blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
d2ba21d4c8e49cd960399f28fa52009def4433a7 | b0b54c7b80ff22fbb7d3910c7f4eaa0fa5e970c1 | /make_master.R | bb91b08e81a48af1ecf6923b6a1b12b46a5823d8 | [] | no_license | yumifoo/finemap | 7880e572fcf333fb1b458eeb65110aeb81dcc5ef | 9d3132c3683c2409ab8d5249e062bd8ceac387fc | refs/heads/master | 2020-09-24T18:33:45.759851 | 2019-12-11T12:08:11 | 2019-12-11T12:08:11 | 225,817,684 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,787 | r | make_master.R | # make input file master file for ld store of each region
# i.e. the master file will be a two lines file, one header, one content
#!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
trait <- as.character(args[1])
maf <- 0.01
library(data.table)
source("/fs/projects/ukbb/yu/src/BOLT2z.R")
################... |
396164188aa3e57a70775fcd7462965fbb555fbb | 49ff0bc7c07087584b907d08e68d398e7293d910 | /mbg/mbg_core_code/mbg_central/LBDCore/R/get_max_runtime_by_queue.R | 0c6f69e9db1cafa6236d3fede652e608b6d8db11 | [] | no_license | The-Oxford-GBD-group/typhi_paratyphi_modelling_code | db7963836c9ce9cec3ca8da3a4645c4203bf1352 | 4219ee6b1fb122c9706078e03dd1831f24bdaa04 | refs/heads/master | 2023-07-30T07:05:28.802523 | 2021-09-27T12:11:17 | 2021-09-27T12:11:17 | 297,317,048 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | r | get_max_runtime_by_queue.R | #' @title Get max runtime of queue
#'
#' @description Check the input string of queue and the maximum runtime available for that queue
#'
#' @param queue String. User supplied queue
#'
#' @return a string of run-time in format \code{DD:HH:MM:SS}
#'
#' @export
get_max_runtime_by_queue <- function(queue) {
if (queue %l... |
e24adca993d3bfff1421491481b681eac72c6c8f | d9b893e267d456d44d7188994a89a078b2902224 | /Homeworks/Homework_3/jachimek_sebastian.R | 3d62fc12253425c15e0b66489bd02ad62c3ab124 | [
"MIT"
] | permissive | wojciechwojnar/PiADwR | d9cfd90de98b7d2d6855e3b6cc7bd919a67881f2 | 80f61be0e383d21c2f11f7c9abb0cbcae2071fee | refs/heads/main | 2023-05-31T01:56:41.687433 | 2021-02-01T23:57:54 | 2021-02-01T23:57:54 | 308,119,264 | 0 | 1 | MIT | 2020-10-28T19:22:49 | 2020-10-28T19:22:48 | null | WINDOWS-1250 | R | false | false | 2,677 | r | jachimek_sebastian.R | library(data.table)
library(dplyr)
library(lubridate)
library(stringr)
gas_file <- list.files("./data", full.names = TRUE)
gas_dfs_dt <- lapply(gas_file, fread)
gas_dt <- rbindlist(gas_dfs_dt)
gas_dt <- gas_dt[, c("State Name", "County Name", "City Name",
"Local Site Name", "Date Local", "Paramete... |
b1e6f1f534b3765d6537e4fc300d5c828017b5c8 | 02da0605981b46d021d0ff9cd321e811c28edf5d | /alluvial_plots.R | 9cdc2b08b5dbbdcef27363e15dbdcc18b0dfdf6c | [] | no_license | turkjr19/alluvial | fb41ffb154848e6e1307d6a28a495fb9b82e8229 | 6bb5f819f70fda7c20ede237a6fb01cfe589c225 | refs/heads/main | 2023-02-27T17:38:51.278320 | 2021-02-01T21:37:35 | 2021-02-01T21:37:35 | 335,090,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,276 | r | alluvial_plots.R | library(tidyverse)
library(ggalluvial)
library(RColorBrewer)
# Load in previously cleaned data from OHL scrape
my_data <- read_csv("~/Documents/hockey/OHL_OIR/my_data.csv")
# create data frame based on team and goal scorer
who_contributed <- my_data %>%
filter(scoring_team == "FLNT",
goal.scorer == "Ty Del... |
c86ccef641f693f21043fe794ddf5d093c2da7e5 | dfe7e6cf00d94cf8f010abc68305f57dc362fcc2 | /utils/recipes/step_recipe.R | eeb9309b9c8c0dd1f850e6c204966104188cbbae | [] | no_license | pedrorio/statistical_learning | 067564aa53967d23169d29ff1df8815d6f4d36ff | 5e630f5d02ca59a57233705b381ef1c42b1e5810 | refs/heads/master | 2022-12-17T21:01:21.819776 | 2020-09-10T21:02:38 | 2020-09-10T21:02:38 | 293,506,305 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,249 | r | step_recipe.R | step_winsorise_new <- function(
terms = NULL,
role = NA,
skip = FALSE,
trained = FALSE,
predictor_column_names = NULL,
outcome_column_name = NULL,
id = NULL
) {
recipes::step("winsorise",
terms = terms,
role = role,
skip = skip,
trained = trained,
predictor_column_names = ... |
ede5c81e9a0eb3e6ce4930dad3d197a0d4fa1d63 | 8765a5aca6a11e92ca8cd69b7f363db50d1ab9cf | /tamu/basic_stat/R/boxcox,yonkers(1).R | 21aa290f71de373a3f2a98f931ecf3e5b6aa11cd | [] | no_license | sadepu1915/data-science | 4f6f041db6d9e5530b7470e656a181b568c5f4d7 | 64f847d50d1cfddb365f3fe9e2e4ebf62c27d626 | refs/heads/master | 2020-05-25T09:58:03.448166 | 2019-05-08T04:45:03 | 2019-05-08T04:59:31 | 42,747,175 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,309 | r | boxcox,yonkers(1).R | x = scan("u:/meth1/Rfiles/ozone2.DAT")
n = length(x)
y = abs(x)
ly = log(y)
s = sum(ly)
yt0 = log(x)
varyt0 = var(yt0)
Lt0 = -1*s - .5*n*(log(2*pi*varyt0)+1)
th = 0
Lt = 0
t = -2.01
i = 0
while(t < 2)
{t = t+.001
i = i+1
th[i] = t
yt = (x^t -1)/t
varyt = var(yt)
Lt[i] = (t-1)*s - .5*n*(log(2*pi*varyt)+1)
if(abs(th[i])<... |
6bdea15b38a7637a68e517032b1a3fc45aea0043 | 57222f96e553dd2802316928f2f2c7825ef05197 | /054-nvd3-line-chart-output/server.R | 54db32f734caf196df93ee43f39e97cdb9af72ea | [
"MIT"
] | permissive | rstudio/shiny-examples | 6815bb4d8198e4b90765926a4865fdef1d1dc935 | c7bf00db4a8a68e579e39ed07d516a33661a853e | refs/heads/main | 2023-08-17T16:35:03.411795 | 2023-08-03T19:51:30 | 2023-08-03T19:51:30 | 13,722,949 | 2,046 | 4,576 | NOASSERTION | 2023-08-03T19:51:31 | 2013-10-20T17:05:23 | JavaScript | UTF-8 | R | false | false | 337 | r | server.R | function(input, output, session) {
output$mychart <- renderLineChart({
# Return a data frame. Each column will be a series in the line chart.
data.frame(
Sine = sin(1:100/10 + input$sinePhase * pi/180) * input$sineAmplitude,
Cosine = 0.5 * cos(1:100/10),
"Sine 2" = sin(1:100/10) * 0.25 + ... |
85972dd76acc4a69ddc59d6d05de6675d5e93fb5 | ee6313c363be9f5ae48a4ddd08fdf68194e408f3 | /plot3.R | 4839bbe6318fddab4f9dcc68673b4e19664df08e | [] | no_license | RechalC/ExploratoryDataAnalysisWeek4 | b2ea13a6c3f54c8528902be9625e57f844bab439 | 8f65bd6895257ad05994fc95ba5b5a6333c7c817 | refs/heads/master | 2020-05-23T17:04:32.424037 | 2019-05-15T16:55:37 | 2019-05-15T16:55:37 | 186,860,896 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 510 | r | plot3.R |
# Call Packages dplyr, bindrcpp & ggplot2
library(dplyr)
library(bindrcpp)
library(ggplot2)
png("plot3.png", width=480, height=480)
# Baltimore yearly emmisisons data
baltYrTypEmm <- aggregate(Emissions ~ year+ type, baltdata, sum)
# plot3.png
chart <- ggplot(baltYrTypEmm, aes(year, Emissions, color =... |
90e45f6d8ae79a55e2f6504f5366c98335b3c369 | 263d8a552164c80e9171e5855aac5d0a82841810 | /MarcoScutari_BayesianNetworksInR/Chapter1: Discrete Case/ex1.4.R | 9ff54916621eed72b479b4cb62595e1bfcef8a8d | [] | no_license | statisticallyfit/RBayesianStatistics | 9d66714fb06410b112337611dfbaa75055c66c8b | 959f2a13719f3f870faccc4d33b474f04e1d8cad | refs/heads/master | 2021-01-23T01:17:05.967248 | 2018-11-23T07:45:13 | 2018-11-23T07:45:13 | 92,862,115 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | ex1.4.R | # 1. In bn.mle compare distribution of O|A with corresponding one from querygrain
junction.mle <- compile(as.grain(bn.mle))
jage.young <- setEvidence(junction.mle, "A", states="young")
jage.old <- setEvidence(junction.mle, "A", states="old")
# P(O)
querygrain(junction.mle, nodes = "O")$O
# P(O | A = young)
querygrain(j... |
2dc5468e5d0041d3344e2a2101945a4c639e8bcb | 6fb04083c9d4ee38349fc04f499a4bf83f6b32c9 | /tests/testthat/test_survfit.R | 78b2a7f1fd63188340fc074a1073cc051229daae | [] | 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 | 2,967 | r | test_survfit.R | Renv = new.env(parent = globalenv())
FLenv = as.FL(Renv)
Renv$data <- sqlQuery(connection,paste0("SELECT DataSetID,Gender,TIME_VAL,STATUS ",
" FROM vwWHAS100 ORDER by 1,2"))
Renv$fit <- dlply(Renv$data,c("DataSetID","Gender"),
function(x)
survival::... |
9cd44ef80531aadc08f7fda4f8a6a4f48252a4ec | a585e4c8eacc2fb59aa5b566a74a7dc7eb28174b | /packrat/lib/x86_64-pc-linux-gnu/3.2.5/rlang/tests/testthat/test-lang-expr.R | 05607e917bdbb2526729d054b0c530886ebc2e8e | [
"Apache-2.0"
] | permissive | sol-eng/connect-roadshow | c86d179621539a7b03e3d6aea5b3b1df590585d0 | 8a1890b8cac9a03bfc07eb5a0a04683ffe063ddd | refs/heads/main | 2021-11-30T00:34:54.811453 | 2019-03-15T19:40:43 | 2019-03-15T19:40:43 | 161,424,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 789 | r | test-lang-expr.R | context("lang-expr")
# expr_text() --------------------------------------------------------
test_that("always returns single string", {
out <- expr_text(quote({
a + b
}))
expect_length(out, 1)
})
test_that("can truncate lines", {
out <- expr_text(quote({
a + b
}), nlines = 2)
expect_equal(out, "{... |
adaf3a3a05645750a6c6c4793ebcf925d6681451 | 4a2272aa2e585ddf752fc7284dacc72ebcfb3b08 | /plot4.R | ecd4b3ed729fa7857d99eec6b68945fb551317db | [] | no_license | nimsathi/EDAProj2 | a263256d217174b249e2b1e2437715ffd4457068 | 229bdd50a93439fdf6b5fd22b93fd24625d3dad1 | refs/heads/master | 2022-09-10T09:32:21.335594 | 2020-06-03T14:49:14 | 2020-06-03T14:49:14 | 269,039,229 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 728 | r | plot4.R | library(dplyr)
library(stringr)
library(ggplot2)
SCC <- readRDS("Source_Classification_Code.rds")
NEI <- readRDS("summarySCC_PM25.rds")
# identify all coal sources
coalSources <- SCC %>%
filter(str_detect(EI.Sector, 'Coal'))
# total emissions from coal sources grouped by year
coalEmissions <- NEI %>%
filter(SCC ... |
91c31ede61a47ecfa39e3edc2addd6979ba04a5f | 4bcb78635e3d5ff3a47b5dcd0e5771f6eb360313 | /cachematrix.R | c28c6d151c42e901e861ec5773b2a0b0f26ab0bd | [] | no_license | zohairh/ProgrammingAssignment2 | 1a0a3857ff22f9249aa3928ad1926c376144b9b0 | 03079848051301d487e0bd3f3ea63058f3905966 | refs/heads/master | 2021-01-17T05:49:58.765997 | 2014-06-21T00:51:50 | 2014-06-21T00:51:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 942 | r | cachematrix.R | ## We will make a special matrix object called CacheMatrix, which will cache the inverse of the matrix within itself
## and contain a list of functions to get and set the matrix and the inverse
## This function creates a CacheMatrix object and optionally initializes the matrix
makeCacheMatrix <- function(x = matrix()... |
3b4c78cc8abd360aeaf2ea0fe4ebffca9da282ce | 461ea396babcd50cf55f83ba7e9b4e50e2588807 | /myFunctions/R/miscellaneous.R | 7bd3430f4b66445803a8f16f2bbf5d57e6e01da1 | [] | no_license | vikram-g/R-Functions | e9f7dca39b41fa3b2c5552dd0013b6f72fa480e9 | aabb1ac6616b75ac23d4985af99036c8fb3c9a27 | refs/heads/master | 2020-03-27T00:33:00.735484 | 2018-08-22T03:27:38 | 2018-08-22T03:27:38 | 145,633,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,801 | r | miscellaneous.R | #Get a percentage string from a decimal
#' Decimal to percent
#'
#' Function takes a decimal value and returns it formatted as percentage.
#'
#' @param x integer or list of interger that need to be formated as percentage
#' @param digits Number of digits after decimal point to retain. Defaults to 2
#' @return The li... |
85a2d77857ec6c05a11963e2e1e53533746e7b1e | 3f7d08494725e4a588519503c842fc5dd3c4e0b3 | /man/attachment.Rd | 9d377e73c4407a3f5c9cffab81563d9ee9efa576 | [] | no_license | kazuya030/gmailr | bcafd1a0e22517cba54a962da7a5adc04420e306 | d8ac8429b5eb5b5aa4a25f955de10ca7f13fd3e1 | refs/heads/master | 2021-01-24T15:53:30.629433 | 2016-01-02T09:58:38 | 2016-01-02T09:58:38 | 48,846,830 | 0 | 1 | null | 2016-01-01T15:34:24 | 2015-12-31T11:06:49 | R | UTF-8 | R | false | true | 820 | rd | attachment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/message.R
\name{attachment}
\alias{attachment}
\title{Retrieve an attachment to a message}
\usage{
attachment(id, message_id, user_id = "me")
}
\arguments{
\item{id}{id of the attachment}
\item{message_id}{id of the parent message}
\item{us... |
f1951a15d1c5e06042c8124ec07308d8f8294497 | 4201e9b754760dc35fc0aeef9df5a8b9d801c47f | /bin/R-3.5.1/src/library/base/R/dynload.R | f13f1c5edd39c2d76cbfab37025c8a549d8ab9c5 | [
"LGPL-2.1-only",
"GPL-2.0-only",
"GPL-2.0-or-later",
"LGPL-3.0-only",
"GPL-3.0-only",
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | lifebit-ai/exomedepth | cbe59cb7fcf2f9183d187f8d466c6620fb1a0c2e | 5a775ae5e2a247aeadc5208a34e8717c7855d080 | refs/heads/master | 2020-03-27T12:55:56.400581 | 2018-10-11T10:00:07 | 2018-10-11T10:00:07 | 146,578,924 | 0 | 0 | MIT | 2018-08-29T09:43:52 | 2018-08-29T09:43:51 | null | UTF-8 | R | false | false | 6,552 | r | dynload.R | # File src/library/base/R/dynload.R
# Part of the R package, https://www.R-project.org
#
# Copyright (C) 1995-2018 The R Core Team
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either ... |
cf07e8f4cf18378d52c32f4c8596869c1b231384 | 991c97b4f8697f7c4635b76f4449d83871dcefe2 | /R/ornament.R | c33aafaec32ca4102f5f53b7f6883dd94e4ee544 | [
"MIT"
] | permissive | r-lib/pillar | 803706f4fa4e7f039005f5a8ad27945869fe7d02 | 92095fcc0fffa6d2b2d42c3ec6017e7501c0c99b | refs/heads/main | 2023-06-08T05:12:41.813170 | 2023-03-26T01:56:15 | 2023-03-26T01:56:15 | 91,374,446 | 146 | 40 | NOASSERTION | 2023-04-05T01:30:09 | 2017-05-15T19:04:32 | R | UTF-8 | R | false | false | 1,337 | r | ornament.R | #' Helper to define the contents of a pillar
#'
#' This function is useful if your data renders differently depending on the
#' available width. In this case, implement the [pillar_shaft()] method for your
#' class to return a subclass of "pillar_shaft" and have the [format()] method
#' for this subclass call `new_orna... |
bd0de9145f52546cd0455e7407344b65cc967d47 | 2a1a9c67e1605068227626c167d3e8d4511ec619 | /cachematrix.R | da8abd794f73dc5742c5dd726625b211f81cfe08 | [] | no_license | Emsobaa/ProgrammingAssignment2 | 8c7349175a28e7b543b3788fade6e839377900ab | 4f7dc9218ab2b6167bb403a37099ac4a16eab93e | refs/heads/master | 2020-12-26T01:37:50.135503 | 2016-04-02T16:28:01 | 2016-04-02T16:28:01 | 55,235,704 | 0 | 0 | null | 2016-04-01T13:48:23 | 2016-04-01T13:48:22 | null | UTF-8 | R | false | false | 1,769 | r | cachematrix.R | ## These functions cache a matrix and its inverse, to avoid recalculating its inverse if it's already known.
## makeCacheMatrix() creates a special matrix object that can cache the matrix itself,
## and can cache its inverse. If "M" is an invertible matrix, and "M-1" its inverse,
## makeCacheMatrix(M) will cache M, a... |
b3ad9fbcbb559a4c82511adb34951cc12a5be7b1 | 0172e34e195b6f2ce39eee04c3282bcd88836c87 | /UdaCityLesson3/diamonds.R | 55cb5594fcddfb69cc287ad26e99eea6dea6041b | [] | no_license | o001705/RFolder | f9f1b47ffce2777f2017ebe40cab7d757b5877a4 | a2bafca2fa036c6e8b02aa5257567d70aeab241c | refs/heads/master | 2021-01-25T09:38:06.679537 | 2017-06-15T04:07:06 | 2017-06-15T04:07:06 | 93,474,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 412 | r | diamonds.R | if (require("ggplot2") == FALSE) {
install.packages("ggplot2")
require("ggplot2")
}
data(diamonds)
summary(diamonds)
df[sapply(df, is.factor)]
print("Number of Observations: ")
nrow(diamonds)
print("Number of Variables: ")
ncol(diamonds)
i <- 0;
print("Number of ordered variables")
for (name in sapply(diamonds... |
9e62f0f5dcb75d70a30bea357f553874b5c2246c | f885f99d0090261317b8528128a1a72958760610 | /man/activity_instance_id.Rd | 1b493b2539dabcd5dea0f3909d981b76f00f941a | [] | no_license | BijsT/bupaR | 7a78d0d15655866264bab2bb7882602804303272 | 19f5e63c7393be690addf3c3977f1d00d0cdbfaf | refs/heads/master | 2021-08-26T06:40:32.388974 | 2017-11-21T23:12:47 | 2017-11-21T23:12:47 | 111,611,796 | 0 | 0 | null | 2017-11-21T23:11:10 | 2017-11-21T23:11:09 | null | UTF-8 | R | false | true | 524 | rd | activity_instance_id.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/activity_instance_id.R
\name{activity_instance_id}
\alias{activity_instance_id}
\title{Activity instance classifier}
\usage{
activity_instance_id(eventlog)
}
\arguments{
\item{eventlog}{An object of class \code{eventlog}.}
}
\descr... |
50e72abc07d89345b81ede3a7eee31fa4d646ceb | c734ebad500a91214f13c0d8cb802cb4b3226564 | /deploy_MultiLabLeaderboard.R | 3379fc560bceb82058508876154e32d7d49d7edd | [
"BSD-3-Clause"
] | permissive | christacaggiano/MultiLabLeaderboard | 5efd35cfa65852c58c4155c248def329d9d64313 | 893747a59792eb4152fa6575a9fbeda1ef69fea4 | refs/heads/master | 2021-05-24T15:38:31.285151 | 2020-04-06T22:58:30 | 2020-04-06T22:58:30 | 253,634,744 | 0 | 0 | BSD-3-Clause | 2020-04-06T22:59:49 | 2020-04-06T22:59:48 | null | UTF-8 | R | false | false | 95 | r | deploy_MultiLabLeaderboard.R | #deploy_MultiLabLeaderboard.R
#install.packages('rsconnect')
library(rsconnect)
deployApp() |
ac4b70f8f0d26ea0d65a9af8bad90a331fa9b641 | 0072f4019005b613c4f0bb322f4ab67167520afb | /man/nse_BB.Rd | 547b5d4919da4eddce4a84411db8d086a200bf87 | [
"MIT"
] | permissive | sudhi1989/nse1minR | 629df41fd3c9bb85b708d4c58f63e01b71efd6f4 | b68eb6c48f2c8856c503a8f343eebcfcf7983602 | refs/heads/master | 2020-07-01T17:08:29.135586 | 2016-11-29T15:57:25 | 2016-11-29T15:57:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 835 | rd | nse_BB.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{nse_BB}
\alias{nse_BB}
\title{Individual stocks 1 Minute Chart data}
\format{A data frame with five variables: \code{id}, \code{date},
\code{time}, \code{open}, \code{high}, \code{low} , \code{close} and \code{vol}... |
a14e5575fa003e06507a407d14c0292fbb1052c7 | c89f5c0195c40aafcb4757be733f7d16dd9116bf | /data/dominated.R | 287a48850bf618320487b13cfe858e1f123f9e4d | [] | no_license | maxconway/PartIII-diss | 44d4bc384f16fe9f5b00e9bccca61dc480263d02 | 09fa04a5dd471bb76c2803062518a57735585636 | refs/heads/master | 2021-01-01T05:39:43.330409 | 2013-05-01T12:04:43 | 2013-05-01T12:04:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 747 | r | dominated.R | stop('this function should be obtained via purl')
dominates <- function(a,b){
# does a dominate b?
any(a>b)&!any(a<b)
}
#helper function
singledom <- function(p,front){
stopifnot(is.data.frame(p) & nrow(p)==1) # this also covers NULL
for(i in 1:nrow(front)){
a=front[i,]
if(dominates(a,p)){
retu... |
28c55f69e8ef0ac1e5bcd93744ba3f681ceade16 | c389484130a8245d4b634c56ca911daf54e8eee9 | /home.R | 857f2a15a72dcf6ba52db56c07bcefe5ce1e5889 | [] | no_license | SARussell-CO/prox | 5d246375078e34e8012b32263c437e5ae23b9a7b | cab50cedafce7875a85c0b8899d89376b8a8c001 | refs/heads/master | 2021-08-23T00:35:15.617034 | 2017-12-01T23:03:08 | 2017-12-01T23:03:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,169 | r | home.R | # This will be the home screen for doing several different functins using R
# Sync r and github 12/1
# Clean up prox data and assign clean data set to the desktop for import
source("proxClean.R")
proxClean()
# Most demos in a specific month for top n canvassers----
source("mostDemos.R")
# Load the data into an... |
7028b7886df63569a93b170b58827487dec40ffc | 89bfd2ed18de2845834ff4754f3be23bf45352cf | /Fig.5_TME_analysis/bioR17.pheatmap.R | 8a6b3fad33bc2c42c130eb0c86c12f1a4e379caa | [] | no_license | luyuitng/ICB_projects | 060162beeed03d5aeecf286f53f015483bb6076f | 2966dee54358759f3692a57df0dabb4a842cbc9e | refs/heads/main | 2023-04-22T06:53:22.576300 | 2021-05-08T15:47:34 | 2021-05-08T15:47:34 | 365,549,511 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 661 | r | bioR17.pheatmap.R | #install.packages("pheatmap")
library(pheatmap)
inputFile="input2.txt"
groupFile="group2.txt"
outFile="heatmap.pdf"
setwd("E:\\Lenvatinib\\TIME")
rt=read.table(inputFile,header=T,sep="\t",row.names=1,check.names=F) #读取文件
Type=read.table(groupFile, header=T, sep="\t", row.names=1, check.nam... |
8b240ed52f7a11f0e932080b03dc7b4ef409afaf | 205bad3b5b7aeb85300e947a8db358b9536cb7b1 | /ensg/miashs/fda/tps/tp1/saison.ufo.R | 9f17c1e11a3e1c5ad82480fed3cf68fc8a8347db | [] | no_license | philippe-preux/philippe-preux.github.io | 744e93a59076a74caf5aeec02fedb383257592f6 | 1e34ad659c214b215134177d8e01f3c4052bfef1 | refs/heads/master | 2023-08-31T03:37:21.383038 | 2023-08-18T13:00:36 | 2023-08-18T13:00:36 | 150,579,919 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,352 | r | saison.ufo.R | ufo <- read.csv ("ufo.us.csv")
ufo$DateOccurred <- as.character (ufo$DateOccurred)
ufo$DateOccurred <- as.Date (ufo$DateOccurred)
ufo$month <- months.Date (ufo$DateOccurred)
month.occurence <- table (ufo$month)
# on ré-ordonne les mis dans l'ordre de l'année
# il faut un as.table() pour que l'objet demeure de classe ta... |
d8e91333ba9732754195dbbac54658021256df6e | 02301ac0beb647d501c19f5f34c7689a98e8dee4 | /man/order_cpp.Rd | 911e81d21e10a9965cab3a4d376e909f064f96e1 | [] | no_license | brenthueth/twosamples | 53fc387eb3796294ac31c0710039f10134b53821 | 6e35086c405459d3aafc276d7dc625f72b54a24c | refs/heads/master | 2020-11-26T18:52:48.921782 | 2018-12-03T16:14:39 | 2018-12-03T16:14:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 412 | rd | order_cpp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{order_cpp}
\alias{order_cpp}
\title{Order in C++}
\usage{
order_cpp(x)
}
\arguments{
\item{x}{numeric vector}
}
\value{
same length vector of integers representing order of input vector
}
\description{
Simply finds the ord... |
fc28df85e6287c56e1079cb1497f44dc4312ffc6 | 820c0a5f34c4e9899db608c6ccbdc3e2e853f2d8 | /man/aki_stages.Rd | c4a929c7d5795689c83c4cea0871d253e223d5df | [
"MIT"
] | permissive | alwinw/epocakir | 887c0fd0b66251c67d922613e1420effff003611 | a1bcd4567fb2d91cde53453dff5991af967c4860 | refs/heads/master | 2023-05-23T06:05:55.385634 | 2023-01-06T12:52:15 | 2023-01-06T12:52:15 | 296,596,576 | 4 | 1 | NOASSERTION | 2022-12-16T10:25:30 | 2020-09-18T11:05:01 | R | UTF-8 | R | false | true | 451 | rd | aki_stages.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aki.R
\docType{data}
\name{aki_stages}
\alias{aki_stages}
\title{AKI Stages}
\format{
An object of class \code{ordered} (inherits from \code{factor}) of length 4.
}
\usage{
aki_stages
}
\description{
Ordered factor of AKI stages
}
\details{
S... |
cf165247d0945e3b2c8eff66007053c3955c74fc | eaa13fef2e1d8e6afe55513435a444224fa0f98c | /man/input_rich.Rd | b5de285d2907e36346e041db306895165337681f | [] | no_license | lshep/LorisWorld | 02b77b58486398eaba13fd2ac7552fa0ab7b6d5e | 4dfc6fefb5e77d25b9c58476f2ccfce135286339 | refs/heads/master | 2021-07-04T14:42:11.130027 | 2020-10-14T14:29:46 | 2020-10-14T14:29:46 | 70,102,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 680 | rd | input_rich.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rich.R
\name{input_rich}
\alias{input_rich}
\title{Rich data input}
\usage{
input_rich(pdata_file, exprs_file)
}
\arguments{
\item{pdata_file}{character(1) The path to the phenotype data file}
\item{exprs_file}{character(1) The path to the e... |
caa0f78e32c8af41de87699e630b2714b594adb2 | 5a83db1fc1af0b72ba8f8ad70293d14e10d14658 | /ggmap.R | 60c409818951d5a7bfcdf39b8bb0dd4d989a2eb3 | [] | no_license | dpebert7/Rsentiment | e3ca9cb3f3025e2bb6f519cbdbdc9085d5218ca6 | a78a0f88ddd48104286c816c86f40252fb380d4b | refs/heads/master | 2020-04-12T09:01:48.488311 | 2016-08-22T22:52:03 | 2016-08-22T22:52:03 | 51,659,917 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,190 | r | ggmap.R | #Ebert/Rider
#Created 16 April 2016
#Purpose: Create sentiment score map of LA2014 data.
#load a little bit of sample data from x
load(file = paste(storage.directory, "x1.RData", sep = ""))
x1 = x1[x1$username != "GoldstarPgh",] #remove friggin Pittsburgh newspaper and its >3000 tweets
x1 = x1[x1$username != "Gold... |
771267e044d2f1b1f8b13d696b0ba1c32d3e6ed4 | f5147eb64730136d2a7d75a0f80325742a0d64b8 | /man/compute_log_lik.Rd | ce032525864e5114e3eeed14624172b1f0222072 | [] | no_license | JohnNay/sa | 04c3a8f84df5af9582660fed443982a3ac1f0fce | ca846dab26341ec271e7f1d9ab382744532cd73e | refs/heads/master | 2021-01-10T16:36:46.461493 | 2016-04-18T18:16:06 | 2016-04-18T18:16:06 | 55,555,996 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 691 | rd | compute_log_lik.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/loss_functions.R
\name{compute_log_lik}
\alias{compute_log_lik}
\title{Compute Negative Log Likelihood}
\usage{
compute_log_lik(prediction, actual)
}
\arguments{
\item{prediction}{Numeric vector same length as actual}
\item{actual}{Numeric v... |
45eb74ca40f256535953fc7be01dc8f6b4eb4f86 | 7560c46d48abf3267c412531fe12857f0a5c8100 | /analise_china.R | 90a6dd9138b2e296d555189d07300fca43f83ae5 | [
"MIT"
] | permissive | marcusfreire0504/desafio-datathon | a00595fdda9255f71fe56afb7692f5beeab9adeb | f23511bf0f643e53ad5d8c551d69b01d3e9b0009 | refs/heads/master | 2022-12-03T02:04:01.720628 | 2020-08-24T22:17:47 | 2020-08-24T22:17:47 | null | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 2,199 | r | analise_china.R | library(xts)
setwd("C:/Users/JOAO VICTOR/Desktop/env/desafio-datathon")
shangai <- read.csv(file = "dado_shangai.csv", fill = FALSE)
hong_kong <- read.csv(file = "dado_hongkong.csv", fill = FALSE)
crypto <- read.csv(file = "dados_crypto.csv", fill = FALSE)
trans_ts <- function(data_prices){
#transforma em time series... |
2220f1c5de9b08d5fc0b71ac701131784595933e | e77f2769ef4601cda5fcea35336f1dd1c6157b59 | /plot1.R | 5f8b7a1dca1fc4375214dd5440e2ecd121af1633 | [] | no_license | Elsabell/ExData_Plotting1 | dd2d9dea8f1cdcb8badb3a3a25fd138146c4a85d | 5c4265c0611c2787576082f3c1a946e4f7414496 | refs/heads/master | 2022-12-05T19:04:37.722536 | 2020-09-01T13:59:05 | 2020-09-01T13:59:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 762 | r | plot1.R | library(dplyr)
hpc_data <- read.table("household_power_consumption.txt", header = TRUE, sep = ";")
# dim(hpc_data); names(hpc_data); head(hpc_data); str(hpc_data)
# length(grep("^(1/2/2007|2/2/2007)$", hpc_data$Date))
hpc <- hpc_data[grepl("^(1/2/2007|2/2/2007)$", hpc_data$Date), ]
hpc$DateTime <- paste(hpc$Date, hp... |
b1c3ca09e91bc052f507cde6399fb799a44786d0 | 695b88a36f548e410d8a4181ed7c6f433c7515a1 | /man/lsmip.Rd | 952961afcc15bff0bba82b0339c7396b274865b2 | [] | no_license | jonathon-love/lsmeans | 16054e0a830df482fd6aa41b5a461535afb8d4bb | c6e91712705647bbd9aa2fa37e65929907fecca9 | refs/heads/master | 2021-01-12T00:31:38.801483 | 2017-08-02T11:31:32 | 2017-08-02T11:31:32 | 78,736,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,336 | rd | lsmip.Rd | % Copyright (c) 2012-2016 Russell V. Lenth %
\name{lsmip}
\alias{lsmip}
\alias{lsmip.default}
\alias{pmmip}
\title{
Least-squares (predicted marginal) means interaction plot
}
\description{
This function creates an interaction plot of the least-squares means based on a fit... |
82a20b9cc9cc4a8f161c5377a12ec0efce411365 | af177c8bb0c9e496574f930e78584fe5c0f2dfe8 | /man/set.Rd | 7a2e2792666ec2608811cb07337c3149c72075ca | [] | no_license | nteetor/roasted | 03055011938b90ef290b9c8b29001862479a900d | ca2b17de3e31655642a7d3c4a10c3d52197cbb2d | refs/heads/master | 2021-01-10T05:49:28.262776 | 2016-10-26T21:19:31 | 2016-10-26T21:19:31 | 51,217,371 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 166 | rd | set.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set.R
\name{set}
\alias{set}
\title{Set}
\usage{
set()
}
\description{
A set object.
}
|
474178be1f01db9cc07ee5bcfb2ae32d87e326ff | 5ac10be9eb74619aa005ba1a7594c15a7b3d8bb8 | /NYC_Housing_Complaints.R | 8f1973807a200a8c712d834a48fed5e30f70f86e | [] | no_license | Gabrielepa/Capstone_Housing_complaints_R | be895d62dca1d049707fe7b52add9c486ffeb6f3 | 0d418161fedc6c31af3cf6dea732714bb884ae60 | refs/heads/master | 2021-05-17T16:22:55.106166 | 2019-09-25T20:28:40 | 2019-09-25T20:28:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,939 | r | NYC_Housing_Complaints.R | ## Library
if(!require(tidyverse)) install.packages("tidyverse", repos = "http://cran.us.r-project.org")
if(!require(caret)) install.packages("caret", repos = "http://cran.us.r-project.org")
if(!require(leaflet)) install.packages("leaflet")
if(!require(randomForest)) install.packages("randomForest")
library(tidyvers... |
fa6da881d7219c075536a62d328e9998d8197500 | cae6eb7fb6d208173c4fb6ea95cdf3b1c38713f1 | /plot3.R | 04a61a1a2a7c3c44773d82a26c4fce4c56ddbe2c | [] | no_license | MMohey/ExData_Plotting1 | 4a2223865acde3eb9ab71b851014cee6c5d07ac4 | 8e94191fa28a140f6b193c6f5191dcf0c9e18aac | refs/heads/master | 2020-12-24T06:59:08.731863 | 2016-11-11T21:21:06 | 2016-11-11T21:21:06 | 73,385,183 | 0 | 0 | null | 2016-11-10T13:35:30 | 2016-11-10T13:35:30 | null | UTF-8 | R | false | false | 1,186 | r | plot3.R | ## plot3.R plots "Sub_metering_1", "Sub_metering_2" and "Sub_metering_3 from
##the "House Power Consumption" Dataset for a specified dates
original_data <- # importing the data set
read.table(
"household_power_consumption.txt",
header = TRUE,
sep = ';',
stringsAsFactors = F
)
original_data$Date ... |
ed81b29687df8669e8b3c44bce300408cb2fc677 | fe5166da0b2085b9ebd741acc9c23694ffc09387 | /man/shinyExplorer.Rd | c20fef4265e4a454eadfe2a0a9247ddd372b88b7 | [] | no_license | DrRoad/shinyExplorer | a7badb864a5e08c62020945197f8f4b33ad01422 | 17be823037dbf4bf9157a81b388640e627db398e | refs/heads/master | 2021-10-21T14:05:47.553903 | 2019-03-04T12:17:34 | 2019-03-04T12:17:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 387 | rd | shinyExplorer.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/shinyExplorer.R
\name{shinyExplorer}
\alias{shinyExplorer}
\title{Run shinyMlr}
\usage{
shinyExplorer(...)
}
\arguments{
\item{...}{[\code{any}]\cr
Additional arguments passed to shiny's
\code{runApp()} function.}
}
\description{
Run a local ... |
11c9df7faaa33f2d90bcd1a91a35adcfdbc1e4be | 3b361820e93c9cbaa7e740b6edbf13c03a1cfcce | /R/stackloss.R | e3890005916210b5393cbe466c64c6413c1df164 | [] | no_license | msalibian/RobStatTM | f8dabc88197be2460f1ba4c95b595e95ff53c1e9 | d542c29816d50889f25649817e3ae5de08946141 | refs/heads/master | 2023-05-14T08:42:27.747789 | 2023-05-09T17:08:37 | 2023-05-09T17:08:37 | 83,067,068 | 14 | 7 | null | null | null | null | UTF-8 | R | false | false | 719 | r | stackloss.R | #' Stackloss data
#'
#' Observations from 21 days operation of a plant for the oxidation
#' of ammonia as a stage in the production of nitric acid.
#'
#' Format: 21 cases and 4 continuous variables.
#' Description: The columns are:
#' 1. air flow
#' 2. cooling water inlet temperature (C)
#' 3. acid concentration (%)
#'... |
49d7646ac3f32fdca864d90f06e0c695ffa76951 | ffb7b79a8b47c09c1fc937b5361acf2cf813b804 | /man/abiotics_rescaling.Rd | a96e438389605ff73ba6b731fce38f5ce29fe0c7 | [] | no_license | GregoireButruille/FishNmix2 | f72be9a9600c56ca570d271ef0f3830c75ce7a63 | ddf5e448336e93c6bc5792e4e89fee98b2cfb66c | refs/heads/main | 2023-07-02T16:24:41.651324 | 2021-08-12T15:04:17 | 2021-08-12T15:04:17 | 377,401,335 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 891 | rd | abiotics_rescaling.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/abiotics_rescaling.R
\name{abiotics_rescaling}
\alias{abiotics_rescaling}
\title{Title Rescale abiotics data to 10 or 30 arcmins}
\usage{
abiotics_rescaling(
flo1k_data,
worldclim_data,
earthenv_data,
minlat,
maxlat,
minlong,
ma... |
3c6e9d3a4339d65002f963ee4bc871cd787ce634 | 9176b0f0ce12a42d1ac2a868e5f41a93d3446405 | /ML/R-script/chap23_randomForest.R | 8aea173a6455bbfe7a25a693321dddfbc2fb008a | [] | no_license | jk-jang/R | 5448f0d53b9c2012f0bc513cdfaf2349d1770aa2 | c70e80484a1980b9330f032f1f20cdc4fa4e61c2 | refs/heads/master | 2020-12-24T06:58:28.970344 | 2017-05-05T09:10:47 | 2017-05-05T09:10:47 | 58,716,839 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,614 | r | chap23_randomForest.R | ##################################################
#randomForest
##################################################
# 결정트리(Decision tree)에서 파생된 모델
# 랜덤포레스트는 앙상블 학습기법을 사용한 모델
# 앙상블 학습 : 새로운 데이터에 대해서 여러 개의 Tree로 학습한 다음,
# 학습 결과들을 종합해서 예측하는 모델(PPT 참고)
# DT보다 성능 향상, 과적합 문제를 해결
#분류절차 : 여러개 Tree 학습 -> 모델 -> 종합(Voti... |
092d0f84bbeecc7a325affeccd0025144186396e | 38d52a7e16b96555f277cb879a69d3f1ba086dad | /R/get_chain_statistics.R | fa4703d5962607d884d0cbdb1d37d59fb8cb6a46 | [
"MIT"
] | permissive | next-game-solutions/tronr | c7ec41a0785536670942c653f0f1500f09e7e692 | e7eb8b1d07e1c0415881ca3259358f707d78b181 | refs/heads/main | 2023-06-19T03:06:34.302241 | 2021-07-12T22:01:05 | 2021-07-12T22:01:05 | 305,829,963 | 7 | 0 | NOASSERTION | 2021-07-12T22:01:06 | 2020-10-20T20:48:08 | JavaScript | UTF-8 | R | false | false | 4,165 | r | get_chain_statistics.R | #' Get historical metrics of the TRON blockchain
#'
#' Retrieves historical values of the blockhain statistics
#'
#' @param days (double): number of days to look back (defaults to 14 days).
#' @param include_current_date (boolean): whether the current date's statistics
#' (as of the query time) should be returned. D... |
a61d04846b8e0d16be935d520cce49588469b6ec | 4fd7dd0ea9f1e73f9d14031d750f4ba02dce3b1b | /man/optimize_refTemp.Rd | e66dc855992ab4fce7e53aab77d3682c58392336 | [] | no_license | jlpesoto/prueba | 8bd4be6551058a064f2e355aa34051336784a454 | 7bcbe619ef35d9e9b60abc539ae11dc5b0966e49 | refs/heads/master | 2020-06-30T17:30:29.359435 | 2019-08-06T17:46:48 | 2019-08-06T17:46:48 | 200,897,474 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,570 | rd | optimize_refTemp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/optimize_ref_temp.R
\name{optimize_refTemp}
\alias{optimize_refTemp}
\title{Optimization of the Reference Temperature}
\usage{
optimize_refTemp(temp_ref0, lower, upper, inactivation_model, parms,
temp_profile, parms_fix, n_times = 100)
}
\a... |
e29a2ca2cec367ddfde8d747eb4dca0b8be2c641 | 9831fdd8ce4d827b271913b536e484555e6196ab | /plot2.R | 9c5df3a4e9394d7cdf95e71031dc2d1bba09dfce | [] | no_license | jlmarrero/ExData_Plotting1 | 9fb11411aa66ca5f9af508af06f3019380cb3d94 | cfe14ab029e7d30622f0d72eedd635ab2afe1c0a | refs/heads/master | 2021-01-18T01:08:21.017507 | 2016-01-11T02:16:04 | 2016-01-11T02:16:04 | 49,390,162 | 0 | 0 | null | 2016-01-10T23:18:58 | 2016-01-10T23:18:57 | null | UTF-8 | R | false | false | 736 | r | plot2.R | # The following script assumes the "Individual household electric power consumption Data Set" resides in your working directory.
# Load data
df <- read_csv2("household_power_consumption.txt", n_max = 100000)
# Subset the data
df$Date <- as.Date(df$Date, format = "%d/%m/%Y")
new_df <- subset(df, df$Date == "2007/02... |
cd9b8003981478ca3846db342f98160e6275fece | f0f76daa6900a2330aa432ea78517c721afae8e6 | /code/Step1_forecast_aq_potential.R | 684f2182d960e23a068091e87a104864fdb3d4f5 | [] | no_license | cfree14/aquacast | cea44f83fdaec3efbe8d94ed26f3c52907d0a7b5 | 04d3936b6e9115ea593cb86d91fed562f7385701 | refs/heads/master | 2022-04-29T01:28:59.820766 | 2022-04-27T18:59:07 | 2022-04-27T18:59:07 | 206,387,817 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,509 | r | Step1_forecast_aq_potential.R |
# Clear workspace
rm(list = ls())
# Setup
################################################################################
# Packages
library(raster)
library(ggplot2)
library(tidyverse)
# Directories
codedir <- "code"
sppdir <- "data/species/data"
outputdir <- "/Volumes/GoogleDrive/Shared drives/emlab/projects/curr... |
76dc885c409db6e6d224b1d66cb0c3a4d8e19d3e | 0f43b7df4006ca85de76f5209b85aa39c649150f | /man/load_extra_iots.Rd | 7aa796485c87429114da6d8b5b72ce9e9da789bb | [] | no_license | sybrendeuzeman/WIOD_package | 29c66e8b17415236421c534c309c1f712cf860ac | edcb5485bd4d49b9a6310d7dfc172f1d8864366c | refs/heads/master | 2020-07-02T04:44:56.131660 | 2020-01-23T18:22:31 | 2020-01-23T18:22:31 | 201,419,749 | 1 | 0 | null | 2020-01-23T18:22:32 | 2019-08-09T07:54:13 | R | UTF-8 | R | false | true | 1,192 | rd | load_extra_iots.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/load_extra_iots.R
\name{load_extra_iots}
\alias{load_extra_iots}
\title{Load extra data to multiple IOTs.}
\usage{
load_extra_iots(iots, extra, directory = get("dir_data", envir = paramEnv))
}
\arguments{
\item{iots:}{List of Input-Output Tab... |
3f1b0213277bdca4ee72a954ba0b0568a2b1797f | 72002076d389156ac665caef39f8f193ab636236 | /identity.r | 8fe51121e0ff9b3bbedf29441eac7e10a8bf0f98 | [] | no_license | Betawolf/panning-scripts | b1050a1616c971a017e093e0826f0184351e01fe | 2db9dcb06429470993d71b199190b0e83f334250 | refs/heads/master | 2021-01-13T13:05:31.342112 | 2017-01-11T21:20:17 | 2017-01-11T21:20:17 | 78,681,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,199 | r | identity.r | #The ROCR package for various plots and performance assessments
library(ROCR)
library(PRROC)
#The data from the attribute comparisons.
ident_all <- read.csv('identity.csv')
# Display the data structure. The origin and target variables identify the
# two profiles involved in the comparison. The block variable identifi... |
5be6353cfccb430d9da98c27a8e3f2f03e961fae | 43eae47269ee5a073218dcda8eed82e3e18c6312 | /man/priors_analyze.Rd | e3997dc11523d5dc39c5f494b4509eb3445a4eb2 | [] | no_license | wlandau/fbseqStudies | 4e494d25165130f95a983ee64d751ba3fb24bd3d | 0169ac5a00d457a261401f926b37e08587eace64 | refs/heads/main | 2021-01-19T04:39:24.890203 | 2017-10-21T02:36:07 | 2017-10-21T02:36:07 | 45,758,088 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 389 | rd | priors_analyze.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/study-priors_analyze.R
\name{priors_analyze}
\alias{priors_analyze}
\title{Function \code{priors_analyze}}
\usage{
priors_analyze(from, to)
}
\arguments{
\item{from}{to directory to save simulations and results}
\item{to}{output directory}
}... |
8ee1841c7d1a776f0675f0a5bbe38d037ef661ba | 1532a1c2779863ab1cf6e3a5d3d7f93c520fd881 | /Forecast_Combo_Flow.R | 1ec08a91d20229f2d13299b1c42a515757d7e6a1 | [] | no_license | gsukisubramaniam/Trend_Forecasting | 5dbc714517f07cf1c8ba1d31e2a8665b892801e1 | bfa6d512ac57ee560e3efc2ce97bd8f75172a1f8 | refs/heads/master | 2020-12-30T10:37:34.532971 | 2017-07-31T06:10:23 | 2017-07-31T06:10:23 | 98,853,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 426 | r | Forecast_Combo_Flow.R | ## Execute this code after loading the data in the table "data" with values in the column name "value"
library(tseries)
actual<-ts(dataset$Value,frequency = 12,start = c(2014,1))
Decompose_Actual <- decompose(actual)
seasonal <- Decompose_Actual$seasonal
trend <- Decompose_Actual$trend
deseason <- actual-seasonal... |
d8df04ac3b8ea21bb8f008cf5b8b75a891329556 | 1a439dc569ec3025b76cb76ff9dd71fe6614ef88 | /createNetCDF/CreatePhaseMap.R | c75c682bf9a906a80827d6209a049ec028dae24e | [] | no_license | seanyx/TimeSeries | f2ca38e1fcea182b3f43404e2b151df5d1c6127b | c671823c74fe14fd632b67890c13be39c54ba975 | refs/heads/master | 2016-09-05T13:59:12.509273 | 2014-08-12T17:55:55 | 2014-08-12T17:55:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,563 | r | CreatePhaseMap.R | library(RNetCDF)
source('~/Dropbox/netcdf_test/CreateNetCDF.R', chdir = TRUE)
source('~/Documents/TimeSeries/createNetCDF/phaseshiftest.R', chdir = TRUE)
olddir=getwd()
setwd("~/Dropbox/netcdf_test/")
data=open.nc("SST_Xiao.nc")
lat=var.get.nc(data,'lat')
lon=var.get.nc(data,'lon')
sst=var.get.nc(data,'sst',collapse=F... |
7895f250666c36e1c2f4dcdeb350bdc330142252 | eb9bbef62511e003f9e9d675b428f83c6f77ac8d | /man/davies_bouldin.Rd | 30748366b7d54b49abb441593a40fe43c26c910e | [] | no_license | baptlano24/clustyanaly | b6495910b6db2bbcef082aeb11335fda304d2b42 | 91176e0f9bb4fc423aca2d3a05be6a6b57f6be17 | refs/heads/main | 2023-01-23T01:00:21.809246 | 2020-12-08T22:34:40 | 2020-12-08T22:34:40 | 319,457,468 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 787 | rd | davies_bouldin.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/davies_bouldin.R
\name{davies_bouldin}
\alias{davies_bouldin}
\title{davies_bouldin function}
\usage{
davies_bouldin(X, clust, distance = "euclidean")
}
\arguments{
\item{X}{data_frame or matrix of features must be numeric}
\item{... |
26cbd4daa57d4f9c3837de08cc372c0c8ce560c4 | 56a409f07b25747e195282126664d11ed9a4d37b | /Preparation.R | 07921a9a1f38e50e5caeb8fef1ababfd1e43a046 | [] | no_license | tanas57/mushroom-machine-learning | 684f43c17794298961349d468bb4f9f12eb0e884 | e3b3acb685db1ccd6dd50de19a0f46b7d0d36798 | refs/heads/master | 2020-06-16T15:30:33.878660 | 2019-05-05T15:17:54 | 2019-05-05T15:17:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,802 | r | Preparation.R | #install.packages("Amelia")
#library(Amelia)
columnNames <- c(
"class", "cap_shape", "cap_surface",
"cap_color", "bruises", "odor",
"gill_attachement", "gill_spacing", "gill_size",
"gill_color", "stalk_shape", "stalk_root",
"stalk_surface_above_ring", "stalk_surface_below_ring", "stalk_color_above_ring", ... |
97b5ba02308e0dfa72f53d05a61bcb4be512665a | 19303ed7628348fdd90abf34431f188ec486237a | /MacheLeafletWidget.R | 7b296548a306ba4c131e0f8f5be9f1bc0ee916c3 | [] | no_license | Hen-son/WebtechUrbanism | 4a8c88016933efe2942010f7548d00399c5778bc | e7cf63fe567172e643b301c22a2c56f792454949 | refs/heads/master | 2022-04-26T20:16:18.003006 | 2020-04-28T17:36:42 | 2020-04-28T17:36:42 | 256,495,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 549 | r | MacheLeafletWidget.R | library(leaflet)
library(dplyr)
webtech_berlin <- read.csv("data/webtech_located.csv", stringsAsFactors=FALSE)
label <- paste(sep = "<br/>", webtech_berlin$Firma, webtech_berlin$URL)
WebtechKarte <- leaflet(data = webtech_berlin) %>%
addTiles() %>% # Add default OpenStreetMap map tiles
addMarkers(lng= ~Lon, lat=... |
bc6f2e8de257f1e16e1b41bdaf7e478cdf107d4f | 114663abec8e5796de6f5ae859547dd31962185f | /indeed_data.R | bde07daff0bbd8043b5bbed3db8b6d4c5336e82a | [] | no_license | pauljchoi2/DataFest | 4ff637add26200cba58a044acd50e56dedffbc2a | 967f41293cacb39e5992fbeddb9bf33fb5301762 | refs/heads/master | 2020-03-14T20:11:16.072407 | 2018-05-02T00:07:02 | 2018-05-02T00:07:02 | 131,772,666 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 539 | r | indeed_data.R | indeed_data <- read.csv("raw-data-datafest2018.csv", header = T , na.strings = c("" , "NA"))
indeed_data$educationRequirement <- as.character(indeed_data$educationRequirement)
indeed_data$educationRequirement[indeed_data$educationRequirement == "None"] <- "0"
indeed_data$educationRequirement[indeed_data$educationRequi... |
6181c25964cf6e94c9b06ba3335544b69a9ec304 | d74e2c19e6e3901ba6c49ae811ae22869f164f8f | /ensemble.R | bcbe2caf23a6a0c8d1fefb67b9589352d6fb9c28 | [] | no_license | noahhhhhh/Melbourne_Datathon_Kaggle | c09604beca03ef334c819a765b8c79aa9b10f5dc | 1039b7f333526c3ff85480e9502586bd1050d8bb | refs/heads/master | 2021-01-17T05:08:22.808019 | 2015-12-11T04:40:43 | 2015-12-11T04:40:43 | 45,720,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,338 | r | ensemble.R | rm(list = ls())
gc()
load("trainEnsemble.RData")
load("valid1Ensemble.RData")
load("valid2Ensemble.RData")
load("testEnsemble.RData")
dt.test.ensemble[, rf:= 1 - rf]
# try simple blending with rf[, 2]
pred.bagging <- rowSums(dt.test.ensemble)
dt.submit <- data.table(Account_ID = dt.test$ACCOUNT_ID, Prediction = pred... |
d2ff39d8c5018df2a41f4b08f25e5332822541f7 | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /paws/man/sesv2_get_deliverability_test_report.Rd | 182348f6b3145378f657429737435478a0e3ff20 | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 619 | rd | sesv2_get_deliverability_test_report.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sesv2_operations.R
\name{sesv2_get_deliverability_test_report}
\alias{sesv2_get_deliverability_test_report}
\title{Retrieve the results of a predictive inbox placement test}
\usage{
sesv2_get_deliverability_test_report(ReportId)
}
\arguments{... |
e98f91deb3dbf16e8287b589720a5d9b684b0f1e | 3faa635909c553b5ec850eb5fba14f57826838b5 | /Demonstrator/MDL Workshop/Acceptance Testing/MDL-Workshop/TestScript_UC51.R | cb98c499b464996dee791677cdae381c8f99e14c | [] | no_license | DDMoReFoundation/testingresources | 6bdfa08288bed1c9e2d9167c2bd1889ac914e59e | c1af2a7610214e059d9bc570200e08fa43416e70 | refs/heads/master | 2021-01-23T02:16:25.321962 | 2016-07-29T11:26:35 | 2016-07-29T11:26:35 | 85,980,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,735 | r | TestScript_UC51.R | #' Executing Nonmem against supported models
#' =========================================================================================
#' =========================================================================================
#' Initialisation
if(!exists(".MDLIDE_WORKSPACE_PATH") || is.null(.MDLIDE_WORKSPACE_PATH... |
de121d19350003c7fab53262d729ac9784466b97 | 0e754612e667b5f44de6194d73a3139bc624f048 | /plotScripts/permutationTestMotifColocalization.R | 4ab585d32df5b5df829873cac388e57efc5ebd72 | [
"MIT"
] | permissive | emsanford/combined_responses_paper | 068d0a26a79533dbdb20ff83f67a764d669dd75f | 6d8d1e6f9e3e660f8acd4e748b5a80b3bb6c6d7c | refs/heads/master | 2021-08-08T01:55:47.880023 | 2021-01-03T23:20:22 | 2021-01-03T23:20:22 | 238,577,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,579 | r | permutationTestMotifColocalization.R | library(tidyverse)
library(here)
joinedTib <- read_tsv(here('extractedData', 'joinedTablePeaksNearGenes.annotated.tsv'))
geneTib <- read_tsv(here('extractedData', 'DeSeqOutputAllConds.annotated.tsv'))
peakTib <- read_tsv(here('extractedData', 'differentialAtacPeaks.annotated.tsv'))
n.permutations.to.make.null.dis... |
10d2c27e807438639ac779a4894de5fc35c2eef7 | 0c90b72ff5b57481b7edb74620333a7c3e3c603f | /R_finance/R_Quant_Portfolio/dynamic_assetallocation_strategy.R | 7825af35608770e2e4044b5b01837dcb05890ee7 | [] | no_license | diligejy/R | 41abd1cc6ef3731366f3196d3d428fd5541215e5 | 1b54334094c54e041b81d45f87c5c07336d62ff9 | refs/heads/master | 2022-09-11T18:13:10.614301 | 2022-08-21T14:57:21 | 2022-08-21T14:57:21 | 210,329,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,726 | r | dynamic_assetallocation_strategy.R | library(quantmod)
library(PerformanceAnalytics)
library(RiskPortfolios)
library(tidyr)
library(dplyr)
library(ggplot2)
symbols = c('SPY', # 미국 주식
'IEV', # 유럽 주식
'EWJ', # 일본 주식
'EEM', # 이머징 주식
'TLT', # 미국 장기채
'IEF', # 미국 중기채
'IYR', # ... |
e706b8e3ce794f17ac02bb29409e093bc4698d1c | 8925be5c960950efecf706f2a1b07472aced61ec | /src/R/run.edgeR.on.simulation.R | d23813cfa6ba7eb89ee4dc136c13681db2405c89 | [] | no_license | heejungshim/multiscale_analysis | fca34a072a2ed80fa1e28b1cb6f464bcc7577409 | b22c566894ec44e5bbb8570fd335697613f626c9 | refs/heads/master | 2021-01-18T23:40:15.643632 | 2016-05-14T19:02:13 | 2016-05-14T19:02:13 | 20,192,741 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,786 | r | run.edgeR.on.simulation.R | ## `run.edgeR.on.simulation.R' contains scrits to run edgeR on simulated data.
##
##
## Example Usage (see command in /mnt/lustre/home/shim/multiscale_analysis/analysis/simulation/sample_size/simulation_manyQTLfinal_v1/sum/edgeR/com/): R CMD BATCH --no-save --no-restore "--args wd.path='/mnt/lustre/home/shim/multiscal... |
b9857ab541c3afa1b25c768f7df06aeca8497447 | 4b02f42b79947ee0014fb3478fa255d1ee363314 | /scripts/annotation.r | ab19af987e62039a70931dbfb4ef027d78ef1b32 | [] | no_license | mavishou/project | 0893b82135322570abd3e5b2373e570f717b0318 | 4b8943a40924ca61b90f1f8ca66b7ed3c18975ef | refs/heads/master | 2021-01-22T05:33:34.865770 | 2014-11-14T14:35:24 | 2014-11-14T14:35:24 | 24,197,639 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 606 | r | annotation.r | anno <- read.table("V4_transcript_annotation_0331.txt", sep = '\t', header=T, stringsAsFactors=F)
bioType <- sort(table(anno[['gene_biotype']]), decreasing=T)
src <- sort(table(anno[['source']]), decreasing=T)
write.table(bioType, file = 'bio_type.txt', col.names=F, quote=F, sep = '\t')
write.table(src, file = 'source.... |
afbdd9ead82bf91d2e28cd12e57345e841f3ae41 | 50bf542824fc7d964a25e3bf30b2c2a9218acd8f | /R/simulate_SIR.R | 632af616f6afd4fd2cffb5cab4fcee19ed10d2d5 | [] | no_license | fintzij/BDAepimodel | 9a00a9796bbd302bace101e85bed05fdc17bb9fb | 0a59d785050401942bad2138cb13af58dc0dc62f | refs/heads/master | 2021-01-21T01:58:15.844456 | 2020-09-15T14:57:40 | 2020-09-15T14:57:40 | 36,148,207 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,460 | r | simulate_SIR.R | #' Simulate an epidemic with SIR dynamics and draw binomial samples.
#'
#' @param obstimes vector of observation times
#' @param params vector of parameters, must be in the following order: beta, mu,
#' rho, S0, I0, R0
#' @param popsize population size
#' @param init_config compartment counts at t0.
#' @param trim ... |
153e87a1a621e96c3c4c2eb86eb358994b2f2e65 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/klaR/examples/TopoS.Rd.R | c1f9a007664a22ab68a9506a56596b8d873549b2 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 355 | r | TopoS.Rd.R | library(klaR)
### Name: TopoS
### Title: Computation of criterion S of a visualization
### Aliases: TopoS
### Keywords: internal
### ** Examples
# Compute S for the MDS visualization of the german business data
data(B3)
plot(cmdscale(dist(B3[, 2:14])), col = rainbow(4)[B3[, 1]], pch = 16)
TopoS(dist(B3[, 2:14]), di... |
e9369f481a5fb265854229c7ece8e5de28c166a1 | 694c5b6e59fd4e0ee09515834bc8b800e2b596cc | /glmerDiagnostics.R | f56c9eed919be61d7cff7a8f7754873464e0830c | [] | no_license | CYGUBICKO/wash | b59985002deeed11dc45859cb7b10734ad3f7a4c | 06335f646cb73893a8f4d6c02e3dbda8409d51dc | refs/heads/master | 2021-07-09T03:32:46.114245 | 2020-08-28T01:41:09 | 2020-08-28T01:41:09 | 187,945,140 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | glmerDiagnostics.R | #### ---- Project: APHRC Wash Data ----
#### ---- Task: Simulation ----
#### ---- Complex Diagnostics ----
#### ---- By: Steve and Jonathan ----
#### ---- Date: 2019 May 06 (Mon) ----
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
library(gridExtra)
library(lme4)
library(lattice)
library(dotwhisker)
t... |
eb05eb43c1c85be99b021562823f8f316990bf4c | f132901efd10d3d2bcf6624bfb0e622cf759c5cc | /run_analysis.R | 0d97352bf7ee49e2921721cf4171fe08705c4cae | [] | no_license | tdwis/course3_project | 2abb8afb8bf297457163a755cee2265eb69602ea | 47cfab1bd3463ba370bee2d036a873374b1cb4a1 | refs/heads/master | 2021-05-04T06:03:20.647235 | 2016-10-16T10:44:45 | 2016-10-16T10:44:45 | 71,021,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,661 | r | run_analysis.R | #read raw data
x1 <- read.table(".\\UCI HAR Dataset\\train\\X_train.txt")
y1 <- read.table(".\\UCI HAR Dataset\\train\\y_train.txt")
s1 <- read.table(".\\UCI HAR Dataset\\train\\subject_train.txt")
x2 <- read.table(".\\UCI HAR Dataset\\test\\X_test.txt")
y2 <- read.table(".\\UCI HAR Dataset\\test\\y_test.txt")
s2 <- r... |
b7c5ea6d48b67c328d949c743be23e049bebbc94 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Introduction_To_Probability_And_Statistics_by_William_Mendenhall,_Robert_J_Beaver,_And_Barbara_M_Beaver/CH9/EX9.5/Ex9_5.R | 6bc299f993ce36c71c9989d6ebd453285a49cc11 | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 593 | r | Ex9_5.R | sample_mean <- 871; # sample mean
hypothesized_value <- 880; # hypothesized value
sigma <- 21; # standard deviation
sample_size <- 50; # sample size
z <- round(((sample_mean - hypothesized_value)/(sigma/sqrt(sample_size))),2)
z # test sta... |
3785dc31d295828658aa0d8c24fdea9d05e05221 | 21cd3dc891f02c973a581c5bce9cde72491e9dbb | /R/SimCMk.R | b4b51f597f4c45e6344884215a0c2799150bc5f4 | [] | no_license | aggounekatia/simCHMk | ff47d35774050663857e4b0640f7de2cc79dfe89 | e7631e447e6cb4e0e767fe38f89b2a7fa54cccd7 | refs/heads/main | 2023-06-04T13:00:32.618294 | 2021-06-30T10:24:26 | 2021-06-30T10:24:26 | 381,660,971 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 401 | r | SimCMk.R | #'simulation d´une chaine de markov a espace des etats fini
#'@export
#'@param P matrix of transition
#'@param mu numeric vector of initial distrebution
#'@param n numeric number of repetition
SimCMk=function(P,mu,n){
m=length(mu)
y=c(seq(1,m,1))
x=c(rep(0,n+1))
t=c(0:n)
x[1]=rdist(y,mu)
for(i in 1:n){
... |
a955b95d786f0f4cefcebcc7d9fa11442aded96e | 064b30bd608a223d8afeb077d32feda26e3c70af | /scripts/randomForestModel.R | 7dd28147dd68a3355838a6566bae639dc0ecbbbd | [] | no_license | dipeshnainani/Bank-Marketing-System-Analysis | 6485cea877499a50dba7c709bc39626d568fbd11 | 9277bd8448b05c07aae703822df373e54fccfc05 | refs/heads/master | 2020-03-10T02:13:25.018558 | 2018-04-11T17:37:44 | 2018-04-11T17:37:44 | 129,131,004 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 948 | r | randomForestModel.R | #Prediction using random forest model
rf.test<-testingDataSet
#Creating vector of target variable
rf.label <- as.factor(trainingDataSet.clean.equal$y)
#Changing yes and no to 1 and 0 for making lable compatible to Random forest model
rf.label <- ifelse(rf.label=="yes", 1, 0)
str(rf.label)
table(rf.label)
... |
098e9c283be61dbc7d7476a39c904c6d0cdccef9 | b19c97da15b61a3f9ec831e454ab38889d5bf555 | /Functions_plotting/dot_plots_per_class_ext.R | 297baffc4e3efd863204b5fec74854078c80ff3f | [] | no_license | smvanliempd/IAVmetabolism | c014b263bc0bc7d5f5aa30585f29cc3bb57eab21 | 49d2a0d16f5d236fa90d519e94f1daca28a99c4f | refs/heads/master | 2022-12-08T17:37:39.648629 | 2020-09-04T10:23:58 | 2020-09-04T10:23:58 | 260,969,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,256 | r | dot_plots_per_class_ext.R | dot.plots.per.class.ext <- function(dat_plot, set, p_effect = 0.8, sel_effect = F, dvc = "png") {
require(egg)
require(grid)
require(cowplot)
# define variables for heatmaps
vars.fc <- c("del_pl_bl","del_d_3i_0i","del_c_3i_0i","del_del_str_inf") #,"del_d3i_c3i"
# define observations for heatmaps... |
ced93b58f13d0de81fa72354e742656b294ba399 | d226838e64a1d55fdaf797893f7468651b725183 | /inst/testScripts/complete/hg19/21.FastaReferenceFile,Bowtie2.R | 237dea7236633a92d86f6f662b5185346e67f52e | [] | no_license | HenrikBengtsson/aroma.seq | 5fd673cc449d9c3b89daf1125e8cc95556d0641d | 6464f1e5e929c423978cf7dcb11ac7018d179a6d | refs/heads/master | 2021-06-21T13:53:21.618898 | 2021-02-10T02:57:15 | 2021-02-10T02:57:15 | 20,848,327 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,017 | r | 21.FastaReferenceFile,Bowtie2.R | ############################################################################
#
############################################################################
library("aroma.seq");
organism <- "Homo_sapiens";
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Indexing a reference genom... |
0087af9ea01fb3c726a990a1792966ca86240cf6 | 1d34ffca722c3de81ab449ad92d621ea2020c306 | /man/group_birds_by_location.Rd | bedb8123171929a5f048ce7b66f41079e9949c48 | [] | no_license | eriqande/gaiah | 9672b4ac51dbf48516592a24e11817be9c80b897 | 58ed0fe099448876602a065d2661f3cc8d5477ea | refs/heads/master | 2023-05-10T20:35:20.629230 | 2023-04-27T21:10:13 | 2023-04-27T21:10:13 | 56,340,908 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,792 | rd | group_birds_by_location.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/isotope-maneuvers.R
\name{group_birds_by_location}
\alias{group_birds_by_location}
\title{Group bird isotope data by locations}
\usage{
group_birds_by_location(
D,
feather_isotope_col,
location_col,
iso_pred_col = "iso_pred",
iso_sd... |
68db751e59f09f91418aa41b47ada1ce21b8c008 | a6d33b17790d652032c0abd8eb55522282302a0b | /quiz.R | 946b1bdeca35d894c279d2281317c94f4a89bccd | [] | no_license | up1/sck-r-programming-101 | 4555d55524a8cb34ff43d2000f57dc8974701f82 | dc8fba0268a3624cb7b597b98df947fe079b15d3 | refs/heads/master | 2022-07-31T03:35:07.218643 | 2020-05-20T18:08:18 | 2020-05-20T18:08:18 | 265,646,632 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 102 | r | quiz.R | f <- function(x) {
2*x^2 + 1
}
x <- c(0:10)
plot(x,f(x), type="l", col="red")
abline(70, 0)
grid()
|
985de799f823a5c52424eeef415578079df7af06 | cc8f3577c8d36b0bbe33a7c5a00ac52274ba58d2 | /man/subsetExons.Rd | 2b7b59d157dd2ed785803556fac6dddd4372fe9b | [] | no_license | mumichae/DASSIE | 89188f3105d1cd7184438b865a44971d4119552c | b0b7ff1528f319e2c8c5368d78487297330f6d96 | refs/heads/master | 2020-12-01T12:37:02.372801 | 2020-01-09T16:19:24 | 2020-01-09T16:19:24 | 230,627,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 476 | rd | subsetExons.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/features.R
\name{subsetExons}
\alias{subsetExons}
\title{Extract first/last exons depending on strand
takes the first hit after sorting}
\usage{
subsetExons(exon_dt, by, which = "first", strand = "both")
}
\arguments{
\item{by}{sorting criter... |
9d084f19ae6b825eba6b018ccea149fb95f1f36e | b90060ec71c5428cbbc3a1514f0d1beef60cac1e | /final_script_BDAP03.R | 5496ba52d59d6050c3ecbcfd24f3bb34f8fdab56 | [] | no_license | Rathi-Nikita/Credit-Risk-Modelling | a4866ea6259b1783df8ca470a48c786934e438e1 | ce08cab4af801c99998e799df8c263452f684cd6 | refs/heads/master | 2021-01-11T17:13:34.388243 | 2017-01-22T20:28:43 | 2017-01-22T20:28:43 | 79,741,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,781 | r | final_script_BDAP03.R | credge_data <- read.csv(file.choose(),header = T,na.strings=c(""," ","- ","_","NA"))
credge_data <- read.csv(file.choose(),header = T)
View(credge_data)
names(credge_data)
attach(credge_data)
str(credge_data)
#keeping copy
data <-credge_data
attach(data)
library(gplots)
library(ROCR)
library(Rcpp)
li... |
b2718c8f1ef3625347140605b5dcfaa6256ac97d | e8133e93f13612f5b0f400e2004a6f71e4f4dc72 | /Unidad 1/Clases/C1-7/C1-7 Regresión lineal.R | 7a12739549b88c1e1a71dabc07860af7d8675d60 | [] | no_license | CristopherA98/Curso_R-UCE | 760049ce8e72c471534d49e76c45a00f2db2c137 | 6d4ed39fad456c05a21cecfcfe0c27e7359d4e75 | refs/heads/main | 2023-01-19T07:31:10.378749 | 2020-11-24T14:43:51 | 2020-11-24T14:43:51 | 303,800,487 | 18 | 15 | null | null | null | null | UTF-8 | R | false | false | 8,220 | r | C1-7 Regresión lineal.R | ####################################
### Curso R - UCE ###
### Clase 07 ###
####################################
# 1. Análisis de regresión -------------------------------------------------
# Prerrequisitos
paquetes <- c('tidyverse','GGally','openxlsx','MASS','hexbin','modelr','l... |
5d3c68eb3ccb38bbc9122f393fa8e69fb663762a | 285aff879570bad28b16013b63de9670bdc33082 | /server.R | 749732b4a9fb9e9c2953cf616a6f6521c9f236eb | [] | no_license | wcm95/Developing-Data-Products | 57aab64d1929e1381dc363863d8cfb19e3acc50f | d066b7f49613a4333b8767fe6516d1956ca05665 | refs/heads/master | 2021-06-10T09:20:18.464649 | 2017-02-15T08:15:05 | 2017-02-15T08:21:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 981 | r | server.R | library(shiny)
library(ggplot2)
library(dplyr)
shinyServer(function(input,output) {
diamonds<-diamonds[,1:7]
dat<-reactive({
data<-subset(diamonds,cut==input$cut & color==input$color & clarity==input$clarity & price %in% seq(input$price[1],input$price[2]))
if(input$asc){
... |
dfb586bb9adbd394c0cb0546a0da592424e2e072 | 464439d8ceb29146335ef2da40b6475c1aa3f9fc | /R/rateModel-class.R | 95951d0c1606feda932571891a577325bc0770f0 | [] | no_license | ndukler/epiAlleleGLM | f5e951b08e3640c94fb5425386a81069d980ae30 | c2cd5bfd86857b0b04ec1e3a33b3f5f32416188a | refs/heads/master | 2020-04-09T20:54:04.745927 | 2018-12-20T22:45:51 | 2018-12-20T22:45:51 | 160,587,360 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,655 | r | rateModel-class.R | #' Class rateModel
#'
#' Class \code{rateModel} holds a rate model for a set of alleles
#'
#' @slot alleleData an allele data object in a locked environment
#' @slot edgeGroups a data.table with four columns: parent, child, edgeGroup. All parent-child combos must be valid for alleleData object.
#' @slot rateFormula A f... |
e1314bfe93bec6ca693ef5c05721fb6be9cf0262 | 7d6ddced6b34b607718441e402656c41543a8a9c | /postgreSQL_R.R | ed6157d1b168a8039149901a662a57e98cd5f9c3 | [] | no_license | prashantbhuyan/prashantbhuyan-install | 19b5cc2f27fd47ff21962dda8e2ce1afd4c41d11 | e136e277f01d2dcf564ab14001c0bd9a88cb6b3a | refs/heads/master | 2020-06-05T00:29:41.157861 | 2015-01-15T18:30:00 | 2015-01-15T18:30:00 | 23,474,586 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,012 | r | postgreSQL_R.R | # Prashant Bhuyan
# R, RStudio and PostregSQL have been properly installed and that the DMwR
# package has been properly loaded and installed.
# 1) What Version of R & RStudio do you have installed?
#
# (Answer) I have installed Version 3.0.3 (2014-03-06) of R. I have also installed
# Version 0.98.501 – © 200... |
80fe4336acd27715fd02bfed7962c2e9132b000b | b6f92a891ba3b05bc556a04e2116a00daf4181c3 | /cleaning.R | e21d76acfa629c7fd45ad23369650bef2f723bf7 | [] | no_license | chanbin-oh/R-programing | 45c4d3a5eac9ec52d65e198a5f582f57bff6e80a | 12d181183e7935cdc4df4dc6320414ab7ef83a77 | refs/heads/master | 2020-05-01T14:49:34.797428 | 2019-05-26T09:25:04 | 2019-05-26T09:25:04 | 177,530,503 | 0 | 0 | null | null | null | null | UHC | R | false | false | 3,095 | r | cleaning.R | ### 극단치와 이상치 제거하기
## 07-1 빠진 데이터를 찾아라! - 결측치 정제하기
# 결측치 찾기
df <- data.frame(sex = c("M", "F", NA, "M", "F"),
score = c(5, 4, 3, 4, NA))
df
# 유의점
# - 문자로 구성된 변수는 NA가 <>에 감싸진 형태로 출력됨.
# - NA 앞뒤에는 겹따옴표가 없다. 따옴표가 있으면 결측치가 아니라 영문자 "NA"를 의미
is.na(df) # 결측치 확인
... |
51d872f381edbfbd4d6d63560d35de7c5d3bf87f | 4551a2ec73889616de3196856c43df6934a2ef72 | /v04 Logistic Regression + Other Files/v04 Logistic Regression + Other Files.R | e47b5a8b4713e9f4cbd0f95a2f2b2b80c128a970 | [] | no_license | gjanesch/Kaggle-Credit-Risk-Competition-Solutions | ee4fb0b4f6b46f5e256b084b734ca961f1014399 | 594a17987344f9af7937b4a911e3ba3c8cbb277d | refs/heads/master | 2020-03-28T09:36:24.058560 | 2018-09-09T18:49:56 | 2018-09-09T18:49:56 | 148,045,876 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,997 | r | v04 Logistic Regression + Other Files.R | # Solution attempt 4: Logistic Regression + Other Files
# Final submission: 2018-06-14
# Submission score: 0.712
# In addition to new tweaks to the data, information from other files will be added in to try to
# improve predictions.
library(ROCR)
library(feather)
library(plyr)
library(dplyr)
has_NAs <- function(x){
... |
d7ae3827b4aa3c09b271be55cba915872dee86c0 | 5f240923dc5b64d11e75dba8f99a305f42b5ef86 | /man/Anthropometry-package.Rd | 00daf0d60cf625312b6565c43bde8418c92ded91 | [] | no_license | cran/Anthropometry | 47cdfbc1f58265fc6284d378c23e339a3bb69f2f | d7cdec4461048d16c08b4c05563acf591764de48 | refs/heads/master | 2023-03-05T22:02:54.232885 | 2023-02-22T09:20:02 | 2023-02-22T09:20:02 | 17,677,730 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,710 | rd | Anthropometry-package.Rd | \name{Anthropometry-package}
\alias{Anthropometry-package}
\docType{package}
\title{
Statistical Methods for Anthropometric Data
}
\description{
Statistical methodologies especially developed to analyze anthropometric data. These methods are aimed at providing effective solutions to some commons problems related to Erg... |
1a99cc73ceaf7a1b2a78788a3e8b6e30d4fff072 | 775b0afd0c99ba3b57eed716463e93bb29bb44d0 | /src/aggregate_frequencies.R | e69274b46ad3f26f87b6a2ce19246bfaa54e267e | [] | no_license | rgorman/test-1 | 85ae1618e9e76808d6e410be69ff198c052dd404 | 66b40000cd69481fc0b188856c3d29016b744d48 | refs/heads/master | 2021-05-12T15:54:09.923902 | 2018-02-26T20:27:04 | 2018-02-26T20:27:04 | 116,996,548 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,353 | r | aggregate_frequencies.R | ## A script to calculate the corpus-wide frequencies of combined variables with values > 0.
## These frequencies will be used to select a subset of combined variables for further processing.
require(XML)
require(tidyverse)
require(stringr)
input.dir <- "./output_2"
files.v <- dir(path=input.dir, pattern=".*xml")
y... |
2a4b515f783584714380bfa62742c2a00724cc06 | d03924f56c9f09371d9e381421a2c3ce002eb92c | /R/UnivarMixingDistribution.R | 7809637f7f56c3fffa7e427e1a228fac4d88874d | [] | no_license | cran/distr | 0b0396bbd5661eb117ca54026afc801afaf25251 | c6565f7fef060f0e7e7a46320a8fef415d35910f | refs/heads/master | 2023-05-25T00:55:19.097550 | 2023-05-08T07:10:06 | 2023-05-08T07:10:06 | 17,695,561 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,767 | r | UnivarMixingDistribution.R | UnivarMixingDistribution <- function(..., Dlist, mixCoeff,
withSimplify = getdistrOption("simplifyD"))
{
ldots <- list(...)
if(!missing(Dlist)){
Dlist.L <- as(Dlist, "list")
if(!is(try(do.call(UnivarDistrList,args=Dlist.L),silent=TRUE),"try-error"))
... |
77d015b116103c2ffe9d9e87562fefbbd21415c7 | 49599cbeb99d70432a688e11e97a4d3160881f6d | /R/make.Mondrian.planning.units.R | 664c1ee57a6f67b72539df6dfab7e3086f0d0bdb | [] | no_license | langfob/rdv-framework-frozen-google-code-export-do-not-change | 260de4eae4de9d4d0465d90e693790d57f411073 | fe7e745223251790e50bc9a412080e45e0cbc709 | refs/heads/master | 2021-01-21T23:29:59.459162 | 2015-07-17T09:30:46 | 2015-07-17T09:30:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,883 | r | make.Mondrian.planning.units.R | #-----------------------------------------------------------#
# #
# make.Mondrian.planning.units.R #
# #
# Make a planning unit map. This is for use with th... |
74e408da4e3301d7617f46026f3e05db8992dc12 | 75977c1f9257c38c45a543b0183184b2b0e34a4d | /R/datasets.R | d7c189eb74d23a2152fc50e5ae16a91a6181ad09 | [
"Apache-2.0"
] | permissive | aimwts/rfaculty | 54ab33411449178f91bdc679e6cee76736794246 | 81962dab67cf56b4804e83d1cd583ea28b61c11f | refs/heads/master | 2022-11-09T21:53:36.303173 | 2020-06-23T13:11:36 | 2020-06-23T13:11:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,782 | r | datasets.R | # Copyright 2018-2019 Faculty Science Limited
#' Copy from Faculty datasets to the local filesystem.
#'
#' @param datasets_path File path on Faculty datasets
#' @param local_path The destination path on the local filesystem.
#' @param project_id The ID of the project; by default, the current project is used.
#' @expor... |
2d169f1da610b692cd8697535f3342147c6e9e7d | b35ffbfd5d78961299f359f02ff96e1002f19173 | /plot3.R | 9dca8da18ed9d413205a3c2fd56e64880f4311bd | [] | no_license | zfkl/ExData_Plotting1 | 7a68bf8b5f309da64e21a775960b04eb65667c90 | 2085870a8583cea265d2b57d8af1b0810ac1a559 | refs/heads/master | 2021-01-09T07:36:12.762317 | 2015-02-08T19:34:45 | 2015-02-08T19:34:45 | 30,475,291 | 0 | 0 | null | 2015-02-08T00:13:47 | 2015-02-08T00:13:47 | null | UTF-8 | R | false | false | 873 | r | plot3.R | house<-read.table("~/household_power_consumption.txt",nrows=1,header=TRUE,na.strings="?",sep=";")
house<-read.table("~/household_power_consumption.txt",skip=397+60*24*15,nrows=60*24*2,header=FALSE,na.strings="?",sep=";",col.names=y,colClasses=sapply(house,class))
house$index=c(1:2880)
plot(house$index, house$Sub_meteri... |
9ddc96ca14b036941b176935f81455fb001fb9f4 | 2457cfad7d128305656d73c79fd0f162d3181608 | /Code/R_Code/Scatboot.R | 7bae3e209d4a461682ceeb04bb2dea8466c5cc2d | [] | no_license | Jadamso/PrettyR | 6b6bcc8331c45364267e9c4a67a31c594fd579a8 | 16cc947bb2f216297113a3189326072d149635ea | refs/heads/master | 2021-01-15T16:51:41.219506 | 2020-03-22T23:08:35 | 2020-03-22T23:08:35 | 99,728,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,532 | r | Scatboot.R | #------------------------------------------------------------------
##################
#' M out of N Bootstrapped Loess Confidence Intervals
##################
#'
#' @param x,y coordinates
#' @param breps number of bootstrap replications
#' @param mfun function to define m out of n subsample
#' @param confidence CI in... |
bb3910d87b3e98d390891b14437ad835fbaadb99 | 2f686ad3fb833cfe417ee7db9297e511e42bdbdc | /ctap_R/ctap_rtools/man/load_ctap_mc_measurement.Rd | 40233b59941e53bce5d319b794d4d581b6e0ef0f | [
"MIT"
] | permissive | RuoyanMeng/ctap | 171ffffef9700b3188ecc99cbdae0e1def8bb4c3 | 192d3fc6d85779b968736a4cfe23afe414f77b42 | refs/heads/master | 2021-04-12T16:20:43.797721 | 2020-03-20T10:48:54 | 2020-03-20T10:48:54 | 249,091,294 | 0 | 0 | NOASSERTION | 2020-03-22T01:22:39 | 2020-03-22T01:22:38 | null | UTF-8 | R | false | true | 648 | rd | load_ctap_mc_measurement.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mc_utils.R
\name{load_ctap_mc_measurement}
\alias{load_ctap_mc_measurement}
\title{Load CTAP MC measurement sheet/table}
\usage{
load_ctap_mc_measurement(mc_file, measurement_sheet,
dateformat = "\%d.\%m.\%Y")
}
\arguments{
\item{mc_file}{c... |
9bad5c9653c42b7465bf767da475db46dd2fa05f | e214f8451eaf187ddcd7f70dbb287147df1884eb | /run_analysis.R | 8a21816d08315d9351e9d21d78bec46c551cf8a4 | [] | no_license | wanghm02/Get_and_Clean_Data | 584de7664561944db45ecd3b222ceecf3a93defc | bc9fe41756502a0ad35a0be26f86aa3f11fc786e | refs/heads/master | 2020-05-23T10:19:34.628098 | 2017-01-30T13:22:22 | 2017-01-30T13:22:22 | 80,420,461 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,129 | r | run_analysis.R | library(data.table)
library(reshape2)
filename<-"Dataset.zip"
## Download and unzip the dataset
if(!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileURL, filename, method="curl")
}
if (!file.exists("UCI HAR D... |
c671eb43b93ec57de4ec59a7380548ea05fe7edd | a0506d64a4cfe8de19554bb914f7299f57782bd1 | /plot1.R | c84284cdf98e8922af164bfbca9560ee6e2cb61d | [] | no_license | wborgmeyer/ExData_Plotting1 | e9972d689d657cdecf8f97b29f288c20da82a563 | cf0be452979e0f8949c52be93930999e6f5bac00 | refs/heads/master | 2020-05-22T21:04:54.723844 | 2017-03-13T15:46:42 | 2017-03-13T15:46:42 | 84,724,599 | 0 | 0 | null | 2017-03-12T12:42:34 | 2017-03-12T12:42:34 | null | UTF-8 | R | false | false | 1,056 | r | plot1.R | library(data.table)
library(dplyr)
#The dates that we will graph for this exersize
begintime <- strptime("2007-02-01 00:00:00", "%Y-%m-%d %H:%M:%S")
endtime <- strptime("2007-02-03 00:00:00", "%Y-%m-%d %H:%M:%S")
# Read the datafile
hpc.df <- read.table("household_power_consumption.txt", header = TRUE, sep=";",
... |
61098a16a1037dc8ff8556c16bfc9cfaa4556157 | 2aa473e524c173313ebbfc757b6a91f7c7b24f00 | /tests/testthat/test-mungepiece.R | a50de932007955ec7a7ae923367436700149ebb5 | [
"MIT"
] | permissive | syberia/mungebits2 | 64052c3756828cef6bc1139106d4929ba02c8e75 | 1b4f0cd2a360856769ccb2b11dfc42f36fa5d84c | refs/heads/master | 2020-04-06T04:16:54.292474 | 2017-09-19T21:27:27 | 2017-09-19T21:27:27 | 29,579,712 | 1 | 2 | null | 2017-09-19T21:27:28 | 2015-01-21T08:32:15 | R | UTF-8 | R | false | false | 22,901 | r | test-mungepiece.R | context("mungepiece")
library(testthatsomemore)
describe("errors", {
test_that("it cannot run a mungepiece without arguments", {
mb <- mungebit$new()
mp <- mungepiece$new(mb)
expect_error(mp$run(), "is missing, with no default")
})
test_that("it cannot initialize a mungepiece with a non-mungebit", {... |
4d0346e1d9418d7acfdebfd91d8e7251e1a6f858 | a8713ffd133f1c887c7f08abc3a47e827548ceda | /man/join.Rd | b5b32c3b277003ebe59e5c061865868925230087 | [] | no_license | cran/disk.frame | 5859ee6faa628ecab4012fc9caabdb4b28336958 | dfb7c3bb5e4ea5f35e4e6432a35dcfbcdb774514 | refs/heads/master | 2023-04-16T19:08:35.501577 | 2023-04-07T16:50:15 | 2023-04-07T16:50:15 | 208,672,993 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,162 | rd | join.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/anti_join.r, R/full_join.r, R/inner_join.r,
% R/left_join.r, R/semi_join.r
\name{anti_join.disk.frame}
\alias{anti_join.disk.frame}
\alias{full_join.disk.frame}
\alias{inner_join.disk.frame}
\alias{left_join.disk.frame}
\alias{semi_... |
7dfbf5cba45abf98691ec25181a33ab91744dd1d | 0e372329ed96ee03c72e787b183fbabe4a6703d8 | /man/write_stderr_msg.Rd | 46c82a51c41ab10af0b7333412e1cf52844231d3 | [] | no_license | haven-jeon/RscriptUtils | 4e45c866e041e9a3af8d5c79d4dd1e745fa0989f | a315742d687c608aeb54604513ab36163d84be3c | refs/heads/master | 2020-06-02T17:06:02.959931 | 2013-01-16T17:22:01 | 2013-01-16T17:22:01 | 7,650,116 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 210 | rd | write_stderr_msg.Rd | \name{write_stderr_msg}
\alias{write_stderr_msg}
\title{write_stderr_msg}
\usage{
write_stderr_msg(msg)
}
\arguments{
\item{msg}{messages}
}
\description{
Write stderr messages with timed informations
}
|
b8994d92d35db39f24a0f6f2509f36904c1c3248 | b4e8faa53a1991ffa007ae95785d8441a1341fe3 | /man/normalise.Rd | bd952f978c71d0d0473e01f89f238d48926e6e41 | [
"MIT"
] | permissive | VCCRI/CardiacProfileR | 6363ef5250593ac9dc865b783bd78321fbeecbed | e231c37f220cd39aa3af982c7dbacf7b571ced7a | refs/heads/master | 2021-01-24T11:27:59.936732 | 2018-06-10T19:56:51 | 2018-06-10T19:56:51 | 123,085,087 | 8 | 0 | null | 2018-05-07T00:36:13 | 2018-02-27T06:40:22 | R | UTF-8 | R | false | true | 698 | rd | normalise.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/profile_generation.R
\name{normalise}
\alias{normalise}
\title{Normalise heart rate in profile}
\usage{
normalise(profile)
}
\arguments{
\item{profile}{profile to be nomalised.
See \code{\link{get_profile}} for a description of profile.}
}
\v... |
b4fb37066c697c2752c657efac80b8dc74ab909c | 397182bd2f6ed4e39dcc3f3d62e89807af03ac5a | /R/init.R | df7c373dbf72f7f76297e284428800951f67a9f4 | [] | no_license | cran/bnlearn | e04a869228c237067fc09d64b8c65a360dada76a | dda6458daf2e0b25d3e87313f35f23d8a1c440c1 | refs/heads/master | 2023-05-14T14:21:07.744313 | 2023-04-29T09:30:02 | 2023-04-29T09:30:02 | 17,694,839 | 58 | 42 | null | 2020-01-03T23:49:19 | 2014-03-13T04:09:21 | C | UTF-8 | R | false | false | 3,448 | r | init.R |
# safe version of setMethod().
tryMethod = function(f, signature, definition, generic) {
# try a first time.
catch = try(setMethod(f, signature, definition), silent = TRUE)
if (is(catch, "try-error")) {
# if it failed, create a generic function ...
setGeneric(f, generic)
# ... and then try again.
... |
fd38fb4c8a1db1489f82c28f17bce2aae0fdc5a1 | fde9c70b67e2ea092f0a3966c8b098b08ad0ffcc | /R/funcRandKmap.R | e0bbb06f0ef7e28e2a54672b16dad68869234316 | [] | no_license | hazaeljones/geozoning | 41d215022b34e6944e4ba7395dc0778c2c49ba48 | c8310ca97a775c4d55807eb3ac3ab1ae73da5334 | refs/heads/master | 2021-01-20T12:37:46.187798 | 2018-02-23T09:44:47 | 2018-02-23T09:44:47 | 90,385,766 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,959 | r | funcRandKmap.R | ##############################################################################
#' generate data
#'
#' @details description, a paragraph
#' @param DataObj =NULL: simulated data with given seed or a data frame with real data
#' @param seed numeric value used to generate simulated data
#' @param nPoints number of generate... |
fb2cd1021604b03ee575b8f9230e1f0d681b2138 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/clttools/examples/dice.plot.Rd.R | b82008e7983ef8718f636c90578ff3565e4495a1 | [] | 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 | 247 | r | dice.plot.Rd.R | library(clttools)
### Name: dice.plot
### Title: Theoretical Probability Distribution Plot of Rolling Dice
### Aliases: dice.plot
### ** Examples
dice.plot(n = 4, col ='red', type = 'p')
dice.plot(3, prob = c(0.3, 0.1, 0.2, 0.1, 0.1, 0.2))
|
b20cb45e52c465e98d89e6998287ab61b7684dbd | 4c7d9073ca83ef771c620c1063b37f44fa2ee8b6 | /draft_code/nls.R | 68871962ab2411fd38b6aa8bc2735d2c3231fd1e | [] | no_license | ilyiglesias/LW | d816fbf3abc7ed95dd18e149c4435d1bd6d16e6a | 1a14d2232c812591ff3eb59e95efb8dd6f3c8d48 | refs/heads/master | 2020-03-19T08:49:27.967932 | 2018-09-26T17:50:15 | 2018-09-26T17:50:15 | 136,235,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,160 | r | nls.R | # an alternative way to plot these length weight values via nls
#NOTE: I was also able to plot these values via nls (non-linear least squares) in case I need to do this again!!
# nls- non-linear regression model- an alternative way to estimate parameters #######################
# i followed instructions from https://s... |
8b0da41be4ea617a438d03707fd3256441429583 | 76540510e51e76171e7559122e60739be2edd5fb | /man/forecast_hmnc.Rd | b667ca2d602b8a279755f06c7bb5c691006f489b | [] | no_license | placeboo/amplify | 0d889633c8a87ad596b410e777877673f682ada3 | 6749fdb2b9d5a4cbbe3282cf029322f2f7ae6519 | refs/heads/master | 2021-07-12T22:30:13.932067 | 2020-09-24T23:19:30 | 2020-09-24T23:19:30 | 202,027,821 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 858 | rd | forecast_hmnc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/forecast_hmnc.R
\name{forecast_hmnc}
\alias{forecast_hmnc}
\title{Forecasting via Harmonic Regression Model}
\usage{
forecast_hmnc(model, par = list(season = c(7, 365.25), K = c(3, 30)), h = 30)
}
\arguments{
\item{model}{An object of class \... |
8eb9b53e08fad31480519dc39091e8857f577d4c | 42cd205591e6acccb6a6a89c8bcb92ae8d17c72f | /TwoWorlds/twoworld-getters-twunity.R | c44df66a49dfdcf84e494edcafe4ad232ffdc6c8 | [] | no_license | hejtmy/two-worlds-analysis | 8d74f882c9727e0da4dd63f56e6fae9a72b82a2a | e404dcb67eed1b2a93a476acaa6a3045091fd3d0 | refs/heads/master | 2022-04-01T11:42:44.052365 | 2020-02-12T01:51:42 | 2020-02-12T01:51:42 | 113,116,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,311 | r | twoworld-getters-twunity.R | ### twunity ----
get_trial_start_id.twunity <- function(obj, trialId){
if(trialId <= 1) return(NULL)
return(get_trial_goal_id.twunity(obj, trialId - 1))
}
get_trial_start.twunity <- function(obj, trialId){
#First trial is started in a center hall
iStart <- get_trial_start_id.twunity(obj, trialId)
if(is.null(i... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.