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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
9a1fab03b036ce268f0a7931ad42b2950a87bf63 | 237bcbdc6b09c57b251191471359eeefb8014410 | /letter_Prokopenko/06-Aquilla_9_23_2021_compare_variants.R | c672f6be251aa3a62788ffd5c34f09cf7fd9ec71 | [] | no_license | achalneupane/rcodes | d2055b03ca70fcd687440e6262037507407ec7a5 | 98cbc1b65d85bbb6913eeffad62ad15ab9d2451a | refs/heads/master | 2022-10-02T20:35:18.444003 | 2022-09-09T20:53:03 | 2022-09-09T20:53:03 | 106,714,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,355 | r | 06-Aquilla_9_23_2021_compare_variants.R | #############################
## Single variant analysis ##
#############################
# Family (FBAT)
NEUP_FAMILIAL_FBAT_ANNO <- read.delim("/40/AD/AD_Seq_Data/05.-Analyses/06-Aquilla_202101/09-Tanzi-replication/01-familial/03-PLINK-QC-files/FBAT/FBAT_rare_variant_analysis_results.csv", header = T, sep = "\t", stri... |
b0cdc307a880283f009a0137cbdfd0f07fefff3a | b599e97542c6df5add3e4b53586097705b10ce74 | /man/micro_nz.Rd | b8b5841f9fb8e1e57ce4f7a44227bbf9cbbd8024 | [] | no_license | ajijohn/NicheMapR | 09c435107b9e4aa0fd5b7982510a65e76680f1ed | 98386659036cc55df840df7339af519a766b88d2 | refs/heads/master | 2021-01-09T05:34:23.544532 | 2017-01-28T07:29:16 | 2017-01-28T07:29:16 | 80,757,456 | 0 | 1 | null | 2017-02-02T18:51:23 | 2017-02-02T18:51:23 | null | UTF-8 | R | false | true | 6,168 | rd | micro_nz.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/micro_nz.R
\name{micro_nz}
\alias{micro_nz}
\title{New Zealand implementation of the microclimate model.}
\usage{
micro_aust(loc = "Melbourne, Australia", timeinterval = 365, ystart = 1990, yfinish = 1990, soiltype = 4,
REFL = 0.15, slope = 0... |
2a5c68f7cdc81c014b842ae48962cf4a6dfefd43 | cf3d35a51ca24a2434826c81730c18904ceab1db | /surfacePlots.R | 759afca741b3fd4a010369ea373c281bbea3c2c9 | [] | no_license | Hardervidertsie/DILI_screen_paper | 2953c4715da122810a31d167dd9c2c7b0d1bcec2 | a09aa51ace36ec4284d5b96c0181e2f3722d9a10 | refs/heads/master | 2021-03-27T08:27:04.852256 | 2017-06-19T14:38:54 | 2017-06-19T14:38:54 | 74,959,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,365 | r | surfacePlots.R | testing rsm for fitting and displaying surface plot
require(rsm)
https://cran.r-project.org/web/packages/rsm/vignettes/rsm-plots.pdf
https://cran.r-project.org/web/packages/rsm/vignettes/rsm.pdf
CR1 <- combined.resp[ treatment %in% "mercaptopurine" & fingerprints %in% "GFP_pos.2m_Srxn1", ]
CR1 <- CR1[, mean(value), ... |
461eca2567536934adf135cd6b7f29482d385fda | 0a906cf8b1b7da2aea87de958e3662870df49727 | /biwavelet/inst/testfiles/rcpp_row_quantile/libFuzzer_rcpp_row_quantile/rcpp_row_quantile_valgrind_files/1610554442-test.R | 7f8dfa57d1bb7f93d07b047f7076cc4c8998628f | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 402 | r | 1610554442-test.R | testlist <- list(data = structure(c(3.17466821391751e-319, 0, 2.8396262443943e+238, 2.8396262443943e+238, 2.8396262443943e+238, 2.8396262443943e+238, 2.8396262443943e+238, 2.83962624009443e+238, 4.06493636881578e-259, 1.06559867695611e-255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(10L, ... |
ea3aeeb08c89f4be23dc115ca0e1d5eed8c2f2ab | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hIRT/examples/coef_item.Rd.R | e882a0ba5161715347aef8f8a9978b2409e5f32c | [] | 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 | 358 | r | coef_item.Rd.R | library(hIRT)
### Name: coef_item
### Title: Extracting Estimates of Item Parameters from Hierarchical IRT
### Models.
### Aliases: coef_item coef_item.hgrm coef_item.hltm
### ** Examples
y <- nes_econ2008[, -(1:3)]
x <- model.matrix( ~ party * educ, nes_econ2008)
z <- model.matrix( ~ party, nes_econ2008)
nes_m1 ... |
170dc5b1e1281c7dfc75bb3f85dda8d18643ae9c | b391a00661c6b5368b6406ede4d93c8a96913417 | /setup.R | f8b8200648653a5f8ef3a54b76bccddf0a9f8e4f | [] | no_license | normhcho/capstone | ad06769b211176f0dc48003434d55825bbc2c04d | ef7adbdbe0db813158e1c5d6b390d8cd6c9f8d0e | refs/heads/master | 2020-03-28T02:53:43.772473 | 2018-09-06T02:46:19 | 2018-09-06T02:46:19 | 147,605,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,489 | r | setup.R | # Loading the libraries
library(tm);library(quanteda);library(stringi);library(stringr);library(data.table);library(dplyr)
blogs <- readLines(file("./en_US.blogs.txt"), encoding = "UTF-8", skipNul = TRUE)
blogs <- iconv(blogs, from = "latin1", to = "UTF-8", sub="")
news <- readLines(file("./en_US.news.txt"), encoding ... |
b2e9d003e327db9715c363b8207e1957a5bbbeb1 | 7912e18deeebf6d99b1c35bf9c7e83f49da0fc8b | /man/omeka_key.Rd | bb88c064e8ccd96d5ef02e3ccefbffbbdb5d2e2f | [] | no_license | giocomai/omekaR | 8fd2b6683dba53f1e52ab618f3b9d9cc958f7ef6 | e7eee70c7ee2cecc5e5988fd2c196da7f5c3efc2 | refs/heads/master | 2020-07-04T06:09:00.856328 | 2014-11-22T19:51:39 | 2014-11-22T19:51:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | rd | omeka_key.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{omeka_key}
\alias{omeka_key}
\title{Get or set the Omeka API key}
\usage{
omeka_key(key = NULL)
}
\arguments{
\item{key}{The Omeka API key to the site that you are using.}
}
\value{
The current Omeka API key, or NULL if none is set.
}
\description{
Pass an Omek... |
245aaa686dd655c3c9bce106d8cb6eb9ae7492af | fed9f3581739dbb8e2b82565ad31b948e17910eb | /pierwszy.R | 568e01270e3b07fbbf926326d597feb741fbf32a | [] | no_license | Omuza/pjatkr | 30a5fbccafd7e10276b8f0d821a4007ae9ed3d02 | 181748c360ba78fa6ffc6736a04d94f2bb5d7aa6 | refs/heads/main | 2023-01-07T11:10:51.944081 | 2020-11-08T10:03:18 | 2020-11-08T10:03:18 | 311,009,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 363 | r | pierwszy.R |
library(devtools)
library(httr)
library(jsonlite)
endpoint<-"https://api.openweathermap.org/data/2.5/weather?q=Lublin&units=metric&appid=ccd2c7f8b414cadf0c4383ce0a541dc2"
getWeather<-GET(endpoint)
weatherText<-content(getWeather, "text")
weatherJson<-fromJSON(weatherText, flatten=TRUE)
weatherDF<-as.data.frame(weat... |
604eec29bd41dc4e689c096d43234ab84cb189a6 | 673e813b89de8f8ccffe671c6b6070026abbc53d | /R/MyPrimers_taqman.R | 3000a74972bf4a0b04df5c9e062f5810dfe8d57d | [] | no_license | jpromeror/EventPointer | 4eaa1f3a6bc653e72afef317517eec42dff41627 | aa24e3a15c6bdbd7b6c950b962b3d24c3eb80950 | refs/heads/master | 2023-05-25T16:48:24.853661 | 2023-05-15T11:14:22 | 2023-05-15T11:14:22 | 80,099,260 | 4 | 0 | null | 2022-11-28T11:24:50 | 2017-01-26T09:01:24 | R | UTF-8 | R | false | false | 358 | r | MyPrimers_taqman.R | #' Data frame with primers design for taqman PCR
#'
#'
#' @name MyPrimers_taqman
#'
#' @return MyPrimers_taqman object contains a data.frame with
#' the information of the design primers for taqman
#' PCR.
#'
#' @format A \code{data.frame} object displays the relative
#' information for primers design for taqman PCR
#'... |
1d321c793a49a2d16a5185122ef47af4c4e2bbf5 | fbd13aa34e784ccae3bdd238cfdcb12ac915470a | /man/RhttpdApp-class.Rd | 94b32860d4a791e4d11958ea8e9a235fa590da05 | [] | no_license | cran/Rook | 676dd56d2d2b0a44f872dca1d5f44e4c8ad78e8e | 933db81fbecf2f5e7a630a130242f13131ea68a9 | refs/heads/master | 2022-11-10T00:38:07.380159 | 2022-11-07T07:50:19 | 2022-11-07T07:50:19 | 17,693,393 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,683 | rd | RhttpdApp-class.Rd | \name{RhttpdApp-class}
\Rdversion{1.1}
\docType{class}
\alias{RhttpdApp-class}
\alias{RhttpdApp}
\title{Class \code{RhttpdApp}}
\description{
Creates a Rook application ready to add to an \code{\link{Rhttpd}} server.
}
\details{
The internal web server allows dispatching to user-defined closures
located in tools:::.h... |
32711b86f92509c64c8f4ed7a0a8c524219d3d1f | 22865e39c7c66cd740bc43313e4b2279f7d75d7f | /R/base.R | 3eff32e4fab0599590d794769e65c17b7c4ea24c | [] | no_license | cran/thematic | b545fc6a1ee118e22a8f8c7e66aa37571c04b655 | 004f263db643a1cc1bffc6e2d374d24942faa239 | refs/heads/master | 2023-08-18T15:27:49.716782 | 2023-08-11T16:30:02 | 2023-08-11T18:30:29 | 334,224,013 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,453 | r | base.R | base_palette_set <- function(theme = .globals$theme) {
base_palette_restore()
codes <- theme$qualitative
.globals$base_palette <- if (isTRUE(is.na(codes))) {
attempt_palette()
} else {
attempt_palette(codes)
}
}
base_palette_restore <- function() {
if (is.null(.globals$base_palette)) return()
att... |
21aa39e154d751e27708eeb8745a50d1265c9f04 | 818dd3954e873a4dcb8251d8f5f896591942ead7 | /Mouse/ClassicalPhenotypes/FV3/QTLanalysis.R | 6c6d587b4fa2700506e66208247fdff2837cdf07 | [] | no_license | DannyArends/HU-Berlin | 92cefa16dcaa1fe16e58620b92e41805ebef11b5 | 16394f34583e3ef13a460d339c9543cd0e7223b1 | refs/heads/master | 2023-04-28T07:19:38.039132 | 2023-04-27T15:29:29 | 2023-04-27T15:29:29 | 20,514,898 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,982 | r | QTLanalysis.R | # QTL analysis
#
# copyright (c) 2014-2020 - Brockmann group - HU Berlin, Danny Arends
# last modified Juli, 2014
# first written March, 2009
#
library(qtl)
setwd("D:/Edrive/Mouse/ClassicalPhenotypes/FV3")
# Analyse the whole F2 cross
cross <- read.cross("csv", file="cross_F2.csv",genotypes=c("A","H","B"), na.string... |
f7ef30ec3880c13fce02825ee83485d5f0ba35b8 | 9d600582590ce8b61b9f3a0b76a209fffa60215c | /netTime1000SamplingDistribution.R | dcc62cf25136039f9e20e039fbebfd27b5607434 | [] | no_license | amlanbanerjee/pathway_to_statistics | f43bd5e6394860962d9134a43e7fe030de121816 | e997f1cc2d9aa406a57e0f19fb205fc11bfb0b1f | refs/heads/main | 2023-06-14T21:34:55.673148 | 2021-07-09T14:59:38 | 2021-07-09T14:59:38 | 378,179,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 882 | r | netTime1000SamplingDistribution.R | library(openintro)
data(COL)
myPNG('netTime1000SamplingDistribution.png', 500, 400,
mar = c(4, 4, 1, 1),
mgp = c(2.7,0.7,0))
set.seed(5)
means <- c()
for (i in 1:1000) {
temp <- sample(nrow(run10), 100)
means[i] <- mean(run10$time[temp], na.rm=TRUE)
}
plot(0, 0,
type = 'n',
xlim = c(70,... |
fa72cf2bbf93a15ec66300c05f5d8c682bf53ba0 | fdf19c5e406df9d9f52409a18bd77e4b120eb87f | /man/limma_stats_fun.Rd | b1b88fc1eceefe944325c4ca7a534b027d10b325 | [
"MIT"
] | permissive | MassDynamics/lfq_processing | d2325cabb0d50779d9ea5beb8596fefb815e0bca | 5480744fbdfc4aea014dec6589e86b3dc2b0f632 | refs/heads/main | 2023-05-12T02:28:48.982754 | 2023-05-04T03:28:00 | 2023-05-04T03:28:00 | 341,805,035 | 4 | 0 | null | null | null | null | UTF-8 | R | false | true | 625 | rd | limma_stats_fun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/limma_stats_fun.R
\name{limma_stats_fun}
\alias{limma_stats_fun}
\title{This function performs the differential expression analysis with limma including all pairwise comparisons
using the condition provided}
\usage{
limma_stats_fun(
ID_type... |
4d21eb8f884c31158f5451de11df50cd299fa643 | c870f9319784e1c3f36b827cc08a3103816c4674 | /IDS 572_Assignment 1_Group code.R | 6fa863bef1aee864abe73862be93cf7bee6e06af | [] | no_license | adriankennyb/assignment_1 | 33f201bc30ceab23f4ecfe3761548d7790c33036 | 4a3985c70182dd2e14ddc977b0218ac7bddefb8b | refs/heads/master | 2023-08-13T23:04:56.868672 | 2021-10-01T23:34:18 | 2021-10-01T23:34:18 | 410,384,355 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,243 | r | IDS 572_Assignment 1_Group code.R | ##IDS 572 - Assignment 1A
##Authors: Jinrong Qiu, Adrian Blamires, Mike Gannon
##Due date September 25, 2021
lcdf <- read.csv("~/Desktop/School/IDS 572/Assignment 1/lcData100K.csv")
library('tidyverse')
library('lubridate')
library('rpart')
library('dplyr')
library('knitr')
library('ggplot2')
library(pacman)
library(t... |
8989ffea60641629b0e808f0f229cc390046805d | e3da04f20a57e0b11677faff78e700dbe05793ed | /NEX2018_08/Project/R/data_analysis.R | 9e2b5e896b3f161d462028997dcb918fb6248aa4 | [] | no_license | salisaresama/NEX | 73417608e9e700febbb53e1771b29adef076a902 | 2a55e12754ac39521080c5f610903b65aa85e8e8 | refs/heads/master | 2020-03-30T19:53:03.807727 | 2019-02-25T20:30:03 | 2019-02-25T20:30:03 | 151,563,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,858 | r | data_analysis.R | # Clear the environment
rm(list = ls())
# Load libraries
PACKAGES <- c('FrF2', 'lattice', 'pid',
'tidyverse', 'nortest', 'lmtest', 'caret')
lapply(PACKAGES, require, character.only = TRUE)
rm(PACKAGES)
# Set up the working directory
setwd(paste0(getwd(), '/NEX2018_08/Project/R'))
# Load source files
... |
a1f77136cfb9c1dc41210743a2eaf25a9f0cbeda | acabe441d5bd5391ff0812169275c67128978c39 | /tests/testthat/test_validate_templates.R | 9434c340227e28e464f509eaeff43f5c6eb26a2f | [
"MIT"
] | permissive | Ashley-LW/EMLassemblyline | 65d448ce6ee760f06904326ca2f3b9f4e475a85e | a37bc32c1feffa4f8a5ae88f158457fd05d4a86e | refs/heads/master | 2022-12-10T17:30:00.850619 | 2020-09-08T23:02:38 | 2020-09-08T23:02:38 | 292,932,246 | 0 | 0 | MIT | 2020-09-04T19:38:35 | 2020-09-04T19:38:34 | null | UTF-8 | R | false | false | 13,450 | r | test_validate_templates.R | context('Validate templates')
library(EMLassemblyline)
# abstract --------------------------------------------------------------------
testthat::test_that("abstract", {
# Parameterize
x <- template_arguments(
path = system.file(
'/examples/pkg_260/metadata_templates',
package = 'EMLassemblylin... |
8ef9d630a1bb2a8a5b8336d146080c3919210cd3 | 8223dbd59aa177f0d7e5c91934a4aa83ef1f1e89 | /caret_example.R | 438f5181cd04eb155ae837b20230303a64f22f5c | [] | no_license | Jong-Min-Moon/GMC-CSVM | 352f21b3f2d3ea92168cfa098ff46550c1c8bed6 | 0bbe3e3c3a3f26ac34182f7dfbfdc4c815624154 | refs/heads/master | 2023-05-15T08:59:52.071397 | 2021-06-09T19:23:03 | 2021-06-09T19:23:03 | 347,348,245 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,897 | r | caret_example.R | getwd()
source("wsvm.r")
## 1. prepare data
set.seed(1)
data(iris)
iris.binary <- iris[iris$Species != "setosa",]#only use two classes
#partition into training and test dataset
idx.training <- createDataPartition(iris.binary $Species, p = .75, list = FALSE)
training <- iris.binary [ idx.training,]
testing <- iris.b... |
e60b30b8977b4ac7905cc55067341663a84e4157 | ee3c321939e7d8899fed042575057c368b6c59e3 | /Project/Code/dus_model_building.R | 4a3c83c90fe895d2e710bbbfc7d79d447ec7a56f | [] | no_license | KBicks/CMEECourseWork | f30e29440077e95c5c87836a2ecc8194d4ee658a | 8cff4651f1ce27ff731137255646e3baccf11387 | refs/heads/master | 2020-03-31T10:54:53.862344 | 2019-08-29T12:04:49 | 2019-08-29T12:04:49 | 152,155,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,180 | r | dus_model_building.R | #!/usr/bin/env Rscript
# Author: Katie Bickerton k.bickerton18@imperial.ac.uk
# Script: dus_model_building.R
# Desc: Building and comparing models for various shark response variables.
# Arguments: None
# Date: 20 May 2019
rm(list=ls())
graphics.off()
# required packages
require(tidyverse)
require(lme4)
require(car... |
3a4a8001e46d5829a59259c352d7c84f8daa5a5a | 851527c2a9663b2aa83462409e66f3da90583f5a | /R/get_num_scale.r | 1c06dcb0cdafaf8d37fd26a0fd210179f09165e2 | [] | no_license | gastonstat/plspm | 36511de2c95df73a010fb23847ef84f7ab680b56 | bd21cb153021aed6ac6ea51ecbd0b856495b2a16 | refs/heads/master | 2022-05-05T19:36:11.853523 | 2022-03-27T00:25:31 | 2022-03-27T00:25:31 | 13,868,484 | 48 | 33 | null | 2016-12-13T07:37:39 | 2013-10-25T18:17:15 | R | UTF-8 | R | false | false | 756 | r | get_num_scale.r | #' @title Non-Metric Numerical Scale
#'
#' @details
#' Internal function. \code{get_num_scale} is called by \code{plspm}.
#'
#' @note
#' scales a matrix X in such a way that mean(X[,j])=0 and varpop(X[,j])=1
#' this means that sum(X[,j]^2) = n
#' if MD, sum(X[,j]^2, na.rm=T) = number of available elements
#'
#' @para... |
44a9393a0067e5ac2bf787e31f3ed53871f096ad | c7aeb17eef74157237662a8b92fefa21a116a15b | /cachematrix.R | 9d1758567d769489f20943f4e202db00e6e405d3 | [] | no_license | abhinavgaikwad/ProgrammingAssignment2 | a8db3755e26a63b454aa2d505a04230686ba4d80 | 2e8f7c0909a84218d908a7aa80e300ca7f10a7d4 | refs/heads/master | 2022-12-17T00:25:12.851387 | 2020-08-02T23:44:32 | 2020-08-02T23:44:32 | 284,554,043 | 0 | 0 | null | 2020-08-02T22:40:14 | 2020-08-02T22:40:13 | null | UTF-8 | R | false | false | 1,098 | r | cachematrix.R | ## Matrix inversion is a computationally an expensive process. This program shows a process
## to cache the inverse of a matrix rather that computing it each time the inverse is required.
## In the following function a special "matrix" object is created that can cache
## the inverse of the input matrix
makeCacheMat... |
67695275f18a89bb8808863f64d836d8ae194d70 | e80ffb7bfb546b42354e29dd9d0c2633e3743ca9 | /R/fn_writesample.R | 5e1ac26e8d11091ae71f7acaa543ae854fe07283 | [] | no_license | shearwavesplitter/MFASTR | 5c417f2499dcbb1df8e56786106e8ebdaa7eeb5e | a533f527cd6a4d2472ff7305f63b7f7c85467ceb | refs/heads/master | 2021-01-21T06:55:13.317019 | 2020-02-17T14:43:59 | 2020-02-17T14:43:59 | 84,286,263 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 864 | r | fn_writesample.R | #' @title Sample data
#' @description Writes out MFAST sample data
#' @param path Path to folder
#' @param type "normal" or "verylocal" sample data
#' @export
#' @examples
#' # Write out MFAST sample events
#' write_sample("~/mfast/sample_data/raw_data")
#'
#' # Write out MFAST verylocal sample events
#' write_sample... |
810af3f0bc5488e7509b5e69f494bae7ebcfa0f7 | ca3d8712ef173397eb9b6e52d73a9e136827e868 | /Rscripts/quant/meth-quant2.R | 8ba67df3f5bac4e774d066bbc1b21060336a3d19 | [] | no_license | hurrialice/metnet | 700953672643a20c1274d43cf6594accdb343631 | 6276d66dd0f042481229867f26348d88214e4c55 | refs/heads/master | 2021-01-23T20:31:47.180568 | 2018-01-19T04:23:57 | 2018-01-19T04:23:57 | 102,867,318 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,447 | r | meth-quant2.R | library(rtracklayer)
library(readr)
library(dplyr)
library(tibble)
library(GenomicFeatures)
library(GenomicRanges)
rm <- read_table2('RMBase_hg19_all_m6A_site.txt')
rm0 <- rm %>% filter(!is.na(score2)) %>% filter(supportNum > 10) %>%
dplyr::select(chromosome, modStart, modEnd, modName, strand,
s... |
a03c19f6b0d468e35d037fd73c551003b47658f6 | e349aed6d2373331d16f07dd89e4a13c21f56d88 | /R.scripts/PEER.Apr.UK.git.R | ac820448d8e777376e39d1fba85a2b01315fd483 | [] | no_license | dcjohnson23/dcj_publications | e60e1ac3d2c010e95c1d9645934fd357aee8d46e | 3be944426fa6cee59be03dde4ab8a3b9f8b9f9cf | refs/heads/master | 2020-07-31T08:37:09.233198 | 2019-10-08T10:18:28 | 2019-10-08T10:18:28 | 210,547,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,097 | r | PEER.Apr.UK.git.R | library(peer)
setwd("/johnson/PEER/")
expr = read.csv("/johnson/PEER/UK.eQTL.gcrma.433.ComBat.Entrez.txt", row.names=1, sep="\t", header=TRUE)
covars = read.csv("/johnson/PEER/gwas.433.eQTL.csv", row.names=1, sep=",", header=TRUE )
texpr <- t(expr)
dim(texpr)
model = PEER()
PEER_setPhenoMean(model,as.matrix(texpr))
dim... |
bc30c5b4c446b49cfde59da8b3704b7a367419ec | 3e6d6f143b0dd56472b70b4238203ea02db2e97c | /CarterHill_PrinciplesOfEconometrics/Chapter9_StationaryTimeSeries/exercise9.5.R | ddd63c19a4ee8d612b5268edce41763fa3d8e83f | [] | no_license | statisticallyfit/REconometrics | 80cee48277e3995df5219ee6515f673e97d1c875 | b61455988f6040ef9ef2d0c36cee241299459461 | refs/heads/master | 2021-01-23T01:13:22.758614 | 2018-11-23T07:57:30 | 2018-11-23T07:57:30 | 92,862,217 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 391 | r | exercise9.5.R | setwd("/datascience/projects/statisticallyfit/github/learningprogramming/R/RStats/learneconometrics/CarterHill_PrinciplesOfEconometrics/Chapter9_TimeSeries")
# QUESTION 9.5 (correlogram for 5.a)
growth <- read.dta("growth47.dta")
growth
growth.ts <- ts(growth, start=1947, frequency = 4)
growth.ts <- lag(growth.ts, -1)... |
f4429605fcf05cb9570adf6e6b64badce54d2f8e | e26db52a0aa8c04aeba460cbb673da1911cd11e1 | /figure_Rscripts/makeFigS2.R | abc793513d58f0337d19212bdf5bd06a386b7567 | [] | no_license | bdesanctis/mode-of-divergence | f108cd13622b7f8cd53518603c47d13847882424 | f31efd047aa18cef71d3a912dd71111d9de9aeb9 | refs/heads/main | 2023-04-11T19:49:46.169050 | 2023-01-10T11:57:27 | 2023-01-10T11:57:27 | 533,055,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,719 | r | makeFigS2.R | library(Hmisc)
library(RColorBrewer)
library(scales)
f <- 123 #this flag indicates that dominance coefficients are drawn from a 'realistic' distribution where large effect mutations have more extreme coefficients
mut <- 'perTrait'
overD <- TRUE
M <- 1
D <- 1000
n <- 20
scenarios <- 'drift'
paths <- c('M','m','f')
sta... |
ee712d48b83f83a96e3c91c5ef3f8b531718f796 | 1a1e0b5c8362d2694f3eeb6337735a35edb76de7 | /Rdatascience/15 - factors.R | 6ca3da5f6362bf0096fd92dcb05fc7520229a521 | [] | no_license | daifengqi/TidyverseStyle | 804adbfdb34f74c6d28aa59c38c25f50681c6236 | ed1e2d949d40fb15644bef1ce65552cf53d3f6d9 | refs/heads/master | 2022-03-04T17:45:32.121907 | 2019-08-13T12:23:18 | 2019-08-13T12:23:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,196 | r | 15 - factors.R | library(forcats)
library(readr)
library(ggplot2)
# a package for dealing with factors
# Creat factors
month_levels <- c(
"Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"
)
x1 <- c("Dec", "Apr", "Jan", "Mar")
y1 <- factor(x1, levels = month_levels)
sort(y1)
x2 <- c("Dec", "Apr", ... |
36362300a0cf52a5d3fe03067fca576f31559998 | 6e07af032188f52b9241bf568d98b159543ec8eb | /ProjectMindmap/DocumentationDatasetT-Total.R | a233886055df1be18cc0f230eae071d40446cb52 | [] | no_license | aumath-advancedr2019/PhaseTypeGenetics | 1919f79f93421786ccc10c69056972f3f9ee9534 | aa66f445b2515232ff4718a7bedef58242f08658 | refs/heads/master | 2020-08-01T15:21:43.487579 | 2019-11-28T09:13:14 | 2019-11-28T09:13:14 | 211,032,468 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 332 | r | DocumentationDatasetT-Total.R | #' The total branch length for a sample size of n = 5,10,20,50 and 100.
#'
#' A dataset containing the initial distributions and subintensity rate matrices
#' for the total branch length (T_Total)
#' for a sample size of n in {5,10,20,50,100}.
#'
#' @format A list containing 5 objects of type \code{contphasetype}.
#'... |
60a2cf167c00e0a3d561386364adfae41a5435cf | 4570d4339e498fa8caaaad6db7296704562d0532 | /webinars/Predictive_Modeling.R | ad3e688229cfa86542211bff3308114a49196457 | [
"Apache-2.0",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | sassoftware/sas-viya-programming | 81e024035a2fec55a17006672fd15069dcdfc8a5 | 947f16955fc7e94b73b5aa5a59010e90abd11130 | refs/heads/master | 2023-05-24T22:21:07.696235 | 2023-05-12T19:26:58 | 2023-05-12T19:26:58 | 62,091,838 | 146 | 154 | Apache-2.0 | 2023-03-25T01:24:58 | 2016-06-27T22:11:06 | Jupyter Notebook | UTF-8 | R | false | false | 8,046 | r | Predictive_Modeling.R | #
# Copyright SAS Institute
#
# Licensed under the Apache License, Version 2.0 (the License);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
898cd516bc9aa2fe5d9c7d891a660f507af6ac68 | 9397a453a4b9c4ddd9988235fe4a8ee6720358c1 | /Process_WholeNL/Metrics_calc_lidRv203.R | 9c16a8c59dcd106f40054ee5e9f1b77afef7e2de | [] | no_license | komazsofi/myPhD_escience_analysis | 33e61a145a1e1c13c646ecb092081182113dbce3 | 5f0ecdd05e7eaeb7fce30f0c28e0728642164dbc | refs/heads/master | 2021-06-04T21:39:58.115874 | 2020-06-18T12:59:53 | 2020-06-18T12:59:53 | 119,659,750 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,529 | r | Metrics_calc_lidRv203.R | library(lidR)
library(e1071)
# reinstall previous lidR version: require(devtools), install_version("lidR", version = "2.0.3", repos = "http://cran.us.r-project.org")
#Global settings
workdir="D:/Sync/_Amsterdam/10_ProcessWholeNL/Test/normalized_neibased/"
#workdir="D:/Koma/ProcessWholeNL/TileGroup_10/norm/"
setwd(wor... |
5434de99fce83ebe664d0f59570e402049832334 | c4c3992f17e63560bb98d7df0438072aab9f1c0a | /hw3/logistic_regression.R | a99f99cc31fd3a73359d3a59e80a6b608f5367e2 | [] | no_license | geluxp/CS584-Machine-Learning | 7ecf615db557de3ef39c5d6739d8e21cf1c858e3 | f3b4615d70fbd3b7a2906e36f7701815e8f218e3 | refs/heads/master | 2016-09-09T21:55:57.754815 | 2015-03-24T22:15:44 | 2015-03-24T22:15:44 | 32,828,424 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,807 | r | logistic_regression.R | ######################
####logistical regression 2 class
######################
##clear the memory
rm(list = ls())
require(cvTools) ##load cross validation package
#Load data
data <- read.csv("D:/KUN_MEI_ASS3/DATA/data.csv")
#Create plot
plot(data$score.1,data$score.2,col=as.factor(data$label),xlab="Score-1",ylab="S... |
149e4c3ae343efef0ecd26a528cf04f57b3ec3b9 | 02e0dd12cd6473d11312f5cd27d655b3e1174cb1 | /time-varying/MGWG_time-varying_20190927.R | d855297a3a524ed0e5410885fd3ca9544cdc27dd | [] | no_license | ices-eg/wg_MGWG | 754b9c93e0d4f31aa2183cbe7279a391e0d36be4 | b22e911b7449ffb9c2137d785a44537012fd2201 | refs/heads/master | 2023-05-27T12:03:25.345328 | 2023-05-25T19:06:37 | 2023-05-25T19:06:37 | 104,081,883 | 6 | 6 | null | 2019-10-07T20:14:42 | 2017-09-19T13:56:56 | R | UTF-8 | R | false | false | 1,314 | r | MGWG_time-varying_20190927.R | #### Libraries
library("ggplot2")
#### Read in files
files <- dir(pattern = "tab1\\.csv", recursive = TRUE, full.names = TRUE)
results <- sapply(files, read.csv, header = TRUE, simplify = FALSE)
results <- lapply(results, function(x) {
colnames(x)[1] <- "Year"
colnames(x) <- gsub("^Fbar[0-9a-z\\.]+", "Fbar", colna... |
eab1a3bac7eb6bd86427381df6ef378fb36fbd02 | b1e1a193db8d4647a2ae1566724beebcfbc2c167 | /index/data/observational/scripts/6_pheno_file.R | 2d1df2bfe8b92ba19f96ae0c8fb2d5db901c82f7 | [] | no_license | mattlee821/000_thesis | 166cef4616ad70ea47a6d558c77c8c4ec0a021b3 | 867c9f08daea61ecca7aa73e660d5001d1315a1b | refs/heads/master | 2022-05-08T08:26:24.394209 | 2022-04-07T09:30:51 | 2022-04-07T09:30:51 | 229,047,207 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,362 | r | 6_pheno_file.R |
# packages ====
library(data.table)
library(tidyr)
library(dplyr)
# children ====
data <- read.table("index/data/observational/data/body_composition/children_body_composition.txt", header = T, sep = "\t")
confounders <- read.table("index/data/observational/data/confounders/children_confounders.txt", header = T, sep =... |
4805a83940faae32d6077ab4c1a85f9f69c6faa1 | c6367467b1ff97c12439e7f3c2a309900223a798 | /data/data_carpentry/merge_mcd14ml_with_gldas2.1.R | 72a71b03e9ad94f1c21486e4eab5577fd51eef8a | [] | no_license | mikoontz/nighttime-fire-effects | 8302bb1133a9a549de43a1ef451160839930380f | 3a1908c7749e8c010f5967674b80185fbcb921f4 | refs/heads/master | 2021-07-22T06:51:21.428062 | 2020-06-22T18:52:12 | 2020-06-22T18:52:12 | 188,514,883 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,708 | r | merge_mcd14ml_with_gldas2.1.R | # Purpose: climate per active fire detection
# Conditional on there being an active fire detection, what is the climate?
library(tidyverse)
library(sf)
library(data.table)
library(tdigest)
library(lubridate)
library(tmap)
library(mgcv)
library(gganimate)
library(viridis)
get_mcd14mlGLDAS <- function(year, download = ... |
25f2d4d0c8786a6f8ac62a6f7ef6a35b04b35bbc | acd9ba63d2780db1550acc7e460f73e5750af052 | /code/plot4.R | 8794086d76e83ab588740f288b66605d16e78cba | [] | no_license | sayy85/ExData_Plotting1 | 2398bfc6fb839c0843f44f554a85718916b611c8 | eeb965971a23b8281f36bd9b2d319b2165f14e2f | refs/heads/master | 2020-12-25T12:28:37.519995 | 2014-08-12T06:49:51 | 2014-08-12T06:49:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 702 | r | plot4.R | plot4 <-function() {
#Read data
source('EDA_P1_ReadData.R')
D<-ReadData()
#Plot the data and save the plot as png
png('plot4.png',width=480, height=480,unit='px')
par(mfrow=c(2,2))
with(D,{
plot(Time,Global_active_power,type='l',ylab='Global Active Power',xlab='')
plot(Time,Voltage,type='l',ylab... |
4bb92b3aca370b1610239eeb724590e1943acfe7 | 993ae53cf1cdbe68427ae5ddfd2845dec78c12e3 | /Rplot4.R | 1febf1128e7055c16c794362d6de8b7b577afdb9 | [] | no_license | HighSpiRitsdx/Emission | d3612050ef6f7ac83c2a4f316982500073eac2f4 | bf54db134790b61ad0de8a579bf42280ea5a566a | refs/heads/master | 2021-01-05T06:17:43.149479 | 2020-02-16T16:56:21 | 2020-02-16T16:56:21 | 240,912,296 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 427 | r | Rplot4.R | ## load data ##
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
library(dplyr)
library(ggplot2)
## plot4 ##
coal <- filter(SCC, grepl("Coal", Short.Name))
NEI_coal <- group_by(filter(NEI, SCC %in% coal$SCC), year)
plot_data4 <- summarise(NEI_coal, Emissions = sum(Emissions))
p <... |
9419d6048fa3eb8f8d14853337538f33abecfbab | 79aa1613f924627f22e4bc60238bd65b3566d61e | /man/SP500.Rd | f23f1cd012a2abc0ef4375de33611ed09c7a3bb2 | [
"MIT"
] | permissive | tsurf101/olpsR | 4d027e7e57bb7650ccd32cd99178464aa6fe6ac4 | 56cdb47725eb1348223355b0c83a42d9f229c1af | refs/heads/master | 2021-05-17T17:33:51.727673 | 2020-03-28T21:36:42 | 2020-03-28T21:36:42 | 250,898,378 | 0 | 0 | NOASSERTION | 2020-03-28T21:36:09 | 2020-03-28T21:36:09 | null | UTF-8 | R | false | false | 2,256 | rd | SP500.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/data_SP500.R
\docType{data}
\name{SP500}
\alias{SP500}
\title{SP500 daily returns}
\format{A data frame with 1276 observations on the following 25 stocks.}
\source{
MSN Money, according to \url{http://www.cs.technion.ac.il/~rani/portf... |
e7b95802d3ee90887bd33bec7902602d8037e3d1 | a2b50507ed58c753f4f48e0e90b4537bd675706e | /tidy.R | 8134230daeb9cb6b8ee8094468d787eb3bb90fd5 | [
"MIT"
] | permissive | datarian/SeelandSettlements | 53f47b8ac5f73942f12fbea2ca0d9c2a7353be55 | 2e2a31fea9b950746cf2649bc6ca0e59d99d4e0f | refs/heads/master | 2021-06-11T04:28:14.133021 | 2019-11-30T08:54:07 | 2019-11-30T08:54:07 | 128,356,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,772 | r | tidy.R | library(dplyr)
library(sp)
dated_woods_raw <- read.csv("./data/belab.csv",
header = T,
stringsAsFactors = F,
sep = ";")
wood_list_raw <- read.csv("./data/Gesamtholzliste_Bielersee.csv",
na.strings = c("-","-... |
00ed61d059d34270a49b97e07ad821355dee8919 | 5fc8ac6bd470f9b0d64b17bd4dbce914667d9b28 | /man/factorpart.Rd | bbaa1fa2b5d79fee85aea0daed1a95052f9d2be9 | [] | no_license | cancer-genetics-utu/heatmapGen2 | 2571134a4f31d2a3d2f50acb4514896bb3401195 | e05237550098f47fec12764e5d90184e1c89c220 | refs/heads/master | 2022-08-01T08:15:28.928071 | 2020-06-02T22:43:47 | 2020-06-02T22:43:47 | 268,923,956 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,986 | rd | factorpart.Rd | \name{factorpart}
\alias{factorpart}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Utility
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
factorpart(fct, col = NULL, label = NULL, cex = 1, vertical = TRUE, width = lcm(1), na.color = "gray80", ... |
62afcb6f9af727f421abbd8e474ba7d319263127 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/photobiologyFilters/examples/rosco.Rd.R | fa771d852fa8cda4f631121e3548131ad4700175 | [] | 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 | 186 | r | rosco.Rd.R | library(photobiologyFilters)
### Name: rosco
### Title: Filter spectra data for Rosco thetrical filters or 'gels'
### Aliases: rosco
### Keywords: datasets
### ** Examples
rosco
|
2fdfaeceed3f79e9b5161761bb829abc61d75d54 | ea3caf9ccef3eca8bf5707f182401ceca55c6d03 | /cleanup credit data.R | edf34456f2e715f76d0d8052327451f5db96146e | [] | no_license | somu2k16/practice_R | c6566f02094eade9784db9c3f3c55c3382e08ad1 | 44e91e7bc81d710644a8180555d513a9c8fb74c0 | refs/heads/master | 2020-08-16T04:39:22.958529 | 2019-10-21T04:51:05 | 2019-10-21T04:51:05 | 215,456,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 948 | r | cleanup credit data.R | library(dplyr)
setwd('C:/Eminent')
loandata = read.csv('LoanStats3a.csv', na.strings = c('.','') )
View(loandata)
dim(loandata)
str(loandata)
result_na = sapply(loandata, function(df) {sum(is.na(df) *100)/ length(df)})
result_na = round(result_na, digits = 2)
class(result_na)
result_na_df = as.data.fram... |
362490617031bc60a4c13f589f8aed3f566f6bbd | 53f05350c45a0b1471560d49c4921d6f9128bd58 | /ui.R | d2625fbec4de9e6966361a9efa6c25e9779ea0f5 | [] | no_license | KellyHu/BST260 | 961c1480d2e12708bd171d766c0bdec40ad8dc9b | 15c1860e7e0efe7d33fe4d7ae7105f398a9225a8 | refs/heads/master | 2020-04-09T05:21:44.148104 | 2018-12-16T16:36:16 | 2018-12-16T16:36:16 | 160,061,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 497 | r | ui.R | library(wordcloud)
library(tidyverse)
library(stringr)
library(tidytext)
library(shiny)
library(rsconnect)
u <- shinyUI(fluidPage(
titlePanel("Word Cloud for text variables in our dataset"),
sidebarLayout(
sidebarPanel(
selectInput("selection", "Choose a variable:",
choices = fo... |
40c0c50235dd1d52a80619ba939c5ef9214f7f58 | 74f962b3643898bb4185a7fb84a6b69cf93c72eb | /man/dot-perma_cc_folder_pref.Rd | a3cb68dfc2dbbba4d6092762ded4f4797191fece | [
"MIT"
] | permissive | QualitativeDataRepository/archivr | 4afecc346132c1a32aee030b7fb701bd2b7557d2 | f7e10573b3c66a79fb82296d5eff4840d7e6d59e | refs/heads/master | 2022-02-26T23:39:45.222070 | 2022-02-09T18:46:20 | 2022-02-09T18:46:20 | 161,853,679 | 5 | 2 | NOASSERTION | 2022-02-08T17:16:05 | 2018-12-15T00:08:33 | R | UTF-8 | R | false | true | 378 | rd | dot-perma_cc_folder_pref.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apitools.R
\docType{data}
\name{.perma_cc_folder_pref}
\alias{.perma_cc_folder_pref}
\title{Global var for the API key for perma.cc}
\format{
An object of class \code{character} of length 1.
}
\usage{
.perma_cc_folder_pref
}
\description{
Glo... |
be62c5b921838d07754d202d1e97ea32ec33e123 | 4709e6dfacdf7bdc5bc1001be4d2879c38cd7344 | /global.R | fba951645f3aed2c73c646b01153e8580e45e3a7 | [] | no_license | mcarmonabaez/crime_mexico_city | d993237ac9ccebc35c15183feff8481c15d36920 | ccfcb33dd88203a95f64291370777b234a483d75 | refs/heads/master | 2021-05-02T16:19:26.212373 | 2018-02-07T22:32:06 | 2018-02-07T22:32:06 | 120,673,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,148 | r | global.R |
# cargar paquetes ---------------------------------------------------------
cargar_paquetes <- function(paquetes_extra = NULL){
paquetes <- c("tidyverse", "stringr", "lubridate", paquetes_extra)
if (length(setdiff(paquetes, rownames(installed.packages()))) > 0) {
install.packages(setdiff(paquetes, rownames(i... |
568d29d1263bca2b8324634964d5a7783ccb9ac2 | e75480f25015787abfcea357570c6dcae1954fe1 | /Lecture_1_20160604.R | f9c9f07d4e583db0a5cd4e5df6cc50b851ecd57d | [] | no_license | poplock100/NYC_Data_Science | 2f113a2e83c17fc41996cccc0669b3b05af60e00 | 184437f556e0f9d0033c683cbf607e1fccd61d17 | refs/heads/master | 2021-01-17T08:27:03.471781 | 2016-07-16T16:39:40 | 2016-07-16T16:39:40 | 63,492,328 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,477 | r | Lecture_1_20160604.R | setwd("c:/Users/Ali/NycDataScience/Lecture_1")
5-
1
a <- 1+1
b <- 2
c <- 3
plot(1:10, 2:11)
### install.packages("ggplot2")
library(ggplot2)
### Basic R
### Arithmetic
1+1*3
### Numerical and string vectors (atomic vector)
c(0,1,1,2,3,9)
c("Hello, World!", "I am an R user")
1:6
### Can't com... |
7f96fae83160024e1108049d6582807e6b33e20c | 82ea05843ae51c2a3a920d2de95b0dfac534ee82 | /size.R | 07cca6d386abe2377a07ce58e1ead94f69a9263e | [
"MIT"
] | permissive | TestingEquivalence/PowerLawR | b15b797b57c6f473f90abf79a59980025f822aea | a92022f072f2f2787d9bfd029d86d7ba5911accb | refs/heads/master | 2021-11-08T19:17:39.225981 | 2021-11-05T14:36:55 | 2021-11-05T14:36:55 | 199,118,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,249 | r | size.R | fullToss<-function(i,parameter){
set.seed(i*1000000)
counting=rmultinom(n=1,size=parameter$n,prob=parameter$p)
if (parameter$test=="asymptotic"){
res=asymptotic_test(alpha = parameter$alpha,frequency = counting,
kmin = parameter$kmin,tol = parameter$tol)
return(res)
}
if ... |
8d826949a6208d285adb25dbf825ef4f9fd6a8eb | 8bd7b647bef8c7b720f1eefb9cf1bfa7f8275b1e | /R/constrppmn.R | 0a879caa9fbe272e62d9de5fe6a1381c3dbd710d | [] | no_license | cran/polyapost | d1680946acb5a18d25b1ea08599df6efd1624ab0 | 395012a4062a89193e93c569538f4bbf9e1f6d6b | refs/heads/master | 2021-10-13T05:27:37.756110 | 2021-10-07T16:00:02 | 2021-10-07T16:00:02 | 17,698,639 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 517 | r | constrppmn.R | #For the subset of the simplex defined by
# A1 p = b1, A2 p <= b2 and A3 p >= b3
# where the Ai's are matrices and the bi's
#vectors of nonnegative real numbers this
#function uses the Metroplis-Hastings algorithm
constrppmn<-function(A1,A2,A3,b1,b2,b3,initsol,reps,ysamp,burnin)
{
checkconstr(A1,A2,A3,b1,b2,b3)
... |
0e326e041a12bfcb18125fd8f735915dfa89c784 | 331635a7ffc237ebc34722d6eb2ae69e0b82c3a2 | /20181116-pong/graphs.R | 9dfde3055fd44a79ba745ba43736c35b5c4d2aa3 | [] | no_license | brmcdonnell/riddlers | 22192dd07288be99f5fcd659d68be9a65a32f3f7 | 674cb31637ed88f29f37078de1945d367895a2ca | refs/heads/master | 2022-04-20T08:36:00.402698 | 2020-03-28T20:44:54 | 2020-03-28T20:44:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 555 | r | graphs.R | library(ggplot2)
pong = read.csv('pong-counts.csv')
pong['err_high'] = pong['means'] + pong['stds']
pong['err_low'] = pong['means'] - pong['stds']
p <- ggplot(pong) +
theme_bw() +
geom_ribbon(aes(x=N, ymin=err_low, ymax=err_high), fill='blue', alpha=0.1) +
geom_smooth(aes(x=N, y=means),
method='l... |
9c2f5ba4eb5cc680f8c9dab4ec6c1192a8e4316d | e99fcd77f0cea82f21f48edece5a974b995c2bdf | /src/models/kingdoms/XGBoost/evaluate_model.R | a6cbb5f4f7ef5d536d9594b4074e7a5b3997b907 | [
"MIT"
] | permissive | Bohdan-Khomtchouk/codon-usage | ef62bfeabf82bcb8533c5b2ebcaf4ed59bf636f8 | 8922418b42d931aea66f7ebcc70d4a84d91faee2 | refs/heads/master | 2023-02-18T14:37:22.349674 | 2023-02-06T17:10:11 | 2023-02-06T17:10:11 | 307,536,175 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,384 | r | evaluate_model.R | ## ----------------------------------------------------------------------------------- ##
## Program to evaluate XGB models using test data
## ----------------------------------------------------------------------------------- ##
library(caret)
library(mltools)
library(data.table)
setwd("~/dev/cuAI/CUTG_ML_paper_data... |
72c92e767a4dfd2e686e91b95443239761a42942 | 88e976cd1f2d50f5fa30129df589bc00d6bee3ac | /R/Crossover.R | 3e57bd5b04bb61364190a8341bad8e15362e2f2b | [] | no_license | yanrongmu/GA | d132cb495af9c3f280ee5bbda04db17572bbeef2 | 56376a54b207493932f01bc59fa5e6860e83b0d9 | refs/heads/master | 2021-08-29T20:47:19.801998 | 2017-12-14T23:58:10 | 2017-12-14T23:58:10 | 113,373,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,191 | r | Crossover.R | ############ This is the Crossover function #########
# It performs the crossover that produces the kid population from
# the parent population
Crossover <- function(Parents){
# Takes as input the pairs of parents
Parent1 <- Parents$Parent1
Parent2 <- Parents$Parent2
P <- 2 * nrow(Parent1)
p <- ncol(Parent1... |
851dcc3d7ddf34c477e9e399b591da2adc9b21af | fb3c0532801f4e30484e0e45eac2f320fe926fef | /R_code/2_TOM_Signed_10samples_spearman.R | 5e8abc1a6285e8f991ef2c0adca5063f5b1c7b9c | [] | no_license | cyntsc/meta-data-arabidopsis | 588a7250c2351b2cec98dc5d8a825e84b05147cc | 6f3f729ae183b9ca9bc1454c84a40244aa19c49d | refs/heads/master | 2023-03-17T07:30:35.525545 | 2021-03-18T01:05:41 | 2021-03-18T01:05:41 | 348,137,458 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,456 | r | 2_TOM_Signed_10samples_spearman.R | ##########################################################
###
### Goal: WGCNA Std / Signed Ntw with dynamic cut off (spearman)
###
### Method: TOM calculation: adjacency ( SIGNED NTW / spearman corr)
###
###
### Made by: Cynthia Soto
### Date: January 29, 2021 / Last update: xxxxx
###
### This is a PhD project ass... |
7edb21c3333332df9e2cf6fe9061c169999e49b0 | 12533f4f5685c6a5b75ab91b918bd0677a332395 | /data_migration.R | e5ac81a821b8c32706baa4982ff41fb16b9b62b3 | [] | no_license | Starryz/VW-Summer-Internship | 7b3f9786f9ac80ae48006883aebb61b16eefbeb9 | d093b9bf5aa2588727441a5515431e274c5a46d0 | refs/heads/master | 2020-06-24T04:48:37.540158 | 2019-08-08T21:07:44 | 2019-08-08T21:07:44 | 198,852,841 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,237 | r | data_migration.R | library(readxl)
library(tidyverse)
library(stringi)
library(readr)
# help match --------------
## for commit
extract_c <- read_csv("/Volumes/GoogleDrive/My Drive/Sustainable_Vision/salesforce_examples/Organization_extract.csv") %>%
rename("GRANTED_INSTITUTION__C" = "NAME") %>%
select(-ORGANIZATION_ALIAS_NAME__C)... |
c9908d534cb36e72bd1e7e3840f6f71cb8d905ec | ff11015a34c325891bac121e9b7432c119b33888 | /R/RedshiftSQL.r | 08a3d5ac02a3ac73f8e2e7aa0ed24df661cd3c90 | [] | no_license | mtreadwell/RRedshiftSQL | 6c52b3e360fc3c6aab9775ac30c13c20723f103b | 53f422f3d27b0bebbf2dd1c42d67991181da6f01 | refs/heads/master | 2021-01-10T23:20:41.145135 | 2016-10-11T13:27:52 | 2016-10-11T13:27:52 | 70,595,593 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,253 | r | RedshiftSQL.r | #' @import DBI
#' @import RPostgreSQL
NULL
.PostgreSQLPkgName <- "RPostgreSQL"
setClass('RedshiftSQLDriver', contains = getClassDef('PostgreSQLDriver', package = 'RPostgreSQL'))
setAs('PostgreSQLDriver', 'RedshiftSQLDriver',
def = function(from) methods::new('RedshiftSQLDriver', Id = methods::as(from, '... |
93c200e9b2913bce62f82faebc9b28088ea9fea5 | 1fc6cdf2b36678fa0096015640ab9e6f14d7aefc | /R/cpgBoxplots.R | ad2679bc61debc6692272fdfd1ba945d77d7fc48 | [] | no_license | clark-lab-robot/Repitools_bioc | e36b4a9912f8fe3c34ab592a02069afe860a6afa | b838a8fd34b2ecc41dd86276bd470bfdae53d544 | refs/heads/master | 2021-01-01T18:37:22.034108 | 2014-04-15T01:49:48 | 2014-04-15T01:49:48 | 2,335,128 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,367 | r | cpgBoxplots.R | setGeneric("cpgBoxplots", function(this, ...){standardGeneric("cpgBoxplots")})
.cpgBoxplots <- function(dm, bins, gcContent, nBins, calcDiff, pdfFile, mfrow, col, ylim, gcCount, cb, sampleNames)
{
if(calcDiff){
title1 <- paste( col, paste(sampleNames,collapse="-"), sep="=" )
}else{
title1 <- paste( paste(c... |
eb77ad35a53a8442a465061f00b49916d001f06a | d204c97fae0f1a3b5a907e59a8b24c649f9c05c0 | /8_anova_and_glm.R | bf7eb336ea7bc05067b9137194a41d20e1d95b4b | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | brasilbrasil/IUCN_threat_analysis | cafdeefc8fda707862efbd695d7e954b68f03949 | c783b645a687d9fcb1bfec6a8fe4a38df9807d48 | refs/heads/master | 2021-01-18T23:06:54.409887 | 2016-11-03T00:50:34 | 2016-11-03T00:50:34 | 72,684,676 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,451 | r | 8_anova_and_glm.R | rm(list = ls()) #remove all past worksheet variables
library(reshape2)
library(ggplot2)
##run this code after merging all IUCN data
sink.reset <- function(){
for(i in seq_len(sink.number())){
sink(NULL)
}
}
#wd="C:/Users/Kaipo Dye/Dropbox/PICCC/Kaipo vulnerability and multiple threats/IUCN_test_analysis_result... |
c8c7669d8b8d64b03c0ad992130c34673d19ca5c | 452eec9695d9de8774598261862d670bb10aac6c | /HW_4.R | 2392c23ddbedba712bcdbef44df61ac8c1727188 | [] | no_license | thangtrinh273/Statistical_Learning_With_R | 9e635e4e260abf708018d64760208e318d84a1ed | 2f4b14f2b5276ac2209ef5bd2ccdd11c0c41e297 | refs/heads/master | 2022-12-31T04:36:49.018626 | 2020-10-19T10:11:18 | 2020-10-19T10:11:18 | 305,336,298 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,274 | r | HW_4.R |
install.packages("foreign")
library(foreign) #necessary to be able to read .dta files
#reading the .dta file into R
program <-read.dta("C:/Users/Kitteh/Dropbox/R/hsbdemo (1).dta")
#OR
program <-read.dta("https://stats.idre.ucla.edu/stat/data/hsbdemo.dta")
#sampling the file for tutorial purposes
subsample... |
e048196ad2bb28b28025afec43e5dadec182a582 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sparseHessianFD/examples/sparseHessianFD.Rd.R | 31ee19a4bd0ea642bc66498bb9c1964b16d24020 | [] | 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 | 926 | r | sparseHessianFD.Rd.R | library(sparseHessianFD)
### Name: sparseHessianFD
### Title: sparseHessianFD
### Aliases: sparseHessianFD
### ** Examples
## Log posterior density of hierarchical binary choice model. See vignette.
set.seed(123)
data("binary_small")
N <- length(binary[["Y"]])
k <- NROW(binary[["X"]])
T <- binary[["T"]]
P <- rnorm(... |
0d0fd166137939849bdeed560714cea3264500a1 | 2bb14f19653e09a4b02007ce8780d1602d7d0c20 | /scripts/find-tail-modifications | d37a241877ab26de38f834203476f59e8528bbdf | [] | no_license | klmr/poly-u | a1c0123b98bbb895bc5c07ff5def0764b92217b8 | 81a2ffad7b973ae7d43970c44563a39318285026 | refs/heads/master | 2021-07-13T06:51:54.881479 | 2017-09-26T11:07:20 | 2017-09-26T11:07:20 | 63,482,171 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,776 | find-tail-modifications | #!/usr/bin/env Rscript
sys = modules::import('klmr/sys')
"Extract the first (non-A) tail modification (if any) and its length, and
writes the resulting table to the standard output"
sys$run({
args = sys$cmd$parse(arg('taginfo', 'input taginfo file'))
io = modules::import('ebi-predocs/ebits/io')
modules:... | |
61e883141af33f9fb64dfde5d2675b67fbca9dd2 | b3921db7e6ac213db389b4f2f5c4cb19e32a3411 | /WANG2021/wbsip.R | 21df159b7174c92138711e9e01faacf6c0deace3 | [] | no_license | 12ramsake/MVT-WBS-RankCUSUM | cebb8c84aeec47c57d816b3281baca5cfd326a2b | e227f96fbf8ac752d78c6f755b71d79647297199 | refs/heads/master | 2023-08-09T08:13:50.768287 | 2023-07-19T20:02:00 | 2023-07-19T20:02:00 | 195,120,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,291 | r | wbsip.R | #returns indices of the intervals
getIntervals<-function(indices,M){
ints<-t(replicate(M,sort(sample(indices,2))))
diffs<-(ints[,2]-ints[,1])==1
if(any(diffs)){
ints[diffs,]=getIntervals(indices,sum(diffs))
return(ints)
}
else{
return(ints)
}
}
#
# checkIfSubInterval<-function(... |
638ea0edac29f3b7db702fdc5710c49729c863f2 | a780373151d932f841e17eed14614b949cc248b6 | /Data_Cleaning_Scripts_DMX_Linkages/Ichthyoplankton_DMX_Linkages.R | 26f2d666d5afa227973e8c78996c2299344baf64 | [] | no_license | NCEAS/dmx-linkages | 56816c309aaa08277670faacec3ecabafcf08a52 | d79983fbfba8cb86280da0c93a64c2cccb1c866f | refs/heads/master | 2020-12-25T17:14:32.804002 | 2016-09-22T21:06:19 | 2016-09-22T21:06:19 | 39,415,949 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 836 | r | Ichthyoplankton_DMX_Linkages.R | ###########################################################
##### Data Cleaning Script - DMX Linkages
##### Ichthyoplankton Data (Arrowtooth, Pollock, Halibut)
###########################################################
## load packages (order matters)
library(httr)
library(plyr)
library(dplyr)
library(XML)
library(cu... |
fb50b8e4e5aa5f0282abbdea00537979a3a309bc | 8a475be4061006487320a7c7e094a8a50453fd70 | /FDA2/lab04.R | 68eeee18d42575b6322394af03f2f0acec0aa97d | [] | no_license | smeds1/Learning | e4f7afd7753fc139b33ba566f4beb154efd84a0d | 05340df2184f2aca48800039b4ba15b09b6034e6 | refs/heads/master | 2020-03-24T01:48:24.083293 | 2019-01-03T23:30:41 | 2019-01-03T23:30:41 | 142,351,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 418 | r | lab04.R | #Sam Smedinghoff
#7/30/18
#Week 4 - Lab 4
library(SDSFoundations)
acl <- AustinCityLimits
#Question 1
tabgen <- table(acl$Genre)
expgen <- c(.25, .25, .25, .25)
chisq.test(tabgen,p=expgen)$expected
chisq.test(tabgen,p=expgen)
#Question 2
tabGenTwitter <- table(acl$Genre,acl$Twitter.100k)
prop.table(... |
7a5817927e23a36bc1418d4e10ea26c4b66380e4 | ee73739bd3314929cd44aa98b6b364f23e72691f | /data-raw/DATASET.R | 520539ab8c2c0502777f7a0b48dc22b99c6778a1 | [] | no_license | irinagain/mixedCCA | 8ee85144bcf7bff615990f244506c37770147d37 | 4c2b63f754582e57654893e50484c42a0f32cdb4 | refs/heads/master | 2022-09-23T22:50:00.459426 | 2022-09-09T21:19:33 | 2022-09-09T21:19:33 | 140,593,736 | 20 | 9 | null | 2022-09-09T21:15:52 | 2018-07-11T15:19:15 | R | UTF-8 | R | false | false | 4,841 | r | DATASET.R | ## code to prepare `DATASET` dataset goes here
# usethis::use_data("DATASET")
# grid is revised with coarser grid on August 20, 2020.
############################################################################################
# For multilinear interpolation approximation for bridge Inverse
#########################... |
553b65d71633d6be42fef47037479394d29136e7 | b9cddf1a01a484252b3e09f52cdf5450ed79ca7d | /R/bayes.R | aad742ba4b3a907f0dc25fa65f16a06efba1b169 | [] | no_license | alvarolemos/machinelearning | 1b436f0bcd954d1cb8ba8ab81b4a2a35787215e6 | 51d1a613bf73ad241a6e75701aaadbf3fd418e26 | refs/heads/master | 2021-07-21T16:40:53.510873 | 2017-11-01T11:19:48 | 2017-11-01T11:19:48 | 103,881,107 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,467 | r | bayes.R | library(foreach)
fitBayesian <- function(X, y) {
# Fits a Naive Bayes model.
#
# Args:
# X: Samples matrix, where each row is a sample and the columns are features
# y: Label vector, with one label for each sample
#
# Returns:
# A list with the following parameters for each label of the training... |
7232424c81d934e1b1b09fd42a2ab3e72f6f180c | 123d808ae2e00215090b17956968b617fb6e0e2a | /pwl.R | 4a06ec4be60ff4b0bb4d441a24331e73d55d5639 | [] | no_license | tudou2015/pwl | 2f79c6e4348a1cba061a6cb0e81330a836949728 | 8e7e25b88c1b2a39333795c175d2d0d6538e16b2 | refs/heads/master | 2020-06-11T06:48:04.321503 | 2016-06-20T14:51:55 | 2016-06-20T14:51:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,061 | r | pwl.R | # Find piecewise linear approximation function for the given data and number of breakpoints (bp)
# Length (l) specifies the minimum lenght of the segments (i.e. length between breakpoints)
# class "pwl" consists of the locations of the breakpoints, coefficients of the equations,
# fitted values, residuals, and mse.
... |
7c7ca20e9caef3899ee0eb252bc67aca0469caeb | 50e8d7f49c8ce112ce0d7d625d3d89728f45cb64 | /drunk/nv.R | d9c052d3ad3608403bb3374c0c1c05dfdc97e3e9 | [] | no_license | MarcosGrzeca/R-testes | dfbaa3786362a631ccc43239990f919957d8b32e | 15449cd4dade39d425c24274f744bd84cbde6650 | refs/heads/master | 2020-04-16T03:34:40.009107 | 2017-08-02T00:14:39 | 2017-08-02T00:14:39 | 68,065,310 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,252 | r | nv.R | library(tools)
PATH_FIT <- "resultados/nv/fit.Rda"
PATH_PRED <- "resultados/nv/pred.Rda"
PATH_IMAGE <- "resultados/nv/nv.RData"
load("rda/alemao_base_completa.Rda")
print("Naive Bayes")
library(caret)
trainAlgoritmo <- function(dadosP) {
fit_nv <- train(x = subset(dadosP, select = -c(alc)),
y = ... |
d90dc5e50479938ece29c7f51c0e70b042a42662 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TAR/examples/LS.lognorm.Rd.R | f0a35688b063d431f3f393d35b2803a04300ae88 | [] | 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 | 389 | r | LS.lognorm.Rd.R | library(TAR)
### Name: LS.lognorm
### Title: Estimate a log-normal TAR model using Least Square method given
### the structural parameters.
### Aliases: LS.lognorm
### ** Examples
Z<-arima.sim(n=500,list(ar=c(0.5)))
l <- 2
r <- 0
K <- c(2,1)
theta <- matrix(c(1,0.5,-0.3,-0.5,-0.7,NA),nrow=l)
H <- c(1, 1.3)
X <- s... |
9ce19ca276209ac27e61b81767e37c11378f8299 | d771ff12fe4ede6e33699704efa371a2f33cdfaa | /R/demo.sum.R | 435f0f53294c2baabf075eaf43fd37ccbce184a3 | [
"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 | 434 | r | demo.sum.R | #' demo.sum - Demo summary dataset with features/measurments (columns) x samples (rows)
#'
#' @docType data
#'
#' @usage demo.sum
#'
#' @format Demo summary dataset with features/measurments (columns) x samples (rows).
#'
#' @author Thomas M Ashhurst, \email{thomas.ashhurst@@sydney.edu.au}
#'
#' @source Thomas M Ashhur... |
acc4cdbabf96ebaec5dcd5c1b1ae2abd8cad7a39 | 67b44263a5b0a57c302845412d653594b611e41e | /man/topmod.plot.wordcloud.Rd | 9a8221aa1cce1f565e14607ee9d45b9f39e2654a | [
"MIT"
] | permissive | arturochian/corpus-tools | 6f562a4b79ca3982663fac37a9791eb346ede5a9 | 1e3bd46e6b227b86e308b41c687c93ac8c077f79 | refs/heads/master | 2021-01-18T03:12:00.054654 | 2015-02-02T12:46:18 | 2015-02-02T12:46:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 392 | rd | topmod.plot.wordcloud.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{topmod.plot.wordcloud}
\alias{topmod.plot.wordcloud}
\title{Plot wordcloud for LDA topic}
\usage{
topmod.plot.wordcloud(m, topic_nr)
}
\arguments{
\item{m}{The output of \code{\link{LDA}}}
\item{topic_nr}{The index of the topic (1 to K)}
}
\value{
Nothing, jus... |
35109db245b1930df5c28f6ddf041f2412ec4a4d | b59d7569583dc3eb4894d5165f432166b1bd740d | /Simulation/Code/Main.R | fd36f6a47800ea1bd62e2dd16ecf06a5062a92c2 | [] | no_license | boyiguo1/MOTEF-Supplementary | 60b033ee08e39aa6f91b54440708ea9ac81949a7 | 5a130faf2e3ae7ed80141c4b309955584c1265f6 | refs/heads/master | 2023-02-28T15:54:04.039571 | 2021-02-10T02:30:19 | 2021-02-10T02:30:19 | 232,177,507 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,105 | r | Main.R | args=(commandArgs(TRUE))
ind <- diag
AR <- function(x){
t <- 1:x
return(0.8^abs(outer(t,t, "-")))
}
if(length(args)==0){
print("No arguments supplied.")
}else{
for(i in 1:length(args)){
eval(parse(text=args[[i]]))
}
}
# Required Library
library(MOTE.RF)
library(glmnet)
library(tidyverse)
library(rand... |
d88407a4f1f2c68b6ddb938961b4198db7386cca | d70a9ad5ef249f67be746186fe2b24ed9a833dc8 | /models/height/mono/Avg_heights.R | 623fa5a1ab5c57eea1ffe1e928ce98a2fac9881d | [
"BSD-3-Clause"
] | permissive | cct-datascience/rangeland-restore | 637fbd5ac72b356c1e666992668d8c8c570d9ad1 | 93567756223058481005408e970109f3a63162ff | refs/heads/master | 2023-04-07T13:10:05.278701 | 2022-11-02T00:56:58 | 2022-11-02T00:56:58 | 376,160,215 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,683 | r | Avg_heights.R | # Average heights per plot
# modeled as a normal distribution
library(rjags)
load.module('dic')
library(mcmcplots)
library(postjags)
library(ggplot2)
library(dplyr)
# Read in data
load("../../../cleaned_data/cover_mono.Rdata") # cover_mono
dat <- cover_mono %>%
mutate(species = factor(species, levels = c("ELTR", "P... |
32f253d9a6893da5087c98b3958b930bad609b1d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kmconfband/examples/noe.Rd.R | 0bf0925cac20822acd8148b3d4effffefda50439 | [] | 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 | 468 | r | noe.Rd.R | library(kmconfband)
### Name: noe
### Title: Noe Recursions for the Exact Coverage Probability of a
### Nonparametric Confidence Band for the Survivor Function
### Aliases: noe
### ** Examples
## A check of the Noe recursion calculations. This result is cited in
## Jager and Wellner's 2005 technical report, Tab... |
cd3eea0cdac67c0c6b001cb4c7423867934ef629 | 1943642c50bfbc19a8e6cfa8953fb5f34cca6d83 | /R/metrics.R | e767d47348c4109c231b481a8651ca74aea939f8 | [] | no_license | diegomattozo/RDisc | 7e19151efb361eec780ea767647d469f5e004810 | bfa5146bddb5c36be6745f802aa1e6344455029b | refs/heads/master | 2022-04-25T16:02:51.245025 | 2020-04-28T02:34:44 | 2020-04-28T02:34:44 | 259,181,853 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 62 | r | metrics.R | chi_square <- function(x, y) {
chisq.test(x, y)$statistic
}
|
f161b0c3952930c3997a1649491e6c7f89233254 | 0986b0e01c2b07b18ed039705c897908e266bdd5 | /units/0_R_Tutorial/assignment_ggplot.r | 41a70bbc2017e348057028b67888d670a27ddce3 | [] | no_license | mtaylor-semo/438 | 8b74e6c092c7c0338dd28b5cefe35f6a55147433 | aab07b32495297a59108d9c13cd29ff9ec3824d3 | refs/heads/main | 2023-07-06T14:55:25.774861 | 2023-06-21T21:36:04 | 2023-06-21T21:36:04 | 92,411,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,192 | r | assignment_ggplot.r | ## Code to create the Alberta Climate Plot in the
## R Tutorial Assignment. Note the use of the
## code to add a degree symbol to the Y axis, which
## is given as an extra credit challenge to the students.
#setwd('biogeo')
library(tidyverse)
#climate <- read.csv('http://mtaylor4.semo.edu/~goby/biogeo/climatedata.c... |
1ef07b52a0c44a5e51052de6de614a0c8e265b5d | 146f3eb628a803d9bb3a37e81b792e0032fc50a8 | /docs/R/functions.R | c676f62811c20de7c59b864ca1ff83f362ed949c | [] | no_license | brychan-manry/AstraZeneca-openFDA-Case-Study | 31b2ed03530f6d01cd3fcd33008f59c6ee3aad2f | 15666032fa3f729d7195843df0de303f2ee47c7d | refs/heads/master | 2022-04-02T23:42:27.679039 | 2020-01-14T04:16:56 | 2020-01-14T04:16:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,888 | r | functions.R | #==============================================================================================================#
# This file contains the general functions for used for generating queries, interacting with openFDA, and
# creating the various plots and tables in the report
#
# Note: given the time limitations these a... |
4a7a9d48193b73f5036e058e3cdc275c55fa8d91 | 4b237cef2143657587c85e657696777b8a0b0a81 | /1.Duc_Le_EDA.R | 44b633aebf1e2281c86b9ab0b2b9de493a8fc052 | [] | no_license | dukele35/credit_evaluation | 297b30d745b03c89b5730fad3798c72efe723d36 | 668a08564cde11f9566e90623813f6e9a9ac209d | refs/heads/master | 2022-11-22T22:23:59.331661 | 2020-07-28T22:55:40 | 2020-07-28T22:55:40 | 282,753,630 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,773 | r | 1.Duc_Le_EDA.R | ##### 1. data preparation #####
# 1.1. list the excel file's sheets
library(tidyverse)
library(readxl)
excel_sheets('Credit_Risk6_final.xlsx')
# 1.2. load the dataset
# 1.2.1 dataframe df1 from 'Training_Data' sheet
df1 <- read_excel('Credit_Risk6_final.xlsx', sheet = 'Training_Data')
View(df1)
nrow(df1)
ncol(df1)
s... |
1c4ac4f55a6b25556434f265e8e94e608e6b13f1 | 17e405a893b652f1da704f0b8c1b4669d8a2dc72 | /plotting_lms.R | 5f6ad55f98d8c79e7306d38c20c6d5160012c276 | [] | no_license | eflynn90/L_and_L | b59fdfb5e4ffb78199e796c14642ecd9e8abc404 | 13777cc04bad354ac06ffd30088bcd398ec88c67 | refs/heads/main | 2023-06-29T17:52:10.341120 | 2021-07-30T15:00:25 | 2021-07-30T15:00:25 | 391,095,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,419 | r | plotting_lms.R | #!/usr/bin/Rscript
##
## EDF 7/30/21
##
library(dplyr)
library(ggplot2)
setwd("~/Downloads/")
## Read in expression and genotype data
expr_gt = read.table("APBB1IPexpr_gt.txt",
header=TRUE,sep='\t')
names(expr_gt)
head(expr_gt)
## Plot expression and genotype data
expr_gt %>%
ggplot(aes(ch... |
e6bf24cd1d1d6e8397c288fb0ace410fd679945b | a39fca6eb72a004709de0baf2e824d4282e4f14d | /R/index.freqDD.R | 456039a4d895582c1ae1b760d47e0b5a62f0e327 | [] | no_license | SantanderMetGroup/R_VALUE | 7258fba9d0ec6a67382e1f5c8b7d6d768fb4f86d | 86042cac384205e832eb8ae9dc918cd3d4202d2a | refs/heads/devel | 2023-07-07T19:34:31.848619 | 2023-06-21T16:46:39 | 2023-06-21T16:46:39 | 15,336,613 | 7 | 1 | null | 2021-11-23T16:59:05 | 2013-12-20T11:17:15 | R | UTF-8 | R | false | false | 611 | r | index.freqDD.R | #' @title Dry-dry probability
#' @description Function to compute the dry-dry probability index.
#' @author Neyko Neykov \email{neyko.neykov@@meteo.bg}, J. Bedia, D. San-Mart\'in, S. Herrera
#' @param ts A vector containing the data
#' @param threshold A float number defining the threshold considered. Default to 1.
#'... |
afb4ee99f43c816a1819f8af2a590e00811295e5 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /netrankr/inst/testfiles/checkPairs/libFuzzer_checkPairs/checkPairs_valgrind_files/1612798799-test.R | 5ca1eabba11792905833e713e6c518772e708d33 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 386 | r | 1612798799-test.R | testlist <- list(x = c(3.07839225763261e+169, 9.07657702144378e+223, 3.87069807020594e+233, 2.14899131997207e+233, 9.2637000607593e+25, 8.90389806611905e+252, 3.59535147836283e+246, 8.79670844719638e-313, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), y = numeric... |
1f93f57a0ee79abf2c2649066822c3b1ca07b504 | cb352086ed786306eabffc2bb30d294301193d3b | /R_codes/codes_utiles/Analysis_functions.R | 3da05dee355010cc220260e99952340bcd75c154 | [] | no_license | MathisDeronzier/mod-lisation-Little_Washita- | 0cda2bdcb992e9588910dd2c0ce4dd46b50fdca6 | 0076db9bbe9682248d37f5629450bdbb6406af37 | refs/heads/master | 2023-08-22T14:24:19.960181 | 2021-09-27T16:24:16 | 2021-09-27T16:24:16 | 397,234,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,189 | r | Analysis_functions.R | #Fonction renvoyant la liste des temps de sécheresse d'affilé
freq_without_rain <- function(pr_serie){
l<-1
k<-0
n<-length(pr_serie)
drought<-FALSE
n_drought<-matrix(0,n)
for (i in 1:n){
if (pr_serie[i]==0){
if(drought){
k <- k+1
}
else{
drought <- TRUE
... |
f2e50df907d0f14c4f6ee14ee2e722af2a92ae9b | 53f6608a8f31d2aa39fae0e899b144c98818ff54 | /man/OSDexamples.Rd | 63ab5ddc0fefe9ee38ecdd3ee0d24ed67dba7341 | [] | no_license | ncss-tech/sharpshootR | 4b585bb1b1313d24b0c6428182a5b91095355a6c | 1e062e3a4cdf1ea0b37827f9b16279ddd06d4d4a | refs/heads/master | 2023-08-20T06:16:35.757711 | 2023-08-08T19:11:52 | 2023-08-08T19:11:52 | 54,595,545 | 18 | 6 | null | 2023-02-24T21:00:28 | 2016-03-23T21:52:31 | R | UTF-8 | R | false | true | 425 | rd | OSDexamples.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-documentation.R
\docType{data}
\name{OSDexamples}
\alias{OSDexamples}
\title{Example output from soilDB::fetchOSD()}
\format{
An object of class \code{list} of length 17.
}
\usage{
data(OSDexamples)
}
\description{
These example data are... |
7e272ea80c837b829e745b58d1de39554e4f200c | 5c30f03837e69425bddea5dc49f1f192576329ba | /Develp_Rproduct/App2/server.R | 6d3332cf04cc97cd11711ef0f84ebbb40f672c2c | [] | no_license | ofialko/Data-Science-Johns-Hopkins-University | 3da23b2a3f7535bddb63da44d0ce550828e4ea58 | 9eb3155c8070c1ce31fc7c6e3ce9c641e72ff946 | refs/heads/master | 2020-03-12T19:57:02.613658 | 2018-06-28T04:04:09 | 2018-06-28T04:04:09 | 130,795,057 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | server.R | library(shiny)
shinyServer(function(input, output) {
mtcars$mpgsp <- ifelse(mtcars$mpg - 20 >0,mtcars$mpg-20,0)
model1 <- lm(hp~mpg,data=mtcars)
model2 <- lm(hp~mpgsp+mpg,data = mtcars)
model1pred <- reactive({
mpgInput <- input$sliderMPG
predict(model1,newdata = data.frame(mpg=mpgInput))
... |
26bc181dec266fb44627e8e84adda2261dc5aaf8 | 565a032b6072a5f4902a53bdc76cd80338a240aa | /Annie_Brinza_week_4_phase_2.R | d80fad647841a4a7b85e1953d456b290d2551c65 | [] | no_license | abrinz/DataScienceAccelerator | e2c8846a74cf556af97b6057491b072950cd27e6 | c42d3884b7da8444033af295eaccad7ee801a4ad | refs/heads/master | 2020-04-02T14:36:30.900223 | 2019-02-04T16:46:41 | 2019-02-04T16:46:41 | 154,531,430 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,428 | r | Annie_Brinza_week_4_phase_2.R | library(tidyverse)
library(nycflights13)
library(maps)
library(fueleconomy)
library(forcats)
##############################################################
# 13.4.6 #1-4
##############################################################
#1
# Compute the average delay by destination, then join on the airports data frame s... |
33bb6961ec2317b5e9e39a74338627e37955c3ac | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/datapack/examples/parseSystemMetadata.Rd.R | afca40083be7b16f5cbcbc883b9663d37e0a220d | [] | 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 | 443 | r | parseSystemMetadata.Rd.R | library(datapack)
### Name: parseSystemMetadata
### Title: Parse an external XML document and populate a SystemMetadata
### object with the parsed data
### Aliases: parseSystemMetadata parseSystemMetadata,SystemMetadata-method
### ** Examples
library(XML)
doc <- xmlParseDoc(system.file("testfiles/sysmeta.xml", pa... |
7084842d99b8b89219638e26ebdc31f2c6f05b5c | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /robustreg/R/robustRegH.R | 2934a241e4571b33398a7096bc24d6e911a8bff6 | [] | 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 | 2,434 | r | robustRegH.R | robustRegH<-function(formula,data,tune=1.345,m=TRUE,max.it=1000,tol=1e-5,anova.table=FALSE){
#psiHuber<-function(r,c){
#middle<-abs(r)<=c
#high<- r>c
#low<-r<(-c)
#h<-middle*r + high*c + low*-c
#return(h)}
bi<-FALSE
if(m==FALSE){bi<-TRUE}
modelFrame=model.frame(formula,data)
X=model.matrix(formula,data)
y=model.ext... |
5c3a0b773dce5120a0804954fc645820f970d187 | 523efae75822b2211dd231513bdc0bc86d908a48 | /mampcg/R/generateMAMPCG.R | 511a52fd56f6e6f42f98124fb0dff4394220a535 | [] | no_license | mgomez-olmedo/mampcg-paperVersion | 603db292625bb5fbe6395db64b76621bb492629c | c727a5d0e7b67ac87832a0e8d204c78ff22d308e | refs/heads/master | 2021-01-10T06:30:47.865416 | 2015-10-26T22:46:32 | 2015-10-26T22:46:32 | 44,955,875 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,975 | r | generateMAMPCG.R | library(bnlearn)
library(parallel)
#'###################### SECTION 1: auxiliar functions #########################
#'#############################################################
#' gets a new id for a model analyzing a given folder. If networks
#' folder contains models artificial1....artificial10 the new model
#'... |
f1dde2c86f15fee2f7443276185fe9bfad465c07 | 02536fad62b930b2e4da91f55d800d805bcb9bce | /staphopia/man/get_samples_by_pmid.Rd | 866ec7e8fdb84113e668ce9aab496d4e6dc901e1 | [] | 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 | 398 | rd | get_samples_by_pmid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tag.R
\name{get_samples_by_pmid}
\alias{get_samples_by_pmid}
\title{get_samples_by_pmid}
\usage{
get_samples_by_pmid(pmid)
}
\arguments{
\item{pmid}{An integer PubMed ID}
}
\value{
Parsed JSON response.
}
\description{
Retrieve all samples as... |
457c0de7b6ff657770d7196b16e3e8a6446b8de0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/drake/tests/test-dsl.R | 85a58332a06a5bfbc502430bca867789be7a43d1 | [] | 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 | 54,281 | r | test-dsl.R | drake_context("dsl")
test_with_dir("nothing to transform", {
exp <- drake_plan(a = 1)
out <- transform_plan(exp)
equivalent_plans(out, exp)
})
test_with_dir("empty transforms", {
expect_warning(
out <- drake_plan(
a = target(x, transform = cross()),
b = target(y, transform = combine()),
... |
0f58141e1c23219275b1b1e28b25c170b1b98b78 | 8b7fe1cfee5ef609a78b28ebb2e994bc100b3811 | /man/ensembl2hgnc.Rd | d6bb9d8c4a46d1eca3bbb7b9d86beed8757e259e | [
"MIT"
] | permissive | letaylor/bioutils | 98b17f8dc21422be4f4ffda26fcff86ce86a823c | cec31913d86a5beec2449b85e45dbf70718354b7 | refs/heads/master | 2020-04-05T14:14:45.284994 | 2019-04-04T21:24:35 | 2019-04-04T21:24:35 | 156,920,447 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 828 | rd | ensembl2hgnc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ensembl2hgnc.R
\name{ensembl2hgnc}
\alias{ensembl2hgnc}
\title{Ensembl ids 2 hgnc}
\usage{
ensembl2hgnc(ensembl_gene_ids, host = "grch37.ensembl.org",
drop_dot_ensembl_id = TRUE)
}
\arguments{
\item{ensembl_gene_ids}{Character vector.
List ... |
4e50d3608c0cb044d346d265163ce336e92a4169 | eda5858803afac2edd220a3edaf3fb8aa64c7b48 | /R/first-last.R | 0d6a2005691afd94f44bc83865bd24ade7d5a57d | [
"MIT"
] | permissive | echukwuka/tidytable | 2a64e38b7971f71aee1f58ae535f9431cd600cf7 | 29136342ef2ea6f99a0a5b8e1a97fd163e405388 | refs/heads/main | 2023-06-03T08:38:56.814415 | 2021-06-21T15:49:52 | 2021-06-21T15:49:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 350 | r | first-last.R | #' Extract the first or last value from a vector
#'
#' @description
#' Extract the first or last value from a vector.
#'
#' @param x A vector
#'
#' @export
#'
#' @examples
#' vec <- letters
#'
#' first.(vec)
#' last.(vec)
first. <- function(x) {
vec_slice(x, 1L)
}
#' @rdname first.
#' @export
last. <- function(x) {
... |
5e1b850e3ee9818124313380fe41c7cdb32572fa | 4f9015f385c8a02ff258414ba931952afb1e6fac | /R-libraries/spm/R/spm.zeroFun.R | a62f253f523e696871044a878d184ce094dcd465 | [] | no_license | NIWAFisheriesModelling/SPM | 0de0defd30ccc92b47612fa93946ef876196c238 | 0412f06b19973d728afb09394419df582f1ecbe4 | refs/heads/master | 2021-06-06T00:15:07.548068 | 2021-05-27T06:07:46 | 2021-05-27T06:07:46 | 21,840,937 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 160 | r | spm.zeroFun.R | #' utility function
#'
#' @author Alistair Dunn
#'
spm.zeroFun<-function(x,delta=1e-11) {
res<-ifelse(x>=delta,x,delta/(2-(x/delta)))
return(res)
}
|
69559e793fcdc67841490612c2102168a0b463d8 | b79edeeacb7c68a5d9de393b7c1979cc3efa9d18 | /DataHandling/fbi_data_handling.R | edcb381461334dcbef5a8ec3a1e04c750489eb90 | [] | no_license | ds-elections/anti-muslim-sentiment | df42c7612b98a72b196ee6eab09dc2a3612fdbda | 15b04c34c35ec74ee73b0adf3e28010980accbbc | refs/heads/master | 2021-01-18T16:53:08.003947 | 2017-06-01T03:24:26 | 2017-06-01T03:24:26 | 86,778,861 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,004 | r | fbi_data_handling.R | library(tidyverse)
library(lubridate)
library(dplyr)
full_fbi <- readLines(file("C:\\Users\\tdounias\\Downloads\\HC 2013 (1)\\HC 2013.txt", open = "r"), skipNul = TRUE)
#Function that counts the nuber of incidents reported by each precinct
count_incidents <- function(x){
if(substr(full_fbi[x + 1], 1, 1) == "B"){
... |
576f5d73358ee3bbafed3db1cc948cdd7527148b | 22793d1a7c9ea29d118695db911a5133607b414d | /public/ecaviar.R | 1a10de1a57b1f2e71e0448fd913939e9a5f83310 | [] | no_license | Sandyyy123/mrlocusPaper | c3ca8c88b7491fe266486c6e6a93bc199a6a402d | 6fadfd8df778122c8c9137429ea2c11f7b5052cd | refs/heads/master | 2023-04-15T03:05:40.835273 | 2021-04-29T14:44:56 | 2021-04-29T14:44:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,989 | r | ecaviar.R | cmd_args=commandArgs(TRUE)
ecav.bin <- cmd_args[1]
dir <- cmd_args[2]
tsv.filename <- cmd_args[3]
ld.filename <- cmd_args[4]
out.filename <- cmd_args[5]
tsv_files <- scan(tsv.filename, what="char")
ld_files <- scan(ld.filename, what="char")
stopifnot(length(tsv_files) == length(ld_files))
# mapping from TSV files to... |
bb95774c86c49ce16e0686c44a5bfa2aae3a6efc | 9262e777f0812773af7c841cd582a63f92d398a4 | /inst/userguide/figures/Covar--Covar_sec2_1_load-plankton-data.R | eff3c79097e0839efde58c19da346796a40441a8 | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | nwfsc-timeseries/MARSS | f0124f9ba414a28ecac1f50c4596caaab796fdd2 | a9d662e880cb6d003ddfbd32d2e1231d132c3b7e | refs/heads/master | 2023-06-07T11:50:43.479197 | 2023-06-02T19:20:17 | 2023-06-02T19:20:17 | 438,764,790 | 1 | 2 | NOASSERTION | 2023-06-02T19:17:41 | 2021-12-15T20:32:14 | R | UTF-8 | R | false | false | 448 | r | Covar--Covar_sec2_1_load-plankton-data.R | ###################################################
### code chunk number 3: Covar_sec2_1_load-plankton-data
###################################################
fulldat <- lakeWAplanktonTrans
years <- fulldat[, "Year"] >= 1965 & fulldat[, "Year"] < 1975
dat <- t(fulldat[years, c("Greens", "Bluegreens")])
the.mean <- ap... |
2f56bb779c307a99073a376e76063b02d930da0b | f733ced3f53a8747b15fbbad3ddd2989599db815 | /sol.R | a7c15b44256b0203e1cb4cd79bc20be62157b8f1 | [] | no_license | yieun0408/TOBIGS | b4752ac03b606224d2d26ab2336b9e8bc804b22d | 0aab07ac6220b024948d71ec2083bf39fe50fb4e | refs/heads/master | 2020-04-23T11:51:51.804447 | 2019-03-05T01:21:54 | 2019-03-05T01:21:54 | 171,150,380 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,561 | r | sol.R | ###################
##주택경매 데이터##
###################
rm(list=ls())
setwd("C:/Users/laep9/Desktop")
#불러오기aq2@qaaa2q
df = read.csv("Auction_master_train.csv", stringsAsFactors = T, fileEncoding="utf-8")
str(df)
#regist = read.csv("Auction_regist.csv", stringsAsFactors = T, fileEncoding="utf-8")
#rent = rea... |
3fde8b01adbf3fbedaf166d45ec67bc5969ba09b | b26682cd791feda1be6b259f4f4d9b037c0c92b0 | /man/pcamix.Rd | e2d714c2fa828bd8ac137d65502bc97976b9f77c | [] | no_license | robingenuer/CoVVSURF | 768ba05a636f011b8fdbe6b6835166fee60d62e5 | c7b909fa81375e478a87e1d8137977f1f45ea0ea | refs/heads/master | 2021-01-20T18:44:54.391659 | 2019-01-07T14:15:31 | 2019-01-07T14:15:31 | 63,255,808 | 0 | 4 | null | null | null | null | UTF-8 | R | false | true | 264 | rd | pcamix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CoVVSURF.R
\name{pcamix}
\alias{pcamix}
\title{Performs splitmix and PCAmix}
\usage{
pcamix(X, ndim = 5)
}
\arguments{
\item{X}{A dataset}
\item{ndim}{Number of dimensions in PCAmix}
}
|
0fb0f8e89822a09c4957e9a59ac7d5aa7fbed5f3 | 86888aaccefb80eb1b6928f511d2ff467d516242 | /man/plot_flight_vertical_time.Rd | 970d3e59fedd7e05d6310f6c1ba5050ff2c9a62c | [] | no_license | caverject10/trrrj | 8948603c055c4c5cb302ea8154f73e18490ad5f9 | 87391882c20ce2e651e3c7344df3aa82e3ea55a2 | refs/heads/master | 2020-08-14T00:21:04.977328 | 2019-09-10T13:31:50 | 2019-09-10T13:31:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 817 | rd | plot_flight_vertical_time.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.R
\name{plot_flight_vertical_time}
\alias{plot_flight_vertical_time}
\title{Plot the vertical profile of the recorded positions of a flight from
lapsed time perspective.}
\usage{
plot_flight_vertical_time(poss)
}
\arguments{
\item{poss}{... |
547248ac9ec74133148e26b54c4db5cea1373d91 | 910a9f85f4712cfb05be5b6a8e0c9c36096aeea6 | /snp_heritability.R | 27293d28c2c7b152ec5a2d0b2005fbf72ea28680 | [] | no_license | lizhihao1990/Cadzow2017_Ukbiobank_Gout | 8345f7edbd2f0142f4eb08ebb2d3233b1eb85d4c | 15eefd10ba5b58cbeb7d4f785971e4c96d6616f3 | refs/heads/master | 2020-05-09T15:29:25.186983 | 2017-08-15T22:24:42 | 2017-08-15T22:24:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 635 | r | snp_heritability.R | library(data.table)
cond <- c('all','winnard','hosp','self','self_ult','ult')
for(gc in cond){
tmp <- list()
for(i in 1:22){
tmp[[i]] <- fread(paste0('/media/xsan/scratch/merrimanlab/murray/working_dir/UkBio/GWAS_all_controls/controls/',gc,'/adjusted/controls',gc,'_age_sex_chr',i,'.assoc.logistic.tsv'))
}
... |
092b118d4b79711286c98670d8aa13f39cf132d2 | a034d355e35d8fa4fc562b8f71a771bca760a877 | /R/plot.results.table.R | 87b741c10ad1b39037bb4a87192c55cef9aedd42 | [] | no_license | tshmak/Tmisc | eeda1df8d449c3df7dd0d91f6ee698b10f6f3839 | 81f224e89a8d2ee9455f5ccfd1eae34e0ef7d8c6 | refs/heads/master | 2021-07-15T13:27:45.526848 | 2020-05-14T05:34:18 | 2020-05-14T05:34:18 | 144,684,908 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,284 | r | plot.results.table.R | plot.results.table <- function(data, x=NULL, y=NULL, fill=NULL, X=NULL, Y=NULL,
wrap=NULL, scales=NULL) {
#' Automatic plotting of results.table...
x <- deparse(substitute(x))
y <- deparse(substitute(y))
fill <- deparse(substitute(fill))
p <- ggplot(data=data, map=aes_string(x=... |
755fa357656d3b802fd4ca725c21ac12f6feb781 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /mcga/inst/testfiles/ByteCodeMutation/libFuzzer_ByteCodeMutation/ByteCodeMutation_valgrind_files/1612802884-test.R | a09f3ee534a36cc78258c171a141990cc023dd57 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 630 | r | 1612802884-test.R | testlist <- list(bytes1 = c(-690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563370L, -690563584L, 147456L, 67108643L, 561577984L, 0L, 0L, 0... |
d0a4f1e17b080c676e34df3a615f736412787fc7 | 4475e9a0ce50c14e83cd6bfadca929d9a7412911 | /MAPI/thetas_density_kernels.R | 2bee427a41189bdf17a06847c8bbca34b8a20114 | [] | no_license | jarvist/MAPI-MD-analysis | 9fa1745a0b639939add6f8e0972cb60be945891e | f742253656a627130a159e501ebf2279d1746225 | refs/heads/master | 2020-05-21T00:27:47.384554 | 2015-08-12T09:58:13 | 2015-08-12T09:58:13 | 19,031,370 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 261 | r | thetas_density_kernels.R | MA_thetas<-read.table("thetas.dat")
t_density<-density(MA_thetas$V1)
p_density<-density(MA_thetas$V2)
par(mfrow=c(2,2))
hist(MA_thetas$V1,breaks=25)
hist(MA_thetas$V2,breaks=25)
plot(t_density)
plot(p_density)
dev.print("thetas_density_kernels.pdf",device=pdf)
|
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