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0818e2d9c5b0df8c3243eb971f117d74b7f2fb6b | 78e656557b5cc6b77f8a30a3792e41b6f79f2f69 | /aslib/man/writeASScenario.Rd | 99c6048e2feb464e118c83081edf018dc1ea7598 | [] | no_license | coseal/aslib-r | f7833aa6d9750f00c6955bade2b8dba6b452c9e1 | 2363baf4607971cd2ed1d784d323ecef898b2ea3 | refs/heads/master | 2022-09-12T15:19:20.609668 | 2022-09-02T17:48:51 | 2022-09-02T17:48:51 | 27,724,280 | 6 | 7 | null | 2021-10-17T17:34:54 | 2014-12-08T16:38:21 | R | UTF-8 | R | false | true | 734 | rd | writeASScenario.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/writeASScenario.R
\name{writeASScenario}
\alias{writeASScenario}
\title{Writes an algorithm selection scenario to a directory.}
\usage{
writeASScenario(asscenario, path = asscenario$desc$scenario_id)
}
\arguments{
\item{asscenario}{[\code{\li... |
31f8050a3061fbb7ccb592167191b02e6eadf599 | 83f845cf9d3987c8d816ca9b0d168c9c90e6cdf4 | /R/helpers.R | ce95b63c1bcd32e5c3a26d02a6fa3fcaae88de55 | [] | no_license | jslefche/piecewiseSEM | e5a572eef4538b6fb2cb0df00fa3e49f7c3c3872 | aac65aafd979b8dbce6c725b11b85123097f6fe7 | refs/heads/main | 2023-06-07T12:30:47.849089 | 2023-06-02T18:45:45 | 2023-06-02T18:45:45 | 22,606,015 | 145 | 53 | null | 2023-03-28T21:10:38 | 2014-08-04T13:55:07 | R | UTF-8 | R | false | false | 16,684 | r | helpers.R | #' Remove random effects from all.vars
#'
#' @keywords internal
#'
all.vars.merMod <- function(formula.) {
if(!any(class(formula.) %in% c("formula", "formula.cerror"))) formula. <- formula(formula.)
if(inherits(formula., "formula.cerror"))
gsub(" " , "", unlist(strsplit(formula., "~~"))) else {
n <-... |
f4e24bb74545ba6aeebc969ce1c9b844a40b57b5 | 6a8a10228612c00e09ea2a33cf0e62c37e7eabb2 | /R/writeVarianceExplained.R | 8cb8205f12fad52f4e48a3a2045ce49a76366c8a | [
"MIT"
] | permissive | avcarr2/MetaNetworkDownloadable | b69b22e56b062aff3225d4399fd894863a713abc | e94d406f676b48dd14d508360bd0093562e224df | refs/heads/main | 2023-01-27T14:15:53.268785 | 2020-12-02T22:38:24 | 2020-12-02T22:38:24 | 316,055,812 | 0 | 0 | MIT | 2020-12-02T22:38:25 | 2020-11-25T21:19:03 | R | UTF-8 | R | false | false | 436 | r | writeVarianceExplained.R | ## Variance explained by eigenproteins
writeVarianceExplained <- function(datExpr,
colors,
MEs){
varianceExplained <- propVarExplained(datExpr,
colors,
MEs,
corFnc = "cor",
corOptions = "use = 'p'")
write.csv(x = var... |
22970a85458b0dba43c5f2ae9277197cfec8e105 | 4a8188222cdb0935f963f4e2b5ef95905586e28d | /dataLoading.R | 38e752371fb55a29fd8e709892dcd0fb4851b079 | [] | no_license | Nevethan/SM-Exercises | dbbddca2521d78deea3258271798db1700b15a23 | 7a472c0f4d6ed53cca4a509c67e3539f69b441aa | refs/heads/master | 2018-05-31T11:36:28.908667 | 2018-05-31T05:45:39 | 2018-05-31T05:45:39 | 120,762,737 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,350 | r | dataLoading.R | #KNN Assignment
library("gmodels")
#source('C:/Users/Bruger/Desktop/SM-Exercises/loadImage.R', echo=TRUE) #This contains Smoothing override method
#source('C:/Users/Bruger/Desktop/Statistical Mashine Learning/BaseFolder/loadImage.R')
source('loadImage.R')
source('Methods.R')
#directory <- 'C:/Users/Bruger/Desktop/Sta... |
8dec3e49ce586b9e4aaa4434b79aed51a2e9bc28 | 0dc7121ee1e033ffca6575849e7ce9bed2c7d0c2 | /R/sharpe.R | cf8c8500daeac3f077602ab6010341fc3464cb1c | [] | no_license | gmahjub/steal-basis-r | 156341e2812eaf721890ad25c2dc2fc304130242 | 67d3db66adb1b3aeb91a167b00220a02afe50502 | refs/heads/master | 2021-03-31T01:11:27.351523 | 2018-08-05T03:13:19 | 2018-08-05T03:13:19 | 125,122,687 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,143 | r | sharpe.R | #' get_period_open_to_close_sharpe
#'
#' Sharpe of open to close returns.
#'
#' @param op2cl_returns_tibble
#' @param window_size
#' @param annualize defaults to FALSE, if annualize sharpe desired, set to TRUE
#' @param scale 252 for daily periodicity, 52 for weekly, 12 for monthly.
#'
#' @return tibble object with pe... |
528164cd8215694371e77f4e7d6f52453e43efc9 | c38a1efc9e7f53b6a9754b55a770f505ddacda8c | /qNewtonNLS/Analysis/analysis_fn.R | 0cd0b123aaea74e55990a7e3e9db4335b1bcc1ba | [] | no_license | bsh2/Experiments | 0b15cf996304b89386690c58acadb106054ab11e | a7434426db486542d3155e4b1bea0c3bf05913dc | refs/heads/main | 2023-04-20T12:21:09.515896 | 2021-05-20T12:05:20 | 2021-05-20T12:05:20 | 369,193,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,264 | r | analysis_fn.R | standardLoss <- function(pars, standardCriterion, loads, standardIndex){
squaredResiduals <- numeric(length(standardCriterion))
inputData <- data.frame(days = 1:length(loads),
loads = loads)
for (i in 1:length(standardCriterion)){
inputSubset <- inputData[1:standardIndex[i], ]
m... |
d38d1fa392a7df78c6396d49815ea5718fdd87d2 | 7e79dc0c40e45872ce00e7921dc110c2891fadea | /clase 02.R | 4ede99738f7f425681d03295aeaaa783e018e253 | [] | no_license | pmtempone/DM_Ec_Fin | 4f29675de38d2c0727abc47c721a9434d6949141 | 13ca54e7e96b1947221067daa0e669067fad8eeb | refs/heads/master | 2020-04-17T22:20:32.818267 | 2016-12-04T16:29:10 | 2016-12-04T16:29:10 | 66,108,683 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 709 | r | clase 02.R | -----#Carga de librerias#-----
library(RPostgreSQL)
library(dplyr)
----#clase 2-----
lift_dm <- function (pos,tot) {
(pos/tot)/(617/194289)
}
#P1 antiguedad < 12
(52/9529)/(617/194289) #lift alto
#ganancia
52*8000-250*9529
#P not 1
(565/(670+183525))/(617/194289)
#visa cuenta estado >10
(79/558)/(617/194289) ... |
1e70521974b8a7d4bc00a2df61cf7d8f4a5be330 | 57399b29b38f1d72bca228495f4da6d3dab0b0ae | /data/geoht/ncep_rf2_geoht_data_process.R | a8d316bc6fcf7bc24072e2357d04cfa3e77c226c | [] | no_license | zpb4/ms_project1 | db1b25f6c09c0b3e7a627c8585168d42a85fc30c | 4ebaad0da991a3e4fc0febbb546fc04d486a00b0 | refs/heads/master | 2020-06-22T19:01:52.968077 | 2019-08-01T16:56:38 | 2019-08-01T16:56:38 | 197,782,135 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,665 | r | ncep_rf2_geoht_data_process.R | #2) Process .nc files into RDS
#NOTE: Downloaded files from 1 Dec 84 to 31 Mar 19, 12539 days
ht<-500
ef<-'mean'#c('cf','mean')
for(k in 1:length(ef)){
nc1<-nc_open(paste("data/geoht/raw/hgt",ht,"_ncep_",ef[k],"_19841201_20190331_hres.nc",sep=""))
nc2<-nc_open(paste("data/geoht/raw/hgt",ht,"_ncep_",ef[k],"_1984... |
4ba49cb1970372b91b45f8252bc35f8cfc25b357 | d175703f8d1de8846380ae92af020ae70ed78843 | /global.R | 6d45cb13843e75e16f2b8d5b9a05bdbbc6e07c53 | [] | no_license | jordaoalves/Analisar-Gratificacoes---IPERN | 3cc54846144f571c5e207bbaf800f32f8b4e0f7a | 298f85b85e7df2e73233e4c19ba11c5f2b5fe1ff | refs/heads/master | 2022-04-24T14:22:16.713522 | 2020-04-27T03:33:45 | 2020-04-27T03:33:45 | 259,187,062 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 881 | r | global.R | library(shinydashboard)
library("shinyjs",warn.conflicts = FALSE)
library("stats",warn.conflicts = FALSE)
library("shiny",warn.conflicts = FALSE)
library("shinyWidgets",warn.conflicts = FALSE)
library(dplyr)
library(pdftools)
library(tabulizer)
library(tidyverse)
library(stringi)
library(stringr)
library(rma... |
c9dc7e2ee4f44bcdfc89684dedeb87c901abb242 | d17028a361bd8af0e1b30b0450d8eed299f5fa82 | /man/expr.dev.test.Rd | 8b5931840eccff45576e90418887d7c539b69684 | [] | no_license | cran/GlobalDeviance | 85518cc4350920dd25b425ab19f1758653fef96e | c198e13686ea5a0c4ee81f955430f0bf0e79266a | refs/heads/master | 2016-09-06T01:38:12.749061 | 2013-09-20T00:00:00 | 2013-09-20T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,073 | rd | expr.dev.test.Rd | \name{expr.dev.test}
\alias{expr.dev.test}
\title{Deviance Test}
\description{
Deviance permuation test.
}
\usage{
expr.dev.test(xx, formula.full, formula.red = NULL, D.red = NULL, model.dat,
test.vars, glm.family, perm = 100, method = c("chisqstat", "permutation"),
cf="fisher", adjust=FALSE, snowfall.args=list(... |
8a61bba2527ba2ab634a00ccc38922a1d8f87c6b | c6e8ce84341637f680872634e3c87bece43cef22 | /dmel_comparison.R | 99407c9a43f1b354bfb10627f804a803471f3547 | [] | no_license | Cy1614/GIA | bb2c8664a0b04ff1e31e00b0ef4c77c835c8bb07 | 74e9a0d5099f24fdc3e674c2cda91927518b968b | refs/heads/master | 2021-08-14T22:52:24.252108 | 2017-11-16T23:14:01 | 2017-11-16T23:14:01 | 109,289,065 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 12,349 | r | dmel_comparison.R | library(rtracklayer)
plot_data<-c()
gtf_data<-c()
file_mat<-matrix(c("genscan_dmoj_clean.txt","genscan_dgri_clean.txt","genscan_dvir_clean.txt","dmoj-all-r1.04.gtf","dgri-all-r1.05.gtf","dvir-all-r1.06.gtf","dmoj","dgri","dvir"),,3)
for( i in 1:3){
#i<-1
pure_gene_out<-fread(file_mat[i,1])
neg_begin<-pure_gene_ou... |
3bcd5ebe419f77311fbcd786fb7669865f6ed3ab | 6603cf711b61df3ca298c727894d88f759ab7a63 | /plot1.r | 1c0e2bcb8252e354e14964f156cf019f0fe5c87e | [] | no_license | ThiDur/-Coursera_hopkins_exploratory_data_analysis_wk1 | b62b43784cb511fe9147ceb8f75a32a14f65d845 | 797a5ee50ef1f77eea9fd6518b51414d83ccc019 | refs/heads/master | 2020-05-22T16:52:00.314647 | 2019-05-13T17:45:57 | 2019-05-13T17:45:57 | 186,438,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | r | plot1.r | library(tidyverse)
tbl_power_consumption <- read_csv2(unz("exdata_data_household_power_consumption.zip", "household_power_consumption.txt"), na=c("", "NA", "?"), col_types=list(col_date(format='%d/%m/%Y'), col_time(), col_number(), col_number(), col_number(), col_number(), col_number(), col_number(), col_number()))
tb... |
1760f7b55fba575147cbc512cdac720dffd37904 | ecfac3a7b04856a1b5a57e71210cd5ab0fb83787 | /Scripts/LiuEtAl-Reanalysis_final_MJ.R | a817ca4629acde1101b952b1a4cb99abbceb0937 | [] | no_license | nemochina2008/Liu_reanalysis | 5d2f4f6150d02afcdeb50520ad3c3fc8bbe508b8 | ecb3e708619df1f8ed96ef2a8fae1351a9f2ed07 | refs/heads/master | 2021-01-23T23:13:26.630115 | 2015-10-20T13:42:33 | 2015-10-20T13:42:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,925 | r | LiuEtAl-Reanalysis_final_MJ.R | #### Preparation and standard Package Loading ####
rm(list=ls())
library(ggplot2)
library(nlme)
library(MuMIn)
library(mgcv)
library(AICcmodavg)
library(scales)
library(lattice)
library(ncf)
library(plyr)
library(raster)
library(sp)
library(maptools)
#### Data loading and format ####
# Load in the supplementary data f... |
048060fe8440cc2453626c4ee9f5daee1e5e5d15 | 11ba9630777d42a9e92b5aea962a8f75ef5a6cde | /man/get_species.Rd | ca33859d8b47991d2cb6492afd421102bc4a3c3d | [
"CC-BY-4.0",
"MIT"
] | permissive | ramiromagno/ensemblr | afb3e03b283ed02bb35dd9cb1ad3d4ab3b912a62 | 23d56975c4be7d6646636d9ff12a44c593a240a0 | refs/heads/master | 2021-11-23T23:18:15.179804 | 2021-11-08T00:23:18 | 2021-11-08T00:23:18 | 198,703,945 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,212 | rd | get_species.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/species.R
\name{get_species}
\alias{get_species}
\title{Get Ensembl species}
\usage{
get_species(
division = get_divisions(),
verbose = FALSE,
warnings = TRUE,
progress_bar = TRUE
)
}
\arguments{
\item{division}{Ensembl division, e.g.... |
4d1f41a70a7fde0b8498e8aa70790c4386868554 | 32d6491e5f646c7110f63ced853c4e125e359af9 | /data scrape.R | d4838944ea121c98c372a4f900cc0c7d70532fa2 | [] | no_license | NoStaples/Twitter-Sraping | 59bc5740c07e90d126bdafe058e643103726e6a4 | 9c826da517fe4503ade6c4caf7f51aa783acc43f | refs/heads/master | 2020-03-30T06:35:22.454945 | 2018-09-29T14:39:48 | 2018-09-29T14:39:48 | 150,872,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,548 | r | data scrape.R | library(rvest)
library(tidytext)
library(dplyr)
library(RSelenium)
rd <- rsDriver()
remDr <- rd[["client"]]
remDr$navigate("https://twitter.com/PattyMurray")
#scraping_twitter <- read_html("https://twitter.com/realDonaldTrump")
#scroll down 5 times, waiting for the page to load at each time
for(i in 1:15){
r... |
f53d4038fff640a4f1436431171fc002b991e353 | ab219b48b72c851385a30aaed62b37707e25c009 | /man/plot_curve.Rd | 1a2a99fa7993640b98170ee5fc15394016d1aac8 | [] | no_license | morphr/morphr | 6526e3f93bedea134e11438a0957bf097071d597 | 0cc8f0ebfe82810f2d6d22ed136aaba0def4e8c0 | refs/heads/master | 2021-04-26T23:33:11.646234 | 2020-09-26T18:42:28 | 2020-09-26T18:42:28 | 124,013,754 | 1 | 2 | null | null | null | null | UTF-8 | R | false | true | 397 | rd | plot_curve.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/viz.R
\name{plot_curve}
\alias{plot_curve}
\title{Plot the curve p with color value}
\usage{
plot_curve(p, colorstr, l = FALSE)
}
\arguments{
\item{p}{coordinates of curve}
\item{colorstr}{color value}
\item{l}{Default value is False, which... |
08268939014270bcacf15d6014f09855b1d51eb4 | c0a85d0ac12178099174f7afbfc09b8eaac54a7d | /R/ApiClientStateGet.r | 30ecdc2a8d2a30052feee10befb887b83f0423bd | [] | no_license | voigtstefan/lykke | a2b7d4f5517c9e2a729c7bb9ea80208a0bfb00e3 | d9739ef6871acf6d0770c586ca779576a0bf1341 | refs/heads/master | 2021-06-15T11:38:32.620773 | 2017-04-10T11:25:01 | 2017-04-10T11:25:01 | 83,903,639 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 252 | r | ApiClientStateGet.r | #' @export
#' @importFrom httr GET
ApiClientStateGet <- function(email) {
base_url <- "https://api.lykkex.com/api/"
get_url <- paste0(base_url, "ClientState", "?", param, "=", email)
res <- GET(get_url)
return(content(res))
}
|
70db918894838b4d32d693e8ec78d985b5b61fcc | 3735a4dd8ad40ca28472adb79f7b24b882c2aedf | /R/dim.R | 628350e6361af712215596d43b745b432697c09d | [] | no_license | kar-agg-gen/DGEobj | efbd8aa9f086a8db97cb159a89a00f51beef51ce | 5382e6de0dbc4289312a58eccbfac657334fe18b | refs/heads/master | 2023-03-01T16:42:28.156818 | 2020-08-17T16:19:52 | 2020-08-17T16:19:52 | 304,116,160 | 0 | 2 | null | 2020-11-20T21:37:50 | 2020-10-14T19:31:31 | null | UTF-8 | R | false | false | 687 | r | dim.R | ### Function dim ###
#' Function dim
#'
#' Reports the dimensions of the assay slot (row = genes; col = samples).
#'
#' @author John Thompson, \email{john.thompson@@bms.com}
#' @keywords RNA-Seq, DGEobj
#'
#' @param dgeObj A class dgeObj created by function initDGEobj
#'
#' @return An integer vector [r,c] wi... |
10782d63c11485004544a47febae687850a4e044 | 532038c73c749dcccf9281a9b2800b2949ae040b | /R/GNARXdesign.R | 84a5841519596bb41944bc46af0f06d57314da36 | [] | no_license | cran/GNAR | 5c83dd0e0515a7694dc2e72ffaafe34787b84ca9 | abfb7ee822930710f64fc9e2be3b06b1eec403dc | refs/heads/master | 2023-05-13T16:52:47.056363 | 2023-04-27T19:10:05 | 2023-04-27T19:10:05 | 150,179,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,571 | r | GNARXdesign.R | #) CHANGES START HERE
GNARXdesign <- function (vts = GNAR::fiveVTS, net = GNAR::fiveNet, alphaOrder = 2,
betaOrder = c(1,1), fact.var = NULL, globalalpha=TRUE,
tvnets=NULL, netsstart=NULL, lambdaOrder=NULL, xvts=NULL)
#) CHANGES END HERE
#) lambdaOrder is a l... |
25763fea582be4a52685f503b167d9b077b8eeb6 | 4cabda4635edd3226a371403d608b6622fb7fd71 | /R/cake_filling_round.R | 673fcfa84dafc6cd9358c975d67572813030ff21 | [] | no_license | randallhelms/cakeR | a67fad8e563ec9dc267c6f2aaa79b3e2fc78ae44 | 923a718c6bc8c2a9354c83373ab94587da8df815 | refs/heads/master | 2021-06-04T16:58:28.637311 | 2020-03-19T21:58:16 | 2020-03-19T21:58:16 | 145,759,920 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,638 | r | cake_filling_round.R | cake_filling_round <- function(d,h,filling) {
require(stringr)
require(tidyr)
require(dplyr)
require(tibble)
d_range <- 6:12
h_range <- seq(3,4,.25)
price_list <- tibble(ingredient = c('icing_sugar','butter','double_cream','chocolate','rum'),
units = c(250,250,200,2500,700),
... |
07fd5e20c4219974424afb6cbd8743e93c333e05 | 970b53258a6b4b54e539ed34c792adde373495d5 | /src/ExploratoryDataPlot.R | faa4357880a641f9e614a14016292d275489f19e | [] | no_license | sfmb-mx/ExData_Plotting1 | 7f9338b298a70c3548243d05243a7412f6d3a2d0 | f7127855ccc9cfbc10ff124bec8bad496c099d9d | refs/heads/master | 2022-09-17T03:55:26.868635 | 2014-08-10T04:28:11 | 2014-08-10T04:28:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,849 | r | ExploratoryDataPlot.R | ### ExploratoryDataPlot.R ---
##
## Filename: ExploratoryDataPlot.R
## Description:
## Author: Sergio-Feliciano Mendoza-Barrera
## Maintainer:
## Created: Sat Aug 9 09:14:48 2014 (-0500)
## Version:
## Package-Requires: ()
## Last-Updated: Sat Aug 9 21:57:18 2014 (-0500)
## By: Sergio-Feliciano Mendoza... |
57620d59d5323da3cd005c6d9f2e9e1c718acbac | 7965c2d3c932f5de20e5431571beba251453654d | /tests/testthat/test_pmh.R | 0b9eabef0c9fd2fc49d6fd651b9ccddf4ee44329 | [
"CC0-1.0"
] | permissive | ZhangAngus/mimicfilters | d8e6419846680e7ff13c4c816f79dccc918b5734 | b6903be8cf89ceb53f15d70919db6874d9a0befe | refs/heads/master | 2020-05-14T19:22:09.134537 | 2016-11-18T21:58:28 | 2016-11-18T21:58:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,011 | r | test_pmh.R | context("testing functions for the past medical history extraction function")
temp_str = "The quick brown fox jumps over the lazy dog.\nPast medical history: pt history of smoking related emphysema. Social history: test"
temp_df = data.frame(id = c(1,2,3),
text = c("blah, blah\nPast medical histo... |
f09417fc75edfd545ce9194c62204fe1be974786 | 5c8d345990b7c849d2842633f0d98d992ea90b01 | /man/graph.diffuseP1.Rd | 2c40b4068afe18f68f068301a8f260b1a22a8f6f | [
"MIT"
] | permissive | BRL-BCM/CTD | 0aaf0fc12efd45b32d397d40e9e62743e7e59bf9 | 67e65f42f329f8c089b7ee35e1621a81f9fd8bad | refs/heads/master | 2023-08-17T16:06:43.246761 | 2023-08-10T06:53:12 | 2023-08-10T06:53:12 | 161,826,954 | 6 | 8 | NOASSERTION | 2023-08-10T06:53:13 | 2018-12-14T18:44:37 | Jupyter Notebook | UTF-8 | R | false | true | 3,059 | rd | graph.diffuseP1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/graph.diffuseP1.r
\name{graph.diffuseP1}
\alias{graph.diffuseP1}
\title{Diffuse Probability P1 from a starting node}
\usage{
graph.diffuseP1(p1,sn,G,vNodes,thresholdDiff,adj_mat,verbose=FALSE,
out_dir="",r_level=1,coor... |
f7d67e1fa353ed46a7ce1229c5c08b7c1ed2431a | eab2d831fb2d60ca810d4a82f00c431f7fd2a74f | /R/make_time_windows.R | fef5b36fde534262ff8d84d7d447e78a03e2b6d5 | [] | no_license | fossabot/circadian-dynamics | 51236723f853713081129502cfb1cb0ffb308530 | 959eebf225045767cee0905792ed87bc43d0b8ca | refs/heads/master | 2022-09-17T02:44:50.481183 | 2020-06-03T16:35:59 | 2020-06-03T16:35:59 | 269,140,681 | 0 | 0 | null | 2020-06-03T16:35:58 | 2020-06-03T16:35:58 | null | UTF-8 | R | false | false | 1,666 | r | make_time_windows.R | # Takes a time series data.frame and returns iterable time windows of any size
#' Create iterable time windows
#' @description Creates iterable time windows for a data.frame
#' @usage make_time_windows(data = NULL, window_size_in_days = 3, window_step_in_days = 1)
#' @param data a data.frame with 2 columns. Column 1 mu... |
afa9f4188bf41a7919c65095850587db6dee0896 | 3a0190e0fd8786e7975de831d4dd1c24619aea25 | /man/abilene.Rd | 702e7a74ca910316a343c6119b5e96b172e3670b | [] | no_license | sguo28/networkTomography | 9c0e5315be379bd2e6078cd48fd1ec15ea115b67 | 4ad68e48b029beb17b617847f5dd7ea85ec7d06f | refs/heads/master | 2021-05-27T03:51:29.681158 | 2014-01-10T03:47:10 | 2014-01-10T03:47:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,915 | rd | abilene.Rd | \docType{data}
\name{abilene}
\alias{abilene}
\title{Abilene data from Fang et al. (2007)}
\usage{
abilene
}
\description{
Data from the 12 node Abilene network from Fang et al.
(2007). Both the OD flows and the topology correspond to
the actual network. This is the X1 dataset from the given
paper.
}
\section{Objects}{... |
e9e33b1af0c1809cf0d8c7d945227a6681563d31 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/encode/examples/encode.Rd.R | 9f89a2587a6321a735544af5ff92661dbc057675 | [] | 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 | 933 | r | encode.Rd.R | library(encode)
### Name: encode
### Title: Encode Factor-like Levels and Labels as a Simple String
### Aliases: encode
### ** Examples
a <- encode(
x = list(
c('M','F'),
c(1:4)
),
labels = list(
c('male','female'),
c('caucasian','asian','african',NA)
)
)
b <- encode(c(1:2),c('pediatric','a... |
d0767a1dd3373bed7a67d63824a7d92a6589166a | 06aca8bcd3aa514e69740b022333fe1502947e82 | /syntax/00_start.R | 192b5c81a99c3229ccebf2f208a4d9cddf4cc2da | [] | no_license | m-sudmann-day/Kaggle-online-news-popularity | c673f0e337fb37aac08c1d42050fd6ae62afdd55 | f81110ab56fc2aaabba734e5a930dc12c35e9fd2 | refs/heads/master | 2016-09-13T18:18:31.986435 | 2016-04-19T22:00:06 | 2016-04-19T22:00:06 | 56,633,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,019 | r | 00_start.R | ################################################################################
# Barcelona Graduate School of Economics
# Master's Degree in Data Science
################################################################################
# Course : Advanced Computational Methods
# Project : Kaggle Competition
# Script ... |
f3ac58bc652c8e4698898ff010432608d47b6cb9 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2019/risk_factors_code/air/paf/save_results_parent.R | 967beb1d90f7f0369d34e326c5f83402b5ec7b5c | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,863 | r | save_results_parent.R | #----HEADER----------------------------------------------------------------------------------------------------------------------
# Author: NAME
# Date: 4/21/2018
# Purpose: Launches save results for air_pm, air_hap and air, new proportional pafs method
# source("FILEPATH.R", echo=T)
# qsub -N save_air -pe multi_slot 1... |
192491c197e3f8ba83a84cdc634be9a6e529de4e | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.networking/man/cloudfront_get_continuous_deployment_policy_config.Rd | cc0e6499c7e9c08e913bd7f9149ac4bbf2fcd312 | [
"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 | 707 | rd | cloudfront_get_continuous_deployment_policy_config.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cloudfront_operations.R
\name{cloudfront_get_continuous_deployment_policy_config}
\alias{cloudfront_get_continuous_deployment_policy_config}
\title{Gets configuration information about a continuous deployment policy}
\usage{
cloudfront_get_co... |
3c4ab216c425711829325b99008b475e7c78c46c | 8d34ff6dee9fcf523ca3202b971e1b39d07fc749 | /man/Tosls.formula.Rd | 0ad29cbf923f23bb6cb7d1cad1ff0245e357ba81 | [] | no_license | cran/tosls | 224bca64ae9d83a7db2e9e4ffab5197868796b11 | 189ebad66e70baa8258e2cb8920a422abc7554b5 | refs/heads/master | 2020-06-05T10:50:31.564993 | 2014-03-31T00:00:00 | 2014-03-31T00:00:00 | 18,368,877 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 270 | rd | Tosls.formula.Rd | \name{Tosls.formula}
\alias{Tosls.formula}
\title{formula}
\usage{
\method{Tosls}{formula}(formula, data = list(), ...)
}
\arguments{
\item{formula}{PIB~INF+TIR|Cap+m2r}
\item{data}{the dataframe}
\item{...}{not used}
}
\description{
formula
}
|
31764ca3aab04c801d87b7d0dd48b23165fc9882 | 975bbef69335a0f5a05c69adee0a61141311c669 | /bin/generateScreenBatchJobs.R | de513ad6acf3003cb9ab2ce04ab2baa6857a5868 | [] | no_license | mzager/crispr-screen-nf | 49386dc7aa15cbf6c706e40852d56476d0bfc12c | d02789306d7e3d6bbfc375b526505b37874e44e7 | refs/heads/main | 2023-07-06T18:03:33.149593 | 2021-08-11T21:15:11 | 2021-08-11T21:15:11 | 394,996,191 | 0 | 0 | null | 2021-08-11T13:20:29 | 2021-08-11T13:20:29 | null | UTF-8 | R | false | false | 3,361 | r | generateScreenBatchJobs.R | options(stringsAsFactors=F)
sampleSheet<-read.csv("/fh/fast/_SR/Genomics/ngs/illumina/solexa/SampleSheets/190812_D00300_0802_BH3FV2BCX3_lcarter.csv")
names(sampleSheet)[3] <- "Sample"
#TO DO: add some sanity checks
#fastx toolkit requires that the sample names be alphanumeric
sampleSheet$Sample<-gsub("-","_",sampleSh... |
8ae0bff341c354d461eb2b69536e847cb4e7e142 | f58d73bb5d624a78c329e79a60d5fb06b4c36837 | /inst/NRM/global.R | 3270fe2b6e3272eeac24687d849a09e426cb645c | [] | no_license | cran/irtDemo | be108fc0c36aa0328f1ed23b5d2c153ed3c0b701 | 3b36e362d74563f404374c8333f11cda023abc70 | refs/heads/master | 2020-04-06T07:01:12.326558 | 2018-04-05T19:29:46 | 2018-04-05T19:29:46 | 57,357,089 | 3 | 5 | null | null | null | null | UTF-8 | R | false | false | 757 | r | global.R | thetas <- seq(from=-6, to=6, by=0.1)
deltas <- seq(from=-6, to=6, by=0.1)
alphas <- seq(from=-6, to=6, by=0.1)
N = length(thetas)
# define probability function
p2num <- matrix(NA,nrow=N,ncol=1)
p3num <- matrix(NA,nrow=N,ncol=1)
p4num <- matrix(NA,nrow=N,ncol=1)
p5num <- matrix(NA,nrow=N,ncol=1)
p2fun <- function(alp... |
cb27b78d71e673e12890c33c6721ade4e9d2371d | af239a6dd830bf1c2d0096796384bcea303a113b | /PSet5/multicontrast.R | 0fa1b0bfe9fc01915d160f22c3616b0b2fcbaa8c | [] | no_license | davidlieberman/SDS363 | 701611bf972b992f63667d56122d0a0574edda67 | 0724a9de3456e8f8c1648f2e035506ea4423e78b | refs/heads/master | 2021-07-06T11:53:04.404758 | 2020-09-12T00:40:48 | 2020-09-12T00:40:48 | 169,842,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,527 | r | multicontrast.R | multicontrast <- function(contrast, data, grouping){
# Groups
groups <- as.vector(as.matrix(unique(grouping)))
# Multivariate Means for Each Variable in Each Group
M <- apply(data, 2, function(y) tapply(y, grouping, mean))
M <- M[match(groups, row.names(M)),]
# Counts for Each Group
N <- table(gr... |
1b22c227b5e808a4f44416caff8a56b8fe40f089 | ffbb81116f468b997ce33496367f01acc667b392 | /man/plotM.Rd | a38152a1af042b2af7cc3323925cda3ccbf2cd7b | [] | no_license | cran/ctrlGene | 2fb9e27eeaf24c5be29d8ee8495cf4a215fbe75c | 735bf747ffc6bd5346df1898a1577b7831d55ce4 | refs/heads/master | 2020-03-30T07:57:35.694817 | 2019-07-04T08:40:28 | 2019-07-04T08:40:28 | 150,977,263 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 609 | rd | plotM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geNorm.R
\name{plotM}
\alias{plotM}
\title{Plots average M of remaining genes}
\usage{
plotM(Mrem)
}
\arguments{
\item{Mrem}{the result returned by function of geNorm()}
}
\description{
This function plots the average expression s... |
a83537741471047ae31e611d4cf9ecf4d696521f | 257bd63361aa846ffdacdc15edaecf84c6364e78 | /rsou/pro2/ex16cor.R | 88e835dd9cf3f0e8dcb7da85c814264307d2d761 | [] | no_license | gom4851/hcjeon | 86dcfd05ce47a13d066f13fe187d6a63142fb9fe | 59a00ca9499f30e50127bb16eb510553e88ace43 | refs/heads/master | 2020-06-04T23:16:08.632278 | 2019-01-15T09:54:08 | 2019-01-15T09:54:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,463 | r | ex16cor.R | result <- read.csv("testdata/drinking_water.csv", header = T)
head(result)
summary(result)
sd(result$친밀도) # 0.9703446
sd(result$적절성) # 0.8596574
sd(result$만족도) # 0.8287436
# 정규성 확인
hist(result$친밀도)
hist(result$적절성)
hist(result$만족도)
# cov : 공분산 확인. 상관계수 확인 하기 위함.
cov(1:5, 2:6) # 2.5
cov(1:5, c(3, 3, 3, 3, 3)) # 0 , ... |
4b13f907b842d179090e469677795e99af01e0d1 | 398f4240620627d11768107f84da044f160aa065 | /text analysis II.R | 6e4c73beb062a95040288acaaa8010e6d18cd8fb | [] | no_license | Yu-study/R_language_script | b70e4351bb60ccf79e2dda7067da7bfd5e5fbc99 | 528083b3d4ab24f26513dc2a908f9760001e3a52 | refs/heads/master | 2020-07-03T11:18:21.626531 | 2019-08-14T07:47:02 | 2019-08-14T07:47:02 | 201,889,768 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 2,481 | r | text analysis II.R | # 设置文档存储位置
# setwd("C:/Users/apple/Desktop/Textasdata")
library(rJava);
library(Rwordseg);
library(tm);
# 安装中文TM包
#install.packages("C:\\SogouDownload\\tmcn_0.1-4.tar", repos=NULL, type="source")
library(tmcn)
library(tm)
library(Rwordseg)
# lecture<-scan(file.choose(),sep="\n",what="",encoding="UTF-8")
# names(... |
d82a1af7e7c3cd92d9cfb9d101f73f7c1d14e439 | a06941ba61a0ee4482c2cee1f80119b40b955b9f | /man/getFolds.Rd | e69fba51332d4652a84c2277bfc930c8ab87e385 | [
"MIT"
] | permissive | lwaldron/LeviRmisc | d44d2ff5a925e85ee359dd13d6ccf144b2547e0c | 410cae0cb9dfb73373b3837333952caece64e3e7 | refs/heads/master | 2021-01-19T01:44:15.971949 | 2016-10-06T18:51:16 | 2016-10-06T18:51:16 | 12,511,219 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 431 | rd | getFolds.Rd | \name{getFolds}
\alias{getFolds}
\title{split N samples into nfolds folds.}
\description{Convenient function for cross-validation
Function by Levi Waldron.}
\usage{getFolds(N, nfolds)}
\arguments{
\item{N}{number of samples}
\item{nfolds}{number of folds}
}
\value{integer vector indicating to which fold... |
202e77b2b2907b5ca671dfcd44077d28ca498bbe | 7be3d7253ab53bc69097d97aca5f380486f97763 | /plot4.R | 53ce5f13a5eb50eb95ff5f6d14f0e6cff5e6871a | [] | no_license | ubuntukeeper/ExData_Plotting1 | 331b3c9a0de30e5e42510f1da5763fecfa314de1 | a72776e48a2572c135961001c11b1578ac6c70a7 | refs/heads/master | 2020-05-29T09:52:04.205395 | 2015-11-08T19:40:53 | 2015-11-08T19:40:53 | 45,789,316 | 0 | 0 | null | 2015-11-08T16:50:39 | 2015-11-08T16:50:38 | null | UTF-8 | R | false | false | 1,335 | r | plot4.R | library(dplyr)
consmp <- read.csv("../supplemental/household_power_consumption.txt", sep=";", header=TRUE,
stringsAsFactors=FALSE, na.strings="?",
colClasses=c("character", "character", rep("numeric", 7)))
consmp_tab <- tbl_df(consmp)
consmp_tab <- consmp_tab %>%
filter(Date ... |
5af9ce057bcc43a345f807357b6d58a4eb45aa08 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/osqp/examples/osqp.Rd.R | 6a1ac9626d09a88bbc5f2d4d631a8c76c1f422d5 | [] | 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 | 638 | r | osqp.Rd.R | library(osqp)
### Name: osqp
### Title: OSQP Solver object
### Aliases: osqp
### ** Examples
## example, adapted from OSQP documentation
library(Matrix)
P <- Matrix(c(11., 0.,
0., 0.), 2, 2, sparse = TRUE)
q <- c(3., 4.)
A <- Matrix(c(-1., 0., -1., 2., 3.,
0., -1., -3., 5., 4.)
... |
347ddd95218ad94cdd510635719058e446450abd | ce208aa19eb9c9068d0ac206df4a16e25e4e11b5 | /8章/8_1_DM購買促進問題/data.R | dcaff0b915d1f993b436b6022dbfa88e77a5c3b8 | [
"MIT"
] | permissive | yukirin/Bayesianstatistics | 9f30cb349f1caed5c761fa342eef088a9c6dedad | 9bc67fb15386392cf0918d7e1bd716d3c56d83a6 | refs/heads/master | 2020-03-10T17:17:26.466509 | 2018-04-14T09:34:45 | 2018-04-14T09:34:45 | 129,496,676 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | data.R | N<-c(200,200)
n<-structure(.Data=c(128,72,97,103),.Dim=c(2,2)) |
ee6085389f17a66aa20ac166d5e7d5d33bff2793 | d771ff12fe4ede6e33699704efa371a2f33cdfaa | /R/package.load.R | d25ba9f843215d788dcefe292f4d12c653307a8e | [
"MIT"
] | permissive | ImmuneDynamics/Spectre | aee033979ca6a032b49ede718792c72bc6491db5 | 250fe9ca3050a4d09b42d687fe3f8f9514a9b3bf | refs/heads/master | 2023-08-23T14:06:40.859152 | 2023-04-27T00:31:30 | 2023-04-27T00:31:30 | 306,186,694 | 52 | 17 | MIT | 2023-08-06T01:26:31 | 2020-10-22T01:07:51 | HTML | UTF-8 | R | false | false | 1,656 | r | package.load.R | #' package.load - a function to load (library) all required packages.
#'
#' This function allows you to load all of the common use packages dependencies for Spectre.
#'
#' @return loads all the common use package libraries.
#'
#' @param type DEFAULT = "general". If "general", then loads packages required for general Sp... |
ddefd4e16dd1038d2eb3386b1e9125c0e5d50709 | 9fff0c4ea727dc15f59c70fcf58d112c27f9181f | /scri_cran/relative_expression_vs_osteo.R | 4a1a61b2bc106cdf5e03b3af7dff9b09994297df | [
"MIT"
] | permissive | kkdang/sage-data-analysis | d58a84daeffad6d3489f9ab00c6d967448c20109 | b78ed29bd74afd7ee1bff27f19b88211b7a012d4 | refs/heads/master | 2020-04-12T09:04:47.222396 | 2018-10-25T22:38:27 | 2018-10-25T22:38:27 | 17,002,799 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,642 | r | relative_expression_vs_osteo.R | #! /usr/bin/env Rscript
# Jan. 30, 2017
# KKD for Sage Bionetworks
library(synapseClient)
synapseLogin()
library('githubr')
source('/Users/kkdang/Computing/rgithubclient_authenticate.R')
sourceRepoFile(sageCode, "scri_cran/process_metadata_validation.R")
setwd('~/Computing/cranio/')
library(biomaRt)
library(gplots)
l... |
539a9e15f9442f8502092153c11dd0673b8b8180 | da7a9bee3e4aec666571e05b38eab56f0bafdc35 | /man/mp32wav.Rd | b0c3caa68974bea71229784123e4e62a6e452bd6 | [] | no_license | fburkitt/warbleR | d7c48b468f4f7c1461ba05d6f28962b1aaac7d97 | 9dea327f39905436067ca83284688764e7cdcddc | refs/heads/master | 2020-03-11T00:35:08.729072 | 2018-04-10T15:01:32 | 2018-04-10T15:01:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,876 | rd | mp32wav.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mp32wav.R
\name{mp32wav}
\alias{mp32wav}
\title{Convert .mp3 files to .wav}
\usage{
mp32wav(samp.rate = 44.1, parallel = 1, from = NULL, to = NULL,
normalize = NULL, pb = TRUE)
}
\arguments{
\item{samp.rate}{Sampling rate at which the .wav f... |
78db908096b5dbd51318b11b1cd717f0aadf0a41 | cae8ea79126f2dd6c62be26fc5791599302b5d70 | /paper_submission.R | a8222c87ab1502ba672a5d98f025e72208ee7f8a | [] | no_license | ggruenhagen3/tooth_scripts | d00e55768435faca5fbfa56fc78a9a1d06ac9ebf | c07200114dc9524c087d13cea15754002bab324d | refs/heads/master | 2023-08-30T21:30:35.373870 | 2023-08-28T14:02:53 | 2023-08-28T14:02:53 | 240,586,917 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 30,692 | r | paper_submission.R | # Load Packages
library("edgeR")
library("Seurat")
library("Matrix")
library("reticulate")
library("cowplot")
library("biomaRt")
library("stringr")
library("dplyr")
library("CytoTRACE")
library("ggplot2")
library('RColorBrewer')
# Load Seurat Objects
combined <- readRDS("C:/Users/miles/Downloads/rna/data... |
e7100b30f8451f37ebe9357a30a886d596297496 | 1f4fb6044e39e1c632c13487fb79f7e5bc836175 | /tests/testthat/test_colours.R | 70c53ce34611814f876708e6d418a265cbb92a1f | [] | no_license | ComputationalProteomicsUnit/cputools | 777a034e14f938ded2fee6672d3da64fb171488a | 17e4584cf1cb3f874dcc60202680e7dcdf306188 | refs/heads/master | 2020-12-24T15:14:22.000702 | 2017-01-10T17:33:21 | 2017-01-10T17:33:21 | 19,713,721 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | test_colours.R | context("colours")
test_that("", {
expect_identical(x <- darken("red"), "#B60000")
expect_identical(lighten(x), "#FE0000")
})
|
b09b165c8067243dc05882f5633929b404372655 | 0939caa7d9fb7dc60da87df12a8daf856fd79551 | /R/RcppExports.R | fc6379a34243fb27c219f595d2e4b8cf25dc1953 | [] | no_license | sjkdfsjkdf/screenmill | 7c2a6ed3388f78cbfe67e49159e1a4e56759299b | 0e0fafc19c0f0d3cf1dd985fc188aa0e4151e653 | refs/heads/master | 2021-01-11T03:39:30.369678 | 2016-10-19T22:29:14 | 2016-10-19T22:29:14 | 71,404,523 | 0 | 0 | null | 2016-10-19T22:27:25 | 2016-10-19T22:27:25 | null | UTF-8 | R | false | false | 390 | r | RcppExports.R | # This file was generated by Rcpp::compileAttributes
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
measureColonies <- function(img, l, r, t, b, background, thresh) {
.Call('screenmill_measureColonies', PACKAGE = 'screenmill', img, l, r, t, b, background, thresh)
}
nearestNeighbor <- function(x, y) {
... |
0d68552cf6cefae4cc8d10776edfde1d7a4ce4bd | 16d00df419d17a6e222e53342344fe89e67b096d | /code/priceImpacts.R | c15debde783c5642a70590d3dd16081367de1c57 | [] | no_license | mattia-cai/SICOMA_2020 | f35b3d9a2677bc583db7b7646489dda8e374842b | de31feb7da697ab6c40e84345ae4a224542bd927 | refs/heads/master | 2023-05-04T19:55:06.828982 | 2021-05-26T13:54:45 | 2021-05-26T13:54:45 | 292,814,843 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,110 | r | priceImpacts.R | rm( list = ls( ) )
# Compute the price impacts using the leontief price model
# Compared to the 1903 version, I have also included a second version
# of the analysis that accounts for case duration
# author: mattia cai
##############################################
### Warning: XLConnect conflicts with xlsx ###
####... |
77a29c222820315dcab66bfdaef79b31d441171f | 708f1b2117c87a9d82694718a95e90988181853f | /01 - R Clustering.r | 1c8e27b41687edeb102bfb67a39053b6c5a0e3f8 | [] | no_license | EugenioGrant/AprendizajeconR | f1494f0ad0cf8b8bd2469c5fe62f4d44a9d18ee0 | 79b0561fd76a5c25189540631222767f4c341c38 | refs/heads/master | 2020-04-26T21:09:29.582319 | 2019-03-04T23:00:37 | 2019-03-04T23:00:37 | 173,833,900 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,138 | r | 01 - R Clustering.r | #...............................................................
# Code: Machine Learning with R
# Obj: Unsupervised Learning: Clustering
# author: E. Grant
#...............................................................
#...............................................................
# 1. Working... |
24f8efd04fb60c9f9c33ae884cd8e6898ef40cfb | 57f780626d36e07c5a824b9e24e092d6110a12c9 | /style.R | ccb0087f0be578f33f900b81f76d5f3123fdfc06 | [] | no_license | phanhung2/rps10_barcode | d173676fd7aed616bdbd3aca1c9bc280c17e759a | 76a5cb1a251df62363202f9593cd731515a5d7a4 | refs/heads/main | 2023-08-18T21:10:26.027377 | 2021-09-23T18:23:06 | 2021-09-23T18:23:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 90 | r | style.R | knitr::opts_chunk$set(echo = TRUE, fig.width = 10, fig.height = 10)
# options(width = 100) |
636653b1f845efe6f7f963f3c6e4e47b244f0b1f | 9b0dcef4d9e58c1feb0a4e80e80c9331cfd36b64 | /man/geom_flat_violin.Rd | 79bd4634aaffaac84f1916d6516201e759602ef0 | [
"MIT"
] | permissive | letaylor/scclusteval | 89b0df0cfa044852c5767cc231a326490fe4b488 | c7ed17bf781b64a3f1822641fad08dc60b2a5909 | refs/heads/master | 2020-09-14T03:56:25.103488 | 2019-09-18T15:13:33 | 2019-09-18T15:13:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 882 | rd | geom_flat_violin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geomflatviolin.R
\name{geom_flat_violin}
\alias{geom_flat_violin}
\title{A Flat Violin plot}
\usage{
geom_flat_violin(mapping = NULL, data = NULL, stat = "ydensity",
position = "dodge", trim = TRUE, scale = "area",
show.legend = NA, inher... |
c5211079c83e1f40a49254da379051b527c00b0d | 3dd1b3bff4d96c8af5bf003b0998e7e91e25943e | /bin/rawSigNBModel.R | 93081943d73749b7c272d1a38627e1a97f91bb22 | [] | no_license | ParkerLab/chromatin_information | 87c6ceaedf4c47df4cdf8a242b41451ab572d483 | 643ea042984096416afd88174410e7c6acc5d72b | refs/heads/master | 2022-03-10T11:19:05.906325 | 2022-03-02T15:27:46 | 2022-03-02T15:27:46 | 206,384,812 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,799 | r | rawSigNBModel.R | #!/usr/bin/env Rscript
# Raw Signal NB binomial modeling
# This will take a raw signal distribution and fit it to NB to
# calculate probabilities scores.
# INPUT:
# 1) Motifs files
# 2) Factor name
# 3) Output file handle
# 4) Column where values are
# (mu) Manual NB mean
# (size) Manual NB overdispersion
# (TP) ... |
b653462335877b12f02160a9df452e1cedea51fc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ibmdbR/examples/idadf.Rd.R | 6156fae14bfe8ad8b7ad5873db3061638f25031e | [] | 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 | 404 | r | idadf.Rd.R | library(ibmdbR)
### Name: idadf,idaSave,idaUpdate
### Title: Query, store and update data in the database.
### Aliases: idadf idaSave idaUpdate
### ** Examples
## Not run:
##D # create connection to DB
##D con <- idaConnect("BLUDB", "", "")
##D
##D # create data.frame from table
##D df <- idadf(con, "SELECT * FRO... |
e73dbbc7def4e79ddd15494d5b72bbb07d394abd | 9948bca9f36c48dbe4329f6e030184c25d56ded4 | /man/nmis.Rd | 40c5fa80fd1f76ea376549afa6ffeb296c4f624e | [] | no_license | Aulide81/estadisticos | 0b75160fec317349fd0a4d20b820f7e538a4a491 | cf79860219eba96fc404eed83516a2852a506fc6 | refs/heads/master | 2020-05-21T04:50:10.973647 | 2019-10-10T14:33:31 | 2019-10-10T14:33:31 | 38,392,518 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 257 | rd | nmis.Rd | \name{nmis}
\alias{nmis}
\title{
Campos no validos
}
\description{
Devuelve el numero de campos en los que hay valor ausente, missing
}
\usage{
nmis(x)
}
\arguments{
\item{x}{
Matriz o data.frame
}
}
\value{
Vector numerico
}
\author{
Emilio Arenas
}
|
3192f3da051c92174dbfbd764281aa0ee74e7e2c | e04b360ce5307d44c775cde976a39ce71e93e89f | /R/globals.R | 66f0e01fcfd237ea3c3e2360e3be096201b9837b | [] | no_license | haukelicht/AnnotationModelsR | efe73983e0f0171c7cf75aa1688f1994aa6626fd | b507e0d5f581adcaea93fc214cbb05cd665afa21 | refs/heads/master | 2020-09-14T11:32:10.682643 | 2020-07-07T07:57:47 | 2020-07-07T07:57:47 | 223,117,022 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 315 | r | globals.R | utils::globalVariables(
c(
"."
, "_item"
, "votes"
, "tie_breaker"
, "majority_vote"
, "_label"
, "est_prob"
, "name"
, "value"
, "_annotator"
, "labeled"
, "est_prob"
, "iter"
, "ll"
, "labeled"
, "est_prob"
, "_prob_"
, "_label_no_"
)
)
|
078e99e1bd1ff6694bc66589cd0e6539386bdbef | e50a4d0be5dba098791f9e33482c88ad822689c9 | /R/readjustWindow.R | 7c2c36d8494aa998f8c27b0a91de32f312ebde80 | [] | no_license | protViz/prozor | 10e2349928f51de53acad1f0cdb49b44cf126846 | c2ec70174d4c6abe555ad5bdcb0615ad5c866387 | refs/heads/master | 2023-07-12T05:05:43.347932 | 2023-06-26T08:29:25 | 2023-06-26T08:29:25 | 41,495,885 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,184 | r | readjustWindow.R | # moves the windows start and end to regions where no peaks are observed
.makenewfromto <- function(windfrom, empty , isfrom = TRUE) {
newfrom <- NULL
for (from in windfrom) {
idx <- which.min(abs(from - empty))
startmass <- 0
if (isfrom) {
if (idx > 1) {
if (empty[idx] < from) {
... |
e13be05be1c60fc01e1d8015f3f3d554c5a64d57 | b60f4edb84e136ae67e2ad78bd69953d8d25f0e3 | /src/02_variant_filtering/03_qc_filtering.R | daab34ccdd509cd8890b7ea88b77f9cc879696b4 | [] | no_license | rivas-lab/sex-diff-biomarker-genetics | 1c7a40f023b8d62ec3dbb6378c78a73047c0036e | 43f7386787ee2ed35717d1c00af444db76d61698 | refs/heads/master | 2022-12-09T22:22:08.380682 | 2020-09-09T21:53:00 | 2020-09-09T21:53:00 | 103,570,042 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,921 | r | 03_qc_filtering.R | # qc_filtering.R
# E Flynn
# 11/9/17
# Code for running initial QC filtering for X, Y, XY (PAR), MT chromosomes.
# Filters by LD, missingness <= 0.1, MAF >= 0.01. We do not have HWE data for X chromosome - this is tricky to get.
DATA.FOLDER <- "data/"
QC.DIR <- sprintf('%s/chr_qc/', DATA.FOLDER)
LD.DIR <- QC.DIR
ge... |
329d41c76aa00314813cdf4d472d8f63d0941f93 | 49ff0bc7c07087584b907d08e68d398e7293d910 | /mbg/mbg_core_code/mbg_central/LBDCore/R/get_populations.R | e2f24a7ee7692c5dad2514ed031aafcafc728c50 | [] | no_license | The-Oxford-GBD-group/typhi_paratyphi_modelling_code | db7963836c9ce9cec3ca8da3a4645c4203bf1352 | 4219ee6b1fb122c9706078e03dd1831f24bdaa04 | refs/heads/master | 2023-07-30T07:05:28.802523 | 2021-09-27T12:11:17 | 2021-09-27T12:11:17 | 297,317,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,053 | r | get_populations.R | #' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param location_set_version_id PARAM_DESCRIPTION
#' @param year_start PARAM_DESCRIPTION
#' @param year_end PARAM_DESCRIPTION
#' @param by_sex PARAM_DESCRIPTION, Default: 1
#' @param by_age PARAM_DESCRIPTION, Default: 1
#' @param custom_sex_id PARAM_DESCRI... |
8709e7b8946880d6ca74f790cd1b3ef59a79394f | f23c29c28a3aa386372d6f0e0e9faae74cf10296 | /man/journals.Rd | 3e3649cd01c5b1a9be39a8e737659bb2854e50b4 | [] | no_license | xiaoningwang/rticles | 1d9318ea9f95d3ff2078a2dec5d95db09a091cc7 | 5e81f6aa7ccfbdfb657b3c5786b56bb5ccb4cf88 | refs/heads/master | 2023-05-11T19:28:08.593412 | 2023-04-25T19:14:14 | 2023-04-25T19:14:14 | 97,591,351 | 1 | 0 | null | 2017-07-18T11:42:17 | 2017-07-18T11:42:17 | null | UTF-8 | R | false | true | 642 | rd | journals.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{journals}
\alias{journals}
\title{List available journals}
\usage{
journals()
}
\value{
A character vector of the journal names.
}
\description{
List available journal names in this package.
}
\details{
These names can be useful... |
7abfc77816c97c778ed6df24ca975dda65a39851 | 5e2016422948ec45305bd96c72ff0690f1f8fe0e | /R/setup.R | cebac0313511676e86ef6bb576f87efa1c2a1276 | [] | no_license | nm-training/rsetup | d45102fbadea50671c3953205afcb067b8951289 | f8c65c455dd80f3791b6e7a9b6cfe8654f9b7d36 | refs/heads/main | 2023-06-24T10:31:08.276688 | 2021-07-25T07:32:39 | 2021-07-25T07:32:39 | 355,864,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,123 | r | setup.R |
# download course file and unzip it
get_course <- function(url_path, set_path) {
options(Ncpus = 4)
# if (require(rsetup)) { remove.packages("rsetup") }
usethis::use_course(
url = url_path,
destdir = set_path
)
}
# get course from url and activate the project
set_project <- function(url_path, set_pa... |
4e962c2a12d8422eb98538d342fb581d8540ded0 | 014aef7c521f0fc8e8fee29789095fc2ccdc10ef | /lab3/q3.R | bbfb5577bf72ccec075a136898b2bdc68a8df114 | [] | no_license | listerys/R-Lab-Kiit | d68096ff6058e1516add5adb0b2bb519cf7f3a32 | bb4ad6ab3bc9e9ae5d3b231cc7a19fcec51acc13 | refs/heads/master | 2022-12-01T06:34:54.990834 | 2020-08-18T04:27:50 | 2020-08-18T04:27:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 273 | r | q3.R | # Title : TODO
# Objective : TODO
# Created by: KIIT
# Created on: 8/18/2020
nterms <- as.integer(readline("Enter the Number of terms "))
n1 <- 0
n2 <- 1
count <- 2
while(count < nterms) {
nth <- n1 + n2
print(nth)
n1 <- n2
n2 <- nth
count <- count + 1
} |
98070c8e975d3445a0356b0fee5be28e94f76dad | 08c557f4e442c3238634fd57f09d966ffa114fc9 | /run_analysis.R | 8a6e494c54578c6bedb449740c4a2b9b1c89d4dd | [] | no_license | vericone4/GettingAndCleaningData | f7a35a4804f5e64920d5eb2ba0cfdfcfc1788f8c | 12e4ed697f521c0fd4adf85e1247ef47185378ac | refs/heads/master | 2021-01-20T02:02:03.459500 | 2014-09-20T06:59:23 | 2014-09-20T06:59:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,864 | r | run_analysis.R | run_analysis<-function(){
#**************I/O for all the given data files**********************#
# set the wd
setwd("~/Data/Venkata/Personal/Coursera/Getting_and _Cleaning_Data/Working_Directory")
# identify the data directory
data_dir<-"Data/getdata-projectfiles... |
5f6264ccbb6f1b5a070ff0fa807834ecf1745f5b | 288b4b6998906714ab368e0ee14c70a4059be4ab | /data-raw/dat.lee2004.r | e5afa0b546435b8f95f268d298ec33035a51436b | [] | no_license | qsh7950/metadat | f6243a382c8c0e3f4c9a0e2cd657edb0ffa3e018 | 5c70fa63d7acfa1f315534fb292950513cb2281e | refs/heads/master | 2021-02-26T06:42:18.937872 | 2019-10-21T21:58:33 | 2019-10-21T21:58:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 140 | r | dat.lee2004.r | dat.lee2004 <- read.table("data-raw/dat.lee2004.txt", header=TRUE, stringsAsFactors=FALSE)
save(dat.lee2004, file="data/dat.lee2004.rda")
|
759950d0a3d53d6da20c54c774c002aaa1c4ac30 | 5b871b8f9db99ab2c88a60bb3a1edbf0fd8aec2f | /R/finish-methods.R | cd258f23e2b0fcdfc4b34ee16a702f11b27042b7 | [] | no_license | JasonHackney/ReportingTools | f461f158ff7813a0e35e54a3201037e0cae89e1c | 2ead9868b44cf9f6829f4aad4d545946ca96250f | refs/heads/master | 2021-08-01T15:05:39.198739 | 2021-07-30T15:38:45 | 2021-07-30T15:38:45 | 100,539,925 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,032 | r | finish-methods.R | setMethod("finish",
signature = signature(
publicationType = "HTMLReport"
),
definition = function(publicationType, ...){
closePage(page(publicationType))
}
)
setMethod("finish",
signature = signature(
publicationType = "DataPackage"
),
definition = function(publicat... |
cd62128b104c26a3e4121de3f938c83d6059121d | d835f0602ed9c05fc66f94b8a43e825b9b76d919 | /archetypes/01_extract_covariates/03_bound_covs_by_transmission.r | ba45e9da04b7d8930df49e530352a89cc6819656 | [] | no_license | InstituteforDiseaseModeling/archetypes-intervention-impact | a41e15ad3b1d5ff00ca52dada12c553fa6fc847e | e7be7be6a689d15da4a8eb658cf80d65a65cbf19 | refs/heads/master | 2023-08-18T09:44:54.709338 | 2021-09-09T04:00:06 | 2021-09-09T04:00:06 | 128,471,001 | 0 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,513 | r | 03_bound_covs_by_transmission.r | ## -----------------------------------------------------------------------------------------------------------------
# Seasonality Classification
# 00_bound_by_transmission.r
#
# Amelia Bertozzi-Villa, Institute for Disease Modeling, University of Oxford
# May 2018
#
# For a given covariate and continent of interest,... |
ca34b804b317eab70584a7011bc9015711ea399a | fbaaee512a18759486d3333f71a407e07b2ba105 | /8_machineLearning/2_SP/PrintHeatMap.R | cd25d0cc21fc3928338c3ec0fbfc5c092af6c4cc | [] | no_license | LeonardoMorenoG/ViPhOGs | 3b3597073552f8fe7933b7b7c749b0ce729c0041 | 63cd5f6ec140a499f3316a2f0528a4ae2e87c1a3 | refs/heads/main | 2023-01-28T08:28:07.466080 | 2020-12-10T14:00:55 | 2020-12-10T14:00:55 | 320,289,671 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 935 | r | PrintHeatMap.R | #!/usr/bin/Rscript
rm(list=ls());
library(gplots);
Inputs<-commandArgs(trailingOnly=TRUE)
#Inputs=c("~/Dropbox/Work/MalCheck/", "ToHeatMap_Consensus.txt", "HeatMap_consenus.pdf", "white", "blue", "red")
##set working directory
setwd(getwd())
#setwd(Inputs[1])
#Load file in R, assumes first row is header and first col... |
e900388d917185aa267f81ea5fd76b874133db16 | 60ed8fb3dbd7199f2efd95857d0544f255024c51 | /redfin.R | c06ef316c210f04b65992df6c414b6d943089f30 | [] | no_license | hack-r/yang | 33e990e592a79160bf73375959802570bf071a81 | c9e9bab0c0d9b8de766a1d304e1ba2a64c46ac2a | refs/heads/master | 2021-01-10T11:04:52.090264 | 2016-04-20T18:41:26 | 2016-04-20T18:41:26 | 54,159,228 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 466 | r | redfin.R | # Options -----------------------------------------------------------------
setwd("C://users//jmiller//Desktop//yang")
# Functions and Libraries -------------------------------------------------
pacman::p_load(data.table, rvest, stringi, sqldf, XML)
# returns string w/o leading or trailing whitespace
trim <- function... |
7ccb870422ac04d102707d5868d9e636a1c1f710 | 2bd971cc829a8639792f615d48fe143bd898a821 | /modules/Init/init_tree.R | fef1dab1e7c82350bd74662e6597130b970ce811 | [] | no_license | DavidBarke/shinyplyr | e2acaf11585c3510df38982401fd83c834932e3d | ddc30c2c2361cec74d524f2000a07f3304a5b15f | refs/heads/master | 2023-04-20T07:43:47.992755 | 2021-05-11T10:56:49 | 2021-05-11T10:56:49 | 250,501,858 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,240 | r | init_tree.R | init_tree <- function(tree, .values) {
# Add pkgs node
pkgs_node <- tree$get_root_node()$add_child(
explorer_class_id = "__group__",
object = Object$new("Package datasets"),
removable = FALSE,
return = "child"
)
# Extract all data.frames out of datasets and fill pkgs_node
add_pkg_datasets("... |
bcead90976b0d2d4688f276950ddcf20ba4dfb9d | 599d35b03b589f634433653629cdc33f7bdc2f17 | /R/computing_priors.R | 5aee5bfd9c7c34c74159ea0def30ed07d3258424 | [] | no_license | francescodc87/IPA | 28f111a7b5866c58f93024623c0cce34ea312f57 | 1325ecfbf52ff277c6fa5c5fc099213ca89a509f | refs/heads/master | 2023-04-09T20:47:37.132055 | 2023-03-28T14:41:20 | 2023-03-28T14:41:20 | 175,849,532 | 7 | 3 | null | 2019-07-29T10:12:02 | 2019-03-15T15:50:50 | R | UTF-8 | R | false | false | 5,225 | r | computing_priors.R | #' @title Computing prior probabilities associated with putative annotations
#'
#' @description
#' This functions takes as input the output of the find.hits() function and computes the prior
#' probabilities for the putative annotations
#'
#'
#' @param Hits The output of find.hits() function
#' @param dataset A matrix... |
a5f8250cc3bb48d6919a9849328d26ea7bb1a4ed | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/futile.matrix/examples/RandomMatrixModel.Rd.R | ebbe4bb78d7287dd41fe727f361b4512eb208d47 | [] | 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 | 355 | r | RandomMatrixModel.Rd.R | library(futile.matrix)
### Name: RandomMatrixModel
### Title: Type constructors for random matrices and ensembles of random
### matrices
### Aliases: Ensemble JacobiMatrix JacobiModel RandomMatrixModel
### WignerMatrix WignerModel WishartMatrix WishartModel
### ** Examples
model <- WignerModel(10)
m <- rmatrix(... |
74fc88d1d2798c429473d1bbfe63c9724b795c8c | 7a91b0eec2b3ab87ef6c868d1203063fa97b43d4 | /R/glmrobMqle-DQD.R | e69fa113740deceb11adb67ac9cbfd64c0017525 | [] | no_license | cran/robustbase | a40f49c769a17af095660947616d9fbbbc3cf1e4 | 335b69f2310bd21ca4cdfc17a2a99ebbcad84017 | refs/heads/master | 2023-06-30T09:52:16.026413 | 2023-06-16T12:30:02 | 2023-06-16T12:30:02 | 17,699,299 | 7 | 8 | null | null | null | null | UTF-8 | R | false | false | 5,620 | r | glmrobMqle-DQD.R | #### Quasi-Deviance Differences --- for Model Selection
#### --------------------------------------------------- -> ./anova-glmrob.R
## MM: These function names are really too long
## but then, they are hidden in the name space ...
## (Maybe it would be nice to do this as one function with "family" .. )
glmrobM... |
e25cdb1602b57d5ef216a60334fb814d2e3ba4f3 | e0ce0f89a6ae408f4dbce9fe784ff0fe3e7c8344 | /R/plotPath.R | 64843e49a4dc1d504d87dc6f99f72636b4e40f84 | [] | no_license | rtlemos/scurvy | 6cc932df8c4e454e5fe0a279828acdfd8af92831 | 5fbcd7ddb69283ecc25c4afbcbf18d315ca6c953 | refs/heads/master | 2020-03-29T14:45:36.104275 | 2019-11-30T23:24:17 | 2019-11-30T23:24:17 | 150,031,915 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,279 | r | plotPath.R | #' Plot a path
#'
#' @param dataset List with data, path, lat, lon, etc.
#' @param plot_data Plot the data?
#' @param lat_bounds Latitude bounds (for zooming in)
#' @param lon_bounds Longitude bounds (for zooming in)
#' @param colored_line Color the path: NA=>no, "group"=> by group ID, "value"=> by group value
#' @para... |
712e084d78d88fd2966bbeda0d788b30e3e61064 | a593d96a7f0912d8dca587d7fd54ad96764ca058 | /man/ml_evaluator.Rd | b4f92afbe7badef5468175be263f1f3eb00ef46b | [
"Apache-2.0"
] | permissive | sparklyr/sparklyr | 98f3da2c0dae2a82768e321c9af4224355af8a15 | 501d5cac9c067c22ad7a9857e7411707f7ea64ba | refs/heads/main | 2023-08-30T23:22:38.912488 | 2023-08-30T15:59:51 | 2023-08-30T15:59:51 | 59,305,491 | 257 | 68 | Apache-2.0 | 2023-09-11T15:02:52 | 2016-05-20T15:28:53 | R | UTF-8 | R | false | true | 4,013 | rd | ml_evaluator.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ml_evaluation_prediction.R
\name{ml_evaluator}
\alias{ml_evaluator}
\alias{ml_binary_classification_evaluator}
\alias{ml_binary_classification_eval}
\alias{ml_multiclass_classification_evaluator}
\alias{ml_classification_eval}
\alias{ml_regre... |
08ef682b822f82024273fc25f220927bdf69069c | 0da2bf00dd26e37b37ede0492b0c1b30ea57085d | /analysis/experiment4.R | 2d7acba8a3f41fb6f7b79976039693bcb779275f | [] | no_license | Srcd-managing-ed/aliens | b1d1ab8b13db818f674a94943c6ab7e2813f2019 | b1deacc7e38e1c5b80bf847db71fb9c364851d7d | refs/heads/master | 2021-01-22T12:49:49.592963 | 2015-06-28T13:20:03 | 2015-06-28T13:20:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,531 | r | experiment4.R | rm(list=ls())
library(plyr)
library(reshape2)
library(ggplot2)
library(lme4)
library(bootstrap)
raw.data <- read.csv("../data/experiment4.csv")
## for bootstrapping 95% confidence intervals
theta <- function(x,xdata,na.rm=T) {mean(xdata[x],na.rm=na.rm)}
ci.low <- function(x,na.rm=T) {
mean(x,na.rm=na.rm) - quantile(... |
4f69423739181e62db3e80c474ffe4bcccadac6e | 0de7dffc4ca5b21247e7017c97943be054f0cfc1 | /Lib/scan_ssms/correlated_from_pvals.R | 6771c4a808208c340ac02ca09ad2ee9df9b735d3 | [] | no_license | morrislab/RNAcompete-S | f29b97d2cc8f4204c43fec0c15dff6d3fc516603 | 433756dd865ef5413989826158d6b1affbb05400 | refs/heads/master | 2021-01-19T01:03:12.935318 | 2017-03-15T22:45:13 | 2017-03-15T22:45:13 | 84,678,329 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 815 | r | correlated_from_pvals.R | #!/usr/bin/Rscript
args = commandArgs(TRUE)
infile = args[1]
outfile = args[2]
library(Matrix)
df = read.table(infile, header=T)
motifs = levels(df$motif_ID_1)
combos = combn(motifs,2)
pvals = matrix(NA,length(motifs),length(motifs))
for (i in 1:ncol(combos) ) {
df_subset = subset(df,motif_ID_1==combos[1,i]&m... |
f79fb335a7a4afccc890775e376dbccb1a61ed69 | 7044839eae96eaec4642a9fd4be56ceca84ca657 | /tests/testthat/test-clustering.R | 98c84c59a165ccdb2d16a2c79205654801520a4c | [
"MIT"
] | permissive | cugliari/iecclust | bb58e11c35bece256d613f0824ad98967e4e441c | 1b6e97a0c317a8f1959b5d927a0787a36726a4de | refs/heads/master | 2021-08-19T19:50:24.825273 | 2017-11-27T09:01:53 | 2017-11-27T09:01:53 | 110,530,261 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,621 | r | test-clustering.R | context("clustering")
test_that("clusteringTask1 behave as expected",
{
# Generate 60 reference sinusoïdal series (medoids to be found),
# and sample 900 series around them (add a small noise)
n <- 900
x <- seq(0,9.5,0.1)
L <- length(x) #96 1/4h
K1 <- 60
s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i)... |
afb9bcd337eab5ef4e418c6c974959149d120ac5 | 0ff9cab1383b811353c8120eba24c629ef05c51c | /code/00_PlotSelection.R | 327d2e4f71622da7bcef34932b98a0d39a670b54 | [] | no_license | miquelcaceres/INFORMED_CaseStudy | 4d0a01abfdebb2b33c766cf2b70e2948b866b07d | 5f1e17f8c28eecc9016849e097de37d6eb557730 | refs/heads/master | 2020-09-19T16:56:12.803211 | 2019-11-26T16:11:19 | 2019-11-26T16:11:19 | 81,959,619 | 0 | 1 | null | 2017-05-17T12:06:41 | 2017-02-14T15:30:36 | R | UTF-8 | R | false | false | 4,827 | r | 00_PlotSelection.R |
# Script to select the plots with pure Pinus nigra stands (>80% BA) within Solson?s
library(medfate)
#setwd("D:/Recerca/Lab/CaseStudy_INFORMED/")
# Load coordinates
ifn3_xy <- read.delim("D:/Recerca/Datasets/IFN/IFN3/ifn3_xy_cat_unique.txt", row.names=1, header=TRUE)
coords = ifn3_xy[,-1]
r... |
e174b7f04e63d582e15829c146a4ff36c01d01fe | 4cf827146404badf6c4ffcc3237187ece23b6084 | /man/Prostate2000Raw.Rd | 25c3a581f28fc099c2b848b5e4ddff3a1ffe13f4 | [] | no_license | Git294/ChemometricsWithR | 1f883099604bfd375a54350ebdc067420e6037fe | 9d15f50972ffa7fe254567c097eab7cbced586c6 | refs/heads/master | 2022-12-06T09:17:15.609101 | 2020-09-02T14:28:44 | 2020-09-02T14:28:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,894 | rd | Prostate2000Raw.Rd | \name{Prostate2000Raw}
\alias{Prostate2000Raw}
\title{Prostate Cancer 2000 Raw Spectra}
\concept{prostate cancer}
\description{A data object of class \code{msSet},
consisting of 654 mass spectra (327 spectra in duplicate) from 2000 to
20000 Da, which were generated from patients with prostate cancer,
benign prostatic h... |
692944faa6455e0b9935615a3edcce5992ff6e08 | 4cee6dec70875ca85f20dd738932be86f361a63e | /pkg/man/findDeseqFactorsForFractions.Rd | 73169680d09e3a52d214294ff6bd855563d2028f | [] | no_license | dieterich-lab/pulseR | 9b7114769b48a305ba0a11357226e8f774b73a20 | 1323b378e95b483c8bda99d6c71befccd45c810f | refs/heads/master | 2021-01-18T20:40:00.474158 | 2018-10-26T10:45:32 | 2018-10-26T10:45:32 | 72,013,067 | 2 | 4 | null | null | null | null | UTF-8 | R | false | true | 612 | rd | findDeseqFactorsForFractions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pulseData.R
\name{findDeseqFactorsForFractions}
\alias{findDeseqFactorsForFractions}
\title{Calculate normalisation factors}
\usage{
findDeseqFactorsForFractions(count_data, conditions)
}
\arguments{
\item{count_data}{integer matrix, colnames... |
68e550f90d7671f6d0a22d73c9bb658c358cf88c | 9072500aa28ba2f603688fa9fba69d6b7f9fd6b7 | /ProjectTraining/NormalDistributions.R | e5c9f1d5b5f5939b5fe4e2f176114f7699ea29d6 | [] | no_license | lchi91/r-training | df4f827cc29096c3063cc6c4a23617a770e75951 | 9c9f7b7d18f0523d4a7165061f2abfd5c7d54b7c | refs/heads/master | 2021-05-22T18:23:14.819224 | 2020-04-04T16:08:25 | 2020-04-04T16:08:25 | 253,038,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 642 | r | NormalDistributions.R | library(downloader)
url <- "https://raw.githubusercontent.com/genomicsclass/dagdata/master/inst/extdata/femaleControlsPopulation.csv"
filename <- basename(url)
download(url, destfile=filename)
x <- unlist( read.csv(filename) )
# make averages5
set.seed(1)
n <- 1000
averages5 <- vector("numeric",n)
for(i in 1:n){
X ... |
c81d43f9d8c920164a897e65a10d7c5b12712a9f | 9150e6280152559008d7cebd796b739f29b1622a | /Assemble_Source_Data.R | b5bc33e17de220e27025977aa47a4e3053c3e58f | [] | no_license | ImprovementPathSystems/Measuring_Abnormality_in_High_Dimensional_Spaces | 30604aea6d1a5c896bcbfba93e47ebc3f3c104c1 | ab4eeaccf1d975ffcad3b1c8bb57bd299ee6ed2b | refs/heads/master | 2021-04-27T08:04:38.126016 | 2018-02-23T16:45:07 | 2018-02-23T16:45:07 | 122,647,240 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 9,164 | r | Assemble_Source_Data.R | #' create a list of all the packages needed to run this R File.
all_packages_needed <- c("reshape", "dplyr","proxy", "matrixStats","R.methodsS3", "zoo","gtools", "Hmisc","knitr","plyr")
#'
#'if an R package that is needed to run this code is not installed, install it.
packages_to_install <- all_packages_needed[!(all_... |
8a31590f7b64c91de922387ab25b6d07e0f18b38 | 4a40b201dc14a357e7f5377a3492652230c5d7eb | /R/00_importing_global-fishing-watch.R | 07cc264e63a98b712be37f7eecdd91893ec11f34 | [] | no_license | fishvice/VMS-training-2018 | dd3e2d06fe1cf35f9b210da11315ac2b2a72d37d | f6063177b5a4d7db8c4a571ea348b4265c59dc3b | refs/heads/master | 2020-03-17T17:41:19.535339 | 2018-05-29T19:07:22 | 2018-05-29T19:07:22 | 133,798,228 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 745 | r | 00_importing_global-fishing-watch.R | library(tidyverse)
files <-
data_frame(path = dir("data-raw/global-fishing-watch/fishing_effort/daily_csvs",
full.names = TRUE),
year = dir("data-raw/global-fishing-watch/fishing_effort/daily_csvs") %>%
str_sub(1,4))
years <- unique(files$year)
for(y in 1:length(y... |
35c9c9bd75ac8821826d61f1b06b14941be541fe | e8c958880769f1d47fcea3fe3a4d47d2417b034b | /MeanPiAnalysis.R | 7da7b8a49db52584b2c4100cbb80c25195181e60 | [] | no_license | melbourne-lab/StochasticGenomicEvolution | c72c623e1fbd0b92362145414271355b38168150 | b0a69c60cf3a7d97c21e0f54151d6b2d2955005a | refs/heads/master | 2023-08-25T19:04:42.812065 | 2018-11-12T18:13:19 | 2018-11-12T18:13:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,444 | r | MeanPiAnalysis.R | # Test the effect of spatial structure on the average reduction in pi values
# for both autosomes and the X chromosome and generate the relevant confidence
# intervals.
setwd("/home/topher/RangeExpansionGenetics/FinalAnalyses/MeanPi")
library(lme4)
library(boot)
library(pbkrtest)
PopPi <- read.csv("PiMeans.csv")... |
85e403f2b35708b1a001c280931d3e64c9b16e1f | ff91ae3a2b38914424363e4e04d2770577167fb2 | /mapping/archive/sqtl.seeker.modified.R | bee804eb964ecf070aadf21406fc06b49c26523f | [] | no_license | ErikSchutte/QTL-Mapping | 6ce9029fcb6f6fe9b40e7484fe49bc220201caf0 | 8bc66e1cba31bc7c00fb3dea161c588bfde2cf99 | refs/heads/master | 2021-01-11T20:54:29.009462 | 2017-01-30T16:14:04 | 2017-01-30T16:14:04 | 79,209,491 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,612 | r | sqtl.seeker.modified.R | sqtl.seeker.modified <- function (tre.df, genotype.f, gene.loc, genic.window = 5000,
min.nb.ext.scores = 1000, nb.perm.max = 1e+06, nb.perm.max.svQTL = 10000,
svQTL = FALSE, approx = TRUE, verbose = TRUE)
{
. = nb.groups = snpId = NULL
check.genotype <- function(geno.df, tre.df) {
apply(g... |
3c28f65c2adbe987d52902226f91badfe5af707c | a42d0875157fc896ed059812e58f729095ee459b | /cachematrix.R | 43ff3047b9931136bdce2c6823fb723b6dbd5e20 | [] | no_license | cmacchambers/ProgrammingAssignment2 | df86edbdd98fb3b66a2b8a7072ba8e9487a63f11 | aef72c149f334ecf62539ba5e618cf98437ba663 | refs/heads/master | 2021-01-18T00:41:31.575833 | 2015-12-22T02:12:23 | 2015-12-22T02:12:23 | 48,401,342 | 0 | 0 | null | 2015-12-22T00:26:28 | 2015-12-22T00:26:27 | null | UTF-8 | R | false | false | 989 | r | cachematrix.R | ##R Programming - Assignment 2
##Two functions that cache and return the inverse of a matrix.
## makeCacheMatrix
## Inputs: 1 Matrix
## Returns: 1 List of 4 functions: set, get, setinverse and getinverse
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
... |
ec5feaa26f9e098b7a0bb907c160a4486f8d90f8 | e07447d03d156edfee5f3b0b899460b10e7d4dc2 | /score_old.R | 47a4b7cd20f90b2719fd78c8fdebae6c19ee8fbe | [] | no_license | rbauer2000/HandsOnR | 887e40fa409d71d3eb67f3adfebe620d2b73dace | 55748893c3b426f7ed25c38f1527f79873149bfe | refs/heads/master | 2021-04-12T16:55:51.540133 | 2020-04-04T14:49:19 | 2020-04-04T14:49:19 | 249,094,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 943 | r | score_old.R | score = function(symbols) {
prize <- 0
all_bars <- FALSE
bars <- c("B", "BB", "BBB", "DD")
if (sum(symbols %in% bars) == 3) {
all_bars <- TRUE
}
}
if (sum(symbols %in% c("B", "DD")) == 3 & sum(symbols %in% c("B")) >= 1) {
prize <- 10
} else if (sum(symbols %in% c("C", "DD")) == 3 & sum(symbols %i... |
e9915e55236a86428b15500d452dd0839ff24752 | 943dd151022cd99b23eda1a23b136226dd6f60ad | /project.R | 2ee90494c2beb31289b6cfda63129fb700aa45eb | [] | no_license | AleksAllav/PracticalMLproject | 7bdcc01ae3453e49bc2b85cae1f98bbb8cce4552 | 96c44bd55e516d41af4cc42e1107bdf99f7a73bf | refs/heads/master | 2020-07-26T06:08:36.235362 | 2019-09-16T14:39:04 | 2019-09-16T14:39:04 | 208,559,576 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,866 | r | project.R | library(caret); library(knitr);library(randomForest)
library(rattle); library(rpart.plot);library(rpart)
library(gbm); library(corrplot); library(ggplot2)
library(colorspace); library(e1071)
#load("pml-testing.csv")
#load("pml-training.csv")
#load data
trainData <- read.csv("pml-training.csv"); dim(trainData)
testData ... |
b2becd77f33788e183b911c503fb14be4d496a13 | cc2d7f64376bdb1acbedae4c00e78cf357761958 | /man/plantGrowth.Rd | 40ddd3eb3a99f2b121f2f90458b5b96325912657 | [] | no_license | jg44/JGTools | 89ea46b0e77da6532ea2b0867a03166d0700931d | b2643a574076d915889afb3615c278483221225f | refs/heads/master | 2023-02-06T07:48:59.976457 | 2023-01-24T15:50:52 | 2023-01-24T15:50:52 | 187,842,688 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 776 | rd | plantGrowth.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plantGrowth.r
\docType{data}
\name{plantGrowth}
\alias{plantGrowth}
\title{Aboveground biomass of imaginary seedlings treated with two levels of N and P in a crossed design.}
\format{
A data frame with 24 rows and 4 variables
}
\usage{
data(p... |
3b578533c4edb2949fe0191c3062e5a67e5ed8df | a5be19737a57491c0dfbe41d068558542b2d5e10 | /R/type_5.R | 138c2dd28ef464c5b93c438e36bc5f28e4a5709b | [] | no_license | cran/jordan | 02ed2dfc77ae1c2d23b7b37016a24d019ce6ee87 | 576f96a2d484e0f60d1a451a465ea6a7984c4380 | refs/heads/master | 2023-03-29T00:43:24.948503 | 2021-04-08T10:00:02 | 2021-04-08T10:00:02 | 355,965,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,529 | r | type_5.R | `spin` <- function(a,V){
stopifnot(is.numeric(a))
stopifnot(is.matrix(V))
new("spin",x=rbind(a,V)) # this is the only place new("spin",...) is called
}
`r1` <- function(x){x@x[1,,drop=TRUE]}
`rn` <- function(x){x@x[-1,,drop=FALSE]}
`quadraticform` <- function(M){ # modelled on lorentz::sol()
if(missing(M)){ ... |
aa4f7613ad963c9d49022e6401022b565b8319de | 590c6ae4469f9741ecd2a6392ea545900a5f78a7 | /exploratory_data_analysis/Project2/loadData.R | e4dbf3fbb6a904fb8cdf6d51116c17a24db0f52a | [] | no_license | grace828822/datasciencecoursera | e7b64f99ddc081c517a0f95de3df5590bbcc3460 | dc013d658d06aa9883cf80ef3fa032113db0fbab | refs/heads/master | 2021-01-23T12:10:23.360915 | 2015-08-10T13:58:20 | 2015-08-10T13:58:20 | 32,438,117 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 128 | r | loadData.R | # Loading the .rds data
NEIdata <- readRDS("data/summarySCC_PM25.rds")
SCCdata <- readRDS("data/Source_Classification_Code.rds") |
9727001daaa63ac966b7982b7cb925c9f082abd2 | e10d735501e104bf4c2ce339c4ab61ad02d88bd7 | /R/package.R | 1268bb1d0a1051a43006c0796e67f94978a3682d | [] | no_license | dots26/ccoevolution | 36933b9bbb76083d80407c54769a5c438d5327e2 | 7c972482727500619d8ed72d6688f8838fc3cef4 | refs/heads/master | 2021-07-08T11:15:06.569323 | 2020-10-07T09:32:50 | 2020-10-07T09:32:50 | 197,369,313 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,299 | r | package.R | # Package Description for Roxygen:
#' This package is an implementation of cooperative coevolution for large scale global optimization.
#' To use this package, several Python modules must be installed beforehand, namely: numpy and PyGMO.
#'
#' Note: This package uses column-major ordering, i.e. an individual should be ... |
cf75ac44b84554143ed4646242d129449a477c0c | 4b588c08a9eb7d236e147ac93cff657c82c09e91 | /man/nhanes_data.Rd | 79b99405a5114b7ad75f6aff027210d3d6139044 | [
"MIT"
] | permissive | monicagerber/mchtoolbox | 131ef106a018c2d2a6b48283ac289b227eff5fb7 | 44086ca5f21bfc9885a3360be9ff5164e8a13655 | refs/heads/master | 2020-03-18T04:51:59.665932 | 2018-05-22T20:19:50 | 2018-05-22T20:19:50 | 134,310,492 | 0 | 0 | null | 2018-05-21T18:42:07 | 2018-05-21T18:42:06 | null | UTF-8 | R | false | true | 541 | rd | nhanes_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nhanes_data.R
\docType{data}
\name{nhanes_data}
\alias{nhanes_data}
\title{National Health and Nutrition Examination Survey (NHANES) example dataset}
\format{A dataframe.
\describe{
\item{cid}{Patient identifier.}
\item{sex}{1 = male, 2 =... |
9fe13f9cc8389c2784d85ae0b7036fa0fd6157fb | ae30b705f1c0618c118a8f33052c21d5d442d69d | /man/createJSON.Rd | c749a9a0e717b0feb68194e1b11c104a26b14d11 | [] | no_license | dselivanov/LDAvis | aa9b8398069a2a7e0583903ecf65fc12eb890fd9 | 176f87e1d89d86aaa7fe29079cb62d217c4d165a | refs/heads/master | 2020-12-25T22:29:52.709582 | 2015-12-22T11:57:50 | 2015-12-22T11:57:50 | 48,425,203 | 1 | 1 | null | 2015-12-22T10:15:37 | 2015-12-22T10:15:37 | null | UTF-8 | R | false | true | 5,625 | rd | createJSON.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createJSON.R
\name{createJSON}
\alias{createJSON}
\title{Create the JSON object to read into the javascript visualization}
\usage{
createJSON(phi = matrix(), theta = matrix(), doc.length = integer(),
vocab = character(), term.frequency = in... |
59f367b7de903c24bb579840477eb8be95579912 | 7a1990f2026969dbc9e826b33faf8ed1754f83c0 | /ai/datacamp/dswr_visual_ggplot/part1/ggplot_pract.R | 68690060628193d91f2e11074b52d03a65b524dd | [] | no_license | slzdevsnp/learn | 5ba8ebf35f134bb1a0c8acbd9054b103e5ab8ac5 | 12731e276af7351a93f1495292889ff7e0c96c9a | refs/heads/master | 2021-06-02T12:47:53.072823 | 2020-01-16T08:48:56 | 2020-01-16T08:48:56 | 150,734,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,135 | r | ggplot_pract.R | # setwd("~/Dropbox/cs/bigdata/datacamp/dswr_visual_ggplot/part1"); source("ggplot_pract.R")
require(ggplot2)
require(tidyverse)
require(data.table)
library(repr)
options(repr.plot.width=4, repr.plot.height=3)
testplot <- function(meansdf)
{
p <- ggplot(meansdf,
aes(fill = condition,
... |
963c13b995373a56d565a043d2f86d1bebb15b64 | da4cadc389b67840e84bdc8d2f744ba1f472ea8b | /RsurveyGroup_1.R | 97570ef1a39cfc19157e0a6a0eb89c405c6dd443 | [] | no_license | Lewis-Barnett-NOAA/STageCompsEstimation | d2ba224d8f9963ee3bcd9df457883c31a40350ca | 8f46353575f51e955b71a36d4df66bfceedaa543 | refs/heads/master | 2022-12-16T11:14:16.365280 | 2020-09-17T22:55:19 | 2020-09-17T22:55:19 | 296,452,520 | 0 | 0 | null | 2020-09-17T22:08:05 | 2020-09-17T22:08:04 | null | UTF-8 | R | false | false | 5,225 | r | RsurveyGroup_1.R | # SET YOUR WORKING DIRECTORY !!
# SCRIPT TO COMPARE AGE ABUNDANCE ESTIMATES BY ALK AND CRL
rm(list = ls())
# Libraries
require(mgcv)
require(fishmethods)
require(reshape2)
require(ggplot2)
# Read data:
cdata = read.csv('data/POLLOCK_CATCH_2015_2019.csv')
ldata = read.csv('data/POLLOCK_LENGTH_2015_2019.csv')
adata = ... |
e501edcd1913ae0895405765d88e1a2a4a31bd6e | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.analytics/man/quicksight_describe_template_definition.Rd | f2e2bc00e0e07718b5e7a375b35fd42b7fb57311 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,268 | rd | quicksight_describe_template_definition.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/quicksight_operations.R
\name{quicksight_describe_template_definition}
\alias{quicksight_describe_template_definition}
\title{Provides a detailed description of the definition of a template}
\usage{
quicksight_describe_template_definition(
... |
85a0de11ac73cd3a32d7f308b227bccdb22125f3 | 8e20060c5475f00e9a513f76725bcf6e54f2068a | /man/edge_attr_names.Rd | 1b5b1c2670525f020f90fc4947a64473921ef0bc | [] | no_license | DavisVaughan/rigraph | 8cc1b6c694ec03c1716d8b471d8f910e08c80751 | a28ac7fe7b45323a38ffe1f13843bb83bdb4278f | refs/heads/master | 2023-07-18T20:34:16.631540 | 2021-09-20T22:55:53 | 2021-09-20T22:55:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,070 | rd | edge_attr_names.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/attributes.R
\name{edge_attr_names}
\alias{edge_attr_names}
\alias{list.edge.attributes}
\title{List names of edge attributes}
\usage{
edge_attr_names(graph)
}
\arguments{
\item{graph}{The graph.}
}
\value{
Character vector, the names of the ... |
750c3523a0d71bde0ea6b32b91c77e2f670d54b5 | 278499567ef34de194ccec599f3843e59ddf1ce2 | /R/eps_CC_loglik.R | 9c1a4c9c3a50a8d6cf3a7291fdf39b864bb345ea | [] | no_license | theabjorn/extremesampling | 3a5850a913a416b0f2d3bf302e3c01e639a29f15 | 29b2846e6fb3d6285745c0b384f47fca64ede967 | refs/heads/master | 2020-05-21T20:29:23.768030 | 2018-12-03T09:34:15 | 2018-12-03T09:34:15 | 62,583,578 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,044 | r | eps_CC_loglik.R | # Log-likelihood function for EPS-CC
epsCC.loglik_ex = function(parameters,data,l,u){
param = parameters
len = dim(data)[2]
y = data[,1]
x = as.matrix(data[,2:len])
n = length(y)
lenp = length(param)
alpha = param[1]
beta = param[2:(lenp-1)]
tau = param[lenp]; sigma2 = exp(tau); s... |
4668dc3771ee38b8344f4d02fbcce69e9cdc4541 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.management/man/managedgrafana_describe_workspace.Rd | f29af6519b2a1262e703d7ff982239d4aae39565 | [
"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 | 619 | rd | managedgrafana_describe_workspace.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/managedgrafana_operations.R
\name{managedgrafana_describe_workspace}
\alias{managedgrafana_describe_workspace}
\title{Displays information about one Amazon Managed Grafana workspace}
\usage{
managedgrafana_describe_workspace(workspaceId)
}
\a... |
d3c763fc1c9dc2881855ba5aed6be54fea6add3e | 596e526d5a2bc3cc8b833096c0f37108ab242b64 | /analysis/shir/shir_hwep.R | 953778f31fa5869f6e4a1987fb4e3497fd76ad68 | [] | no_license | dcgerard/hwesims | f967c92549f9ddcc690376f0374097da73be74a6 | 99aa7963e638509279d78a40ea2b2f09ec305cdf | refs/heads/main | 2023-04-13T15:33:15.612702 | 2022-07-25T18:08:05 | 2022-07-25T18:08:05 | 338,132,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,277 | r | shir_hwep.R | ######################
## Fit hwep on Shirasawa data
######################
# Number of threads to use for multithreaded computing. This must be
# specified in the command-line shell; e.g., to use 8 threads, run
# command
#
# R CMD BATCH '--args nc=8' shir_hwep.R
#
args <- commandArgs(trailingOnly = TRUE)
if (length(... |
490eec9375945dbc1268219e8f220f438c67ecf7 | 3ee48064cf4a49718e56368aab3518da43335df5 | /inst/NEWS.Rd | 46297ba169273a3f33b4a46ff9b507daa7decb04 | [] | no_license | Shians/SingleCellExperiment | 23b4a476bc6880be98f0bfa0893f50d40c8d2171 | f46c5345d01529371635100839c22cb714476083 | refs/heads/master | 2021-07-06T22:43:12.669217 | 2017-09-28T04:35:35 | 2017-09-28T04:35:35 | 105,102,435 | 0 | 0 | null | 2017-09-28T04:30:18 | 2017-09-28T04:30:17 | null | UTF-8 | R | false | false | 194 | rd | NEWS.Rd | \name{SCEnews}
\title{SingleCellExperiment News}
\encoding{UTF-8}
\section{Version 0.99.4}{\itemize{
\item
New package SingleCellExperiment, for representation of single-cell genomics data.
}}
|
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