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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
7b77fb1c62a8a71752bf3a461f8446cdbeb65e60 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/stylo/examples/zeta.chisquare.Rd.R | 4a7443a29e948eb666939df7cebc4987274840a7 | [] | 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 | 245 | r | zeta.chisquare.Rd.R | library(stylo)
### Name: zeta.chisquare
### Title: Compare two subcorpora using a home-brew variant of Craig's Zeta
### Aliases: zeta.chisquare
### ** Examples
## Not run:
##D zeta.chisquare(input.data, filter.threshold)
## End(Not run)
|
d3044e6dff6d08c19d2842394f5cc779da15098b | 5b04bc3cbd6f8f59c9b9e7f0963fcf94b10ab95f | /man/yeastInterProDesc.Rd | 4a120b0595ffd730cd5ba73016f2426d4f5c74a6 | [] | no_license | Distue/termEnrichment | c895c83a4d5f49aa1a45adbb7f46b512e229da4c | 5783ce7a7b382ade5dcbcd3a53e680edeb41445c | refs/heads/master | 2021-01-19T11:14:22.428019 | 2020-03-04T12:24:39 | 2020-03-04T12:24:39 | 87,942,469 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 394 | rd | yeastInterProDesc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{yeastInterProDesc}
\alias{yeastInterProDesc}
\title{yeast inter pro description}
\format{\code{tibble} instance}
\source{
Biomart, Ensembl 88
}
\usage{
yeastInterProDesc
}
\value{
\code{tibble} instance
}
\descript... |
260312fa5eaec47e5cd8211e1ddef0ce3644238b | 87909bcf22ecc26b3cdb0f2e72e605114317bb37 | /R/additional.R | 85abc1863e75cd393118ad8d712f2b8d4d641482 | [] | no_license | rbagd/dynfactoR | 4322eff7461f89ca283f012dc2ea0b0cf7017f62 | cd92d901e5dce4d1d3c68f0f336a632035b872d4 | refs/heads/master | 2022-11-11T01:00:47.865367 | 2022-10-17T07:25:42 | 2022-10-17T07:25:42 | 38,105,065 | 24 | 11 | null | null | null | null | UTF-8 | R | false | false | 609 | r | additional.R | #' Estimate a p-th order vector autoregressive (VAR) model
#'
#' @param x Data matrix (T x n)
#' @param p Maximum lag order, i.e. VAR(p) will be estimated
#' @return Estimated parameter matrix, residuals and regression model
#' independent and dependent variables
#' @examples
#' x <- matrix(rnorm(50*2), nrow=50, ncol=2... |
a1ee3d4cf69013c1f2f03171b1fc409ba762be09 | 8e20241c4310c0c52d11a3fd505c458c330312ef | /metalcomposit-thermal-conductivity/server.R | 5da5a7c372df19d825ec938ddb48ede2964e3191 | [] | no_license | AnatoliiPotapov/shiny | efe54cc1ec6bad04552a88176d58b3c60bc55f8b | 6d208a040d13129a72cfc55086a75f4ec0e1ccea | refs/heads/master | 2020-12-28T21:18:09.739405 | 2015-11-25T12:16:31 | 2015-11-25T12:16:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,531 | r | server.R |
# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://www.rstudio.com/shiny/
#
library(shiny)
library(ggplot2)
library(reshape2)
source("./method/method.R")
source("./math/fit.R")
source("./plot/plot.R")
pi = 3.1415926
ParseInfo ... |
116f3c1361307a1bfdc8ec6b5d01d0b7047d3dd5 | 4766928560ace79430e299a0cbfc56830338f989 | /Week 2/gradientDescent.R | 79c295f5d682a6717e329bdc845c2351ec9548d0 | [] | no_license | Tatortreiniger91/Coursera_Machine_Learning | 7690e4e176fe70e1c2cda1018093d611df64cd49 | 19aa017fc6ce275eb3f40f7cf7c8b2147659343b | refs/heads/master | 2020-03-11T03:29:24.646192 | 2018-05-17T14:32:57 | 2018-05-17T14:32:57 | 129,748,941 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 838 | r | gradientDescent.R | #GRADIENTDESCENT Performs gradient descent to learn theta
# theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
# taking num_iters gradient steps with learning rate alpha
# ====================== YOUR CODE HERE ======================
# Instructions: Perform a single gradient step on ... |
90fe3f90717a57cc64f68862991864524a0bbcff | 9bb3920636dfbf3ee32c19b3be6d265379bf5a28 | /man/oceanTime_GetTimes.Rd | 40d687de68f588c7fc40954749c0e5e9b6d49441 | [
"MIT"
] | permissive | wStockhausen/wtsROMS | 549ccbbf04f4fd09f350243e503f454a0c0a489f | ec48097404da8e00ca3d5876ac0fd7fb15989035 | refs/heads/master | 2022-06-28T01:00:20.409440 | 2022-06-08T19:26:13 | 2022-06-08T19:26:13 | 244,923,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 904 | rd | oceanTime_GetTimes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/oceanTime_GetTimes.R
\name{oceanTime_GetTimes}
\alias{oceanTime_GetTimes}
\title{Get a dataframe of ocean_times associated with files in a folder}
\usage{
oceanTime_GetTimes(
path,
pattern,
ref = as.POSIXct("1900-01-01 00:00:00", tz = "... |
23e3a41b24874183bdc44b74e80bb0f47ecf5eec | 7c949f9ec55c15e9ffe8862fad0688a3e5421441 | /man/code_CDR.Rd | c00a74a823ad3089864b85d80e17ef6fa3052d4a | [] | no_license | thebackman/CDRalg | 723436e37aab797b3d29b246588d99019f8efc68 | 4f273c9b46e139a7dd2f82f57604c6e4c115e06c | refs/heads/master | 2021-01-02T08:34:53.084401 | 2017-10-25T13:57:41 | 2017-10-25T13:57:41 | 99,021,245 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 665 | rd | code_CDR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/code_CDR_user.R
\name{code_CDR}
\alias{code_CDR}
\title{Assignment of CDR rating}
\usage{
code_CDR(df, id_name = "lopnr", deb = FALSE)
}
\arguments{
\item{df}{a data frame}
\item{id_name}{the name of the primary ID variable in the data set}
... |
d3dd8939c783d1b54a6fb1907d34754254ce2e33 | 8dfee68e3695253eb9aa719a2571ea5607a5311b | /R/drive_auth.R | 210487572ad8cbb5c7752b2c4b3bdba6fb43e967 | [
"MIT"
] | permissive | fuentesortiz/googledrive | 49e7384a0749fbb9870821541e7b8e3ca1d7f735 | 20ffe8cb87ef180246fd3a94e00010879117aaa1 | refs/heads/master | 2023-03-07T17:37:20.406535 | 2020-11-19T21:47:22 | 2020-11-19T21:47:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,764 | r | drive_auth.R | ## This file is the interface between googledrive and the
## auth functionality in gargle.
# Initialization happens in .onLoad
.auth <- NULL
## The roxygen comments for these functions are mostly generated from data
## in this list and template text maintained in gargle.
gargle_lookup_table <- list(
PACKAGE = "... |
e61e9301e7312cf399639666607ebb3a0348cb09 | b4a6ab0f66c3d79588eb64b237a36e198ba60999 | /setup/rpackages.R | 62f51d28ecb34b58438b8ea13e714fa6cdad227e | [] | no_license | Blinket/dotfiles | f69d6945d0b963beef45f86a68bdfa38779c64d9 | a4e896c68bc0c37e453c0053c355b22d6e7c7a5d | refs/heads/master | 2020-07-21T08:00:29.027731 | 2019-07-17T08:18:20 | 2019-07-17T08:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,045 | r | rpackages.R | getthese <- c('caret',
'classInt',
'DBI',
'data.table',
'devtools',
'doMC',
'doParallel',
'dplyr',
'forecast',
'ggmap',
'ggrepel',
'gridExtra',
'httr',
... |
c4b9780ce885ed2f43bcc7dfe1f834a9e6cb3633 | 976163cb410214d74152c6982e64b3efac572833 | /simplex/Harjoitustyo_t.r | a9c845c7c7565be3a8b867af7ebde703cf293325 | [] | no_license | Jylital/projects | 8fad4faa281b6712749d9859628468650c54a5ed | ca4396671d826dbc7fdd8b78acd8057be6286b91 | refs/heads/master | 2020-11-29T15:32:53.683250 | 2017-04-07T08:21:31 | 2017-04-07T08:21:31 | 87,477,175 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 6,330 | r | Harjoitustyo_t.r | # require(matrixcalc)
require(lpSolve)
simplex.part <- function(constants, G, b, P, V)
{
while(TRUE)
{
#if(is.singular.matrix(G[, P]))
#{
# cat("Keskeytys.\n")
# return(NA)
#}
# Edellä oleva saattaa hidastaa suoritusta aika paljon suuremmissa systeemeissä,
# joten sitä ei välttämätt... |
7476d08f1fe4c3b8459736e9c2c3d9122454e5e0 | 0d70251d94495adee8e5f6b3e534c4f6ad27afc6 | /R/guide_rect.R | 3144e11b5cc5c10af4755252f6b129398c84fcf9 | [
"MIT"
] | permissive | teunbrand/ggchromatic | cea7aa0f19de6035662aa388cf70212f43f8ebfc | 7ed7b22c02dee6972f2006d9366c249170157ed0 | refs/heads/master | 2023-03-24T01:29:05.160303 | 2021-02-24T21:56:41 | 2021-02-24T21:56:41 | 329,685,767 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,342 | r | guide_rect.R | # Guide constructor -------------------------------------------------------
#' Chromatic colour rectangle guide
#'
#' The colour rectangle guide is a specialised guide for chromatic scales. It
#' maps two channels of a chromatic scales along the x and y axes and renders a
#' rectangle raster displaying the colours.
#'... |
20629c0078a97d73ecdf8ea3eb5e5a0bec87bacd | 37c0a409c4f06dfac2365fb792a953f59758f245 | /R/write_ASAP3_dat_file.R | 111dfe849e839f932d380f2216e9edddc0d5b49b | [
"MIT"
] | permissive | cmlegault/ASAPplots | 8a3aee8a79137dd8911305397430965aa18ae683 | 75adfd7cf889a5a2b66b6ef0a4dbe22b51aa2084 | refs/heads/master | 2021-07-13T00:48:41.370521 | 2021-03-22T21:02:40 | 2021-03-22T21:02:40 | 87,811,600 | 3 | 4 | MIT | 2021-03-19T14:02:42 | 2017-04-10T13:04:09 | R | UTF-8 | R | false | false | 2,094 | r | write_ASAP3_dat_file.R | #' WriteASAP3DatFile
#'
#' Function to write ASAP 3 dat file (modified from Tim Miller's text_datwrite.R file).
#' @param fname full directory and file name to be created (including .dat suffix)
#' @param dat.object R object containing all the necessary information
#' @param header.text text put run description line i... |
87a95321c707067a4c8edf49747df4ca31643947 | 61cba4dee2d95f6d0186e28a741f81d83c5b0983 | /ReadingData/API.R | 818bc905f653e0cdf7d42bc5c099f1d933a70b0c | [] | no_license | xpmanoj/R-Examples | ad9db962f465159cd54c53e3b0e2cf98b4b6f1ae | c0422d5f8f24f0c3f7403540e2026d634e99b235 | refs/heads/master | 2020-05-17T14:13:37.029907 | 2014-08-16T02:30:03 | 2014-08-16T02:30:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | API.R | # Creating an application
https://dev.twitter.com/apps
# Accessing Twitter from R
myapp = oauth_app("twitter",
key="yourConsumerKeyHere",secret="yourConsumerSecretHere")
sig = sign_oauth1.0(myapp,
token = "yourTokenHere",
token_secret = "yourTokenSecretHere")
h... |
14d684955a2ad282c16240989b11fcc05a9d2260 | 31b24efedd2709563bfc8e622e54c4d8a500ed1c | /Assignment II/batsmen.r | ef29e11d13aa2d427500820ed8a42c1813a14c9d | [] | no_license | VishalAmbavade/R-projects | 248d6b85489be1097369e85033b2ea8873f8e0fa | 4f59fe1bb3b4f864a79c8caac0284459ce7796eb | refs/heads/master | 2020-04-23T10:04:10.030506 | 2019-05-14T09:12:57 | 2019-05-14T09:12:57 | 171,087,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,156 | r | batsmen.r | odibatting2007 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2007odibattingrating.csv")
odibatting2008 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assignment II\\Player Ratings\\2008odibattingrating.csv")
odibatting2009 <- read.csv("D:\\Vishal\\III year\\Data Analytics\\Assi... |
d5bcf34fcafc170127afc3cccdd66230c498739c | fbc5705f3a94f34e6ca7b9c2b9d724bf2d292a26 | /edX/DS Visualization/gapminder/Ex3 selecting desired columns.R | 6eafe79bf715f3a05d0b4dd607f47e0a165a17c3 | [] | no_license | shinichimatsuda/R_Training | 1b766d9f5dfbd73490997ae70a9c25e9affdf2f2 | df9b30f2ff0886d1b6fa0ad6f3db71e018b7c24d | refs/heads/master | 2020-12-24T20:52:10.679977 | 2018-12-14T15:20:15 | 2018-12-14T15:20:15 | 58,867,484 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 188 | r | Ex3 selecting desired columns.R | library(dplyr)
library(dslabs)
data(gapminder)
df <- gapminder %>%
filter(continent == "Africa" & year == "2012" & fertility <= 3 & life_expectancy >= 70) %>%
select(country, region)
|
52d942140eb5726cdbbdb126ba458c12e8e92bd3 | 6514db5b170ef26891a5e02e27440c0701b55802 | /man/RMs_sets.Rd | aab5a98a421239d6e46e643d070dc3ef004196e5 | [] | no_license | AProfico/Arothron | cb8efc56d2fc0b7cdd73a9f6c7be95ac49bd4674 | 788c59e73a505d6bbeec96829e5bf6de976206c4 | refs/heads/master | 2021-11-03T19:38:59.399331 | 2019-04-26T10:53:42 | 2019-04-26T10:53:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 418 | rd | RMs_sets.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RMs_sets.R
\docType{data}
\name{RMs_sets}
\alias{RMs_sets}
\title{example dataset}
\usage{
data(RMs_sets)
}
\description{
Array containing the landmark coordinates of the reference sample for Digital Alignment Tool example
}
\auth... |
49589437e6cc30599986fe94c92ef763c8b83bc6 | 53db6f69689baa4154d45d62b33a93367f3cd434 | /thekarefunction.R | 473c6ad26e76255f8cfb3c59a232f47a2cff9657 | [] | no_license | TonyNdungu/Supervised_learning | 89d2c5e21345dd9aed14dc87b1dabd6e0624c197 | ce3799699862ddac82cbfe208227569b461f971d | refs/heads/master | 2020-04-22T05:13:35.170541 | 2019-03-27T13:35:32 | 2019-03-27T13:35:32 | 170,152,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,199 | r | thekarefunction.R | ###############################################
# Step 1: Load all the necessary libraries, Oauth and etc
require(twitteR)
require(plyr)
require(stringr)
require(RCurl)
require(ROAuth)
require(ggplot2)
# Step 2: We need to import the files containing the positive and negative words
pos = scan('/Users/tony//D... |
bafeb539ff78f4c0e98f5e0b56142281bc299a2e | 688185e8e8df9b6e3c4a31fc2d43064f460665f1 | /R/spectralclass.R | ca0e06ed5adec229ad21e6b4311a5d7768f795a4 | [] | no_license | IPS-LMU/emuR | 4b084971c56e4fed9032e40999eeeacfeb4896e8 | eb703f23c8295c76952aa786d149c67a7b2df9b2 | refs/heads/master | 2023-06-09T03:51:37.328416 | 2023-05-26T11:17:13 | 2023-05-26T11:17:13 | 21,941,175 | 17 | 22 | null | 2023-05-29T12:35:55 | 2014-07-17T12:32:58 | R | UTF-8 | R | false | false | 11,174 | r | spectralclass.R | ##' Function to test whether the object is of class "spectral"
##'
##' Returns TRUE or FALSE depending on whether the object is of class "spectral"
##'
##'
##' @param dat An R object
##' @return A single element logical vector: TRUE or FALSE
##' @author Jonathan Harrington
##' @seealso \code{\link{as.spectral}}
##' ... |
099bbe660201001b7794202c4aa273f1796f8f75 | 0f2981c025b2bdc6fa6030abd0cdbd3dbd853149 | /run_analysis.R | 77d9c34fb1201ec2fb9ad8ce5e2ab937739422f3 | [] | no_license | rainbowsaurus/run_analysis | cc5ff9186234a0e420933f0c9d7e4fd0a1329283 | 88db753cd6278ebf02723b88f528e57f6424c6fa | refs/heads/master | 2020-06-04T02:52:00.732758 | 2014-07-26T23:15:27 | 2014-07-26T23:15:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,840 | r | run_analysis.R |
# UCI Har Dataset unzipped in working directory
# Read separate datasets into R
xtestdata<-read.csv("UCI Har Dataset/test/X_test.txt", sep="", header=FALSE)
ytestdata<-read.csv("UCI Har Dataset/test/y_test.txt", sep="", header=FALSE)
xtraindata<-read.csv("UCI Har Dataset/train/X_train.txt", sep="", header=FALSE)
ytra... |
f9f1e88c96c027439a7deb0e346ad6c8a6578a63 | 6e0a10823d35c92efd3d4bc90f3ad5a4ae721782 | /server.R | a408f6ff9e5e5395d2fc4b9fec9dde7ac464761e | [] | no_license | DrRoad/btc_predic_shiny | d5aa9add1e57f1f1f6e73a88cfb83303dbc3d2b9 | 765f1d77e43bd650383ce020723d60d11ddfe100 | refs/heads/master | 2022-01-14T14:23:25.257033 | 2018-10-27T00:36:54 | 2018-10-27T00:36:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,736 | r | server.R | # server.R
library(quantmod)
library(ggplot2)
library(Quandl)
library(dplyr)
source("func.r")
toDate <- function(x) as.POSIXct(x,origin="1970-01-01")
z <- read.zoo(file = "sub_total_datset_652015",header=TRUE, sep=",")
sub_total_dataset <- as.xts(z)
no_date_dataset <- sub_total_dataset
x <- Quandl("BC... |
531986796bc7c0772ce597900d3bf9cd300269b9 | f60f72f511cf4c3af8ce3bf72f71d9d26c06801b | /capstone/app/ui.R | 24320302205157c9cd8cf33aa9b7b83c3e7f8441 | [] | no_license | markczhang/coursera_datascience_with_r | 3b238a3533361438c0024f072ea8b00fa752b1f0 | 7f157a9f018c431cd5a53d5f178275ccf2e79080 | refs/heads/master | 2022-10-20T14:09:23.333598 | 2020-06-18T19:36:55 | 2020-06-18T19:36:55 | 137,272,668 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 822 | r | ui.R | library(shiny)
shinyUI(fluidPage(
titlePanel('Word Predictor'),
sidebarLayout(
sidebarPanel(
em('This app uses an N-Gram model to predict/suggest for the next word given the previous content you have typed.
By default, the model... |
99522acdf1e1aab1327beff89f614117e961710f | d746fef241f9a0e06ae48cc3b1fe72693c43d808 | /ark_87287/d77g6c/d77g6c-004/rotated.r | 708b47907ea41bbdf855382329020f6bbdf92237 | [
"MIT"
] | permissive | ucd-library/wine-price-extraction | 5abed5054a6e7704dcb401d728c1be2f53e05d78 | c346e48b5cda8377335b66e4a1f57c013aa06f1f | refs/heads/master | 2021-07-06T18:24:48.311848 | 2020-10-07T01:58:32 | 2020-10-07T01:58:32 | 144,317,559 | 5 | 0 | null | 2019-10-11T18:34:32 | 2018-08-10T18:00:02 | JavaScript | UTF-8 | R | false | false | 199 | r | rotated.r | r=359.97
https://sandbox.dams.library.ucdavis.edu/fcrepo/rest/collection/sherry-lehmann/catalogs/d77g6c/media/images/d77g6c-004/svc:tesseract/full/full/359.97/default.jpg Accept:application/hocr+xml
|
f4695563f89c57548325da9c28317b7141223d76 | a1ca395cd4db65d52e95c5b396a8e4687a3171a8 | /man/kriging.quantile.grad.Rd | efd82e6d55534465e733e45d8b35010e848368c8 | [] | no_license | ProgramMonkey-soso/DiceOptim | 6784806c23332eb47aa28813fdfa2c2719387e0b | 8da55d4235754de56168e0a029ec37cae6012ece | refs/heads/master | 2022-11-04T21:50:41.469095 | 2020-06-29T16:10:03 | 2020-06-29T16:10:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,314 | rd | kriging.quantile.grad.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kriging.quantile.grad.R
\name{kriging.quantile.grad}
\alias{kriging.quantile.grad}
\title{Analytical gradient of the Kriging quantile of level beta}
\usage{
kriging.quantile.grad(x, model, beta = 0.1, type = "UK", envir = NULL)
}
\arg... |
e61b4b0d1f3cf63018507d9f9c6b66900113994a | 991d72b16c087afb9835502757fa69f38e5ce79a | /R/balance-statistics.R | d0d7768e1851f5b5407c36880e68966994e736a5 | [] | no_license | ngreifer/cobalt | a1862b212efb254a55a8913a814d4971aaa43ea2 | 42c1ac803a8bae3916833d669f193a7f06c4d89e | refs/heads/master | 2023-08-03T18:58:45.744235 | 2023-07-28T03:44:14 | 2023-07-28T03:44:14 | 63,369,821 | 63 | 13 | null | 2022-10-13T07:20:51 | 2016-07-14T21:07:03 | R | UTF-8 | R | false | false | 22,692 | r | balance-statistics.R | #' Balance Statistics in `bal.tab` and `love.plot`
#' @name balance-statistics
#'
#' @description [bal.tab()] and [love.plot()] display balance statistics for the included covariates. The `stats` argument in each of these functions controls which balance statistics are to be displayed. The argument to `stats` should b... |
a5892bc1ac3bc06e4990177197d64f2ec685c5c3 | eff907031716ec2fe0870c6bca31f35df0ad5b62 | /analysis_scripts/libraries.R | 18d9d77c42ebb6a6e0ce45fdcd02f5bef86ee131 | [] | no_license | rhi-batstone/outdoor_event_survey | a7ad06033884bfae564499eb17a11d9a1661f967 | a62933e5b93c5350f0617a2f4b4a7aae60226ff6 | refs/heads/master | 2022-11-18T13:59:48.642755 | 2020-07-15T18:02:07 | 2020-07-15T18:02:07 | 271,045,142 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 221 | r | libraries.R | library(tidyverse)
library(janitor)
library(xlsx)
library(tidytext)
library(viridis)
library(leaflet)
library(extrafont)
library(sf)
library(mapview)
library(wordcloud)
library(reshape2)
library(lubridate)
library(knitr)
|
694425b8eaae5fbee2d381b176684dadb9aaee14 | de5c92cdd473daa28707ca840c97fe5ffd3b4135 | /script2_0.R | f9c4f40e1f1258344ef5a8be9acb17c194d5fdd4 | [] | no_license | stamnosslin/stamnosslin.github.io | 4829ed78050ebf91a0877dd7b762ab9703f56c1d | d17d11a3aec2fd385fa9d380371be859a9e03bce | refs/heads/master | 2021-01-10T23:23:12.223275 | 2019-11-19T09:06:46 | 2019-11-19T09:06:46 | 69,728,420 | 1 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 657 | r | script2_0.R | # Script2. Analyzing data from a psychoacoustic experiment (under construction)
#
# The data is from a listening experiment (ALEXIS 106) involving blind and sighted
# listeners, conducted at the Gösta Ekman Laboratory in 2016. The experiment measured
# auditory thresholds for abilities potentially important for ... |
695f5f98d28f0bbf356a018ef47cb07e569cb412 | b694b44e564a59cb034cff1930d8611fc68e39c2 | /stocks_r.R | 4b769060d644bde87de179717f8364669a390c5b | [] | no_license | DheepthaB/StockMarketAnalysis | 4479f91cf389320bc3bcc8e6992e9f7b4c1e06dc | 46b43d955cdbe83e8fc6427917c3950c29e822be | refs/heads/master | 2021-08-08T23:31:19.601213 | 2017-11-11T16:38:08 | 2017-11-11T16:38:08 | 104,391,766 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,686 | r | stocks_r.R | r=read.csv("WIKI-AAPL 6M.csv")
sm50=SMA(r[c("Close")],n=50)
em10=EMA(r[c("Close")],n=50)
r$Close[50:60]
sm50[70:80]
em10[70:80]
plot(r$Close[70:90],col="blue",type="l",xlab="Days",ylab="Closing prices and SMA50 values")
lines(sm50[70:90],col="red")
legend( x="bottomleft",legend=c("Buy","sell"),col=c("red","red"... |
188af7e1a064d7e652729d586d1e8b100f4bb665 | 0d2eea91a487c186c05112bc77bba162833562b0 | /NAMIBIA_trawl_data_analysis.R | 7f6df2ddf500147fed7d763848231a423b73eca5 | [] | no_license | steffenoppel/Namibia | c63ead223969c6c2577f3631b6f42d3a10a38c76 | b8560dbc6b2653c7cb72b08ba2e339bd43d131b9 | refs/heads/master | 2021-07-02T18:45:52.168227 | 2021-01-08T11:00:57 | 2021-01-08T11:00:57 | 209,339,239 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 31,582 | r | NAMIBIA_trawl_data_analysis.R | ### ##########################################
###
### Namibia trawl bycatch analysis
### DATA PREPARATION AND ANALYSIS
### based on scripts by Tim Reid, March 2018
###
### ##########################################
### cleaned up by Steffen Oppel 23 August 2019
### only retained data cleaning/preparation - d... |
ccf7534a024950d530d22653ad4849bfb2fe7860 | e5fa76d7fadb2eeb7d7407155f39b98df19425bd | /bookdown-master/codes/tempo_simu.R | d8fb573d5275b396ed7c4123ae2647763f6f5d49 | [
"CC0-1.0"
] | permissive | andreamirandagz/random_demography | 5afb710d4f3868229283a75bb9b4605d2c366001 | 26d7915c4b360c149a39b23a75e7dd0cff18d424 | refs/heads/master | 2022-12-10T03:24:04.858146 | 2020-08-25T16:58:40 | 2020-08-25T16:58:40 | 268,879,687 | 0 | 0 | null | 2020-08-25T16:58:41 | 2020-06-02T18:35:26 | null | UTF-8 | R | false | false | 5,482 | r | tempo_simu.R | ## Shift simulator
## We simulate births according to their original timing and then
## shift them by age and time according to a continuous shift function.
## We then show that changes in the mean age of the shifted births can
## be used to recover the original birth counts.
## Finally, we consider the case when th... |
7697abd121b6c727cbd389022bca4edce11be53d | 5966fe8c6d639bb3c92f6e5de2c55644a081d702 | /man/vision.Rd | bfe45fa2319c116f8748b767132c23ab366229ec | [] | no_license | lwjohnst86/seer | 6e4af7300557559443ca3ce7abc8813c0e645f69 | 7df2352bac17473b7a6ca2eff49352e931479a13 | refs/heads/master | 2021-01-18T23:43:29.388807 | 2018-02-26T22:27:27 | 2018-02-26T22:27:27 | 45,485,157 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 694 | rd | vision.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vision.R
\name{vision}
\alias{vision}
\alias{vision_simple}
\alias{vision_sparse}
\title{Vision experience (aka plot theme).}
\usage{
vision_sparse(base_plot, base_size = 12, base_family = "Helvetica")
vision_simple(base_plot, base_size = 12... |
cf9b401eaeb8ea93ebfcad5d361584c32bb1a794 | cb0fc27028b0ce887292a99dbad269abda3b9c82 | /man/permutations.Rd | 3fa76183aced8155f4b2de1bed2c2e27bcd58b94 | [] | no_license | cran/windfarmGA | ce1a14c585cf9170f6b54c166cf6c6e8f55ecf36 | d8ec83780dbd26f50e6ea79a5d376f87507dc33c | refs/heads/master | 2021-06-01T13:51:52.840809 | 2021-05-05T14:20:03 | 2021-05-05T14:20:03 | 95,279,120 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,281 | rd | permutations.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/crossover.R
\name{permutations}
\alias{permutations}
\title{Enumerate the Combinations or Permutations of the Elements of a
Vector}
\usage{
permutations(n, r, v = 1:n)
}
\arguments{
\item{n}{Size of the source vector}
\item{r}{Si... |
00eaee718f5be926b967cfd64562b96504a14d4b | 2b595692f51b784b49bf7a67351e6013065e3116 | /plot1.R | fd74230650d43c3e83abe9dd6d15b64b6063401c | [] | no_license | 0xts/ExData_Plotting1 | 39be914c53cdf603879c8a74224959fdc6588a0e | 8a2410e5e3fd9fbe4d787843732c46f3efb5c2c6 | refs/heads/master | 2023-06-12T14:04:38.762286 | 2021-07-04T08:23:11 | 2021-07-04T08:23:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,178 | r | plot1.R | file <- "./household_power_consumption.txt"
if(!file.exists(file)){
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", destfile = "./data.zip")
unzip("./data.zip")
}else{
if(dir.exists("exdata_data_household_power_consumption")){
data <... |
86909d86f526ad2136c53f3fb2f92f50f1ec5ae1 | 80c9b1baa262883d69f9f155b662142ec5b1888b | /scripts/ensembl_details.R | 409b307c691d0e5581411907a59ce51deebe96d5 | [] | no_license | russHyde/bfx_201909 | b054eaf72073b29fc3d4bd2f71f291445cb6c486 | 58923e9eb3e83e2c64d647a1647a71f0d5212b33 | refs/heads/master | 2020-07-04T16:10:44.143091 | 2019-10-01T13:59:54 | 2019-10-01T13:59:54 | 202,333,892 | 3 | 0 | null | 2019-10-01T13:53:45 | 2019-08-14T11:07:37 | Python | UTF-8 | R | false | false | 1,525 | r | ensembl_details.R | suppressPackageStartupMessages({
library(magrittr)
library(readr)
library(rtracklayer)
library(argparse)
})
###############################################################################
# copied from snakemake r script: `get_ensembl_gene_details.smk.R` in
# `rnaseq_workflow`
parse_gtf <- function(gtf_path)... |
5955e962e4b729e0bce541e89d26db095f718506 | bda47498922e92041f38c335f25aa695b8f117ee | /plot_agb_drivers_restor_TS.R | 59e13923552a2ce32a854b60ad676e5ab9f65f80 | [] | no_license | Kochal/permanence | c6eb565fc37bd9bf3a13393df79b5807bfec7ffc | 7d14c69c8b8902945f9ab6df72c7d99c3dae82af | refs/heads/master | 2023-03-27T22:17:59.241134 | 2021-03-12T01:05:20 | 2021-03-12T01:05:20 | 346,888,716 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,698 | r | plot_agb_drivers_restor_TS.R | library(ncdf4); library(ggplot2); library(reshape)
source('/home/akoch/scripts/R_code/fun_Utility.R')
# source('~/Documents/R_code/fun_Utility.R')
# wdir <- '/home/ucfaako/Documents/future_forests/data/'
carbon <- 't3C'
carbon_dir <- 'totc'
carbon_name <- 'Total carbon'
wdir <- paste0('/home/terraces/projects/LPJ_futu... |
b1dad782d86cc67ead8cd74f953251a9a4d401c7 | 3aee1d334563dd323bd7dd16f19036234948b7c5 | /R/count_taxa.R | 42a32e5cc9cc428d0879f22ee4ffc61da4163c0f | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | helixcn/plantlist | 78f020be3cecca367b28314d974e8c8920fecf20 | 7362677dbb4d304c311699c76c1ca75e741c362b | refs/heads/master | 2022-08-17T02:53:53.556080 | 2022-08-02T05:38:05 | 2022-08-02T05:38:05 | 50,150,703 | 21 | 15 | null | null | null | null | UTF-8 | R | false | false | 6,212 | r | count_taxa.R | #' Get the number of taxa under each taxonomic level for a dataframe generated
#' by the function CTPL
#'
#' Compute the number of species under each family, each genus, and each group.
#' See the value secion.
#'
#' Simple function summarizing the number of taxa under each taxonomic
#' level.
#'
#' @param checklist_da... |
fed674a900d91199fdd8780f4166bbe6abefc27e | b77d7957713e2219cda85b7d0ebcfe0e605bc49c | /rna_seq_home_page/view/report_more_view.R | ab3970b5c4fcf0f5ab5af48de4ec82971061b2b6 | [] | no_license | fxy1018/RNA_Seq_Report | a696ef3db28c3a566fb571c4b24d13be5d96b48c | 2f25e8eddf521427b2f29bbea2c851f8ee8fcea0 | refs/heads/master | 2021-08-23T00:08:33.776380 | 2017-12-01T20:50:04 | 2017-12-01T20:50:04 | 104,396,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | report_more_view.R | report_more_view <- function(expNum) {
navbarMenu("More"
# ,
# tabPanel("Download",
# mainPanel(
# DT::dataTableOutput("downloadFileTable"),
# downloadButton("downloadFiles", label="Download Selected Files")
... |
7b9420be8a1e9bd2aec2463aad1ddb3a3e8322de | 202775810402a48a177b2c7f5b20f3bf45809342 | /R/FUNCOES/FN_LIMPA_XPROD_V3.R | bc06944e1657dfd323ed8ef490b215bcfbe5238e | [] | no_license | emirsmaka/CLASSIFICA_PRODUTOS | a4cd8db15fdc64666e169ebb4a6bf4c0256a2188 | d96810d502bc5ffad07ad454f2879b6c8b989d40 | refs/heads/master | 2023-04-16T12:42:56.126200 | 2021-04-26T11:31:32 | 2021-04-26T11:31:32 | 297,635,969 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | FN_LIMPA_XPROD_V3.R | fn_limpa_xprod <- function(x){
x$PROD_XPROD_LIMPO <- gsub("[[:digit:]]|[[:punct:]]"," ",x$PROD_XPROD)
x$PROD_XPROD_LIMPO <- str_trim(x$PROD_XPROD_LIMPO,side = "left")
remove_words <- c("cx|ml|cartao|six\\s?pack|cxa|\\ssh\\s|l?gfa|\\sl\\s|\\slt(\\s)?|vd|\\sx\\s|ln|\\s(c)\\s|npal|un(id)?|\\scom\\s|ttc|pct?|\\sc\\... |
71bb614dcd2a3a4e7efb63f42ca16b711e99e793 | 85cbc4bd64a653bd3acc21a994620968a9ef659e | /notebooks/exploratory/DESeq2Analysis copy.R | 4de4ecd5c52dbf675e2e69cdd83a9f4baea8c602 | [] | no_license | TaniaJes/module-4 | 12687a3a1b5e8a82b4af7d36cbc7a6f35d149dd2 | 10bb6c88a9d696339d42654a471a6d5852d2adda | refs/heads/main | 2023-01-05T21:00:59.253392 | 2020-11-01T20:09:05 | 2020-11-01T20:09:05 | 308,375,911 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,381 | r | DESeq2Analysis copy.R | ##########################################################################################
# This script performs differential gene expression analysis with DESeq2 #
##########################################################################################
### LOAD REQUIRED LIBRARIES
library("DESeq2")
library("org.Hs.... |
9975b5014bb574c4cfbd3c8f93a77c6b30c7be07 | 4c51fdcafa3b1a3d044face76ce0c51683ddf6b0 | /R/simulation_multi_genes.R | e526a0a2f9aaf5ffdabea42aee37b4500e56fda9 | [
"MIT"
] | permissive | JiayuSuPKU/JointModelQTL | ae8fe5a1cd20f56c92eff8b807e204bae5e26434 | 0deb158845ae4add8cdecccfcbc34b8861915d77 | refs/heads/main | 2023-04-21T09:35:46.640387 | 2021-05-26T14:11:09 | 2021-05-26T14:11:09 | 357,774,407 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,921 | r | simulation_multi_genes.R | #' Simulate read for multiple genes
#'
#' This function simulates read counts data from negative binomial (total read counts)
#' and beta-binomial (allele-specific read counts) distributions for multiple genes
#'
#' J>=1, K=1, L=1
#'
#' \code{T ~ NB(mu, phi)}
#' \code{A_alt ~ BB(T * prob_as := N_as, prob_alt, theta)}
#... |
50df26289c6f0e1abc3bede98f4629896d679aee | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/inbreedR/examples/simulate_r2_hf.Rd.R | 0ba43c8f9eae280702381364f85ee867b89214ec | [] | 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 | 463 | r | simulate_r2_hf.Rd.R | library(inbreedR)
### Name: simulate_r2_hf
### Title: Calculates the expected squared correlation between
### heteorzygosity and inbreeding for simulated marker sets
### Aliases: simulate_r2_hf
### ** Examples
data(mouse_msats)
genotypes <- convert_raw(mouse_msats)
sim_r2 <- simulate_r2_hf(n_ind = 10, H_nonInb = ... |
102890852ea10bdf3d50bf5375aa8e683ae660e2 | b9b32fcdfd3f2387cc85e2690ee610010dfdf930 | /man/clear.labels.Rd | 7fb64ab32738b4870420047ada87df6e69b99988 | [] | no_license | jfontestad/danMisc | cc49fb89240f128c6a055c8fe65c238af68d0cfc | f3dccbe0a8f9415c268b633e8985aa5a3b1c45df | refs/heads/master | 2022-03-06T07:28:55.044264 | 2019-11-23T10:23:59 | 2019-11-23T10:23:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 358 | rd | clear.labels.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.r
\name{clear.labels}
\alias{clear.labels}
\title{supprime tous les labels de HMisc pour eviter les problemes avec dplyr}
\usage{
clear.labels(x)
}
\arguments{
\item{x}{data.frame}
}
\description{
supprime tous les labels de HMisc p... |
66d1d48ef954b863878cd672563f99378968cec4 | 1dc0ab4e2b05001a5c9b81efde2487f161f800b0 | /experiments/keel/noisy/cn/cn_sonar.R | ac4f3b7cf0387910c7dfb4dcb9839215641a6f10 | [] | no_license | noeliarico/knnrr | efd09c779a53e72fc87dc8c0f222c0679b028964 | 9f6592d1bbc1626b2ea152fbd539acfe9f9a5ab3 | refs/heads/master | 2020-06-01T02:44:34.201881 | 2020-03-13T13:30:52 | 2020-03-13T13:30:52 | 190,601,477 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,204 | r | cn_sonar.R | # Sonar -------------------------------------------------------------------
sonar_5cn10_nc_5_1tra <- read.keel("data/keel/noisy/cn/sonar-5cn10-nc/sonar-5cn10-nc-5-1tra.dat")
sonar_5cn10_nc_5_1tst <- read.keel("data/keel/noisy/cn/sonar-5cn10-nc/sonar-5cn10-nc-5-1tst.dat")
sonar_5cn10_nc_5_2tra <- read.keel("data/keel/n... |
add79c93218f58ddfa090c4361eb2bac40ab8544 | 1e81deb64a22c92d6cd53842ae7d8be0c3e9d49b | /2021-08-24_lemurs/01-get-data.R | 3cbdef318344d5e296233ac1bf110f520198b702 | [
"BSD-2-Clause"
] | permissive | jmcastagnetto/tidytuesday-kludges | 5bfea5ccd4640df4e9e5367794dbb09594ac32a3 | 13dcb24694acff3839a7e1322d725e80bb146ae0 | refs/heads/main | 2023-04-07T04:33:13.715619 | 2023-03-29T03:46:12 | 2023-03-29T03:46:12 | 193,815,379 | 9 | 3 | null | null | null | null | UTF-8 | R | false | false | 322 | r | 01-get-data.R | library(readr)
taxons <- read_csv("https://github.com/rfordatascience/tidytuesday/raw/master/data/2021/2021-08-24/taxonomy.csv")
lemurs <- read_csv("https://github.com/rfordatascience/tidytuesday/raw/master/data/2021/2021-08-24/lemur_data.csv")
save(
taxons,
lemurs,
file = "2021-08-24_lemurs/lemurs-data.Rdata"
... |
d05cc7672816b013d5c518957091c185c4240f71 | 17cabbd6156cc0ab06c3970b06bbad61e984f698 | /R/ca.R | 786d3d2c9a865189465aeb587d0c3a8769b98b4b | [] | no_license | cran/visae | ff51ea4ecedf9ce63f11101277882574017db6c2 | 7dc5b7997c4e76c7a5905b7ed63fec924382412c | refs/heads/master | 2023-08-29T15:01:06.887431 | 2021-11-10T22:40:02 | 2021-11-10T22:40:02 | 334,227,668 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,210 | r | ca.R | #'Correspondence Analysis of Adverse Events
#'@param data data.frame or tibble object.
#'@param id unquoted expression indicating the
#'variable name in \code{data} that corresponds to the id variable.
#'@param group unquoted expression indicating the
#'variable name in \code{data} that corresponds to the group va... |
b61ef76db6562fe6b744073b943aa41e410aa623 | e9fc4d886ca490bc8c0537ca7ef6ede2ef7c7f78 | /man/ateRobust.Rd | abbd435595b6cab88c4079c0042ed5d006f20775 | [] | no_license | bozenne/riskRegressionLight | b6f06a2f1d4af13a5a1e4b1ce5a68ea539ce2faa | 0d3fff3062876935e478a84b1e361614a9d851e3 | refs/heads/master | 2020-05-19T19:56:26.426169 | 2019-05-06T13:50:59 | 2019-05-06T13:50:59 | 185,192,171 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,679 | rd | ateRobust.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ateRobust.R
\name{ateRobust}
\alias{ateRobust}
\title{Average Treatment Effects (ATE) for survival outcome (with competing risks) using doubly robust estimating equations}
\usage{
ateRobust(data, times, cause, type, formula.event, formula.cen... |
e861ca000662343f6c7a6090520debea38064900 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /esreg/inst/testfiles/G1_fun/libFuzzer_G1_fun/G1_fun_valgrind_files/1609889430-test.R | f5cf0263b4c2097a272406f35cffb4e5af5c03f9 | [] | 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 | 108 | r | 1609889430-test.R | testlist <- list(type = 0L, z = 4.84176071611214e-305)
result <- do.call(esreg::G1_fun,testlist)
str(result) |
631c4c2a0003219abaed573fd13a74b7cf703a0a | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_Makkink/AFL_ET0_Makkink/ET0_Makkink_valgrind_files/1615848961-test.R | 13189247daa41368dcdcf21edc686ab09df40881 | [] | 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 | 439 | r | 1615848961-test.R | testlist <- list(Rs = numeric(0), atmp = numeric(0), relh = c(3.19860037215742e+129, -1.22227646714106e-150, -2.48280557433659e+258, -9.13799141996196e-296, -1.88918554334287e+52, -4.11215093765371e-273, 1.93031268583159e-314, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... |
aba37ca81ffd509b82bbcaee513153b2d85f4b75 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/cgraph/examples/const.Rd.R | e01dae360a820b62e93d5136fa2187c4369664d0 | [] | 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 | 236 | r | const.Rd.R | library(cgraph)
### Name: const
### Title: Add Constant
### Aliases: const
### ** Examples
# Initialize a new computational graph.
x <- cgraph$new()
# Add a constant with value 1 and name 'c' to the graph.
const(1, name = "c")
|
7fa771f715cf47865f6013ef078f180194399913 | aaa2fa57565a8689d15eb60fc564f44d3e8b8b1f | /Tender_Exploratory Analysis.R | 0ed2d4c79372662c4b8b0060823b069a438a3694 | [] | no_license | cocaangle/Sam-s-club-Cusomter-Membership-renewal | f33c900296c5b4dcc6931eea5e24870d1a6e45ec | f3684768b954ae01979c6f74b681907d0920779a | refs/heads/master | 2020-05-07T01:05:17.829433 | 2019-04-14T04:54:17 | 2019-04-14T04:54:17 | 180,258,990 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,421 | r | Tender_Exploratory Analysis.R | #load packages
library(data.table)
library(caret)
library(dplyr)
library(ggplot2)
#import and read data
pos=fread("pos.txt")
tender=fread("tender_type.txt")
member=fread('members.txt')
dmm=fread("dmm_gmm.txt")
#Split training and testing dataset(1:1)
set.seed(5)
member_sample=member[sample(nrow(member), 20000), ]
... |
9cc535ef460c34eda8d9e892850c3dba8c6c3bc3 | a1fa0f12726f2c4afa8b95a68e66c882f8794377 | /modeling/old/finley_code/model-FH.r | 0b83af81b98afd4b2937f2e9ca126a33b62fc1e0 | [] | no_license | Reed-Statistics/thesis_white | f71628496102c4622a8a1c3f32f9476267c735b2 | e046e7c627baa097e63a13046578c1344b2053f6 | refs/heads/master | 2023-05-28T15:52:07.929835 | 2021-06-09T17:32:29 | 2021-06-09T17:32:29 | 295,543,496 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,278 | r | model-FH.r | ##=========================================================================
## FH MODEL
## see Appendix A of You & Zhou (2011) for Full Conditional Distributions
##=========================================================================
## y := m x 1 matrix of direct estimates for response variable (i.e., Aboveground... |
d57734886941b38a9b9c5f558f641e305b84bf6b | b335df05ada92baaa9c6db7f9a8b2102f7ee05b0 | /plot1.R | 6c8071777c7cf194a1184c031e9d1ff64646466b | [] | no_license | joerglandskron/ExData_Plotting1 | bca4880768b77df5c07f89d65daafe4dbbc8f6f7 | ce047d3146ec5874e363c2f2b187812c9896aa3b | refs/heads/master | 2021-01-16T21:06:48.996508 | 2015-09-13T13:30:03 | 2015-09-13T13:30:03 | 42,396,408 | 0 | 0 | null | 2015-09-13T13:15:59 | 2015-09-13T13:15:59 | null | UTF-8 | R | false | false | 1,555 | r | plot1.R | ##Coursera
##Data Science Specialization Signature Track
##Exploratory Data Analysis
##
##Programming Assignment 1
##plot1.R
#This is only for my own computer
#WorkingDirectory <- "Q:/Eigenes/Joerg/Buero/Coursera/Data Science Specialization/4-Exploratory Data Analysis/Programming Assignments/PA1"
#setwd(WorkingDirecto... |
f717efe192e34563ea2a8df7b0b6bb3743d87403 | 79aa6188960a85b751e7bc47d566b3a0fb92354f | /app_startup.R | 66e237ca75342f830c3e6cd85e55267cd5373d26 | [] | no_license | jimscratch/shiny_big_long | 43b2046074dd23502d3139de6054d0f6f36afc7f | db2106faa04b5cf48a52b3c0a9bf3bae14051f61 | refs/heads/master | 2022-11-25T15:12:24.315835 | 2020-07-31T09:45:38 | 2020-07-31T09:45:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 561 | r | app_startup.R | library(tidyverse)
library(DBI)
library(RSQLite)
source('package/game.R')
set.seed(1234)
tbl_segment <- tibble(
name = LETTERS[1:4]
, compare_alpha = c(1.01, 1.01, 15, 15)
, compare_beta = c(15, 15, 1.01, 1.01)
, compare_trend = c(0, 0, 0, 0)
, freq_shape = 1
, freq_scale = 1
, freq_trend = c(-.02, .0... |
e717417e811aa5e2ca2961f1c92d331f2a025b78 | 86b083f0b1e16c7f5d20379901ab3c2eae6061cf | /PAP_coding_demo.R | 978c71587d3b81644612abb0a052f5f7533b88b1 | [] | no_license | freedmanguy/PAP_coding_app | cb0d416a135c725fe922b78c1c735951f4dc53e9 | 424a24b095f04ebf780f9f1f44e15e96f54adb9e | refs/heads/main | 2023-01-02T13:02:15.786585 | 2020-10-27T23:28:13 | 2020-10-27T23:28:13 | 305,813,776 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,552 | r | PAP_coding_demo.R | filename <- "demo.RDS" # change according to the relevant filename
user <- "[user]" # change to the name of the coder
shinyurl <- "papcodingdemo" # change to the name of the app (affects url)
library(shiny)
library(dplyr)
library(rdrop2)
library(DT)
library(rvest)
mydatetime <- function(){
temp <... |
e35c66dde2267381fbf58d91e93e5c5ea618d3df | 19b2bada91976d8b36cfaa1315cb02a228009b59 | /Utilities/MergeReviews.R | d73dfe6d0930aa78397f89b11304602a48585eeb | [] | no_license | DiegoPergolini/TextMiningProject | 7667cc034e937919808f71b51eb2e8bc90268365 | 0a6955a40c7022c9f3571dba0bcedc0841971bc1 | refs/heads/master | 2020-04-17T14:28:25.656045 | 2019-01-20T16:06:49 | 2019-01-20T16:06:49 | 166,658,431 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | MergeReviews.R | filenames <- list.files(path="./",pattern="*.csv")
print(filenames)
## [1] "abc.csv" "pqr.csv"
## Full path to csv filenames
fullpath=file.path("C:/Users/diego/OneDrive/Documenti/R/Mixed",filenames)
## Print Full Path to the files
print(fullpath)
## Merge listed files from the path above
dataset <- do.call("rbind",l... |
0bf3d041d4c5ad720620cbe02a2311038ae0bdb8 | 6a676c52142be89288e532bdb9b20fb77445143e | /run_analysis.R | 05186c78bf120482569e138744fa567b47184625 | [] | no_license | skneils/tidydata | eb3c7486b253da8719f9f140f18847dbe6db228b | 14817cfd761b11043bce9bc1bfbfa509a869ea16 | refs/heads/master | 2016-09-03T04:10:00.433694 | 2014-04-27T18:15:39 | 2014-04-27T18:15:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,875 | r | run_analysis.R | # load test data from ./UCI HAR Dataset/test/
testx=read.table("./UCI HAR Dataset/test/x_test.txt")
testy=read.table("./UCI HAR Dataset/test/y_test.txt")
tests=read.table("./UCI HAR Dataset/test/subject_test.txt")
# load train data from ./UCI HAR Dataset/train/
trainx=read.table("./UCI HAR Dataset/train/X_train.txt"... |
df471e90a3c32d3185e11499846704769c9330a7 | 7e1602b2e2885c211dee1fad93bf3cb5d823945a | /HW2/Code.R | 69e77b2221d8f859add39c02f0873f31f3f1ed6e | [] | no_license | CharlesYWL/ECS132 | f773e237159347882d24a27e1ba849b9368a76d3 | d73b6f068da05d3289418cab43ebbc16357ee48d | refs/heads/master | 2020-05-14T19:15:49.126061 | 2019-06-13T05:08:12 | 2019-06-13T05:08:12 | 181,926,290 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,637 | r | Code.R | #Weili Yin,912603171
#####P1
sample(0:4,prob=c(.292,.4096,.2312,.068,.064))
sample(0:4,1,prob=c(.292,.4096,.2312,.068,.064))
sample(0:2,2,prob = c(.5,.4,.1))
ls1 <- sample(0:2,10000,prob = c(.5,.4,.1),replace = TRUE)
var(ls1)
getL2 <- function(){
L1 <- sample(0:2,1,prob = c(.5,.4,.1))
NumofPeople <- L1
i <- 0... |
fb75bb57482a987deb93681536fc115a49634d6a | 5a2be4b810a31ce3da1fa58fc91a87faabcc7af0 | /Week3/project.R | aba7c886b023d9363ada3c148986289ca438a4f4 | [] | no_license | chanyayun/Data-Science-Programming | 85b582c847f5afa66f28493a61519c120b1639ef | e0ed329d675e53a630622ac9985b07df051b7f8c | refs/heads/master | 2020-06-16T22:55:24.515071 | 2019-07-28T15:55:29 | 2019-07-28T15:55:29 | 195,725,784 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,939 | r | project.R | library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(tm)
library(qdap)
library(ggplot2)
data <- scan("Week3/data/cnn_2019.txt", what = character(), encoding = "UTF-8")
data_source <- VectorSource(data)
data_corpus <- Corpus(data_source) %>%
tm_map(removeNumbers) %>%
tm_map(content_transformer(to... |
6927d888415363deedeba90d1aa0536bad4c4f74 | 2a1407b8e6552a0caae86d5fa0bb8c898fa4288e | /man/score.Rd | 7fe42cd65a40592b94eedd09e5ec6d88cf74b182 | [] | no_license | weinroth/corncob | 29753c06be6941aaa22d8c77044534a37e6d1dd9 | 667280f7eec3ad5715f7a4caa3ecebd7a6601777 | refs/heads/master | 2020-03-28T10:00:18.393993 | 2018-08-14T19:45:11 | 2018-08-14T19:45:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 489 | rd | score.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/score.R
\name{score}
\alias{score}
\title{Compute score}
\usage{
score(mod, numerical = FALSE, forHess = FALSE)
}
\arguments{
\item{mod}{model fit from bbdml}
\item{numerical}{Boolean numerical score. Not as stable. Defaults to FALSE}
\item... |
4deb775b986630e78defa591884656265eeb9bc9 | 71e23a80daa5d4ac060303733719a25734ad6229 | /Big.test.r | 544c61c9fac52c224292802e8ce6c370c4176ee1 | [] | no_license | PaulPyl/h5array | e82920a63122242701c66fccd1d212b8564e3a0f | 2905c2d653f74bff3d3e32b80be1385c5e0432ca | refs/heads/master | 2021-06-03T15:22:59.654944 | 2016-02-03T13:33:25 | 2016-02-03T13:33:25 | 39,744,744 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,142 | r | Big.test.r | require(h5array)
x <- h5arrayCreate(tempfile(), "BigData", c(1e4+1,1e4+1,20), "double", chunk = c(1e3, 1e3, 20))
dimnames(x) <- list( NULL, NULL, letters[1:20] )
x[1,,1] <- 23
h5ls(getFileName(x))
writeDimnamesToFile(x)
h5ls(getFileName(x))
y <- h5array(getFileName(x), location = getLocation(x))
dimnames(y)
dimnames(x)... |
10f31d094b54f9db56e18357e675bcfdc79e4a84 | 0e20fdf781cf8489e6d2fe66aaff5d627d4d0f15 | /animated_bar_plot_in_R_for_male_names.R | 61da54d97114de070f12eccbf93cdde60cd7a82b | [] | no_license | hhnnhh/animated_bar_charts_in_R | fc1a47cffed343840b050453cd7e02f7263a4182 | 0aef7ae105708eac6e52caabd09e7eef716325ca | refs/heads/master | 2020-09-01T12:07:25.916987 | 2019-12-11T22:30:46 | 2019-12-11T22:30:46 | 218,954,781 | 1 | 0 | null | 2019-11-01T09:26:37 | 2019-11-01T09:26:37 | null | UTF-8 | R | false | false | 3,786 | r | animated_bar_plot_in_R_for_male_names.R | library(tidyverse)
library(gganimate)
## --> needed for the nice design "theme_tufte" = optional
#library(extrafont)
#library(ggthemes)
## --> for rendering the GIF
#library(gifski)
#library(png)
setwd("C:/Users/hanna/Dropbox/R_wissen/animated_bar_charts_in_R/data/")
list.files()
getwd()
setwd("./mostfrequentnames/"... |
601ab21c65fd78b6532e7cdb07b6257773c9abd0 | d121f587f7e0678030d33a4c5428e594c5978dad | /man/log2_transform.Rd | f73129687fca20665c98ac0bcef831462749d4a5 | [
"Apache-2.0"
] | permissive | kauralasoo/eQTLUtils | fcf0907721b3a8f19fe68e611cecb4f16d7a0c9d | 26242562a4e244334fd9691d03bc1ef4d2d6c1d9 | refs/heads/master | 2023-03-05T19:10:45.247191 | 2023-03-03T13:33:08 | 2023-03-03T13:33:08 | 149,779,618 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 443 | rd | log2_transform.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/array_qc_utils.R
\name{log2_transform}
\alias{log2_transform}
\title{Log2 transform intensity values (same approach as in function lumiB)}
\usage{
log2_transform(mat)
}
\arguments{
\item{mat}{Matrix of intensity values}
}
\value{
Log2 transfo... |
84fa69c7beb4a30a7bd672b2fa0090b7c5adf904 | 81a2fa3228451179b12779bb0149398cbfc8e9b1 | /R/naOmit.R | e0b4827187c5a8dc361d86c6c02896db55fc9bf2 | [] | no_license | cran/wrMisc | c91af4f8d93ad081acef04877fb7558d7de3ffa2 | 22edd90bd9c2e320e7c2302460266a81d1961e31 | refs/heads/master | 2023-08-16T21:47:39.481176 | 2023-08-10T18:00:02 | 2023-08-10T19:30:33 | 236,959,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 810 | r | naOmit.R | #' Fast na.omit
#'
#' \code{naOmit} removes NAs from input vector. This function has no slot for removed elements while \code{na.omit} does so.
#' Resulting objects from \code{naOmit} are smaller in size and subsequent execution (on large vectors) is faster (in particular if many NAs get encountered).
#' Note : B... |
8cbf14612c903b8ef49242b3fcd1edcae4aed925 | f99ce07d94ccb52745532c16a83afef1cb0c9121 | /man/crd3r3.Rd | d9429d827803e60e07d74a8d12f5d2ff9f42f44a | [] | no_license | cran/cosa | e3b3b2e04fa9d86386611055347acfeb536dc500 | 6cc728f618446c71cef36c5951f70a1d16a903be | refs/heads/master | 2021-11-29T14:07:23.589664 | 2021-11-20T21:50:05 | 2021-11-20T21:50:05 | 120,439,174 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 8,343 | rd | crd3r3.Rd | \name{crd3}
\alias{crd3r3}
\alias{crd3}
\alias{bcrd4f3}
\alias{cosa.crd3r3}
\alias{cosa.crd3}
\alias{cosa.bcrd4f3}
\alias{power.crd3r3}
\alias{power.crd3}
\alias{power.bcrd4f3}
\alias{mdes.crd3r3}
\alias{mdes.crd3}
\alias{mdes.bcrd4f3}
\title{Cluster-level Regression Discontinuity (Three-level Design, Disc... |
2ce7d70e39affa223ad9939201d5f079016fe0ad | 0084280ad5d1400c280c110c402d3018b7a129af | /R/snv/maf-comparison-tcga-pcwag-glass.R | 1e79c98221ebad1d14f3904440b9b2929f3505fb | [
"MIT"
] | permissive | fpbarthel/GLASS | 457626861206a5b6a6f1c9541a5a7c032a55987a | 333d5d01477e49bb2cf87be459d4161d4cde4483 | refs/heads/master | 2022-09-22T00:45:41.045137 | 2020-06-01T19:12:30 | 2020-06-01T19:12:47 | 131,726,642 | 24 | 10 | null | null | null | null | UTF-8 | R | false | false | 2,731 | r | maf-comparison-tcga-pcwag-glass.R | #######################################################
# Comparisons of maf files from TCGA (PCAWG vs. GLASS-WG Snakemake)
# Date: 2018.08.15
# Author: Kevin J.
#######################################################
# Local directory for github repo.
# Use Kadir's linker file to identify the samples that overlap betw... |
44d2132073c69729996f3fd885c8256eaf767c02 | 7043c353662b2e238e31357bff5bf1dc8ba2abad | /R_source/geo_dbscan.R | ac7b90515ef23db9349c282ed101503411fef4af | [] | no_license | gafalcon/tweet_topic_modelling_clustering | afb8fa19e99916b630992bb001164e9a8344b057 | 4b6c500e2fe07e384eb1dd2bee7b2791873acb9b | refs/heads/master | 2023-07-01T19:03:07.446023 | 2021-07-26T15:46:58 | 2021-07-26T15:46:58 | 50,520,851 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,546 | r | geo_dbscan.R | #Db Scan of a single user
library(fossil);
library(dbscan);
usr_ids <- list.files(getwd()) #read usrs_ids files
#usr_ids <- read.csv("user_ids", header = FALSE); #Read user ids
#usr_ids <- usr_ids[1][,1]; # transpose column to row
lats <- double(length(usr_ids)); #initialize lats and longs
longs <- double(length(usr_i... |
37292b2c4e1ca321ae95c28e6f4315b8cfeed743 | 536cf445a4a9465270d79f0c37e0eb4f8d61403a | /man/exfm20.Rd | bea9ab8e7fd57dd707fda7da204bc4977be56897 | [
"MIT"
] | permissive | sollano/forestmangr | a01cc90925e582c69c3c77033c806adae5b38781 | e14b62fafd859871277755cfa097d8d032a4af82 | refs/heads/master | 2023-02-19T21:53:44.213144 | 2023-02-15T21:57:42 | 2023-02-15T21:57:42 | 152,138,499 | 13 | 7 | NOASSERTION | 2022-12-11T22:43:03 | 2018-10-08T19:59:13 | R | UTF-8 | R | false | true | 1,164 | rd | exfm20.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exfm20.R
\docType{data}
\name{exfm20}
\alias{exfm20}
\title{Inventory data of a natural forest in Brazil}
\format{
A data frame with 12295 observations and 18 variables:
\describe{
\item{cod}{area code}
\item{transect}{plot number}
\ite... |
4e914b95d207326ae9491931fbf81f9e487cc092 | aa6cce49e3cb87260b6546e0829b72dc79e415d8 | /code/NB_exercise.R | bf890598cd66edc0b4438cc22f8dc70b0a0347b7 | [] | no_license | anthonyhung/MSTPsummerstatistics | 037fc7499345aea4c0c4fb9aac6f15eeb6fdb347 | dc53f74f5c1ca44d06ff9d443d71df8acc2fd5ef | refs/heads/master | 2021-07-09T17:33:53.439306 | 2020-06-23T14:55:00 | 2020-06-23T14:55:00 | 150,667,061 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 314 | r | NB_exercise.R | mush_data <- read.csv("data/mushrooms.csv")
#Create a Naive Bayes classifier to predict whether a mushroom has bruises or not. No need to perform any cross-validation, just split your samples into a 20% test set and 80% training set. What is the AUC of the ROC curve for your model?
library(caret)
library(pROC)
|
6919222a94ace3c0f604a539c10717c8803b26a9 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.customer.engagement/man/ses_delete_identity.Rd | 0dfd18cf5de51a638391652d7aba6e14dfd5d731 | [
"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 | 638 | rd | ses_delete_identity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ses_operations.R
\name{ses_delete_identity}
\alias{ses_delete_identity}
\title{Deletes the specified identity (an email address or a domain) from the
list of verified identities}
\usage{
ses_delete_identity(Identity)
}
\arguments{
\item{Ident... |
dc6ca005e7dbf7767344afd0edd9715ba3b17647 | 6957f0d9b9ebc660cf9031794269f929cdcb6de9 | /medical_prediction/gene information extraction and analysis/decision_tree_2mer.r | 216c254cb99d00c52d26d80f748ceed0c72144f7 | [] | no_license | jializhou/undergraduate_projects | 24ab6c77a6fcfb02f162017738cec82c557b7307 | f59aef33fdfbae59df60535b6f5b28133139c94e | refs/heads/master | 2021-05-31T04:26:22.301177 | 2016-03-21T01:31:57 | 2016-03-21T01:31:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,085 | r | decision_tree_2mer.r | setwd("C:/Users/I311161/Desktop/gencode/whole gene/transcripts/k_mer/2/1")
library(rpart)
library(ROCR)
##training set##
foreground_train <- read.csv("foreground_train.csv",header = F)
foreground_train[,17]<-1
background_train <- read.csv("background_train.csv",header = F)
background_train[,17]<-0
train_set<-rbind(for... |
0e0f88f2f583de235dfc6abe1e1e1cec6084326d | 85d8f91b58f912130362bd6415bbdb5e2e0cc7c0 | /tests/testthat/test-flipformat.R | d8c15b26550406e940c846bf6ec791529d8c1ba7 | [] | no_license | Displayr/flipRegression | c8ab22ffc875ca09deac2ec01ffaf5371501c860 | 871819d800ebb24a7331336bd4cfea24b35afb48 | refs/heads/master | 2023-08-21T21:39:02.916680 | 2023-07-19T05:50:48 | 2023-07-19T05:50:48 | 59,715,681 | 7 | 5 | null | 2023-08-03T07:19:54 | 2016-05-26T03:09:43 | R | UTF-8 | R | false | false | 14,277 | r | test-flipformat.R | context("flipFormat tests")
data(bank, package = "flipExampleData")
zformula <- formula("Overall ~ Fees + Interest + Phone + Branch + Online + ATM")
sb <- bank$ID > 100
attr(sb, "label") <- "ID greater than 100"
wgt <- bank$ID
attr(wgt, "label") <- "ID"
bank$dep <- (unclass(bank$Overall) - 1) / 6
attr(bank$dep, "labe... |
8f9434a4b7d42e83f4165010dc1ddf67ec03320a | f6a65b54568fdc34f0a66796191e34950c383115 | /man/makeSweave.Rd | bd46c88856557ffa96bec1214ce82b992e2abb8f | [] | no_license | cran/indirect | b60113a5c188434b29a426b1e36bbb8c3a5414d5 | 8ad3d93f172db38c9cab34a04998ab55bb6c1ad8 | refs/heads/master | 2022-02-17T05:15:23.814030 | 2022-02-09T04:30:02 | 2022-02-09T04:30:02 | 128,393,529 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,379 | rd | makeSweave.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/elicit_functions.R
\name{makeSweave}
\alias{makeSweave}
\title{Function to create summary document from a saved elicitation record.}
\usage{
makeSweave(
filename.rds = "",
reportname = "",
title = "Elicitation record",
conta... |
91733dd0efa331cfe7bf241c8a60305d8dfbd5ac | 40f4cb44ab742a168ca3f82d36a3e38dcaa6f844 | /R/dumpNcbiTax.R | 26cb4b6148b78d30665e628ea6251a7935cc3e53 | [] | no_license | sankleta/BED | 34e3f91fceffbb1164e65ab8a4cb24e6431b898b | 85c5c5ba4bbc927155d454dc6612512c7b197805 | refs/heads/master | 2021-04-30T05:55:28.535605 | 2018-02-06T11:18:59 | 2018-02-06T11:18:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,172 | r | dumpNcbiTax.R | #' Feeding BED: Dump tables with taxonomic information from NCBI
#'
#' Not exported to avoid unintended modifications of the DB.
#'
#' @param reDumpThr time difference threshold between 2 downloads
#' @param toDump the list of tables to load
#' @param env the R environment in which to load the tables when downloaded
#'... |
3c9a5f088df36e621d6335356fb3f9b72405ce4a | 556f65c5ef3c3cec789b58b29c684ea0f772eb34 | /src/M11_elasticnet.R | 424fa537f7cd36ea444bbfab8bf9b08edcc8fefa | [] | no_license | adamcone/higgs_boson | 4696b3fa1684e039b94362062f506f8c1543ce09 | 0ac7e3475e0c48d7ad8a8362d8bac6c2c735a4ef | refs/heads/master | 2021-01-20T18:38:51.564684 | 2016-07-14T17:50:21 | 2016-07-14T17:50:21 | 63,354,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,002 | r | M11_elasticnet.R | setwd('/Users/adamcone/Desktop/projects/Kaggle/code')
load('kaggle.RData')
library(dplyr)
library(glmnet)
library(caret)
# M11: 0 jets, no candidate mass estimate.
# No jets recorded, so all of the continous jet metrics are undefined.
# Removed PRI_jet_num and PRI_jet_all_pt because both are constant at 0
#... |
e0c77d67ea45eaac9a04897cab5522cd889a758c | 3bc4732d0260fc8865da8354efe9778915bd53d6 | /R/reader.R | f2e8c7ecf5964634194944a9a93c3b05253414b6 | [] | no_license | tpopenfoose/fitFileR | a92c63d8e2fd5cbfe1832b45cf87213ebb456191 | 3e152b5b11702d7e51d7afd96a08db03f304de63 | refs/heads/master | 2022-11-06T11:42:42.724848 | 2020-06-11T15:34:32 | 2020-06-11T15:34:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,819 | r | reader.R |
#' @export
readFitFile <- function(fileName, dropUnknown = TRUE, mergeMessages = TRUE) {
data("data_type_lookup", package = "fitFileR", envir = parent.frame())
tmp <- .readFile(fileName)
all_records <- .renameMessages(tmp[[1]], tmp[[2]], merge = mergeMessages)
for(i in names(all_records)) {
all_... |
3e04d9887e81ac45c9bef14892ba683195f2ee64 | 3c85d8c213dfb13f7ab69c300b93e8f419831a02 | /cachematrix.R | fa09384e95afb77bb3b847de33750f8ae5bb3701 | [] | no_license | jimtheba/ProgrammingAssignment2 | d876da24b83483b07d38a08e4c2bc94ace04eb49 | bb0d901cb887da70be87d282b3f1f0a97fc5cabf | refs/heads/master | 2021-01-20T17:29:09.437614 | 2015-07-26T18:25:17 | 2015-07-26T18:25:17 | 39,733,827 | 0 | 0 | null | 2015-07-26T17:08:58 | 2015-07-26T17:08:57 | null | UTF-8 | R | false | false | 1,855 | r | cachematrix.R | ## The purpose of the two functions makeCacheMatrix and cacheSolve
## is to created a cached matrix object that holds the inverse of
## a matrix. This allows for the computation to be done just once
## instead of computing repeatedly as needed.
## This first function creates the matrix object that can cache its invers... |
8658288683daf0e8733197940f1be161b8285f16 | aece010c3572eaf59a791569ae60fec62a260ee6 | /man/emodel.object.Rd | 1b4dc65da8a1e0a7ef5e8fda01d8de55addb25e3 | [] | no_license | cran/msm | edb92247a14b77f5a6726a80623884f29cce20e2 | fa420503596991f9e0c5e903474c1e24954c9451 | refs/heads/master | 2022-12-03T03:59:27.043063 | 2022-11-28T16:30:02 | 2022-11-28T16:30:02 | 17,697,695 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,117 | rd | emodel.object.Rd | \name{emodel.object}
\alias{emodel.object}
\title{Developer documentation: misclassification model structure object}
\description{
A list giving information about the misclassifications assumed in a
multi-state model fitted with the \code{ematrix} argument of
\code{\link{msm}}. Returned in a fitted \code{\link{m... |
25b2c8c2bce38d7e959f69b7b5c77f0a45d288c3 | 638a9479734ffdc5c504ccf4d9438a04803648dd | /StephensMacCall_EW.R | 812e30c074be322f9f41a607dd4ce6ef50e421be | [] | no_license | ellewibisono/Chapter3 | 886096949a46fea493c7c9f5faaabf8e025b57c7 | c26f6b5d9450f90abe5fc05c671d4c6ab217c1e1 | refs/heads/master | 2022-11-26T23:55:23.347896 | 2020-07-24T21:15:12 | 2020-07-24T21:15:12 | 273,325,685 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,181 | r | StephensMacCall_EW.R | #Load in data fron PgAdmin
m <- dbDriver("PostgreSQL")
con <- dbConnect(m,host='localhost', port='5432',user="postgres", password="fishy", dbname="ifish_03242020")
rs2 <-dbSendQuery(con, "SELECT f.oid, f.var_a, f.var_b, f.fish_genus, f.fish_species, f.lmat, f.lopt, f.linf, f.lmax, f.fish_code, s.cm,
d... |
6fd7ceeafe580f43dc65ce3aef9a0c473f322a48 | 70520c160449323e7a03dbdcbd85bc723e2a2f58 | /exercise.r | 27b147f5b55a95897b4ee48926a82ebcaae17f59 | [] | no_license | davemfish/maptime-r | 72e3b1b3ba10f56a54510d490619355d1c79a3c8 | 54efd29e8f4fc84dec92a25e29fe5099bcb4025d | refs/heads/master | 2021-01-20T20:31:59.808782 | 2016-07-09T19:46:27 | 2016-07-09T19:46:27 | 60,197,037 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,537 | r | exercise.r | # Install packages if needed:
# packages <- c("rgdal", "rgeos", "leaflet", "RColorBrewer", "raster")
# install.packages(packages)
## load packages:
library(rgdal)
library(rgeos)
library(leaflet)
library(RColorBrewer)
library(raster)
## set a working directory
setwd("/home/dmf/maptime-r")
## load species data from a... |
a273e0068a0b31b5d14437c60595347662811b37 | fc6a16ff52ee0a1aed32706a2bc3e0bbd7594f8f | /data-wrangling/lubridate/datetime.R | 69f137c4a1d5a855697637f367de94cbd5f862c9 | [] | no_license | rsquaredacademy-education/tutorial_slides | 80931f8ac3ec07f56c90c1f8b0fe1196761f2322 | f4597c32b1111f73d3e91d974c7417cce8ade64a | refs/heads/master | 2022-12-11T19:18:30.401786 | 2020-09-07T11:02:52 | 2020-09-07T11:02:52 | 97,691,482 | 0 | 1 | null | 2020-09-07T11:02:53 | 2017-07-19T08:19:31 | HTML | UTF-8 | R | false | false | 3,908 | r | datetime.R | ## load libraries
library(lubridate)
library(dplyr)
library(magrittr)
library(readr)
## origin
lubridate::origin
## today
now()
today()
am(now())
pm(now())
## read case study data
transact <- read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/transact.csv')
transact
## day, month and year... |
43c4e3f479b654db2c098e9dd369a711ef329e02 | 1b515549f0d9689d7c08d9b19d1078839e0cc39c | /run_analysis.R | 50d2c6d3b10bae5c364cc85993539fb4a5bb5287 | [] | no_license | za-gor-te-nai/getting-and-cleaning-data | 21856ba3757318c8fb4d26f8b00f6c7a91bb59d0 | 97f6d2d5c8903f4e28221bfb1ded55fad2f7252d | refs/heads/master | 2020-05-05T11:03:12.871267 | 2019-04-07T14:38:11 | 2019-04-07T14:38:11 | 179,972,718 | 1 | 0 | null | 2019-04-07T14:12:06 | 2019-04-07T13:49:37 | R | UTF-8 | R | false | false | 2,255 | r | run_analysis.R | rm(list=ls())
graphics.off()
require(readr)
require(dplyr)
## load variable names
con <- file('UCI HAR Dataset/features.txt', open='r')
varNames <- readLines(con)
close(con)
## read the training data set (561 features?)
xTrain <- read_table('UCI HAR Dataset/train/X_train.txt', col_names=F)
## read the activity de... |
43ebc4ef9e6f911755c92dcda2e0c202933c4cfa | 28eb040ee4e8487e5c6cc43a8fb014b427f95c90 | /read_data.R | bd6677e0a8a34d9280fe76cf43f965dfd5de8733 | [
"MIT"
] | permissive | blatoo/testOutskewer | b963512b06d5e6ab034a91d2970beb41a1e9900a | 432e023b7ad1b043f669c667f9a5865c3d6b4dbc | refs/heads/master | 2020-06-04T07:31:34.264821 | 2015-07-21T06:42:36 | 2015-07-21T06:42:36 | 39,428,028 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,510 | r | read_data.R | #get my databases
setwd("D:/Ying/coding/RStudio/outskewer")
mydataset <- local({
data <- read.csv(file="data/my_results.txt", sep = "", stringsAsFactors = FALSE, header = TRUE)
data$x <- as.numeric(data$x)
data$yes <- as.numeric(data$yes)
data$maybe <- as.numeric(data$maybe)
data$no <- as.numeric(data$no... |
c282a7d384e02cfaf15569f30f44c833a4ec849b | a00a5de7e7e1226a095af8e871c24656cc138a55 | /Figure1B_Broader_Molecular_Descriptors_043020.R | dcf928256fe459fbd84dbbe27df224f1f7b8febb | [] | no_license | ndfriedman/WES-RECAPTURES-IMPACT | a3d4ebfd8134ab0158e8c994a910157c2750334b | 9cde92d724145b6f6ea54cc171203ffe86b1cf14 | refs/heads/master | 2022-09-06T23:21:35.019606 | 2020-05-31T09:01:03 | 2020-05-31T09:01:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,421 | r | Figure1B_Broader_Molecular_Descriptors_043020.R | #libraries & custom functions
suppressWarnings(library(data.table))
suppressWarnings(library(plyr))
suppressWarnings(library(dplyr))
suppressWarnings(library(stringr))
suppressWarnings(library(ggplot2))
suppressWarnings(library(reshape2))
suppressWarnings(library(forcats))
suppressWarnings(library(RColorBrewer))
librar... |
e26437e1cce0c5299d34545621137c6ac4f8fba7 | 1586a418d5d558c1c1c8d00d781fc6df4797053a | /R/create_db.R | 2e98f5ba195a8d4d63fc4509de88dbcd62244540 | [] | no_license | CRI-iAtlas/iatlas-data | 95a6ee640e34167e4657860e91266683d2031691 | 606b1a8f89be6cd4a2523fc8bcc059125f3466d8 | refs/heads/staging | 2020-12-08T08:02:25.380880 | 2020-06-25T23:17:51 | 2020-06-25T23:17:51 | 232,928,065 | 1 | 1 | null | 2020-06-24T22:17:52 | 2020-01-09T23:45:22 | R | UTF-8 | R | false | false | 692 | r | create_db.R | # Global function that may be used to spin-up, create, or reset the Postgres DB.
# env may be "prod", "dev", "test", or NULL. If NULL is passed, it will default to dev.
# If "prod" is passed as the env argument, the shell script will NOT be executed.
# reset may be "create", "reset", or NULL. If NULL is passed, it won'... |
ed1646eb5bec73f0b17e300ed35e457ab7dcb0f6 | 57cf2b17ad01b78b9f7f4c7e4e229c48dcd3b2f3 | /R/lowcost.matrix.rep.R | fe13a1e559d4f19a92f77163d424a103e1cc3d20 | [] | no_license | tf2/CNsolidate | b5c62014f4c06751697d2332df7e7787bdf3faa7 | c856aa54604d5a0e8cfcd4eb9790bef70cc8c7f4 | refs/heads/master | 2021-01-22T09:16:50.336608 | 2015-06-24T07:57:56 | 2015-06-24T07:57:56 | 9,937,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,067 | r | lowcost.matrix.rep.R | `lowcost.matrix.rep` <- function(dat, segLen, cpPex) {
s = seq(1, length(dat[,1]), by=segLen)
so = segLen
cpPex = (segLen/100)*cpPex
starts = NULL
stops = NULL
mens = NULL
lens = NULL
for(x in 1:length(s)) {
st = s[x]
tst = st
ss = 1
if (so > length(dat[,1])) {
so = length(dat[,1])
... |
53e3d401b40e13a2081e666891e34caefa43f934 | e73e42b43f6f539ab626881c190f9d3cd61056f9 | /files/Barret/qtdot.r | 71e325657a88e61c5447e39fdc672c662f1723cd | [] | no_license | ghubona/cranvasOLD | 59fd1aa314b618779b716aaad0bf42aa777964fd | 119682c49a656a515a9acdc4d32f5392f92dfc4d | refs/heads/master | 2021-01-09T05:19:45.170404 | 2010-12-16T20:38:16 | 2010-12-16T20:38:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,223 | r | qtdot.r | #' Create a dot plot
#' Create a dot plot from 1-D numeric data
#'
#' http://content.answcdn.com/main/content/img/oxford/Oxford_Statistics/0199541454.dot-plot.1.jpg
#'
#' @param data vector of numeric data to be made into a histogram
#' @param horizontal boolean to decide if the bars are horizontal or vertical
#' @para... |
7c046b03b3e1f9be01a4637d3ee52f7c11bad728 | 69b7540b543e5a08f4af3da605ab58cff8f8d1f4 | /analysis.R | 335aaba2f2eae73b33e12881cba79793143591fe | [] | no_license | leosaenger/members-stats-workshop | 19a1affbdd2ce8d30913e5ea4e579fc43fa3cb0e | f53ac9b295613ebd65416c0e9d66780b2f9e451b | refs/heads/main | 2023-03-04T17:42:03.631249 | 2021-02-11T00:43:26 | 2021-02-11T00:43:26 | 336,910,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,468 | r | analysis.R | # r supplemental bootcamp materials
# stats supplement
# leo saenger feb 6 2021 for HODP
library(tidyverse)
library(ggplot2)
library(estimatr)
# set it to wherever you have it
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# let's take some data - 3044 firms in Cambridge/Somerville w/ less than 150k in... |
820c8f397d49716bb55cda812774d4e675418e57 | b6aba9b09f3fd4672c17c0311f5ee3eafdb29968 | /Codes/FeatureSelectionMDS/Suvrel_normalization.R | e1722f84debbdc84aa47e95e9262e1445dcf8c75 | [] | no_license | tambonis/GA_RNA_Seq | fc31d0e0674b334adfdf727555149aa2a42b7585 | 4ddb2e45e5d37f6e4437c1f3234b05346f0fc03b | refs/heads/master | 2022-03-01T13:03:41.041835 | 2019-11-05T13:23:45 | 2019-11-05T13:23:45 | 105,914,262 | 1 | 0 | null | 2017-10-09T12:05:04 | 2017-10-05T16:34:12 | null | UTF-8 | R | false | false | 624 | r | Suvrel_normalization.R | ################################################################################
##Mean 0, variance 1 normalization.
##Tiago Tambonis, 06/15.
################################################################################
Suvrel.normalization <- function(counts, group){
sd_g <- sqrt(apply(counts, 1, var))
... |
ed618e6519d2bcce1edc3b5e4d3abfd0d641f455 | 2ed991300219427268e844e3dd37012ec0000f5f | /R/StatisticFactory.R | b58ebea708f9c011ccf27220285f47dcd45d12f5 | [
"MIT"
] | permissive | riccardoporreca/powerly | 3ceeba85900ff11f30ad9afe588f3c66cd40fe8e | 725d11819fa35cc4c75351c12b367a580a83f010 | refs/heads/main | 2023-08-26T01:51:31.984149 | 2021-11-08T08:39:23 | 2021-11-08T08:39:23 | 425,935,717 | 1 | 0 | NOASSERTION | 2021-11-08T17:40:31 | 2021-11-08T17:40:31 | null | UTF-8 | R | false | false | 343 | r | StatisticFactory.R | #' @include PowerStatistic.R
StatisticFactory <- R6::R6Class("StatisticFactory",
public = list(
get_statistic = function(type) {
return(
switch(type,
power = PowerStatistic$new(),
stop(.__ERRORS__$not_developed)
)
... |
504b475a3d77b7e7b8eba712923e50dfe515aced | ba95ca23cd4d1463fba07d6f88cdfa5fb0e7ebce | /man/downloadDB.Rd | 811e87c9c1667b7f792dbb8955fac0d12db56fe8 | [] | no_license | adrianacarmo/FunctSNP | b0ae937ac0dfb09f54a2658bc066b8cc24c58c2a | 1b52238c1d203d2c3df8ef49f7f18cbe5cbbf7f6 | refs/heads/master | 2021-01-18T02:39:48.461738 | 2010-02-01T00:00:00 | 2010-02-01T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,421 | rd | downloadDB.Rd | \name{downloadDB}
\alias{downloadDB}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Download pre-assembled species-specific databases
}
\description{
Download or update one or more pre-assembled databases for selected species.
}
\usage{
downloadDB(speciesCode, db.list=FALSE)
}
%- m... |
a9ab90264f28cd5da7b4bd34f52ad8551fce922d | 7612f4d040ba14b99544587e357f46466f85c1eb | /R/TFM_R/.Rproj.user/F4600158/sources/per/t/21A29B5C-contents | 128cc8ffa6bb34eca9830fac79a19d73aab12dc8 | [] | no_license | vargasde/TFM_EAE | 765c0ab900cf2498cf385f224e0d227bad4b5441 | 9b3475010fac30965da0cc3d49a16200caa4cfee | refs/heads/master | 2020-04-21T09:47:38.170681 | 2019-06-09T12:47:32 | 2019-06-09T12:47:32 | 169,455,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,786 | 21A29B5C-contents | #TFM
#Importar data individual
# multas <- read.csv("MultasMAD.csv", header = TRUE, sep = ",", fill = FALSE, blank.lines.skip = FALSE)
#Path para ubicacion de archivos
folder<- "Users/Diego/OneDrive/Documents/Maestr?a/Clases/TFM/Codigos/TFM_EAE/Datos"
#Lista de archivos .csv a hacer read
file_list <- list.files(path... | |
551a791a6a972882e09d39ac10bfebace7821df5 | f8e58a4b8ee11502f1b07c08e93ae96de19574e1 | /R/gen.stat.miss.R | 2177e9dbaf8a6fabd6c4fc8cf90463ce2413fb73 | [] | no_license | cran/ARTP2 | 648609beb0d95088aabe373208f0070e8bc4a863 | 3d4401daa50050ac020c4612a2b819913bd2f549 | refs/heads/master | 2021-01-15T15:25:21.875869 | 2018-11-30T20:30:03 | 2018-11-30T20:30:03 | 51,500,259 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,206 | r | gen.stat.miss.R |
gen.stat.miss <- function(resp.var, null, family, G, X, lambda, options, cid){
if(options$impute & any(is.na(G))){
msg <- paste0('Imputing missing genotype with means in chromosome ', cid, ': ', date())
if(options$print) message(msg)
for(j in 1:ncol(G)){
id <- which(is.na(G[, j]))
if(... |
56101eda034121c808f36a622fad8fdd82901ef1 | 5c2374557193bd5a741aa36bf44532dc462003ae | /tests/testthat/test-block.R | 90040e0b1a4c6d6ada9bd51330d89e09186b594b | [] | no_license | andrie/pandocfilters | fab302e760702ec437188bb0da23a4b92d08c255 | 2fa1f1ee40168c4ccb6ac5c294877b5cc8349c53 | refs/heads/master | 2021-09-14T11:06:43.009929 | 2018-02-23T20:00:03 | 2018-02-23T20:00:03 | 105,012,941 | 0 | 1 | null | 2017-09-27T12:02:42 | 2017-09-27T12:02:41 | null | UTF-8 | R | false | false | 4,285 | r | test-block.R | if(interactive()) library(testthat)
context("block")
context(" - Plain")
test_that("Plain", {
## Test Str with Plain
x <- pandocfilters:::test(list(Plain(list(Str("Hello R!")))))
expect_equal(x, "Hello R!")
x <- pandocfilters:::test(list(Plain(Str("Hello R!"))))
expect_equal(x, "Hello R!")
} )
contex... |
008e25c387e5f0051875dd66a029919f05d8d669 | 5399a61f1c003bbc9eca544331b5d695667748f6 | /man/getPia.Rd | 73d63a12e3d9e37f5de5d6b9df959fcaa5f2f45b | [] | no_license | twjacobs/oasdir | 432da05d953d65aee89c1a484d0f7c335773a50f | f80745e60b4da16ee34425fe7bcb7ea0146f6c0f | refs/heads/master | 2021-07-17T08:42:15.372815 | 2019-07-24T13:36:38 | 2019-07-24T13:36:38 | 97,974,383 | 0 | 1 | null | 2018-03-23T23:57:24 | 2017-07-21T18:03:09 | R | UTF-8 | R | false | true | 746 | rd | getPia.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calculations.R
\name{getPia}
\alias{getPia}
\title{The Primary Insurance Amount (PIA) is the actual Social Security
benefit. It is based on the average of the highest 35 years of
indexed earnings.}
\usage{
getPia(piaBends = NULL, aime = NULL)... |
646c0c8c6f24debc978373af7ae73193653fa805 | 487e11bb17dbb49cca33e607e663be0e3292fc31 | /data-raw/student-survey.R | 32e2193a36b956ff42219c44f120c9465abd0c8d | [
"MIT"
] | permissive | IVI-M/dsbox | d1d0f4a4b87d56c622e4fdb0ea85e3e54722c25f | a7c4430491e602164f6584ea7c5e36a3be7b5501 | refs/heads/master | 2021-09-23T15:29:06.834150 | 2018-09-25T05:09:28 | 2018-09-25T05:09:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 168 | r | student-survey.R | # Read csv, save as rda.
library(tidyverse)
library(here)
library(usethis)
student_survey <- read_csv(here("data-raw", "student-survey.csv"))
use_data(student_survey)
|
21fcc8a138b3a08ffb99ea9e020703347cdf8b7f | 36adeff66a7d5a822a3d9971a28ae91bae3bf140 | /R/ClassMethods.R | f134eff5393bdb8a04908039582e7fa683e6798b | [] | no_license | cran/GAS | befe93225465c4d51cf0a2943e83cef6438c2239 | e588e3a10bf22cb7dff4a49a848baac63d743c3f | refs/heads/master | 2022-02-20T20:59:36.560819 | 2022-02-04T09:30:12 | 2022-02-04T09:30:12 | 65,930,586 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 40,874 | r | ClassMethods.R | setClass("uGASFit", representation(ModelInfo = "list", GASDyn = "list", Estimates = "list", Testing = "list",
Data = "list"))
setClass("mGASFit", representation(ModelInfo = "list", GASDyn = "list", Estimates = "list", Data = "list"))
setClass("uGASSim", representation(ModelInfo = "lis... |
c3fa7ffac715552409518febba7fc8c4a85c5c1b | bc3a58c0f3abd24f4f64f641152c09b79efefe38 | /man/PCASNPSDS.Rd | 9ca5556f4851854e47700297f0d01fdc5eb0c59c | [
"MIT"
] | permissive | isglobal-brge/dsOmics | 96aa2594cbe009f2899d99fdc5be43a96f50d6bf | 78fee19320cdf360db7ec1aed2fb07ee4c533951 | refs/heads/master | 2023-04-07T09:23:17.202083 | 2023-03-15T09:31:40 | 2023-03-15T09:31:40 | 158,839,360 | 1 | 12 | MIT | 2021-02-02T10:21:06 | 2018-11-23T13:55:17 | R | UTF-8 | R | false | true | 644 | rd | PCASNPSDS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/PCASNPSDS.R
\name{PCASNPSDS}
\alias{PCASNPSDS}
\title{Principal Component Analysis (PCA) on SNP genotype data}
\usage{
PCASNPSDS(gds, prune, ld.threshold)
}
\arguments{
\item{gds}{\code{GDS} object}
\item{prune}{\code{bool} \code{... |
b2185e0a589a5b9f8d25d68074448ade913cc725 | 2145787ee6f08e741dc6e6d78364781d394f6c12 | /hla3.R | f27a95b3fb5292e5c6102e3a8e8024fcfcc79af2 | [] | no_license | RonSchuyler/HLAEpitopes_R | 5d61ea44cde9bd35d63651b9346df69a91a0c999 | f4e7e187e90524c08365e409865c2b86cc0ebfa6 | refs/heads/master | 2020-06-07T08:32:04.745504 | 2019-07-24T18:00:30 | 2019-07-24T18:00:30 | 192,974,678 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 38,430 | r | hla3.R | #
# 10/1/07
# Functions to read data files and create hashes.
#
# Read file mhc.csv
# Return a hash of alleleName:count
get_allele_counts_slow <- function(loci="HLA.DRB1", dx="Control", file_name="../data/mhc.csv"){
# dx = diagnosis: "Affected" | "Control"
# "Control" = "Matched Control" + "Random Control"
m... |
6bc6f7be5822de9380598efec56c832279bb1480 | 439933a3fb21a29240ab4b04aebaced0569248be | /Mixed model post processing/orig/non SS/Cumulative Chinook Distribution.R | 345b63c45a7e90e9c3901055d8bae60ee4369041 | [] | no_license | nwfsc-cb/spring-chinook-distribution | e47b5e39f5ce2ab8f20413085bc13249ef3bec37 | 5bff26b6fe5102a16a9c3f2c13d659b7e831e03e | refs/heads/master | 2023-08-08T03:35:36.302066 | 2023-08-01T16:35:04 | 2023-08-01T16:35:04 | 128,123,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 54,550 | r | Cumulative Chinook Distribution.R | #### # MAKING SALMON DISTRIBUTION SURFACES
library(ggplot2)
library(reshape2)
library(dplyr)
# READ IN POSTERIOR FILE FROM MODEL FIT OF INTEREST:
results.dir <- "/Users/ole.shelton/GitHub/Orca_Salmon/Output files/_Mixed Results"
code.dir <- "/Users/ole.shelton/GitHub/Orca_Salmon_Code/Mixed model post processing"
s... |
4ce12d5d341edd7bf76187cefc6f09da8d841931 | bb72c39fdddaec1fe09483c61877980d86e49a00 | /code/summary_functions.R | bd6b79227c28bd7a38660bb75cac69738c200dd7 | [] | no_license | adorph/fireandfrag_reptile_msom_example | acd717764631183ba419c6eb19bf08fc0e55685a | 7407ee1854e14e59fd6c5819bb09a0a8a95c48a2 | refs/heads/master | 2022-12-24T15:02:32.988569 | 2020-09-22T04:03:49 | 2020-09-22T04:03:49 | 297,508,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,517 | r | summary_functions.R | ##
## This file contains summary functions used to evaluate model fit and how species respond to environmental
## correlates. Parts of these functions are based on code from Kéry M & Royle JA (2016) (Applied Hierarchical
## Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS:... |
a092947224986120fa5cafa12f34cda4d04d90fa | d7f68113ba841857d68f2ac452bcda91fe373cf0 | /Insight/Scripts/Imaging Data Import and Cleaning.R | 27820d0a48c4b0b272be654ee339bdfedd8b23e0 | [] | no_license | ramyead/Insight-Project-kNOw-Care | bc731398d3a49ac803af08d70ca58beb9a38d09d | 654c0203bc51036f671a14c17e259c8fa4f17b47 | refs/heads/master | 2021-01-17T11:58:33.172780 | 2017-06-26T00:45:01 | 2017-06-26T00:45:01 | 95,390,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 609 | r | Imaging Data Import and Cleaning.R | pacman::p_load(stringr, ggplot2, car, effects, lme4, lmerTest, dplyr, reshape2, tidyr, sjPlot, nlme)
imaging = read.csv("Data/Hospital_Revised_Flatfiles/Outpatient Imaging Efficiency - Hospital.csv")
imaging.n = distinct(imaging, Measure.ID, Measure.Name) %>% as.data.frame()
imaging2 = select(imaging, Hospita... |
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