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f853836c66fb6bf2500836043dfb2bd5b292e6c7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/QFRM/examples/HolderExtendibleBS.Rd.R | fa9c576b8b2dbab5a1e972abd18520ce43c9982c | [] | 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 | 517 | r | HolderExtendibleBS.Rd.R | library(QFRM)
### Name: HolderExtendibleBS
### Title: Holder Extendible option valuation via Black-Scholes (BS) model
### Aliases: HolderExtendibleBS
### ** Examples
(o = HolderExtendibleBS())$PxBS
o = Opt(Style='HolderExtendible',Right='Call', S0=100, ttm=0.5, K=100)
o = OptPx(o,r=0.08,q=0,vol=0.25)
(o = HolderEx... |
f4cceaf856273f415b7848c95a1696180baf2c5d | 452ec71b7cae302f0710163e0617ad6382d9e1c3 | /man/GL_test_stat.Rd | b896b634032ee98213126a54001c77fd5839a666 | [] | no_license | umich-biostatistics/corrsurv | 05247bd29ff362ebacd6c9e6101641e2d29cb7f8 | 444c88c7be8493ad62167d8fb140a3de9578909d | refs/heads/master | 2020-08-26T19:56:01.580397 | 2020-01-10T21:00:31 | 2020-01-10T21:00:31 | 217,128,847 | 0 | 0 | null | 2020-01-10T21:00:32 | 2019-10-23T18:40:38 | R | UTF-8 | R | false | true | 778 | rd | GL_test_stat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TM.R
\name{GL_test_stat}
\alias{GL_test_stat}
\title{Calculate test statistic for Ghosh and Lin method}
\usage{
GL_test_stat(p_GL, time, data1_format, data2_format)
}
\arguments{
\item{p_GL}{p parameter}
\item{time}{time to event}
\item{dat... |
ad19ac174fa705aa0ff78e89ff77835dd01c5c61 | 1c0be95fb6ebfd304d2f524403c032cd4e2cce0b | /Discriminant Analysis/distinguish.bayes.R | fe7c8c054c9bf8cc399c0676df9cc159a5ea5e0e | [] | no_license | luweihao/duoyuan | f42300edc5f29679f6a5ee90df68632a653b595a | 9e9b59b7a26bbc99234de6c842cf07ac3ca74140 | refs/heads/master | 2021-03-16T08:44:19.964636 | 2018-01-29T16:38:12 | 2018-01-29T16:38:12 | 119,410,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,428 | r | distinguish.bayes.R | distinguish.bayes=function(TrnX, TrnG, p=rep(1, length(levels(TrnG))), TstX = NULL, var.equal = FALSE)
{
flag=0
if (is.null(TstX) == TRUE){
TstX=TrnX
flag=1
}
if (is.vector(TstX) == TRUE) TstX=t(as.matrix(TstX))
if (is.matrix(TstX) != TRUE)
TstX=as.matrix(TstX)
if (is.matrix(TrnX) != ... |
9fe614a8d4b898c7e265a8344869180aab0d9b46 | 9555bdc33d1c2f5b497de5cd4fcd91329bcae3d7 | /289R_HW4_20170924.R | c44b8c9536263ff31d9ea2fe450952b0742cc7bb | [] | no_license | lovefreedomval/201708 | 4f0291ec001288c6d43c368a63e21bf468186f84 | ddf1976d4cda979deb54cc34c1b91e220e00d09b | refs/heads/master | 2021-01-19T19:02:10.216490 | 2019-04-07T14:43:54 | 2019-04-07T14:43:54 | 101,182,167 | 0 | 0 | null | null | null | null | BIG5 | R | false | false | 542 | r | 289R_HW4_20170924.R | # 公正的骰子，必須出現三次六點才可以停止 - 請問總共要投幾次？ - 投擲的歷史紀錄為何？
dice_flips <- c() # 投擲紀錄
dice_vector =c(1:6) # 定義骰子的內容/結果
i <- 1
while (sum(dice_flips==6) < 3){ # 什麼條件下要一直執行
dice_flips[i] <- sample(dice_vector, size = 1) # 用sample函數骰骰子, 並把每次結果紀錄給dice_flips
i <- i + 1 # while迴圈往後交棒=指定下一次
}
dice_flips # 印出投擲紀錄
len... |
8af4b19e9236be7a2a0e1510b37367ee8584e072 | fbbf4719a5fac80d1c540231c0003aef6aa444c2 | /unix_shell/2nd_script.R | b9610f83ebb3347f4abdaefa16eb3f634771cc78 | [] | no_license | elenamdo/Software-Carpentry-Workshop-2018 | c8fd456a9e7024ae6d135c7f3413193cffe0030a | 5d82bd3694788a1b7d95c41e6500ea4366a60d75 | refs/heads/master | 2020-03-29T14:41:13.812178 | 2018-09-23T22:01:34 | 2018-09-23T22:01:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 802 | r | 2nd_script.R | #2nd script
#This script computes the average GDP for a country using
#clear away old variables
rm(list = ls())
#location of the data
filename <- 'Data/gapminder.txt'
#read the data file
gapminder <- read.table(filename, header = TRUE)
getAverageGdpPerCapita <- function(country, gapminder){
# select countryw here... |
6950c100c1a5ebdfd260b5e4a5681fb05c5854a1 | c6da4424c172b71477fe5c8420d0ab69d3b277cf | /man/RPChoose.Rd | 476662d2ab93a8b838966bbd61d6e4e728b1b0a9 | [] | no_license | cobrbra/RPEnsemble | 68702aaf1bca8bf605407f93cabfec6ee0f1feef | d1161d8e9352a98eb0cbb1a224db91f420edd66f | refs/heads/main | 2023-08-26T13:17:08.488723 | 2021-10-09T12:03:53 | 2021-10-09T12:03:53 | 337,134,340 | 0 | 0 | null | 2021-02-08T16:18:04 | 2021-02-08T16:18:03 | null | UTF-8 | R | false | true | 2,315 | rd | RPChoose.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RPChoose.R
\name{RPChoose}
\alias{RPChoose}
\title{Chooses projection and produces predictions}
\usage{
RPChoose(
XTrain,
YTrain,
XTest,
d,
B2 = 10,
base = "LDA",
k = c(3, 5),
projmethod = "Haar",
estmethod = "training",
.... |
38ce6798df197382f49f967677d4e6fbfd642111 | 0775b1dc2ff15385e26d01bf80b482b5c69d7bf4 | /cachematrix.R | 848d7c1418fa8af945bdcad331164e81b426ad3a | [] | no_license | jbirnbaum92/ProgrammingAssignment2 | 25536a59b654f2501c84f553cece45d4553f1517 | 390e28373367d208fb5125a36c69b31aac8c9a77 | refs/heads/master | 2021-01-18T05:27:40.355170 | 2016-02-19T18:13:23 | 2016-02-19T18:13:23 | 52,067,627 | 0 | 0 | null | 2016-02-19T06:48:38 | 2016-02-19T06:48:38 | null | UTF-8 | R | false | false | 1,115 | r | cachematrix.R | ## Matrix inversion is a costly computation an there is benefit to caching it rather than computing repeatedly.
## The First function creates an object that can cache its inverse.
## The second computes the inverse returned by the function above. If the inverse has been calculated already, it will simply be
## recovere... |
3f400ee3e902b90081454d27a048c2ea0e848f4b | 3bba09703406414cc64349f5bdefeaf133034932 | /Code/Old_Code/16a_LUR_BC_Averaged v1.R | 3cd663e93861903e627ac3e17a206c44fb157adc | [] | no_license | smartenies/ECHO_Aim1_BC_ST_Model | cb79815ea7d6b3e8d3680b60b90f08ae2c5067f8 | c69da5a28ad4c4ff16d69df5d5c5a097d4c6e826 | refs/heads/main | 2023-02-17T22:39:49.307080 | 2021-01-19T20:23:54 | 2021-01-19T20:23:54 | 319,451,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,969 | r | 16a_LUR_BC_Averaged v1.R | #' =============================================================================
#' Project: ECHO LUR
#' Date Created: Febrary 17, 2020
#' Author: Sheena Martenies
#' Contact: Sheena.Martenies@colostate.edu
#'
#' Description:
#' Preliminary LUR for BC
#' Averaging all data at the sampling locations-- essentially takin... |
0e7714b9cf43e1e94f08ce1ff2872f960716e3d8 | 14abfaec6c704d8ea1799fa8f2dfe834d66953b1 | /One-off analyses - Copy/prescreen research questions.R | e3e6264a8071647ee7c803529e9376c29d7134e4 | [] | no_license | jrliebster/getting-and-cleaning-data | d7d119125750006f74c52ff81f396fb91dd8ade5 | b30aa5f7a1287bd5dece503885cd0f74ff126aa0 | refs/heads/master | 2021-01-22T18:06:47.183111 | 2017-08-23T20:47:59 | 2017-08-23T20:47:59 | 100,740,016 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,162 | r | prescreen research questions.R | #load packages
library(pacman)
p_load(readr, dplyr, janitor, tidyr, ggplot2)
prescreen_ratings <- read_csv("prescreen data with research questions.csv")
prescreen_ratings[prescreen_ratings=="Strongly Agree"] <- 6
prescreen_ratings[prescreen_ratings=="Agree"] <- 5
prescreen_ratings[prescreen_ratings=="Somewhat... |
6bed4241fd8f36188ae790ff098f9301331b1fe5 | 834a593c957282895297c90344f6d9b3062b77d9 | /Robustness Checks/Data_Generation.R | 3b9b34d56bada39a5b7079a8cb4158dab67cdb1a | [] | no_license | haroonatcha/CCEnrollmentPrediction | 27442685d255f1e2299edc34059f62d87b78ceda | be0846b9fdf04e24297c1bd4cc19806a9e263a3c | refs/heads/main | 2023-06-17T14:02:35.403103 | 2021-07-02T20:21:00 | 2021-07-02T20:21:00 | 372,040,081 | 0 | 0 | null | 2021-07-12T05:43:45 | 2021-05-29T18:04:48 | HTML | UTF-8 | R | false | false | 19,919 | r | Data_Generation.R | library('truncnorm')
library('rlist')
library('forecast')
library('scales')
library('ggplot2')
library('reshape2')
#everything in this file is just a combination of the data generation and
#modeling files up to the final section which shows how I generated the
#aggregate fit values
# Initialize variables ------------... |
336d861bb7d301b48eaa500d611c4cb5349eef99 | d0c80b1c3fa3b584152a399581c3b0792811be6d | /man/possol.Rd | fd73de02d48c29d927a78d71a5aefeeef327af50 | [] | no_license | belasi01/asd | 4dbeb7d2da9bbb3398a8bae6ee1d3e22ec34067d | 16118ee6b49419ad0c26774f69158a2dc8b95347 | refs/heads/master | 2021-08-06T10:08:13.324759 | 2021-06-22T16:16:37 | 2021-06-22T16:16:37 | 72,467,268 | 1 | 2 | null | 2020-05-27T18:14:33 | 2016-10-31T18:43:12 | HTML | UTF-8 | R | false | true | 711 | rd | possol.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/possol.R
\name{possol}
\alias{possol}
\title{Compute the Sun position from the location and time on Earth}
\usage{
possol(month, jday, tu, xlon, xlat)
}
\arguments{
\item{month}{is an integer for the month number (1 to 12)}
\item{jday}{is th... |
79d366da85b183db7ccc1b3c5086e42fcbea2a8a | c1d43bdf6a75b9485dcb74a93c8466741d13452e | /Statistica/Repartitii de va.R | ee3a15a5235960d46bb68c5de7b12c9df3f7d213 | [] | no_license | theodormoroianu/SecondYearCourses | bc981e9beb08e3c52945b6aa8f54a5cba7a35545 | 9dc399319172432b989c6f525fa51344ac8d13d1 | refs/heads/main | 2023-06-05T14:23:33.370022 | 2021-06-24T20:11:27 | 2021-06-24T20:11:27 | 300,949,174 | 10 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,254 | r | Repartitii de va.R | #Repartitii de v.a.
#1.d+nume_repartitie=functie de masa(caz discret)/functia de densitate(caz continuu)
#dgeom(x,p)
#dbinom(x,n,p)
dbinom(3,5,0.4)
#P(X=3)
#dexp(x,lambda)
dexp(3,1)
#NU mai e o probabilitate
#2. p+nume_repartitie=functia de repartitie
# pbinom(x,n,p)
#P(X<=x)
pbinom(3,5,0.4)
#3. r+nume_repartit... |
3c89eee8e83277a7492e987060d201400937055c | d5cc7f10f54e30f84a7b1c9de22fd07de14a4e8c | /Evaluacion_01/Scripts/smith-waterman.R | 3d83474b4ac6b633a7f65864482f24f3543deb36 | [] | no_license | KariVillagran/Bioinformatica | af174123b144abf407bf10477b5bdf197ce322af | 94c9f2dd54ef6bf5fddfeeb34ce1bb63f7645fd4 | refs/heads/master | 2021-01-11T00:36:58.422315 | 2016-12-20T19:26:21 | 2016-12-20T19:26:21 | 70,535,612 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,793 | r | smith-waterman.R | install.packages("stringr")
library(stringr)
# Algoritmo Smith-Waterman
smith <- function (s1,s2)
{
matriz = matrix(nrow=nchar(s2)+1,ncol=nchar(s1) + 1)
s1 <- paste("",s1)
s2 <- paste("",s2)
colnames(matriz) <- c(unlist(strsplit(s1, "")))
rownames(matriz) <- c(unlist(strsplit(s2, "")))
matriz <- inicial... |
bb5b7295f3ee66cbae9755eac5bf77af6d4f52fb | 83ced69bbb0e163f85287138690794a863ef9956 | /cachematrix.R | 52eb521bc06db41456bffae9a320168e592f411a | [] | no_license | GerhardStimie/ProgrammingAssignment2 | 5c6b416fe791087cb946a3ddead586226b0bbc2b | 082051c7bc27914ec6135ac3ba6b90435ada1101 | refs/heads/master | 2020-04-02T21:18:47.517849 | 2018-10-27T10:45:10 | 2018-10-27T10:45:10 | 154,794,393 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,719 | r | cachematrix.R | ## Assignment 2 submission
##
## This source file contains 2 functions that:
## 1. Create a list of functions to calculate the inverse of a passed matrix
## 2. Store the resulting inverse matrix in cache
## 3. Return the cached matrix upon request if the inverse has been calc... |
c47136061a2fd8ab14d44e5179443aa24b09b060 | 08c48f2627281810fe2a4a37bb1e9bc5c03eeb68 | /Huan_link_all_script/R/x86_64-pc-linux-gnu-library/3.4/VennDiagram/tests/test-Three.R | 436974b194de4a0499a4744d01357ca7461617c6 | [] | no_license | Lhhuan/drug_repurposing | 48e7ee9a10ef6735ffcdda88b0f2d73d54f3b36c | 4dd42b35e47976cf1e82ba308b8c89fe78f2699f | refs/heads/master | 2020-04-08T11:00:30.392445 | 2019-08-07T08:58:25 | 2019-08-07T08:58:25 | 159,290,095 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,885 | r | test-Three.R | #Testing using package testthat for detailed error messages
library(testthat)
#Get the testing function applied to compare the two venn diagram objects
source("testFunction.R");
#load in the reference plot data
load("data/plotsThree.rda");
#Suppress plotting for sanity
options(device=pdf());
#initialize the testing... |
d6ea173e105368e1528deecc0ec05f7064019e36 | 03b13ddf39e2c7f1cab356ea589775cc31612a7e | /man/bhl_namelist.Rd | b85f076eaa71fafcccc23eb2210e6c5e2ceb383a | [
"MIT",
"LicenseRef-scancode-public-domain"
] | permissive | firefoxxy8/rbhl | e7f0b134584bda13561fd0d4a77691b4f145f025 | b046f5748342d2256242941213a9555157dca0ed | refs/heads/master | 2020-03-27T19:32:33.766244 | 2018-08-25T15:21:59 | 2018-08-25T15:21:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,523 | rd | bhl_namelist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bhl_namelist.R
\name{bhl_namelist}
\alias{bhl_namelist}
\title{List the unique names.}
\usage{
bhl_namelist(startrow = NULL, batchsize = NULL, startdate = NULL,
enddate = NULL, as = "table", key = NULL, ...)
}
\arguments{
\item{startrow}{fi... |
3fcfa3cb4d0113cb6a838d89179668397e41ca70 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RNetLogo/examples/NLGetAgentSet.Rd.R | 43611515bf29ab4db7823105c9c60a52403561dd | [] | 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 | 1,533 | r | NLGetAgentSet.Rd.R | library(RNetLogo)
### Name: NLGetAgentSet
### Title: Reports variable value(s) of one or more agent(s) as a
### data.frame (optional as a list or vector)
### Aliases: NLGetAgentSet
### Keywords: interface NLGetAgentSet RNetLogo
### ** Examples
## Not run:
##D nl.path <- "C:/Program Files/NetLogo 6.0/app"
##D NLS... |
52b345ec3b0512f2e1fa76b51339082a77c37039 | 551d324609cc89855800ef120341c25e53ca7f92 | /Data Processing/pca.R | 9aab04ce8074d3a3164279b66a46c9566487817b | [] | no_license | GiorgosTsal/Machine-Learning-in-R | d8fc235bab6c830604c62535594f2673faf98b5a | e677c8d6488569e92ebf0a664068f0d30fedbb4d | refs/heads/master | 2022-04-01T21:00:00.340468 | 2020-02-03T09:38:41 | 2020-02-03T09:38:41 | 234,272,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,026 | r | pca.R | #in order to set current directory as root
script.dir <- dirname(sys.frame(1)$ofile)
setwd(script.dir)
cat("\014") # for clearing console or use Ctrl+L
rm(list=ls()) #fir clearing env
#install.packages("factoextra")
library("factoextra")
#Load the data and extract only active individuals and variables:
data(decathlon... |
9314582f61eb152200a24d179369cfa65666bd45 | 5875db7aaae2fb33c2097008bbc91ffa677ba7fa | /R/selection_alg.R | 89d26465d0a3c128287b70a57ff7434ca5f95096 | [] | no_license | ClimDesign/fixIDF | a4e086a63cc6ce6d786614773b5b900be0d5c4ea | e8c36ac9f7b721c02412a95cba8e1b4e43a3a755 | refs/heads/main | 2023-08-12T03:50:39.609330 | 2021-10-07T11:54:04 | 2021-10-07T11:54:04 | 333,784,713 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,555 | r | selection_alg.R | selection_alg=function(quant_bay,maxit=1000,strategy="up",save.history=TRUE,seed=NULL){
if(is.null(seed)==FALSE){
set.seed(seed)
}
curvehistory=list()
n=length(quant_bay)
bestguess=c()
pvec=rep(0,n)
quantilevec=rep("50%",n)
quantvec=rep("50%",n)
quantilehistory=quantvec
names(pvec)=1:n
possi... |
b99eb3348df3626ea98280a03ea70e1218d4e57d | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleaiplatformv1.auto/man/GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics.Rd | 7a04422d40f8e85b6c9147d4ab9c6b75a19b3db1 | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,439 | rd | GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aiplatform_objects.R
\name{GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics}
\alias{GoogleCloudAiplatformV1SchemaModelevaluationMetricsTextExtractionEvaluationMetricsConfidenceMetrics}
\title... |
5e633c50c9a400aaabf71e2a4fba8a4fb9a69f9c | 012b34b9323b72a8a4a6e2a365849b69a9a78286 | /Project 1 Plot 2.R | 220cab4351c3edbb1d27f440a35fbf1fc989df7b | [] | no_license | Shivens/Coursera | 7e3585643e8026f9ec6e93e7a5eb2abe4f4a3082 | 7f95b6ee936b39b004134abdba6344520237a63b | refs/heads/master | 2021-01-23T08:34:55.330442 | 2015-02-02T14:49:08 | 2015-02-02T14:49:08 | 29,438,173 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | Project 1 Plot 2.R |
# To execute just copy the relevant sections and exceute (Ctrl+R)
############ DATA INPUT - Reading file ######################
rm(list = ls())
#0) Ensure file "household_power_consumption.txt" is in source directory
#1) Reading the .TXT file correctly, by skipping initial rows
mydata=read.csv("household_power_consump... |
95c37b7ebb1ae57527ea7d65c8baa76086d14e68 | 007e32a803059d789dcc66b99dd14913b2e9489f | /Code/Modified_LC.r | 7d2010407eed914d758a3dfe89e6eee72b3edee6 | [] | no_license | NanduDara/Mobile-Data-Offloading | cd4e8e94b22f35cd6750d3a2675b6ecbafd2b2da | a2bd8bb4991ac44fcf5bf86880d08543b733b71f | refs/heads/master | 2020-08-05T17:04:51.127393 | 2019-10-03T16:31:08 | 2019-10-03T16:31:08 | 212,626,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,413 | r | Modified_LC.r | #There are 10 data items in increasing order
n <- 10
m <- 3
z <- sample(30000:40000, n)
z <- sort(z)
#Local Cost
l_c <- matrix(data=0, nrow=n,
ncol=m)
l_e <- runif(m, min = 0, max = 1)
l_e <- round(l_e, 3)
l_e <- sort(l_e)
l_t <- runif(m, min = 0, max = 1)
l_t <- round(l_t, 3)
l_t <- ... |
c42153e04fca44ffc456a4dab52807698c9d1e62 | fea763229750657d1f22b2fb970b9abb357c72bb | /InClass/.Rproj.user/99D7318E/sources/per/t/D1344DEF-contents | efd14083342e698d5a979847dd466c31d22ea6ad | [] | no_license | AbigailCastro17/SYS2202-Data-and-Information-Engineering | 8b9547f203ce7dc04a178414a5583481bfc798a1 | ae0002c643537e86d3e2865929e4054aec35786a | refs/heads/main | 2023-04-30T22:25:24.315274 | 2021-05-19T04:39:23 | 2021-05-19T04:39:23 | 368,744,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,166 | D1344DEF-contents | # You can convert this into an .Rmd if you are comfortable with it.
# The name of your file should include your Zoom team number. Please make sure to pick a consistent name with your teammates.
# ----------------------------------------------------------
library(dplyr)
library(nycflights13)
library(tidyverse)
View(... | |
6601b89c588bce8478a9196adc1b93082d9d6605 | 7caa535fa86544482ae18d5408012edc9fbc5ddd | /man/InitBinaryFA.Rd | d9ff93aea19430d21e015d1c069c4509bf0c3930 | [
"Apache-2.0"
] | permissive | kant/scBFA | 7e11800a7bf6b4fa1fa963b51a22b6168e8cf554 | 82913a20ccaafd8622d51fc4634a854344920c82 | refs/heads/master | 2020-12-11T16:41:58.304222 | 2019-08-22T06:39:28 | 2019-08-22T06:39:28 | 233,900,004 | 0 | 0 | null | 2020-01-14T17:44:16 | 2020-01-14T17:44:15 | null | UTF-8 | R | false | true | 2,056 | rd | InitBinaryFA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BFA.R
\name{InitBinaryFA}
\alias{InitBinaryFA}
\title{This function should be called to initialize input parameters into the
main scBFA function}
\usage{
InitBinaryFA(modelEnv, GeneExpr, numFactors, epsilon, X = NULL,
Q = NULL, initCellcoef... |
4a1d8c4a6cd9b3f438b651d5db738a2fec4aa75a | 45745857ce9ef0eafd57201cf1a966095fe0c54c | /.Rproj.user/5205562C/sources/s-6E0280BE/3CC48C71-contents | 6b550abf8d1967c8b24403814e463235981358bb | [] | no_license | anuran-roy/learning-R | 3fade44be5ffbfb150cfa92340a9a31464ae2384 | 797b60963c15a693ecf89078c195867ade0a9f84 | refs/heads/main | 2023-06-23T10:38:36.308879 | 2021-07-16T15:29:36 | 2021-07-16T15:29:36 | 386,683,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,201 | 3CC48C71-contents | library(ggplot2)
ggplot(mtcars, aes(x="disp", y= "mpg")) #, aes(x=read)) + geom_bar()
data(iris)
# data(package = .packages(all.available = TRUE))
IrisPlot <- ggplot(iris, aes(x= Sepal.Length, y=Petal.Length, colour=Species)) + geom_density()
print(IrisPlot)
data("airquality")
OzonePlot <- ggplot(airquality,
... | |
b8ca5da92ee4af63b11a1e20b42365ba73f7a92e | ca04b59fa5778544808aac5f8e8d4f1a7f890b1e | /man/hi-package.Rd | 4c89fe8d7bf1b6de94790e4aaaa1bdfebfe1cfdb | [] | no_license | elistein/capm | 72cba9ea2652d8da9518406ecfa1854ee23f9cdf | 9c3757449f597ba1fb58d6405382673f8db60029 | refs/heads/master | 2020-04-06T03:49:19.543078 | 2013-09-16T22:12:27 | 2013-09-16T22:12:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,157 | rd | hi-package.Rd | \name{capm-package}
\alias{capm-package}
\alias{capm}
\docType{package}
\title{
Customer Analytics Using Probability Models
}
\description{
Fit latent variable models to understand who your customers are and what they will do next. Contains maximum likelihood estimation routines for a variety of models, as well as S3 m... |
488607410f11196006653cf76dc5d98eae3a528a | d32b0504ce7158272bba512bfba6ba2a6f01b07e | /man/flexCrossHaz-package.Rd | 4db4bc7a3d2014e768c9482fead76c07f1acd9b7 | [] | no_license | cran/flexCrossHaz | 0834f8da50c7d7c36286bd4223a1f2b96ddea6df | 1790e7dfd7eb9e0387e98b9681665c0415b48a43 | refs/heads/master | 2020-06-06T04:00:09.243786 | 2010-03-25T00:00:00 | 2010-03-25T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,627 | rd | flexCrossHaz-package.Rd | \name{flexCrossHaz-package}
\alias{flexCrossHaz-package}
\alias{flexCrossHaz}
\docType{package}
\title{
Flexible crossing hazards in the Cox model
}
\description{
Estimation of Cox model with flexible time-varying effects via P-splines and possible crossing points.
}
\details{
\tabular{ll}{
Package: \tab ... |
0fc8d9da0cedff0c7de284d03ac5c79309172d99 | a7d3f3e36460e71c4b34f0c95f955818ed2727f4 | /tests/testthat.R | aa1883a04673201ff4d35285c29b2171083421e5 | [
"MIT"
] | permissive | etiennebacher/shinyfullscreen | 9585779741b4d9788040d970eb51f140377fe2c4 | 81ca0b905e4636d942dca01feb3017662c7254b7 | refs/heads/master | 2023-05-04T06:07:53.131718 | 2023-04-20T14:31:03 | 2023-04-20T14:31:03 | 320,671,350 | 31 | 2 | NOASSERTION | 2021-01-11T19:51:12 | 2020-12-11T19:58:35 | R | UTF-8 | R | false | false | 74 | r | testthat.R | library(testthat)
library(shinyfullscreen)
test_check("shinyfullscreen")
|
1fba8ba67c8de19414a1dfacd27e22daec5c3291 | 438a9cb09f3afcc8cc9da4622028abe408fb15ac | /R_code/demo_R.r | b58ab3153877e084c2363be1d832a28b10fee567 | [] | no_license | Stuart-Aitken/ISS | a4cf29684e80f95f761ab0694b9daf90d0e8f03a | 7c8c738665df796b78338f779a88a1b2531575ff | refs/heads/master | 2020-07-23T09:47:30.621173 | 2019-09-11T08:36:23 | 2019-09-11T08:36:23 | 207,518,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,307 | r | demo_R.r | # Copyright (C) 2019 The University of Edinburgh
# Author Stuart Aitken MRC IGMM stuart.aitken@igmm.ed.ac.uk
# All Rights Reserved.
# Funded by the Medical Research Council
# https://www.ed.ac.uk/mrc-human-genetics-unit
library(e1071);
library(Hmisc);
source('functions.r');
quartz(width=12,height=4)
par(mfrow=c(1,... |
5eba58ca4211fa2c8fce55b05ba0a9fed35d5817 | ffcb30f62bb5a82ce1fd709d456f64d1e910e4e3 | /src/update-depth-charts.R | cf678356f2705acec4cc5a59954deee438fedab7 | [
"MIT"
] | permissive | nflverse/nflverse-rosters | 9d3deef3aea215db279c89bc3672964a851c8af9 | 265468baa09d27a94efad2776e194135b721e59f | refs/heads/master | 2023-08-03T10:15:17.147507 | 2023-08-01T02:32:08 | 2023-08-01T02:32:08 | 295,816,576 | 1 | 2 | NOASSERTION | 2023-09-06T06:06:31 | 2020-09-15T18:32:02 | R | UTF-8 | R | false | false | 3,275 | r | update-depth-charts.R | scrape_teams <- function(season) {
h <- httr::handle("https://www.nfl.info")
r <- httr::GET(
handle = h,
path = glue::glue(
"/nfldataexchange/dataexchange.asmx/getClubs?lseason={season}"
),
httr::authenticate("media", "media"),
url = NULL
)
teams_df <- httr::content(r) |>
XML::xmlP... |
8fce9518157facc8b8cb2fa04788ecf6245b02ec | c15c4062d360fd5f18e4718097352391e8439b90 | /Scripts/Env_variables/env.nc_to_csv.R | abade36ac4e3ab02da8c4c8f38150d153f30c382 | [] | no_license | EveTC/Pararge_aegeria_morphometrics | f2292737c57aaf8f0aa685ad7f776f7e09c27184 | 78b1f79dd8c9d9c4b155762b451feb8043eee58a | refs/heads/master | 2023-06-20T10:57:59.580931 | 2021-07-19T09:52:38 | 2021-07-19T09:52:38 | 262,007,077 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 55,143 | r | env.nc_to_csv.R | ### Bring in environmental variables as .nc files and make to csv
## Environmental dataset: HadUK-Grid Gridded Climate Observations on a 1km grid over the UK
### N.B. I can not directly porovide the environmental data. It is available through the CEDA archives and the Met office (for 2018 temp data).
## Follows htt... |
a71acb7de50877519ebcacc8c87bb8ec32432c73 | 4db6edaf2be0fe5ce5a8932e71a39954dc73cc7e | /scripts/00_Main.R | fc3056da2e0733b18168ad7ccfe3bdfc2150b0ec | [] | no_license | RSGInc/cmap_freight_model | 60999aced8db8df55002f5deab900d786615bd9b | aea542c286cbece5a18fc2d5a7bb277c80d39afa | refs/heads/master | 2021-01-20T14:01:58.429355 | 2017-07-28T19:12:29 | 2017-07-28T19:12:29 | 82,729,904 | 0 | 0 | null | 2017-02-21T21:39:50 | 2017-02-21T21:39:49 | null | UTF-8 | R | false | false | 10,260 | r | 00_Main.R | ##############################################################################################
#Title: CMAP Agent Based Freight Forecasting Code
#Project: CMAP Agent-based economics extension to the meso-scale freight model
#Description: 00_Main.R controls the model flow and sources in ot... |
10492fb098f22e7c1aedd90fa42930958df15627 | 6e0e38fc926c0d51340903674fb2d0d08eccdd9c | /src/tab_table1.R | cc1ebb10b829c129830620b735fab58daab30dd9 | [] | no_license | eribul/NH_luxation_infektion | a512f0039f2c50cbdd93d2ea9d9931f0cf3ef217 | 4370ef63748fbb59d80880ebd69254f8e45c8f89 | refs/heads/master | 2023-04-16T17:26:38.085338 | 2022-01-28T17:39:31 | 2022-01-28T17:39:31 | 259,828,410 | 0 | 0 | null | 2022-01-28T17:38:35 | 2020-04-29T04:58:49 | HTML | ISO-8859-1 | R | false | false | 5,363 | r | tab_table1.R | suppressMessages({library(ProjectTemplate); load.project()})
load("cache/df.RData")
dft1 <-
df %>%
mutate(
Charlson = replace(CCI_index_quan_original, CCI_index_quan_original > 1, "2+"),
Elixhauser = replace(ECI_index_sum_all, ECI_index_sum_all > 2, "3+"),
RxRiskV = cut(Rx_index_pratt, c... |
d67e614ff6bb4e70c79d6983072c3ad12f116c60 | ac5cf1135044426715a39d041348afd25c68d585 | /Code/Sandbox_fishtree.R | c4b5ae7c2a189ddaee9fd78b70f3eb2efe2b4f05 | [] | no_license | OScott19/ResearchProject | a173773130ed4c3ae151530691a514b96ac692c2 | 7f39f7202ee0643985d9b239309033a3e4f3c41d | refs/heads/master | 2022-11-11T20:09:06.123108 | 2020-06-26T14:52:36 | 2020-06-26T14:52:36 | 234,543,392 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 108 | r | Sandbox_fishtree.R | anewtree <- read.tree(file = "../../../Downloads/fish_trees", fill = T,
quote = "")
|
40fcc17dee8c558fe453fabc1d0e14bd1d828fe7 | 45b9583d47dfcca8cd18875a1d24480e7443c0b4 | /examples/demo-html.R | 044e88d20689bae07b13d5b06896a833d6cf475c | [] | no_license | araastat/BIOF439Online | a0ab26503781eb5c3b7b1b9905ab08bb21dac891 | 290b43bcff1c5a87f8c6285025bf5a4d6c410cb5 | refs/heads/master | 2022-11-15T02:43:38.550469 | 2020-07-15T09:02:45 | 2020-07-15T09:02:45 | 274,275,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,293 | r | demo-html.R | ## exams ----------------------------------------------------------------------------
## load package
library("exams")
## exam with a simple vector of exercises in R/Markdown (.Rmd) format
## -> alternatively try a list of vectors of more exercises
myexam <- c("boxplots.Rmd")
## exams2html -------------------------... |
6def559c67b5e5fe9c3e10d6457eb6d1382256d3 | c3d2fb0a02e5eabbd0234860186f246651c9fb39 | /R/Economics/unemploy-fx-match-r3.r | e152e95dd2a9ab2b242c9a87199f2fabf6425198 | [] | no_license | ppbppb001/Snippets | 09216417c52af40c947114bc106aee97776674f7 | 49e254eecd55f5e777d87c3f06c720cb881fb2dd | refs/heads/master | 2023-08-14T08:16:07.814866 | 2023-08-07T11:57:15 | 2023-08-07T11:57:15 | 93,360,154 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,107 | r | unemploy-fx-match-r3.r | #-------------------------------------------
# <unemploy-fx-match-r3.r>
# Improve the target table with
# unemployment and FX data colmum
# date matcing
#
# [2017-06-14] - New calculation added
#--------------------------------------------
# Key Constants: ........................
lookup.dateformat <- "%d-%b-%y"... |
99761262520ee3f3be965d76be17146544e668d3 | 3aa98ae7a9734c7891d0493a443b58acf497f36d | /s6_results_sim_setting_2.R | 30505fd05a85ed2e14d9bd891f91fc5c45adf427 | [] | no_license | LTTTDH/SING | bca9dd4ab9ea8e0550686c978a8add46bca7ce2f | 650fc4fff7429387a059d97c14495dd86fd4a161 | refs/heads/master | 2023-05-27T15:06:48.905382 | 2021-03-18T20:53:55 | 2021-03-18T20:53:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,865 | r | s6_results_sim_setting_2.R | # Simulation setting 2
# Error calculation for all methods, and creation of figures
# Load the results of joint ICA
load("jointICA_LargeScaleindiv10.Rda")
# Load the results from separate approach (rho = 0)
load("sepJB_LargeScale.Rda")
# Load the results from SING (from small rho to large rho)
load("out_indiv_small.Rd... |
9d582dfbf3cf7afbf5772572b2be71e2d40d841e | d2ac85674d6812fe3f606094bae82ea089659609 | /Scripts/gammbootstrap.R | 4b5d5a9ef85abb0ccf2a58e0b5d8ae8afb46cf77 | [] | no_license | LabNeuroCogDevel/R03Behavioral | 2a98e71917b1f35a4affe08298e32f9100df3b93 | f743b316ac00aa3381eb72ae08c47b3c87891ebf | refs/heads/master | 2020-09-23T07:19:38.313210 | 2019-12-05T22:19:06 | 2019-12-05T22:19:06 | 225,437,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,855 | r | gammbootstrap.R | #!/usr/bin/env Rscript
library(dplyr)
library(tidyr)
library(lubridate)
library(ggplot2)
library(lsmeans)
library(mgcv)
library(itsadug)
library(lme4)
library(lsmeans)
library(stats)
library(psych)
library(parallel)
library(lme4) # bootMer
library(MASS) ## for mvrnorm
library(cowplot)
lunaize_geomraster<-function(x){
... |
a4cba23932c4b29624ef7cdb966270daa112f03b | 559713216d4fe05838b1450981d8f6a2bd838135 | /profiling/6.bode_new_data/6.D.3_collect_ik.R | 9750886746f95c0c82565e7b8e9dd7537f9c5f79 | [] | no_license | yangjl/phasing | 6ac18f067c86d225d7351dfb427b6ae56713ce1b | 99a03af55171dac29b51961acb6a044ea237cb3a | refs/heads/master | 2020-04-06T06:58:38.234815 | 2016-06-10T00:40:36 | 2016-06-10T00:40:36 | 38,838,697 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,241 | r | 6.D.3_collect_ik.R | ### Jinliang Yang
### use impute_parent in CJ data
#library(imputeR)
get_ik <- function(path="largedata/ik", pattern="kid_geno"){
files <- list.files(path, pattern, full.names = TRUE)
message(sprintf("### found [ %s ] files!", length(files)))
kgeno <- read.csv(files[1])
for(i in 2:length(files)){
... |
a3c14298d96055e7b1048cce5ee35774f9462496 | e2f37b60e1cd4fdf9c002cd267a79f2881b248dd | /inst/examples/plots.R | 4defeb34a58399cd8c8c97c278c5830c0d301ee2 | [
"CC0-1.0"
] | permissive | cboettig/pdg_control | 8b5ac745a23da2fa7112c74b93765c72974ea9b9 | d29c5735b155d1eb48b5f8b9d030479c7ec75754 | refs/heads/master | 2020-04-06T03:40:50.205377 | 2017-10-17T02:47:24 | 2017-10-17T02:47:24 | 2,390,280 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,229 | r | plots.R | # file plots.R
# author Carl Boettiger, <cboettig@gmail.com>
# date 2011-11-16
# creates extra plots accompanying Reed.R
# for stat plots
require(ggplot2)
require(Hmisc)
require(pdgControl)
## FIXME Once standardized, all these plots should become package fns
## Reshape and summarize data ###
dat <- melt(sims, id="t... |
072218ce67cb9a3b7f45d8f6d186c16931646936 | 2b106b4488e294b561de4cdd8492d5341229d6d4 | /man/zoom_mat.Rd | 738a6eee491967ae5236e9281508bc0b6a69099e | [
"Apache-2.0"
] | permissive | ysnghr/fastai | 120067fcf5902b3e895b1db5cd72d3b53f886682 | b3953ad3fd925347362d1c536777e935578e3dba | refs/heads/master | 2022-12-15T17:04:53.154509 | 2020-09-09T18:39:31 | 2020-09-09T18:39:31 | 292,399,169 | 0 | 0 | Apache-2.0 | 2020-09-09T18:34:06 | 2020-09-02T21:32:58 | R | UTF-8 | R | false | true | 529 | rd | zoom_mat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vision_augment.R
\name{zoom_mat}
\alias{zoom_mat}
\title{zoom_mat}
\usage{
zoom_mat(
x,
min_zoom = 1,
max_zoom = 1.1,
p = 0.5,
draw = NULL,
draw_x = NULL,
draw_y = NULL,
batch = FALSE
)
}
\arguments{
\item{x}{x}
\item{min_zoo... |
932a7eddfbc5d15b41bb619b29ace1c21700e56c | 29585dff702209dd446c0ab52ceea046c58e384e | /Rvcg/R/vcgIsosurface.r | 1fdffd374cf32782140620386b1c1fe1faaf6fd8 | [] | 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 | 3,612 | r | vcgIsosurface.r | #' Create Isosurface from 3D-array
#'
#' Create Isosurface from 3D-array using Marching Cubes algorithm
#'
#' @param vol an integer valued 3D-array
#' @param threshold threshold for creating the surface
#' @param spacing numeric 3D-vector: specifies the voxel dimensons in x,y,z direction.
#' @param origin numeric 3D-ve... |
50f17fa60bbf09ced6d6df84553261ea82d7207c | 2c6465921e8d18a9133cd61727765dd7a07beaea | /Simulation Gestational Diabetes WHO/rotina_simulacao_20120909.r | 30bd64d07691b03abd04f7ecfc1848da1f726ee0 | [
"MIT"
] | permissive | isix/Othprojects | ea260b2b91a1e5880b98b382432c09614cf69b5a | f53c443e3314a0097f2abd9b872dd45d55e8e275 | refs/heads/master | 2020-03-08T08:04:38.878648 | 2018-06-20T04:32:01 | 2018-06-20T04:32:01 | 128,011,680 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 20,604 | r | rotina_simulacao_20120909.r | setwd("C:/Maicon/20120907")
# Settings
n <- 1000000 ;
alfa <- 0.05
# The Beta distribution with parameters shape1 = a and shape2 = b has density
#
# G(a+b)/(G(a)G(b))x^(a-1)(1-x)^(b-1)
#
# for a > 0, b > 0 and 0 = x = 1 where the boundary values at x=0 or x=1 are defined as by continuity (as limits).
# The mean is a... |
b48ad84de11d0785e766aed446dae5db9751610e | 62ef84d9a05abca20217017fbaeebba3df67384f | /rfunctions/sensEffectRatioMod.R | 2cf237bac0c06792636276a85258c7d08bb29843 | [] | no_license | jasa-acs/Biased-Encouragements-and-Heterogeneous-Effects-in-an-Instrumental-Variable-Study-of-Emergency-Ge... | b3c833501ada3ece38bdffbc97cd5fd86b9ee944 | 36e916f2c6d6fe09ddc6fec2738e9fd85500080c | refs/heads/master | 2023-02-14T03:25:02.006415 | 2021-01-04T20:03:59 | 2021-01-04T20:03:59 | 325,870,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,245 | r | sensEffectRatioMod.R | ###########
#sensEffectRatio
###########
sensEffectRatio = function(index, treatment, outcome, dose, null=0, DE = "both", MO = T, ER = T, alternative = "two.sided", alpha = 0.05, Gamma.vec = 1, calculate.pval = T, continuous.relax = F)
{
PVAL = calculate.pval
require(gurobi)
require(Matrix)
ns = table(... |
55883d7d658865914f93f348ac8437505f3e54fc | a61f94187639c226c06164ecb92924076d27bf4c | /R/rc.R | 2f33bddc915b7f5839bd434ebe8318b478bc173a | [] | no_license | DevinOrman/mlbstats | 90b7c53b3f0846df34664507660f9457ebe7760c | 11a10adeeba2db2e065f733a102cfb963fa8fb1e | refs/heads/master | 2020-05-17T11:41:20.979656 | 2019-04-26T20:37:56 | 2019-04-26T20:37:56 | 183,688,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 269 | r | rc.R | #' RUNS CREATED FUNCTION
#'
#' This function measures how many runs a batter has contributed
#' @param x Dataset containing batting statistics
#' @keywords rc
#' @export
#' @examples
#' rc()
rc <- function(x){
RC <- x$TB * (x$H + x$BB)/(x$AB + x$BB)
} |
4d12babe3c1de1821b6f7ec37a3bb42a78111b54 | d68441b6311721a84d0210c371a1a94b2eb5f261 | /R/bin_smooth_median.R | 17d83dfca9928883561bec2ba35c873a40e08873 | [] | no_license | jasdumas/dumas | 0e787cb29037cbfac331af108cff0f28c758b513 | 84aedfdd0e095e3a20d07877120a86e7b5d64f8b | refs/heads/master | 2020-04-06T05:37:36.930368 | 2017-07-17T19:24:24 | 2017-07-17T19:24:24 | 38,554,253 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 377 | r | bin_smooth_median.R | ## ---- Smoothing function - median ----- ##
#' Bin smoothing by median
#'
#' @param x a numeric vector or list of numbers
#' @param bins a numeric vector of how many bins
#'
#' @return a list
#' @export
#'
#' @examples bin_smooth_median(x = c(21, 15, 26, 26, 28, 29), bins=2)
bin_smooth_median <- function(x, bins) {
... |
5f81a65ae5de56a03b4e5af00b9e4661c1f523dc | 6b799b4098ca2a7d9878bfe7ea79c42d1a526c18 | /Date formats and tests of significance no answers.R | 1a9dcafb4a9a0d5048d584e7f8ad8822a4c8e7f9 | [] | no_license | SaraH545/SecondaryDataClass | 11f2fa69d0b41a9b5ddb89ae7a4e35de1bb049c1 | 002f42d160f5cc2ccfc0d01aee3cb34738a729d7 | refs/heads/master | 2020-03-12T17:25:27.902499 | 2018-04-23T19:19:57 | 2018-04-23T19:19:57 | 130,735,371 | 0 | 1 | null | 2018-04-23T18:41:34 | 2018-04-23T17:52:26 | R | UTF-8 | R | false | false | 1,881 | r | Date formats and tests of significance no answers.R | ##Date Formats
#Convert character variables to dates (https://www.statmethods.net/input/dates.html)
strDates <- c("01/05/1965", "08/16/1975")
dates <- as.Date(strDates, "%m/%d/%Y")
#INDEPENDENT ACTIVITY: Convert the string dates to abbreviated month, two digit year
#(HINT: Use link above for help)
#Chi squ... |
aa5f74d5fb813b9e44cd8550089b6f591ab60507 | 45c63420097a1a5047693e7ee25502ad03afa6aa | /man/convert_dates_r_to_twfy.Rd | 83ab69b38d4391b97b69fe46f3bd29e216ca2bab | [] | no_license | jblumenau/twfyR | 2f08fb7c8cd9a013d4024ed8c52daeacf15f2aaf | 5b6b14164e60d0d297f1460d43e9099fa9eec081 | refs/heads/master | 2021-01-12T02:50:32.057012 | 2019-10-23T11:25:43 | 2019-10-23T11:25:43 | 78,113,610 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 360 | rd | convert_dates_r_to_twfy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{convert_dates_r_to_twfy}
\alias{convert_dates_r_to_twfy}
\title{convert_dates_r_to_twfy}
\usage{
convert_dates_r_to_twfy(x)
}
\arguments{
\item{x}{R date object to convert}
}
\value{
Character object
}
\description{
Generic func... |
852df6e3434dfeebb536f7e44ea3f2b292499ded | 4ac98b6f1473ed8fcfb9de05951c33d51559dd73 | /man/regroup.read_table.Rd | e95fa3fc5cfbfaebe09b0368d83638c1337c91b2 | [] | no_license | stephenshank/RegressHaplo | 4331b082618a61073fcbb9564eaa8a17296a0e9f | cd55f2cc9ca540a2b18eedc4bd3a6e432541487a | refs/heads/master | 2020-04-19T07:11:04.600592 | 2017-08-25T13:37:46 | 2017-08-25T13:37:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 646 | rd | regroup.read_table.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read_table.R
\name{regroup.read_table}
\alias{regroup.read_table}
\title{Merges identical read over an edited read table}
\usage{
regroup.read_table(df)
}
\arguments{
\item{df}{a read_table object}
}
\value{
a read_table object
}
\description... |
d4e029856ca255f454aab8269ea0e0d2b5035990 | c838300b1609d3abd37b45179f960416be3d2d7b | /R/s.s.test.R | 876bbc6f88d6f492516ce369620e1cf3fb5c9159 | [] | no_license | kennylouie/kdevtools | 2ab5c07a975c5093c619d8e3778bd93450e38d70 | bef5361aacb4ef3a72b2d142eaaeac7721441945 | refs/heads/master | 2021-09-10T02:14:56.956855 | 2018-03-20T17:12:19 | 2018-03-20T17:12:19 | 125,908,564 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,561 | r | s.s.test.R | #' Summary statistics by group
#'
#' Summary and statistical comparison of group characteristics
#' @importFrom car leveneTest
#' @param dat dataframe with desired summary statistics to be calculated
#' @param samplelabels the name of the column in which has the group parameter you are interested in
#' @param ign... |
c1a519090be54bba87eaffd35637a06c8733dd3b | 6f2e5eee9737dbfdcac5e917e9eff9c28d2ad8ae | /man/kinship.Rd | 593fefe3fc95eecfc4bd3ab685a74947da3a896f | [] | no_license | oywpan/alphaSimHlpR-1 | b12bba3b433c40230e6971c931a586e22005653a | cf17441d15cd2f1e32f21efedccd8023518bb1be | refs/heads/master | 2022-04-09T07:10:08.866640 | 2020-03-17T16:18:52 | 2020-03-17T16:18:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 226 | rd | kinship.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alphaSimHlpR.R
\name{kinship}
\alias{kinship}
\title{Title}
\usage{
kinship(M, type)
}
\arguments{
\item{type}{}
}
\value{
}
\description{
Title
}
|
5b40cc69b18e1f08479d0a0cfe6bc343c86d991a | 9c246553377432d7130b9d07de4b82d2adf203f1 | /inst/doc/MCPModGeneral-Vignette.R | ba188dbf0957030078db8286ff5696c31a264fb5 | [] | no_license | cran/MCPModGeneral | edfeca44adb3b304c8ad37d3b81e7d7defe2e717 | 2924fc0a062748a8dca24cf2645f9c1d9422731f | refs/heads/master | 2020-12-22T01:04:58.231614 | 2020-02-19T16:50:04 | 2020-02-19T16:50:04 | 236,624,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,249 | r | MCPModGeneral-Vignette.R | ## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup, include = FALSE, fig.width = 3, fig.height = 3---------------
library(DoseFinding)
library(MCPModGeneral)
## ----powMCTGen, fig.height=3, fig.width=5--------... |
742f57bee0cc258d6d0ec1ddb7eb0b979940ffcf | 8866f2576324045f7f57bf02b87433bd3ed34145 | /R/ci_heatmap.R | 4159e8b46bd8dada772d33a0f732c1682519b870 | [] | no_license | cran/rock | 31ba91c6be5bff97c1659b3a8c3e5fbe6644f285 | 61999cb18c02680719a96b8ec3d0f33010849270 | refs/heads/master | 2022-12-26T21:02:05.960658 | 2022-12-13T11:30:02 | 2022-12-13T11:30:02 | 236,884,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,274 | r | ci_heatmap.R | #' Create a heatmap showing issues with items
#'
#' When conducting cognitive interviews, it can be useful to quickly inspect
#' the code distributions for each item. These heatmaps facilitate that
#' process.
#'
#' @param x The object with the parsed coded source(s) as resulting from a
#' call to [rock::parse_s... |
4d55897b692a549e4f08254df625659c5d7b4f00 | da93a36d25fbf1f3c3072624d4b1cccbac3c127b | /R_scripts/chromHMM_block_potential.R | 3c3327919d30abbe25db0c61ff442a86140376a8 | [] | no_license | hzauleibowen/TE_landscape | 9a86db98d03c8d31e4ccba88fa22a11826bd21e4 | 52ed632ad84f6003c698cffbfc0321829ea57307 | refs/heads/master | 2023-05-08T03:21:45.733823 | 2020-06-10T19:20:33 | 2020-06-10T19:20:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,207 | r | chromHMM_block_potential.R | # Creates a combined dataframe with the potential for TEs to be in each chromHMM state
# Using the standard rules, requiring overlap with the center of a 200bp bin,
# Or requiring overlap with the center of a chromHMM block
## block_potential: Number of samples each TE is in each chromHMM state, overlapping chromHMM ... |
7da6f4dfe178f7e2f0960500614317924728bd40 | d4acf50c38fa67affec3cf9feaaa0d0ab3fbe083 | /basic api and NPL copy.R | 5c894968907e02d602b4d0857bd5c256969324b3 | [] | no_license | crimono/group2_project | b0fbab38e0cbacdd1860b868bfbb96908db1ac62 | f7d683cc868aada0944492938981860a9676a4d5 | refs/heads/master | 2020-04-08T12:25:32.707091 | 2018-12-19T19:00:01 | 2018-12-19T19:00:01 | 159,346,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,716 | r | basic api and NPL copy.R | library("rtweet")
library("SentimentAnalysis")
library("plyr")
library("sentimentr")
#state dataset built in r in order to get the center of each state
#compute the radius and build the geocode string for the twitter download
usa <- as.data.frame(state.x77)
for (i in 1:50){
usa$x[i] <- state.center$x[i]
usa$y[i] ... |
28aa2b7714b1f21106c256b856166e86d2aacea5 | cb4abb6553d4697cefdc10b4de027f83fafdceb7 | /2.2.r | e57c2e965df5664bdbec6d0aab13106de4843b2d | [] | no_license | royb3/statistiekMetR | f90b4a0bd00bb7495637106365dcdd1697aa549b | 0df60a4fa661020d58f489b7b92095694d8388e2 | refs/heads/master | 2021-01-10T22:04:17.351092 | 2015-10-29T07:37:11 | 2015-10-29T07:37:11 | 42,578,771 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,127 | r | 2.2.r | #2.2a
nValue2a <- 2*30
kValue2a <- 20+15
answer2a <- factorial(nValue2a) / factorial(nValue2a - kValue2a)
print(paste("2a = ", format(answer2a)))
#2.2b
nValue2b <- 2*30
kValue2b <- 20+15
answer2b <- factorial(nValue2b) / (factorial(kValue2b) * factorial(nValue2b - kValue2b))
print(paste("2b = ", format(answer2b)))
#2... |
f71ee827edf4bdcaa68feec402903ed000766ed9 | 4c38de2cc0b8cb8372a4d54a28ba7b1c4c572f75 | /Code/Twitter_Data.R | f5fad11fb873f474a79dc091b459a23957fd9917 | [] | no_license | kalaamlabs/Streaming-Analytics-using-Hive | 3c8258ff19c8f0c450f6bbdaaa47e6230502cc67 | e8a8d1ec575a29f08bdf820f558a053ef03a4dfc | refs/heads/master | 2021-05-14T16:54:44.637606 | 2016-10-12T17:55:16 | 2016-10-12T17:55:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,331 | r | Twitter_Data.R | setwd("C:\")
#devtools::install_version("httr", version="0.6.0", repos="http://cran.us.r-project.org")
#install.packages("httr")
library(RCurl)
library(ROAuth)
library(streamR) ... |
dd03d69499f485c4553540b968383534c4411fb6 | 2ef7bff5de5b8ba586c21057524eb5823e763197 | /R/predict.dsm.R | 272a453cfde311c8a83f01d937c87da476459891 | [] | no_license | dill/dsm | 3ec6fa21a979d378b70627b3dc686d40abd48b65 | cc8f82b89ae6c6091d0ad1b27094ba0bdd351554 | refs/heads/master | 2021-01-18T12:18:08.019145 | 2014-02-18T16:12:40 | 2014-02-18T16:12:40 | 3,394,162 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,813 | r | predict.dsm.R | #' Predict from a fitted density surface model
#'
#' Make predictions outside (or inside) the covered area.
#'
#' @param object a fitted \code{\link{dsm}} object as produced by \code{dsm()}.
#' @param newdata spatially referenced covariates e.g. altitude, depth,
#' distance to shore, etc. Note covariates in this dataf... |
49363cef69bea0e8eb319789d27c8478c5db1e74 | 3af91945083aa604efc778ea52a17ad60766948b | /matthew_Patient-overlap-jcedited.R | 8fa818f58a891281712586d4c0e59ed9b5483775 | [] | no_license | cjieming/R_codes | fa08dd1f25b22e4d3dec91f4fb4e598827d7492f | 5b2bcf78dc217bc606c22f341e1978b5a1246e0c | refs/heads/master | 2020-04-06T03:53:50.030658 | 2019-06-30T07:31:35 | 2019-06-30T07:31:35 | 56,031,249 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,892 | r | matthew_Patient-overlap-jcedited.R | #################################################################
## this script is adapted from Matthew Kan's script in plotting
## Patient-overlap in AD-studies
setwd("/Users/jiemingchen/Documents/transplantation/a_donor/immport")
library(RImmPort)
library(DBI)
library(sqldf)
library(plyr)
library(RMySQL)
libra... |
883029ff91ede28a0b888e6de497b0c3e7e169c2 | 3edd74c94cfb00593982abd66986897a8b35c350 | /man/djqpd.Rd | f890cc8bbdb2371385b51223b135e428bb3a943a | [
"MIT"
] | permissive | bobbyingram/rjqpd | f83abad29998e71eb4c655a9ead406165ade2cfd | ddcbf480393eb1bf2739cd28638d799f1cd6323c | refs/heads/master | 2022-12-24T14:02:11.375062 | 2020-09-28T20:17:13 | 2020-09-28T20:17:13 | 289,718,873 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 636 | rd | djqpd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/djqpd.R
\name{djqpd}
\alias{djqpd}
\title{Density function of Johnson Quantile-Parameterised Distribution.}
\usage{
djqpd(x, params)
}
\arguments{
\item{x}{vector of quantiles}
\item{params}{jqpd object created using \code{jqpd()}}
}
\value{... |
3df852dba069971ba2510eb11879f18083e09b34 | 520246ced10aa690003c1c4658480b43bfdd38cf | /server.R | 12886bdfcda480d6f64286ecdb0c096c8d2ad1a0 | [] | no_license | DNAReplicationLab/plotGenome | aa95873aff602009d542b055edac3c55d6a27fe7 | f521dcbc4691bb918b2e8166168745545fccbb02 | refs/heads/master | 2020-03-28T10:50:07.258601 | 2018-11-08T15:22:21 | 2018-11-08T15:22:21 | 148,150,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 71,162 | r | server.R | library(shiny)
library(ggplot2)
library(colourpicker)
source("plotGenomeFunctions.R")
load("www/plotGenome.RData")
options(shiny.maxRequestSize=30*1024^2,shiny.trace=F,shiny.fullstacktrace=F,shiny.testmode=F)
function(input,output,session) {
## initialise reactive values here
names <- reactiveValues(
bed = NULL... |
120aa7acca0c11b6b6d04d716244fd24208e9af6 | b1a059096bb0205bf316ad14125ea8f7ebda51d8 | /lib/feature2.R | c4937c6c9f003aebd258aefac2ea8c8a1e407b25 | [] | no_license | TZstatsADS/Fall2018-Proj3-Sec2-grp5 | bf251ac90b49d21a40a5b8ee9d42a1140e3f6b87 | 65cf18bf6a27307e9fe09864e203e13c32877812 | refs/heads/master | 2020-03-31T23:10:38.749169 | 2018-11-23T08:05:06 | 2018-11-23T08:05:06 | 152,645,139 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 8,898 | r | feature2.R | #############################################################
### Construct features and responses for training images###
#############################################################
### Authors: Chengliang Tang/Tian Zheng
### Project 3
# feature with 9*9
# helper function to get the value for the neighb... |
6243217e27c268f274662183feb1ffe2a687ff70 | 1dd7892ae8546fdee168ca69fc2ddba36c9028e6 | /plot2.R | 4fba428fb8289f488227e163a0dc9a9d161b3ecb | [] | no_license | csllam/ExData_Plotting1 | 378a8a1a17999283c0c3a041be46ab24e0fec86c | 84489aa7945576e64e27ca326ceadb204bacdad9 | refs/heads/master | 2021-01-17T06:16:59.054905 | 2015-02-08T06:34:38 | 2015-02-08T06:34:38 | 30,482,589 | 0 | 0 | null | 2015-02-08T06:00:52 | 2015-02-08T06:00:52 | null | UTF-8 | R | false | false | 612 | r | plot2.R | ##plot2.R
Dat<-read.csv("exdata_data_household_power_consumption/household_power_consumption.txt", sep = ";")
DAT<- subset(Dat, subset = Dat$Date == "1/2/2007" | Dat$Date == "2/2/2007")
DAT$Global_active_power <- as.numeric(as.character(DAT$Global_active_power))
DAT$Date<- as.character(DAT$Date)
DAT$Time<- as.character... |
9ef52befdbad092adcbbe1087b9a936f6521be4b | 85a0e45a8d85ab80d6bcf48560dec3c2e502a4d7 | /nightLightsExample.R | cad00f0712e0a3fcbce93f47cce44561fc00f571 | [] | no_license | garrett-w-powers/rgeeTraining | 1fcb335f07b0466338e9abffe7ce9fc908446f5b | f60376bc658b3718a48cff1c5e3f6f8d8fca23c5 | refs/heads/main | 2023-04-25T07:19:22.946641 | 2021-05-19T14:55:12 | 2021-05-19T14:55:12 | 368,894,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 928 | r | nightLightsExample.R | library(rgee)
ee_Initialize()
#add a band containing image date as years since 1991
createTimeBand <- function(img) {
year <- ee$Date(img$get('system:time_start'))$get('year')$subtract(1991L)
ee$Image(year)$byte()$addBands(img)
}
#map the time band creation helper over the night lights collection
collection <- e... |
f6f3aef35688ab53457318992e0e77b296566940 | 3de36a93bafc5f58aaaeb316d2d7bf7c774e2464 | /R/rgl.isomap.R | dda53bc2e127d6e2b7deeedc9e68fc151494037e | [] | no_license | vanderleidebastiani/vegan | fc94bdc355c0520c383942bdbfb8fd34bd7b4438 | dd2c622d0d8c7c6533cfd60c1207a819d688fd1f | refs/heads/master | 2021-01-14T08:27:15.372938 | 2013-12-17T18:19:10 | 2013-12-17T18:19:10 | 15,258,339 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 266 | r | rgl.isomap.R | `rgl.isomap` <-
function(x, web = "white", ...)
{
require(rgl) || stop("requires package 'rgl'")
ordirgl(x, ...)
z <- scores(x, ...)
net <- x$net
for (i in 1:nrow(net))
rgl.lines(z[net[i,],1], z[net[i,],2], z[net[i,],3], color=web)
}
|
8cf724c795e58eab20878c6e2d645c97582ca2b4 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/A1/Database/Letombe/renHorn/renHorn_400CNF1280_2aQBF_62/renHorn_400CNF1280_2aQBF_62.R | 02f17c6035b78733c4cd9526b73b2e08c2907089 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 77 | r | renHorn_400CNF1280_2aQBF_62.R | 369759c0130d583c2cc5984e4fe756b1 renHorn_400CNF1280_2aQBF_62.qdimacs 400 1280 |
232ce5c1707cefaf8aec68f469d6b5fca783a7f7 | e2c647ffbf27b64d2e20e0f6266e65f06582f651 | /run_analysis.R | eb4a8c6114cb0df4f109a88b2365f8e7b5aaf66e | [] | no_license | i-digital/CleaningData_Assgn | 3b591a329084f348819971fe8894fa4df6db94c8 | 1dfdc552862142c6168dc9cc39f19ff0cabe276c | refs/heads/master | 2016-08-10T22:34:05.047901 | 2016-02-13T10:30:52 | 2016-02-13T10:30:52 | 51,641,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,165 | r | run_analysis.R | #setwd("/Projects/GIT/MOOC/Data Science/assignments/CleaningData_Assgn")
## Activity Labels
activityDF <- read.table("UCI HAR Dataset/activity_labels.txt", header = FALSE, stringsAsFactors = FALSE)
## Features
features <- read.table("UCI HAR Dataset/features.txt", header = FALSE, stringsAsFactor = FALSE)
## Read Tes... |
5f3d798eb99ebcd1f50b366f3a1727a577639a51 | 822c73ded025bf2d03903809aa240b7fe78799aa | /R/functions_for_testing.R | 72bc017b7f5fdd8acd0852ac878c7109e573107c | [] | no_license | Nekojou/masters-thesis | 0bedf25b5685763921aa82420602af8c4c9f332f | ba2907e34368f047bd55009b2f1ae108e14d2bba | refs/heads/master | 2021-09-10T07:58:56.128194 | 2018-03-22T13:24:58 | 2018-03-22T13:24:58 | 112,517,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 649 | r | functions_for_testing.R | # generate some test samples
# according to simulation study 1 (weibull distributions)
# but without varying the parameter alpha2
test.generateTestSamples <- function(numberOfCases=2,montecarloRepetitions=10)
{
samples<-matrix(data.frame(), nrow=numberOfCases, ncol=montecarloRepetitions)
for(casesIterator in 1:numb... |
ff2a85a5b29d82e8cadb2e513e725bb78d990b31 | ded62e5f272ada0a34f658d4b6ba25dcd3f08a2c | /Code/PredictCM_RAMspp.R | b0e8faac9103a661d78ddc8d7255f31c34715275 | [] | no_license | bselden/RAM_FishClim | 39a1819d5f274badf3d8555195dd3f8d7ab2c258 | 5b17a9ad81fae13bdef0cc9ce38325152e2ad613 | refs/heads/master | 2020-05-02T07:50:49.087082 | 2019-06-24T20:24:07 | 2019-06-24T20:24:07 | 177,828,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,809 | r | PredictCM_RAMspp.R |
library(data.table)
library(Hmisc)
load("Data/hauls_catch_Dec2017.RData", verbose=T)
ram.stocks <- readRDS("Data/ram.stock.us.rds")
setorder(ram.stocks, scientificname)
### Species catch by haul
catch.dt <- as.data.table(dat)
haul.dt <- as.data.table(hauls)
### Classify hauls into RAM regions
haul.dt[,"subarea":=i... |
f6fbac84de7523408b7c97d877757ddd7434d1ec | 6228e9c9be718a2d59665a455cdd42f212b0b732 | /R/rem_mv.R | 50ed3a92ae2905132e177885d4a14aa31d656bb4 | [] | no_license | csbl-usp/MetaVolcanoR | ef6080be107274fb5a26caf0c845c0eda7bd5c98 | c0f64a47d566b294bfffdc0b72fb1be789e5cf8c | refs/heads/master | 2023-08-18T08:07:29.869316 | 2019-11-04T09:38:37 | 2019-11-04T09:38:37 | 150,426,737 | 18 | 1 | null | 2023-08-10T08:19:09 | 2018-09-26T12:56:31 | R | UTF-8 | R | false | false | 7,894 | r | rem_mv.R | #' @importFrom parallel mclapply
#' @importFrom topconfects normal_confects
#' @importFrom methods new 'slot<-' show
#' @importFrom plotly as_widget ggplotly
#' @importFrom htmlwidgets saveWidget
#' @import dplyr
setOldClass('gg')
setOldClass('ggplot')
#' An S4 class to represent MetaVolcanoR results
#'
#' @slot in... |
7779a9aa3d30c66664ee79124649214fb0b0e277 | 57079c65f2ca12dcc58ed9cbe54e32b093345627 | /limma.R | 41c0794d119fe9f6b147be44efbc2c7bc88fcb31 | [] | no_license | h3aknust/Assessing-differential-expression-analyses-tools | 97eeeb507a84c63c731cd2426769353c152e49e2 | 4ac65b7ba270ac492dbd2f0fd8e37d2c65a8420e | refs/heads/master | 2021-02-09T23:39:13.835516 | 2020-03-02T09:55:44 | 2020-03-02T09:55:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,611 | r | limma.R | ##
#This code computes for differentially expressed genes using limma
##
#Import package limma
library("limma")
library("edgeR")
#First create a DGEList object using the edgeR package:
dge <- DGEList(counts=df)
#Create a design matrix
design <- cbind("1"=1,"1vs2"=rep(c(1,2), each = nrow(metadatah)/2))
#apply scale n... |
3f6233a3b04c067cd58487ab4acca0555cc2996c | d38fe23893d143f54550ea2f851ae5fec759e3de | /utils.R | 61d9a8cf8193651295a21cb111445c8bfb4a9be5 | [] | no_license | danioyuan/r_utils | 025f9753faeb32343ae0bd3e5ef4cf7ae7ba0022 | 913b9df777d7f3fda1638389a4fc52b5d11b29a8 | refs/heads/master | 2020-07-28T15:17:21.907006 | 2016-11-10T18:16:37 | 2016-11-10T18:16:37 | 73,408,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 788 | r | utils.R | currency2Num <- function(x, na.string = "N/A") {
# Extract value and currency symbol from a currency string.
# Eg. "-CND$0.08", "$-1.89B"
if (is.na(x) || x == na.string) { return(rep(NA, 2)) }
m <- regexpr("^([+-]*)([^\\d+-]+)([\\d.+-]+)(\\D*)", x, perl=T)
if (m < 0) { return(rep(NA, 2)) }
ss <- attr(... |
f885aa95ff4ce1de12263549c8600244e6eb28de | eb27c112efa6f461c722551526e80b162c7b54ea | /R/sp_lit_parse_names.R | 5f8c6c2449148cd9bcb59ddd5cf2d3b159bb8415 | [
"MIT"
] | permissive | sckott/spplit | 493df117c40e681381f4e37a2f36ecba6225be5e | 9a663449bbc751b069876847d325e35c2808adda | refs/heads/master | 2023-05-23T18:17:37.596992 | 2020-09-24T01:40:14 | 2020-09-24T01:40:14 | 48,463,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,235 | r | sp_lit_parse_names.R | #' Parse scientific names
#'
#' Depends on non-CRAN package rgnparser
#'
#' @export
#' @param x An object of class `sp_lit_text` or `sp_lit_text_one`
#' @param progress (logical) print a progress bar. default: `TRUE`
#' @param ... arguments passed on to `rgnparser::gn_parse_tidy()`
#' @details make sure to install gnp... |
76c2b2e9b3f764c2c30231eb4d7082532172869e | dcfd7d7140ff5f4d52a90c148262892a4b041b79 | /man/drcfit.Rd | f6ef824f86d482137afc26946a6775ca1ee9e24e | [] | no_license | DrRoad/DRomics | 364fa7e6a0ed3419a106e7ec3fca27b9beee80ab | 94b9d5a0b975698044a12588058e0a910de45d29 | refs/heads/master | 2020-07-04T08:58:14.593775 | 2019-07-26T12:18:59 | 2019-07-26T12:18:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,817 | rd | drcfit.Rd | \name{drcfit}
\alias{drcfit}
\alias{print.drcfit}
\alias{plot.drcfit}
\title{Dose response modelling for responsive items}
\description{
Fits dose reponse models to responsive items.
}
\usage{
drcfit(itemselect, sigmoid.model = c("Hill", "log-probit"),
progressbar = TRUE, saveplot2pdf = TRUE,
... |
33ff0587622e9a89506501b3f4eaef8784d9565a | 6058ae780cde6ec6117a3fc86a4c1c26b578650e | /R/seq_clatworthy_williams.R | 265d8c173fc237236a1329250642faa69e6653e4 | [] | no_license | mjg211/xover | 88243eb2f99a76502057f67924725fda1796dd39 | 9e8bb48dcc735d78aa9a8d14f236a8ba13609866 | refs/heads/master | 2020-03-26T14:55:03.702584 | 2019-10-15T10:40:29 | 2019-10-15T10:40:29 | 145,011,976 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 462,981 | r | seq_clatworthy_williams.R | #' Clatworthy-Williams cross-over design specification
#'
#' Specifies cross-over designs based on combining Clatworthy (1973) designs
#' with Williams (1949) designs.
#'
#' \code{seq_clatworthy_williams()} supports the specification of designs based
#' on combining designs from Clatworthy (1973) with designs from Will... |
7bf416a6179abd388fb53100d6e99930455a448c | ee9287f407efab94c3a598916d4e92777eb55143 | /R/documentation.R | ac0dbe0056db81d1e15b287df51eca5ee99a3d1d | [] | no_license | jarretrt/tci | fc8dae33f9aa73f505cf96cb513ef2665015e889 | 23fb6a721a277709eb351ce5f7e60daed9f9fafb | refs/heads/master | 2023-01-24T22:55:32.769055 | 2023-01-18T01:33:04 | 2023-01-18T01:33:04 | 217,107,508 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 809 | r | documentation.R | #' tci package documentation
#' @title tci_documentation
#' @name tci_documentation
#' @description Functions to implement target-controlled infusion algorithms
#' @details This package contains functions to implement target-controlled
#' infusion (TCI) algorithms for compartmental PK models under intravenous administr... |
20e83fc12719052179fc1e488714d8408b4cd34e | 06196479af789edf48792949519336743ee442b8 | /R/util_style.R | 7ab2dabcb10905039ed43224e62d4f11cb54c532 | [
"MIT"
] | permissive | jeksterslabds/jeksterslabRutils | c35a33eda24ad28cd9722fcf77c5338a89a44d55 | 4410e7d512b41aa678dcfaf9611d3be3b683a33d | refs/heads/master | 2021-08-10T07:01:43.903835 | 2021-01-15T06:12:54 | 2021-01-15T06:12:54 | 241,089,145 | 0 | 1 | NOASSERTION | 2020-02-17T11:41:04 | 2020-02-17T11:17:31 | null | UTF-8 | R | false | false | 2,522 | r | util_style.R | #' Style `R` and `R` Markdown Files
#'
#' Styles all `R` scripts and `R` Markdown files
#' in a given directory.
#'
#' @author Ivan Jacob Agaloos Pesigan
#' @inheritParams util_lapply
#' @param dir Character string.
#' Directory.
#' @param recursive Logical.
#' If `TRUE`,
#' recursively style all `R` scripts (`.R`, `.r... |
9e7c2478c6a1fe0a0d99505a05ae9509066788f4 | 77ef73c072c75fc92d313d404fa1b6df50a53e40 | /R/cyto_map.R | 5abfead6a34d37deaf493c0bcefd16ed5cafcaa6 | [] | no_license | DillonHammill/CytoExploreR | 8eccabf1d761c29790c1d5c1921e1bd7089d9e09 | 0efb1cc19fc701ae03905cf1b8484c1dfeb387df | refs/heads/master | 2023-08-17T06:31:48.958379 | 2023-02-28T09:31:08 | 2023-02-28T09:31:08 | 214,059,913 | 60 | 17 | null | 2020-08-12T11:41:37 | 2019-10-10T01:35:16 | R | UTF-8 | R | false | false | 21,441 | r | cyto_map.R | ## CYTO_MAP --------------------------------------------------------------------
#' Create dimension-reduced maps of cytometry data
#'
#' \code{cyto_map} is a convenient wrapper to produce dimension-reduced maps of
#' cytometry data using PCA, tSNE, FIt-SNE, UMAP and EmbedSOM. These
#' dimensionality reduction functio... |
7e789984c852b955c3391250199d04180c3f6cc0 | 8d4902d586f2a7f3f2b57d6a2ce0e0afefa8bc22 | /R Code - Plots/plot2.R | b97cd4341f90cbfdde4e4dc64d1a1fcec7ca8c8b | [] | no_license | mfaryna/ExData_Plotting1 | 63bbf5e502ea03cae527a411dfcfedec243961bd | 4289bd4fa086258d5fc9b6cf3886b4a8b3be773f | refs/heads/master | 2020-12-27T01:45:05.732913 | 2014-12-06T11:37:02 | 2014-12-06T11:37:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,400 | r | plot2.R | ## ========== DOWNLOADING AND UNZIPPING DATA ========== ##
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url, destfile = "data.zip")
Sys.setlocale("LC_TIME", "English") ## changing time locale (windows) to present ab. of days in English
dane <- unzip("dat... |
b601053f6279f9c56df7457a494c95e5e27146c2 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googleiamv1.auto/man/AuditData.Rd | b5f95aaffa33490368f323914a8529c0ffb70181 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 574 | rd | AuditData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iam_objects.R
\name{AuditData}
\alias{AuditData}
\title{AuditData Object}
\usage{
AuditData(policyDelta = NULL)
}
\arguments{
\item{policyDelta}{Policy delta between the original policy and the newly set policy}
}
\value{
AuditData object
}
\... |
4a6c8eb20bc353897916b309014d78979cb72b9a | dd34df468ab31496c86449db8861d49a8fe5ae39 | /cachematrix.R | cbffb0feabc79b0940ceb87f3303e0b04f0808bf | [] | no_license | HerbN/ProgrammingAssignment2 | 2c880feaa1b0221c3072e0da975738132ea7aa44 | bf59f8adf7b1e0466275df944c9e13bd72beae95 | refs/heads/master | 2021-01-18T15:32:30.241501 | 2015-07-26T15:41:10 | 2015-07-26T15:41:10 | 39,729,109 | 0 | 0 | null | 2015-07-26T14:51:57 | 2015-07-26T14:51:56 | null | UTF-8 | R | false | false | 1,966 | r | cachematrix.R | ## This is a pair of functions to create a container object for large matrices
## and allow certain values for them, initially just the inverse, to be precomputed
## allowing higher programming speed by avoiding repeated calculation
## makeCacheMatrix creates a list container with:
## 1. A matrix
## 2. Four functions:... |
9a5e039ccbb4e83f4498ffaa058e5d9344d031c3 | 529a7db69b0643d9d7fe3c91cb48428789b482c8 | /man/download.dbcan.Rd | 55d62dace91a776bd9ea83e96cf346f41d328dca | [
"MIT"
] | permissive | ukaraoz/microtrait | 0b039191a178a9e674cd4c64b00a25ba88e99c42 | ad2b5aacc775336d9f45d47e4fa4a75a2ab49073 | refs/heads/master | 2023-04-27T03:51:19.650028 | 2023-04-25T16:04:58 | 2023-04-25T16:04:58 | 283,821,755 | 20 | 2 | null | null | null | null | UTF-8 | R | false | true | 349 | rd | download.dbcan.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prep_hmmpackage.R
\name{download.dbcan}
\alias{download.dbcan}
\title{Prepare dbcan database (download and subselect)}
\usage{
download.dbcan(dbcan_version = 8, dbcanhmmdb_selectids_file, dbcanhmmdb_file)
}
\description{
Prepare dbcan databas... |
6971a65636ae6af92524c848d1b99bde95e749d7 | 84343f1887e8c93a7d3de4d5e791b08c1ee9d37c | /server/vacancy/vacancyServer.R | 96a1d50c75db28beddd3c71abc53c7c6381e8fb2 | [] | no_license | chambox/bullhornShinyDash | cf162d20905dc2f2e628067514603605ab028fd7 | dff94c50941cb9d05b96334e28f34d43b78b5777 | refs/heads/master | 2020-04-27T17:44:34.229939 | 2019-03-08T12:44:03 | 2019-03-08T12:44:03 | 174,535,116 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,235 | r | vacancyServer.R |
# filter recruiter
output$vacancy_clientCopFilter <- renderUI({
clientCop <-
vacancy_rdata$clientCorporation[!vacancy_rdata$customerDpt == "NULL"]
clientCop <- as.list(unique(clientCop))
selectInput(
"vacancy_clientCopFilter",
"Select client coperation",
choices = clientCop,
selected = "BNP ... |
dc8e1f4122aa420ad21a88e19f63542caffba96e | b0a915648ff80798c5aba7883826c9c4d7aa71eb | /man/cytoHeatmaps.Rd | 20bfa40a08ef210d8a98e70c533baedd9c4f7919 | [] | no_license | KoenAStam/cytofast | 69a13ad655dd71dd2c430f163b0a12fc73bdfede | 98d2625aded79f4e2459cf59b2724f0ba44e8730 | refs/heads/master | 2022-06-10T02:29:57.346697 | 2022-05-23T13:42:51 | 2022-05-23T13:42:51 | 151,564,984 | 3 | 3 | null | 2020-03-17T10:15:03 | 2018-10-04T12:03:01 | R | UTF-8 | R | false | true | 1,508 | rd | cytoHeatmaps.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cytoHeatmaps.R
\name{cytoHeatmaps}
\alias{cytoHeatmaps}
\title{Draw heatmaps for cfList}
\usage{
cytoHeatmaps(cfList, group, legend = FALSE)
}
\arguments{
\item{cfList}{a cfList object.}
\item{group}{one of:
\itemize{
\item a character vecto... |
0f1457cfc7aba767e4a0dd92065f21d782be1e50 | 915c84dc61471ff1518fa2aec586a606119fcf1e | /Analysis.R | f084ee7f1e1b0504cd2028bebfee0e7ab576b5de | [] | no_license | chriswardchrisward/Data | d8411fbf7b5a404efdd000091ecf37f8b16229d4 | 39a18fc5c0395799eb431386a81856fda4dd49c4 | refs/heads/master | 2020-08-10T18:06:58.086072 | 2015-05-17T23:12:35 | 2015-05-17T23:12:35 | 35,786,520 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,589 | r | Analysis.R | ###### Set Up ######
# 1.
setwd("~/Training/Data")
dat <- read.table("Activity.csv", header = T, sep = ",", as.is= TRUE)
# 2.
dat$interval <- as.factor(dat$interval)
dat$date <- as.Date(dat$date, format="%m/%d/%Y")
dat$day <- weekdays(dat$date) #Adds day of week
dat$weekday <- ifelse(weekdays(dat$date)=="Saturday" | we... |
033404b3a31f38c27bc6e9c5c6da2ebb399821e7 | 66a8e5c4ccc3cccf48ed696fce117c3a6df4a0f8 | /R/stepcAIC.R | 652288c5cd4fed61e2b177a4326a9396e3771e80 | [] | no_license | davidruegamer/cAIC4dev | 3ce3a312cdb9f2793bc558bd78f9feccc0fa89f3 | 56ccc8b2f1287d10e28ad1e5c08a1d2d773df0ee | refs/heads/master | 2023-08-16T19:52:47.613189 | 2023-08-10T17:08:16 | 2023-08-10T17:08:16 | 47,450,068 | 3 | 5 | null | 2019-06-23T08:55:09 | 2015-12-05T09:27:56 | R | UTF-8 | R | false | false | 24,994 | r | stepcAIC.R | #' Function to stepwise select the (generalized) linear mixed model
#' fitted via (g)lmer() or (generalized) additive (mixed) model
#' fitted via gamm4() with the smallest cAIC.
#'
#'
#' The step function searches the space of possible models in a greedy manner,
#' where the direction of the search is specified by th... |
690106c4f1cafe9341312cc5087d812263b80cfe | e4f02038bf4a8b1e6f5a017cbc547bf33be9fd3d | /CRISPR/pptm_functions.R | 6e7e35d3e79b970d0ecf354e35907e0d2807301c | [] | no_license | rahulk87/GeneFunctionCodes | 00e29a3e027566ec12fcbf9235dfca371bba4fff | 777140c7c41f14908cdda794fd137f4cf7ebe3be | refs/heads/master | 2021-01-12T02:47:26.826820 | 2019-10-16T02:48:23 | 2019-10-16T02:48:23 | 78,104,858 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | pptm_functions.R |
get_pptm <- function(x){
x_total_count_sum <- sum(
x$T0
)
T0_pptm <- x$T0 / (x_total_count_sum / 10^7)
x_total_count_sum <- sum(
x$Drug_1
)
Drug_1_pptm <- x$Drug_1 / (x_total_count_sum / 10^7)
x_total_count_sum <- sum(
x$Drug_2
)
... |
d98fcd30658bfee10ab859ddbb9a6ba1e0515766 | b702b7798cdb9182331b81f0cdebc4f6cdd8e2c7 | /run_analysis.R | 8e6e1251d612e8efae7e770ce82081a6b62e6f98 | [] | no_license | jocmom/GettingCleaningData | 003ecd197ff32ebeb2c108e027d7a3c40dbb2e4c | 65e9aab198013edfe8d2e23132e0488f54e93b6a | refs/heads/master | 2021-05-27T13:41:06.970373 | 2014-10-27T09:43:43 | 2014-10-27T09:43:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,309 | r | run_analysis.R | # Getting and cleaning data course project
run_analysis <- function() {
# use "dplyr" library for fast joins
library(dplyr)
library(tidyr)
############################################################################
# get 561 features
features <-read.table("./Dataset/features.txt",
... |
be530e38404293a4aeabaddb6041e97bffcea1b4 | 4ab245990f25f69185636ba2180875293ba5a249 | /grolu.R | 7093aa268a67e93025e27cd95f2543b9524f4b5b | [] | no_license | RomanKyrychenko/bubbles | dcbb8b681497b778dbc2c11d3638910772ba6498 | bc9223fd609bd496608aa42cd0f8c112fcecd778 | refs/heads/master | 2021-01-20T00:28:48.698769 | 2017-08-02T20:59:59 | 2017-08-02T20:59:59 | 89,138,262 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,511 | r | grolu.R | Sys.setlocale(,"UK_ua")
library(shiny)
library(ggplot2)
library(readxl)
library(readr)
library(png)
library(grid)
library(extrafont)
library(stringi)
unzip(".fonts.zip",exdir = "~/",overwrite = T)
system('fc-cache -f ~/.fonts')
tele <- rasterGrob(readPNG("1.png"), interpolate=TRUE)
net <- rasterGrob(readPNG("2.png"),... |
3d7273369b82d5005bb61d5a8ecde2fac719fe81 | c677505fded0544d5b12900f4f2be751251ef5af | /man/plotRDA.Rd | b4eb98ee3941ac1da68ed24163e18f6b3d32567b | [
"MIT"
] | permissive | isglobal-brge/MEAL | 03659b5f1b98120fd408f17cbd7553c6f414219f | ee9eebb76cd67b56d5be65842832274ff45e8e29 | refs/heads/master | 2021-05-15T00:34:10.007907 | 2021-05-05T15:50:14 | 2021-05-05T15:50:14 | 103,267,394 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 875 | rd | plotRDA.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ResultSet_plotRDA.R
\name{plotRDA}
\alias{plotRDA}
\title{Plot RDA results}
\usage{
plotRDA(object, pheno = data.frame(), n_feat = 5, main = "RDA plot", alpha = 1)
}
\arguments{
\item{object}{\code{ResultSet}}
\item{pheno}{data.frame with th... |
d08387736b554e77d20afe257a2e0edc38a4cce8 | 828e41f70d8f4d2c86e232d62e515e85df099204 | /graphs/work_sample.R | 7861dcdb24b9a48094e09ec7068e9c83dbfd89af | [] | no_license | jgsogo/muia_tfm | 6f759006521ac7065b8af51303ac8333f9c8f92c | 8a15b239ca6ef58d515fb0b4a51f7eae41230358 | refs/heads/master | 2021-01-23T07:10:19.398193 | 2015-08-17T08:06:31 | 2015-08-17T08:06:31 | 40,876,491 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,304 | r | work_sample.R | # Work over results
library(data.table)
require(ggplot2)
library(reshape)
source("summarySE.R")
parse_sample <- function(file) {
data <- data.frame(read.csv(file=file, header=TRUE, sep="\t"))
}
plot_synset_tol <- function(data, title, save_path) {
# Credit: http://www.cookbook-r.com/Graphs/Plotting_means_an... |
1444b6f5df3421d6ade8aa6658e4da13e3dddd2e | 189176e43cd9fe06ab4ab8ff698565db8427f0c7 | /analysis/source/draw_data/draw_data.R | 657dc48ae3e5af5adf841ac39018525f51a13cf1 | [
"MIT"
] | permissive | memonb1226/Practice-Task | 3c9743ba60edaed5428356af521040fd5ecfcecf | f3ab358f0e072cffd0c9e90851b14ccba7b5dba3 | refs/heads/master | 2022-12-27T01:42:08.184599 | 2020-10-07T03:08:47 | 2020-10-07T03:08:47 | 298,068,106 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 343 | r | draw_data.R | library(yaml)
library(MASS)
CONFIG <- yaml.load_file("config_global.yaml")
main <- function() {
simga <- matrix(c(1,.5,.5,1),2,2)
mean <- rep(0, 2)
draw <-mvrnorm(10000, mean, simga)
write.table(draw, sprintf("%s/data.csv", CONFIG$build$draw_data), sep=",",
row.names = FALSE, col.names = FALSE, ... |
aacec07dfafdae72294e4e392b6866b7ee2ad148 | 6467756ff4bc6f6a28b5f2c63421b2d36c163815 | /Ass 1 Recycling Vectors.R | ba3547167ed01198b4a2359513c8f056627df35d | [] | no_license | monicamajora/R-Assignment-1 | a37305a21c326a6919e0087ad1de4a6638b615f9 | 894c79bdc85a79340add15712aa86700cb88d157 | refs/heads/master | 2020-04-13T04:57:13.301212 | 2018-12-24T10:00:41 | 2018-12-24T10:00:41 | 162,976,780 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 543 | r | Ass 1 Recycling Vectors.R | getwd()
#1...Prescriptive Analytics used to predict the future outcomes? FALSE
#....Base R packages installed automatically?TRUE
#2... R operates on vectors of the same length, so if it sees two vectors of different lengths in a binary operation, it replicates (recycles) the smaller vector until it is the same leng... |
f0138edd7aa8327f90c1ce581b1a685cc6bd6835 | 735af472776c3e90a4d337df478a3f9b6daf5c73 | /cleanScript.R | 99b18ae997454904c1d9a3ce5859551e95143d41 | [] | no_license | acottman1/dataFestTrainingCensus | cbbac9f4a6fb8735378540970d0e28d51aa2296b | aeb0b24e5e8ad4d616ae0f9eef468f85d502a99d | refs/heads/master | 2021-01-03T09:25:42.630838 | 2020-05-28T02:28:15 | 2020-05-28T02:28:15 | 240,020,122 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 222 | r | cleanScript.R | #remove special characters, commas from character data and save it to a DF
clean <- function(x){
as.numeric( gsub('[^a-zA-Z0-9.]', '', x))
}
dat$avg_Agg_HH_INC_ACS_13_17 <- clean(dat$avg_Agg_HH_INC_ACS_13_17)
str(dat)
|
92f9099e249750c521de9e4884cf377cd075311b | f54ee006bee856ea6d10eeb71e64f928cac526a2 | /Modelo Revenue 2/functions/logistic/build_prod_tables_log.R | c6dd0e0e28a8f03a0173732aa2012cfcf765cdaa | [] | no_license | lover2668/oil | a6f89b8ebf46d312c0cc853c59418abbd0c8ef19 | a62b4e07d3aaa1fab5e0064c74ef224ad5ecf3be | refs/heads/master | 2020-04-26T03:08:48.746437 | 2017-03-13T20:31:14 | 2017-03-13T20:31:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,783 | r | build_prod_tables_log.R | build_prod_table_log <- function(transport) {
file_name <- paste('data/log_metrics/Escenarios_',transport,'.xlsx',sep='')
#read all data and returns
temp_table <- read.xlsx (file_name,sheet = 1)
temp_table <- temp_table[temp_table$prioridad==1,]
out_table <- data.frame(num_cuad=temp_table$numVehicl... |
1044d513e8bcfba9163107a15f9831e329b33824 | 257ffc3438528729b62bc3e7abc24eea2be6193e | /man/shud.filein.Rd | 90a5fb69794533986559d177ce1777f9948130f6 | [
"MIT"
] | permissive | SHUD-System/rSHUD | 91e1ae7f077cf5efa52575a32ed4e692ed8034b9 | 1915a9cf2b241a1368b9768251b2f140454bd94e | refs/heads/master | 2023-07-06T11:07:18.335307 | 2023-07-01T15:08:11 | 2023-07-01T15:08:11 | 224,737,854 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 746 | rd | shud.filein.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Project.R
\name{shud.filein}
\alias{shud.filein}
\title{Prepare Input file of SHUD model
\code{shud.filein}}
\usage{
shud.filein(
projname = get("PRJNAME", envir = .shud),
inpath = get("inpath", envir = .shud),
outpath = get("outpath", ... |
3720402f86ee69b5b186414019e14f6abd04a829 | 9f9011d2f7fa105a392a56bae77e89a807a97abb | /mse_error_rho.R | 0b5b7f6feb26aa99452ae898145b768d4410fc1d | [
"MIT"
] | permissive | CTsicarius/ESN_R | 4d40fc60e04287001fc6f90e0aaa8d408ad87125 | 3cd4b7f72666e6c81f8fe0e2a6786d69198a676c | refs/heads/master | 2020-03-30T23:14:01.676874 | 2018-10-24T06:53:47 | 2018-10-24T06:53:47 | 151,694,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,862 | r | mse_error_rho.R | library(MASS)
library(Rcpp)
library(RcppArmadillo)
wd <- getwd()
source(paste(wd, 'var_utils.R', sep = '/'))
source(paste(wd, 'esn_utils.R', sep = '/'))
#SIM PARAMETERS
N_sim <- 1
print_det = FALSE
#DATA PARAMETERS
seed = 42
N <- 1
Ny <- 1
Nu <- 1
PHI_ro = 0.2
N_test <- 10000
sigma <- 0.1
#ESN PARAMETERS
Nx <- 5000
w_r... |
677ebe6dc9e8e2ac02bb78b81c3bb95a3d3fec76 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Statistics_For_Business_And_Economics_by_Anderson_Sweeney_And_Williams/CH10/EX10.2a/Ex10_2a.R | 61ed0ecc03607d6fcdc88e46a74c296994cd1532 | [] | 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 | 832 | r | Ex10_2a.R | # Page no. : 415 - 417
# Inference about the Difference between the two Population Means Sigma 1 and Sigma 2 Unknown
s1 <- 150
s2 <- 125
n1 <- 28
n2 <- 22
xbar1 <- 1025
xbar2 <- 910
point_estimate <- xbar1 - xbar2
numerator <- ((((s1)**2 /n1) + ((... |
c8a7c1058789d490d0ff687f343765745d8fc7b3 | 9acda93c7ff9fd5510edae289a4be49d1f8b2503 | /R/DispConPwrAll.R | df402a2ba8d4f28268dfa13ff82606d101d418a4 | [] | no_license | cran/CP | 143eff1ddd628372b7ddb22db8c77a0d2ddf436d | bccf2da45a9ac30b5c02045d7c9fba69eda3bd13 | refs/heads/master | 2023-05-26T21:51:35.320827 | 2023-05-19T13:20:05 | 2023-05-19T13:20:05 | 25,158,240 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,495 | r | DispConPwrAll.R | DispConPwrAll <- function(gamma.theta.0.exp,
gamma.theta.0.nonmix.exp,
gamma.theta.0.nonmix.wei,
gamma.theta.0.nonmix.gamma,
group1.name, group2.name) {
## Prints the calculated conditional power.
##
#... |
fd5e23054fca3ab2b232a8811275c64defc142b8 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/libFuzzer_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1612735762-test.R | c3110e3762638ddd1119ac022b3de72af8878ff9 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 532 | r | 1612735762-test.R | testlist <- list(doy = c(-2.14820462865696e+139, 1.19979472795022e-309, -2.08399199030036e+139, -2.14820462865696e+139, -2.14820462865696e+139, -2.14820462865696e+139, 2.77447923393688e+180, 2.77448001762435e+180, 2.75117951516237e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.77448001... |
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