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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2db90bbd0bd0cfee54ee74082e6621c63c0a272c | 672982793c40413d9c9e941b0fd31bca5936960b | /plot1.R | 4f457f97ad269305988e80cbc11a8e93b11871f7 | [] | no_license | Giackgamba/ExData_Plotting1 | 59a5ccb3c91cac9a2d45f1c39d7b8b439fe72ca6 | 3754fbf2ef916c645cca9b02d487816925806d8b | refs/heads/master | 2021-01-15T18:32:15.377669 | 2015-03-06T13:03:34 | 2015-03-06T13:03:34 | 31,661,691 | 0 | 0 | null | 2015-03-04T14:40:27 | 2015-03-04T14:40:27 | null | UTF-8 | R | false | false | 784 | r | plot1.R | ## Download the zip file and store in a temporary file,
## unzip it and read only the relvant data
temp <- tempfile()
download.file('https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip',
destfile = temp)
data <- read.table(unz(temp, 'household_power_consumption.txt'),... |
fd7c7eb33a160f652ce16fc6f89cdd0e13705a08 | c1fe5ed7db9ad19e2b257b6ef21a4095a92b6689 | /dist/glitch-js.min.js | 6162c976420315b2dfa9e6e556f89a2bddf27984 | [] | no_license | BubbleMakersLab/glitchjs | 83dbd8d272c6df9f29cdd22eeade691510cdff6d | 656c0d7ed9e560cc7ca095167ee26d07db88da0b | refs/heads/master | 2020-05-16T18:28:27.986091 | 2019-04-25T16:24:32 | 2019-04-25T16:24:32 | 183,226,066 | 0 | 0 | null | null | null | null | UTF-8 | R | true | true | 402,891 | js | glitch-js.min.js | !function(e){var t={};function n(r){if(t[r])return t[r].exports;var o=t[r]={i:r,l:!1,exports:{}};return e[r].call(o.exports,o,o.exports,n),o.l=!0,o.exports}n.m=e,n.c=t,n.d=function(e,t,r){n.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:r})},n.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.d... |
0dfef1c87eb94d2965ca002a765e8fc1e74a2dfd | 28750d2d90cb45173a956cc8542ebda0d076e6dd | /Mincome/Codes/Regressions/eventhistory_final.R | e8b4c1adb7aeaa3b0eb48bab28b37033b55bd709 | [] | no_license | DrSnowtree/MINCOME | 994e1060e84aba0d5944b8ead720b4eae8512534 | aa8f41cae82b995e3290e56a6487dc9c71ca04e9 | refs/heads/master | 2021-07-25T20:26:21.767288 | 2020-09-22T13:50:57 | 2020-09-22T13:50:57 | 220,972,475 | 0 | 0 | null | 2019-11-19T13:51:34 | 2019-11-11T12:04:15 | null | UTF-8 | R | false | false | 2,751 | r | eventhistory_final.R |
setwd("W:/WU/Projekte/mincome/Mincome/Data")
library("lme4")
library("stargazer")
library("ggstance")
library("jtools")
library("coefplot")
library(dplyr)
#remove the 5 women not included in the baseline analysis
bpid <- basepay[, 1]
bpid <- as.data.frame(bpid)
bpid <- bpid %>% rename(FAMNUM=bpid)
... |
aa5b5a6a69f34eaf37838c1cdb349b2910ef5120 | 178087fd666375abeb10fc4f9f23230d2438dc21 | /R/peel.one.r | 345254e60df24689601dfd46b98d95338c886859 | [] | no_license | cran/sdtoolkit | 736adf447b9c59c9a795f7f4735d34985dfac0a2 | 8e9767f73b1266de8edf37744c7325a7e36c6497 | refs/heads/master | 2020-05-29T13:14:47.569985 | 2014-02-16T00:00:00 | 2014-02-16T00:00:00 | 17,699,521 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,261 | r | peel.one.r |
#peel.one -
#the main change I made was adding a new quantile function pquantile, that handles
#things more appropriately for PRIMs purposes (see that function).
`peel.one` <-
function(x, y, box, peel.alpha, mass.min, threshold, d, n,
peel_crit)
{
box.new <- box
mass <- length(y)/n
... |
c3592310caf66024a1754d1e55fc97883127b9b9 | 6a192ede793c1aa1c63c5dc388d87f704d4c8f02 | /Unit3 Logistic Regression/Unit3_Framingham.R | 8f3472d4c097bbc7de52991db727a4ca2f2ef322 | [] | no_license | jpalbino/-AnalyticsEdgeMITx | 454341f8e69cc615ab7b8286ba9bbee9df66026d | 9d6b152f5ac2ca9728e5363face0ba0e50da879c | refs/heads/master | 2021-01-18T08:12:17.814468 | 2019-11-05T21:33:18 | 2019-11-05T21:33:18 | 57,457,758 | 0 | 0 | null | 2016-04-30T19:14:06 | 2016-04-30T19:14:05 | null | UTF-8 | R | false | false | 1,260 | r | Unit3_Framingham.R | # Unit 3, The Framingham Heart Study
# Video 3
# Read in the dataset
framingham = read.csv("framingham.csv")
# Look at structure
str(framingham)
# linear regression
fit<-lm(TenYearCHD~., data=framingham)
# Load the library caTools
library(caTools)
# Randomly split the data into training and testing sets
set.seed... |
f5d5964fdc204b829076bdcc6dc414f7ea2e9e3b | c98d6f40abe3e3ad60569ae52e499de4ed6ab432 | /man/shinySNPGene.Rd | abce4041492fe6bba0f8e950098cd3ca7bf654e2 | [] | no_license | byandell/qtl2shiny | 6ad7309b7f4b6bf89147560e23102b4ac5f93620 | e342ce39f2a30ea4df4010aac61822e448d43e20 | refs/heads/master | 2023-05-11T06:04:00.877301 | 2023-04-30T20:22:25 | 2023-04-30T20:22:25 | 78,020,416 | 2 | 3 | null | 2018-01-17T14:31:31 | 2017-01-04T14:00:43 | R | UTF-8 | R | false | true | 850 | rd | shinySNPGene.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shinySNPGene.R
\name{shinySNPGene}
\alias{shinySNPGene}
\title{Shiny SNP Association}
\usage{
shinySNPGene(
input,
output,
session,
snp_par,
chr_pos,
pheno_names,
snp_scan_obj,
snpinfo,
top_snps_tbl,
gene_exon_tbl,
proje... |
26630ec32403bed953b66db01c0ee5fc21b75116 | b4dc1ccbba0146fefb2a62bb6b557c27e4ec793e | /man/get_playlist.Rd | f035eb848ecbf88361511c4f4b15eca75e177cf1 | [] | no_license | Marcow12/antaresXpansion | 863b7e5501412c8c85bb5f7353681e518d0a7bdf | 1c53ebf646ef02f52c104a49d709972afcd0bdfd | refs/heads/master | 2021-01-20T04:58:41.105372 | 2017-08-07T11:17:00 | 2017-08-07T11:17:00 | 101,404,694 | 0 | 0 | null | 2017-08-25T13:06:11 | 2017-08-25T13:06:11 | null | UTF-8 | R | false | true | 640 | rd | get_playlist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulation_settings.R
\name{get_playlist}
\alias{get_playlist}
\title{Get playlist of simulated MC years}
\usage{
get_playlist(opts = antaresRead::simOptions())
}
\arguments{
\item{opts}{list of simulation parameters returned by the ... |
d9c5649cdcb49f157bb3d56e73a1a58d6b59ed97 | 2364af0d602947c317ec092c7c6d919624c303ae | /plot1.R | 58d9998e1da485577ed3c8e3f0f9110ba2dfbf8b | [] | no_license | leej3/dss_exploratory_analysis | fb77d2fd07137394f46400c10893072239ab77ea | bf42895af65d45bb16276936a28806b8021fd0f0 | refs/heads/master | 2021-01-15T17:37:07.013494 | 2015-09-13T21:30:54 | 2015-09-13T21:30:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,380 | r | plot1.R | plot1 <- function() {
# Function plots a histogram of the global active power
# Data of electricity consumption as reported in the UC Irvine power consumption dataset. 1st and 2nd of February 2007 were assessed
#Data downloaded manually from :
#https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_cons... |
3faca4eb360dbf6a35d97d78a78c5a9ce9f7997d | ae45b1826a92420b6bf229e5b07e1f1a06ab622b | /ui.R | 8ac3c1ef2878ecc053cfcb790718875fe726320f | [
"MIT"
] | permissive | dathanasakoglou/clust-app | 6a230b718205b9458d7a675ec8d7d449abd245a2 | c78a83a2a2b8aa74fa907137f3623f1e3a8e3698 | refs/heads/master | 2021-08-14T22:50:10.277927 | 2017-11-16T23:16:12 | 2017-11-16T23:16:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,513 | r | ui.R | library(shiny)
library(shinydashboard)
library(highcharter)
#---Header---#
header <- dashboardHeader(title = "Clustering App")
#---Sidebar---#
sidebar <- dashboardSidebar(collapsed = FALSE,
sidebarMenu(
# sidebarSearchForm(textId = "searchText", buttonId =... |
ad5ca46f8a34f8661598faa4e25da4baf0a5768e | e8733b08964cdec4a24ca5a6f8c60fab04dc4dd7 | /scripts/testing.R | da45e74563559698a731be40ef126a947cd0a7f0 | [] | no_license | andreasolden/ssb-api-og-shiny | 3befd81f3dc7a9df2f7016afbbe1aff5fa5d4f74 | ce4bae88bf15a6e43c8b282f80898a243bff78df | refs/heads/master | 2020-04-17T17:54:33.355885 | 2019-03-10T16:41:30 | 2019-03-10T16:41:30 | 166,803,955 | 1 | 1 | null | 2019-01-23T12:59:50 | 2019-01-21T11:36:59 | R | UTF-8 | R | false | false | 2,138 | r | testing.R | #Testing
options(scipen=10000)
library("checkpoint") #Checkpoint assures us that we use the same package versions
checkpoint("2019-01-21") #As they were at the date set in this function. i.e "2019-01-21"
library(stargazer)
library(scales)
library(skimr)
library(tidyverse)
library(stringr)
library(readr)
library(tidyver... |
5cea03785f41876efa703992c2f767085a67b3f6 | cce287cfff55807ac72c5f43c24cddf83adbe4f4 | /man/write_prms_dimension.Rd | a0445ab0b10d4ebf7b2847e1014b90fbe9604ff5 | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer",
"CC0-1.0"
] | permissive | smwesten-usgs/prmsParameterizer | d55f362eff98482438d46786a81ba3c35613a8c1 | ea00e68f24b615b87ccac0e6cb5a7f54c8a95521 | refs/heads/master | 2020-04-06T04:01:03.271356 | 2017-02-24T19:23:58 | 2017-02-24T19:23:58 | 83,072,983 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 612 | rd | write_prms_dimension.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/write_prms_dimension.R
\name{write_prms_dimension}
\alias{write_prms_dimension}
\title{Write PRMS dimension information to a PRMS parameter file.}
\usage{
write_prms_dimension(fname = "", dimname = "one", dimlength = 1,
dHeader = FALSE)
}
\... |
7c884b63c9606251111774cd2f1d4d8bd189963b | 68249e521dca2c4ef3b6b4491abea093bf5e15d1 | /project4/Q2-5.R | fee49fbee653adf129f60e8caebc487c791a514d | [] | no_license | lx950627/Large-Scale-Network-Analysis-and-Mining | 2194f9cef33a6b3dd6a3671e618dadd1e181c338 | 38d901ffae9f66087da503a601ae7f838d86c8b8 | refs/heads/master | 2020-03-15T14:40:21.087300 | 2018-06-13T01:29:59 | 2018-06-13T01:29:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,534 | r | Q2-5.R |
library(igraph)
library(data.table)
edge_list <- fread("C:\\Users\\IfyourRtheone\\Desktop\\UCLA\\2018Spring\\edge_list.txt", sep='\t', header=FALSE)
names(edge_list) <- c('from','to','weights')
edge_list <- edge_list[duplicated(edge_list[,1:2]) == F]
edge_list <- edge_list[edge_list$from != edge_list$to]
... |
ff2e16013db62726ef105d9309799798d2ef40e6 | 9c2296c877a283325c3998f4b4574cf7574c0512 | /Test_DRIMSeq_0.3.3_sQTL_permutations.R | 92dad39f500f1738a977d04b8a5aa241e6d1573d | [] | no_license | gosianow/drimseq_package_devel_tests | e7bee32bc8111d295f4e3c8598aad98cf1a9a178 | c555beaf708f38f984e015e6bf53114ce129481f | refs/heads/master | 2021-01-18T15:08:32.488731 | 2017-07-18T13:04:03 | 2017-07-18T13:04:03 | 56,255,862 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,472 | r | Test_DRIMSeq_0.3.3_sQTL_permutations.R |
# R32dev
# Created 7 Apr 2016
rwd <- "/home/gosia/multinomial_project/package_devel/Test_DRIMSeq_0.3.3_sQTL_permutations"
dir.create(rwd, recursive = TRUE)
setwd(rwd)
library(DRIMSeq)
library(GenomicRanges)
library(rtracklayer)
library(ggplot2)
### Load sQTL data
data_dir <- system.file("extdata", package... |
345f4fb2afbad20c57ffeb1527c5244a0cc4dbd3 | 62df50b1f31e9330bd42d853674dc6d40ee383a8 | /baypass_daph_interp.R | 82eb5c17e8a6d9cc7968bc52025b1985d4ff6f83 | [] | no_license | andbeck/BayPass | 2d9cb9c566bbe130768d5d4baaa514f371454393 | af336af08b133e9d95399f047ead1dffe1118143 | refs/heads/master | 2020-07-09T14:05:28.604565 | 2017-03-22T16:29:49 | 2017-03-22T16:29:49 | 66,954,036 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,497 | r | baypass_daph_interp.R | # setups
library(corrplot)
library(ape)
library(ggplot2)
library(gridExtra)
library(dplyr)
library(ggthemes)
library(qqman)
source('baypass_utils.R') # this is in the repo
setwd('/Volumes/TTYLMF')
# Covariance matrix
omega<-as.matrix(read.table("anaprEnvfile__mat_omega.out"))
omega<-matrix(omega, 8,8, byrow = TRUE, ... |
c92f491f8677a4079f5b2c89a70d2cb7fa44dbc6 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fechner/examples/regMin.Rd.R | b96abb9833ced11cc6a773e622c1463b4fe6e864 | [] | 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 | regMin.Rd.R | library(fechner)
### Name: regMin
### Title: Artificial Data: Regular Minimality In Non-canonical Form
### Aliases: regMin
### Keywords: datasets
### ** Examples
## dataset regMin satisfies regular minimality in non-canonical form
regMin
check.regular(regMin, type = "reg.minimal")
|
a838be60502310044ae76480ecce0282ddce6ec5 | 30cf71cda7b873411ed66ad0eb78a0ed21bb26ba | /webgestalt.R | 53da1c58cc3b3f306b68ca8ab7d92d653cfdc7b7 | [] | no_license | TNO/immune_health_textmining | 74050cb82c19d7e1818ee34488b90c1a24d68352 | 4190c86191c80dd0abd228ac230a237b23939729 | refs/heads/master | 2020-09-09T11:42:21.133609 | 2020-05-15T12:02:11 | 2020-05-15T12:02:11 | 221,438,037 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,725 | r | webgestalt.R | webgestalt <- function(topic){
library(devtools)
install_github("bzhanglab/WebGestaltR")
install_bitbucket("ibi_group/disgenet2r")
install.packages("WebGestaltR")
library("WebGestaltR")
library("disgenet2r")
library(httr)
library(jsonlite)
library(lubridate)
options(stringsAsFactors = FALSE)
library(... |
7d487735dbc51f252aadb46344401a9f0afca49b | 2f15b2dc16de0471e7bee43f6739b6ad8522c81d | /man/selection_bit_map.Rd | 67494e44d6ea386c8cc285791c2e07dd508275be | [
"MIT"
] | permissive | billster45/starschemar | 45566be916c95778727a3add3239143d52796aa9 | 5f7e0201494a36f4833f320e4b9535ad02b9bdc1 | refs/heads/master | 2022-12-20T13:45:06.773852 | 2020-09-26T03:44:12 | 2020-09-26T03:44:12 | 298,796,838 | 1 | 0 | NOASSERTION | 2020-09-26T11:10:30 | 2020-09-26T11:10:30 | null | UTF-8 | R | false | true | 593 | rd | selection_bit_map.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fact_table_incremental_refresh.R
\name{selection_bit_map}
\alias{selection_bit_map}
\title{Generate a record selection bitmap}
\usage{
selection_bit_map(table, values, names)
}
\arguments{
\item{table}{A \code{tibble}, table to select.}
\ite... |
e98b96736514cd2c5af5b3bd3d111cab5711e5ee | f3079beec1719b26e22114da21a8612f725bd8c5 | /2015_codeClinic/2_CodeClinic_ImageAnalysis/isthisacroppedversionofthat.R | 4b123d11fcce931b55622290ee9c8b8ce6208374 | [] | no_license | CheolsoonIm/CodeClinicR | d7712b510f2bf4325be71acafbe4874d74799c49 | c2664ebad2fe2bded13a0f204343938f4044865a | refs/heads/master | 2021-09-13T04:14:08.280424 | 2018-04-24T22:37:21 | 2018-04-24T22:37:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,485 | r | isthisacroppedversionofthat.R | # function to test if one image is a cropped version of another
# needle - image that might be a cropped version
# haystack - image that might be a parent version
# returns TRUE or FALSE
isthisacroppedversionofthat <- function(needle,haystack) {
# needle <- paste("imagesToAnalyze/","460249177a.jpg",sep="")
# ... |
768d2d175e20fdb1f2ada194e906d7950dc67cfc | 4789c9f646348cee93918b3edce7ad81e8b92b40 | /man/correlations.to.adjacencies.Rd | 9fb3afe03f4e12e6d3476cf63c1609a805a0de5d | [] | no_license | cran/brainwaver | 19316a65e8b358efd5441acb9fa8f86a407ab16d | 6f0d05b26b1270a2de63b0c86e36b4e23c1f30d3 | refs/heads/master | 2021-01-16T19:14:15.079321 | 2010-08-09T00:00:00 | 2010-08-09T00:00:00 | 17,694,878 | 0 | 1 | null | 2014-09-06T01:50:20 | 2014-03-13T04:10:12 | R | UTF-8 | R | false | false | 2,265 | rd | correlations.to.adjacencies.Rd | \name{correlations.to.adjacencies}
\alias{correlations.to.adjacencies}
\alias{ideal.wavelet.levels}
\alias{distance}
\title{Produce adjencency matrices for a given number of edges}
\description{
Given a correlations thingy as produced by \code{const.cor.list},
produce a list of adjacency matrices fiddled to have a pre... |
d48b183d352d2067ae15ab466b1045bfd3bccd00 | 29585dff702209dd446c0ab52ceea046c58e384e | /lfl/R/tail.fsets.R | 1eceafa39de78498d4deb1d6286df24be360ff88 | [] | 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 | 250 | r | tail.fsets.R | tail.fsets <- function(x, n = 6L, ...) {
if (!is.fsets(x)) {
stop("'x' is not a valid 'fsets' object")
}
v <- vars(x)
s <- specs(x)
class(x) <- setdiff(class(x), 'fsets')
return(fsets(tail(x, n=n), vars=v, specs=s))
}
|
20125db2ca70df4bb063e600b86e329c7ad02a1e | 58c16d88f72cdbd25567464d26f028849ec07768 | /cachematrix.R | 79d8afb0f4bff8aece2f0b3e7534bb9af8ff72cf | [] | no_license | rfquah/ProgrammingAssignment2 | 9a183abb23d8e01312bbb0ef9eff587b8a970ed6 | fcaf9b91ddaaedd1069fe93083e1ea68a97baf92 | refs/heads/master | 2020-12-26T00:53:44.894633 | 2014-11-23T14:18:23 | 2014-11-23T14:18:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,965 | r | cachematrix.R |
## makeCacheMatrix is a function that takes a matrix as its argument,
## and sets im (the inverse matrix) to NULL in the function environment.
## Contained within the makeCacheMatrix function is the function set,
## which sets im to NULL in the global environment. But the function set
## is only defined within makeC... |
72ec46f335798a8f3886004ce7b774152315da5f | c0a08d09bb804cbfd9c186ba4e5c75dfb3e4faee | /R/TSGMM_PP.R | 59362e0a69c57881daedab16110fc34e4aef8f00 | [] | no_license | lalondetl/GMM | bd1b21f694a35c09484dfb6e99aa974a6f6a48f6 | 628889324529b0b86f55a4d2054bed5442af0b40 | refs/heads/master | 2018-07-05T06:26:23.000514 | 2018-05-31T20:57:15 | 2018-05-31T20:57:15 | 119,891,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,626 | r | TSGMM_PP.R |
#' Two-Step Generalized Method of Moments, Truncated Count Component of Longitudinal Hurdle Model
#'
#' This function calculates the Generalized Method of Moments (GMM) parameter estimates and standard errors for the zero-truncated count component ("positive Poisson") of a hurdle model for longitudinal excess zero co... |
b718936b316fba85246b6031b065d80fee946c7e | 4bedace4382076f899e3969043a4e22a1c5da62c | /Testing 2 file.R | 749dea5983c319286eced40da7cbe2b4c9261cc8 | [] | no_license | muchandifungam/Test-data | 5bef5bd5a118569d6abe77a456347392ea19acd9 | d64e975d998639afdd8a774c98272f1d63453b0d | refs/heads/master | 2023-03-29T02:53:34.299863 | 2021-04-01T18:37:07 | 2021-04-01T18:37:07 | 352,073,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 44 | r | Testing 2 file.R | a <- (1:10)
b <- c(1:10, nrol=2,ncol=5)
a
b
|
5b00e0d3327050c2401ceddd8ea37f50f94bf05f | 75ec5fea203bbe5b46867bd3da5d85480bd9f71d | /High-dimension/Cross Validation Steps.Work.R.R | 5d096a1bb4b33d9bbf19540a76c733bcd4faffe2 | [] | no_license | mshasan/EmLassoSCAD | a4db9c16eabd16b42693b07d0f97b327ebf9c57a | 517c5d3fa7db94eee964a687591a666c0b250eed | refs/heads/master | 2021-01-20T09:57:19.429087 | 2017-05-05T01:20:20 | 2017-05-05T01:20:20 | 90,310,856 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,185 | r | Cross Validation Steps.Work.R.R | ## Cross Validation for Glasso
## Row Elimination (every row considered as a fold) for one rho
set.seed(100)
cv.gl<-function(p,q) # p rows and q culumns
{
rho<-.01
y<-matrix(rnorm(p*q),ncol=q)
cv.s<-numeric(p)
for(k in 1:p)
{
if (k >= p) {x<-y[0:(k-1), ]} else # Elimination of kth-fold
{if (k... |
9d980c567eb013deb292e4267a9583e108347380 | 37d272c2e369a1eb1e55ca7d38ceb9911d42dd05 | /man/extract_from_cellranger.Rd | 1cc6b660a46dc54cca814b0c0431fbd947ab4a06 | [] | no_license | shambam/scgex_navigator | 3afc69c769442707c496b5f1ad6fbb0bb52c77a0 | 361407a6848c972ed117b5f4d3b6399a3b944930 | refs/heads/master | 2021-01-22T23:54:18.501033 | 2017-05-31T09:12:26 | 2017-05-31T09:12:26 | 85,680,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 551 | rd | extract_from_cellranger.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataprep_funcs.R
\name{extract_from_cellranger}
\alias{extract_from_cellranger}
\title{Extracts the components need from a cellRanger aggrergated file}
\usage{
extract_from_cellranger(path, build = c("mm10", "hg38"))
}
\arguments{
\item{path}... |
9313c73dfcbf8323a7f6617d48a14deff9132cd1 | 295b502d7e367edfa0ee4017f1a7b6a4135211d3 | /R/predict.ELMCoxBoost.R | 06e34a5a40f27a8e594556ade9d8c246bc5bf7c3 | [] | no_license | whcsu/SurvELM | e5b09b504af20ad5e322c687504090cf19689cb9 | c9297f6bd29ff3448e84d1420aeed1215e611ba9 | refs/heads/master | 2021-05-11T02:52:36.034952 | 2020-01-28T08:57:32 | 2020-01-28T08:57:32 | 117,897,717 | 10 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,755 | r | predict.ELMCoxBoost.R | ##' Predicting from An Extreme Learning Machine Cox Model with Likelihood Based Boosting
##' @title SurvELM predict.ELMCoxBoost
##' @param object An object that inherits from class ELMCoxBoost.
##' @param testx A data frame in which to look for variables with which to predict.
##' @param ... Additional arguments fo... |
6ca58b6159703592173cfae73c22a8d8c25709b4 | e56c98512229172467f1f4f99870ed2aac5324cd | /man/sdm.Rd | 54c5e1d8363b0940d3ab63f53b8f736ef4bcde6f | [] | no_license | babaknaimi/sdm | 6de65e769562adedca326adaaf66518870830e62 | 63ec623526e3867158a4847e3eff1c5d3350552b | refs/heads/master | 2021-11-25T10:59:02.624170 | 2021-11-11T05:35:37 | 2021-11-11T07:19:16 | 39,352,874 | 18 | 7 | null | null | null | null | UTF-8 | R | false | false | 3,676 | rd | sdm.Rd | \name{sdm}
\alias{sdm}
\alias{sdm,ANY,sdmdata,character-method}
\alias{sdm,sdmdata,.sdmCorSetting,ANY-method}
\alias{sdm,ANY,sdmdata,.sdmCorSetting-method}
\title{Fit and evaluate species distribution models}
\description{
Fits sdm for single or multiple species using single or multiple methods specified by a user ... |
b895df96ad71d67acdac1b6bcd3feda3018bfe70 | bad016da9e32faadd8800ff5e65f57cb6909479c | /ui.R | 0d266877650adf147d1647cbba94fbc5ba363e6f | [] | no_license | kevindaymath/NCEmissionsShiny | a6636c3ec9602b5f95c76380f610e21461186a8d | 0692faa1284e544cb5093dff37e0b950cdb6ec97 | refs/heads/master | 2020-03-21T01:26:02.603890 | 2018-06-20T15:25:08 | 2018-06-20T15:25:08 | 137,942,356 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,309 | r | ui.R | options(warn=-1)
library(shiny)
library(leaflet)
library(DT)
load("data\\data.RData")
# Define UI for dataset viewer application
fluidPage(
# Application title
titlePanel("EPA Emissions Data (in tons)"),
# sidebarLayout(
# sidebarPanel(
# helpText("Select a county below"),
... |
5cc219aa3f04c735d84503be238277f24c890db4 | 8a03ba8694baec69d38cb30f3198fc1d770b467b | /R/graph_mwra_sewage_data.R | 38ecd92d9bfdae03678e8cdf47efa6a47586c7e4 | [] | no_license | smach/WrangleMACovidData | c7817e841d16698bb84e9566fefe08dfc5c941a1 | cdbb0f9cb1aa22447155aca520db241ee3b6d46d | refs/heads/main | 2023-02-20T08:11:01.434747 | 2021-01-22T00:32:18 | 2021-01-22T00:32:18 | 287,988,156 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 925 | r | graph_mwra_sewage_data.R |
#' Generate interactive plotly graph of MWRA sewage testing data
#'
#' Data from http://www.mwra.com/biobot/biobotdata.htm generated by import_mwra_sewage_data() function. See mwra-data vignette for details.
#'
#' @param mydf dataframe of MWRA sewage test data from import_mwra_sewage_data()
#' @param mytitle charact... |
0d685a41b32d1b2d07aeb02b53802e021259538e | 6dcd7c7215b226abf5ed6736b9dc118af31e7466 | /man/all_object_size.Rd | 3a50b744ea8096e14eb59feaaf88f273de3e3d0f | [
"MIT"
] | permissive | adrientaudiere/MiscMetabar | b31a841cdac749a0074a0c24c7f8248348c64a22 | 2cb7839d26668836aac129af7115dea0a52385c0 | refs/heads/master | 2023-08-31T06:57:15.546756 | 2023-08-23T09:10:52 | 2023-08-23T09:10:52 | 268,765,075 | 7 | 0 | NOASSERTION | 2023-09-06T10:12:57 | 2020-06-02T10:02:00 | R | UTF-8 | R | false | true | 487 | rd | all_object_size.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/miscellanous.R
\name{all_object_size}
\alias{all_object_size}
\title{List the size of all objects of the GlobalEnv.}
\usage{
all_object_size()
}
\value{
a list of size
}
\description{
\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/s... |
734f5489eab1d5272b3d54f2e7ca3ee27dc0d58f | 5307b4b351ab14d178ce00850f3b336ceb58c8e6 | /markov_smoking_probabilistic_novgam.R | 1b6542e0c025b31ab21e00a1827b7b892acc5bb5 | [] | no_license | dearku/Markov-model-without-VGAM | 02e1f9bc27ba124250889ca2609f57a07cc590b2 | 590398230db8c90d0ee6d316726c08a9e5459ce3 | refs/heads/main | 2023-05-23T10:31:21.294633 | 2021-06-17T15:52:27 | 2021-06-17T15:52:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,476 | r | markov_smoking_probabilistic_novgam.R | # Smoking Cessation Markov model
# Edited to use rbeta() instead of rdiric() to avoid dependency on VGAM
# Howard Thom
# Load necessary libraries
# If not installed use the following line first
# install.packages("BCEA)
library(BCEA)
# Set a random number seed so results are reproducible
set.seed(1002435)
... |
c50434076e713a9cf75f241d6e3e4e93777e1ecb | 6fb04083c9d4ee38349fc04f499a4bf83f6b32c9 | /R/wilcox.test.R | 253541fd0f101a9644451b158ee0fed57648d416 | [] | no_license | phani-srikar/AdapteR | 39c6995853198f01d17a85ac60f319de47637f89 | 81c481df487f3cbb3d5d8b3787441ba1f8a96580 | refs/heads/master | 2020-08-09T10:33:28.096123 | 2017-09-07T09:39:25 | 2017-09-07T09:39:25 | 214,069,176 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,724 | r | wilcox.test.R | #' Wilcox Test.
#'
#' An S3 class to represent wilcox signed Rank test Performs one- and
#' two-sample Wilcoxon tests on vectors of data; the latter is also
#' known as \code{Mann-Whitney} test.
#' If only x is given, or if both x and y are given and paired is
#' TRUE, a Wilcoxon signed rank test of the null that the ... |
9d2fb41b4fd15ba85423613fd0f5babc3e329f6a | 2e37e4e3506c814f0449fae6971c4e10440e5e01 | /tests/testthat/testsummary.zoonWorkflow.R | 725e72d716bf594b7b41a7b1f44b20e0ac50d8d8 | [] | no_license | cran/zoon | f5c03c31d417238c9be03c7a2bca133c71749fa3 | cd5903ef805b4ed31fb4c2b338c32f02f1fee1e3 | refs/heads/master | 2020-04-06T21:25:50.085542 | 2020-02-28T15:30:02 | 2020-02-28T15:30:02 | 48,091,536 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,252 | r | testsummary.zoonWorkflow.R | context("summary.zoonWorkflow")
test_that("summary.zoonWorkflow tests", {
skip_on_cran()
set.seed(1)
expect_is(
summary(workflow(
occurrence = UKAnophelesPlumbeus,
covariate = UKAir,
process = Background(n = 70),
model = LogisticRegression,
output = SameTimePlaceMap... |
7ed38451def847b1ced89ec383c3acad5ed812bc | 269eff99a621cde7e6f26e9fd27a031a4a66ea38 | /R-language-projects/K means clustering-Wine/Kmeans-Wine.R | 16189748ee66c53fd1fe6e2d3f64d5bd230eacb0 | [] | no_license | KrushnaWakode12/Data-Science | d7e1734bdff99e5a2d5203259cd9ee1b0280b6fa | 9f6695e8fc8520bce249a8bc04f074222b9728c9 | refs/heads/master | 2020-10-01T22:12:53.780791 | 2020-02-25T20:24:22 | 2020-02-25T20:24:22 | 227,634,547 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,481 | r | Kmeans-Wine.R | #define libraries required for program
library(ggplot2)
library(cluster)
#Read input files
df1 <- read.csv('winequality-red.csv', sep = ';')
df2 <- read.csv('winequality-white.csv', sep = ';')
#add wine labels
df1$label <- sapply(df1$pH, function(x){'red'})
df2$label <- sapply(df2$pH, function(x){'white'})
... |
5653e5a8eb9213da1a14c265e0f559cc39d84cff | 959c359d37be00c71250fcc8f82d47382ad60637 | /cachematrix.R | 5059b3fb8f4ebd735e177fc6c73102b7a6abe8eb | [] | no_license | harinimukund/ProgrammingAssignment2 | ff93864e14087b204fc834fe7cd5ece978e2e04c | a6f1ab4035d040511617274a2d0d65236a9928e5 | refs/heads/master | 2021-01-17T15:34:14.356221 | 2015-12-23T23:23:05 | 2015-12-23T23:23:05 | 48,455,639 | 0 | 0 | null | 2015-12-22T21:50:10 | 2015-12-22T21:50:09 | null | UTF-8 | R | false | false | 1,243 | r | cachematrix.R | ## The program below is divided in 2 parts.
## 1) The program makeCacheMatrix keeps a list of values in memory
## 2) The program cacheSolve calculates the Inverse of an matrix if it is not cached already.
## The makeCacheMatrix takes in a matrix and returns a list
## which is used later used by the function cacheSolve... |
1b297616b288f667bff9c1643ce9fd047c37306e | 8e4e6474a8fce97066e9231c0fe4b0a83ab841ff | /Ridgelineattempts/20191016_jmcastagnetto_ridgelines.R | b6231da8fee6981d6c7bc07c20507777f7a303dc | [] | no_license | microbesandmud/dataviz | 51d51fae8e797701347a381d1472d0f6db57b0a0 | c2ea09ece34068b3776671a681f3feefd80d9fef | refs/heads/master | 2020-09-01T09:47:42.489507 | 2020-05-16T07:14:48 | 2020-05-16T07:14:48 | 218,933,769 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,715 | r | 20191016_jmcastagnetto_ridgelines.R | install.packages("lubridate")
install.packages("gganimate")
install.packages("extrafont")
install.packages("ggdark")
install.packages("ggridges")
library(tidyverse)
library(lubridate)
library(extrafont)
library(ggdark)
library(ggridges)
library(tidyverse)
df <- read_csv(
"20191016_simple_biopiles.... |
34fbaf0757a0c3866435fa44235e0e4bc78c83b0 | 8bc0348da53579f6d7cb45d7a60db9eafd04b7eb | /R/supp_rarefaction_curves.R | ceb9d6b318cafd5a5c9976b4ed963bea60e8cb59 | [
"MIT"
] | permissive | RadicalCommEcol/multitrophic_feasibility | 24fef12cfbd4b59af1c9837ee40b52c3c40122f7 | fd98486f0638cc280da79b648727a344f38bd4ee | refs/heads/main | 2023-04-15T18:36:02.170317 | 2023-02-07T09:18:58 | 2023-02-07T09:18:58 | 540,769,909 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,165 | r | supp_rarefaction_curves.R |
# Calculate rarefaction curves of species interactions
# for each type of interaction observed (plant-plant, plant-herb, plant-pol)
# INPUT
# interaction matrices (/data/*_matrices.RData)
# OUTPUT
# rarefaction curves for every interaction and local community
# -----------------------------------------------------... |
666ab3a89936c7e521dd8f188ad7da1c79b6fdd0 | 0ce1da8e088edfb8c0f55f0473c2cc1356f590dd | /man/logit.Rd | 250c1004341b250c0e6388f13684ea9e7c50107b | [] | no_license | azolling/EBmodules | 89cf57929870f176e15ce808e19da600206bec08 | a114f566cc24621bafb3bedb343ba79b77bc80ce | refs/heads/master | 2021-01-19T19:19:02.468274 | 2017-04-23T16:39:59 | 2017-04-23T16:39:59 | 88,411,378 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 373 | rd | logit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bayesian_code_final.R
\name{logit}
\alias{logit}
\title{logit function}
\usage{
logit(p)
}
\arguments{
\item{p}{a vector with values between 0 and 1 which we want to take logit.}
}
\description{
This function computes the logit of a quantity
... |
48c2c0b4e1f91ace8ad59ccac4bd03f6e95fc04b | cad6b67bfd5bd73dc217346bb029ddb9fb0ef510 | /man/bubblesOutput.Rd | 07cd42b1b207389cfb4267ec8a4a72e8aafd2a2c | [
"MIT"
] | permissive | jpmarindiaz/bubbleCloud | 8e0008766f3ed568a3c4f1f94185edf8d9183e0b | 9747838ee6e6cced0d8f20f7d1487d6cfe82e96a | refs/heads/master | 2020-03-29T21:24:46.356142 | 2015-05-24T16:38:13 | 2015-05-24T16:38:13 | 31,972,021 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 313 | rd | bubblesOutput.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/bubbles.R
\name{bubblesOutput}
\alias{bubblesOutput}
\title{Widget output function for use in Shiny}
\usage{
bubblesOutput(outputId, width = "100\%", height = "500px")
}
\description{
Widget output function for use in Shiny
}
|
ef90d761310be8deb34e1ac8f6c99fbbdb27faf9 | 5b2f016f1298c790224d83c1e17a425640fc777d | /chol/forestPlot.R | 1cdd68ff55ea69802444d0d85dd5c7eb5a61ac5c | [] | no_license | Shicheng-Guo/methylation2020 | b77017a1fc3629fe126bf4adbb8f21f3cc9738a0 | 90273b1120316864477dfcf71d0a5a273f279ef9 | refs/heads/master | 2023-01-15T20:07:53.853771 | 2020-02-28T03:48:13 | 2020-02-28T03:48:13 | 243,668,721 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,554 | r | forestPlot.R | install.packages("metafor")
library("metafor")
rm(list=ls())
load("CD4_RGSS_data_beforecombat.RData")
phen<-read.csv("CD4_RGSS_clinical.csv")
methdata<-CD4_RGSS_data_beforecombat
dim(phen)
dim(methdata)
head(phen)
i=500
Seq<-paste(phen[,3],phen[,2],sep="_")
mean<-tapply(as.numeric(methdata[i,]),Seq,functio... |
07ea18b037f6e6e418a08a4ca9813cc05cf84f4f | 541a192813be04a1793959edd57dc7abb7834e22 | /R_code/old/test_multivariate.R | a8e6fa3208035e3016043f17aef666d1b1c88bbd | [] | no_license | jumping2000/MasterThesis | 03b2abc057fbfde4914c26dd03643b3424d5268f | f0b5dc44ae7e9e87380497d0d03bdfbf86f0e693 | refs/heads/master | 2021-10-26T16:00:09.891289 | 2019-04-13T16:56:07 | 2019-04-13T16:56:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,086 | r | test_multivariate.R | ###################################################
############### Multivariate Calibration ##########
###################################################
rm(list = ls())
source("MultivariateMertonModel.R")
##### chiamata delle librerie
library(tseries)
library(mvtnorm)
library(pracma)
library(DEoptim)
library(s... |
97945a1d115e12ab395cc1c2acb1ba0e74d7ff08 | 0b622b091c8d3ccc1fbcb36f35abb8c2b23744e0 | /M565FinalProject/Code/Chuck/img_proc/createMaskFromEncodedFile.R | 8b7b8a47da57df4077bdff1e82ee70cd5b83c75d | [] | no_license | chuckjia/B565-DataMining | 12a8cba020a53910e172797ef587affb07d91263 | 42d7dd37470f644abbc30d70c888225f92716ecd | refs/heads/master | 2021-09-27T22:10:47.901946 | 2018-11-12T05:14:57 | 2018-11-12T05:14:57 | 119,938,162 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,793 | r | createMaskFromEncodedFile.R |
# install.packages("png")
library("png")
# install.packages("rstudioapi")
library(rstudioapi)
createMaskFromEncodedFile <- function(outerFolder, encodedFile, newFolder) {
dset <- read.csv(encodedFile)
ndpt <- nrow(dset)
prevFolder <- ''
imgmat <- matrix(0, nrow = 1, ncol = 1)
maskNo <- 1
... |
f526dc2dbe04bbcc5a8a5a20d96621fba8916695 | b61ea73d01d708ad9f337bae20a112ba233f8483 | /man/validate_region.Rd | c6438a68a35982d5e89ef31cc17a2665404219b3 | [
"MIT"
] | permissive | rnevils/youRtube | 2466b490f290948c59e22e93356829d73d3c9aeb | bf0f3963a364a558405d001c4fc4cbc0deafa1a6 | refs/heads/master | 2022-06-12T05:58:55.552068 | 2020-05-05T01:56:51 | 2020-05-05T01:56:51 | 260,322,960 | 0 | 1 | MIT | 2020-05-05T01:58:06 | 2020-04-30T21:33:37 | R | UTF-8 | R | false | true | 441 | rd | validate_region.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_top_videos.R
\name{validate_region}
\alias{validate_region}
\title{Validates region inputted by user}
\usage{
validate_region(key, region)
}
\arguments{
\item{key}{Your YouTube API key}
\item{region}{String or numeric value inputted by u... |
195a54846bf3727a3c4c0bc1d055175e5314bb7a | d80b92f205586c7cbae986ba0a41236b682c4db4 | /R/summary.glmmEP.r | 189c26b0c10651575550546f408cff986b855c40 | [] | no_license | cran/glmmEP | fdfe931681175cc95ce7df6609ca846e85c490b7 | 31f200bb27d92f2da8b6ebe5c64f461867360659 | refs/heads/master | 2020-03-18T21:11:51.476444 | 2019-10-15T07:19:35 | 2019-10-15T07:19:35 | 135,265,706 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 240 | r | summary.glmmEP.r | ########## R-function: summary.glmmEP ##########
# For summarising the glmmEP() fit object.
# Last changed: 18 JAN 2018
summary.glmmEP <- function(object,...)
return(object$parameters)
########## End of summary.glmmEP ##########
|
2a11d795f40ab166148a6b67ee57b41341c9bf0b | f708aec3211a52cad14d9fc5d7cfc1b8253758cf | /profiling/1.R-basic/1.C.3_Part3-Visualization.R | 203a7391c455e4487521e2c411ee6c02d60e4f59 | [] | no_license | jyanglab/labworkshop | 93aa1f7f9f0cbb2525d69496ba96fe883e23dba7 | bd4cbe61ccaa1fa19c7c4784cf91be31972f8d0f | refs/heads/master | 2020-05-24T12:24:59.596074 | 2019-07-19T21:15:31 | 2019-07-19T21:15:31 | 187,267,972 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,738 | r | 1.C.3_Part3-Visualization.R | #
# Let's visualize our data
# ========================
#
# So far we have covered:
#
# - data types in R
# - reading in data
# - subsetting data
# - reading documentation
# - using functions
# - saving data
#
# Of course, we haven't used one of R's most powerful assets: graphics. This
# section is dedicate... |
759bed9279aac4bf56c6ec956de3b4a55d89814f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rwt/examples/denoise.Rd.R | 88840158ef08f1d297d7a9ca47f5c0b3205d2ecd | [] | 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 | 544 | r | denoise.Rd.R | library(rwt)
### Name: denoise
### Title: Wavelet-based Denoising
### Aliases: denoise denoise.dwt denoise.udwt DWT.TRANSFORM.TYPE
### UDWT.TRANSFORM.TYPE MAD.VARIANCE.ESTIMATOR STD.VARIANCE.ESTIMATOR
### SOFT.THRESHOLD.TYPE HARD.THRESHOLD.TYPE MAX.DECOMPOSITION
### CALC.THRESHOLD.TO.USE DEFAULT.DWT.THRESHOLD.M... |
ffdbc57185f3a51120d2e8e7461fe20ba3a920f9 | 865e787ca5d51f3d4b3c5b8af1af3976baa2bc95 | /man/eval_M_Z.Rd | 5fd896c245f8669107ff5c3918ca13639ce0937c | [] | no_license | wgmueller1/mmppr | f43066e12caf90664f8c34f2a8035b34a9a3f082 | 63f971e550a4a232638b9b1470bf3ebc6e78a266 | refs/heads/master | 2021-01-15T17:20:28.159453 | 2014-06-27T12:39:54 | 2014-06-27T12:39:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 261 | rd | eval_M_Z.Rd | % Generated by roxygen2 (4.0.1.99): do not edit by hand
\name{eval_M_Z}
\alias{eval_M_Z}
\title{eval_M_Z}
\usage{
eval_M_Z(M, Z, prior)
}
\arguments{
\item{M}{}
\item{Z}{}
\item{prior}{}
}
\description{
This function evaluates p(M|Z)
}
\examples{
eval_M_Z
}
|
d45f20e3d7c5a088dee4576d9511332a6068c954 | cea373fba99a36d39f64b9de0a748e0b8bda8a37 | /05_function/venn_intersects_upgrade.R | ee17baff925d49f676764e6dec5aabe514473914 | [] | no_license | TheJacksonLaboratory/wild_AD_mic_scRNA | 6f949456cf42e297be9970bc03e6f169ba562be5 | fb9cb603ba1e05af4f518945ca714508dc8181cb | refs/heads/master | 2023-03-15T13:59:23.071302 | 2021-03-03T02:14:48 | 2021-03-03T02:14:48 | 314,021,339 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,158 | r | venn_intersects_upgrade.R | ## generate intersection table compatible with Vennerable library
library(Vennerable)
library(tidyverse)
venn_intersects_upgrade <- function(x_list){
tmp <- Venn(x_list)
intersect_name <- tmp@IndicatorWeight %>% rownames()
Weight <- tmp@IndicatorWeight %>% as_tibble()
names(Weight) <- str_remove(names(Weight)... |
ea5da434b25d6375e2ed0214167cefd95dc98d04 | 84af2a5d4cc82c218c6ea63ef6a3f8fdc299c1ac | /R/SETAR_model.R | 15e9c1f61cff2ac0c034504d525410a09a0e47c6 | [] | no_license | cran/NonlinearTSA | c173ef17fad7165d3f13ad3034ae3b6de4520f4b | 8fa55067e97fa0d4ceafe62999b03169c6dca109 | refs/heads/master | 2023-02-25T02:38:07.707991 | 2021-01-23T15:30:02 | 2021-01-23T15:30:02 | 270,956,992 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,870 | r | SETAR_model.R | #' SETAR model estimation
#'
#' This function allows you to estimate SETAR model
#'
#' @param y series name,
#' @param delay_order Delay order,
#' @param lag_length lag length
#' @param trim_value trimmed value, .15, .10, .5
#' @return "Model" Estimated model
#' @return "threshold" the value of threshold
#' @... |
e0d65c7d7c45679eed779fb0ddd58503fd972a87 | bad08314942d890670cb8186827e93387f8242cb | /R/oneWayAnova.R | 72b55f3e8a8e081d1f9d8ee2b04e685c9c3c4d9b | [] | no_license | stamats/MKmisc | faaa5a4bc04d015143fcd2d468bc11aa12ef5633 | e738e1f1b18899af42c1149335c6ee063e9de80c | refs/heads/master | 2022-11-25T06:06:56.692986 | 2022-11-19T15:35:13 | 2022-11-19T15:35:13 | 33,780,395 | 10 | 2 | null | 2015-06-29T18:02:53 | 2015-04-11T15:13:48 | R | UTF-8 | R | false | false | 321 | r | oneWayAnova.R | ## Modification of function Anova in package genefilter
oneWayAnova <- function(cov, na.rm = TRUE, var.equal = FALSE){
function(x) {
if (na.rm) {
drop <- is.na(x)
x <- x[!drop]
cov <- cov[!drop]
}
oneway.test(x ~ cov, var.equal = var.equal)$p.value
}
}... |
1cbbd4a1513bd7180a49830e2d7d9d5433e8a19e | c1f1d1615f7f3eb62382fcdf38c6eb96c6ec0040 | /LuasStrike/LuasStrike.R | 3854720a10d9f4f71bea0e0e99871f19383be077 | [] | no_license | mryap/SentimentAnalysis | fc9c333ad9401fca5017211d86b3f16ffd998c09 | b0cb3cbd39dcbc9b71cd286ec00b175059ca0237 | refs/heads/main | 2023-01-23T15:56:38.557807 | 2020-12-07T14:35:23 | 2020-12-07T14:35:23 | 319,342,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 893 | r | LuasStrike.R | install.packages("base64enc")
library(httr)
library(twitteR)
library(base64enc)
consumer_key <- 'kDbigJNR6f9BMu6o2Sy95IJp2'
consumer_secret <- 'oXB0795wCX0KwaMC8kT3DgUk45Zv94Bu2DMFVqak0dYUTordEk'
access_token <- '9465632-warwshtU6ax4XsrHYPc5llNWMe17xqAczznh6AH0NN'
access_secret <- 'XULeCzfftRUqUUrzj3Hla2Uv29... |
75ed534f5eda2a2411fe48ab1f4d44c8908f6a67 | 1888f6a3e9892150524e392e9288456c4564ef62 | /cachematrix.R | 33a35d813f4e2dd9ec736d92847800fd692061a6 | [] | no_license | PKathib/ProgrammingAssignment2 | 1e07012bcf0628746309835a1d59f7d4df9ce3cb | 717b70cf749a1682c52814259109a6d6847d985a | refs/heads/master | 2021-01-18T10:15:01.807760 | 2016-02-20T04:22:27 | 2016-02-20T04:22:27 | 52,133,547 | 0 | 0 | null | 2016-02-20T03:02:41 | 2016-02-20T03:02:39 | null | UTF-8 | R | false | false | 1,813 | r | cachematrix.R | ## We are creating two functions in R.
## 1. makeCacheMatrix, 2. cacheSolve
## Both these functions are used to get the inverse of the matrix and then to cache them for future use
## as matrix inversion is an expensive process.
## # 1. Set the value of the passed matrix
## # 2. Get the value of the passed matri... |
f86bfd05d7c53d64912a0eec5c121e8623f11dc4 | 860efbde82499c1cc307e36b57f6af41fe37225e | /man/analyze.gain.Rd | 9c3f0ac1bfe5604822bb4bdf71c71aeff6ac85d3 | [] | no_license | cran/gainML | 92a2ffb79ca5026e9e509edcdc1cc43b151ddb92 | f85e402726004d6f9a31f812cc0a66bf83eabffc | refs/heads/master | 2020-12-21T23:34:19.542072 | 2019-06-28T12:40:07 | 2019-06-28T12:40:07 | 236,601,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 7,466 | rd | analyze.gain.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analysis_total.R
\name{analyze.gain}
\alias{analyze.gain}
\title{Analyze Potential Gain from Passive Device Installation on WTGs by Using a
Machine Learning-Based Tool}
\usage{
analyze.gain(df1, df2, df3, p1.beg, p1.end, p2.beg, p2.end... |
f62778905382e394986d0aa870672235479ce5ca | 36bf489230433e0e8ae7b88cf6a9f9b0cf299f15 | /plot3.R | abe8a8c2470c3b7a8a58314a54142b4e60cca3e4 | [] | no_license | samermounir/ExData_Plotting1 | 27fcc7c3ee881dbebad56e5483f9f4de207c27d3 | fad4e75e97e34b4df78dfdcd7f980524c1adede8 | refs/heads/master | 2021-01-18T00:13:59.859161 | 2014-12-05T15:23:05 | 2014-12-05T15:23:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,549 | r | plot3.R | ############################################################
##You have to extract the data into your working directory##
############################################################
## Step 0 using the lubridate library
library(lubridate)
## Step 1 read the data into the elecdata including doing all the adjustments
#... |
26f6d8cc7130dd909e5fb4ccf6489c38b76bd59e | d8e6354d5fcc6f3f1202fccccaf816f11d6a0518 | /R Files/ps2.r | a08ca5c4d635a1bb22403c123ad9f9b9b550c2b2 | [] | no_license | nishidhvlad/Repository | 4f7ff19c7c4cb92f53fc6d3f4df79dc2e82a7a04 | b10e664b6d9912bb49f5d20d0ab16562f76a90de | refs/heads/master | 2020-03-26T04:29:44.084478 | 2018-08-12T22:36:21 | 2018-08-12T22:36:21 | 144,506,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,510 | r | ps2.r | ################################
# QUESTION 1
################################
rm(list=ls(all=TRUE)) #remove all variables
library(data.table)
context1 <- fread("attend.csv") #read data file
attendrt <- context1$attend/32
hwrt <- context1$hw/8
summary(context1)
model1 <- lm(termGPA~priGPA+ACT+attendrt+hwrt, ... |
95d53883542d5fda33b19b8597cd71444dcc106c | 93aed4eda5fe4e23d8f0f042b1180449eac9517c | /RakregiszterScraper.R | 41c8294647faa0a26a8bdd84492496cb51ab9d23 | [] | no_license | tamas-ferenci/RakregiszterVizualizator | 543467171eae1456247608a8926f93dfc9304595 | 1e0085f80f56cbb3940d9704c549f5bcedfa704d | refs/heads/master | 2021-10-11T16:05:16.592672 | 2021-09-28T11:50:36 | 2021-09-28T11:50:36 | 146,289,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,442 | r | RakregiszterScraper.R | library(data.table)
ftfy <- reticulate::import("ftfy") # https://github.com/rspeer/python-ftfy
years <- rvest::html_attr(rvest::html_elements(rvest::read_html("http://stat.nrr.hu/"),
xpath = "//select[@id='edit-eve']/option"), "value")[-1]
counties <- rvest::html_text(rve... |
ae0f1cda9c105b53bfed5381bda4bd0a10f99489 | 91ec04b21cd17e36a6784865aee08dc729c0a4ea | /cachematrix.R | 4f6373d7292ea2218d913027d5e08b4669803b9a | [] | no_license | praneets/ProgrammingAssignment2 | a1816cf217581e522439c56cb24682b78a8b3c2c | 2bf6dd3af99a78887ceddf874720bb148c5cbe90 | refs/heads/master | 2021-01-18T06:56:08.406543 | 2014-05-24T10:34:39 | 2014-05-24T10:34:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## This function creates a list of functions that will work on the argument x.
## Argument x will be initiated when this function is called. The four
## functions are set, get, setinv and getinv. The cache for this function is
## the v... |
e8b8aa76d9ba5ee1e5a0a0ac0de8a8177a071a79 | 7e3f188372012ed9635facb1a2a3b0bab71cef48 | /man/check.assign.with.multiple.sol.Rd | af4ee984a5a8761afb2e72812ef58b574fd3c8aa | [] | no_license | skranz/RTutor | ae637262b72f48646b013b5c6f89bb414c43b04d | f2939b7082cc5639f4695e671d179da0283df89d | refs/heads/master | 2023-07-10T03:44:55.203997 | 2023-06-23T05:33:07 | 2023-06-23T05:33:07 | 11,670,641 | 203 | 61 | null | 2020-06-17T16:11:34 | 2013-07-25T20:47:22 | R | UTF-8 | R | false | true | 694 | rd | check.assign.with.multiple.sol.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tests_for_ps.r
\name{check.assign.with.multiple.sol}
\alias{check.assign.with.multiple.sol}
\title{Checks an assignment to a variable with up to 5
possibly correct solutions}
\usage{
check.assign.with.multiple.sol(
sol1,
sol2,
sol3,
s... |
a26c00b2b8303340e429d29189fd12a1ab572a8d | 8c353819bc833ce88ff5c1f2d27f31d40ac2b162 | /data-raw/ice.R | 8ad25391d5c60711f944c13ae30087f322b42cbd | [] | no_license | AustralianAntarcticDivision/SOmap | 6e1e91ec59a59be6471ce1b940c9363154f949b7 | 0297ee8aea87015e32a2d3e4f9b009d00a549d29 | refs/heads/master | 2023-03-12T07:42:44.477742 | 2023-03-07T02:44:57 | 2023-03-07T02:44:57 | 155,124,496 | 24 | 5 | null | 2023-02-02T22:18:42 | 2018-10-28T23:06:11 | R | UTF-8 | R | false | false | 295 | r | ice.R | ice <- raadtools::readice(latest = TRUE)
date <- getZ(ice)
psproj <- "+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0"
ice <- setZ(raster::projectRaster(ice, crs = psproj), date)
ice[ice < 1] <- NA
usethis::use_data(ice)
|
694e779bee0d6b4b6aaa894421c0a2c7f09e32f3 | 71aedc7b7f4e70697b09b8786b835df061b2ade9 | /man/creds_from_file.Rd | d333268d3d1e191fdd9c15a737a3916ecfeabb93 | [] | no_license | jflournoy/scorequaltrics | e6027483aedeb16c3ff07956b803511ebed5dc78 | f3114b315b96ed05d99b7950b538a3c2c11ce0ae | refs/heads/master | 2022-04-28T11:10:43.565376 | 2022-04-21T20:30:07 | 2022-04-21T20:30:07 | 247,803,512 | 1 | 0 | null | 2022-04-21T20:29:57 | 2020-03-16T19:48:30 | R | UTF-8 | R | false | true | 537 | rd | creds_from_file.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{creds_from_file}
\alias{creds_from_file}
\title{creds_from_file}
\usage{
creds_from_file(creds_yaml = "credentials.yaml")
}
\arguments{
\item{creds_yaml}{The .yaml file that contains the user's credentials}
}
\description{
Reads ... |
25e35d17b26ca4f9ae8efef448ebc185567ae0c4 | 2c40d0d8a09d8808acb6187cbc98b091758e3c2e | /ClassifiyTracks.R | 21b037f4e657336ded0bc051acfb8d33b487d58b | [] | no_license | thorstenwagner/spie-photonics-europe-2016 | f98e9b30791fac90b8e5aaa7111611cfbc3bc971 | 9e443d3da321265b83565785cbd1dbc4078679d3 | refs/heads/master | 2021-01-10T02:33:47.630061 | 2016-02-17T09:08:33 | 2016-02-17T09:08:33 | 51,763,237 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,760 | r | ClassifiyTracks.R | #The MIT License (MIT)
#
#Copyright (c) 2016 Thorsten Wagner (wagner@biomedical-imaging.de)
#
#Permission is hereby granted, free of charge, to any person obtaining a copy
#of this software and associated documentation files (the "Software"), to deal
#in the Software without restriction, including without limitation th... |
4a2cc4760686d57dc5b0170b2758eec38d200826 | 05a9f722bfd91a75144ebf840f296f932b5baf20 | /BrestNewlyn/correlationNewlynSubPolarGyreIndex.R | 1cf96d201f55445c1de1a2fff0c09cfe36cb49ec | [] | no_license | simonholgate/R-Scripts | 05118e4e92118a506eaf29bf6fa9aca4ea3f9477 | 89ab9ee9da1bbce10f4dc9a422259dda64748689 | refs/heads/master | 2020-05-17T14:48:02.345740 | 2012-06-21T11:19:58 | 2012-06-21T11:19:58 | 4,738,138 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,773 | r | correlationNewlynSubPolarGyreIndex.R | library(fields)
library(robust)
source("~/Dropbox/BrestNewlyn/matrixMethods.R")
load("~/Dropbox/brestNewlynData/analysis/paper/correlationACRE/brestNewlyn.tot.ps.RData")
newlyn.full.p.start <- 1916
newlyn.full.p.end <- 2008
newlyn.full.p.yrs <- c(newlyn.full.p.start:newlyn.full.p.end)
hatun.period <- c(which(newlyn.f... |
5bf467df96d204dc4f41df885374134a4639c872 | 36881f038bac0454ac2f1adaabf472074e38fd1d | /Code/man/module.input.Rd | 92f49cee6032e8782b51ad10c35cad96f645da5b | [] | no_license | PriSomeda/longleafGY | d3a3580de4d86e73ebe3f7e422d81cd906ed0f39 | bffa7bbfbc5d5d476b6a5a1809df73f35968d147 | refs/heads/master | 2020-03-18T20:54:43.555997 | 2018-06-03T21:47:15 | 2018-06-03T21:47:15 | 135,246,715 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 4,219 | rd | module.input.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/module.input.r
\name{module.input}
\alias{module.input}
\title{Module of input tree- or stand-level data to prepare it for further simulations.}
\usage{
module.input(TYPE = "PLOT", TREEDATA = NA, AREA = NA, SI = NA,
HDOM0 = NA, AGE0 = NA, B... |
32fe4b0ca157ec62e7d2ef35dc729f27370af628 | 47f1ebbeb9e1c2f639926da5fc42cd7ae508f350 | /man/makeACSdf.Rd | cdc05e1171ed01c0fcde74d2351e014bd0d6a1cc | [] | no_license | maxsibilla/spew | ee72183a2aa43a31025247134bafdc1d4a6fb6e6 | 375526cef25f7029e12d4148d37ac11a3560a6f7 | refs/heads/master | 2021-01-20T17:46:47.928742 | 2017-05-10T14:23:38 | 2017-05-10T14:23:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 438 | rd | makeACSdf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/acs-tables.R
\name{makeACSdf}
\alias{makeACSdf}
\title{make a more usable df from acs objects}
\usage{
makeACSdf(acs_obj)
}
\arguments{
\item{acs_obj}{class "acs" from acs package}
}
\value{
a formatted df where the rows are the geographies a... |
1df62e558423ddffbd4cefbc028566006a147e4c | c73f0d6bf7ba22627fdbf8b2fca89ba4c8058c9a | /plot1.R | d33e2c919976256df4ab67fe80ade70e8950e040 | [] | no_license | wills8/ExData_Plotting1 | d457495b6e5f05d988553d518e9ea477457633f9 | 3c9c1baaa97ad0ffdb785e92912dfdfbfbc2e1aa | refs/heads/master | 2020-06-11T10:27:19.429294 | 2019-06-26T15:47:32 | 2019-06-26T15:47:32 | 193,931,514 | 0 | 0 | null | 2019-06-26T15:28:03 | 2019-06-26T15:28:02 | null | UTF-8 | R | false | false | 616 | r | plot1.R | # John Hopkins Exploratory Data Analysis Project 1
# Data Source: UC Irvine Machine Learning Repository
# Data Title: Electric Power Consumption
# PLOT 1
# Read data in and subset data
data <- read.table("household_power_consumption.txt", sep = ";", header = TRUE, na.strings = "?")
subData <- data[data$Date %in% c("1... |
1490c84dc423983e6e8c6e4bd4c2bde9b86a8bbe | 506e917f1a30059c0d61d897e099f178c85098b8 | /R/perf.metric.R | 3b90a0cf3026a2b7491faa5c90b8f4067a46d640 | [] | no_license | pierrecattin/thesis-resources | 03262e980857493a208fd11198503c6af0170091 | 6a321aa377a00f82a86e5f61d6170ab0ba849f52 | refs/heads/master | 2021-06-22T08:00:42.441620 | 2018-08-24T15:23:18 | 2018-08-24T15:23:18 | 136,451,744 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 907 | r | perf.metric.R | #' Performance Metric
#'
#' @param freq.acc data frame containing at least columns Freqency, Accuracy and Sr if metric is "mean.sr"
#' @param metric c("mean.accuracy", "mean.sr")
#'
#' @return Performance metric computed as a mean accros frequency levels
#' @export
#'
perf.metric <- function(freq.acc, metric){
freq.g... |
21278bd12ef8df0699e678ad4c3d27435c659e99 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/event/examples/hboxcox.Rd.R | b51d4897789bc4a8d534d0da4926997cbe62416b | [] | 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 | 177 | r | hboxcox.Rd.R | library(event)
### Name: hboxcox
### Title: Log Hazard Function for a Box-Cox Process
### Aliases: hboxcox
### Keywords: distribution
### ** Examples
hboxcox(2, 5, 5, 2)
|
49da1f7aad633a382beca45ef50434fdad798ef0 | 452042d9a5cb876a90a9cb1f4c802d0f4b1453c7 | /R/espn_draftpicks.R | 960d25cf2de4af8e7be24d927068311c13f4b90c | [
"MIT"
] | permissive | jpiburn/ffscrapr | dc420370f6940275aaa8cb040c5ec001a25268b8 | 4e7bda862500c47d1452c84a83adce7ee1987088 | refs/heads/main | 2023-06-02T00:09:09.670168 | 2021-06-12T15:52:23 | 2021-06-12T15:52:23 | 377,976,824 | 1 | 0 | NOASSERTION | 2021-06-17T22:42:26 | 2021-06-17T22:42:26 | null | UTF-8 | R | false | false | 677 | r | espn_draftpicks.R | #### ff_draftpicks - ESPN ####
#' ESPN Draft Picks
#'
#' @param conn the list object created by `ff_connect()`
#' @param ... other arguments (currently unused)
#'
#' @describeIn ff_draftpicks ESPN: does not support future/draft pick trades - for draft results, please use ff_draft.
#'
#' @examples
#' \donttest{
#' conn... |
93dee8c29ef9feca00f6d2ebac62209cbdfe320b | fc70b4b8f15ec7062ad57714ad81441015b559b8 | /inst/app/server/projection/reactive.R | f1e0211f94a765b97451476ebff7c15d08b27376 | [] | no_license | jackolney/CascadeDashboard | 02aa85dc78e6ab916ba6e01328b45f483d81b0c0 | 25e29abd233ba365501900c800f81ae0beadc0c6 | refs/heads/master | 2020-07-07T05:58:55.039907 | 2017-04-04T07:58:23 | 2017-04-04T07:58:26 | 66,279,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | reactive.R | plotOptimCostImpact.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_909090.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_DALYs.ranges <- reactiveValues(x = NULL, y = NULL)
plotOpt_DALYs_909090.ranges <- reactiveValues(x = NULL, y = NULL)
|
9999cb982cd61e7077d24bd65b833c1a2cfe5041 | fc96da2f9cac0702e0caa7b40ca9a732ad798bcb | /scripts/archive/sfa_gwis_gwastools/sfa_gwis_gwastools_fhs.R | 61f1e9e96448d3906a4dc5fc305be56b06c452b6 | [] | no_license | echoheqian/whi-diet-response | d452df48fd1c04b9c5eb81be8255727edeeb35ec | 0372f9c7fc5e48ae4b7121a504b6979ed175fa38 | refs/heads/master | 2023-04-24T14:25:19.611522 | 2021-05-05T15:54:31 | 2021-05-05T15:54:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,544 | r | sfa_gwis_gwastools_fhs.R | silent <- lapply(
c("knitr", "tidyverse", "cowplot", "doParallel", "kableExtra",
"glmnet", "broom", "GWASTools", "SNPRelate"), library, character.only=T)
args <- commandArgs(trailingOnly=T)
dv_withEx <- args[1]
rf <- args[2]
main_effect_threshold <- args[3]
INT <- function(x) qnorm((rank(x, na.last="keep") - 0.5) / ... |
a4d53fa74f9bee0d737b0768e888af7e71b70c80 | 47f4ff8f58149e3b7301b455f47754aa041dc8f0 | /server.r | fe844b38aa4c4010141b9584b5d7315d428963ed | [] | no_license | ashish9308/SentimentAnalysis | fa6fa7fb4fdab3cfa53e54fc5fec94ca0b311477 | 43ef62aea356ddaa3e4474933b48b9a5fe9175ce | refs/heads/master | 2021-05-04T22:17:10.619013 | 2020-01-30T13:18:04 | 2020-01-30T13:18:04 | 120,026,675 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,132 | r | server.r |
#We need to have an app created at https://dev.twitter.com/apps before making any API requests to Twitter.
#It's a standard method for developers to gain API access, and, more importantly,
#it helps Twitter to observe and restricts developer from making high load API requests.
shinyServer(function(input, outp... |
c9d6f73cafa41b2d19f208c9076e338465389131 | e2ad154f7a7001a2a393141993432bd04a5889b2 | /man/getQuizTemplate.Rd | 7e20beb055396b4c8bedc363a816c36ae6af2e2f | [
"Apache-2.0"
] | permissive | takewiki/learnr | 0dab59ce219986e97727cc03042f6f66af3e497c | 0a9b4bc729d30e7a546bf840e7711c18a2930d36 | refs/heads/master | 2020-03-16T23:01:31.349360 | 2019-06-21T08:25:05 | 2019-06-21T08:25:05 | 133,061,854 | 1 | 0 | Apache-2.0 | 2018-05-11T16:13:46 | 2018-05-11T16:13:46 | null | UTF-8 | R | false | true | 586 | rd | getQuizTemplate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getQuizTemplate.R
\name{getQuizTemplate}
\alias{getQuizTemplate}
\title{获取测试的模板数据，提供中英文2种格式}
\usage{
getQuizTemplate(path = "./", lang = "en")
}
\arguments{
\item{path}{文件的路径，默认为当前项目目录}
\item{lang}{语言的内容，默认为英文，也可以为中文，指定为cn即可}
}
\value{
实际没有返... |
6254a6d45a9a01b7e604f3c8d9372260f12d555a | fa60f8262586afbf25096cfb954e5a9d391addf7 | /R_Machine_Learning/r_14_2(Diabetes_Random_Forest).R | cdc504b04b3676eb70bf5b5bcdeb52d913ab49e1 | [] | no_license | pprasad14/Data_Science_with_R | ce5a2526dad5f6fa2c1fdff9f97c71d2655b2570 | 3b61e54b7b4b0c6a6ed0a5cc8243519481bb11b9 | refs/heads/master | 2020-05-05T08:56:39.708519 | 2019-04-06T20:42:11 | 2019-04-06T20:42:11 | 179,884,402 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,759 | r | r_14_2(Diabetes_Random_Forest).R | #load data
dataset = read.csv("Diabetes.csv")
# another way to make train and test set without caTools
set.seed(123)
id = sample(2, nrow(dataset), prob = c(0.7,0.3),
replace = T)
table(id)
training_set = dataset[id==1,]
test_set = dataset[id ==2,]
#building descision tree
library(rpart)
classifie... |
68bf46f9dc4ca5efe521539d493391236f6c30af | af901bc01d668ecd411549625208b07024df3ffd | /man/string.Rd | af2f09ee1ac52f16fe237465c602ac8dc02c566d | [
"MIT",
"BSD-2-Clause"
] | permissive | r-lib/rlang | 2784186a4dafb2fde7357c79514b3761803d0e66 | c55f6027928d3104ed449e591e8a225fcaf55e13 | refs/heads/main | 2023-09-06T03:23:47.522921 | 2023-06-07T17:01:51 | 2023-06-07T17:01:51 | 73,098,312 | 355 | 128 | NOASSERTION | 2023-08-31T13:11:13 | 2016-11-07T16:28:57 | R | UTF-8 | R | false | true | 1,522 | rd | string.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-encoding.R
\name{string}
\alias{string}
\title{Create a string}
\usage{
string(x, encoding = NULL)
}
\arguments{
\item{x}{A character vector or a vector or list of string-like
objects.}
\item{encoding}{If non-null, set an encoding mark... |
3e213b4053723f1798a3b80f6be684766efc5618 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hutils/examples/ahull.Rd.R | 6e5347a52456c24062a17a338815729e85c2c5e9 | [] | 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 | 169 | r | ahull.Rd.R | library(hutils)
### Name: ahull
### Title: Maximum area given x and y coordinates
### Aliases: ahull
### ** Examples
ahull(, c(0, 1, 2, 3, 4), c(0, 1, 2, 0, 0))
|
60ce03c6825db3146b2016dfb8a1fea2ca85d0b5 | b352edcb8ffea55c1b1bf4fae9be834213acf550 | /plot3.R | a1dd2a3a27a8dc6fddd1a9ec2b9a62074df25197 | [] | no_license | connorburleigh/ExData_Plotting1 | b4db52f2ce2fda22f94cbcc093e79ac2672d957b | cda28544f8fe8f2d952c4dd76b97a75fff8ad5c9 | refs/heads/master | 2021-01-24T23:35:09.938602 | 2015-08-07T16:28:03 | 2015-08-07T16:28:03 | 38,980,661 | 0 | 0 | null | 2015-07-12T22:37:24 | 2015-07-12T22:37:23 | null | UTF-8 | R | false | false | 1,268 | r | plot3.R | ## downlaod.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip","/Users/connorburleigh/Coursera/power_consumption.zip",method="curl")
library(lubridate)
wd<-getwd()
temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumptio... |
112befc89ac2ba26e62ecdc9bbfad9dc1c8337d0 | dabe6fa5a3caf17d8b0c3dab939c614e3143775a | /2_model/src/evaluate.R | 5794101f0602c6858876abae1475f76a8e59e35f | [] | no_license | wdwatkins/lake-temperature-neural-networks | d0c166183adaf82ef6562312a6fcd49a864ff255 | 626e569cb397f4d67299ebf3d80e1e8fa5dc4057 | refs/heads/master | 2020-03-25T05:36:10.759004 | 2019-11-16T01:29:16 | 2019-11-16T01:29:16 | 143,456,068 | 1 | 0 | null | 2018-08-03T17:37:01 | 2018-08-03T17:37:01 | null | UTF-8 | R | false | false | 505 | r | evaluate.R | evaluate_model <- function(model_list_ind, dat_ind, rmd_file, site_id, output_html, priority_lakes) {
model_list <- readRDS(as_data_file(model_list_ind))
dat <- readRDS(as_data_file(dat_ind))
lake_name <- lookup_lake_name(site_id, priority_lakes)
meets_data_criteria <- priority_lakes %>% filter(site_id == !!sit... |
3e5989032e601345b8f6b7339a53589c6d2ce34b | 44143d0c480e1cabf87f2c44909afe2aa85bd67c | /man/summary.QTE.Rd | 20bd6aba13590fb3629f767ab70d0eb8fa88c3bc | [] | no_license | bcallaway11/qte | c383e991a3969e9e50e30477e701ee11c500d574 | 09830e766b7f9e28643928e9a170f73d9c4c0bcf | refs/heads/master | 2023-08-30T21:43:34.342973 | 2023-08-15T21:37:43 | 2023-08-15T21:37:43 | 19,584,525 | 8 | 5 | null | null | null | null | UTF-8 | R | false | true | 352 | rd | summary.QTE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qte.R
\name{summary.QTE}
\alias{summary.QTE}
\title{Summary}
\usage{
\method{summary}{QTE}(object, ...)
}
\arguments{
\item{object}{A QTE Object}
\item{...}{Other params (to work as generic method, but not used)}
}
\description{
\code{summar... |
4d6c59c59edba85969b7d9d698d3e7fe2413af0c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rmutil/examples/Levy.Rd.R | e353872507527841c666297726f470c46f1de7e9 | [] | 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 | 210 | r | Levy.Rd.R | library(rmutil)
### Name: Levy
### Title: Levy Distribution
### Aliases: dlevy plevy qlevy rlevy
### Keywords: distribution
### ** Examples
dlevy(5, 2, 1)
plevy(5, 2, 1)
qlevy(0.6, 2, 1)
rlevy(10, 2, 1)
|
9e9d7379d34f764b0a04d122e853e89f168da823 | d125c6d235454381dfc70bf01056563af9ae071c | /R/children.R | f12451bc5a4ad34508f30e7935b7de0faf117413 | [
"MIT"
] | permissive | dmkaplan2000/taxize | 02e68eef6e62fcb5233c11928b5bd47b0cf339e3 | 5b6c4479eb7188d0289d7df6aff3b1a9d61422df | refs/heads/master | 2021-01-22T10:51:43.095152 | 2015-06-18T15:44:07 | 2015-06-18T15:44:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,015 | r | children.R | #' Retrieve immediate children taxa for a given taxon name or ID.
#'
#' This function is different from \code{\link{downstream}} in that it only collects immediate
#' taxonomic children, while \code{\link{downstream}} collects taxonomic names down to a specified
#' taxonomic rank, e.g., getting all species in a family.... |
8aa0bc862701f663f54287dbb3cd3e47dd8bceee | 40eccb8e26853d23ea3d35ac15225a420333ad55 | /spinGameSamples.R | 9fef52cafa95987e02290a5bde72590a68a27416 | [] | no_license | dinhtuanphan/CoolProjects | 72df0fe4f1dd56f85bb883a1eb3821a9c4a4159b | 2eaffa8c3d278d1c4cbc52bb2fb3c1e6403c937a | refs/heads/main | 2023-08-28T12:27:39.514791 | 2021-10-01T19:20:03 | 2021-10-01T19:20:03 | 370,442,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 243 | r | spinGameSamples.R | runs <- 100000
spin <- function() {
expectation <-
sum(sample(c(1/2, -1/4, 1/2), size = 10, replace = TRUE))
if (expectation < 0) {
return(1)
} else {
return(0)
}
}
prob <- sum(replicate(runs, spin())) / runs
print(prob)
|
a50094e817c264cd3018be07080152adfcc67fcb | 747803a7abca38892a07ae46239ef58300d7bc0c | /exercises/exercise1_vectors.R | 73b8dca0acc66ef3218206db775577dc03197306 | [] | no_license | m-nabais/neurasmus_rmarkdown_workshop | 0b07d7a84acd49c9c7d00a86f5e9c35c0f283cc6 | 29c986910572a8638f6542ac504ff87ebed5ea34 | refs/heads/master | 2022-11-06T13:03:08.217465 | 2020-06-13T13:49:55 | 2020-06-13T13:49:55 | 268,495,378 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,625 | r | exercise1_vectors.R | #########################
## Example 1 - vectors ##
#########################
a <- c(1,2,5.3,6,-2,4) # numeric vector
b <- c("one","two","three") # character vector
c <- c(TRUE,TRUE,TRUE,FALSE,TRUE,FALSE) #logical vector
d <- c("1", 1, "TRUE", TRUE, "Hello")
##########################
## Exercise 1 - vectors ##
######... |
be00aaf3a7a91dbe70b0b45a1b7b3639b17ce785 | f634745ad3168a636a5d6a1de2b339aab108b9f5 | /digit.R | 83f7d3d69b39fdc755e8999e6ebeb91655082a87 | [] | no_license | anupamsingh81/grades | 33c49826ea436b02d005419344118ccdc2d9c2e2 | 2058a2ed5631e81981cc09420dccd6bb8ec2af59 | refs/heads/master | 2021-09-09T01:44:45.980347 | 2018-03-13T07:08:55 | 2018-03-13T07:08:55 | 125,007,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 541 | r | digit.R | library(digitizeR)
app <- wpd.launch()
plot(res_2005$V1,res_2005$V2)
library(tidyverse)
res_2005$V1
res_2005 =
res_2005 %>% rename(marks=V1,percent=V2) %>%
ggplot(aes(marks,percent))+ #stat_smooth()
geom_line()
res_2005 %>% filter(V1<81)
pnorm(60,mean(res_2005$V1),sd(res_2005$V1))... |
1d87a0af5ba21b0bc716568435b597cff6471373 | 1a1a686b70a443f0d61b0e6903323a396c18c212 | /R/ct_update_databases.R | 8351c9201f7ccf8c237c4765e3648fad48d77dac | [] | no_license | cran/comtradr | 8d3e6d4073a49b215bce43d181f1f7daab61a2bf | aa054cbec4893182cae0b78dee951c0c21b46477 | refs/heads/master | 2022-04-29T19:10:49.795077 | 2022-04-20T05:32:29 | 2022-04-20T05:32:29 | 87,315,342 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,128 | r | ct_update_databases.R | #' Check for updates to country/commodity databases
#'
#' Use of the Comtrade API requires access to the Comtrade countries database
#' and commodities database. The \code{comtradr} package keeps each DB saved
#' as a data frame in the package directory, as Comtrade makes updates to
#' these DB's infrequently (roughly ... |
ceebb0d882ce5f257cadee2776171d2ea64df92c | f351a9b16a1fd6da52b2c2fa00bda484e7ef6f1c | /SNP_analysis/snp_PCA2.R | de9196602b67688db7e1eb22fb0197b7f3a0cdfa | [] | no_license | tania-k/Friedmanniomyces_popgen | 19f7b105ba28d78367752b73ab416b3477787a6e | 816e8e98fe6ac693729c0a58ef8dbde3484a8d8a | refs/heads/master | 2023-04-12T14:30:20.450499 | 2022-11-20T11:35:33 | 2022-11-20T11:35:33 | 568,384,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,832 | r | snp_PCA2.R | library(gdsfmt)
library(SNPRelate)
library(dplyr)
library(wordcloud)
library(tm)
library(ggfortify)
library(ggplot2)
library(plotly)
library(phyloseq)
library(ggrepel)
library(ggbiplot)
library(RColorBrewer)
gdsfile = "plots/snps_selected.gds"
vcf.fn <- "vcf/CCFEE_5001_v1.All.SNP.combined_selected.vcf.gz"
if(!file.e... |
b2fd28ac5b667477314e144d2b8cb30adcd22e69 | 5d8eb44c6dccda49d67ccd48052909a12cc7da30 | /GSE22544_Bootstrap.R | 0ed3138717f140b9e1e1a6bb92835054a15fc0f6 | [] | no_license | justjooz/research-experience | c797b75a439ca9003b52ada3ed372ddf84f6aedc | a467ce10f4d5fe2722f18f066100913e84465e82 | refs/heads/master | 2020-06-25T00:01:16.290799 | 2019-08-03T04:23:30 | 2019-08-03T04:23:30 | 199,132,600 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,575 | r | GSE22544_Bootstrap.R | # this R script shows the process to obtain significant genes, binarize them, and do a 1000x bootstrap
# followed by using a distance matrix/heat map
# and confusion matrix to obtain the F-score, recall, precision
# Set working directory
setwd("D:/Code/RE/My R scripts")
# ============================================... |
04a4d26728b3ad230536941168e6750d5f018ec8 | cb4b8d511a14f1655120bb8737266296c5e46059 | /R/birds/Density_stuff/species_density.R | 2db88f5f2b324b5609ea3b48b77826a9a62997a4 | [] | no_license | Josh-Lee1/JL_honours | 40361e2f8b78fac9676ff32a8e0ce7a0603f6152 | db6792a039d824fdb518f9e06c3cc27ecca6da8a | refs/heads/master | 2023-03-29T22:28:19.500012 | 2021-04-15T04:40:20 | 2021-04-15T04:40:20 | 295,877,409 | 0 | 0 | null | 2021-03-16T06:17:06 | 2020-09-16T00:02:18 | HTML | UTF-8 | R | false | false | 2,535 | r | species_density.R | library(tidyverse)
library(Distance)
library(mrds)
library(lme4)
library(sjPlot)
library(sjmisc)
birds <- read.csv("Data/Raw/Birds.csv")
birds$Treatment<- with(birds, paste0(Formation, Fire))
birds <- birds %>%
group_by(Site) %>%
mutate(species_richness = n_distinct(Species)) %>%
rename(distance = Distance) %>%... |
bbb4666d4f79f89a569434d303a011e9115e34d3 | c1b66b5db23476c16ddd62b1afbcda48d4bde719 | /cluster_analysis/opt_clust.R | 0012fe32eb0bd6e0511b89a6e58e28fee4066b79 | [] | no_license | venice-juanillas/eib-tools | 38e19a67864fc70b8db29199db173f082c15cca1 | 4a946819ca1676c8947c3ac4ee92e71e0259fbaa | refs/heads/master | 2020-03-18T06:17:22.384260 | 2018-05-22T09:08:26 | 2018-05-22T09:08:26 | 134,386,147 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,045 | r | opt_clust.R | #######################################################################
# Umesh Rosyara, April 10, 2018
# CIMMYT
# optimum QTL: This function is used to do find optimum number of QTLs
######################################################################
rm(list = objects()); ls() # CLEAR 'WorkSpace' (R ... |
1b525f2b1e8ee1b277560c8243d3e03d2ca9e29c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/NISTunits/examples/NISTsqrFtTOsqrMeter.Rd.R | aa6f91ddb02690cd2f0f2657af0cb1be8592f16a | [] | 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 | 202 | r | NISTsqrFtTOsqrMeter.Rd.R | library(NISTunits)
### Name: NISTsqrFtTOsqrMeter
### Title: Convert square foot to square meter
### Aliases: NISTsqrFtTOsqrMeter
### Keywords: programming
### ** Examples
NISTsqrFtTOsqrMeter(10)
|
2be2268a6b800ca061515b7b087704183f09dd22 | 949041354ceeea0eaf534ef0f108c43382279a23 | /landscape.R | 318198236ec0b5e8055ca6e92619b9deb546d4ed | [] | no_license | cbig/dsexplained | 9b87a9647494b702771044be764e6555520a8f56 | 6e55e7862e351e746dac7aa7f04227ab2882079b | refs/heads/master | 2021-01-10T13:33:47.587174 | 2015-10-12T12:45:59 | 2015-10-12T12:45:59 | 44,098,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,368 | r | landscape.R | # Name: simulate_landscape.R
#
# Author: jlehtoma
###############################################################################
## Function create.landscape can be used to create landscapes of varying
## complexity.
## Params:
## x - matrix of coordinates, or vector of x coordinates
## y - vector of y coordinates
##... |
fb2d27438f912c91e1b59f9b8ebad22b10144050 | 19e11f1eee51bf74e3c903490c1f8f962387e765 | /tests/viterbi_test.R | dafb4c65c784ff764fa48ef49a5754de18f8fb60 | [] | no_license | Una95Singo/MarketStates | 8630040e7cfed3cc9b5e28d9f2ae235c849e0229 | d7e4ba80f0f42a797e0b9fc8387ea1e735d068e9 | refs/heads/master | 2022-02-18T08:31:55.124928 | 2022-02-06T17:00:48 | 2022-02-06T17:00:48 | 235,183,871 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,442 | r | viterbi_test.R | # Test script for the viterbi algorithm
# una singo, SNGUNA003
# 9 January 2020
# import R functions ------------
source("R/viterbi.R")
# libraries ---------------------
library('tidyverse')
library(readxl)
# Test Case 1 -------------------
data = read_excel("data/Test/viterbiDataset.xlsx")
vit = viterbi(D=data[, ... |
1c9eb611fab72ff2af9433c921ec39be52f131de | e6f1fbb059464a6e6580c13bfb12daddc6b45374 | /MakeZipGraphOnNPI.R | a7175691520aa3579f3d5e5235a79895eb854bbc | [] | no_license | thuhale/AHClustering | f98e6ea47dab761dc5ea036ae6cfe8c7c9863e8e | 3501799b71ad1d4e5cdd5e63ad8b000447ece007 | refs/heads/master | 2021-01-18T22:16:24.077827 | 2017-02-07T05:52:57 | 2017-02-07T05:52:57 | 72,385,602 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,853 | r | MakeZipGraphOnNPI.R | rm(list = ls())
library(readr)
library(Matrix)
library(sparseAHC)
library(zipcode)
data(zipcode)
library(geosphere)
library(igraph)
##CUSTOM FUNCTION
# reorganize the clus
reorganize = function(df, clus){
col = which(colnames(df) == clus)
unique_label = sort(unique(df[,col]))
ind = c(1:length(unique(df[,col])))
map... |
5a8f86881bdaa88e56d9d954040fbc8377933ada | 55fe7eeb9397100fcd544c2b8bcecfcbcabdbb06 | /REPSI_Tool_02.00_Mesaurement_Data/Query_99999_YYYY-MM-DD_HH-MI-SS.HS.R/Query_81002_2007-01-21_11-22-19.96.R | e0ccc80f76a90b047d608086f83ae78e43286b63 | [
"Apache-2.0"
] | permissive | walter-weinmann/repsi-tool | d5e7b71838dc92d61c1a06a2c7f2541a0c807b32 | 5677cdf1db38672eff7f1abcf6dca677eb93a89c | refs/heads/master | 2021-01-10T01:34:55.746282 | 2016-01-26T05:31:17 | 2016-01-26T05:31:17 | 49,252,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,458 | r | Query_81002_2007-01-21_11-22-19.96.R | if (exists("A_A")) remove("A_A")
if (exists("A_U")) remove("A_U")
if (exists("C_A")) remove("C_A")
if (exists("C_U")) remove("C_U")
A_U<-c(3878102,3863925,3953180,3779476,4356739,3983607,3931598,3958639,3835372,4538178,4005537,3990393,3967956,3912965,3948423,4173723,4004452,4097618,5126453,3787584,3937871,3864664,37895... |
7419854581469ee2d763e740d7760c3ee95111be | 497153f9a15f53b5b2b4ce0d375ea7f9848d75bb | /src/06-CORUM_Shuffle_Results.R | fe92c61b5ab19e988d6a99710962b26c71f58189 | [] | no_license | joshbiology/pan-meyers-et-al | b43b4299e56ff979fe4bce751574e7bbca8244d8 | 2d72ea626e2c8f4422cc5413bbd835f462a20a62 | refs/heads/master | 2022-01-07T00:16:14.916776 | 2018-05-18T19:26:57 | 2018-05-18T19:26:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,895 | r | 06-CORUM_Shuffle_Results.R | library(igraph)
library(ggridges)
library(ProjectTemplate)
load.project(override.config = list(munging=F, cache_loading=F))
if (config$threads > 1) {
library(doMC)
registerDoMC(cores=config$threads)
do_parallel <- T
} else {
do_parallel <- F
}
out_dir <- file.path("./output/empirical_shuffle", Sys.... |
4bbcc027882a9dc7f77b73de3a6cecac607871bd | eff63f358252d5fe474e215fd11c17bdf5e5f716 | /man/tune_and_update.Rd | 9449468d73c3e35cac9f7d37aad9b0683cd32845 | [] | no_license | gabrielcrepeault/xgbmr | 0fc50af27f93a2e469f4d50e770bb285641f9df9 | 50701662bf9900b6d1fc6fb631a9f887f92958fd | refs/heads/master | 2020-09-13T13:45:34.814325 | 2019-12-21T11:50:08 | 2019-12-21T11:50:36 | 222,803,677 | 4 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,104 | rd | tune_and_update.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tune_and_update.R
\name{tune_and_update}
\alias{tune_and_update}
\title{tune_and_update}
\usage{
tune_and_update(learner, task, param_set, plot = T, show.info = T, root)
}
\arguments{
\item{learner}{Un objet R de classe "Learner"}
\item{task... |
0bca72f3bbd87fcebf9f6eb00cbbaed89a726b80 | c89182ec5149c2959bd2ec41e4bcdf754c10e3fb | /cashematrixexample.R | a60f9c8320bb2793002976bacc725d24f10b6542 | [] | no_license | DmitryFesenko/ProgrammingAssignment2 | b576222bb3b8fa6c04f599d79558074f04d853a0 | 7f944a2392368a71b8afcf339b3d7e7fa4bc538b | refs/heads/master | 2021-01-12T06:38:48.246118 | 2017-01-01T20:18:08 | 2017-01-01T20:18:08 | 77,404,305 | 0 | 0 | null | 2016-12-26T20:01:31 | 2016-12-26T20:01:31 | null | UTF-8 | R | false | false | 934 | r | cashematrixexample.R | makeVector <- function(x = numeric()) {
m <- NULL #begins by setting the mean to NULL as a placeholder for a future value
set <- function(y) {
x <<- y #defines a function to set the vector, x, to a new vector, y,
m <<- NULL #and resets the mean, m, to NULL
... |
96c34bf6648fe5652ce1bf025bea1f2dfae892b2 | 1fa56b40529a4b720d6e3a5b4720d3e2e47e5c8d | /test.R | f02f5306bfbb944334009499a0a366134ce54c3f | [
"MIT"
] | permissive | elserch/findYourWine | f0364aa945efa73f06773fc1e50da50f52674d0f | 0a551bd64aea4b1747421b61495d3dfa3b6ce2d2 | refs/heads/master | 2020-03-22T15:19:20.005615 | 2018-09-16T20:22:19 | 2018-09-16T20:22:19 | 140,243,832 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,153 | r | test.R | install.packages("dplyr", dependencies = TRUE)
install.packages("ggplot2")
install.packages("forcats")
install.packages("DT")
install.packages("ggformula")
#install.packages("ddply")
library(ggplot2)
library(dplyr)
library(ggformula)
# load file
wine_reviews <- read.csv("/Users/christianelser/github/findYourWine/wine... |
d36cc90bf9ed7ed95fb9291f2b655b148ab868a5 | be66c01c8da7d84562f54195ffedc26488362db0 | /TwitterAnalysis.r | 017fa71c5d2d951ada1e67682f5571a12fadc037 | [] | no_license | ashwin-srinivas7/Twitter-Flu-Trends | 2035d2c2d410a543959e4864f21d01abd6c30d1a | b75740e6551b94bbf4f1392151879ecccf954d9b | refs/heads/master | 2020-12-09T14:40:08.855719 | 2020-01-14T06:39:29 | 2020-01-14T06:39:29 | 233,336,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,401 | r | TwitterAnalysis.r |
setwd("D:/Study Material/Projects/Twitter Flu Analysis/Outputs")
# ------------ Install and load all libraries
if (!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("mkearney/rtweet") #install dev version of rtweet
install.packages("httpuv")
install.packages(... |
363b9c28b8610515135c921c3734b3bac79a9db6 | df28d71337c2d4551fdb0e1b02c4d7e26ad5c58e | /ARIMA_Aviation.R | ad8a5851dc93a9f718511cb3859c20acea1d040c | [] | no_license | AkshayChopade07/R-Codes | 891242066d95cbfd7724975b3ea3344a8bff8f72 | 6d8a2737ce6a6ae139846458677453ee390f6a15 | refs/heads/master | 2023-04-29T12:41:19.215814 | 2021-05-20T06:47:49 | 2021-05-20T06:47:49 | 263,832,190 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 894 | r | ARIMA_Aviation.R | install.packages(c("forecast","fpp","smooth","tseries"))
library(forecast)
library(fpp)
library(smooth)
library(tseries)
# Converting data into time series object
# Loading Aviation Data
aviation<-read.csv("C:/Users/Immortal/Documents/R/Aviation.CSV") # Aviation.csv
View(aviation)
amts<-ts(aviation$Sales,frequ... |
2de6c17f68cecd0527a79290a4899dc642c30665 | dbe8b293b4654cac8ab2f1be4febd54eef4a45a9 | /R/spCdfplot.R | f2aa4291acb5f6c52ddc78aa5145462fbbfb78f8 | [] | no_license | cran/simPopulation | 2555bda823b1e2190a7a76881fa3254d350a8ea2 | fcd172cf8c91d1b23ee29fa4e52a111ed08fb72b | refs/heads/master | 2021-01-19T14:58:56.505267 | 2013-12-10T00:00:00 | 2013-12-10T00:00:00 | 17,699,673 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,126 | r | spCdfplot.R | # ---------------------------------------
# Author: Andreas Alfons
# Vienna University of Technology
# ---------------------------------------
spCdfplot <- function(x, ...) UseMethod("spCdfplot")
spCdfplot.default <- function(x, weights = NULL, cond = NULL, dataS,
dataP = NULL, approx = NULL, n = 100... |
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