}
|
e7d8c64c9fde3c640c6e1cdac614da81e5507ad4 | 29585dff702209dd446c0ab52ceea046c58e384e | /tmap/R/split_tm.R | d4ed1ea59c2c978793ae782c918862d36cf40122 | [] | 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 | 1,341 | r | split_tm.R | split_tm <- function(gp, nx, order_by) {
gpnx <- lapply(1:nx, function(i){
g <- mapply(function(x, o) {
oid <- if(is.null(o)) NULL else o[[i]]
mapply(get_i, x, names(x), MoreArgs = list(i=i, n=x$npol, oid=oid), SIMPLIFY=FALSE)
}, gp, order_by, SIMPLIFY=FALSE)
})
names(gpnx) <- paste0("plot", 1:nx)
... |
b350b0ae0e54b11ae2bd54366f9417f76d7cc6a1 | a48793031ba127d1635bb0a80070896f89a48a40 | /ANLY_506_1_5Rscript.R | d90f77a4126ec284ee242272e337d9f7d7073e40 | [] | no_license | EAMMensah/ANLY_506_Code_Portfolio | c83f363d9762d83f72a1e66e57a8cd0b11bd10cc | 59755a8e7df8981ae07ad826a5f2765e003e90bf | refs/heads/master | 2020-06-25T15:18:11.094568 | 2019-07-29T00:27:48 | 2019-07-29T00:27:48 | 199,350,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,957 | r | ANLY_506_1_5Rscript.R | # week 2 - Base plots
# plotting a histogram
library(datasets)
hist(airquality$Ozone)
# plotting a boxplot
airquality <- transform(airquality, Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")
#plotting a scatterplot
with(airquality, plot(Wind, Ozone))
# ... |
953db8f934af43374cc72e3ea8e658b864f38d9c | 66e04f24259a07363ad8da7cd47872f75abbaea0 | /Joining Data in R with data.table/Chapter 4-Concatenating and Reshaping data.tables/2.R | 41120205891e9c5955217b46c20a580e1e5a4603 | [
"MIT"
] | permissive | artileda/Datacamp-Data-Scientist-with-R-2019 | 19d64729a691880228f5a18994ad7b58d3e7b40e | a8b3f8f64cc5756add7ec5cae0e332101cb00bd9 | refs/heads/master | 2022-02-24T04:18:28.860980 | 2019-08-28T04:35:32 | 2019-08-28T04:35:32 | 325,043,594 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,315 | r | 2.R | # Concatenating a list of data.tables
# A list of data.tables has been loaded into your R session: gdp. Its elements contain a data.table for each continent, each data.table containing the gross domestic product (gdp) in the year 2000 for the countries in each continent (data sourced from the Gapminder foundation). You... |
8c432ec07ab667d6e6745ea2176be6102a96a283 | 9f2df28e9a44cf50c0249030121621f6b4172f16 | /man/FAMEoutliers.Rd | 5108cc02401fd7e43e6d0fdfda1ad8258083f10f | [] | no_license | acinostroza/TargetSearch | 7246e8c473403a72cd295ce6b6924c2b1766ae41 | 31ffae96fcefdeb8cd73138efd789fe28b511ca4 | refs/heads/master | 2023-07-05T20:46:15.736431 | 2023-06-26T13:10:43 | 2023-06-26T13:10:43 | 126,149,138 | 4 | 0 | null | 2019-08-19T09:46:32 | 2018-03-21T08:47:25 | R | UTF-8 | R | false | false | 2,180 | rd | FAMEoutliers.Rd | \name{FAMEoutliers}
\alias{FAMEoutliers}
\title{ FAME outlier detection }
\description{
A function to detect retention time marker (FAME) outliers.
}
\usage{
FAMEoutliers(samples, RImatrix, pdffile = NA, startDay = NA, endDay = NA,
threshold = 3, group.threshold = 0.05)
}
\arguments{
\item{samples}{ ... |
a109e1e6c1d7ce3068e4ca37a7c26ddbf6742635 | 0e5948d9b3bfe27ebc00cbb97c7095a68b10c29d | /data.prep.R | 290227beb645e941434d2fccccf06aa72a69fb55 | [] | no_license | asherstnev/MDSP.final.project | 4a33c992a2138ec3d8a609c7aa5efae2e7b57532 | 46b95ff3b5b0058381d58b382b1ed56b8e733d05 | refs/heads/master | 2021-01-13T04:45:29.352922 | 2017-01-23T20:26:47 | 2017-01-23T20:26:47 | 79,108,969 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,498 | r | data.prep.R | library(data.table)
library(compare)
######################################################################################
# load raw data
load.raw.data <- function(in.file) {
raw.data <- fread(in.file)
# rename some columns
setnames(raw.data, "Loan ID", "loan.id")
setnames(raw.data, "Cu... |
9359d82167a6fb7171416d2ff2b9923abc058895 | bf1e79e2ee906acf1a6b5b414f3e78dc16d1e437 | /man/source_han.Rd | 7b73f32673f031a2b9cdbdb68ecc5996f0fbd108 | [] | no_license | cran/showtextdb | 496a08b240ee3cdd76823085ced5c7d622b0bf5e | b1e4f2d1da68de68c1ade7d550d5334dcb62f510 | refs/heads/master | 2021-08-06T09:13:47.076173 | 2020-06-04T07:10:02 | 2020-06-04T07:10:02 | 31,942,397 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,048 | rd | source_han.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/source_han.R
\name{source_han}
\alias{source_han}
\alias{source_han_sans}
\alias{source_han_serif}
\title{Meta-information for the Source Han Sans/Serif Fonts}
\usage{
source_han_sans(lang = c("CN", "TW", "JP", "KR"))
source_han_serif(lang =... |
401b1b2ce83c6fd0ede102f93b578f43189af07d | 147706932a9deff1d9b12fed0783047110ba2191 | /plot3.R | 8cd48c211f7f44cc79c0fed441d75dea94431d57 | [] | no_license | ok-datascience/ExData_Plotting1 | 023755f6b8ce302da85d27a775b986af67cd3364 | 8f5d63de3275aede7afb3d5d29e39339317c60a9 | refs/heads/master | 2021-01-09T07:31:00.187201 | 2015-01-09T13:40:41 | 2015-01-09T13:40:41 | 28,893,617 | 0 | 0 | null | 2015-01-07T02:08:16 | 2015-01-07T02:08:15 | null | UTF-8 | R | false | false | 931 | r | plot3.R | # read sample data
allData <- read.csv('../exdata1/household_power_consumption.txt', sep = ';',header = T, na.strings="?")
# convert separate date and time fields to POSIXlt date type
allData$Date <- strptime(paste(allData$Date, allData$Time), "%d/%m/%Y %H:%M:%S")
# subset sample data
plotData <- allData[(as.Date(allD... |
48d11d776782c27a8889b08811d6a3cce6f74726 | 2975fba6bf359214c55e7d936f896a5a4be3d8f5 | /man/FGR.Rd | e2953ddf5ab5936fa9c3907af4f5afa8f347d06b | [] | no_license | tagteam/riskRegression | 6bf6166f098bbdc25135f77de60122e75e54e103 | fde7de8ca8d4224d3a92dffeccf590a786b16941 | refs/heads/master | 2023-08-08T03:11:29.465567 | 2023-07-26T12:58:04 | 2023-07-26T12:58:04 | 36,596,081 | 38 | 14 | null | 2023-05-17T13:36:27 | 2015-05-31T09:22:16 | R | UTF-8 | R | false | true | 3,109 | rd | FGR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FGR.R
\name{FGR}
\alias{FGR}
\title{Formula wrapper for crr from cmprsk}
\usage{
FGR(formula, data, cause = 1, y = TRUE, ...)
}
\arguments{
\item{formula}{A formula whose left hand side is a \code{Hist} object -- see
\code{\link{Hist}}. The ... |
1d4eb10c55c65858a58863438538809528c3cc8b | e573bc7fd968068a52a5144a3854d184bbe4cda8 | /Recommended/survival/R/frailty.gammacon.R | 6be48fb433864ec947dc4959d6d489b410ef6bcd | [] | no_license | lukaszdaniel/ivory | ef2a0f5fe2bc87952bf4471aa79f1bca193d56f9 | 0a50f94ce645c17cb1caa6aa1ecdd493e9195ca0 | refs/heads/master | 2021-11-18T17:15:11.773836 | 2021-10-13T21:07:24 | 2021-10-13T21:07:24 | 32,650,353 | 5 | 1 | null | 2018-03-26T14:59:37 | 2015-03-21T21:18:11 | R | UTF-8 | R | false | false | 768 | r | frailty.gammacon.R | # $Id: frailty.gammacon.S 11166 2008-11-24 22:10:34Z therneau $
# Correct the loglik for a gamma frailty
# Term2 is the hard one, discussed in section 3.5 of the report
# The penalty function only adds \vu \sum(w_j) to the CoxPL, so this
# does a bit more than equation 15.
#
frailty.gammacon <- function(d, nu) {
... |
7159a21b55f9fbce8bbe0e25c94e44224e9f0e02 | 4ae65ee9e98ab3e9b279c5af16cf15bbc364d34e | /years 2015-18.R | 67f2b47519449d999a6df58965bb57a322957f90 | [] | no_license | emcbride09/Are-points-the-most-important-NBA-Championship-factor | d5fd5ce601036e867553eaf0d59d04d0598ce7d0 | 26004c28a1a61a65694a6ef5b7282bd8e2f237fb | refs/heads/master | 2023-01-31T12:09:01.517622 | 2020-12-15T13:56:20 | 2020-12-15T13:56:20 | 158,214,059 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,346 | r | years 2015-18.R | require(tidyverse)
url <- "https://www.teamrankings.com/nba/stat/points-per-game?date=2016-06-20"
fifteenstat <- read_html(url) %>%
html_nodes("table") %>%
.[[1]] %>%
html_table()
fifteen <- fifteenstat %>% select(Team, `2014`, `2015`)
url_17 <- 'https://www.teamrankings.com/nba/stat/points-per-game?date=... |
d3c4e7ed9cb88d0d778e929389586e3b9c55f0a6 | af7d0c29242eb096032eddbdeeceeb702b0f34b7 | /Code 4.R | a8e2857af96b68d84d2f517cfd26ae2775c420cf | [] | no_license | SoylabSingh/PATRIOT | b749746819283c18caf41135bbdaa16c240b5c8f | 3e0a605b93c911980d38fc3444d41d73517436a5 | refs/heads/master | 2023-08-01T08:45:13.264077 | 2021-09-09T17:38:39 | 2021-09-09T17:38:39 | 291,137,928 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,453 | r | Code 4.R | #Install packages needed
install.packages("tidyr")
install.packages("stringi")
install.packages("stringr")
install.packages("plyr")
install.packages("dplyr")
install.packages('rrBLUP')
install.packages('data.table')
library(tidyr)
library(stringi)
library(data.table)
library(rrBLUP)
library(plyr)
library(d... |
305093f5954c67991fdf336598fb683d6b810e37 | 7058b908bbb57fa425c6a0829ff08c2f3159fd7c | /cachematrix.R | fe7f4af743a0998a742cfadca71a8b6e35eb780e | [] | no_license | defaultersan/ProgrammingAssignment2 | 2dad32fdb1ec7288430f181031933e081b9bb45e | f5e69ed2a9953b840933384f3e51dd8b63290c41 | refs/heads/master | 2020-12-15T03:08:32.525439 | 2014-05-25T22:33:15 | 2014-05-25T22:33:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,667 | r | cachematrix.R | ## makeCacheMatric function is in turn exposing various functions to
## get/set the value of a matrix
## get/set the inverse of a square matrix
## cacheSolve function computes the inverse of a matrix.
## If the inverse matrix has already been calculated (and the matrix has not changed),
## then re... |
b368a89c983c9a83368f17189173d1075a20f6d7 | 85b80782f63b3482d8aa6317934c704b626387e4 | /Modelos/Lufthansa_LHA_DE/Luf_10a19.R | c9908a53d513f2ffb29a07b738b559da93431dc0 | [] | no_license | MathNog/UndergraduateResearchProject | 23ab0a6bb6d53ba54353d6bf1c4980ea11a500da | 404de409d2746006fb19316f4c84becaab2e33c7 | refs/heads/master | 2023-07-13T03:23:28.074473 | 2021-08-31T01:04:31 | 2021-08-31T01:04:31 | 286,582,042 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 4,427 | r | Luf_10a19.R | library(forecast)
library(tseries)
library(FitARMA)
#Setar diretório correto
setwd("C:/Users/Matheus/Desktop/PUC/IC/CiasAereas/Modelos/Lufthansa_LHA_DE")
#Leitura dos dados -> preços BOEING
arquivo=read.csv(file="LHA_DE2010a2019.csv")
#Selecionando apenas o AdjClose -> podemos ignorar os outros
AdjClose=arquivo[c(6)... |
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