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values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
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values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
37a94c8b4a0c2494709bafa11a77d80f535438ea | 2b3f05bc191a15317aedc443850f432513aec737 | /R/RcppExports.R | eec31ac519cba4c4e470917cfdb4e103849c6a7e | [] | no_license | ThinkR-open/utf8splain | 2f435e613b22727276dc3dffbe8644ba80dcda97 | 2fb7ed57c773ba5d462b7ab79a340eddb31b113e | refs/heads/master | 2021-01-02T08:54:14.154004 | 2017-08-29T13:34:43 | 2017-08-29T13:34:43 | 99,092,294 | 9 | 0 | null | null | null | null | UTF-8 | R | false | false | 225 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
parse_binary <- function(s) {
.Call('_utf8splain_parse_binary', PACKAGE = 'utf8splain', s)
}
|
2947d56cc0414657e6565153bea29d3f76b43420 | 4d07eecae0429dc15066b34fbe512b8ff2ae53ea | /mds/ps/wt2-ps-test.R | 7f42f49b1a116c1d723cefabf68308014add17e7 | [] | no_license | distanceModling/phd-smoothing | 7ff8ba7bace1a7d1fa9e2fcbd4096b82a126c53c | 80305f504865ce6afbc817fff83382678864b11d | refs/heads/master | 2020-12-01T09:31:24.448615 | 2012-03-27T18:35:45 | 2012-03-27T18:35:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,689 | r | wt2-ps-test.R | # function to run simulations on the wigglytop 2 domain
# Copyright David Lawrence Miller 2009.
source("mds.R")
samp.size=250
noise.level=0.05
## create a boundary...
bnd <- read.csv("wt2-verts.csv",header=FALSE)
names(bnd)<-c("x","y")
## Simulate some fitting data, inside boundary...
gendata<-read.csv("wt2truth.... |
5051a47c62490c87ce335541fe1d1d4f9c3dc03b | b8e977c2cadf840e3e75b3ec143a5a63407ae84f | /PS4_i.R | e5a6f5d3750555ebd5b8e89362f8c7725d090364 | [] | no_license | HaixaingZ/ESE5023_Assignments | fae2164cc940fd444002344fbc8ca5515429b023 | d826eb82a1eac8ebe268634ddbbb0f1332bddf72 | refs/heads/main | 2023-02-14T21:52:01.412769 | 2021-01-08T11:38:51 | 2021-01-08T11:38:51 | 302,874,984 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,238 | r | PS4_i.R | #1
library(tidyr)
library(dplyr)
library(ggplot2)
tracking_data <- read.csv("Tracking_data.csv", header = T)
td_tbl <- as_tibble(tracking_data)
names(td_tbl)
#boxplot flyhing hight average
td_tbl %>%
filter(Longitude != 200) %>%
ggplot(aes(x = UUID, y = Geoid_height, color=UUID))+
geom_boxplot(na.rm ... |
69ceae7a7dac7fcd019ec5904a7aca1cc8657712 | fc71968aeab6975d4fedff2d2f9f9208d2ba2f5f | /gp_all/gp_hetsked/3_post.R | 97cecb9c9c925985a1d7825508743f7f7bfcf9c3 | [] | no_license | tkmckenzie/tyche | 855c3032fc3087e3fde3b02c264bfcbbabab3806 | ac2bb9aafda1b195136a21ddeec6f41d3c540b15 | refs/heads/master | 2023-08-23T04:56:17.892335 | 2023-08-10T22:40:33 | 2023-08-10T22:40:33 | 151,305,485 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,182 | r | 3_post.R | library(abind)
library(ggplot2)
library(MASS)
library(rstan)
setwd("~/git/fortuna/gp_hetsked")
rm(list = ls())
load("data.RData")
load("gp_hetsked_fit.RData")
stan.extract = extract(stan.fit)
#Prediction functions
cov.exp.quad = function(x.1, x.2, alpha, rho){
N.1 = length(x.1)
N.2 = length(x.2)
alpha.sq ... |
f647b6285bbb0cb99b5e8cde235c3c55db7241d4 | c5baacf45414864179c18c4878af5464e103ece8 | /Lab18/CLI/calling_py_from_r.r | b128168597eb0dbf87d451a76a5df33d396202a3 | [] | no_license | VladimirShleyev/Method_R_doc | ed1cbbd9b59cc1cec445e87a9e5696f665f83065 | 85aa7c64e3816108f0e84a0ff1efa11cc8e37d3b | refs/heads/master | 2023-07-16T00:29:57.871114 | 2021-09-03T12:13:19 | 2021-09-03T12:13:19 | 286,023,236 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 600 | r | calling_py_from_r.r | # -*- coding: utf-8 -*-
command = 'python'
#обратите внимание - одиночные и двойные кавычки нужны, если в Пути есть пробелы
path2script = "py_script.py"
# строим вектор из аргументов
string = '"3423423----234234----2342342----234234----234i"'
pattern = "----"
args = c(string, pattern)
# добавляем Путь к скрипту как ... |
c27e963372ce8c2bf5f76d216636ec96c493ba7c | 042d2ef2214c0d2a2c24dc9a4d89ffca871f000b | /tests/test.R | 92ffc3075d32848ed5d342c8ea7ea7c1ce4ff0b6 | [
"MIT"
] | permissive | Afsharov/heartspot | f866c7fe559a186476c1db36811ec1ca9ad7db22 | 3c896ce47040e891aad8e972c7e30d1b85d6144b | refs/heads/master | 2020-07-24T08:12:16.665421 | 2020-06-10T09:39:50 | 2020-06-10T09:39:50 | 207,860,297 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 153 | r | test.R | library(shinytest)
library(testthat)
context("Test shiny app")
#open shiny app
app <- ShinyDriver$new('path_to_shiny_app')
#stop shiny app
app$stop()
|
41899649ff551b10200cb76811a2d0d5c3ac4983 | afdd69685e8073c8259d4617a6e0bd49e8a98681 | /TestScript.R | 150ceee3c85b8412c1c9c423ac5f11c331899ccf | [] | no_license | fcavaco/ProgrammingAssignment2 | 28a5802e279b0cd4db791d2ee4cbdfc34a45e314 | c1f127ef86d39792e1d75aac26aea8df51127467 | refs/heads/master | 2021-01-12T20:15:44.643130 | 2015-01-25T03:25:05 | 2015-01-25T03:25:05 | 29,701,312 | 1 | 0 | null | 2015-01-22T21:31:01 | 2015-01-22T21:31:00 | null | UTF-8 | R | false | false | 780 | r | TestScript.R | #source('./cachematrix.R')
# Generate an invertible matrix
set.seed(123)
n=2000 # matrix dimension
M <- matrix(runif(n^2),n) # my randomnly generated matrix
#Mi <- solve(A) # my matrix inverse calculation
# create a cache (list of functions) matrix
cache <- makeCacheMatrix(M)
# 1. test... |
9593d390b95f651b0d4689565e1da54c614bfbdb | 29585dff702209dd446c0ab52ceea046c58e384e | /texmex/R/hist.evm.R | bef3f735fadd79a42af04f632356d812dfba0876 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,349 | r | hist.evm.R | hist.evmOpt <-
function(x, xlab, ylab, main, ...){
# Want parameters as a matrix with one row for passing
# through to family$rng etc.
a <- t(x$coefficients)
u <- x$threshold
if (!is.finite(u)){ u <- min(x$data$y) }
# FOLLOWING if BLOCK COMMENTED OUT TO ACCOUNT FOR DIFFERENCE
# BETWEEN GEV ... |
4817bd8f6b2a2a02488f081f69445f4a7d661d31 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/OTE/examples/Body.Rd.R | f9f8721ca655b892c0ac45d3cca58dc6c64ee72f | [] | 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 | 167 | r | Body.Rd.R | library(OTE)
### Name: Body
### Title: Exploring Relationships in Body Dimensions
### Aliases: Body
### Keywords: datasets
### ** Examples
data(Body)
str(Body)
|
8b49758248cff1c5d13b75e874d1206e6e4d4345 | 37c46074f9e03ad7fbdc577cf6c528795182f861 | /server.R | eff4fbb05c86ca39fa3c07ba4daaaf4c498eff7a | [] | no_license | Eotoke/DDP_Assignment | 469ee1558ae91be529b537f59a508304f7b8bec9 | f18dd5e7b091ab977fa8d3828c6a25a3f1d54837 | refs/heads/master | 2016-09-06T07:51:06.998156 | 2015-04-26T17:12:07 | 2015-04-26T17:12:07 | 34,621,562 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 739 | r | server.R | library(shiny)
library(datasets)
## start of first time setup
#load mtcars
data(mtcars)
#fit a linear regression model based on mpg, hp, wt, am, cyl
fit <- lm(mpg ~ hp + wt + factor(am) + factor(cyl), data=mtcars)
## end of first time setup
shinyServer( function(input, output) {
# Generate an HTML table view of ... |
75397306deea40148dbebb541a91df84c715722a | 3980ac1a2de7fe2c607d3b7b38d93a8dd0855337 | /example_scripts/intro_to_R.R | 0457ed5be7466dc6e45e811e5b5d41246f27bc10 | [] | no_license | ea-guerette/ACC-From_thin_R | 5bda9da9c08c992be9403e33a4466f1447fad39e | 71aa7a79fd75d0839ca00d8d769b47e720a4344c | refs/heads/master | 2022-11-24T21:32:34.727001 | 2020-07-27T02:32:07 | 2020-07-27T02:32:07 | 258,040,303 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,460 | r | intro_to_R.R | #A script (.R file) lets you save your work
#It also lets you write comments on your code, by using #
#to run a line of code, press ctrl+enter while the cursor is on it
#to run several lines of code, you can highlight them with the mouse, then press ctrl+enter.
#You can also use the Run button
# Tab autofills variabl... |
7295a32437f9f21145f36982862b46a6be6f38ba | aac5ce74aab15e438641afdda0a92c10803a3256 | /Clusttering/K-Means/Kmeans_plotly_BaseCredito.R | 234d449fca8b7b84984b27b7874b01f4cad48917 | [] | no_license | joscelino/Machine_Learning_em_R | 778b530754103826c2400fe9683e5ac5475048c8 | 698cf1ce17867a6c43a6091657b72f75b529fba7 | refs/heads/master | 2023-03-08T16:24:37.999374 | 2021-02-17T14:44:52 | 2021-02-17T14:44:52 | 281,133,419 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,919 | r | Kmeans_plotly_BaseCredito.R | library(readr)
library(dplyr)
library(plotly)
# Funcao para identificar NAs em colunas de data frames
funcaoNA <- function(df){
library(pacman)
pacman::p_load(dplyr, tibble)
index_col_na <- NULL
quantidade_na <- NULL
for (i in 1:ncol(df)) {
if(sum(is.na(df[,i])) > 0) {
index_col_na[i] <- i... |
f405d7f8ea4502525ef634ecfbdde1659760f5c4 | 1b473280443b277ef942c21ed2f786da252dea35 | /R/op_base.R | c9d673ecd3e906dbb0f89fb92b73a7e56875b4b4 | [] | no_license | jtuomist/OpasnetUtils | d52def972ceb5daa0c3076ced4672fe577478863 | 7bbc38d71188f6bab3bfd969584817cca28f2a2d | refs/heads/master | 2021-07-09T22:38:10.228825 | 2020-07-09T16:13:37 | 2020-07-09T16:13:37 | 150,073,444 | 0 | 1 | null | 2019-04-27T13:48:32 | 2018-09-24T08:24:19 | R | UTF-8 | R | false | false | 306 | r | op_base.R | # Old functions that are now wrappers for the opbase-family for compatibility reasons
op_baseGetData <- function(dsn, ident, ...) {
return(opbase.data(ident, ...))
}
op_baseGetLocs <- function(dsn, ident, ...) {
stop('Deprecated method! op_baseGetLocs')
#opbase.old.locations(dsn, ident, ...)
} |
cb6f7072ed818cce5dc5723d6f031d82d8516066 | 5bd1261a6fa4a9207e634f05b27ac599ca79cabc | /Microsphere Persistence in Larvae Exp. 2/Graph_Latex.R | 8448f1895acb18b4cb58596780e20afef65773c8 | [] | no_license | miadrianna/Masters_Summer_Project | 2ee217519f595ac4aa6639ae7d79a77a9ae59331 | 68ebb51d85f6a9a8fa8353edc9809c1825a6f459 | refs/heads/master | 2020-07-12T22:19:18.879915 | 2019-08-28T14:11:35 | 2019-08-28T14:11:35 | 204,920,655 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 690 | r | Graph_Latex.R | #This code is to create the bar char for the latex treatment
data <- read.csv("./Graph_Data.csv")
data$instar <- c(1.5,5.5,9.5,6.5,11.5,10.5)
df <- data.frame(
"0"=c(12,12,3),
"1"=c(NA,9,7),
"2"=c(NA,NA,2)
)
df <- t(df)
rownames(df) <- c(0,1,2)
colnames(df) <- c(2,3,4)
svg("Graph_Latex.svg", width ... |
b37a1ed5965673a5238a870198ea0612025d04fc | 65d0a9128f2dc04cd97cea6ad2719fe4e78d6a29 | /man/add_authors.Rd | f09d6f3398d184d027a7870a203f344293181cc2 | [] | no_license | systats/librarrry | 668adf7008cd077407e76435c709bec03a3376a3 | 0a219f0d95101cf0febd3e9c3e8e47dfd022b2af | refs/heads/master | 2020-04-11T01:06:20.407275 | 2018-11-29T16:08:40 | 2018-11-29T16:08:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 652 | rd | add_authors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/main_scrapping.R
\name{add_authors}
\alias{add_authors}
\title{add_authors}
\usage{
add_authors(ids, n_batchs, batch_size, path = NULL)
}
\arguments{
\item{ids}{A tibble containing a column dc_identifier}
\item{n_batchs}{Corresponds to the n... |
9c265f4cdf525325a164051e033b4b56f8603b5b | 2a43faf3c612f631f6048b0004f7c519f0aa0e29 | /R/qtl2pleio-package.r | a4b5a49b779ef5fe6829009bd4e31e471dcbdbc0 | [
"MIT"
] | permissive | fboehm/qtl2pleio | a78bddec1374a428c218927ebe8af0d9375dc9a8 | d3406f399e2245502e101ed78f9d680b9827638d | refs/heads/master | 2021-07-22T04:43:58.057545 | 2021-07-13T20:20:18 | 2021-07-13T20:20:18 | 104,493,705 | 10 | 2 | NOASSERTION | 2019-06-29T18:34:46 | 2017-09-22T16:06:34 | R | UTF-8 | R | false | false | 584 | r | qtl2pleio-package.r | #' qtl2pleio.
#'
#' Testing pleiotropy vs. separate QTL in multiparental populations
#'
#' @useDynLib qtl2pleio, .registration = TRUE
#' @name qtl2pleio
#' @docType package
#' @importFrom Rcpp sourceCpp
#' @importFrom stats profile
NULL
.onUnload <- function (libpath) {
library.dynam.unload("qtl2pleio", libpath)
}
... |
6f2050aec5f67e29ae57d8f48d1a34343def97bc | b1364236128077ae0a1ebc81f692268fb5df5ba3 | /run_analysis.R | a16690554465843a8dd235fa31b0beabf4a48845 | [] | no_license | ppandolf/getdata-015 | 55c2f6db3f9e8ae1940fa035d6e1f95406f3cbe0 | 431537bf7ae8655da934ae0ad128ebceced5e836 | refs/heads/master | 2020-05-30T01:28:16.385489 | 2015-07-25T00:25:02 | 2015-07-25T00:25:02 | 37,830,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,141 | r | run_analysis.R | #################################################################
# run_analysis.R compiles physical activity data collected
# from the accelerometers from the Samsung Galaxy S smartphone
# please see README and code book documentation for details
# this script is written to read input files from the
# /data/UCI HAR... |
115cc1da95d3edce37572a9f24a18236a25d378a | e36b28012ea9ce785b0767e3498669ed9b377f1b | /Package/PPLS.Rcheck/00_pkg_src/PPLS/man/meta_EMstep.Rd | 057581074f7ac51e3e9844704a1f411aac3a7adf | [] | no_license | selbouhaddani/PPLS | 00bad52886159f085ca406a2a1d309472cdb231c | b553e605325782fe5a2ea137bb89b729730701ee | refs/heads/master | 2020-12-14T16:11:02.777289 | 2018-01-23T07:28:55 | 2018-01-23T07:28:55 | 41,370,610 | 0 | 2 | null | 2016-05-18T12:27:02 | 2015-08-25T15:11:17 | C++ | UTF-8 | R | false | true | 1,239 | rd | meta_EMstep.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/EM_W_multi.R
\name{meta_EMstep}
\alias{meta_EMstep}
\title{Performs one EM step (use \link{PPLS} for fitting a PPLS model)}
\usage{
meta_EMstep(X, Y, W. = W, C. = C, Ipopu = as.factor(rep(1:2, c(N/2,
N/2))), params = replicate(2, list(B_T =... |
2e047e435c9dea5e3f18b1401e6bf5a121ce9fe4 | 623a2c07758165f333c79da129f8b4ee196c936c | /SVM.R | 279e8ce39c418f2fd6212da540508985b5a39791 | [] | no_license | duccioa/01_STDM_WashingtonDC-Crime | 65ed3f4dd5b3e57207646770a816f816411ad9fd | a69d2f2836533c76b9e6952ee33acf03c130d40e | refs/heads/master | 2021-01-10T12:24:54.891266 | 2016-03-30T19:13:49 | 2016-03-30T19:13:49 | 53,758,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,066 | r | SVM.R | source('./FUN_runSVM.R')
param_df = data.frame(Configuration = c('Std', 'A', 'B', 'C','A_opt','D'),
Type = c('C-svc', 'C-svc', 'C-svc', 'C-svc','C-svc', 'C-svc'),
C_value = c(10, 1, 100, 100,1, 1),
Kernel = c('rbfdot', 'rbfdot','rbfdot','rbfdot','rbfdot','polyd... |
4f6d542e0a16dd77e29b3c6007a24eb04b3954aa | 3bd2304fb88609fa21a6012aa436085832a4a70e | /run_analysis.R | f483f52df4a687028810aa7e0a36da866acf9ebb | [] | no_license | dexterwang/GettingAndCleaningDataAssignment4 | 4e6a8b36adb93ca46e89d4a47269045930582819 | 129ab4825058d49a48b4a8d0a626f03a81f04760 | refs/heads/master | 2020-12-31T04:56:31.005801 | 2016-04-12T09:15:15 | 2016-04-12T09:15:15 | 56,044,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,854 | r | run_analysis.R | #1. Merges the training and the test sets to create one data set.
# Load the "X" part of train & test data and merge them together
x_train <- read.table("./UCI HAR Dataset/train/X_train.txt",header=FALSE)
x_test <- read.table("./UCI HAR Dataset/test/X_test.txt",header=FALSE)
x_train_n_test <- rbind(x_train,x_test)... |
9b7b6e09757ab881e7ae87c1219bcadd91322943 | b404a06211d0702b8b4ed40d9a8b05ba3009f02e | /R/RankingWeekRanks.r | b613d432f662e05d971df5791082131b436c75c1 | [] | no_license | saiemgilani/cfbd-api-R | 2b94b8fbeff9462f3eeeee467f932bb2b22c4432 | 84535ae89b8b08eb4f63a7f136d62948a3f34def | refs/heads/master | 2023-03-05T17:49:10.113757 | 2021-02-15T15:24:20 | 2021-02-15T15:24:20 | 339,117,428 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,790 | r | RankingWeekRanks.r | # College Football Data API
#
# This is an API for accessing all sorts of college football data. It currently has a wide array of data ranging from play by play to player statistics to game scores and more.
#
# OpenAPI spec version: 2.3.5
# Contact: admin@collegefootballdata.com
# Generated by: https://github.com/swag... |
4eaacdae4df98aa23c9fabc03d6b2770505d948d | 7ee80c5a6957496471b8060013036b6ee7320937 | /sortOutNames.R | f96a8dc9b8bfdd50361f7ccfdc5ee9bc148ffc8d | [] | no_license | Sloth1427/paper_currencies | 6ee376e130ee12c9d6bce8a79415535ade77a272 | 9c0bd07aac47bb0b5f9512af63647ba26154babf | refs/heads/master | 2022-12-19T10:10:30.564802 | 2020-09-11T16:02:30 | 2020-09-11T16:02:30 | 294,474,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,105 | r | sortOutNames.R | #Turn author names from "surname, firstname middleinitial" to "surname initials", e.g. "Bloggs JR"
#remove any leading " "
trim.leading <- function (x) sub("^\\s+", "", x)
trim.trailing <- function (x) sub("\\s+$", "", x)
#trim.leading(R01data2005_2015_mainPI_Only$SURNAME_INITALS)
trim.trailing(R01data2005_2015_mainP... |
d65621ea753e947f829a33b808fca00cbc9d6887 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/runner/examples/streak_run.Rd.R | 39e28e4f33d593086810e18dcc21061887d814e9 | [] | 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 | 506 | r | streak_run.Rd.R | library(runner)
### Name: streak_run
### Title: Running streak length
### Aliases: streak_run
### ** Examples
set.seed(11)
x1 <- sample(c("a","b"),15,replace=TRUE)
x2 <- sample(c(NA_character_,"a","b"),15,replace=TRUE)
k <- sample(1:4,15,replace=TRUE)
streak_run(x1) # simple streak run
streak_run(x1, k=2) # streak ... |
a668dcbac6e11610d68253e0287e90f3742eb53b | 724fa2771be1a900d6e935ea0ce79f5c54698098 | /snippets/mvr_ext.R | 9e302c5dd9ed8ba66c3d07ea7d10c70535b270b0 | [] | no_license | Katiedaisey/chemometrics | b4dbc656ae2ca8cc74ff219f9121f325becd455e | 9b82ea3103f664bda75085d965682bacb6d6ad91 | refs/heads/master | 2021-01-10T19:02:08.671377 | 2015-04-24T14:20:46 | 2015-04-24T14:20:46 | 30,512,418 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,340 | r | mvr_ext.R |
RMSEPCV<-function(reg,plot=T){
#RMSEPCV from plsr/mvr/pcr
CV<-NULL
for(i in 1:reg$ncomp){
CV<-append(CV,sqrt(sum((prop-reg$validation$pred[,,i])^2)/20))
}
#%variance from plsr/mvr
#prop variance
prop_exp<-NULL
for(i in 1:reg$ncomp){
a<-prop-reg$fitted.values[,,i]
prop_exp<-append(prop_exp,100*(var(prop)-v... |
7fce3ca0dfd3cd14852366a114e5bf8ff21296e2 | 69b4c16c230cba3c065b3bad440f305e3b5474e4 | /hirano2000/hirano.R | 21c978ef0bf57568e7b01bf82512dd657ea68e56 | [] | no_license | ptoulis/little-projects | 13a46f3b8b7d098690949be42658000f68d39dcf | 588226c7238f32d9c184607ea10ee6e9c18c08eb | refs/heads/master | 2021-01-19T00:19:36.439675 | 2014-10-03T16:20:01 | 2014-10-03T16:20:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,071 | r | hirano.R | ## Stat 240 Homework 2. Analysis of Hirano et. al.
##
## Panos Toulis, panos.toulis@gmail.com
source("terminology.R")
library(coda)
get.compliance.probs <- function(theta, X) {
# Computes Ψ = (ψ_it) = P(Ci=t | Xi, θ)
#
# Returns: (N x p) matrix of probabilities.
#
N = nrow(X)
p = ncol(X)
Pred = X %*% t... |
4cd1e2b33ac6b7fef542575fe9660a4b39483ac4 | 6e913f34e5c51da1f1dc936d79bb30d23313a6cf | /tests/testthat/test_hy_stn_regulation.R | e258e271b670b406f06046d2399d630ae3e3237b | [
"Apache-2.0"
] | permissive | ropensci/tidyhydat | 8c81e07d08089aee73173676d6d579f6c1fbcf5b | 00ee7ba237416536e0c5de92692c92ac5f5b0cd2 | refs/heads/main | 2023-08-31T06:49:38.270327 | 2023-08-17T21:59:32 | 2023-08-17T21:59:32 | 100,978,874 | 78 | 21 | Apache-2.0 | 2023-08-17T21:58:25 | 2017-08-21T18:01:23 | R | UTF-8 | R | false | false | 1,257 | r | test_hy_stn_regulation.R | test_that("hy_stn_regulation accepts single and multiple province arguments", {
stns <- "08NM083"
expect_identical(unique(
hy_stn_regulation(
station_number = stns,
hydat_path = hy_test_db()
)$STATION_NUMBER
), stns)
expect_identical(length(unique(
hy_stn_regulation(
station_number... |
e738363d14d363bf5aa4c5c0734b04e20fc90cd8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phenofit/examples/PhenoExtractMeth.Rd.R | 17e50669eb5f08ddc87560c05d66cb1ad95a592b | [] | 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 | 626 | r | PhenoExtractMeth.Rd.R | library(phenofit)
### Name: PhenoExtractMeth
### Title: Phenology Extraction methods
### Aliases: PhenoExtractMeth PhenoTrs PhenoDeriv PhenoGu PhenoKl
### ** Examples
library(phenofit)
# simulate vegetation time-series
fFUN = doubleLog.Beck
par = c(
mn = 0.1,
mx = 0.7,
sos = 50,
rsp = 0.1,
eo... |
e59d98d30f1c00687559bb68aa081261c2d067d4 | 44a3fad6338a63ac5417b1e52e47420c0e013f45 | /R/ConfidenceBands.R | 7fc52cad2ca1cb66dc5db361c8ffb3c4083bbf44 | [] | no_license | cran/ExtremalDep | 4faac60ce0040262a98410edc6488ddf939ad9bd | 18238416ddb6567610c4457dc332316272dbd16e | refs/heads/master | 2023-03-06T18:03:59.304908 | 2023-02-26T14:40:02 | 2023-02-26T14:40:02 | 236,595,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,820 | r | ConfidenceBands.R | #############################################################
### Authors: Giulia Marcon and Simone Padoan ###
### Emails: giulia.marcon@phd.unibocconi.it, ###
### simone.padoan@unibocconi.it ###
### Institution: Department of Decision Sciences, ###
### Univer... |
ebb42bec320b7a59e21a5102681df819c97de508 | a5fc86001154f8b81c28009e351df7c43ae1ff5a | /inst/script/make-metadata.R | 9ebbf3e7234aeafe8d9de9d397fea560375a7a49 | [] | no_license | Liubuntu/SeqSQC | e0e8ab9fa786d8424b74e7e764bfe04855a30bb7 | 3527554184d8747d898057fc7af883ea6b502d0d | refs/heads/master | 2021-06-02T05:37:37.061464 | 2020-02-14T19:23:18 | 2020-02-14T19:23:18 | 240,587,303 | 0 | 1 | null | 2020-11-29T21:11:18 | 2020-02-14T19:50:18 | R | UTF-8 | R | false | false | 1,582 | r | make-metadata.R | library(AnnotationHubData)
meta <- data.frame(
Title = "DNA-Sequencing dataset from the 1000 Genomes Project",
Description = paste0("DNA-seq data from the 1000 Genomes Project ",
"containing 22 AFR, 22 EAS, 21 EUR and 22 SAS samples. ",
"there are eight known r... |
ec033458d5b2fee0855afa7ad2b86a1f41584e4a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spatstat/examples/WindowOnly.Rd.R | 33570ed06b7dd32327be27238bde67ba7cde1d34 | [] | 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 | 454 | r | WindowOnly.Rd.R | library(spatstat)
### Name: WindowOnly
### Title: Extract Window of Spatial Object
### Aliases: Window.ppm Window.kppm Window.dppm Window.lpp Window.lppm
### Window.msr Window.quad Window.quadratcount Window.quadrattest
### Window.tess Window.layered Window.distfun Window.nnfun Window.funxy
### Window.rmhmodel ... |
1e70e3d419481490f1169cd50f4cc221b2ce26fd | d7e0f91fed1200959ccb51307c266c3a647f25ea | /man/executeExtraction.Rd | 152fc7adea7f087236a956dcb5f1485010c46818 | [
"Apache-2.0"
] | permissive | UKVeteran/CancerTxPathway | 4049f0a5ca0790f8fe287795c9e9dec69fffe0c1 | e72fea56d38a9cfe1acca47e05f82c1d1e0c5094 | refs/heads/master | 2022-04-09T07:56:14.771258 | 2020-03-09T15:09:08 | 2020-03-09T15:09:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 608 | rd | executeExtraction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Main_RegimenExtraction.R
\name{executeExtraction}
\alias{executeExtraction}
\title{Main}
\usage{
executeExtraction(connectionDetails, oracleTempSchema = NULL,
cdmDatabaseSchema, cohortDatabaseSchema,
vocaDatabaseSchema = cdmDatabaseSchema... |
91b11bed0a913903f74d9c266f622737fd203bf4 | d255d28ece6cbc2967ef00c014eafc859cf68141 | /RHRV/SplitPowerBandByEpisodes.R | 361645b393cc40bb5e128c8596deaff302ade7b7 | [
"MIT"
] | permissive | med-material/ArduinoLoggerShinyApp | 0fd645b7d721efd9bf005c12b46030402e98b35e | a1af24786df19dd4bbd909b5189d77ece88ac49f | refs/heads/master | 2023-08-03T17:01:19.812307 | 2023-07-28T13:11:35 | 2023-07-28T13:11:35 | 228,591,063 | 1 | 1 | MIT | 2020-02-18T11:16:30 | 2019-12-17T10:24:41 | R | UTF-8 | R | false | false | 4,129 | r | SplitPowerBandByEpisodes.R | SplitPowerBandByEpisodes <-
function(HRVData, indexFreqAnalysis = length(HRVData$FreqAnalysis), Tag="",
verbose=NULL) {
# ------------------------------------------------
# Splits Power Per Band using Episodes information
# ------------------------------------------------
# Tag -> specifie... |
6babd448c84ab9a1f7e07080645d8c8782a7f71f | b8f9a74a91ae75c4d8b5270a4b7a36104c29528e | /TestFunction.R | cdc276c451abe85ecb5d34882e5b988f273dab87 | [] | no_license | yuliangzhang/ProgrammingAssignment2 | aa23bf02a767ad30e3dba58fe2b8d7e642648d5f | 834f2ef7e3a356c2636aab7964f77cf6ab442bd7 | refs/heads/master | 2021-01-14T08:39:16.966959 | 2015-01-23T13:24:51 | 2015-01-23T13:24:51 | 29,729,872 | 0 | 0 | null | 2015-01-23T11:27:35 | 2015-01-23T11:27:34 | null | UTF-8 | R | false | false | 116 | r | TestFunction.R | tmp <- matrix(c(1,2,3,4,5,6,7,8,7,3,9,2,15,7,23,11),4,4)
tmp
x <- makeCacheMatrix(tmp)
cacheSolve(x)
cacheSolve(x)
|
cd27f2904ba7d4a70f6db4129a61bd4200e35c27 | 0abf16c147a819cf5fd9bb4ce380cf4f2222bb8d | /Statistics - Duke University/002_Inferential_Statistics/Week 1/Lab/LabQuiz/LabQuiz.R | 64c31ba6d5b57c10af2bc5d7939caae074562919 | [] | no_license | bhunkeler/DataScienceCoursera | 6c6c17f5808cd6a8e882f7558ca32e70b9b39b30 | 4ae8c176acbb5b2d78ff08379a856c4afefea8f8 | refs/heads/master | 2022-05-01T14:26:22.738900 | 2022-03-11T16:56:07 | 2022-03-11T16:56:07 | 43,755,669 | 52 | 120 | null | 2022-03-24T19:07:28 | 2015-10-06T14:23:57 | Jupyter Notebook | UTF-8 | R | false | false | 5,908 | r | LabQuiz.R | # ========================================================================================================================================
# Load Libraries
# ========================================================================================================================================
library('dplyr')
library... |
d762c5f92910a30cc33ddab2eda01f31022d9ae1 | 57a18b3e750c7e1d89af3d98778a5cea9de75555 | /tests/testthat.R | acfa222eb1ecf3422da2aa396dadc8a5a1c50e59 | [
"MIT"
] | permissive | piotrekjanus/aiRly | b97ce15d1e15c0701926738551ecf1598881e8d8 | 093f6f4756d0f18af3c7445845fa47f5b624deb0 | refs/heads/master | 2021-03-02T22:17:11.450643 | 2020-03-19T22:31:01 | 2020-03-19T22:31:01 | 245,910,217 | 0 | 0 | NOASSERTION | 2020-03-12T18:40:16 | 2020-03-09T00:22:38 | R | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(aiRly)
test_check("aiRly")
|
f686ad8c037a930e56cfb56ab22853db6a14460f | f6c3107039eca2c6295fefc456bc5b369d3f4472 | /run_analysis.R | 35255411b39a4f39e23254fa0ab0930455285fdb | [] | no_license | jatinchawda1503/run_analysis | 36d47d5cc368cb1f91b127526f9849ea585e8e66 | 8a4acb68bf2a0269c0a90d8af65904fad85ac355 | refs/heads/master | 2020-10-01T01:07:12.504411 | 2019-12-11T20:23:30 | 2019-12-11T20:23:30 | 227,416,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,307 | r | run_analysis.R | library(dplyr)
library(data.table)
#Setting File
get_file <- "Coursera_DS3_Final.zip"
url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
directory <- getwd()
#checking the file and downloading it
if (!file.exists(get_file)){
download.file(url, get_file, meth... |
bec83956493cdac967e1b7b206d45b72405b7f2a | 4ea05b7ebcfc00552c3f5cb2d596a63cf146a7e9 | /create_data.R | 7c2ff4b8cdfd00e81f7041d85a0abc4d8b8390fc | [] | no_license | joranE/single-skier | 28837b65e841a5657310805015ff0d52ff9f5be8 | bef5c429d7bde84992a15c572bdaeef403ef41f9 | refs/heads/master | 2020-05-20T02:45:31.500010 | 2017-10-04T03:35:38 | 2017-10-04T03:35:38 | 35,615,791 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 698 | r | create_data.R | library(statskier2)
library(dplyr)
conl <- db_xc_local()
x <- ss_query(conl,"select * from main")
x$cat2[x$cat2 == ""] <- NA
maj_ind <- x$cat1 %in% c('WC','WSC','OWG','TDS')
x <- split(x,maj_ind)
#XC_FAC <- load_xc_conv()
x[[2]] <- x[[2]] %>%
mpb() %>%
standardize_mpb()
x[[1]]$mpb <- NA
x <- do.call("rbind",x)... |
0cf94b5ec28e50317c12829f70576a856162a071 | 2d71ce9dffea7ca2c9c9e1d1efaf1b27ba303d63 | /games_howell.R | fcf1daf00ac7e65ea662d60239bdfad1a4e73043 | [] | no_license | sgelias/cerrado-yeasts | 590f144b83f15537510509c18d71095d1aab9b12 | 81165829679125c7ff588f325d94e02a575a974a | refs/heads/master | 2020-12-26T11:43:46.271494 | 2020-06-05T18:18:33 | 2020-06-05T18:18:33 | 237,498,345 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,208 | r | games_howell.R | #' @title Games-Howell post-hoc test
#' @name games_howell
#' @description This function produces results from Games-Howell post-hoc tests
#' for Welch's one-way analysis of variance (ANOVA) (`stats::oneway.test()`).
#'
#' @inheritParams pairwise_comparisons
#'
#' @importFrom stats ptukey qtukey
#' @importFrom utils ... |
e7929bb3220fdd8b6a18d1726026982fbee81c59 | 41a58a02a504850bfa7dea1bc897e08f82a47680 | /man/theme_eq_timeline.Rd | 714cc6e527e7adf1d62f3883fd9e1dbfce5bb6ce | [] | no_license | rafaelcb/NOAA | e543afe61f03f23f60e202d9c0847d171bc1817f | 21f5ea6dedc2f285414a0097d188a13dee5324c5 | refs/heads/master | 2021-01-01T17:35:04.187428 | 2017-10-09T03:16:43 | 2017-10-09T03:16:43 | 98,105,781 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 616 | rd | theme_eq_timeline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geoms.R
\docType{data}
\name{theme_eq_timeline}
\alias{theme_eq_timeline}
\title{Theme for geom_timeline plot}
\format{An object of class \code{theme} (inherits from \code{gg}) of length 7.}
\usage{
theme_eq_timeline
}
\description{
This them... |
fd5e5b2d5321af72b962d9caea3c25691483fc8c | 5bf3987cf8d91e5fb8427fb82342bb158a24472e | /cachematrix.R | ca7ea27764171a2e2624ee8c23002ab605a9a8af | [] | no_license | ivancostabernardo/ProgrammingAssignment2 | fc89f49df2c95216bca4588471951a9fd00f3446 | 6f638e53493f131e3725866693a1cad3cf12e8d2 | refs/heads/master | 2020-12-25T09:09:12.548024 | 2016-07-01T00:31:58 | 2016-07-01T00:31:58 | 61,958,038 | 0 | 0 | null | 2016-06-25T19:17:19 | 2016-06-25T19:17:19 | null | UTF-8 | R | false | false | 1,041 | r | cachematrix.R | ## The following two functions work together to calculate the inverse of a given matrix,
## caching the result and thus avoiding unnecessary calculations.
## This function returns a list with four functions:
## 'set' sets the value of the matrix;
## 'get' gets the value of the matrix;
## 'setsolve' sets the value of t... |
dd46660a52bb06272321d272b8ecd460bd26e416 | e7a8b0ad922d03cfb245c08821332ce8ce7ee333 | /plot3.R | 9373b8bc04ff70b3afdefd87339b6cd96a385096 | [] | no_license | YeshwantBhat/ExData_Plotting1 | 6de0d7a5260d47205b78bf6c24a61a6f6acd7889 | 8671c54296debc80f913be5c0d8dc8f642ab8ef8 | refs/heads/master | 2021-01-15T08:16:10.209744 | 2015-07-11T14:39:19 | 2015-07-11T14:39:19 | 38,778,730 | 0 | 0 | null | 2015-07-08T20:39:21 | 2015-07-08T20:39:21 | null | UTF-8 | R | false | false | 1,119 | r | plot3.R | householdpower<-read.csv("household_power_consumption.txt",h=T,sep = ";",nrows=2075259,stringsAsFactors=FALSE)
str(householdpower)
head(householdpower)
householdpower1<-subset(householdpower,householdpower$Date =="1/2/2007")
str(householdpower1)
head(householdpower1)
householdpower1<-subset(householdpower,householdpowe... |
1430cf28b7674529e5afbabaae2b502f5db1249b | 78eb646b4ef3565ef1442cad2a47e2e152dcea81 | /Housing Price/ISLR- Housing Price.R | 569b71362b4877d5869424b8552f6e9f16faac24 | [] | no_license | sxd213/dataScienceML | 89b4ed2200fdb62ec95c57abd9acae16e7f1ccb7 | f87ad39626459c0f8ba6b356df1412c0c9ab1e9e | refs/heads/master | 2021-01-23T06:14:37.892482 | 2018-01-21T03:41:50 | 2018-01-21T03:41:50 | 93,012,311 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 714 | r | ISLR- Housing Price.R | rm(sales, tsales, sales1, train, test)
dir <- 'D:\\MyStuff\\Housing Price'
setwd(dir)
sales <- read.csv('kc_house_data.csv', stringsAsFactors = FALSE)
#Back up data set
tsales <- read.csv('kc_house_data.csv', stringsAsFactors = FALSE)
str(sales)
attach(sales)
summary(price)
# basic manipulation
sales$sale... |
1a4106669268d00e81ae74103c11944128582482 | dcede2b512a9d572c53eca741a56fcd745308100 | /workflow/scripts/reduced-dimensions/plotClusteredPCs.R | 5be12d7d79057a594ab4b6286a47914c773a8e05 | [
"MIT"
] | permissive | jma1991/DiasTailbudData | 1c9a71d8f3364a26f67eba96626070e352ca4477 | 0e37976fdd79c7972066ded83c789edf6a23acbe | refs/heads/main | 2023-06-09T19:59:11.627780 | 2021-07-05T09:00:44 | 2021-07-05T09:00:44 | 310,294,386 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,289 | r | plotClusteredPCs.R | #!/usr/bin/env Rscript
set.seed(1701)
main <- function(input, output, log) {
# Log function
out <- file(log$out, open = "wt")
err <- file(log$err, open = "wt")
sink(out, type = "output")
sink(err, type = "message")
# Script function
library(ggplot2)
library(scales)
library... |
6dc735a15d6b92f67945d1d83ada85690395de78 | 639ae17e0f7f9fba6dccb7387281741c3594256e | /FINVIZ_NEWS_SCRAPE.R | 92e2d7f4c446a2b029cf5cd476b04f31f93144db | [] | no_license | jgQuantScripts/FinViz-News-Scraper | 14c12688a25e66a8e4306186cb2c8beac614a171 | 8adde15bedd485660e920600f7bda307e13fd6c5 | refs/heads/main | 2022-12-28T14:56:52.621388 | 2020-10-13T00:27:08 | 2020-10-13T00:27:08 | 302,469,968 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 939 | r | FINVIZ_NEWS_SCRAPE.R | require("rvest");require("stringr")
ticker = "ZM"
getFinNews = function(ticker)
{
Sys.sleep(5)
url <- paste0("https://finviz.com/quote.ashx?t=",ticker)
# read finviz news data
data <- read_html(url)
# copy xpath
data = data %>% html_nodes(xpath = "//*[@id='news-table']") %>% html_table()
tmp = do.call(rb... |
c78ca370c8ae9e995fe7620970cfaae817af4625 | 6d0ab19a101dc52bb2b110ad4d168043b1c0f298 | /changes.R | 53731b526118abec1bf60359ee7d045cd163db6c | [] | no_license | jjchieppa/Nocturnal-gs_Panicum-virgatum-UNF | edaa9d306d147cb8cdb10a0cebca5816af825a5e | 79c2519bcd16845675e13f97ec55f5777a534e11 | refs/heads/master | 2021-11-23T23:08:45.769587 | 2021-10-27T14:01:58 | 2021-10-27T14:01:58 | 250,525,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30 | r | changes.R | # I'm making some BIG changes! |
2bd70e8b5a607743fa667505a59eb81165344a01 | 7a914c0e1004de6cff6180a4f728293d53931b6d | /create_xcpfiles.R | ff21f2f0dd5894c75b1c47f36bea5a6def8caeed | [] | no_license | tientong98/xcpEngineTutorial2 | 23657cbb83f72749fa33e08d2e11748b288c72ff | e79384f96b65f745814ecb231daedc1aa9553d0f | refs/heads/master | 2021-01-09T04:34:28.417062 | 2020-02-21T23:12:47 | 2020-02-21T23:12:47 | 242,247,453 | 1 | 0 | null | 2020-02-21T23:18:59 | 2020-02-21T23:18:58 | null | UTF-8 | R | false | false | 2,206 | r | create_xcpfiles.R | library(dplyr)
lut <- read.table("lut.tsv", stringsAsFactors = F)
names(lut) <- lut[1,]
lut <- lut[-1,]
rownames(lut) <- NULL
lut[,1] <- rownames(lut)
write.table(lut, "lut_updated.tsv", col.names = T, row.names = F, quote = F, sep = "\t")
# network mapping copying from James's script
#NETWORK_MAPPING = {
# 1: ... |
425a096add48e66814825a6737ead6bdf2dc1358 | 8a313266928dc5e985050ebaeb1feb6fe43a2929 | /ftd_new.R | 026764fbe26a19b585ae29964ea0a393ad4e571d | [] | no_license | mt-christo/ej | b4a2c283285256f9fd3961b0f67dd89be20f890a | 265a9c171eb64e5e4e5ed7d2e2053908b1d946d4 | refs/heads/master | 2022-12-12T12:03:41.654039 | 2019-10-26T03:22:06 | 2019-10-26T03:22:06 | 102,653,282 | 0 | 1 | null | 2022-12-08T00:54:12 | 2017-09-06T20:07:26 | R | UTF-8 | R | false | false | 10,848 | r | ftd_new.R | # source('rapache_eval1.R')
library(credule)
library(bindata)
library(foreach)
source('ftd_func.R')
y_tod = c(0.04,0.07)
y_sigmas = c(0.005,0.01)
y_as = c(0.15,0.1)
rfr_in = 0.01
spread_years_in = c(5,5)
basket_years_in = 5
rec_rate_in = 0.4
time_step = 0.5
n_steps = basket_years_in/time_step
# Building LOG IR and ... |
fb6e4f1d8bc914e27f54a94373b6ebc7c3f0eef9 | cdf278819965268d0a7cafaff9790c6f2b2134bf | /inst/tests/testthat.R | 67be2c1203d5cf04cbf30e2f5b7eef3e85a057ef | [
"MIT"
] | permissive | owenjonesuob/BANEScarparkinglite | 0668f6183b365a109e65f07832a71bf1f0da14f7 | 5297944cc611a6e4d27b9f154de48303a3a8ff48 | refs/heads/master | 2021-01-15T22:47:29.756145 | 2020-04-13T14:48:04 | 2020-04-13T14:48:04 | 99,914,416 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 81 | r | testthat.R | library(testthat)
library(BANEScarparkinglite)
test_check("BANEScarparkinglite") |
515517b00eba9a2f8618dc5565e08f9d70794016 | 5dc933b42d4030657d090ceb4bfb28a1a9e5ad2e | /Problems 3/Norm2Exp.R | 3e857ab727f37cc81c294f4d11d57f2dc46b5f06 | [] | no_license | ilia10000/LearningR | 7e8369cd85567be4ed1a1a4221d3e702f82f062f | a345aa8e6ec1301fdc9ecb7bcfd4319b0959e58a | refs/heads/master | 2021-01-21T13:57:21.145375 | 2018-10-14T20:23:40 | 2018-10-14T20:23:40 | 50,978,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 439 | r | Norm2Exp.R | #Generate normal distr. using realizations of unif. distr.
RNORM = function(U,V){
theta = 2*pi*U
R = sqrt(-2*log(V))
return(c(R*cos(theta), R*sin(theta)))
}
# Generate Exp(2) from N(0,1)
# Note that Exp(2) = (1/4)Exp(1/2) = (1/4)Chi(2)
U1 = runif(1,0,1)
V1 = runif(1,0,1)
Norm = RNORM(U1,V1) #2 independent r.v.s from N... |
8880e8d8e78f4361a363adb56f43e9bde5bb1179 | d5b8ecc661a851d3194c1eb9531767e69cad39fc | /MS_Toolbox_R/aaft.R | 1a50eb65b71e4c8030aa4beb938168d0a34fa47b | [] | no_license | tomstafford/microsaccades | 6df2f25269d4dc6c46763293336de53d0e361475 | 4d2986893d505a05ff80fe025e84634ec0c7701b | refs/heads/master | 2020-03-21T02:32:37.636524 | 2018-06-20T10:59:21 | 2018-06-20T10:59:21 | 138,003,528 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,077 | r | aaft.R | #============================================================
# Function aaft() -- Microsaccade Toolbox 0.9
# (R-language Version)
# Authors: Ralf Engbert, Petra Sinn, Konstantin Mergenthaler,
# and Hans Trukenbrod
# Date: February 20th, 2014
#============================================================
#-------------... |
2e77830906c88c012f4796661834b84f8abb728d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/adegraphics/examples/triangle.class.Rd.R | 9eb81fada755c493b74ffb03654d66eebefc19c3 | [] | 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 | 509 | r | triangle.class.Rd.R | library(adegraphics)
### Name: triangle.class
### Title: Ternary plot with a partition in classes (levels of a factor)
### Aliases: triangle.class
### Keywords: hplot aplot
### ** Examples
data(euro123, package = "ade4")
fac1 <- euro123$plan$an
df1 <- rbind.data.frame(euro123$in78, euro123$in86, euro123$in97)
trian... |
2b87ea458b3bd62f83721bd998d1d97cf08e4a8c | 2935d597895945d2a32b6701f75e918405533a57 | /H3K9me2/snakemake_ChIPseq/mapped/both/peaks/PeakRanger1.18/ranger/p0.05_q0.05/genome_wide/regioneR/noMinWidth_mergedOverlaps/TE_family_vs_H3Kmod_genome_wide_peaks_chr.R | 5e88e19773fc4e4d4e2f047635a373056896abf5 | [] | no_license | ajtock/wheat | 7e39a25664cb05436991e7e5b652cf3a1a1bc751 | b062ec7de68121b45aaf8db6ea483edf4f5f4e44 | refs/heads/master | 2022-05-04T01:06:48.281070 | 2022-04-06T11:23:17 | 2022-04-06T11:23:17 | 162,912,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,414 | r | TE_family_vs_H3Kmod_genome_wide_peaks_chr.R | #!/applications/R/R-3.3.2/bin/Rscript
# Plot bar chart of log2(observed:expected) peaks overlapping other features
# Usage:
# /applications/R/R-3.3.2/bin/Rscript TE_family_vs_H3Kmod_genome_wide_peaks_chr.R "Histone H3 lysine modification peaks" 10000 chr1A
library(ggplot2)
library(ggthemes)
dataName <- "Histone H3 ... |
20987eac7ec47065b18192932ee3e0fc1d7722a5 | 935f498683c4e523243abd80a87c57ca637294f5 | /R/repo_events.R | 1199f2a1d32627baae16b2c5c2f6b88778bcfe1f | [
"MIT"
] | permissive | jasmine2chen/gitevents | 358e68893b6e61de11f0c21c2d72d1c8efa433a8 | f7fc5de145c26a5b27a8e4691838af7a0fe4d998 | refs/heads/master | 2022-11-15T22:13:37.577235 | 2020-07-09T23:35:06 | 2020-07-09T23:35:06 | 278,493,093 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,667 | r | repo_events.R | # library(httr)
# library(jsonlite)
# library(dplyr)
#' Get a list of events from a GitHub respository
#'
#' Returns the 30 most recent events from a public repository (within the last 90 days), including all metadata, as a list of lists.
#'
#' @param owner string
#' @param repo string
#'
#' @return list
#' @export
#'... |
a43fd63b7925f9e71084f379411c5323d2131833 | 1326b5fec78ed753676df9d275590fed2bbac4e9 | /man/plot.permuteTest.Rd | 3c129d125763473d21075538a3f8bee4b60a54f5 | [] | no_license | lhsego/glmnetLRC | c0d4c06c7ee9d84acc93337166fca67211381b15 | 8ff225c512269ca6d8f402e28600df654df4a8e2 | refs/heads/master | 2020-12-25T16:35:53.010136 | 2017-10-19T21:24:17 | 2017-10-19T21:24:17 | 51,790,797 | 1 | 0 | null | 2016-02-15T22:37:38 | 2016-02-15T22:37:38 | null | UTF-8 | R | false | true | 690 | rd | plot.permuteTest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot.permuteTest.R
\name{plot.permuteTest}
\alias{plot.permuteTest}
\title{Plot the null distribution of the permutation test}
\usage{
\method{plot}{permuteTest}(x, ...)
}
\arguments{
\item{x}{an object of class \code{permuteTest} returned by... |
d731b7a3c1e7794c32902386aa84e0a144877c0e | 01fe688a807a88373d7bde3158b3363d9072373a | /RStudio/0813/0813.R | 7c0c5b1017a83242d8903af3daa0dbc06d31644c | [] | no_license | tmddnr712/BigData_DB | eeb7610182bedc23be43fc4effd35460bfbbe8c7 | 97216a344b621347af01c7b57a4216fd773ed94a | refs/heads/main | 2023-07-11T07:50:34.199117 | 2021-08-24T08:48:47 | 2021-08-24T08:48:47 | 383,033,628 | 0 | 0 | null | null | null | null | UHC | R | false | false | 2,778 | r | 0813.R | # Perceptron의 구현
x1 <- runif(30, -1, 1) # 균등 분포
x2 <- runif(30, -1, 1)
x <- cbind(x1, x2)
Y <- ifelse(x2 > 0.5 + x1, +1, -1) # y 값을 결정
plot(x, pch=ifelse(Y>0,"+","-"), xlim=c(-1,1), ylim=c(-1,1), cex=2)
abline(0.5, 1)
calculate_distance = function(x,w,b){ # forward propagation 순전파
sum(x*w) + b
}
linear_classifier ... |
6233b98ffd446ec7ee38f1c80d252f1ff097f831 | fc76f853b699f5e1ffa9a03c4e2725773ada429a | /functions.R | 3824df0c082ea24a4dfe59f4142faaf6f4e48c0a | [] | no_license | bmschmidt/ACS | d255d9b2dc888ecddd35f1e689028bc10becc613 | 711272c3ac19a270fa3f135567c2619608faf583 | refs/heads/master | 2021-01-25T03:26:56.080707 | 2014-05-22T22:04:10 | 2014-05-22T22:04:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,321 | r | functions.R | library(dplyr)
library(ggplot2)
returnWeights = function(weights=1:80) {
weights = read.table("weights.txt",sep="\t",header=F,colClasses=c("numeric","integer","integer"))
names(weights) = c("SERIALNO","sample","weight")
}
writeOutFields = function() {
#
counts = persons %.% group_by(FOD1P) %.% summarize(count... |
5e5523c11d86d3f3b634c1c7e3dc35f4f65469a9 | e983b9ddb154e040349277335ad18731ec8d97f8 | /TimeseriesChurnRate.R | 7261aed55154f83b0e6c647144c1e6e74a821658 | [
"MIT"
] | permissive | blendo-app/TimeseriesChurnRatePrediction | 8f8861f6a596fff87ed789974fd35bd916872cf6 | eb621c6181543acc64d15b8ebb4e54b1aa90b0ff | refs/heads/master | 2021-01-19T12:52:42.507444 | 2017-09-07T09:20:35 | 2017-09-07T09:20:35 | 88,054,793 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,483 | r | TimeseriesChurnRate.R | library(sqldf)
library(glmnet)
library(randomForest)
library(ggplot2)
library(caret)
data<-table_1_
data$totalpractions<-0
data$totalactions[is.na(data$totalactions)]<-0
data$avgactions<-0
data$practions<-0
data$days_since<-0
#create email count
data$mailCount<-NA
data$mailCount[1]<-1
for(i in 2:(nrow(data))){
if (... |
49a3046ab180d6fdcadb4d1c5bdcc391652523dc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/soilDB/examples/get_extended_data_from_NASIS.Rd.R | 034e06a3890f844f7c3f733d702387f5154c13ad | [] | 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 | 381 | r | get_extended_data_from_NASIS.Rd.R | library(soilDB)
### Name: get_extended_data_from_NASIS_db
### Title: Extract accessory tables and summaries from a local NASIS
### Database
### Aliases: get_extended_data_from_NASIS_db
### Keywords: manip
### ** Examples
## Not run:
##D # query extended data
##D e <- get_extended_data_from_NASIS_db()
##D
##D # ... |
ff72e24e25087cffcadcf5fbf98689497eb6f55f | 9fe822d020259841a5ee28b94232f36b75930a75 | /run_analysis.R | 385f53aba816f610bf70e447f60f771f4d34fc87 | [] | no_license | teykitt/R_Getting_And_Cleaning_Data | d0e079a9fba80d2e7f997dd52f258b80b4c37ee9 | a2882106733241677fdcd1b1eccf9eeb33728e1e | refs/heads/master | 2020-03-18T13:55:59.067729 | 2018-05-25T08:06:45 | 2018-05-25T08:06:45 | 134,818,429 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,434 | r | run_analysis.R | # The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set.
#
# Review criteria
# The submitted data set is tidy.
# The Github repo contains the required scripts.
# GitHub contains a code book that modifies and updates the available codebooks with the data to indicate all ... |
b87484a49f50396b3a56f3cb91940e7edc231841 | ac771259d6e3469b75e0fdac251839ab1d070767 | /man/vtlEvalNodes.Rd | 6b18581fdbd931eb047f6687e4e9d0a600b5132b | [] | no_license | amattioc/RVTL | 7a4e0259e21d52e8df1efe9a663ca20a7d130b15 | 630a41f27d0f5530d7c3df7266ecfaf25fe4803a | refs/heads/main | 2023-04-27T17:52:39.093386 | 2021-05-14T09:22:24 | 2021-05-14T09:22:24 | 304,639,834 | 0 | 1 | null | 2020-10-19T19:19:41 | 2020-10-16T13:46:02 | JavaScript | UTF-8 | R | false | true | 1,381 | rd | vtlEvalNodes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vtl.R
\name{vtlEvalNodes}
\alias{vtlEvalNodes}
\title{Evaluate a list of nodes}
\usage{
vtlEvalNodes(sessionID, nodes)
}
\arguments{
\item{sessionID}{The symbolic name of an active VTL session}
\item{nodes}{The nodes to be evaluated}
}
\desc... |
83d382457ff967393bc0b2c54561660d7a432412 | 96504fbe9d4dcee2e31089d00aabda46dc3950f1 | /R/predict.snqProfitEst.R | 7afd7cf204a49faf1bdc840f9f2d0cea22349618 | [] | no_license | cran/micEconSNQP | a326acf97d635454d7ead9571a28766e82a79bfb | 99c0891b02a42e1ea9546e449f8951ed79326581 | refs/heads/master | 2022-07-30T17:18:03.410662 | 2022-06-21T10:30:02 | 2022-06-21T10:30:02 | 17,697,476 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,084 | r | predict.snqProfitEst.R | predict.snqProfitEst <- function( object, newdata = object$data,
se.fit = FALSE, se.pred = FALSE, interval = "none", level = 0.95,
useDfSys = TRUE, ... ) {
nNetput <- length( object$pMeans )
nFixed <- length( object$fMeans )
nObsOld <- nrow( object$data )
nObsNew <- nrow( newdata )
modelData <- ... |
df2883e135e23e0128ede1ecf0eeb203e87e8cb3 | 98249747ca7a0b15bf34d0fd3fe9389d1bb4052a | /man/AnovaTest.Rd | c177c3d907a34026b48d175a9c516ce3e86f3f00 | [] | no_license | suraj-yathish/MCSlibrary | a5fe2cdbfd74764fec9996e9c3c294afa18a40ab | 77e1be2cf25d47b2784c8eb1bb2e3d278fb62ace | refs/heads/master | 2020-03-14T10:51:06.997578 | 2018-06-04T09:13:57 | 2018-06-04T09:13:57 | 131,577,606 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 517 | rd | AnovaTest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AnovaTest.R
\name{AnovaTest}
\alias{AnovaTest}
\title{Function to perform Anova test}
\usage{
AnovaTest(data, VIF1, VIF3, defect1, defect2)
}
\arguments{
\item{Collect}{Chisq values of all the metrics involved in LR, Defaults to dataset, VIF1... |
d0583fb82eba5f4b315359402c54d9875d3ddf15 | 305209120a483d820dde9f4a790709e2ca21b83f | /x.R | c772527082c3c4938c941f76c4b508756fc89d6d | [] | no_license | misal6/DataProductsSlides | cbd5627465c3f86533430505019bc5842ac575df | 3fbb150f66fc950421a4cd5e4671856d0c92210c | refs/heads/master | 2021-01-25T05:21:48.421253 | 2015-01-24T11:09:14 | 2015-01-24T11:09:14 | 29,774,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 340 | r | x.R | library(shiny)
library(datasets)
library(forecast)
idxdata=ts(EuStockMarkets[,"FTSE"])
idxarima=auto.arima(idxdata)
fcst=forecast(idxarima,h=260)
resfcst=tail(as.data.frame(fcst),1)
curval=round(tail(idxdata,1))
fctval=round(resfcst[,1])
pct=round(((fctval-curval)/curval)*100)
print(paste("Return on Investment : "... |
81d1cffee017ea1c64ac580a8fd0c50683c583e9 | e6acff9db72596867cdbcba70a8b3711cc97a317 | /methylationPattern.R | 4f25edcfb77d06e9816447928edcfb39394fb720 | [] | no_license | johnmous/methylationScripts | bb86118e9e4fae6440625803500b137f2832e0c5 | e401f74f21f522e7fb9752f84c3aff982fdeec0f | refs/heads/master | 2021-08-23T20:51:03.295658 | 2017-12-06T13:46:33 | 2017-12-06T13:46:33 | 113,179,998 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,921 | r | methylationPattern.R | ## Author: I. Moustakas
## Title: Get the methylation patterns and count them
## Usage: methylationPattern.R bismarkCpG outputDir sampleName
library("reshape2")
library("stringr")
args = commandArgs(trailingOnly=TRUE)
if (length(args) != 3 ) {
stop("CpG file from bismark (CpG_OB_*), amplicons table and output dire... |
927c2f0923afb9e0c8a50d10c4ec9cb9b893b76b | 6cf9a94de51479dc65dad3608a4b315ba289a36f | /man/top_rows_overlap-matrix-method.rd | a141d72f0478789e21c7b03d9df9fe152e01c161 | [] | no_license | NagaComBio/cola | 818c3afdab7e140d549ab9ebf6995a882c967cf5 | 304b3cf771e97ced7f4b20388815b882202cdd84 | refs/heads/master | 2021-04-27T08:31:19.184145 | 2018-02-26T10:00:07 | 2018-02-26T10:00:07 | 122,491,685 | 0 | 0 | null | 2018-02-22T14:45:23 | 2018-02-22T14:45:23 | null | UTF-8 | R | false | false | 990 | rd | top_rows_overlap-matrix-method.rd | \name{top_rows_overlap-matrix-method}
\alias{top_rows_overlap,matrix-method}
\title{
Overlap of top rows from different top methods
}
\description{
Overlap of top rows from different top methods
}
\usage{
\S4method{top_rows_overlap}{matrix}(object, top_method = all_top_value_methods(),
top_n = round(0.25*nrow(objec... |
749b60e39b146617dcbe44ee4f8d5b02bb9370c3 | 5ca8793fd39a818675306047c861e8c32965022a | /website/Old_source/Function/man/pi_wrapper.Rd | 34d105151ba420aa7baca489d4e6fa865b9d1dfc | [] | no_license | SOCR/TCIU | a0dfac068670fa63703b8e9a48236883ec167e06 | 85076ae775a32d89676679cfa6050e683da44d1d | refs/heads/master | 2023-03-09T01:28:28.366831 | 2023-02-27T20:27:26 | 2023-02-27T20:27:26 | 188,899,192 | 8 | 5 | null | 2022-11-12T01:56:25 | 2019-05-27T19:33:38 | R | UTF-8 | R | false | true | 580 | rd | pi_wrapper.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pi_wrapper.R
\name{pi_wrapper}
\alias{pi_wrapper}
\title{pi_wrapper}
\usage{
pi_wrapper(x)
}
\arguments{
\item{x}{A numeric value}
}
\value{
A number that satifies one of the 4 cases specified below
}
\description{
Define a pi-... |
4e1a9adbb6b88c2c36a421f4a4b139aeb12f8fe3 | 9c6c10769dd5d0d2b0604b5c5d5d43ad3635fd20 | /R/blblm.R | 4f0c85c61f42cbaf4607f3582df5b1aaf8247b88 | [] | no_license | anmeiliu/blblm | ec06281c75b7e996d5b0f14b38522ad7a67c1550 | 9960626bc0facc4c2753e81de6fd6aeee4403bb6 | refs/heads/master | 2022-11-04T07:31:02.449451 | 2020-06-11T06:02:47 | 2020-06-11T06:02:47 | 268,934,386 | 0 | 0 | null | 2020-06-03T00:03:29 | 2020-06-03T00:03:29 | null | UTF-8 | R | false | false | 13,451 | r | blblm.R | #' @import purrr
#' @import stats
#' @import utils
#' @import future
#' @importFrom magrittr %>%
#' @details
#' Generalized Linear Models with Bag of Little Bootstraps
"_PACKAGE"
## quiets concerns of R CMD check re: the .'s that appear in pipelines
# from https://github.com/jennybc/googlesheets/blob/master/R/googles... |
0057533961429bdc781a3063bca2a8ec46919914 | 49af06a3dd58afae35d379610bf745fb555d6150 | /plot3.R | 8c41508da34c521a95eebe86598cdc1ef6dd72de | [] | no_license | RidzuanMo/EDA-Project-2 | 5643897a62efdde600b86fd58771a419f360878b | 772742b2fb31dbc0cd5014ebb6c165ec1fc43cc8 | refs/heads/master | 2021-01-13T13:56:07.272633 | 2016-12-13T15:19:34 | 2016-12-13T15:19:34 | 76,142,070 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 622 | r | plot3.R | require(ggplot2)
require(plyr)
# load NEI datasource
NEI <- readRDS("summarySCC_PM25.rds")
# filtering Baltimore City dataset
baltimore <- subset(NEI, fips == "24510", select=c("year", "type", "Emissions"))
# source type
pm0 <- ddply(baltimore, .(year, type), summarize, Emissions=sum(Emissions))
pm0 <- transform(pm0... |
fd9c2a7858ef6239c251bfcf372681d28ec5c7a0 | b838ef0d0389fb8731439ff615abc24da5854448 | /R/RDSProject/R/poisson_reg_quiz.R | d00fa8dea2508b35a4c0bf4ceb8a89956f5994b8 | [] | no_license | sadiagit/DataScience-R | a0bdece78fd0e92053c5f2e6e6e972bd7358619a | 1e90cdccec5173803b83972b9bf5b2577bcf1778 | refs/heads/master | 2023-03-16T07:11:37.499153 | 2021-03-09T00:25:57 | 2021-03-09T00:25:57 | 282,094,726 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,655 | r | poisson_reg_quiz.R | #log(E(yi)) = log(lamda) = b0+b1*x1_i+b2*x2_i
b0=1.5
b1 = -0.3
b2 = 1
x1_i = 0.8
x2_i=1.2
log_lam = b0+b1*x1_i+b2*x2_i
lam = exp(log_lam)
#2.If tt is the amount of time that we observe, and \lambdaλ is the rate of events per unit of time, then the expected number of events is t \lambdatλ and the distribution of the n... |
8e3f11b3adf9f7868b28afa67f53ee2fa8f2b07e | 7dda987e5bc0dea30143ad52f190b41bc06a1911 | /ClusteringCorrelation/segmentation3.R | 8675b5d3ae770aee035afa21cbd7f01bb7548ab2 | [] | no_license | bgbutler/R_Scripts | 1b646bc4e087e8d80b52d62fed8eb20841ed2df7 | b10e69f8781eb2a19befe5f80f59863a0f838023 | refs/heads/master | 2023-01-25T04:08:44.844436 | 2023-01-12T16:00:55 | 2023-01-12T16:00:55 | 28,370,052 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,774 | r | segmentation3.R |
#load in the libraries
library(MASS) #for LDA
library(dplyr)
library(ggplot2)
library(FactoMineR)
library(class) #for knn
library(rpart) #other machine learning
library(caret) #machine learning package
library(dendextend)
library(dendextendRcpp)
library(xgboost)
library(Metrics)
library(gmodels)
... |
f5e414efd44578ed7e306f6eddb70755fa11fce0 | 9ec240c392225a6b9408a1636c7dc6b7d720fd79 | /packrat/src/backports/backports/R/import.R | de409746cd14b24555ea0a347690522cabf3a6d8 | [] | no_license | wjhopper/PBS-R-Manual | 6f7709c8eadc9e4f7a163f1790d0bf8d86baa5bf | 1a2a7bd15a448652acd79f71e9619e36c57fbe7b | refs/heads/master | 2020-05-30T17:47:46.346001 | 2019-07-01T15:53:23 | 2019-07-01T15:53:23 | 189,883,322 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,360 | r | import.R | #' @title Import backported functions into your package
#'
#' @description
#' Imports objects from \pkg{backports} into the namespace of other packages
#' by assigning it during load-time.
#' See examples for a code snippet to copy to your package.
#'
#' @param pkgname [\code{character(1)}]\cr
#' Name of the package ... |
03cb29211af47aa893e15bd14c580932bbd60a71 | e4c8af552f8801a088ca91a6cffe77689089d5d7 | /src/Analysis/0-archive/2b-regress-10day-body-unadj.R | fbe4c57eaf2ae09e8d5a8d4168e894b1d31fd81f | [] | no_license | jadebc/13beaches-coliphage | eb6087b957dbfac38211ac531508860f48094c15 | 3d511ffa91a6dd5256d6832162ea239c1dbbad28 | refs/heads/master | 2021-06-17T03:38:00.805458 | 2017-04-27T22:50:06 | 2017-04-27T22:50:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,172 | r | 2b-regress-10day-body-unadj.R | ##########################################
# Coliphage analysis - 6 beaches
# v1 by Jade 7/13/15
# This file conducts maximum likelihood regression
# to estimate prevalence ratios
# Results pooled across beaches
# unadjusted analyses
# 10 day gi illness
##########################################
rm(list=ls())
libr... |
43fcd9e4cd7b0f3da856c48f0a7437f3395a7801 | 69ed15a883dfbc2d67023d436dbb4cb9742b3970 | /man/checkIfVarinaceExcist.Rd | d158ff894698fc8f2c38dbe7f532e69a7776be86 | [] | no_license | joh4n/JGTools | 57f163463b107028509243260e80f7f05f847dd5 | 7418f924665c03791e758da7bc3112cd6b2022d9 | refs/heads/master | 2021-06-20T08:26:36.857149 | 2017-06-17T13:35:56 | 2017-06-17T13:35:56 | 77,140,153 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 748 | rd | checkIfVarinaceExcist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/smalUtilityFunctions.R
\name{checkIfVarinaceExcist}
\alias{checkIfVarinaceExcist}
\title{Checks if variance excits for selected columns in a data frame, and retuns the column names where variance excist}
\usage{
checkIfVarinaceExcist(names, d... |
57275cf28e205647c505e7e0d157bc3e6e9003be | f6b8edceb2d8b5344adf8a327f0407c7f3c0246a | /tests/testthat/test-compute_sdim.R | 2ba4f70faa37af746bd1bfde2a40e5775c511622 | [
"MIT"
] | permissive | bcjaeger/cleanRbp | e5c59d7b86c353985fe346cffd919c0704e629ba | ec654d7599c4fe5847c77bb28aede3466814e593 | refs/heads/master | 2023-05-04T17:01:12.770293 | 2021-05-25T16:35:49 | 2021-05-25T16:35:49 | 275,244,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 670 | r | test-compute_sdim.R |
test_that("standard inputs work", {
mean_bps = runif(n = 100, min = 80, max = 190)
sd_bps = runif(n = 100, min = 1, max = 3)
mean_bps[5] <- NA_real_
# cmp_sdim_values(mean_bps, sd_bps, method = 'A')
# cmp_sdim_values(mean_bps, sd_bps, method = 'B')
expect_is(cmp_sdim_coefs(mean_bps, sd_bps), 'numeric'... |
1ee888ec4aae81efeaf399510fc8c86a812e5206 | e04a048b646228814b5154aab3f883136d1dfff3 | /man/GRSDbinom.regressGENO.Rd | 2b04fb72367f48f834febf4173fc581417064525 | [] | no_license | elijahedmondson/HZE | be105263a3ec7a8fa5c5c7716d5f8459f2ff21c6 | 7ee3ecb514c43af8c08123472410389efc4491a5 | refs/heads/master | 2021-01-15T15:49:39.608472 | 2016-09-05T17:12:26 | 2016-09-05T17:12:26 | 48,752,928 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 444 | rd | GRSDbinom.regressGENO.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GRSDbinom.regressGENO.R
\name{GRSDbinom.regressGENO}
\alias{GRSDbinom.regressGENO}
\title{Association mapping on autosomal chromosomes with binary variable outcomes.}
\usage{
GRSDbinom.regressGENO(obj, pheno, pheno.col, addcovar, tx)
}
\autho... |
5caf88f1d9b21f6de612c2468c1364b9f413965f | 6d43aae6ce66f18f462cd5e923bf6e3503c336c9 | /R/vg_calc.R | 157a9c373363e538f5dfd99e5feb907e81fc6570 | [] | no_license | jnghiem/bfasttools | 984ee841e74568af11e57cbd0406e791ed7b8161 | 7d7834d81222193ed37119a4ea9cbd9a3d48fd69 | refs/heads/master | 2020-03-23T20:39:46.005392 | 2018-07-27T20:20:14 | 2018-07-27T20:20:14 | 141,771,122 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,188 | r | vg_calc.R | #' Perform variogram analysis on \code{bfastSpatial()} shift and slope
#' statistics
#'
#' This function uses the \code{gstat} package to compute variogram statistic
#' for the shift and slope output from \code{bfastSpatial} output. This function
#' uses the exponential variogram model.
#'
#' This function returns a da... |
308689b0fb6b3d9198f78dae02f5f2871a630161 | 0d927c92c7f5fd72d588103541241b1fc1ae9ed0 | /R/Settings.R | f6d9a8a85ab45345d928f38305f4a63f323f90b4 | [] | no_license | ClaudioZandonella/DMGC_Meta | 33d176a003495683b98e5193d61381584f40cf4f | 62d86ae789e034a91766704923c553e9827f51ae | refs/heads/master | 2022-11-11T11:57:25.865327 | 2020-07-02T14:22:04 | 2020-07-02T14:22:04 | 261,465,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,434 | r | Settings.R |
################################
#### Project settings ####
################################
#---- R packages ----
library("conflicted")
library("tidyverse")
library("metafor")
library("clubSandwich")
library("drake")
library("gridExtra")
library("MAd")
library("visNetwork")
# packages_list <- c("confli... |
4844693d2942318ff5ce76177917f6af04d00bc9 | 29585dff702209dd446c0ab52ceea046c58e384e | /EpiBayes/R/summary.ebhistorical.R | 0fcea43e90aa2e04ea7dbed04cfffa542989d083 | [] | 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 | 4,043 | r | summary.ebhistorical.R | #' @title
#' Summary Method for EpiBayes Historical Object
#'
#' @description
#' This function gives summary measurements for posterior distributions of cluster-level
#' prevalences across all time periods considered. It does so by examining the object
#' output by the \code{\link{EpiBayesHistorical}} funct... |
8bdb52c522b1aaa95e302b245403d3401ceeb017 | 056be6c657db6dd94561ded6782a978e0f5995ed | /br/br_robust_sample.R | 6ca35732a348a8dcd8ecd6015f3c195eff318b29 | [
"MIT"
] | permissive | lunliu454/infect_place | 9f4e90ed8900e1376cbe6ebd78c903d313c5e564 | 46a7a2d69e74a55be8cc0c26631fd84cda2d23bb | refs/heads/main | 2023-07-24T14:18:34.144798 | 2021-09-03T09:06:11 | 2021-09-03T09:06:11 | 402,244,291 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,503 | r | br_robust_sample.R | library(dplyr)
library(lfe)
input <- read.csv("br/br_input.csv", stringsAsFactors = F)
output <- data.frame(intervention = 1 : 10)
for (i in 1 : length(unique(input$code))){
m <- felm(log_win7_Rt_estimate ~ win7_stay_at_home + win7_school_close +
win7_office_close + win7_shop_close + win7_restaurant_cl... |
e06f80e8bbd7946278e69f42b63cf2a33d046aba | 7fc82291996c34238ce232548f88ee2dd761df49 | /R/foreigners.R | 27d636e6ef00a941e2877317de314b4f12991eb3 | [
"MIT"
] | permissive | ZajacT/ASIA1 | 125a9c1f6413458cf5dfaf9a9ae8a82742aae006 | f1ba2a637a71603a2adfee6589414d0dac6eda34 | refs/heads/master | 2021-06-30T04:13:42.998667 | 2018-12-20T14:16:50 | 2018-12-20T14:16:50 | 134,391,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,346 | r | foreigners.R | #' @title Lists foreigners and their admissions status
#' @description Function lists foreigners and their admissions status and writes
#' results to a file
#' @param registrations optionally path to the file with data on registrations
#' @param scores optionally path to the file with data on recruitment scores
#' @par... |
07f190927c0b1458ac192b556a3518df283accd3 | b75e75cffe78ac9b8e7067cac738408ae2d7d634 | /Utils.R | 82d485a726c9306ecabcc044d081330b12b73064 | [] | no_license | redsnic/SteamAnalysis | 29e16f58a38111f5721441153c4d26b0a440d14c | 2786a5c2381689fc7fdbdfef83d17caf0e058154 | refs/heads/master | 2022-12-08T11:13:06.438605 | 2020-08-29T18:03:34 | 2020-08-29T18:03:34 | 286,234,354 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,105 | r | Utils.R | # rimuovi caratteri speciali che possono essere problematici in caso di join
clean.text <- function(df,colname,er){
colname <- enquo(colname)
df %>% mutate( !!colname := map_chr(name, ~ gsub(er, "", . ))) %>%
mutate( !!colname := tolower(!!colname)) %>%
mutate( !!colname := map_chr(name, ~ gsub(... |
6ba9cb10ef93cb123ff4d90c9da1129568ed9368 | cd5ba89c012f5bc3c14bc6f74a457d0305dc6e24 | /POLYCHORIC_R.R | 14ad354e54d94225c52a8ac6d3ac173b95eda2d8 | [] | no_license | soy2padre/misspatt | 16f6be068b570ede1b8e61e8d707c4c74d602f59 | 78c68d9f4f1b0c93232f767c97c7f3f23181a678 | refs/heads/master | 2021-04-05T11:14:47.780461 | 2020-10-21T12:53:49 | 2020-10-21T12:53:49 | 248,550,410 | 0 | 1 | null | 2020-03-25T01:23:28 | 2020-03-19T16:22:31 | HTML | UTF-8 | R | false | false | 3,445 | r | POLYCHORIC_R.R | # Brian P. O'Connor
# https://github.com/bpoconnor
POLYCHORIC_R <- function (data, method='Revelle', verbose=TRUE){
if (is.integer(data) == FALSE) {
if (all((data - trunc(data)) == 0) == FALSE) {
cat("\nThe data matrix does not appear to consist of whole numbers and is therefore not appropriate
... |
e6fc8e6ad5f58db3fcc4625c4ec3c7c845c3c418 | 6059195ce6e1a4cdee3346acb689c26aa42d8c7d | /R/data.R | 682ce496eae63a05512443a7084359193b1e65c7 | [] | no_license | jleluyer/signet | 2e7d3534241ccd591305b8590398283f681280ed | b488d6d1fb3c0e223e815a75e3374621decb452f | refs/heads/master | 2021-01-21T20:25:49.861599 | 2017-04-18T20:37:42 | 2017-04-18T20:37:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 245 | r | data.R | #' Datasetscores.
#'
#' A dataset of gene scores
#'
#' @format A data frame with 17918 rows and 2 variables:
#' \describe{
#' \item{gene}{gene identifier}
#' \item{score}{gene score}
#' ...
#' }
#' @source Daub et al., 2013, MBE.
"scores"
|
6be091ca7fe99be6856ce761b5e2b3cdb597000d | a2c983c2ef6f9d3ff4aaf1c7ad8cf59e4c9b7fe4 | /stock.R | 7496b3a62039b2c67e9fdd2eb98f26b867646f00 | [] | no_license | isaac-altair/Multivariate-Analysis | d426402d2b1d40a5b5d4f1718bc6fe484a7fc21a | d6739481504fc41e9d6c74d81d8ba36e86fdbebd | refs/heads/master | 2020-03-10T20:57:58.269688 | 2018-04-15T06:44:15 | 2018-04-15T06:44:15 | 129,581,833 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 488 | r | stock.R | T = read.csv("YahooStock.csv", header=TRUE)
names(T)
head(T)
P = T[ , 2:11]
names(P)
plot(P[,1],type='l',ylim=c(0,100))
# plot several time series
points(P[,3],type='l',col=3)
points(P[,4],type='l',col=4)
points(P[,5],type='l',col=5)
p= prcomp(P,scale=TRUE)
summary(p)
p$rotation
plot(p, type="l")
... |
8b0ae8f489c922b79279e140dac0c9b205f72fbc | f2ccc4dd8363a7279365524c1b15b895e3e9166c | /man/plotAsso.Rd | ff01388cad6a0f0646354c26872fd610c9232b28 | [] | no_license | cran/IntegratedJM | 79406b2b18f459e09534659691a794e762d7f296 | ec82f6ce916fdc8305b2e09a317b097a27c0f754 | refs/heads/master | 2021-05-04T11:22:59.262537 | 2017-08-03T21:37:38 | 2017-08-03T21:37:38 | 48,081,992 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 727 | rd | plotAsso.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Functions.R
\name{plotAsso}
\alias{plotAsso}
\title{plotAsso}
\usage{
plotAsso(jointModelResult, type)
}
\arguments{
\item{jointModelResult}{Data frame, containing the results from the fitJM function.}
\item{type}{Characte... |
71c15b84a5364d47ff06d2eec77ba0d5a2481bda | d60af7d5b9ff9f1f5455039b5523e32fe6e17b8c | /MachineLearningHW2.R | 7fa4c16bf803f4a3a52ad34303202520d93925ff | [] | no_license | TanalpSengun/MachineLearning | 3f11c36c44938ddff31294f37b30a4a2d08a602a | 527cbcde0b96fb510d29647a19bb915c9d7fcac2 | refs/heads/master | 2022-12-02T10:59:55.890178 | 2020-08-07T12:36:10 | 2020-08-07T12:36:10 | 285,816,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,665 | r | MachineLearningHW2.R |
#this is the safer version of log in case of the threat log0.
safe_log <- function(x1) {
return (log(x1 + 1e-100))
}
# reading the data to the memory
#headers are false for taking into account the first data .
data_set <- read.csv("hw02_data_set_images.csv", header = FALSE)
true_y <- read.csv("hw02_data_set_... |
1ee3bfcd2934945fcb6d96f51797a644a2c336a8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/quanteda/examples/dfm.Rd.R | 04e966501c8ec50877a3483b2d4ca816c7ae52b7 | [] | 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 | 2,817 | r | dfm.Rd.R | library(quanteda)
### Name: dfm
### Title: Create a document-feature matrix
### Aliases: dfm
### Keywords: dfm
### ** Examples
## for a corpus
corpus_post80inaug <- corpus_subset(data_corpus_inaugural, Year > 1980)
dfm(corpus_post80inaug)
dfm(corpus_post80inaug, tolower = FALSE)
# grouping documents by docvars in ... |
12f8d54acdcfb2007a5a9da687671d3a7f4cca13 | 29585dff702209dd446c0ab52ceea046c58e384e | /TimeProjection/R/projection.R | b577310db2c55e14ad14ab011a3c773fedfcc1dc | [] | 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 | 4,364 | r | projection.R | #' Time Projection
#'
#' Project dates to lower dimensional subspace.
#' Extracts components year, month, yday, mday, hour, minute, weekday,
#' bizday and season from a date object
#'
#' @param dates date or datetime objects
#' @param size either "narrow" or "wide". If narrow, returns a data frame
#' containing the... |
51672775b945f86aecc88faed2f9f806470f8fc1 | 786f434f2f65fc35339283f64ad86cbc2f363300 | /Library/Mode.Sample.R | 446610f15066d86564793217f8fd58281eaa30db | [] | no_license | amberjaycocks/HIVProbPrEP | e483036089b5a8f281c9a66a11a729ca7b0911aa | b90676f5c4498076c997cc37f83f7273517dc3ae | refs/heads/master | 2020-05-18T11:46:00.081053 | 2014-06-17T20:40:39 | 2014-06-17T20:40:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 105 | r | Mode.Sample.R | Mode.Sample <- function(param){
r <- as.array(param$Mode)
names(r) <- rownames(param)
return(r)
} |
780ced967a4e0a8a3b11bdec7446671e21a3f87f | 08df3ba8636658bfb564264d578ec5d700571d33 | /using_refnet_troubleshooting/mapping_addresses.R | 3978249b224a137025be09f4795713d7aa063eef | [] | no_license | aurielfournier/refnet_materials | cbc1c5163051e977478d2887ccdd5dd745303987 | e0ce97910caec345a3894d1088d7b487dcfd9d23 | refs/heads/master | 2020-03-27T02:17:38.835246 | 2018-11-01T15:44:54 | 2018-11-01T15:44:54 | 145,779,762 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 387 | r | mapping_addresses.R | library(ggmap)
library(tidyverse)
library(stringr)
library(stringi)
library(refnet)
load("./output/eb_refined.Rdata")
world <- map_data("world")
zz <- address_lat_long(data=eb_refined)
plot_addresses_points(data=zz)
plot_addresses_country(data=zz)
s <- net_plot_coauthor(data=zz)
s$data
q <- net_plot_coauthor_count... |
1e2ec94390c0cd33d2a13b1866d0eddb5d049bc3 | 5a9956727d7a12f0bf2c697c486a49c2f37ed8c3 | /man/RFcd.Rd | d371456e2f2e44775a206db3577669694d772cc0 | [] | no_license | mlondschien/hdcd | fb1bfcf9315bcda74632e86c415d22357193f5d9 | c6dd90aceb7921b25d18f9395771415080a6b530 | refs/heads/master | 2023-02-04T08:14:54.664927 | 2020-12-29T18:48:35 | 2020-12-29T18:48:35 | 280,871,404 | 2 | 0 | null | 2020-10-22T16:41:27 | 2020-07-19T13:29:06 | R | UTF-8 | R | false | true | 519 | rd | RFcd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hdcd.R
\name{RFcd}
\alias{RFcd}
\title{Random Forest change point detection}
\usage{
RFcd(x, delta = 0.1, control = hdcd_control())
}
\arguments{
\item{x}{A matrix with observations in rows}
\item{delta}{Minimal relative segment length, defa... |
cc8be77b94d03598b78c824af24524cd61935995 | f5a491f6cb11aca7d592ddebe2a75e6bbd28055d | /run_analysis.R | 7cd83d8e45f90a1a433337c8605d4deedcaabe8f | [] | no_license | Lunoj/Coursera_Data_Cleaning_Project | 6ccf973cda00953f131cc185ecd7ee631f6a08c3 | 45dd9f1c5565c3315d0745c1e6a4af9bf84e53ea | refs/heads/master | 2021-01-25T09:32:34.380153 | 2017-06-09T13:33:18 | 2017-06-09T13:33:18 | 93,854,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,159 | r | run_analysis.R | # load required library
library(plyr)
library(dplyr)
library(data.table)
# download file
if(!file.exists("./data")){dir.create("./data")}
fileurl <- "http://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileurl,destfile = "./data/Dataset.zip")
#Unzip file
u... |
23971f6b8db163df2ee20b728791a6d279256321 | 94dcbff4ef2072f5a5ecbb95af1f259f31ad3b20 | /man/int.est.rm.Rd | c73a85ba0d554059eabaeed964576f13d54fb4bf | [] | no_license | DistanceDevelopment/WiSP | bf51406076ded020098f4973003eafc05a45d437 | e0e2665d6b3b49ba634944b4bb7303be41620e5a | refs/heads/master | 2021-06-05T02:54:08.957306 | 2020-09-14T20:03:59 | 2020-09-14T20:03:59 | 9,773,511 | 0 | 1 | null | 2020-09-14T09:30:06 | 2013-04-30T15:05:50 | R | ISO-8859-1 | R | false | false | 3,238 | rd | int.est.rm.Rd | \name{int.est.rm}
\alias{int.est.rm}
\title{Removal Method Abundance Estimation: Interval Estimate}
\description{
This function estimates the animal population size for the current survey sample of the simple removal method.
}
\usage{
int.est.rm(samp, ci.type = "boot.nonpar", nboot = 999, vlevels... |
aeeaeaddaec912f6ab3879dc7399147d64f2e889 | c005a2c57e87a330a2412997bfd38fd85c917f4e | /code/script_Feb2018.r | 68971bd1870ed84d9f7228b93b6c26e22e1bf0cd | [] | no_license | richardli/RandomizedVAstudy | a383ef89b631a764f52bf97d526de20067c48ae4 | 4f2f4059f86db2d025704e3f5033935097f588d2 | refs/heads/master | 2021-01-17T14:57:00.721793 | 2018-05-16T18:04:00 | 2018-05-16T18:04:00 | 51,721,626 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,660 | r | script_Feb2018.r | ###################################################################################
## This script perform the experiments 7 and 8 for the new data
## from 3 sites in India
###################################################################################
remove(list = ls())
library(openVA)
sites <- c("Amravati", "... |
d7c7bb5f0b20d60ddd6e431096725e8eae388002 | 04cf2ed7a926c177b264a7663115d3c7d76bacb1 | /week2/corr.R | 87a9fb883cea01f6b5b3cb7ba2e799b4d8e72907 | [] | no_license | GiovanniCassani/R_programmingAssignments | 9ecca208a87b9e63de6c4f26cd6ce7ec259dfae8 | 01173f61eea0d3de445bd0e7abfdadb3e619bbcd | refs/heads/master | 2021-01-10T19:05:30.586608 | 2015-01-15T15:48:06 | 2015-01-15T15:48:06 | 29,242,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 438 | r | corr.R | corr <- function(directory, threshold = 0) {
files <- list.files(directory)
corrs <- numeric(0)
idx = 1
for (i in seq_along(files)) {
file <- paste(directory, files[i], sep = '/')
dataSet <- read.csv(file)
rows <- na.omit(dataSet)
if(nrow(rows) >= threshold) {
... |
c1bbf629ccf4af6eadc8dc7a369631c3c3a13e2b | 7fee2033e776446ef9990582811b956f70176874 | /R/klein.R | 73c043262c4ba97a4119cbee179a0cef1673893f | [] | no_license | tallulandrews/scRNA.seq.datasets | 4930fd1a45ec3cf24b2a43253ce627df86692470 | 43e4957a3cb4d46f566133955242b2919dd7eadc | refs/heads/master | 2021-01-21T20:47:39.616331 | 2017-05-23T21:32:46 | 2017-05-23T21:32:46 | 92,283,314 | 0 | 0 | null | 2017-05-24T11:07:10 | 2017-05-24T11:07:10 | null | UTF-8 | R | false | false | 1,035 | r | klein.R | library(scater)
# load data
d0 <- read.csv("klein/GSM1599494_ES_d0_main.csv", header = FALSE)
d2 <- read.csv("klein/GSM1599497_ES_d2_LIFminus.csv", header = FALSE)
d4 <- read.csv("klein/GSM1599498_ES_d4_LIFminus.csv", header = FALSE)
d7 <- read.csv("klein/GSM1599499_ES_d7_LIFminus.csv", header = FALSE)
d <- cbind(d0,... |
88a648b476700deb2d94c7af625943adf134a286 | 40a6affe413e20f6b76b0fa6626cbddc0a568b3b | /vignettes/amigaDiskFiles.R | ffdb5ba6f5417fe397145bb6dd9a9ee7c19109ef | [] | no_license | zbarutcu/adfExplorer | 7e3f79a5b8b32754ef174ac9ccd2da8217a61739 | 5fb85322fa42e8f9e0a2067b413302d8e4539ef1 | refs/heads/master | 2023-07-25T22:42:38.574018 | 2021-09-05T11:28:10 | 2021-09-05T11:28:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,197 | r | amigaDiskFiles.R | ## -----------------------------------------------------------------------------
library(adfExplorer)
blank.disk <- new("amigaDisk")
## -----------------------------------------------------------------------------
## Create with constructor:
blank.block <- new("amigaBlock")
## Extract the first block from an amigaDis... |
b5e95110aa1fad4a4cfeeace3580696f123f93d2 | 6acd86b9f9e76bb0eb3c08c650e16354d32eee77 | /traclus/partitioning.R | 3b62076b4daa7489242b84b1f47381b32e1f8553 | [] | no_license | yuen26/hntaxicab-shiny | 4fab3117f965240546fe275f1ff2b57cec2c9794 | 211d99995ee07152a7ab0d2489d501b70c44c222 | refs/heads/master | 2021-08-27T23:31:13.422521 | 2017-12-10T19:27:18 | 2017-12-10T19:27:18 | 99,350,497 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,664 | r | partitioning.R | # ===================== MDL functions ======================
descriptionCost <- function(trajectory, startIndex, endIndex) {
startSegment <- trajectory[[startIndex]]
endSegment <- trajectory[[endIndex]]
distance <- measureEuclidDistance(startSegment, endSegment)
if (distance < 1) {
distance <- 1
}
retur... |
9c3e4a078e6aa933992b89420c11a9b9a474edc7 | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googledfareportingv27.auto/man/TagSetting.Rd | 168b62e9139f520c5680f111f17e7bf76c1cef5b | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 806 | rd | TagSetting.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dfareporting_objects.R
\name{TagSetting}
\alias{TagSetting}
\title{TagSetting Object}
\usage{
TagSetting(additionalKeyValues = NULL, includeClickThroughUrls = NULL,
includeClickTracking = NULL, keywordOption = NULL)
}
\arguments{
\item{addi... |
a2ee5f148a770b42656407949409dfd3e41e5948 | 69a62f8dab62e35a0fcb2f23bfd35bc4f401324f | /tests/testthat.R | 413a30f4492fce3ade4f63b15b652ea27e806b0c | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | kleinschmidt/daver | d86f880374094bcfe96ea7683a04e85c96ae30e1 | 501c8dfbf77af49ff512beae7d09e582dd8adc94 | refs/heads/master | 2021-01-19T04:37:35.600284 | 2018-03-28T19:32:21 | 2018-03-28T19:32:21 | 46,999,898 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 54 | r | testthat.R | library(testthat)
library(daver)
test_check("daver")
|
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