blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
be9992d5fc5ff7890d48fe6dcfda38815f7d163c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/iotools/examples/readAsRaw.Rd.R | 66c254dc7b84318be5b18abddc4331f9df98452b | [] | 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 | 337 | r | readAsRaw.Rd.R | library(iotools)
### Name: readAsRaw
### Title: Read binary data in as raw
### Aliases: readAsRaw
### ** Examples
mm <- model.matrix(~., iris)
f <- file("iris_mm.io", "wb")
writeBin(as.output(mm), f)
close(f)
m <- mstrsplit(readAsRaw("iris_mm.io"), type="numeric", nsep="\t")
head(mm)
head(m)
unlink(... |
1cbe5c4bdebb040b0a86b7b115b1e8627e3c1308 | 3252907f13eb34741fb51b7288abf43bebecc312 | /plotSeqsProbHeatmap.r | e2f45b67b9158ad3e57823a6de1ccc152ff26aea | [] | no_license | NarlikarLab/exoDIVERSITY | 5ef3d8570ac22629009fcec25021351b60137155 | d646fc655d0cf686a11ce29de4731f32fb7a4f6d | refs/heads/master | 2023-08-14T01:34:11.678808 | 2021-10-05T10:35:49 | 2021-10-05T10:35:49 | 310,792,452 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,223 | r | plotSeqsProbHeatmap.r | library("RColorBrewer")
library("plotfunctions")
args=commandArgs(trailingOnly=TRUE)
infofile = args[1]
inpfile = args[2]
outfile = args[3]
seqprobs = read.table(inpfile)
info = read.table(infofile)
modes = as.integer(rownames(table(info[,2])))
modeOrder = seq(min(modes),max(modes))
lseqs = cbind(info[,2],seqprobs[,2... |
9bd89e27651e3a74b3f7ff190b5ca7fd3cc8abbb | c847169d8ce43d5e31a0a428b4d97e5a7c8c230a | /by/process_surface.R | 5cbc6e2839ef6f6275cd0e97997a8e297bbaa12a | [] | no_license | xuehangsong/dvz_dust | 992e03056f6d0c81bf150bd06ce3e32d5761c4fe | 75ac29bdee5191648bef8253d527d851787e7e99 | refs/heads/master | 2020-03-19T07:27:33.848850 | 2018-10-02T21:57:44 | 2018-10-02T21:57:44 | 136,115,282 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,128 | r | process_surface.R | ## scripts was named as <solute.flux.v3.R>
## plot all figures for single case
## written by Xuehang 04/11/2018
rm(list=ls())
library(RColorBrewer)
#simu.dir="/pic/projects/dvz/xhs_simus/dust/fy18/by_9a/"
simu.dir="./"
scripts.dir="/people/song884/github/dvz_dust/by/"
# load model parameters
source(paste(scripts.di... |
2de6c5ac073e25336a3a7b875d0a5abe416613ac | 29585dff702209dd446c0ab52ceea046c58e384e | /ProgGUIinR/inst/Examples/ch-RGtk2/ex-RGtk2-color-tool-button.R | 44ea85285c0f69c48fe9efd54630d2a88c0df438 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,727 | r | ex-RGtk2-color-tool-button.R | ### R code from vignette source 'ex-RGtk2-color-tool-button.Rnw'
###################################################
### code chunk number 1: rgtk2-mennus-toolbar-color-button
###################################################
gdk_color <- gdkColorParse(palette()[1])$color
color_button <- gtkColorButton(gdk_color)
... |
47acd1a6d03da0b747791f794624adb189cd85d8 | 4df3882cd1d21190b10894a5eea39ba5ebbf7c5d | /usernames.R | 2bb4bdff342aaaaeb317152bdb389613aea64580 | [] | no_license | yk2684/NYC-Theatre-Reviews-Scrape | 4714f0511dddf6e88e194635cd9e26d33b2911ec | 94fde09a358b330de7c13e284144658149fdbfa8 | refs/heads/master | 2021-05-09T05:07:20.213072 | 2018-01-28T21:37:20 | 2018-01-28T21:37:20 | 119,298,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,418 | r | usernames.R | library(rvest)
library(stringr)
library(data.table)
getwd()
#setting up working direcotry
setwd("Your_working_directory")
#clear environment
rm(list=ls())
#Load in the show link and add the page numbers - will loop through each page to scrape the usernames
#i.e. ('https://www.show-score.com/broadway-shows/once-on-thi... |
46677c0676c2022e3f4e909ec8e3e1b8e27ac159 | c4d14f572811fb5c7e94d9c97568c158574f330f | /ui.R | 52ca1a7479d6f56830febc77bf0351a3bc625d55 | [] | no_license | bigstewosu/Champs | 5e4d5048ab7b817ff67c16d8345daaf1945bfd25 | 2c3ee2ccdb2ed57146965571ac8fc7ef8aadb52e | refs/heads/master | 2021-07-25T14:52:54.181296 | 2017-11-07T22:32:09 | 2017-11-07T22:32:09 | 109,896,677 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,464 | r | ui.R | ## Course Project Data Products Coursera
## Author: "Dale Stewart"
## date: "Nov 7, 2017"
## Build User interface (ui.R), which will control the layout and appearance
## Load Shiny library
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Number of Major Championships per State in 4 Major U.S. Sp... |
70b85b1a7aff68bc7f02bc90fdc2db18fcce2b86 | c0e4fe657bc91bebdd99dc609e30d6779a050923 | /src/miller_signatures.R | bd931d00939ef8b8f799d9cc121b7e7232895712 | [] | no_license | caramirezal/tcd8ExscSeq | 491d2983ce64d2f2f4087dd69359adaa2ad8f9c2 | 900b785ac62f7b1e23babe49cf7ae7bdd52abe7b | refs/heads/master | 2022-11-10T17:45:43.873223 | 2022-11-02T10:37:22 | 2022-11-02T10:37:22 | 248,523,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,365 | r | miller_signatures.R | ## Visualization of scarches results
## Dependencies
library(Seurat)
library(dplyr)
library(readxl)
library(rio)
library(dittoSeq)
library(viridis)
set.seed(333)
## Setting working directory
path2project <- '/Users/carlosramirez/sc/tcd8ExscSeq/'
setwd(path2project)
##################################################... |
0626c82cb528022680148e6dfcfd5a66ec832518 | 75716773a68c416be5b297bb278f6c036729891b | /cacheSolve.R | 8f8be12206e5ac4c39bac9d94c545a9ac94a0263 | [] | no_license | umaircheema/datasciencecoursera | 466e12c06ff049d3ecaff267bec9b48a8084886a | aef42c65bc6d678d0db42723f30bc5af107db712 | refs/heads/master | 2016-09-05T19:16:36.180907 | 2015-08-21T10:11:08 | 2015-08-21T10:11:08 | 40,152,003 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 211 | r | cacheSolve.R | cacheSolve <- function(x, ...) {
invc <- x$getinverse()
if(!is.null(invc)) {
message("getting cached data")
return(invc)
}
data <- x$get()
invc <- solve(data, ...)
x$setinverse(invc)
invc
} |
626761451e495ba8b33b9c0155ac03b4dc085c07 | 135725be7153ff6ec6cc79b8db29cdea80ecc875 | /man/gwasLog.Rd | 97fdf137e8474d3f72a362e8714ff8b096963877 | [] | no_license | cran/CollapsABEL | f2969755a8fe831eeab9c4971ddd1488a1e39b81 | b3be3f831ba5e9500d016aca0ca768a367e79c84 | refs/heads/master | 2021-01-10T13:18:22.127650 | 2016-12-11T19:35:07 | 2016-12-11T19:35:07 | 55,400,560 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 356 | rd | gwasLog.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/2_plGwas.R
\name{gwasLog}
\alias{gwasLog}
\title{Plink log fie}
\usage{
gwasLog(pl_gwas)
}
\arguments{
\item{pl_gwas}{PlGwasC object.}
}
\value{
character. Path to log file.
}
\description{
Redirect stdout to this file when plink is running.
... |
6b2fd42c781825652d89acedf9da8943b37089b2 | 48d2c8117c4604e32bef0752f16447641bd82718 | /electability/R/ProcessPolls.R | c10a21c8476aa490628060c1f67a8fcd86e2a9cb | [
"MIT",
"CC-BY-4.0",
"LicenseRef-scancode-public-domain"
] | permissive | tmcintee/electability-2020-pub | b3334cf5ada9c74a43f5cdc9bbb5742cfef290d1 | 5dd97241c7551633890020b4a5ce92eff78dc468 | refs/heads/master | 2020-12-13T09:14:24.949548 | 2020-01-16T17:23:56 | 2020-01-16T17:23:56 | 234,372,855 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,883 | r | ProcessPolls.R | ProcessPolls <- function(poll_file,candidate_vector)
{
polls_raw <- read.csv(poll_file,stringsAsFactors = FALSE)
nameVector <- as.character(polls_raw$candidate_name)
list_names <- str_split(nameVector," ")
for(i in 1:length(list_names))
{
first_name <- list_names[[i]][[1]]
last_name <- list_names[[i]]... |
c7350e6a53ef721986edf955ed916c85f8a3df3a | c2f267114e9024f6fea4fbc2c3ce530fd44ee753 | /man/is.surveyorStats.Rd | 7308ed82eae807e4acccbe0d17431e3f420d0d99 | [] | no_license | andrie/surveyor | a1e0596be1d910162be0e36c897f8ff11ee150de | b6724141687e31f193e87747e53e4a2b00187ae0 | refs/heads/master | 2016-09-09T23:47:00.528333 | 2012-08-28T16:35:20 | 2012-08-28T16:35:20 | 1,082,414 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 292 | rd | is.surveyorStats.Rd | \name{is.surveyorStats}
\alias{is.surveyorStats}
\title{Test object for membership of class "surveyorStats".}
\usage{
is.surveyorStats(x)
}
\arguments{
\item{x}{Object}
}
\value{
TRUE or FALSE
}
\description{
Test object for membership of class "surveyorStats".
}
\keyword{internal}
|
d054b7b352fba137f3b59453687fc8551de0d6ac | 7853c37eebe37fa6a0307e0dd9e197830ee6ac71 | /man/cuDeviceGet.Rd | fff5d64937e5a874ac90ade9cbbb00b8a029c81a | [] | no_license | chen0031/RCUDA | ab34ffe4f7e7036a8d39060639f09617943afbdf | 2479a3b43c6d51321b0383a88e7a205b5cb64992 | refs/heads/master | 2020-12-22T22:47:37.213822 | 2016-07-27T03:50:44 | 2016-07-27T03:50:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 528 | rd | cuDeviceGet.Rd | \name{cuDeviceGet}
\alias{cuDeviceGet}
\title{Returns a handle to a compute device}
\description{ Returns a device handle given an ordinal in the range [1,
\code{cuDeviceGetCount}()].}
\usage{cuDeviceGet(ordinal)}
\arguments{
\item{ordinal}{Device number to get handle for}
}
\seealso{\code{\link{cuDeviceGetAttribu... |
d46dca1ec68204520689b8a629639e62d2d076d0 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/6890_0/rinput.R | d804caf5b7ed9107a4f03d8ef672e7016a1d0d91 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("6890_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="6890_0_unrooted.txt") |
308d6679c2a7c5e03cb39ddfed2d4053ebf3a478 | 0c3fa446b7aa8e5f48201e139fd7e19c351ae54d | /src/run_apeestim/R/apePredPost.R | f77017ea27b57100314935bcb6640425c193ab0e | [] | no_license | mrc-ide/covid19-forecasts-orderly | 477b621129ebc93dbd4e75ae4d54fbda0ad1669c | 2210ea70dc7b38d91fc1678ab0c2bb9b83c5ddcb | refs/heads/main | 2023-02-06T17:58:18.937033 | 2022-08-02T16:39:50 | 2022-08-02T16:39:50 | 254,094,782 | 33 | 12 | null | 2022-08-02T16:39:51 | 2020-04-08T13:25:08 | R | UTF-8 | R | false | false | 2,980 | r | apePredPost.R | ######################################################################
## Negative-binomial posterior and APE score
# From: Parag, KV, and Donnelly, CA. (2019) “Optimising Renewal Models for
# Real-Time Epidemic Prediction and Estimation” BioRxiv: 835181.
################################################################... |
b422b8d013001bdfa5779bcd6c3752e5d9e89ad5 | 8ff96e8db40d0688ddc93c749245883e13a5de88 | /man/filesInTreeStructure.Rd | 5be4f05114883ba6312f02c27cd866108647ac01 | [
"MIT"
] | permissive | McClellandLegge/shinydashtabs | 6614372257a960520d26c42a15c670fac5ebe180 | 79d2e530c89e7e42ad7c678ec780188e980fa6e2 | refs/heads/master | 2023-01-20T00:36:40.928833 | 2020-11-20T22:10:26 | 2020-11-20T22:10:26 | 305,006,404 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 463 | rd | filesInTreeStructure.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/files-in-tree-structure.R
\name{filesInTreeStructure}
\alias{filesInTreeStructure}
\title{Determine a directory file structure}
\usage{
filesInTreeStructure(file, pattern = NULL)
}
\arguments{
\item{file}{The root directory or file}
\item{pa... |
69123b0f426b2924344cd8c21a6bc43f05529c7f | f2aa5c24567724bd3d2d20c21ae4cba4d7e268be | /R-Programming-By-Example-master/Chapter09/fibonacci.R | 9675d85dd3a87952c623eaf1bd3412ceb882b459 | [] | no_license | cyrsis/RSandBox | ab3a2548cbd2a0abaf6fcf2e8f2d42951d3ab80f | b647cda2e30702de408b1e223cb898d2bb05c01a | refs/heads/master | 2020-03-21T19:50:21.523273 | 2018-06-28T09:31:36 | 2018-06-28T09:31:36 | 138,972,521 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 551 | r | fibonacci.R | #
# Chapter 09 - Improving Performance: Fibonacci
#
# R Programming by Example
# Omar Trejo Navarro
# Packt Publishing
# Mexico City
# 2017
#
# ---- fibonacci-recursive
fibonacci_recursive <- function(n) {
if (n <= 1) { return(n) }
return(fibonacci_recursive_1(n - 1) +
fibonacci_recursive_1(n - 2))... |
9f7e3221b54c2cad812394f12187426f21c37e9c | f46ff3940e124c95c1a6b092eb9faf84f772a489 | /man/dint-package.Rd | eb9b0f956757855c17b7497031d2d89f966b5057 | [
"MIT"
] | permissive | s-fleck/dint | 6724a9e673f351ef03f8b82df7cb1b0d90109c07 | 16925fe23cb007d996cadbef6d702d459d44e7c2 | refs/heads/master | 2022-11-07T09:54:49.632281 | 2022-10-17T06:24:14 | 2022-10-17T06:24:14 | 130,039,666 | 13 | 4 | NOASSERTION | 2020-03-03T12:54:28 | 2018-04-18T09:38:21 | R | UTF-8 | R | false | true | 828 | rd | dint-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dint-package.R
\docType{package}
\name{dint-package}
\alias{dint}
\alias{dint-package}
\title{dint: A Toolkit for Year-Quarter, Year-Month and Year-Isoweek
Dates}
\description{
S3 classes and methods to create and work
with year-quart... |
fc40c7adf12302276648b236722fc076ef1d5fda | dcbd82e150369094a73253fa48cdd2887e1dd778 | /IO_M2.1_new/Analysis_Ben_Loading.R | 1d5051e7e43084b11610c78d829199bdbc2ed463 | [] | no_license | yimengyin16/Model_Main | 0e8140ba296d1115bbbc64bcffefb05ac7a4fe86 | b63573407aaa0f6489d96083bf2fe09e843ca43f | refs/heads/master | 2022-07-03T22:44:47.660461 | 2018-02-22T01:36:15 | 2018-02-22T01:36:15 | 27,349,525 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,545 | r | Analysis_Ben_Loading.R | rm(list = ls())
library(reshape2)
library(plyr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(knitr)
library(magrittr) # to use %<>%
library(zoo)
library(grid)
library(gridExtra)
library(stringr)
library("readr")
library("readxl")
library(xlsx)
source("Functions.R")
IO_folder <- "IO_M2.1_new/"
#*********... |
ab8aac73ba030136d13666c77449e2b236d7cf1b | a001932cb35ad0f5f1874999044ea1768870502f | /talks-and-lectures-master/CSC-UCSB/R/clusterR/montecarlo.R | 5ce13e79b74f746d88824e25951221e8638bd20e | [] | no_license | UCSBMRL/Center-for-Scientific-Computing | 619bd772d82ea1cb4ef57c11cc0b2c43b1e206e4 | 0584e2845c782ff5f4182b529894379a164e56c8 | refs/heads/master | 2021-08-22T08:29:39.362297 | 2017-11-29T19:01:13 | 2017-11-29T19:01:13 | 112,514,483 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,076 | r | montecarlo.R | # Burak Himmetoglu
# 10-25-2016
# bhimmetoglu@ucsb.edu
#
#
# Example: Serial Monte Carlo with for loop
#
# Number of simulations
nSim = 1e+5
# Exact result
exact <- function(x = 1, nDim){ ((pnorm(x*sqrt(2)) - 0.5)*sqrt(pi))^nDim }
# Function for Monte Carlo calculation
funMC <- function(nDim){
vec = runif(nDim, mi... |
9feab174dc4588676edb9d64a6df6db5a509d761 | 7747a3fdf0fdc57b767d8ed199b323afb4d491a2 | /man/sim_detect.Rd | 21306a2958cdd61fb9d6d75007fad1e22fd10fc1 | [] | no_license | ianjonsen/simsmolt | dcafaad041d6caa29cd573cd543dbeab7e14868a | 09c9a8b8132bedaa499dd71c5c2fc6e2439256eb | refs/heads/master | 2022-07-28T10:03:06.683400 | 2022-07-07T14:13:08 | 2022-07-07T14:13:08 | 155,731,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 843 | rd | sim_detect.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sim_detect.r
\name{sim_detect}
\alias{sim_detect}
\title{simulate acoustic transmissions & detection, using \code{simulate} & \code{sim_setup} output}
\usage{
sim_detect(s, data, delay = c(50, 130), burst = 5, noise = 1)
}
\arguments{
\item{s... |
2df169fd43e928124a9c25347a629c443ce2117f | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /lefko3/R/lefko3-package.R | cff774b0d367de3181808804eb7789d1503caaf5 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,685 | r | lefko3-package.R | #' @title Historical and Ahistorical Population Projection Matrix Analysis
#'
#' @description This package creates population projection matrices for use in population
#' ecological analyses. The particular specialty of the package is the construction
#' of historical matrices, which are 2-dimensional matrices compri... |
2b50ad98367696c52822e270071089b655f628a2 | d255d28ece6cbc2967ef00c014eafc859cf68141 | /RHRV/LoadBeatVector.R | 01434b8e251b542f2b93bce9b72f9366991e2f32 | [
"MIT"
] | permissive | med-material/ArduinoLoggerShinyApp | 0fd645b7d721efd9bf005c12b46030402e98b35e | a1af24786df19dd4bbd909b5189d77ece88ac49f | refs/heads/master | 2023-08-03T17:01:19.812307 | 2023-07-28T13:11:35 | 2023-07-28T13:11:35 | 228,591,063 | 1 | 1 | MIT | 2020-02-18T11:16:30 | 2019-12-17T10:24:41 | R | UTF-8 | R | false | false | 2,408 | r | LoadBeatVector.R | #' Loads beats positions from an R vector
#' @description Stores the beat positions from an R vector under the
#' \emph{HRVData} data structure.
#' @param HRVData Data structure that stores the beats recording and information
#' related to it
#' @param beatPositions Numeric vector with the heartbeats occurrence's time... |
77403b4cd7d31b86c5061726432d6f6a42471aa0 | 9f4949d8dfd39f94d75fc06b95b5b584f7ea04e3 | /miscfunc.R | bc7859e36e4e4a827c6108010edd80756e742a03 | [] | no_license | sickel/cowplot | 8be407904f2faa5c1a445f8782b20d215e955432 | 22391752b77bbddb9b527ea428113d8fbfc0ea7a | refs/heads/master | 2021-01-10T03:49:34.124346 | 2015-01-27T18:18:02 | 2015-01-27T18:18:02 | 8,456,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,944 | r | miscfunc.R | cumuldata=function(lok,type='grazing'){
sqlbase=paste("select count(*) from ",lok,"_adjusted_model where result='",type,"' and newperc <=",sep='')
ps=c(0,10,20,30,40,50,60,65,70,75,80,85,90,95,96,97,98,99,100)
sets=c()
for(p in ps){
sql=paste(sqlbase,p)
rs=dbSendQuery(con,statement=sql)
ret=fetch(rs... |
724d007b0aad92c975b5461815dfbdf67500274e | 1b6c2393630ef796e147a5538f14742cfbd14a13 | /Rpkg/man/exm_madvise.Rd | c51831225ae3b3002388081e9759d2d98f91f702 | [
"BSD-1-Clause",
"BSD-2-Clause"
] | permissive | bwlewis/exm | 2ba55286cad19152f5b309c97d269adbda04c66b | d6ca3936fc5850c4d02641dd705f6264703e1cdd | refs/heads/master | 2018-11-16T02:58:56.304005 | 2018-09-20T13:50:16 | 2018-09-20T13:50:16 | 17,552,567 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 503 | rd | exm_madvise.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exm.r
\name{exm_madvise}
\alias{exm_madvise}
\title{Lookup the exm backing file for an object}
\usage{
exm_madvise(object, advice = c("normal", "random", "sequential"))
}
\arguments{
\item{object}{Any R object}
\item{advice}{Mapping type, on... |
0e63aa0785cd3b33502f211642dbe937194e514a | c87282580bd8547d4def283cf2f738c270046676 | /man/getAssistLeaderByYear.Rd | 068495fa63b9a379fbf9ee2c3488d205339a5fe2 | [] | no_license | rpcvjet/ModernDSHW | 6ee72c0995c1c604226a5a6861ec6a87a6ceaf2d | b935b51944d8b7935756a0b43939b769e51ff00e | refs/heads/master | 2020-06-04T11:03:40.428227 | 2019-06-14T21:40:49 | 2019-06-14T21:40:49 | 191,995,223 | 0 | 0 | null | 2019-06-14T21:40:50 | 2019-06-14T19:23:25 | R | UTF-8 | R | false | true | 314 | rd | getAssistLeaderByYear.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getAssistLeaderByYear.R
\name{getAssistLeaderByYear}
\alias{getAssistLeaderByYear}
\title{getAssistLeaderByYear}
\usage{
getAssistLeaderByYear(year)
}
\arguments{
\item{year}{}
}
\description{
gets assist leader by year
}
\examples{
}
|
b1256250088a57d713f8b78541fd1952066f85c0 | c1c42390b20504827f3e939689bdda634bbac931 | /stats/top-n.R | 1b7c642a0c59eb28251a77d25b8acb13f84a04be | [] | no_license | jithinjohn7/FaultLocalizationResearch | ddde1aa6dcfb50150b2f7d91853fe931bc674a94 | 965f47678c672e3f15c272e385944ebf4f793cc0 | refs/heads/master | 2020-04-03T23:56:10.022504 | 2018-12-07T17:08:18 | 2018-12-07T17:08:18 | 155,635,143 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,197 | r | top-n.R | # Script that computes the ratio of defects that the best mbfl, sbfl, and hybrid
# techniques localize in the top-5, top-10, and top-200 of the suspiciousness ranking.
#
# usage: Rscript top-n.R <data_file_real_exploration> <out_dir>
#
# Read file name of the data file and the output directory
args <- commandArgs(trai... |
dbefb8ee58ba9f1a55e26a2e7e20cba4e753f29b | 316aa351ff9f4b242fd79cf388aecd5bd5ad4f21 | /man/fromInd.Rd | 26b06b1665b0d6cda37c45915915fe6d984ca980 | [] | no_license | jmhewitt/dsdive | 5d24b6f9d3e39e9d8fc33c12b6172126a3769dd0 | bb55540452917d0c81e039e9ed6fb79f9b233131 | refs/heads/master | 2022-09-23T06:18:55.498742 | 2020-05-28T19:36:01 | 2020-05-28T19:36:01 | 224,232,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 572 | rd | fromInd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/indexing.R
\name{fromInd}
\alias{fromInd}
\title{Converts array indices to (x,y,z) coordinates}
\usage{
fromInd(ind, x.max, y.max)
}
\arguments{
\item{ind}{index in an array}
\item{x.max}{maximum value of x coordinates}
\item{y.max}{maximum... |
8ce06702d1089fdb2beeac578aa71885ac285cfe | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mondate/examples/format.mondate.rd.R | 90404f288920574d352355c90a179ab17c058849 | [] | 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 | 289 | r | format.mondate.rd.R | library(mondate)
### Name: format.mondate
### Title: Format a mondate
### Aliases: format.mondate
### ** Examples
(b<-mondate(1)) # end of first month of millennium
format(b) # "01/31/2000" -- with quotes -- in the U.S. locale
format(b, format="%Y-%m-%d") # "2000-12-31"
|
b8d2895fab5a959021c479af5a63c365b4ac1017 | 39ada063feadfa4fd38a2258e7bf1205a505be1d | /man/directpvalcombi.Rd | 26432883d410011fe01ce2333138a057311758b4 | [] | no_license | cran/metaMA | 4846d0144431eea33be142968721310d07555be9 | 4ca8d9ae282eded228568d173d15ecc61b49c428 | refs/heads/master | 2022-04-30T10:23:17.194643 | 2022-04-12T15:02:36 | 2022-04-12T15:02:36 | 17,697,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,262 | rd | directpvalcombi.Rd | \name{directpvalcombi}
\alias{directpvalcombi}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Direct p-value combination }
\description{
Combines one sided p-values with the inverse normal method.
}
\usage{
directpvalcombi(pvalonesided, nrep, BHth = 0.05)
}
%- maybe also 'usage' for ... |
6c7592a365b106443166a8b20533dc36ec193689 | 3dfcad5e4ca29823a6e7899dcd22aaf7f5df971c | /R/CopyNumberDataSetTuple.R | b43029156a0b6c5d47e4fbe34306b2d913aa4616 | [] | no_license | HenrikBengtsson/aroma.core | f22c931029acf55f3ad2fdb6eb3bc2f0d2ba04e4 | 1bf20e2b09f4b8c0ca945dfb26fdf1902c187109 | refs/heads/master | 2022-12-01T09:01:49.295554 | 2022-11-15T18:29:17 | 2022-11-15T18:29:52 | 20,845,682 | 2 | 3 | null | 2018-04-21T02:06:48 | 2014-06-15T01:32:43 | R | UTF-8 | R | false | false | 2,753 | r | CopyNumberDataSetTuple.R | setMethodS3("getAverageFile", "AromaMicroarrayDataSet", abstract=TRUE)
# AD HOC. This is currently only implemented in the AffymetrixCelFile class,
# but should be implemented by others too, alternatively be replaced by
# a "better" method. /HB 2009-11-18.
setMethodS3("getXAM", "AromaMicroarrayDataFile", protected=TRU... |
3ef96cdac75901fd28d03f4967d7ba489ec30348 | 639e8fc3a59d319dfd1ea257570e4f4d71a55dd7 | /Course3_DataClean/week 2/hdf5_intro.R | 8c43cb65d17439cf85fb55e419a4924d8f3b53e8 | [] | no_license | Bttobar/Bttobar-JH_datascience | fc4bdac65dba3948c575ce5130ad48fc7dcf2e1f | 40bfbb305eafaf597c3b5fbc2fb5477e5e8082d1 | refs/heads/master | 2023-01-08T00:37:27.622459 | 2020-11-02T02:16:56 | 2020-11-02T02:16:56 | 291,361,665 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,512 | r | hdf5_intro.R |
# HDF5 otro formato para guardar los datos.
# Caracteristicas: para guardar large data sets + alta variedad de tipos
# groups => group header (name + attrib)
# => group symbol (lista de objetos)
# datasets => header (name + metadata como datatype ...) + arreglo de la data.
# www.hdfgroup.org
if (!requireNam... |
a566c9702a1d96ff350b714a8426df086082e50b | e55eba9dc09a0eaea779a5fb71e878b53b037a79 | /tests/testthat/test-linear.R | 8915c4cda056e41b533d8b00d86960ed7728900a | [] | no_license | LTLA/InteractionSet | cb76c66d4e2cbd357c0eac022c0f90fd750e54f2 | 62cd50ab8dd16807b257621dc68078d7fdfaa350 | refs/heads/master | 2023-06-28T10:57:07.566004 | 2023-06-13T21:11:31 | 2023-06-13T21:11:31 | 45,793,595 | 3 | 3 | null | 2016-01-11T14:11:08 | 2015-11-08T18:43:25 | R | UTF-8 | R | false | false | 4,961 | r | test-linear.R | # Testing the linearize function for InteractionSet objects.
# library(InteractionSet); library(testthat); source("test-linear.R")
set.seed(200)
N <- 50
all.starts <- round(runif(N, 1, 100))
all.ends <- all.starts + round(runif(N, 5, 20))
all.regions <- GRanges(rep(c("chrA", "chrB"), c(N-10, 10)), IRanges(all.starts, ... |
3a8408ee8aa782f0dc381cfca03cc60304cda71c | 9046be09971d3d8ed2c997cc9524593d00a65d74 | /R/boxM.R | a741f32863eb6b0459d4967eaed4cb000ea74359 | [] | no_license | GabrielHoffman/decorate | e915a43b078d2cebe0db5e1f4381e039767e09c8 | 9b4109aab277e8e884e387772d567db00e0f6562 | refs/heads/master | 2023-05-28T10:53:47.066330 | 2023-05-17T18:28:03 | 2023-05-17T18:28:03 | 167,074,817 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,949 | r | boxM.R |
# Compute log determinate based on only the first N eigen values
det_partial = function( C, tol=1e-10 ){
if( ncol(C) != nrow(C) ){
stop("Matrix must be square")
}
dcmp = eigen( C, only.values=TRUE)
evalues = dcmp$values
# log determinant is sum of log eigen-values
# partial sum of 1:N value... |
a09cdccb658883650c243f278e5a328a303baa6e | 354066554dd1c3ca7a2d45e3673a2da86330f816 | /R/grafico.R | 178ed77cf1b7a218830d19f3568f982ebe34474b | [
"MIT"
] | permissive | elemosjr/planejamento | e8bfe3d0d9d255b1216a91d91a0a880fa2f3cb0c | f7cd310c6a59533b8d7b7ec8b03391a874f0cb51 | refs/heads/master | 2023-04-30T19:34:33.232372 | 2021-05-20T00:54:16 | 2021-05-20T00:54:16 | 366,199,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,464 | r | grafico.R | #' Base para gerar graficos de comparação
#'
#' @import ggplot2
#'
#' @description Funcao base para graficos de comparacoes multiplas.
#'
#' @param dados Data frame com colunas separadas para os tratamentos, resultados, e caso haja, blocos
#' @param x String com o nome da coluna dos tratamentos
#' @param y String com o... |
23abc5b40604038d2264359e7b232d7ee07b6f1b | 15b43be459adec4f54507aafe759f61f1eadd5d5 | /man/as_search.Rd | 9033c6bd8e48fd60fd0429e4af354ec25a66b101 | [
"MIT"
] | permissive | jpiaskowski/rtimes | 7d5c5ffe3bc250f2ee1d5bee98a10dbd98c141b6 | 4eaef46ca60a35c879db68edba6a697418693850 | refs/heads/master | 2022-01-28T10:42:27.313696 | 2019-07-19T17:12:07 | 2019-07-19T17:12:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,427 | rd | as_search.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as_search.R
\name{as_search}
\alias{as_search}
\title{Article search}
\usage{
as_search(q, fq = NULL, sort = NULL, begin_date = NULL,
end_date = NULL, key = NULL, fl = NULL, hl = FALSE, page = 0,
all_results = FALSE, try_flatten = FALSE, ... |
836b023482c60609ad46b8bba9d26e4446b37c3e | 887d5ff66955a6e9110d95398a8d08bcb3b0ba48 | /pollutantmean.R | 62dbd2d8f13b73cf998682f30e10264adc4d5ba2 | [] | no_license | yelangya3826850/R_Language | a26342120b99430b2953feaac08798466174628f | f571536ce249bac4bc99d78264201b02bdc97ef0 | refs/heads/master | 2016-09-05T22:47:51.734170 | 2014-07-20T14:03:23 | 2014-07-20T14:03:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,577 | r | pollutantmean.R | pollutantmean <- function(directory, pollutant, id = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'pollutant' is a character vector of length 1 indicating
## the name of the pollutant for which we will calculate the
## mean; either "sulfate" or "n... |
dccb42c6f28f6412ce38adecb0e78466e3a415da | 914c515a6c85356ee148950f5d93be59ee112b4c | /man/wmed.Rd | 40e516cfff24e5a16fc32d1d4f837ad7b86dbec7 | [] | no_license | Mufabo/Rrobustsp | 917fb8e9a353ffc7a838544fa5cd1d39eae34f6c | 1700f4fed27e63cec6dfb67a14a208d8f46e2418 | refs/heads/master | 2022-06-14T22:15:50.045970 | 2022-06-05T12:13:32 | 2022-06-05T12:13:32 | 222,286,547 | 0 | 3 | null | 2019-12-19T14:17:46 | 2019-11-17T17:36:00 | R | UTF-8 | R | false | true | 1,101 | rd | wmed.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Regression.R
\name{wmed}
\alias{wmed}
\title{weighted median}
\usage{
wmed(y, w, verbose = F, tol = 1e-07, iter_max = 2000)
}
\arguments{
\item{y}{: (numeric) data given (real or complex)}
\item{w}{: (nubmer) positive real-valued weights. In... |
68c20f33351cbd027c10ccbeb9ab6186dc0403e5 | c007f1b6847cf9909102d63d440cf8399a083412 | /R/get_cpt_functions.R | efb29abbf3be5170202869ae6ccb2beb85029c89 | [] | no_license | aarmiller/delayDX | 36c67444f475e624a48ec6c9f78ab1ec44c44f03 | fe3d715c6fc4b68ec564979660529843cf313349 | refs/heads/master | 2023-06-16T18:09:01.527192 | 2021-07-09T15:37:06 | 2021-07-09T15:37:06 | 274,458,924 | 1 | 1 | null | 2021-07-09T15:37:06 | 2020-06-23T16:43:49 | R | UTF-8 | R | false | false | 6,019 | r | get_cpt_functions.R |
#' Return outpatient core information for a specific list of cpt codes
#' @name gether_proc_data_outpatient
#' @param table_name The specific outpatient core table to access
#' @param cpt_codes The cpt codes of interest
#' @param proc_vars List of variables to return
#' @param db_path Path to the database
#' @param co... |
616e986d574add0665bce4d883a48322161fd546 | 360df3c6d013b7a9423b65d1fac0172bbbcf73ca | /FDA_Pesticide_Glossary/triazole-1-propaneni.R | 30e97ed3bd6f0234d277772dd35fbca31e05e2d1 | [
"MIT"
] | permissive | andrewdefries/andrewdefries.github.io | 026aad7bd35d29d60d9746039dd7a516ad6c215f | d84f2c21f06c40b7ec49512a4fb13b4246f92209 | refs/heads/master | 2016-09-06T01:44:48.290950 | 2015-05-01T17:19:42 | 2015-05-01T17:19:42 | 17,783,203 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 276 | r | triazole-1-propaneni.R | library("knitr")
library("rgl")
#knit("triazole-1-propaneni.Rmd")
#markdownToHTML('triazole-1-propaneni.md', 'triazole-1-propaneni.html', options=c("use_xhml"))
#system("pandoc -s triazole-1-propaneni.html -o triazole-1-propaneni.pdf")
knit2html('triazole-1-propaneni.Rmd')
|
40b98333e44b4c9f9b42ababa5311e0a26d6dc4a | 8dda6c31530411c6328481a3b63ac0bb27320741 | /R scripts/code_for_week10_part1.R | 8568877404e93ff14dd330a0dc24f9031832f413 | [
"MIT"
] | permissive | guhanavel/DSA1101-Introduction-to-Data-Science | fc1ccf855a2313a9ab8e04a03094cd9031755ae3 | 2c5e7f1ef53c2cac77ab3ad20c6d9e2a78e2ac15 | refs/heads/master | 2022-04-17T16:18:39.661539 | 2020-03-17T15:24:39 | 2020-03-17T15:24:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,158 | r | code_for_week10_part1.R | install.packages('arules')
install.packages('arulesViz')
library('arules')
library('arulesViz')
data(Groceries)
Groceries@itemInfo[1:10,]
Groceries@data[,100:110]
apply(Groceries@data[,100:105], 2,
function(r) paste(Groceries@itemInfo[r,"labels"], collapse=", "))
itemsets <- apriori(Groceries, parameter=list(... |
09b66a9010223a8171c4e93a75f452bf1701b26f | e351e76746b37fb9c3b2bf0f2fd24eff8c542647 | /HandsOnTSA/Chapter 02 Working with date and time objects.R | b4677b5ab068f4c6cbd1e23379c26ab877bc953b | [] | no_license | PyRPy/TimeSeries_R | 2a26fcba2cd8fc2388f543e4f8944a20c9199bfb | a94394f1a2882157d4f58c915592f77f568cae83 | refs/heads/master | 2022-05-20T02:34:47.970015 | 2022-03-14T01:02:38 | 2022-03-14T01:02:38 | 156,481,087 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 8,081 | r | Chapter 02 Working with date and time objects.R | # Chapter 2 Working with date and time objects
# -------- Code Chank 1 --------
# The Sys.Date function get the current date from the system
date <- Sys.Date()
date
class(date)
# The Sys.time function get the current date and time from the system
# The defualt format is POSIXct
time_ct <- Sys.time()
ti... |
9ea9d6d1ee3185a71be20f5ebbf2f0bfc19e65fd | 0e92c0b362b230341f9cc31207df8139dbc3ac18 | /R/mean.R | b5ab70572e342130a3833bead2378b4f130594d2 | [] | no_license | cran/raster | b08740e15a19ad3af5e0ec128d656853e3f4d3c6 | dec20262815cf92b3124e8973aeb9ccf1a1a2fda | refs/heads/master | 2023-07-09T20:03:45.126382 | 2023-07-04T10:40:02 | 2023-07-04T10:40:02 | 17,699,044 | 29 | 35 | null | 2015-12-05T19:06:17 | 2014-03-13T06:02:19 | R | UTF-8 | R | false | false | 5,927 | r | mean.R | # Author: Robert J. Hijmans
# Date : October 2008
# revised: October 2011
# Version 1.0
# Licence GPL v3
.deepCopyRasterLayer <- function(x, filename="", ...) {
out <- raster(x)
if (canProcessInMemory(x)) {
return( setValues(out, getValues(x)) )
} else {
tr <- blockSize(x)
pb <- pbCreate(tr$n, la... |
1d4ed04086a1263489c786cfca34f3fb7ee52a3a | 02536fad62b930b2e4da91f55d800d805bcb9bce | /staphopia/man/get_samples.Rd | 7b1f78f9505690f91be34318b2f3166378b3cd0e | [] | no_license | staphopia/staphopia-r | ef22481b947e0717cbcdda1ae7e7755fd0af1b88 | df19fa91421e18a990d861b5d138698acf0a731c | refs/heads/master | 2023-08-07T15:43:10.300845 | 2023-08-01T21:06:34 | 2023-08-01T21:06:34 | 59,863,793 | 4 | 3 | null | 2018-04-09T17:17:13 | 2016-05-27T21:18:55 | HTML | UTF-8 | R | false | true | 377 | rd | get_samples.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sample.R
\name{get_samples}
\alias{get_samples}
\title{get_samples}
\usage{
get_samples(sample_id)
}
\arguments{
\item{sample_id}{A Sample ID}
}
\value{
Parsed JSON response.
}
\description{
Given a sample ID(s) return sample information.
}
\... |
52ada2b7749f07062889809c04bd4fc35850bed3 | 568962b96f03714be82940029fe707facf4bffe0 | /man/read10xVisiumWrapper.Rd | 525d73d8f6de5bba59505a6cd28356363de0714e | [] | no_license | LieberInstitute/spatialLIBD | 7064a3ddebd5de3be65556ad738a3816fd6296a4 | 66f7b7ba72be86ed65ed70bb1251f314bcdc332e | refs/heads/devel | 2023-07-07T09:15:10.011687 | 2023-07-04T02:19:03 | 2023-07-04T02:19:03 | 225,913,568 | 53 | 13 | null | 2023-04-28T13:08:17 | 2019-12-04T16:46:09 | HTML | UTF-8 | R | false | true | 3,048 | rd | read10xVisiumWrapper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read10xVisiumWrapper.R
\name{read10xVisiumWrapper}
\alias{read10xVisiumWrapper}
\title{Load data from a 10x Genomics Visium experiment and make it spatialLIBD-ready}
\usage{
read10xVisiumWrapper(
samples = "",
sample_id = paste0("sample",... |
3a509d1b4b6fc6bd1a61e7af7da86a29ecf0b3d5 | 536cf445a4a9465270d79f0c37e0eb4f8d61403a | /man/similarity_matrix.Rd | cf7812582e9ead1e2cbd92d2e45030d3ac2609ba | [
"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 | 2,714 | rd | similarity_matrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/similarity_matrix.R
\name{similarity_matrix}
\alias{similarity_matrix}
\title{Get the similarity matrix of an area}
\usage{
similarity_matrix(
df,
species,
comparison,
NI_label = "",
index = "Sorensen",
dendrogram = FALSE,
n_gro... |
36f1fe2bbeb461c3288dfac242f0dd040554a5aa | f4d77c60cc21b50656522d272b49243b5993d571 | /01_gene-level-analysis/utils/load_data.R | 5e66618bd50e5b0bf727c940e5d7fc98d8e6b479 | [] | no_license | tanminkang/CB2-Experiments | 453c20a723bcc8563a18da73109d293690596454 | 8c5245c7bd63987f01cffb20a57a00218e9d7924 | refs/heads/master | 2020-09-29T16:22:50.469016 | 2019-03-25T21:33:13 | 2019-03-25T21:33:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 669 | r | load_data.R | load_data <- function(study_id) {
require(tidyr)
require(magrittr)
require(dplyr)
require(glue)
mk_df <- function(f) {
read_csv(f) %>%
mutate(
method = basename(f) %>% str_replace(".csv", ""),
dataset = f %>% strsplit("/")) %>%
mutate(dataset=dataset[[1]][3]) %>%
filte... |
10b369e0106b6d204f960350f66aaa40a9785aa9 | 0a8b6890acc567c4536af126c0c0d1a197b6f62a | /code/model.R | 1cec36aa45168f1898683e5da1c16107e17b7365 | [] | no_license | xhq318/628module1-bodyfat | eeaa57c1296473e353f4866cbe96a60ad466ae15 | 0baf4626bdde3e3295b14b94bc8dd2149d895f44 | refs/heads/master | 2020-04-18T14:04:57.252018 | 2019-02-07T05:46:40 | 2019-02-07T05:46:40 | 167,579,647 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,436 | r | model.R | dat=dat[,-2]#remove density
#use stepwise method
library(leaps)
steps <- regsubsets(BODYFAT~., data = dat, nvmax = 4,
method = "seqrep")
summary(steps)
#create accuracy matric
model=c()
R2=c()
#one predictor
step1<-lm(BODYFAT~ABDOMEN,data=dat)
summary(step1)
model <- c(model, "step1")
#two pre... |
dd1842986a3c85a57c6b788b7be0fe74467caa76 | 6f47a7832483a437b68cd73e025d4a389c1ed2a9 | /R/newfunctions-Survival.R | 06fb0b32acf2b2604030dcf51502af8032d0ce8a | [] | no_license | cran/partDSA | 6ba5af11a537621b9c41a6dd34ecb425a2ff8939 | 2ccc836d61817c037b175a29565fa2089672b3ca | refs/heads/master | 2021-01-22T08:19:35.583443 | 2017-01-05T09:39:59 | 2017-01-05T09:39:59 | 17,698,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,132 | r | newfunctions-Survival.R |
## This is the top level of the survival function which is called. The first if clause is for Brier and it repeats the lower level survival function the number of times
##corresponding to the length of the brier.vec. It then sums the risk over these times. The second if clause is for IPCW and just runs the lower ris... |
8832253ba9ca92169c1fa82ef2404539db16674f | ec9ad043b7eb8c868e972fc21011b89e32a0433e | /man/fars_read.Rd | 9974f94504dfc161cc14848365135bbe983a7ebf | [] | no_license | olb1605/FARS_Package_Coursera | ef65a1063435af897926a4780b2518ef1409481b | f16459798041f02cef306f24d67bf77763a29310 | refs/heads/master | 2020-03-14T03:59:55.807754 | 2018-04-28T21:13:33 | 2018-04-28T21:13:33 | 131,431,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,341 | rd | fars_read.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fars_functions.R
\name{fars_read}
\alias{fars_read}
\title{FARS functions
These functions provided are using data from the US National Highway Traffic
Safety Administration's Fatality Analysis Reporting System (FARS), which is
a nationwide c... |
7244facc3899312413b6aad91a1d9e34c003366b | 7c0ce79410cb02e36fc524346a3c316c2108376b | /tests/testthat/test-save-configuration.R | da29389040c9f81a9bcb921412e2b43d660e6fd2 | [] | no_license | bheavner/minidtools | 471ba86953ec06e9467298cf6c3d17f3262d3e97 | 7be9895f40c93b318f169c3cfc00cd354477ccd0 | refs/heads/master | 2022-11-27T05:59:11.045330 | 2020-07-29T14:46:37 | 2020-07-29T14:46:37 | 61,151,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,964 | r | test-save-configuration.R | context("test_save_configuration - unit tests")
test_that("save_configuration() writes expected JSON", {
config <- configuration(
server = "http://minid.bd2k.org/minid",
user = "Jane Smith",
email = "jsmith@example.com",
orcid = "0000-0003-2898-9044",
code = "XXXX-XXX-XXXXX-XXXX-XXXX"
)
sav... |
cbb3431f38ded2dfaf3d7be92035d0d6f375b6f7 | f1de7628bebc854846128bddddbeccee79f65102 | /plot1.R | eab3cb1504cc5ddd4fa356d55a93c70276bf2dba | [] | no_license | nilomartin/ExploratoryAnalisysAssignement1 | e83c007829237f55df19b44b59bb6a5467a6f9c0 | bcfc1a87c7a68637cbe90a8bc10e8efe28db25cc | refs/heads/master | 2023-03-19T13:02:58.251614 | 2021-03-08T17:35:06 | 2021-03-08T17:35:06 | 345,739,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 616 | r | plot1.R | #using sqldf package to subset while reading the file
library(sqldf)
data<-read.csv.sql("household_power_consumption.txt", header = T, sep = ";",
sql = "select * from file where Date == '1/2/2007' OR Date =='2/2/2007'")
#converting Date data to date class
data$Date<-as.Date(data$Date, "%d/%m/%Y")
#c... |
cef609b850c55876b92272d129188cf4ca541e59 | ba702a1551dd6c01e19b6d7aaec19c2130ae66d8 | /R/expression/DE/preprocess-GSE76097.R | 51f5873e0dc0a28462d640b9aad58afb07c91aa5 | [
"MIT"
] | permissive | skinnider/spinal-cord-injury-elife-2018 | a6d5d971a1eef566a68283d2b3d8feaebe9b73a6 | a8e26008d1bc34ff98a88cd42ee32ebf5d467252 | refs/heads/master | 2020-03-25T12:06:23.001331 | 2018-09-09T19:33:39 | 2018-09-09T19:33:39 | 143,760,533 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,501 | r | preprocess-GSE76097.R | # Preprocess the Anderson et al. RNA-seq dataset.
setwd("~/git/spinal-cord-injury-elife-2018")
options(stringsAsFactors = F)
library(tidyverse)
library(magrittr)
library(preprocessCore)
library(org.Mm.eg.db)
source("R/expression/DE/DE-functions.R")
# set expt
expt = "GSE76097"
# read SOFT file
lines = readLines("data... |
ec530aab54f71c80f4e8716346076c34e9a21211 | f9963618c8d61c7ea5ab1d7ea6c201975f04fe4e | /R/expressedGenes.R | e31f0363b44be92e3002efe19790ea0699186020 | [] | no_license | Kraus-Lab/groHMM | 7f31d0f0ac026e99dd7c8eaab74bb5b5411973c3 | 514f58a04befc2d3c9c8e10ce63ce30f5f05560e | refs/heads/master | 2022-10-19T16:00:39.726593 | 2022-09-28T20:51:02 | 2022-09-28T20:51:02 | 47,837,375 | 1 | 1 | null | 2015-12-11T16:23:24 | 2015-12-11T16:23:24 | null | UTF-8 | R | false | false | 7,910 | r | expressedGenes.R | ###########################################################################
##
## Copyright 2013, 2014 Charles Danko and Minho Chae.
##
## This program is part of the groHMM R package
##
## groHMM is free software: you can redistribute it and/or modify it
## under the terms of the GNU General Public License as... |
e1f3577609e6b6fc1c3066a6fcbc47bbf8bcb9b4 | 565132aa83ca2a741b21c7fde1f00e1ab9ef1cd2 | /BMA_development/SF_mimic/indicator_bma/run_sims.r | 7a7d09d50c8c05ce9b072343e3e8beb3a28da981 | [] | no_license | JHHatfield/BRCindicators | e1c61744887b01a567982d2d47fe9e42038949b2 | fb80e7aa0b31025e4f0d1eac2f57993eeaa0380b | refs/heads/master | 2022-02-21T22:23:52.130275 | 2019-10-16T14:51:35 | 2019-10-16T14:51:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,167 | r | run_sims.r |
######################################### download and install the package
library(devtools)
install_github(repo = 'drnickisaac/BRCindicators')
library(BRCindicators)
# load the help file
vignette("BRCindicators")
######################################## simulate some data
data_in <- simulate_indicator(nsp=25, n... |
1f1984215dca34649f6f0aa683bcf24ece735244 | d7400f2feeb54bc3a7bf1048d7040c1f29c55f54 | /man/orderedLasso.cv.Rd | 0fcf16753674de07bd2f394587a4ad0d152f7e78 | [] | no_license | cran/orderedLasso | 30e9238aa217024635982aace7da38969be1b2b6 | 6e3d1b2291e543aedbfbf91c6b3ec9a4fd8c47ba | refs/heads/master | 2021-06-03T05:32:39.972806 | 2019-01-07T11:21:23 | 2019-01-07T11:21:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,226 | rd | orderedLasso.cv.Rd | \name{orderedLasso.cv}
\alias{orderedLasso.cv}
\title{Cross-validation function for the ordered lasso}
\usage{
orderedLasso.cv(x, y, lamlist = NULL, minlam = NULL, maxlam = NULL,
nlam = 50, flmin = 5e-04, strongly.ordered = FALSE, intercept = TRUE,
standardize = TRUE, nfolds = 10, folds = NULL, niter = 500,
iter.... |
b8fa7192057fd4272f2034e956a110ed903e64b5 | b8a19cc9c443d367da8ce10c435a8c7d9bbffa9b | /tests/testthat/test-convert_dates.R | c412ab8080fa453299334393ee74f0c0521d8fad | [
"MIT"
] | permissive | g3rley/healthcareai-r | 9b0a68cc5406f2af2a85dc5318769a94075787a6 | 9b00c1be1daaa5c387ecee910d7571684447e4ff | refs/heads/main | 2023-07-21T19:08:15.174485 | 2022-09-01T17:16:55 | 2022-09-01T17:16:55 | 451,652,775 | 1 | 0 | NOASSERTION | 2022-01-24T22:20:57 | 2022-01-24T22:20:57 | null | UTF-8 | R | false | false | 3,709 | r | test-convert_dates.R | context("Testing convert_date_cols")
# Setup ------------------------------------------------------------------------
d <- data.frame(
a_DTS = c("2018-3-25", "2018-3-26"),
b_DTS = c("2018-03-25", "2018-03-26"),
c_DTS = c("3-25-2018", "3-26-2018"),
d_DTS = c("03-25-2018", "03-26-2018"),
e_DTS = c("Mar 25 2018... |
5f77cd8dcf4c5149de92a627b66fc151f0317336 | a16f28d10e26710c831a6f66902abd985c51c096 | /analysis_Step7.R | 290540b66b0a0c2299176d85af3f409df1bef6e9 | [] | no_license | sandyschumann/cogflexreplication | 2e9dbf81aae7fcb935f7d7aeee78aeb1f8e74d1e | 1b06dda8c3cc3f8a818296c208cffe4dc528a8c2 | refs/heads/main | 2023-05-31T03:11:07.058933 | 2021-06-24T08:31:27 | 2021-06-24T08:31:27 | 333,808,201 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 827 | r | analysis_Step7.R | ###Goals: Complete additional analyses to examine scale properties of willingness to fight and die
###Requirements
install.packages("lavaan")
library(lavaan)
###Confirmatory factor analysis Model 1: one latent factor
CFA_model1<- 'progroup =~ killandfight_1+killandfight_2+killandfight_3+killandfight_4+killandfight_5+... |
67c2998ef3dcaac05bb2c036093df19854e7d4e4 | e105a5cc2ab6cdb71c788e5957ff2e8cbf27dfc7 | /scripts/1.Pre-processing/A. Predictors/remove_extra_lines_from_dbf.R | bed05d15396726f7693199a4988ea9069ee01708 | [] | no_license | mrc-ide/DENV_risk_maps | 214c4c199457f4173dccd9b0e7524fd879faf3c9 | 32a1ca0669757d3d015dbfa02798aac27500d0e1 | refs/heads/master | 2022-03-30T01:50:45.494730 | 2020-01-21T09:09:02 | 2020-01-21T09:09:02 | 120,633,767 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,449 | r | remove_extra_lines_from_dbf.R | rm(list=ls())
# load R packages
library(maptools)
# load data
all_countries_in_world_shp_admin_1 <- read.csv (file.path("output", "datasets", "all_adm_1_countries.csv"),
header = TRUE, sep = ",", stringsAsFactors = FALSE)
all_countries_in_world_shp_admin_2 <- read.cs... |
f69dc712a1e531c1df704544c433909c1c84e0a6 | 08a550500164fd09ba110fc9a9403053406c5801 | /Jackie/text_analysis.R | b18f67acdd2ffbecf78b4e8461c0f4517232ba67 | [] | no_license | jliangus/Final | 85acf4cc86bf9c3be8b3783187bfb54bfcb956fb | 4292fb447bafbfb22fe519e67f74a6600bc0d11c | refs/heads/master | 2020-04-12T00:21:44.620952 | 2018-12-18T00:28:49 | 2018-12-18T00:28:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,342 | r | text_analysis.R | #linear model
Model <- function(corpus){
dtm<-DocumentTermMatrix(corpus)
sparse<-removeSparseTerms(dtm, sparse = 0.6)
term_freq<-findFreqTerms(dtm,1000)
dtm1<-as.matrix(sparse)
dtm<-TermDocumentMatrix(corpus,control=
list(dictionary=term_freq,removeNumbers=TRUE,
... |
08fc558f1dbde3b3e2ac8a1076ca9f83fca690b3 | 140126d428822112ccbdb7f7044e8e5a209f2607 | /main.r | 8b60bcf29fbed32c6692c77cfe2381c5fb593964 | [] | no_license | mziegler611/ProgLanProj-R | 3a2ef07e3de66ff25fad3e0c4691347ba3b992ca | d40acf7abde73831eab7e01fdc20841d05655302 | refs/heads/main | 2023-01-15T20:22:15.798859 | 2020-11-20T01:56:46 | 2020-11-20T01:56:46 | 312,638,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,436 | r | main.r | #--------------------------------------
#Author: Lei Chen, Morgan Ziegler
#Date: November 17,2020
#Comp321 final
#--------------------------------------
source("/Users/leianna/Documents/321F20/ProgLanProj-R/functions.R", chdir = TRUE)
#source("/Users/morganziegler/Desktop/ProgLanProj-R-main/functions.R", chdir = TRUE)
... |
7d2105b188bb2bc4e3dd4f6bbe7d8ecd4548f317 | 20746cd95fee322979cc3464f01e8970796d1d2e | /data/union.R | 9e2bc8a19bdad99080f23060df419de5ddad809c | [
"Apache-2.0"
] | permissive | 92amartins/hacka-ime | a27730b9e9341ddf54271d2acdedecc9db4525bc | 5a06c7364a90b7bced738477b3768f10781b47e1 | refs/heads/master | 2021-06-21T19:29:14.105379 | 2017-08-20T11:34:07 | 2017-08-20T11:34:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,124 | r | union.R | library(dplyr)
setwd("../../data/")
paths <- c("cs/sarajane.csv",
"cs/clodoaldo.csv",
"cs/karina.csv",
"cs/fabio_kon.csv",
"cs/ivandre.csv",
"fis/alencar.csv",
"fis/salvadori.csv",
"med/kalil.csv",
"med/ludhmilla.csv",
... |
373b445dbe0695616da49881b8f25cd9e8d1710d | e213599e52e0f943a5dd99aa0f4716ebc3493fc3 | /Figure/FigureS13/S13.04.A.02.R | 0f9de0feb0653a0dee71e57b3d1425a7a376a31f | [] | no_license | hegu2692/VAEN | d78a6580b2430babe62702f9b775cd2e741a84a1 | 69b90b80c1c1419679c51c65a681c071fdba2779 | refs/heads/master | 2023-05-07T14:36:49.427975 | 2021-05-30T06:25:42 | 2021-05-30T06:25:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,134 | r | S13.04.A.02.R | #setwd("/path/to/VAEN/Figure/FigureS13")
source("../../code/multiplot.R")
library(ggplot2)
library(ggrepel)
abc = read.table("FigureS13.A.txt", as.is=T, header=T, sep="\t")
dat.plot = data.frame(FC = as.numeric(abc[,5]), log10p=-log10(abc[,4]), nMutProp = as.numeric(abc[,7]), color = as.factor(abc$color) )
la... |
bdcc1138611c71e38d99bea0fd872d438c45d60e | bc304dc82564cf44b7fe73d8722f1ac6e680dacc | /BiHLaws/script/2018_exctract_laws_and_votes.R | 7466b96a689f174114e2c5b6b5e207205726c90d | [] | no_license | rs2903/BiH | 7f993a06724e6a72a73810d2f59823c67dba8882 | 6c2f9b7413634ea0e0bd347cd74756cb989986ae | refs/heads/master | 2020-06-19T04:09:30.983391 | 2019-07-12T09:34:33 | 2019-07-12T09:34:33 | 196,551,852 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,100 | r | 2018_exctract_laws_and_votes.R | # description -------------------------------------------------------------
# scraps links to laws
# scraps info on developments pertaining to each law => basis for 2018_law_analysis
# scraps from each law development links to votes
# scraps pdfs behind each link on votes => basis for tesseract_extract_individual_vote... |
60c3fab7e264d4e7124aa4a2ef11fcb9b68cc724 | 1115ed2b4ab8073a9fb7937387ff2758258ed4dd | /Simulation/sim_bootstrap.R | f31068dd1f4cac8cb04ccf089f10c0bdfc594d05 | [] | no_license | chxyself25/Change-pointDetection | e343bc8cf47e141f579ab6f6b136257087f72a72 | 4a1b1b60074efb887d1f71c2b83ca3630fc9e409 | refs/heads/master | 2022-12-03T10:29:51.145653 | 2020-08-08T21:07:10 | 2020-08-08T21:07:10 | 285,974,409 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,810 | r | sim_bootstrap.R | library(doParallel)
library(devtools)
library(fda, lib.loc = "/home/xchang/R/x86_64-pc-linux-gnu-library/3.6/")
library(Rcpp, lib.loc = "/home/xchang/R/x86_64-pc-linux-gnu-library/3.6/")
library(fdapace, lib.loc = "/home/xchang/R/x86_64-pc-linux-gnu-library/3.6/")
sourceDir <- function(path, trace = TRUE, ...) {
for ... |
9732e975ef39296a643c9d80d8671038b8e2f207 | 5e20d81caa0e6585588a4474c73025f900b4c43c | /section3_assessment.R | d9c634c9adb1c589e0d52c92a1464d50d3ec0e33 | [
"MIT"
] | permissive | RitRa/edx-Data-Science-Professional-Certificate | 9bb39e906ef6e4fdb4ee7d7b6e29fd3010ca79e4 | 82228d4f623d451dda0e658181d361b91ce5a553 | refs/heads/master | 2022-12-25T21:07:03.926941 | 2020-09-14T18:45:23 | 2020-09-14T18:45:23 | 291,427,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,351 | r | section3_assessment.R | library(dslabs)
data(heights)
options(digits = 3)
heights
#Q1. First, determine the average height in this dataset. Then create a logical vector ind with the indices for those individuals who are above average height.
#
# How many individuals in the dataset are above average height?
summary(heights)
#68.3
isCl... |
bc0b89d0d65c026009dbae6c5ad8ab400d834188 | 1e705bd7f0363075e574393ea0d61795a01a8752 | /assigment/analysis.r | 87e56707f4b7777dc447c0cbe459a22ac01c66ff | [] | no_license | cajoho99/ese | ed24b445971dc14bacc05bb7d2e55440ba11acd8 | 64b40ce2db7195f353f6b0eed1a8dc8d2a4583b1 | refs/heads/main | 2023-08-18T20:41:43.567631 | 2021-10-15T17:43:27 | 2021-10-15T17:43:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,746 | r | analysis.r | library(rethinking)
library(readr)
path <- paste(getwd(), "/assigment/data.csv", sep = "")
print(path)
d <- read_delim(path,
delim = ";",
col_types = cols(
.default = "?",
category = "factor",
technique = "factor"
)
)
d$category_id <- as.numeric(d$category)
d$technique_id <- as.nume... |
068db692af260e2bb773202021210d2cc21e2836 | 9719ea69f693adfddc62b27eaf948fc7b16f6ad0 | /R/map_nests.R | 1c15f1b3081c86af50a2522eea052286bf3454e8 | [] | no_license | dbca-wa/wastdr | 49fe2fb1b8b1e518f6d38549ff12309de492a2ad | 5afb22d221d6d62f6482798d9108cca4c7736040 | refs/heads/master | 2022-11-18T01:00:41.039300 | 2022-11-16T08:32:12 | 2022-11-16T08:32:12 | 86,165,655 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,667 | r | map_nests.R | #' Map tagged Turtle Nests interactively.
#'
#' \lifecycle{maturing}
#'
#' @details Creates a Leaflet map with an interactive legend.
#' The maps auto-zooms to the extent of data given.
#'
#' @param data The output of \code{wastd_data$nest_tags}.
#' @template param-wastd_url
#' @template param-fmt
#' @template param-cl... |
ef0466d608c7567b45fff1babf81a84053082c34 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.management/man/ssm_describe_maintenance_window_tasks.Rd | 819be7ef4be49da5bb43e91d328c9da1ac3ac316 | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 2,382 | rd | ssm_describe_maintenance_window_tasks.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ssm_operations.R
\name{ssm_describe_maintenance_window_tasks}
\alias{ssm_describe_maintenance_window_tasks}
\title{Lists the tasks in a maintenance window}
\usage{
ssm_describe_maintenance_window_tasks(WindowId, Filters, MaxResults,
NextTok... |
689fe1be6c77710e6afe4712c844caab77069df6 | 29585dff702209dd446c0ab52ceea046c58e384e | /Scale/R/Scale.R | 84a0c4e46cc73f7854d1c99bf9484083c093f4a9 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,095 | r | Scale.R | Scale <- function(data=data.frame(), orders=list(),
orders_id=integer(), reverse=c(),
items=character(), col_names=character()){
# A class to represent scale data
out <- list(data=data, orders=orders,
orders_id=orders_id, reverse=reverse,
ite... |
f90f7066f5ec64708dab498354751f67eb6f7f5c | 306a2f9b10c63884e56f3eddd77c83c3b96df55e | /Sims_svn/gensys/tag/gensys/man/qzdiv.Rd | 0a05c53ee28fba6f32ab08cf2c80c5a596472687 | [] | no_license | Allisterh/VAR_Sims_rfvar | 0f74bda155a1d2e0768dcacc08f7a5fa6de8eddd | befb94ec18da7a7c88c581249e2382e86a319ebc | refs/heads/master | 2023-03-17T13:32:11.188302 | 2017-10-11T19:15:22 | 2017-10-11T19:15:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,072 | rd | qzdiv.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/qzdiv.R
\name{qzdiv}
\alias{qzdiv}
\title{qzdiv}
\usage{
qzdiv(stake, qzlist, flip = FALSE)
}
\arguments{
\item{stake}{the real number about which the roots are sorted}
\item{qzlist}{the qz transform list. The \code{qz} from the QZ ... |
52fc74a7093eabfd80cbecb9874db313ccfeeb6f | 0aaecd6991a7f16759a1f8d2b3be6093f8a183af | /inst/snippet/mom-norm.R | 2408ab2ed76e7bbd70dbfaa7811b64e6798b7557 | [] | no_license | cran/fastR | 3f0e3959dad4e5d361c341eb6bea670eab6bfdcc | 572a5dc31e5aa85af4126662f95268329179c87b | refs/heads/master | 2021-01-21T04:55:14.927487 | 2017-07-27T19:52:06 | 2017-07-27T19:52:06 | 17,695,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 108 | r | mom-norm.R | x<-c(57.9, 70.8, 86.3, 92.3, 94.2, 117.0, 118.4, 122.4, 125.8, 134.4);
mean(x);
sd(x);
sqrt(9/10 * var(x));
|
fb8be5fef246bef0c0eb3b6a987c50cc8d5380b9 | fd570307c637f9101ab25a223356ec32dacbff0a | /src-local/specpr/src.specpr/newplot/setwav.r | fcd104ca5f6155ce9242bc62f2875725add3d8f0 | [] | no_license | ns-bak/tetracorder-tutorial | 3ab4dd14950eff0d63429291c648820fb14bb4cb | fd07c008100f6021c293ce3c1f69584cc35de98a | refs/heads/master | 2022-07-30T06:04:07.138507 | 2021-01-03T22:19:09 | 2021-01-03T22:49:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,405 | r | setwav.r | subroutine setwav
implicit integer*4 (i-n)
#ccc Version date: @(#)setwav.r 2.3 08/08/85 12:49:31
include "../common/pltcnt"
real*4 temp
if (iptype == 1 | iptype == -1) {
if (xlabel==' ')
#OLD way xlabel = 'Wavelength (!m!m)'
xlabel = 'WAVELENGTH (!m!m)'
} else if (iptype == 2) {
if (wminp==0.0) { ... |
d80c9f3d9e433bfd80468f91187428301bc7cf85 | e9f297561abaa4d9870f760507705fda34983183 | /man/arthropodaPhyloProfile.Rd | 9ddd0c83402a3f45b56e260961a933ef815e74a6 | [
"MIT"
] | permissive | mueli94/PhyloProfileData | f1c846fc029b45aa5ba47e294ec291c82e75d4f8 | 00a7a81a022c2994511063b5c2849edfcf95e69d | refs/heads/master | 2020-05-25T22:41:22.562566 | 2019-05-23T14:18:29 | 2019-05-23T14:18:29 | 188,020,000 | 0 | 0 | MIT | 2019-05-23T13:56:06 | 2019-05-22T10:54:35 | R | UTF-8 | R | false | true | 764 | rd | arthropodaPhyloProfile.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{arthropodaPhyloProfile}
\alias{arthropodaPhyloProfile}
\title{Phylogenetic profiles of Arthropoda taxon set}
\format{A data frame with 5 columns and 54656 rows:
\itemize{
\item{geneID}{Gene ID, e.g. "97421at665... |
123639a8ed37194215effc535ffa34c0d2a843b9 | dec5e66aefd642fcc8a9b51438806f5284e5f58f | /Src/GetSatisfaction 1.0.R | 519fc29fec0ba57cf5005f72ec3d58c008913d03 | [
"Apache-2.0"
] | permissive | satcos/HappyNurse | dde546034563bf33f8b2fae33cebedd1efea7a77 | ce34fdc32a9b129ad8e460a9c855b63e85798a66 | refs/heads/master | 2021-01-10T23:39:11.836287 | 2016-10-10T04:00:42 | 2016-10-10T04:00:42 | 70,410,197 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 432 | r | GetSatisfaction 1.0.R | # Input: void
# Output: Void
cat("\nLoading GetSatisfaction 1.0.R")
GetSatisfaction <- function(modelSchedule, Solution, noOfOptVar) {
modelSchedule <- modelSchedule[, 3:22]
Solution <- Solution[, 3:22]
Solution[modelSchedule == 0] <- 0
modelSchedule[modelSchedule < 0 ] <- 0
modelSchedule[modelSchedule > 0 ] <... |
d46bfae38dfac291de1c15e3b0476534abea8891 | e8584fdf4b9302610483dcb8e87740b238f50cb7 | /plot1.R | b8f6614a768c4f733ded4aa105fc7d4ad71408fd | [] | no_license | roseapple0803/PM2.5Plots | c08c2ef69027fadbd1259f3af577d429c823eacd | 18fec1837ad175eea488226baebce87ab2fd9570 | refs/heads/master | 2016-09-10T18:36:01.695020 | 2015-08-08T14:09:24 | 2015-08-08T14:09:24 | 39,744,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,796 | r | plot1.R | library(plyr)
library(dplyr)
## go retrieve these two files on the remote site if they don't exist
drawPlot1 <- function()
{
##Search and download packages if not present
if("ggplot2" %in% rownames(installed.packages()) == FALSE)
{
install.packages("ggplot2")
}
library(ggplot2)
if (!file.exists("summar... |
d783dadc7eb720195300c6ee3a74d2da57d5a317 | e943294fe503d00513175402cc3f81089618e409 | /man/rebuildPackage.Rd | 63eab8289e881e3dadb45aa8ad3a1d8e279e7be6 | [
"MIT"
] | permissive | sfeuerriegel/ResearchGroupTools | 395aa520315003b7626b7a5707a03df963059d3e | 96c0238f7261d471d522628be941a32aa9fc042a | refs/heads/master | 2020-04-24T01:22:59.561316 | 2017-02-03T17:21:50 | 2017-02-03T17:21:50 | 66,145,324 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 480 | rd | rebuildPackage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/devtools.R
\name{rebuildPackage}
\alias{rebuildPackage}
\title{Builds, loads and checks package}
\usage{
rebuildPackage(updateReadme = FALSE)
}
\arguments{
\item{updateReadme}{Flag whether to update the README.Rmd (default: FALSE).}
}
\descri... |
63eda15f450107d36dfecc3aa26e49f8095e0cf1 | 31d259895d0abb1625edc9b3f118bfafdcdddd3b | /HW/SPRMHW.R | 75261d6af621c665ad35139d85061bfc6339e3cd | [] | no_license | cortlandwatson/Experimental-Design | 0f9f66e9c6149253669c9e8a77aac59cf5560783 | 6ded6b3e52a2a9640b28a4fbac161b56815bc8f9 | refs/heads/master | 2020-03-19T06:54:30.214227 | 2018-07-20T15:50:20 | 2018-07-20T15:50:20 | 136,065,928 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,533 | r | SPRMHW.R | ####SPRM HomeWork
library(mosaic)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(CARS)
library(pander)
library(MASS)
library(gplots)
library(car)
### 3
Auxin <- read_csv("HW/auxin.csv")
Auxin$plant <- as.factor(Auxin$plant)
View(Auxin)
#Descriptive
favstats(Auxin$days~Auxin$plant)
favstats(Auxin$days~... |
8a86c1abb8dd095e95ddadd2afdf222d9db2bede | 6a4e4d968f5b8d0aefc4177a0d794a607e10f9f0 | /man/pmle.norm.Rd | f664071523a69297c3f2502fedca8c45f9d3cc84 | [] | no_license | cran/MixtureInf | 07ebf123aadd2a2eeddcf370690434fd24a287ee | aa55d75f9e34568f00f5dc240f35e09a7b74fcca | refs/heads/master | 2021-01-18T21:19:28.145574 | 2016-04-07T01:10:26 | 2016-04-07T01:10:26 | 17,680,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,464 | rd | pmle.norm.Rd | \name{pmle.norm}
\alias{pmle.norm}
\title{
compute the PMLE or MLE of the parameters under a mixture of normals with unequal variance
}
\description{
Compute the PMLE or MLE of the parameters under a mixture of normals with unequal variance.
}
\usage{
pmle.norm(x, m0 = 1, lambda = 0,
inival=NULL, len = 10... |
034620aeb1124d9d2d8b1d089209ef42dbc13a43 | 37b38b3858dd099c8b878d8e90f8b90d3d8c559d | /Taxi_Data.R | f1a96ce400df588d5524fd39babba803c3989908 | [] | no_license | sanket1sinha/Data-Science-with-R | 8b3fc66c7c5f037831719c27cb0190843107624b | bcabf6873511f9b2457e3b47b90930f9c4fb48ca | refs/heads/master | 2021-01-24T01:01:54.816192 | 2018-02-25T02:11:18 | 2018-02-25T02:11:18 | 122,792,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,658 | r | Taxi_Data.R |
###
##Part a)
#Loading the green_tripdata_2015-09 dataset into taxi_df
library(data.table)
taxi_df <- fread("https://s3.amazonaws.com/nyc-tlc/trip+data/green_tripdata_2015-09.csv")
##Part b)
# Reporting number of rows and columns in the dataframe taxi_df
number_of_row=nrow(taxi_df)
number_of_column=ncol(taxi_d... |
6a3689bb4908b5ea55e84d6c5d497cd06ff462bb | 48aa070e16208befa4c2181ac48ad0fbd9e4fa4b | /merging_NRE_data.R | 0ec71df4cf064a4049581648d9110dfca0e0c94b | [] | no_license | saahithi1/Research | 81ce56cdd437987310d1054284037c419a337f2d | e0d88fd82a39bf9bb006a983c0137cd05cf4b65b | refs/heads/master | 2021-07-09T17:30:44.743986 | 2020-06-22T20:56:09 | 2020-06-22T20:56:09 | 143,492,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 552 | r | merging_NRE_data.R | setwd("/Users/saahithi/Desktop/Research/merging")
# Reading in data
coding <- data.frame(read.csv("AnneChrisMerged.csv", header = TRUE))
NRE <- data.frame(read.csv("Latest_NRE_data_feb_2018_w_group.csv", header = TRUE))
# Looking at column names
names(coding)
names(NRE)
# Renaming column B in coding to match column ... |
0c1a3bcf2f003f9b17bbb98b1edb60d47e5fa894 | d1be17ffee0a234f18792f993e92b923d1ba6ba1 | /script.contours_for_google_earth.R | 57d2cbe5c66abd785a9cf253c66dcde4d9bf4604 | [] | no_license | AndresEGonzalez/GIS_TOPO_KML | e86263ecf9ec32d00999980d25b21cb70efd0813 | 69bd3946ce5f706e1b4f2014fe869b27195cecf9 | refs/heads/master | 2021-01-20T18:54:41.068991 | 2016-08-02T19:18:49 | 2016-08-02T19:18:49 | 63,365,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,379 | r | script.contours_for_google_earth.R | # SCRIPT CREATE GEARTH KML FILES WITH CONTOURS LINES (.tif RASTER)
# USE AFTER THE -> script.GIS.hgt.to.tif.R
rm(list=ls())
# sudo apt-get install libgdal1-dev libproj-dev
# http://stackoverflow.com/questions/30424608/r-error-in-fetchkey-lazy-load-database
# http://stackoverflow.com/questions/15248815/rgdal-package-... |
d88716da03fd4ff4ce1af48c5bceb2eed901e4df | f2478ed514a0559e433fb8f20c20fd2ea631c8f9 | /man/DataSource.Rd | 28bbae8c5434c5f275489577f506574375f83ae7 | [
"CC0-1.0"
] | permissive | qdread/useeior | 44555c6fc941a5750ce46a36243a22a4820d36ce | 53494293c34ca53a2bb9e92c418476754003bf80 | refs/heads/master | 2020-09-15T13:39:34.957252 | 2019-11-22T23:20:04 | 2019-11-22T23:20:04 | 223,462,878 | 0 | 0 | CC0-1.0 | 2019-11-22T18:32:36 | 2019-11-22T18:32:35 | null | UTF-8 | R | false | true | 531 | rd | DataSource.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ConfigDocumentation.R
\docType{data}
\name{DataSource}
\alias{DataSource}
\title{DataSource}
\format{A list of data source attributes
\describe{
\item{Title}{string, Name of source},
\item{DataYear}{numeric, year of data},
\item{Author}{st... |
4eb17a76dea046bd4a2f22f34bed1930b070f78e | 2bdff965877caa16d587e27879e899abf48f174e | /gp.combat.R | 1956ef3628462b4dd8e368db72a7eb9d3b4d9e06 | [] | no_license | genepattern/ComBat | 0c56f358384f377b3c3832dcd7372439587e7c2e | f4ce91f00bbfc79c755069182fa84e6896d2bdf2 | refs/heads/master | 2023-04-14T15:37:48.305636 | 2021-02-27T00:54:20 | 2021-02-27T00:54:20 | 52,296,184 | 0 | 0 | null | 2021-02-27T00:54:20 | 2016-02-22T18:40:14 | R | UTF-8 | R | false | false | 7,765 | r | gp.combat.R | setup <-
function(libdir)
{
source(paste(libdir, "ComBat.R", sep=''))
source(paste(libdir, "common.R", sep=''))
#setLibPath(libdir)
#install.required.packages(libdir)
}
parseCmdLine <- function(...)
{
suppressMessages(.parseCmdLine(...))
}
.parseCmdLine <- function(...)
{
args <- list(...)
input.file.name ... |
7c4d586b46fb2b32a267459562075d22860187f6 | f19dc4ea6af08834bff90a22b2786be169c07db1 | /R/ChibLL.R | 0082551ae93f42acb1a83bc7698209eca51784f0 | [] | no_license | hoanguc3m/fatBVARS | 71df1bff45b963f06258d2efa4e92036a6e7209b | 472ea0e344975bb4538182e3e5302708c9fd1a7c | refs/heads/master | 2022-12-21T13:42:35.405562 | 2022-12-12T18:00:38 | 2022-12-12T18:00:38 | 226,090,978 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 44,099 | r | ChibLL.R | #' Marginal log likelihood of BVAR model
#'
#' This function returns a marginal log likelihood density of BVAR-SV-fatTail model.
#' @param Chain The fatBVARSV object from command BVAR.
#' @return The marginal log likelihood density of of BVAR model
#' @export
#' @examples
#' \dontrun{
#' marginal_dens1 <- marginalLL(Ch... |
90a0a0a2f061612ba399a4d3715aeddacfb5db49 | 57eeb8bc61e099a71dee6291de36de05aa3014a2 | /run_Analysis.R | c251d95ed9ac4cc1dbd8f16a8879633e8dd6e415 | [] | no_license | anatesan/DataCleansingFinalProject | 26c9c1c43b7bd8027ff6bc388860d92ec51cdb29 | d19877d41cb9e5e73997441be0c204aec3ad8f7b | refs/heads/master | 2021-01-17T17:15:21.397316 | 2016-06-06T06:51:54 | 2016-06-06T06:51:54 | 60,500,457 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 644 | r | run_Analysis.R | source("tidyHAR.R")
source("createHARAvgDataset.R")
# This is just a driver script that relies on the workhorses - tidyHAR() and createHARAvgDataset() functions.
# Does not do much else than calling those 2 functions.
run_Analysis<-function() {
# create the tidy data set for HAR data
print... |
243c38a4f25dd86fe05ab3749492001ce6fe5753 | 6d94af002ecbd876df3e22c33ec69d11d985ed37 | /man/ExpPower.Rd | 752ed9e0aa1a7266ead53dddc2d9898fd81d44e5 | [] | no_license | statwonk/reliaR | c82365b33dcab7a66d87f70bc2887608b305c265 | f35ce9fbe14e7a0cf89ad4880b2e666879fdc2a0 | refs/heads/master | 2021-05-14T02:28:29.110165 | 2010-10-28T00:00:00 | 2010-10-28T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,973 | rd | ExpPower.Rd | \name{ExpPower}
\alias{ExpPower}
\alias{dexp.power}
\alias{pexp.power}
\alias{qexp.power}
\alias{rexp.power}
\title{The Exponential Power distribution}
\description{
Density, distribution function, quantile function and random
generation for the Exponential Power
distribution with shape parameter ... |
b0cc12b729a3d44b0bd5ac0877f584ac3caad3aa | 35e85a8945e271680d569d91336cd5584c5f2297 | /Load_Bgen/LoadBgen.R | ebe8c61840308217b8c5dcf510b3e58d361b3c3a | [] | no_license | zmx21/polyresponse | 377f2f15477b85c4754b6b0ffef231ef03de272a | 800bbf6c2d7037933fcd12cf44d29f5e408266f0 | refs/heads/master | 2021-07-13T11:25:30.640388 | 2020-08-11T09:52:31 | 2020-08-11T09:52:31 | 178,338,183 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,979 | r | LoadBgen.R | library(dplyr)
library(rbgen)
library(abind)
library(DBI)
library(RSQLite)
LoadSNPAnnotations <- function(sql_path,sql_db_name,rsid){
curwd <- getwd()
setwd(sql_path)
con <- RSQLite::dbConnect(SQLite(), dbname = sql_db_name)
#Get annotation info of rsid
annotation <- do.call(rbind,lapply(rsid,function(x) RSQL... |
8868fc28b6fb9eb831d3e36072b2d7f494f49e7e | e6d1b0df8392757469026cd5c3b74a9b65c7a966 | /ensemble2.R | 5139c1a2658387c7644df78d466f31b19d86da67 | [] | no_license | miguel-conde/Kaggle_SCS | 1dd8e6dd11bfc7ac240bf349b51a88214a341bb8 | 7af5f6504cb4d13decf6c55ff410b0c82c39af1d | refs/heads/master | 2021-01-10T06:30:57.408488 | 2016-04-14T07:53:30 | 2016-04-14T07:53:30 | 55,915,625 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,282 | r | ensemble2.R | # Initiate ----------------------------------------------------------------
source("common.R")
# Start Parallel Processing
cl <- makeCluster(max(detectCores()-1,1))
registerDoParallel(cl)
# Data --------------------------------------------------------------------
# Load tidy_train and tidy_test
load(file = file.path... |
8cccc122ade4a52486eaeb4397c34945819458d0 | 085d56e1c39afa030dd000512a7385c70bb3570e | /3_Script/1_Code/03_Processing_Data/batch_ItemBaseData.R | 8e1eb14167fc077625b408013bbbe9306b9c0209 | [] | no_license | datvuong/OMS_Data_Update | 86fb0e48a12482b28d80c401b9e68ad36a7d7ae2 | 178c4695322d8a1dbdb5c9f2f5d9ec83fada5d62 | refs/heads/master | 2021-01-16T23:15:17.800460 | 2016-07-06T05:05:03 | 2016-07-06T05:05:03 | 62,691,886 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,438 | r | batch_ItemBaseData.R | args <- commandArgs(trailingOnly = TRUE)
ventureShort <- as.character(args[1])
source("3_Script/1_Code/00_init.R")
source("3_Script/1_Code/01_Loading/00_LoadingData_Init.R")
source("3_Script/1_Code/03_Processing_Data/UpdateSOITimeStamp.R")
source("3_Script/1_Code/03_Processing_Data/GetSixMonthData.R")
source("3_Scrip... |
33a97838b7aa086fd7b357f496c75b3c119787ba | 3b6fd9148a6c86cd05fc3b9db05cee8b7cbb96fa | /R/outside.R | b5a157e6d8066b90e140029f90d45f2aacdf7f1e | [
"MIT"
] | permissive | markusdumke/fluid | 604faa7023539c843d44ff6d970663e507c18068 | e7c4c566495e9f76d6b4bacec475ab8a1bb04686 | refs/heads/master | 2020-04-18T09:41:31.133937 | 2019-09-23T18:48:40 | 2019-09-23T18:48:40 | 167,443,315 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 543 | r | outside.R | #' Inside / Outside
#'
#' @param .x Vector to test.
#' @param .y Vector or list of length 2.
#' The first element is interpreted as the lower bound,
#' the second element as the upper limit.
#'
#' @return A logical vector of the same length as `.x`.
#'
#' @export
#' @import data.table
#' @rdname outside
#... |
76ad120514e5e550aae4d296f3508ca81f068d10 | 2b4ad1be75d23c3081ab01797822ecf49cab7488 | /plot2.R | 73e375d8cc45ebba9611b93c79450edd3671727f | [] | no_license | galc/ExData_Plotting1 | 44cb8c24f06daec488e3a2a0ad57fc2a1a19cbbe | 6a3ce21f9f022b567a0541a39f8e7ebe4897c03a | refs/heads/master | 2021-01-22T00:46:13.954241 | 2014-05-10T17:28:49 | 2014-05-10T17:28:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 942 | r | plot2.R | ## plot2.R - Timeline Plot of Global_active_power
## Read Data
data <- read.table('Data/household_power_consumption.txt', sep=';', header=T,
colClasses = c('character', 'character', 'numeric', 'numeric',
'numeric', 'numeric', 'numeric', 'numeric', 'numeric'),
... |
91c1756ad208b7149389295e2123e1ea20e97665 | 146d7de2db9f1bffeaa24e7f01200c38ea2e9d75 | /tests/testthat/test_wtr.layer.R | 199d32630b410e2da4175ffa25dcbf2adffeafc9 | [] | no_license | lawinslow/rLakeAnalyzer | 13e9b11da4b44042fea0dd88210d7e47a9557c5c | 69749f58a665358dac8263a2d08d92f9100f1ff3 | refs/heads/master | 2021-01-21T03:13:08.229100 | 2018-09-13T19:49:51 | 2018-09-13T19:49:51 | 25,272,757 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,295 | r | test_wtr.layer.R | context("Testing wtr.layer")
data("latesummer")
test_that("Ensure that unconstrained and specified layer approach result in the same answer", {
expect_equal(
wtr.layer(depth = latesummer$depth, measure = latesummer$temper)[, 1:4],
wtr.layer(
depth = latesummer$depth, measure = latesummer$temper,
... |
e3d6a25cf6b10053e7c8d91917793b3dd5ff79ff | c8ce461387ababba10c5116dd047325d873a17a4 | /diversification_analyses/Castor/louca_pennell_stats_truncated.R | 43303677940ecc7bc86bb0cfc7d51851006f7b10 | [] | no_license | elsemikk/tyranni | 8ff58e6c39e2f402766d13fda413b3504023ec78 | 859f162e55124c72603f25b21172ac0b63188203 | refs/heads/master | 2023-05-01T05:57:18.018181 | 2021-01-29T16:15:03 | 2021-01-29T16:15:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,859 | r | louca_pennell_stats_truncated.R | setwd("tyranni/diversification_analyses/Castor")
getwd()
library(ape)
library(phytools)
library(castor)
# Get mean crown age of families
sample.data <- read.csv("../../Species_name_map_uids_Jetz.csv", header=TRUE)
family.map <- sample.data$howardmoore.family
names(family.map) <- sample.data$tipnamecodes
tree <- read... |
8b94be5fbb2e51db451edc4c55942cb3f555b20e | aebca85114388224fc24481fdfce04be048110db | /man/getModels_Caret.Rd | e1ac41f7bdcef7698bdc42601e18800905201871 | [] | no_license | mssm-msf-2019/BiostatsALL | 4f79f2fbb823db8a0cbe60172b3dcd54eac58539 | 0623dd13db576b2501783b31d08ae43340f2080b | refs/heads/master | 2020-05-25T15:16:01.949307 | 2019-05-21T18:11:12 | 2019-05-21T18:11:12 | 187,864,190 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,261 | rd | getModels_Caret.Rd | % Generated by roxygen2 (4.1.0.9001): do not edit by hand
% Please edit documentation in R/getModels_Caret.R, R/getModels_Caret_JCR.R
\name{getModels_Caret}
\alias{getModels_Caret}
\title{A function that tells you what are most N disimilar models available in caret for a certain problem (tag)}
\usage{
getModels_C... |
7be4f4bc6bb8151a0caad3d14d35e98bdc90e7bb | 448e3fbc77e3290c1c4196c2258b3d444c8c0d45 | /R/facets-procreads.R | e9b6393352d4cdd07ca2904bf455cd180d7586a0 | [] | no_license | mskcc/facets | 1fe15ddabe7b157c4ffdeddb048797c8a1f4f83b | f3c93ee65b09fc57aaed22a2eb9faa05586a9dc0 | refs/heads/master | 2023-06-09T01:38:44.938368 | 2021-10-12T12:04:39 | 2021-10-12T12:04:39 | 35,636,429 | 135 | 71 | null | 2023-06-04T17:04:27 | 2015-05-14T20:54:49 | R | UTF-8 | R | false | false | 4,616 | r | facets-procreads.R | # heterozygous and keep flags of the SNPs
procSnps <- function(rcmat, ndepth=35, het.thresh=0.25, snp.nbhd=250, nX=23, unmatched=FALSE, ndepthmax=1000) {
# keep only chromsomes 1-22 & X for humans and 1-19, X for mice
# for other genomes (gbuild = udef) nX is number of autosomes plus 1
chromlevels <- c(1:(n... |
fcedbc2d4e6638a86d576846792d88f87aa2acf9 | 04335cc0a2db41c50d2083600c3ceb19efe16db9 | /videoseries/05_Maximum_likelihood.R | cd97c595fb78a7f1312ada29566385932148253f | [] | no_license | tchintchie/VU-Supervised-Learning-Parametric-and-Semi-Parametric | 33ae29a55699a19cef7f59d6e04a3fff2ea110e0 | d388acbf6460ff1e222fb9607eba7c7e03d9a170 | refs/heads/master | 2023-03-20T08:04:11.498705 | 2021-03-14T08:59:03 | 2021-03-14T08:59:03 | 347,587,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,255 | r | 05_Maximum_likelihood.R | rm(list = objects())
library(ggplot2)
data("airquality")
data <- na.omit(subset(airquality, select=c(Ozone, Solar.R, Wind, Temp)))
head(data, n = 2)
# prepare response vector y
y <- data$Ozone
head(y)
# covariate matrix X
X <- as.matrix(cbind("(Intercept)" = 1, subset(data, select = -Ozone)))
head(X, n = 2)
beta ... |
2bb8401b2808dfc2e1b3cb37ee0f0ff5bc94a9cd | 0b84d6023413e08965e0ce2cca29c6431ddc3d53 | /codes/main/step8_results.R | 338f3a998ca639b73864ac9ab0a7969af86f5d0d | [] | no_license | jasa-acs/Covariance-Based-Sample-Selection-for-Heterogeneous-Data-Applications-to-Gene-Expression-and-Auti... | 8d615fe9b73991ebf066bbc545aeebadfd31ec75 | 1ed8f1f509098c8d34b92496b7258b28d2309d4f | refs/heads/master | 2022-08-30T02:09:03.023416 | 2020-05-26T20:27:21 | 2020-05-26T20:27:21 | 266,931,290 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,105 | r | step8_results.R | if(verbose) print(paste0(Sys.time(), "Start of step 8: Compiling the results"))
validated_genes <- covarianceSelection::validated_genes$Gene
num_pfc35 <- length(intersect(genes_pfc35, validated_genes))
num_nodawn <- length(intersect(genes_nodawn, validated_genes))
num_our <- length(intersect(genes_our, validated_g... |
56b18129783b7131ba1a1d1215538ffd638c9fab | 9e6c6d3ea78d408a6746fcdeca6ff0d3a8a3308c | /R/biomisation.R | 3d14172921930a9c760bff5c4b02468a1ee37383 | [] | no_license | stineb/rbeni | 36f28d38f58301d2af24255e9d63fe5ac6809ebe | 2f9d26d0a286c550cb90ee9d30a1f2b6c3b112f6 | refs/heads/master | 2023-02-18T22:18:52.856980 | 2023-02-16T17:29:09 | 2023-02-16T17:29:09 | 167,402,490 | 3 | 6 | null | 2020-09-25T09:35:32 | 2019-01-24T16:49:15 | R | UTF-8 | R | false | false | 6,500 | r | biomisation.R | ## ----------------------------------------------------------------
## BIOME CLASSIFICATION ALGORITHM FOR LPX-Bern OUTPUT
## Using variables GDD, FPC_GRID, height
## after Prentice et al., 2011
## Adopted from Fortran subroutine 'findbiome.F' implemented within
## LPX-Bern.
## b.stocker@imperial.ac.uk
## --------------... |
4c4745bff1e7beb899e18c25f24652f3c4f43bd3 | 2beba1c70d0b5ee75f083e71cf186dc826a1aca2 | /plot4.R | c77e6c5826b23057fbf33c470e92cfa8a7fb38ae | [] | no_license | thomasemurphy/ExData_Plotting1 | 5bb3b253b1e13acab265417e37b1f583e19bc4dc | 6ce2f47cab3891b917fc3052a77f6ee29f9987a2 | refs/heads/master | 2020-12-13T16:23:42.133979 | 2020-01-31T05:01:37 | 2020-01-31T05:01:37 | 234,470,790 | 0 | 0 | null | 2020-01-17T04:31:36 | 2020-01-17T04:31:35 | null | UTF-8 | R | false | false | 1,645 | r | plot4.R | load_data <- function(filename="household_power_consumption.txt"){
all_data <- read.table(filename, header=TRUE, sep=";")
all_data[,"dateTime"] <- as.POSIXct(paste(all_data$Date, all_data$Time), format="%d/%m/%Y %H:%M:%S")
feb_data <- subset(all_data, dateTime >= as.POSIXct("2007-02-01", format="%Y-%m-%d") & date... |
5bc180e1225a046df9a66eaea7bcb744915fce9a | cc23547152744fdc4af49bd25f4c72cc1af44067 | /tests/testthat/testloadGEO.R | 5e241b2599f669d4c67b30ceb00edb2a7032279f | [] | no_license | rsablina/phantasus | 0ea1e7ed8fb4b5a7e8649658aefc3930c924a111 | 60bec95d906666bb80681c8aa44ffe685131f03c | refs/heads/master | 2021-04-27T19:24:29.074081 | 2018-02-18T07:39:27 | 2018-02-18T07:39:27 | 122,356,605 | 0 | 0 | null | 2018-02-21T15:48:53 | 2018-02-21T15:48:53 | null | UTF-8 | R | false | false | 2,077 | r | testloadGEO.R | context("Load GEO and its utils")
library(jsonlite)
library(Biobase)
test_that("loadGEO finishes with result", {
options(phantasusMirrorPath = "https://genome.ifmo.ru/files/software/phantasus")
x <- loadGEO("GSE27112")
expect_is(x, "json")
ess <- protolite::unserialize_pb(readBin(fromJSON(x), what="r... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.