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68fa8e6293d198e8b0f46a59eedca9788571d459 | fa5eb1a6e94be9be5d1bc19d1807c6ed2983b2d0 | /libbase/R/attach.local.R | cf6e9f401e94281790b008b5bf596a1abddcd32b | [] | no_license | bereginyas/rlib | 57c8a4f3548b34ba9a69dd3774ab127cbd4632be | f511254f1ed46f5a7d43eea7884cf31ef2cda9ca | refs/heads/master | 2022-01-07T12:54:04.819043 | 2019-05-25T05:34:24 | 2019-05-25T05:34:24 | 67,099,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 860 | r | attach.local.R | ## local attach, not added to search path
attach.local = function(object, fields=NULL, excludeFields=NULL, overwrite=TRUE, envir=parent.frame(), ...) {
if (is.null(fields)) {
fields <- names(object)
# Don't try to attach non-named elements
fields <- setdiff(fields, "")
}
# Note: we cannot do 'fields... |
4740a9aa2f9ecfa99c77b4cbdf74586cc08fc23c | d0b5ca282def5cda68c9adead9ba4db72acadb62 | /CanadaCOVID19/R/plot_Ca.R | 8b14b43a2b405ee0b4b52d4ab050118296bf79e3 | [] | no_license | YujieWang95/package | ed1deeed110f22145927affa252d18c1b297f7dd | d6fed1c2f8fed06ad5f115f9b05474f75819a499 | refs/heads/main | 2023-05-03T03:39:31.824917 | 2021-05-10T19:42:55 | 2021-05-10T19:42:55 | 342,692,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,812 | r | plot_Ca.R | ##' @description Show the new cases/new deaths/cumulative cases from the day the first case occured to a chosen day in Canada.
##'
##' @title New cases/New deaths/Cumulative cases by day in Canada
##' @name plot_Ca
##' @author Yujie & Jiahui
##' @usage plot_Ca(stopdate,type)
##' @param stopdate "yyyy-mm-dd" Doub... |
4be77388d9980bff944be28594ac5fbdada4135e | 9d4e9bf93975e5f48eb10d4f4949b6e1ff302e40 | /train/0a-quick_pipeline.R | 7a73d404848c9496bbce17f173e18dbc341d48f7 | [] | no_license | kota7/kgschart-r | d8ccc87e39f43a078e88e2f013563b941c95c7f5 | d97f9012b7d7e84a4543123e127ec3f73cc7febd | refs/heads/master | 2022-09-16T01:04:42.168707 | 2022-08-13T02:44:29 | 2022-08-13T02:44:29 | 89,450,572 | 0 | 0 | null | 2017-07-02T13:29:16 | 2017-04-26T07:19:49 | HTML | UTF-8 | R | false | false | 2,695 | r | 0a-quick_pipeline.R | # quick implementation of pipeline-like functionality
Flatten <- function(...)
{
fit <- function(data) {}
transform <- function(data)
{
dim(data$x) <- c(dim(data$x)[1], dim(data$x)[2]*dim(data$x)[3])
data
}
self <- environment()
self
}
PCA <- function(n, ...)
{
model <- NULL
fit <- function... |
d93d56a144e83027027cef51778adeaa275d4045 | a5394f0a48914e5278f7172648af749e9cd60005 | /R/ens.rpart.R | cfa1f56e414ab0989c9751f39376debd915f6d3a | [] | no_license | cran/ensemble | ff086efb0d873ad397f84bd2d0e10c80ddb9d25d | f7af10dfd3df1fa0acae260bca2c4aa9d3139039 | refs/heads/master | 2020-05-30T18:02:42.316016 | 2000-12-07T00:00:00 | 2000-12-07T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,857 | r | ens.rpart.R | ens.rpart <-
function (formula, data = NULL, weights, na.action = na.rpart,
method, model = FALSE, x = FALSE, y = TRUE, parms, control,
...)
{
call <- match.call()
if (is.data.frame(model)) {
m <- model
model <- FALSE
}
else {
m <- match.call(expand = FALSE)
m$m... |
0b281fe4ec1fc4a9b38f6359b6a7c6312fbca33b | e216da99e347be74e0d2ef0021c50db3c7d5de50 | /R/mouseHumanConversion.R | 4169a5a11cc128be4ced752477ca2f15c333c6c7 | [] | no_license | vincent-van-hoef/myFunctions | e6afc3b997d7593a24a4841e671a084bc2897474 | 0299622df24169a1881a38a5dc799e645fab6926 | refs/heads/master | 2021-05-05T00:38:46.894937 | 2018-07-25T11:41:45 | 2018-07-25T11:41:45 | 119,551,523 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 553 | r | mouseHumanConversion.R | #' Basic function to convert mouse to human gene names
#' This function converts a vector of mouse gene symbols to human gene symbols.
#' @param x A vector of mouse genes
#' @export
convertMouseGeneList <- function(x){
human <- useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse <- useMart("ensembl", dataset... |
749c08c7a2e4f01f47942d90f31753323eb94999 | d8a28f2f5a8d532da596a433aa75348187befa76 | /functions/func_preprocess.R | b36a171ed19338d05684ebfa290c7e969f594ef8 | [
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | nreinicke/EVIPro-Fleet-Gen | 9340e5139024a7e1997ec5c55e89df90a946126d | 3892d9eefeaa57801ff3daa12b18fa2383d74220 | refs/heads/master | 2023-01-10T15:45:36.795840 | 2020-07-10T19:20:13 | 2020-07-10T19:20:13 | 283,615,589 | 1 | 0 | NOASSERTION | 2020-07-29T22:34:45 | 2020-07-29T22:34:44 | null | UTF-8 | R | false | false | 2,575 | r | func_preprocess.R | # Author: Schatz Energy Research Center
# Original Version: Micah Wright
# Date: March 01, 2019
# Description: Processes and saves the evi sessions and time series load profile for all ambient temperatures.
# Version History
# 2019-09-12 Max: minor edits, run on SUV data
# 2020-05-04 Jerome: turn into formal functi... |
9525362f5ecfc818f87022c2139a8e9f3a9cb880 | 8f9f5362319ba7c15cc92d7a2acd9988cc8607f1 | /R/vis.R | 4bd762f5fc864654f4c8f222393ebd676c41a725 | [
"MIT"
] | permissive | mc30/disnap | 2afc2cc7b7289fa50beb3041d59a4c13850b2fa3 | 66433b6f62cae28a923b81f4c27a9a50ae06d0ae | refs/heads/master | 2020-05-19T04:03:13.219263 | 2020-01-16T10:07:27 | 2020-01-16T10:07:27 | 184,816,176 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,094 | r | vis.R | ###############################################
# Visualisation
###############################################
#' @title Plot PPP objects
#' @description Plot all PPP objects from the provided list.
#'
#' @param lst List of PPP objects.
#'
# @return .
# @details .
#'
#' @author Mikhail Churakov
#'
#' @export
plotPP... |
c11324e1ac83667b9fc56d0ac7c5616ac1736a1d | fd9f494cd9746cf6f4fefbe57e6eee8fb9f38a13 | /inst/examples/Discrim_Install.r | 6e1d107e48070291fb0c2edbc0018e2844f89b44 | [] | no_license | PingYangChen/DiscrimOD | 30936fa300b90f4f5aec65190584cac9fa921fae | fbc04af687d80fa8b304dcf7550a579ac846794f | refs/heads/master | 2022-02-05T20:24:10.014705 | 2022-01-23T06:06:50 | 2022-01-23T06:06:50 | 92,010,316 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 696 | r | Discrim_Install.r | # File Name: Discrim_Install.r
# Description:
# To use the **DiscrimOD** package, one need to have R packages 'devtools',
# 'Rcpp' and 'RcppArmadillo' installed in advance. This file is written for
# users to install all the required R packages and our **DiscrimOD** package.
# Please copy the codes below and ... |
a2e8cc4641b960389e91cb9dde50329844be5069 | 4b711eacdc4b76b14c3ebc08cc582ed9a4e5ce84 | /AndroidPrediction/Analysis.R | 35c9de02d8160b7762ac849887acd3ed9203470a | [] | no_license | FRSB/AndroidDataCollection | d0d6172974ea9e469e41ee1772af9dd8c86a0e6e | 40296d906b22d8b3bf66a78f1b23a161427918fa | refs/heads/master | 2021-08-05T10:25:54.236822 | 2021-02-04T07:52:54 | 2021-02-04T07:52:54 | 8,315,037 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,370 | r | Analysis.R | rm(list = ls())
# install required libraries and packages
.libPaths("lib") #remove this line to use your personal library instead
#install.packages("hash")
#install.packages("HMM")
# load required libraries and packages
library(hash)
library(HMM)
source("MarkovChains.R")
source("DataTransformation.R")
source("Evaluat... |
55a5cef162b6d7c823df6d16ef6aad9998c514b6 | c55835fb6a8930fed3483260d63ac86078058986 | /preamble.r | 222e564be80e2a43b61b4f2be217411395b23b38 | [] | no_license | ppreshant/R_chip_analysis | bc4244fbf7ababd78673387812f591448103f250 | d4098b49b586cfb4ca6cee2dbf73145ac5a19fb4 | refs/heads/master | 2021-01-19T05:27:07.458828 | 2016-10-07T21:33:22 | 2016-10-07T21:33:22 | 62,277,637 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,207 | r | preamble.r | setwd("E:/R files_array comparision") # change it to the dropbox location of the R files_array comparision folder
# give the path of the input files (it should be the same unless changed)
folo <- 'results/Kinase/F_late_2dayexp' # for Fgfr2 arrays
# folo <- 'results/Kinase/J_6dayexp' # for Jak2 arrays
# reads the lis... |
a36d784487b79a124f4ad5064ed85ea2bd5e560e | 3618885dabc16828de4fa1175fb0dd17a153a060 | /day013.R | c0655ea204f821acf0049075478f0e18d8c007b9 | [] | no_license | nhoteling/AdventOfCode2020 | 9044f41a603422a9ff655d8ee38bb45003de7e0c | 68f1ee65bc14ed921f2927751c7581ecffa82104 | refs/heads/main | 2023-02-17T02:55:23.079038 | 2021-01-13T20:07:51 | 2021-01-13T20:07:51 | 319,969,533 | 0 | 0 | null | 2021-01-13T20:07:52 | 2020-12-09T13:45:33 | HTML | UTF-8 | R | false | false | 967 | r | day013.R | library(stringr)
fdata <- readLines("data/day013.txt")
tm <- as.integer(fdata[1])
ids0 <- unlist(str_split(fdata[2],pattern=","))
ids1 <- as.integer(ids0[ ids0 != "x" ])
len <- length(ids1)
d <- lapply(seq_len(len), function(i) {
v <- seq.int(from=0,to=tm+ids1[i],by=ids1[i])
df <- data.frame(id=ids1[i], tm= v[ v>... |
7dd5aa475598153e114cefe86bab630d064f41a2 | f0c51db62ce23cbafec52f447cee4a8837023be8 | /Solution to Medium test by Xing.R | 7aa8fbe91a3a287ecafdc563b4ed869d3cdca319 | [] | no_license | XingXiong/gsoc2017 | e665ba2db25bc8f1a14e24f25568605af47916a3 | c58aa1ddd81db527be1a4c38a06ac697e1863950 | refs/heads/master | 2021-01-22T23:58:26.996677 | 2017-03-31T05:26:10 | 2017-03-31T05:26:10 | 85,685,068 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,658 | r | Solution to Medium test by Xing.R |
###############
# Almost the same as easy test2,I get ".SN li" from the website structure.
#
crawl_species <- function(validurl,outputfilename1,outputfilename2){
require(rvest)
require(stringr)
url1 <- validurl
species <- url1 %>%
read_html()%>%
html_nodes(".SN li")
species <- gsub(... |
7925c2655ada98dfb5698f5768332e28de581a97 | 08c16d791ad8250be127f90dc62b8cab8942ef86 | /R/factors.R | 941bc318431aaf54a4aed367838805196ef1e2a8 | [
"MIT"
] | permissive | mrdwab/mathrrr | a4b2327eb34d41af6e8be88477ea076e5b1e2f3c | aee28db6761a737ac869780506dc8400668face1 | refs/heads/master | 2022-11-21T04:28:33.611664 | 2020-07-17T23:52:09 | 2020-07-17T23:52:09 | 278,999,650 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,369 | r | factors.R | #' All Factors of a Number
#'
#' The `factors_of` function returns a numeric vector of the factors of a
#' provided number.
#'
#' @param x The number that you want to find the factors of.
#'
#' @return A numeric vector.
#' @examples
#' factors_of(8)
#' @export
factors_of <- function(x) which(!x %% seq_len(x))
NULL
#' ... |
74544470b23afe4942a5f97973cb6337723f1fa2 | 248db17ce191339720d3651a5eae817e03af789e | /R/get_package_and_url_names.R | 8d085a918d6eab818f7b2eafa168f53d7d850bb4 | [] | no_license | rzhao621/SDS230 | bf4b872c99ed7eefbf67d87040953525220284bc | b12be38bd0ce6c24f543526076c1968eb8ce506f | refs/heads/master | 2023-07-16T23:42:21.482823 | 2021-09-02T12:42:32 | 2021-09-02T12:42:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,034 | r | get_package_and_url_names.R |
#' @import httr
# should set this to the name of the package
package_name <- "SDS230"
# should set this to the github user name of the repository owner
github_user_name <- "emeyers"
#' Get the package name and version number
#'
#' Returns the name of the package and the version number. This is useful to
#' che... |
487e4cc715da2c56b3ad363633c988c2f0cf9312 | a7dd545bd4529bce3364fbd8078e26ad86499aea | /by-member-pressure-contours/1998_assimilation/adjusted_contours.R | 2042440541a1968c47584152133463fddcd8f903 | [] | no_license | philip-brohan/weather.case.studies | 335d15e4bc183f0139b56d411cc016f98b7be0b0 | 2139647d51156e1cd7f227b088fdc66ea079920e | refs/heads/master | 2021-04-18T20:21:05.017207 | 2018-06-19T16:36:45 | 2018-06-19T16:36:45 | 42,944,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,158 | r | adjusted_contours.R | #!/usr/common/graphics/R/R-3.1.0/bin/R --no-save
# Modern period
library(GSDF.TWCR)
library(GSDF.WeatherMap)
library(parallel)
library(lubridate)
Year<-1998
Month<-1
Day<-1
Hour<-0
d.total<-30 # Number of days to be rendered
version<-'3.5.1'
members<-seq(1,56)
GSDF.cache.dir<-sprintf("%s/GSDF.cache",Sys.getenv('SCR... |
b23c90351300bf364c98b5ef1a88c95ca30d8cf8 | e7e4643a435d8f77f22dd229fd1bd9c298cde75e | /man/integrateFunction.Rd | f82f9a423ae0d971753c1a601fd21e867ddc9e9a | [] | no_license | califano-lab/MOMA | 74c5c1bb3b8c99352bb4b94912e3749022161816 | 852825b00474055b076b3564698c6d02fe8fdeb0 | refs/heads/master | 2023-04-06T13:14:06.110621 | 2020-06-04T20:54:19 | 2020-06-04T20:54:19 | 145,617,655 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 642 | rd | integrateFunction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cluster.r
\name{integrateFunction}
\alias{integrateFunction}
\title{Numerical integration of functions}
\usage{
integrateFunction(f, xmin, xmax, steps = 100, ...)
}
\arguments{
\item{f}{Function of 1 variable (first argument)}
\item{xmin}{Nu... |
6d0aa7d352906a8bf3e951e38e43e5bd70464337 | aac51389396f601727bf2dbcbbb15829f1726026 | /HW2.R | 4d5b41584aa3cbbbaf2097e80860a6417acfec1f | [] | no_license | yadevi/UMich_HS_650 | a463d7e3b38a13d6176ecd5b062cfb349129d75b | ee1bad288a6d1ecafe92ece65b673fe9bab9d298 | refs/heads/master | 2021-06-23T09:43:45.030738 | 2017-08-23T17:02:49 | 2017-08-23T17:02:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,527 | r | HW2.R | library(rvest)
library(gmodels)
library(ggplot2)
library(reshape2)
library(plotly)
library(MASS)
library(unbalanced)
library(GGally)
library(mi)
library(betareg)
# Q1
# Load the following two datasets, generate summary statistics for all variables, plot some of the features (e.g., histograms,
# box plots... |
fc9d2c0600d9b2f0282475862f98a90de681f5cc | 20e0483ed898440420db9d234e2b85be29284376 | /R/outs_tp.R | befb724f1e126e843850f203868ecfd5672895ba | [] | no_license | cmaerzluft/TexasHoldEm | 52e2cbe118a78cf8b7ac92b0c7991d823325d1ef | e8cb1a57fe70de394f41b466fb5d3ca4c48cde46 | refs/heads/master | 2021-06-18T11:49:36.022764 | 2021-06-11T06:44:52 | 2021-06-11T06:44:52 | 150,686,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,887 | r | outs_tp.R | #' Outs to make a Two Pair
#'
#' @param cards Cards from hand, including pocket cards and all community cards
#' @param stage What part of deal was just performed, "flop" or "stage"?
#' @param output IN PROGRESS. Should function return number of outs ("counts") or the cards themselves ("cards")
#'
#' @return Either cou... |
c72c875cd736855dd06bfecdd78daf3ac36f1af5 | 817267ad6ee388294faf0c42a0b06aa3af6551b2 | /portfoliotheory.R | 6e4254c45c74a6b7991b256028d26ab80027023b | [] | no_license | tgwisner/MS_thesis_R_code | d7c293bd81fdd160313fecbc82134f5686268e02 | 91dc5917779b3549e8caf9bb2a0e6135459a2949 | refs/heads/master | 2021-01-10T02:28:09.846862 | 2016-03-24T22:23:32 | 2016-03-24T22:23:32 | 54,678,107 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | r | portfoliotheory.R | library(stockPortfolio)
# Funds (change to indices?)
# vbmfx - US bond
# vtiax - int'l stock
# vtsmx - US stock
returns <- getReturns(c("VBMFX", "VTIAX", "VTSMX"), freq = "day", get = "overlapOnly")
model <- stockModel(returns, freq="day")
mvp <- optimalPort(model, Rf=0)
portPossCurve(model, riskRange = 6, add=FALSE... |
a4d9263782f67ffe059095994c8ac62358460b03 | f245521e63b59e37470070092b7d1d38a87b2e48 | /plotCover.r | cc03a442bd8e99c01faa8b960739de06c6b69a5c | [] | no_license | douglask3/UKESM-land-eval | 3c10d10eba32bcef1e7b2a057db3b22fdf2fd621 | aad3f6902e516590be02585ad926bfe1cf5770bf | refs/heads/master | 2021-08-17T06:32:10.736606 | 2021-07-14T12:57:13 | 2021-07-14T12:57:13 | 242,747,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,502 | r | plotCover.r | source("cfg.r")
library("plotrix")
library(maps)
graphics.off()
obs_files = c(IGBP = "igbp", CCI = "cci", VCF = "VCF")
vars = c(Tree = "tree", Wood = "wood", Shrub = "shrub", Herb = "herb", Grass = "grass",
"Total\nveg. cover" = "bares")
limits = seq(0, 0.9, 0.1)*100
cols = c('#ffffe5','#f7fcb9... |
df9a7042171b77792494eb2ad857867a7cce3c67 | dd1fa9020beb9b0205a5d05e0026ccae1556d14b | /itwill/R-script/chap18_ClusteringAnalysis.R | 3a3c40783ef5c4ede2088f1ab89bcacb8b05e671 | [] | no_license | kimjieun6307/itwill | 5a10250b6c13e6be41290e37320b15681af9ad9a | 71e427bccd82af9f19a2a032f3a08ff3e1f5911d | refs/heads/master | 2022-11-13T11:55:12.502959 | 2020-07-15T08:14:21 | 2020-07-15T08:14:21 | 267,373,834 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,674 | r | chap18_ClusteringAnalysis.R | # chap18_ClusteringAnalysis(1)
###################################################
# 군집분석(Clustering)
###################################################
# 고객DB -> 알고리즘 -> 군집
# 알고리즘을 통해서(패턴으로) 근거리 모형으로 군집형성 - 규칙(rule)
# 변수에 의해서 그룹핑되다.
# 변수 적용 : 상품카테고리, 구매금액, 총거래금액
# 유사성 거리에 의한 유사객체를 묶어준다.
# 거리를 측정하여 집단의 이질성과 동질성을 ... |
ccf47650fc1cb96834a9b64c445efc68a3f33f1c | a1d5615adff3d432c9d22f450d67d1340e81bec4 | /R/headlines.R | c40dccf68193ea0174a4690d5d2875e1fc8a7387 | [] | no_license | phebepalmer/textclassificationexamples | 922b0faeb513c114ee507edb4d722111bf121b3e | 7965fb2fa26e5aec98773c7b0374abb5558e5bf5 | refs/heads/master | 2022-12-05T18:27:47.196889 | 2020-08-20T16:29:20 | 2020-08-20T16:29:20 | 281,805,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,135 | r | headlines.R | #' Headlines
#'
#' This data comes from Chakraborty et. al., which combines headlines from
#' a variety of news and clickbait sources. Much of these headlines contain
#' subject matter innapropriate for classroom use. Given the volume of headlines
#' containing such language (especially for clickbait == TRUE), this fil... |
9dae839eaa871f186aa1a4de28626d1789e8b1c4 | 29585dff702209dd446c0ab52ceea046c58e384e | /CNVassoc/R/mixture.R | 7f0bf2ea903899bf24cb6680c2f9d2377db4ff76 | [] | 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 | 2,265 | r | mixture.R | mixture <-
function (intensities, num.class, mix.method, threshold.0, threshold.k,
mu.ini, sigma.ini, pi.ini, var.equal)
{
method <- charmatch(mix.method, c("mixdist", "mclust", "EMmixt"))
miss.threshold0 <- missing(threshold.0)
miss.thresholdk <- missing(threshold.k)
if (!miss.threshold0 && ... |
9d87d7d03291d64b2851fabf6dbb34194dbb6e9e | 607fb8ae7ca5550de01bbb86304e6b4cf0e24152 | /Tem que ver isso ai.R | 89886ebf48773a0aeecb91a7ad105b452facc225 | [] | no_license | rodolphoBernabel/Curiosidades-eleitorais-2016 | 301090b55fd61f38b6b600edefa109f7db303bf2 | fead587bc1b152ed5a7365198bb6af1e8e157fd2 | refs/heads/master | 2021-01-24T11:39:50.834161 | 2016-10-14T18:17:29 | 2016-10-14T18:17:29 | 70,204,761 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,182 | r | Tem que ver isso ai.R | ################################
# Curiosidades Eleitorais 2016 #
# Rodolpho Bernabel #
# 2016-10-06 #
################################
#limpa a área
rm(list=ls())
#lê o arquivo (substitua o caminho para o seu diretório)
despesas_candidatos_2016_brasil <- read.csv("C:/Users/Rodo... |
edf9b957fad52d6342d94facebff5d44c58b0226 | 3ad58e81afb376d43220fc138a088bc47c63e8ff | /tests/testthat/test-openfair.R | 9c15e600bd8b3d6db37cf49e6280d20d5404896f | [
"MIT"
] | permissive | redzstyle/evaluator | 11fe5425631c482592c697d0ca676f416e57419e | e81a72ca6536395b58fda9cc2a21d6cb78723740 | refs/heads/master | 2020-04-27T09:28:14.889581 | 2019-02-05T16:59:11 | 2019-02-05T16:59:11 | 125,245,691 | 0 | 0 | NOASSERTION | 2019-02-10T22:06:23 | 2018-03-14T16:58:20 | R | UTF-8 | R | false | false | 9,385 | r | test-openfair.R | test_that("Sample TEF", {
set.seed(1234)
tef <- sample_tef(params = list(n=10, 1, 10, 100))
expect_is(tef, "list")
# ensure that the list has the required elements
expect_equal(names(tef), c("type", "samples", "details"))
# ensure that the samples matches the number requested
expect_equal(length(tef$sampl... |
ee28ef661ca6c7d02f0a3d84c559bef29ba5cc7e | 6dfb737d4f74bff5392f26ffc101aa84d0695d3a | /inst/shiny/server.R | fa1f6174d9f847685f0374b8b331c13550f27833 | [] | no_license | cran/weightr | e66651ef7d65e554bb6782b4ce5e96c7fcdba910 | af3c9bc0df2ff17ce2dbdc8e0a412ec479ea94b7 | refs/heads/master | 2021-01-01T05:03:10.816984 | 2019-07-06T17:00:10 | 2019-07-06T17:00:10 | 56,994,714 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 24,197 | r | server.R | library("shiny")
library("foreign")
library("gridExtra")
library("ggplot2")
library("plotly")
source("weightfunction.R")
shinyServer(function(input, output, session) {
filedata <- reactive({
inFile <- input$file1
if (is.null(inFile))
return(NULL)
return(read.table(inFile$datapath, h... |
e4360e5f786f320a40c16c791ec474342cd78bed | 36f9d3d21e7bf2b897c01b8c80ccda2a7b9e1c65 | /cachematrix.R | 99152820d11a44072a9e58f4557862511da3efad | [] | no_license | plagi/ProgrammingAssignment2 | 09bd255df708a78d830c6a983dd7b464f4a6ebea | ff96de2c4320f2b2a3820d2a83e8f8a7a653e547 | refs/heads/master | 2021-01-14T11:20:53.372462 | 2014-05-15T19:27:11 | 2014-05-15T19:27:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,022 | r | cachematrix.R | ## The first function, makeCacheMatrix creates a special "matrix",
## which is really a list containing a function to:
## set the value of the matrix
## get the value of the matrix
## set the value of the inverse matrix
## get the value of the inverse matrix
makeCacheMatrix <- function(x = matrix()) {
im <- NULL
... |
26de36118549eb8ccdc0e9335c8aa196befea3aa | 5caa953c51a26a6bd1be24ddd78c8b6fb04eeaf1 | /main/server.R | c11b23ac2edb85a359116b77a27221b153d110ea | [] | no_license | tintinthong/hugo | adb121eaa1321d712d7dd1f1e45d9aed124f86db | 6e7fc5d7f99cc6932f6c67f506fec729e0cfa525 | refs/heads/master | 2020-05-24T15:49:40.340583 | 2019-05-18T10:04:16 | 2019-05-18T10:04:16 | 187,340,902 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,983 | r | server.R | #....:....1....:....2....:....3....:....4....:....5....:....6....:....7....:....8
function(input, output, session) {
#session
dataMat<-"here"
#sourced for each session
#source('all_sessions.R', local = TRUE)
output$plotJT <- renderPlot({
# Render a barplot
hist(data,
... |
203559af8f371c068ea3538791583db4f9b3c6b5 | 187414dcb264fb49d82507a099fd5fdca6e55e38 | /R/pkg/inst/worker/worker.R | 7fc4680bad10e5b2f6b0a4272483712957bf3d17 | [
"BSD-3-Clause",
"CC0-1.0",
"CDDL-1.1",
"Apache-2.0",
"LicenseRef-scancode-public-domain",
"BSD-2-Clause",
"LicenseRef-scancode-unknown-license-reference",
"EPL-2.0",
"CDDL-1.0",
"MIT",
"LGPL-2.0-or-later",
"Python-2.0",
"LicenseRef-scancode-generic-cla",
"LicenseRef-scancode-free-unknown",... | permissive | apache/spark | 8aeba2d80465a262acc95781ede105a5b5886f6d | 60d8fc49bec5dae1b8cf39a0670cb640b430f520 | refs/heads/master | 2023-09-04T04:33:36.058199 | 2023-09-04T03:48:52 | 2023-09-04T03:48:52 | 17,165,658 | 39,983 | 32,449 | Apache-2.0 | 2023-09-14T19:46:24 | 2014-02-25T08:00:08 | Scala | UTF-8 | R | false | false | 10,355 | r | worker.R | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... |
b1143c4c5e36f9c6bedac09eba732656fc60d8ca | 53851868e25801999033fe8d7c3150b73e7dde65 | /R/aegean/XTentModel.r | 14aa21f3d2dd2bc233568c20692dc64e632532c8 | [] | no_license | xuzhikethinker/PRG | bb7e75d27f9da7611d3c26f10bb083ec69025487 | 25b971f6e65ef13f80d3a56732e4bb6d4502bb55 | refs/heads/master | 2016-09-06T02:27:18.042949 | 2013-03-27T18:17:53 | 2013-03-27T18:17:53 | 9,262,600 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,204 | r | XTentModel.r | # produces XTENT models
inputDir="input/"
rootName="aegean39S1L3a";
typeName="_v1_3e-1.0j0.0m0.5k1.0l4.5b1.2D100.0MC_r4_";
methodNumber<-1
numberRows=39
distanceMeasure<-1
mainTitleOn=TRUE
source("criticalAngleDendrogram.r")
useAnglesOn=FALSE
methodNumber<-1
distanceMeasure<-1
criticalAngleDendrogram(inputDir, rootN... |
b03c09756c86406840d02792e3798222a43bded5 | 6d790f6448781672395a339fa7ed4bb6890ffb1e | /R/KF-interfaces.R | 95e798471c4216c9f65e764ff27561a6152d1880 | [] | no_license | cran/KFKSDS | fac6899e084a7348f0c32e4ac9a8b1208a052d36 | 764548b72421436a4e617acfeeed1451e6cfb024 | refs/heads/master | 2016-09-05T13:47:26.383362 | 2015-01-28T00:00:00 | 2015-01-28T00:00:00 | 17,680,127 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,291 | r | KF-interfaces.R |
KalmanFilter <- function(y, ss,
KF.version = c("KFKSDS", "StructTS", "KFAS", "FKF", "dlm", "dse"),
KF.args = list(), check.args = TRUE, debug = FALSE)
{
KF.version <- match.arg(KF.version)[1]
a <- if (check.args) {
make.KF.args(ss, KF.version, KF.args)
} else KF.args
P0cov <- if (is.null(KF.args$P0c... |
6d9e13a6bef81201d101aea0c738211a77a1a3c1 | eeae86290d4b0bcc11ce82dc8b03cc75613de1ce | /fig1/F1_src_LoadReadCounts.R | 86f82e1d4ec8de44e7fbac7fe23984953ad45184 | [] | no_license | hjanime/TT-seq_mESC_pluripotency | 07b438a7a4e3d1cecd3da9758b4329c1392dec0a | 654e4176570cbd79e5ad6cfef3f3b2cfa67e2cf9 | refs/heads/master | 2023-08-02T11:25:25.136642 | 2021-10-03T21:08:37 | 2021-10-03T21:08:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,837 | r | F1_src_LoadReadCounts.R | #_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#
# read in kallisto tx counts on gencode.vM17.annotation and spike-in RNAs
# and save reads counts for normalization
#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#_#
# load kallisto counts, mm10
filenames <- sort(list.files('../data/kallisto_output', full.names = T)) # count with comb... |
15aacca9334ccb8b230421432c04766c30f8b159 | b9aba21008fb49a8b4ffb4f04bdf66a4cafaef9d | /SF Salries Code/SVM.R | fd612e3ebc6c6a9112329f0218ff129773fb2257 | [] | no_license | wztaylor/Final-Project-DATA-495 | 66cb7d03d69804d0205eb0e1ad7494fd7267e94d | 7c8799c2a18be62507a7d07353039c6b87021eaf | refs/heads/master | 2021-01-09T20:47:03.842013 | 2016-06-02T23:12:52 | 2016-06-02T23:12:52 | 60,303,503 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 688 | r | SVM.R | ########################################
# SVM #
########################################
library(e1071)
library(rpart)
index <- sample(1:nrow(completedata),round(0.50*nrow(completedata)))
train <- completedata[index,]
test <- completedata[-index,]
## svm fit and prediction
svm.model <... |
e24bdf5ec2cbb3f556f3c84c26a5399ac21d0ded | 03ac171669a60b57e772941dfeb6be966e916808 | /R/single_a_id.R | 5fb6145a379d274ebaa2c27c3b315c54e8a21aee | [
"MIT"
] | permissive | michaeldumelle/DumelleEtAl2021CopepodSentinel | 8a37d4a260f6bacc3ec704d394e56ac9df2281bd | 2da0ba403f5eabcf78836891ee4047578d7ee0f4 | refs/heads/main | 2023-08-23T10:47:24.998662 | 2021-10-04T23:43:44 | 2021-10-04T23:43:44 | 339,199,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,317 | r | single_a_id.R | ## Compute sentinel station statistics with an auxiliary stations
##
## `single_a_id` computes statistics for a single sentinel station
## corresponding to a single auxiliary stations
##
## @param s_id A character vector of unique sentinel station identifiers. If
## this variable is not specified, the \code{sentinel()... |
7514959e8f030ad1cccebb3f4cbf289b85319bde | cc81dbbfa460d0d474c7e6618f19158c7bacbe3d | /params_pb_se.R | 6575a0088fc134a5d42b5929459f922d6d1c204d | [] | no_license | raimund-buehler/shinyapp | 72be622de701d0e1f50794a7f691263b150401e5 | c9667a18d10b9aba996c950aa514fd406513851a | refs/heads/master | 2023-02-11T07:06:08.022763 | 2021-01-11T10:24:07 | 2021-01-11T10:24:07 | 278,086,703 | 0 | 1 | null | 2020-09-08T07:22:10 | 2020-07-08T12:44:04 | R | UTF-8 | R | false | false | 326 | r | params_pb_se.R | # set parameter for sterne & egger regression
if(input$go_SE > 0){
params_pb_se <- list(
pb_se_zval = SEres$res$zval,
pb_se_pval = SEres$res$pval,
thres_se_pval = input$SE_p
)
} else {
params_pb_se <- list(
pb_se_zval = NA,
pb_se_pval = NA,
thres_se_pval = NA
)
}
params... |
a44b0dcf38b7bf7bcda4e7546f5f0a072b47ba13 | aafa44abd35bd74bd3a5203e3d5ffcc7ef2775b5 | /inst/extdata/junk/quasiTpm.R | 3123d47aa8f87471d1470613e0cae58347eb2718 | [] | no_license | arcolombo/rToolKit | df1d53c9d1384217fbc9b4af4cd7fd14ff566aca | ad9dfcf44c044947fc442b0690bd23f26641f8a2 | refs/heads/master | 2021-03-24T12:34:39.090176 | 2018-02-17T06:32:52 | 2018-02-17T06:32:52 | 66,577,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,286 | r | quasiTpm.R | #' @title quasi normalization of TPM by bundling
#' @description resulting quasi-normalization of TPM of a bundle numbers should now have variances which behave similarly to those of counts. this may be good enough to use the bundle wise TPM quanties for batch normalization and other shenanigans, then multiply through ... |
9ba71f999a3de6f54b8d16a3f5b7f9cea2026fca | 0fbc58702c39addfa7949391d92533922dcf9d49 | /inst/examples/song-words-hclust.R | 7f03886091a475469205585d16e848f92fa26035 | [] | no_license | yihui/MSG | d3d353514464f962a0d987efd8cf32ed50ac901a | 8693859ef41139a43e32aeec33ab2af700037f82 | refs/heads/master | 2021-11-29T08:12:02.820072 | 2021-08-15T17:14:36 | 2021-08-15T17:14:36 | 1,333,662 | 30 | 12 | null | 2021-08-15T17:14:37 | 2011-02-06T05:42:53 | R | UTF-8 | R | false | false | 312 | r | song-words-hclust.R | # 宋词作者层次聚类谱系图
load(system.file("extdata", "SongWords.rda", package = "MSG"))
SongCorr = cor(SongWords) # 词风相关矩阵
song.hc = hclust(as.dist(1 - SongCorr))
par(mar = c(0.5, 4, .2, 0.1))
plot(song.hc, main = "", cex = .8, ylab = "高度")
rect.hclust(song.hc, k = 4, border = "red")
|
50d1e6c5c83fb79be0135da85219a92a592276e8 | 6bb000638105bea5d968913d53a2bdd750ad0fba | /src/data/clean/mobility/safegraph/weekly-patterns/process_weekly_patterns.R | 02dc4e6c144744ddb47dad69acd3b4934bf5dc88 | [] | no_license | alecmacmillen/covid-research | 3db91ec3f59ae76f23c563deedfc1a80f422bb99 | cefa45c53fff4241402c8bd74f8135257e33118f | refs/heads/master | 2022-10-17T19:17:09.934590 | 2020-06-13T05:26:30 | 2020-06-13T05:26:30 | 266,205,353 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,896 | r | process_weekly_patterns.R | ###################################################
# process_weekly_patterns.R
# Script to replicate data build for county-level POI and visits
# As described in Allcott et al (2020) Appendix A.1.1, step 3
# Merge county name from POI data build
###################################################
rm(list = ls())
libr... |
9540b5ad95c11c404719ffda7ca98320e3c8ac2c | 6f1c32b7c1686a2f618e0fee812e96f0e1b054a3 | /R/ipak.R | 86671a4ba609bbb2f143dd34940f047cbef22c7e | [] | no_license | xtmgah/Tmisc | f1033c3cfdb10bf5c6d957c6ffd1c888d3047249 | 305a123f90bacb0139b56ecc9d61ebee14dcf7cf | refs/heads/master | 2021-01-20T17:07:02.385416 | 2015-04-20T19:58:58 | 2015-04-20T19:58:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 312 | r | ipak.R | #' Install packages
#'
#' Shortcut to \code{\link{install.packages}}
#'
#' @author Stephen Turner
#' @keywords keywords
#'
#' @param ... Arguments to be passed to \code{install.packages}
#'
#' @export
#'
#' @examples
#' \dontrun{
#' lsp(Tmisc, pattern="un")
#' }
ipak <-function(...) install.packages(...)
|
afa509cac365ecefda5fbf33eb41c1bbd4b307b6 | bc4816180d9f01c5215fee4fd8742c5ccf14add1 | /plot1.R | bc530630640b40bda7d0866631fbed476af3ebd5 | [] | no_license | singhs32/ExData_Plotting1 | 46f36a83fe0060047957475dcd4964233eff62f9 | 5f0f0e78a473212e1c71f9aa717433c1bfa045ca | refs/heads/master | 2020-12-25T03:40:24.047673 | 2014-08-09T23:06:12 | 2014-08-09T23:06:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 712 | r | plot1.R | plot1<- function()
{
## reading entire file
data<-read.table("./household_power_consumption.txt", header = TRUE, sep=";",na.strings=c("NA","?"),colClasses=c("character","character","numeric","numeric","numeric","numeric","numeric","numeric","numeric"))
## subsetting the data to get data we... |
14aa27a0f164f3720b66c1d3508892519aa3e63d | 35902ea94d808a4cf80050b3eba004d3fc2eef9d | /scripts/posterior_predictives_out_sample.R | 502bc0c6a5c9c17b0e9bb3fabcf81c38a76e6aec | [
"CC0-1.0"
] | permissive | Kucharssim/WALD-EM | 79f2099b3490bba479f4a6700d524cbd71e607d4 | 9a34d02ba2f565b0291099b495f2683a0b0562b7 | refs/heads/master | 2023-01-06T05:08:52.399454 | 2020-11-10T10:55:43 | 2020-11-10T10:55:43 | 248,290,398 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,606 | r | posterior_predictives_out_sample.R | library(tidyverse)
library(rstan)
library(here)
library(tidybayes)
library(patchwork)
library(imager)
library(ggforce)
ggplot2::theme_set(ggplot2::theme_classic(base_size = 14))
ggplot2::theme_update(axis.ticks.length = ggplot2::unit(6, "pt"),
axis.text = ggplot2::element_text(size = 15)... |
04c001257acdd9713a4fccf6f4d5fd2a85627ec9 | e7bb2f29beacbc7d61b22f4a85dc5699ed76c5fb | /R/forest_lake.R | bf216a6d700e30bfb4fa7a354795465decd91b3a | [] | no_license | jmzobitz/degreeDay | 789ab92f42032799e25e7f22d7ba9b10be102f89 | f72110fc10b528eebff8cec8cdbcbb4846e2c8bd | refs/heads/master | 2020-04-10T03:07:03.957284 | 2019-01-24T19:06:36 | 2019-01-24T19:06:36 | 160,733,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 636 | r | forest_lake.R | #' Measured minimum and maximum daily temperature from Forest Lake Weather Station
#'
#'
#' \itemize{
#' \item year. Year of measurement
#' \item day. Day of the year
#' \item min_temp. Minimum temperature (degrees Celsius)
#' \item max_temp. Maximum temperature (degrees Celsius)
#' }
#'
#' @docType data
#' @ke... |
14ab6799773b5898c89b56ff5b6190f6b88a0281 | 501684023d91f6de5617d1b13b2e3367dc919473 | /gToolbox/man/dds_heatmap_rld_vst.Rd | de529253884a76f89810b0ea3e8beac41f8df7f1 | [] | no_license | aqibrahimbt/BioMarkerAnalysis | 8c55d4d1085bb370e84def48ab47e63aa781f708 | f37edf645e5e8abdd991ddbbb088b41775b18812 | refs/heads/master | 2022-01-08T23:34:57.680090 | 2018-08-22T10:30:07 | 2018-08-22T10:30:07 | 147,839,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 699 | rd | dds_heatmap_rld_vst.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/de_analysis.R
\name{dds_heatmap_rld_vst}
\alias{dds_heatmap_rld_vst}
\title{Generates heatmap for the top {n} genes from Regularized Log transform
of the count data and outputs to JSON file}
\usage{
dds_heatmap_rld_vst(dds, subset, datasets,... |
a78fcd6f03733b0491b0f01774dce96f41621bbf | c334867663e1786211cc398daa051a722478844b | /Sorting.R | c853891c70a243804c7d4b94183764fcedd5f4f8 | [] | no_license | Dinesh5191/Practice | ae01fb86a3fbac710a0d5baa9f284a82e9c7d12f | 661313e6e519015d89d92b4fdb65d72184b36f9b | refs/heads/main | 2023-03-09T04:10:41.506974 | 2021-02-23T07:06:27 | 2021-02-23T07:06:27 | 339,984,329 | 0 | 0 | null | 2021-02-23T07:06:28 | 2021-02-18T08:32:18 | R | UTF-8 | R | false | false | 3,567 | r | Sorting.R | # We may create vectors of class numeric or character with the concatenate function
codes <- c(380, 124, 818)
country <- c("italy", "canada", "egypt")
codes
codes <- c(italy = 380, canada = 124, egypt = 818)
codes <- c("italy" = 380, "canada" = 124, "egypt" = 818)
codes
class(codes)
codes <- c(380, 124, 818)
country <-... |
1bf21310c4c92b8c7dd646b384c6c8a31dddebea | f2ed007678fb657948af026f26c87d268d4afc70 | /R/distancesDistribution.R | c4ff87b17c00f13a624a9bef42279cf8203ded8e | [] | no_license | cfyy/Distances | 12998e04e0204563d3fc0bdf90cf851b46b9ada5 | 56a3f1f3f0833f0285410f4f6a28bb7bd1f801fc | refs/heads/master | 2022-04-03T05:15:13.345382 | 2020-02-03T14:52:46 | 2020-02-03T14:52:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 942 | r | distancesDistribution.R | distancesDistribution <- function(data) {
#require("dbt.classifiers")
#require("dbt.pareto")
# author: Raphael Päbst
warning('Not verified, may not work properly. Please check results')
maxNrOfPoints <- 1000
nrOfPoints <- nrow(data)
if (nrOfPoints > maxNrOfPoints) {
percentage <- (maxNrOfPoints / nrOfPoints) * 100... |
9c9799f6e41b7a0820dd1539db319691fe613984 | e28711ce5ece5984dfd14c934938e3fce1468306 | /man/rSemiCov.Rd | fcbe0ef83c7d5f36a0592c8fdb8b2a7c6eb15665 | [] | no_license | jonathancornelissen/highfrequency | 7387098c0998f2fb719cad4acff6b25bea781720 | 967dc40e0f7688f1e4d89ca5244ad1ac2b7810d4 | refs/heads/master | 2022-12-15T23:19:42.120107 | 2022-12-05T21:01:34 | 2022-12-05T21:01:34 | 7,306,202 | 125 | 62 | null | 2022-12-05T21:01:35 | 2012-12-24T11:15:54 | R | UTF-8 | R | false | true | 3,883 | rd | rSemiCov.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/realizedMeasures.R
\name{rSemiCov}
\alias{rSemiCov}
\title{Realized semicovariance}
\usage{
rSemiCov(
rData,
cor = FALSE,
alignBy = NULL,
alignPeriod = NULL,
makeReturns = FALSE
)
}
\arguments{
\item{rData}{an \code{xts} or \code{da... |
991e60a6eb5533ef82095161a985b42c49165545 | 67c2633786ebaf36b649b0c07f7544e09cf9a924 | /man/mechanismStability-class.Rd | 9461d1691bcf0f0c29708411271a251310fc92ec | [] | no_license | privacytoolsproject/PSI-Library | 6343cb34cf28a8736807e2bc95990d2c7bbe3756 | adaa32e941dc2832b0a719886d863e29f81808ec | refs/heads/develop | 2021-03-27T10:22:03.874212 | 2020-02-12T19:02:20 | 2020-02-12T19:02:20 | 82,702,513 | 6 | 6 | null | 2020-02-12T19:02:21 | 2017-02-21T16:33:22 | R | UTF-8 | R | false | true | 273 | rd | mechanismStability-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mechanism-stability.R
\docType{class}
\name{mechanismStability-class}
\alias{mechanismStability-class}
\alias{mechanismStability}
\title{Stability mechanism}
\description{
Stability mechanism
}
|
28665107fea2f089df84b7b4b4e2eb1f35f52fb5 | d85c04b9fe18a217ccfdb4b1a8ffe50db783676e | /shp2raster_function.R | ab44df26fa4007285534a96bc7d1e73cdccd6349 | [
"MIT"
] | permissive | hmorzaria/Biodiversity | bc76024521fdcdeb8bb0d75bdc619997df102a6f | 1169a2312f27f07716177715bf10a237ee723516 | refs/heads/master | 2021-01-10T10:06:23.677131 | 2016-01-21T23:16:14 | 2016-01-21T23:16:14 | 49,678,635 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,069 | r | shp2raster_function.R |
##https://amywhiteheadresearch.wordpress.com/2014/05/01/shp2raster/
shp2raster <- function(shp, mask.raster, label, value, transform = FALSE, proj.from = NA,
proj.to = NA, map = TRUE) {
require(raster, rgdal)
# use transform==TRUE if the polygon is not in the same coordinate system as
... |
d6484d7c08e993703d8a8486db38d014bf6e0d7e | 4180e1de7f766fd0065f33559481ff730470598a | /analyze/analyze.r | 9a03d0f5a173b16473c4497830140351a113e8e5 | [] | no_license | rhema/chrome-study-tracker | 77bcdeb24f6f1a64f8c66410953870d886fbd02b | b23509e33575b10b0bcb59ccfde42bd198885468 | refs/heads/master | 2020-05-20T06:16:07.318084 | 2012-12-20T16:03:06 | 2012-12-20T16:03:06 | 6,184,455 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,705 | r | analyze.r | #check first and second
#check difference between first and second or difference between two results
#setwd("/Users/administrator/code/js/chrome-study-tracker/analyze")
setwd("/Users/rhema/Documents/work/gitwork/chrome-study-tracker/analyze")
study_data<-read.csv('second.csv',header=TRUE)
metrics<-cbind('collected','... |
499ab5b8679d46432dd14f519cb980e2c1eea139 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmomco/examples/is.gev.Rd.R | 7d37ad383bda359331615e8c42753d0916191622 | [] | 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 | 356 | r | is.gev.Rd.R | library(lmomco)
### Name: is.gev
### Title: Is a Distribution Parameter Object Typed as Generalized Extreme
### Value
### Aliases: is.gev
### Keywords: utility (distribution, type check) Distribution: Generalized
### Extreme Value
### ** Examples
para <- pargev(lmoms(c(123,34,4,654,37,78)))
if(is.gev(para) == T... |
58ecbf8272d7031f52179f24bf9387b16fcb8b07 | 7311333635a0711c86a84b2badd571c5a0bd42b6 | /man/entropy.Rd | bec81761d1957e0e12e93bacb075eaf0c32b623a | [] | no_license | cran/infotheo | ef9d11f5a605df1d584b93cdaafd92e4797250ee | a0a3450b5ed66f49fadb98a0509baeb7e78f167d | refs/heads/master | 2022-04-29T07:31:02.112855 | 2022-04-08T10:00:24 | 2022-04-08T10:00:24 | 17,696,780 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,841 | rd | entropy.Rd | \name{entropy}
\alias{entropy}
\title{entropy computation}
\usage{entropy(X, method="emp")}
\arguments{
\item{X}{data.frame denoting a random vector where columns contain variables/features and rows contain outcomes/samples.}
\item{method}{The name of the entropy estimator. The package implements four estimators ... |
0f217d9ee57a0b9713ef4b8c274eaefccb57d80d | 37a1b0e96b6a224b1df0a6c680ca02b34dcb581b | /R/summary_tibble.R | 27d5d44bc4ee8cdac4eaa76577e5e4b18fe5497e | [] | no_license | HuntsmanCancerInstitute/hciR | a75fcb4a4afb674477bcd9d26bb03a511228f78a | 418a81899a31c4def7f9a7aef315f872a1a56700 | refs/heads/master | 2023-08-18T08:03:25.771541 | 2023-08-08T18:19:02 | 2023-08-08T18:19:02 | 67,628,344 | 9 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,585 | r | summary_tibble.R | #' Summarize tibble columns
#'
#' Summarize tibbles with many columns like TCGA metadata. Summaries for large tibbles
#' with many rows will be slow and not recommended.
#'
#' @param x a tibble
#' @param y a vector
#'
#' @return A tibble with column names, types, number of unique values and NAs,
#' minimum, maximum an... |
1d804ab821c38103e607e12f2b856cceb23c173c | ec3947959f93dd1d5112080db031c70fbd2cc127 | /R/bfastTemplate.R | 6ef4433d124e1b162bfbfb358e3a0708f5e10848 | [] | no_license | npp97-field/gimms_iran | 77e7c104665d75464367246630d9bb110d801f27 | de17f54d1991801173e14763f6d90565b33af11e | refs/heads/master | 2020-03-27T07:10:01.810316 | 2015-12-09T07:51:40 | 2015-12-09T07:51:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | r | bfastTemplate.R | bfastTemplate <- function() {
## gimms raw data
gimms_files <- gimms::rearrangeFiles(dsn = "data", full.names = TRUE)
gimms_raster <- raster::stack(gimms_files[1])
## reference extent
library(rworldmap)
data("countriesCoarse")
spy_iran <- subset(countriesCoarse, ADMIN == "Iran")
## crop ... |
20ae555786a9c5dabe31fbdb9d064d4dba8a1ea3 | dbfff49801233324ee40f1ff559303d9ad23421d | /SG-RNA-seq/stress_granule.R | 47c30c13be5839079b435c2b9d57116b3bad62a5 | [] | no_license | JungnamChoLab/CodonOptimality | 62008e68ebc14c6c4090da7544c840c5ea2e1d38 | 65ce2bafd87a383f07ba7dd8b50ca36182039802 | refs/heads/master | 2023-06-16T05:44:07.424611 | 2021-07-02T05:04:46 | 2021-07-02T05:04:46 | 274,046,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,280 | r | stress_granule.R | # Use the raw read count got from featurecounts to do DEG analysis and got SG-enriched and depleted transcripts
g <-read.delim("stress_granule_raw_read_count")
colnames(g)[7:10] <- c("Total1","Total2","SG1","SG2")
gg <- g[,c(1,7:10)]
g <- merge(t_type,gg,by.x="V1",by.y="Geneid")
table(g$V2)
library("DESeq2")
gg <- data... |
740f5af505460c584aee73561a6cca3c8b2d8083 | a5a1dfa861d42495ea1013c42f8edd460ca89561 | /hcasmc_specific_eqtl/plot_hcasmc_specific_eqtls_pval.R | ad02e3cbcc4713ae1254c174988576a41ac69c64 | [] | no_license | chanibravo/hcasmc_eqtl | 4cca8abab75d63196d005294bf42d291128fac24 | 0c4f25b5a336349d8e590f2ac357ce0519e16032 | refs/heads/master | 2021-09-20T05:52:29.831159 | 2018-08-05T05:23:01 | 2018-08-05T05:23:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 346 | r | plot_hcasmc_specific_eqtls_pval.R | library(data.table)
library(dplyr)
library(dtplyr)
library(cowplot)
eqtl=fread('../processed_data/eqtl_and_atacseq/specificity.mean.sorted.10000.eql.txt')
setnames(eqtl,c('sid','pval','beta','se','tissue'))
pdf('../figures/hcasmc_specific_eqtl/qsiTop1e5.pval.pdf')
ggplot(eqtl[tissue=='HCASMC',],aes(-log10(pval)))+geom... |
60ad4d1dfaeef9e47e7d035a71fbcd1d2076c38a | 928f72ab4f9d8fd643c3c010ee732ea1c195a3a2 | /tests/testthat.R | 7c72829fbb30d444baba56f2168a68809d39de3f | [
"MIT"
] | permissive | vegawidget/ggvega | 362a3f5df2edeb2177f9dbdd68c21a605359e185 | e9a98a3db7d894b5378b6d13f32b435af693a4ed | refs/heads/master | 2023-01-04T13:49:40.936592 | 2021-10-22T23:05:09 | 2021-10-22T23:05:09 | 186,412,239 | 48 | 3 | NOASSERTION | 2023-01-04T07:47:28 | 2019-05-13T12:08:11 | HTML | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(ggvega)
test_check("ggvega")
|
2a7c70752e83ca3e04900dadd0df796fcec181c2 | 7b13f708f1b834a158b5637750f3577d3d5ac7d8 | /geo_2016_repeatable.R | d5095ad2d17f36f3d177d84e370b2ff377348253 | [] | no_license | cal65/Geography-of-Cal | f98712ec4add52ea3b3d31926857b7f677987392 | 8b2c62f2853d71d35d7c6ee76d6752ac03e065b4 | refs/heads/master | 2021-01-09T21:45:16.508728 | 2017-02-20T05:54:50 | 2017-02-20T05:54:50 | 52,335,968 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,684 | r | geo_2016_repeatable.R | setwd('/Users/christopherlee/Documents/CAL/Real_Life/Geography/')
library(ggplot2)
library(geosphere)
library(ggmap)
library(RColorBrewer)
library(sp)
library(plyr)
G_merged<-read.csv('G_merged.csv')
G_merged$Date.Begin <- as.Date(G_merged$Date.Begin)
G_merged$End.Date <- as.Date(G_merged$End.Date)
mapWorld<-borders("... |
29031792f881c0a42c51375b958f78130656f57b | 45ab1e397b5fc69ba84c8f5dfb66c09b79bca4c6 | /Course_II/R/pract/pract3/task4.r | 3d85d9f1b7aad9cf3e8c017d8d751367e012e985 | [
"WTFPL"
] | permissive | GeorgiyDemo/FA | 926016727afa1ce0ee49e6ca9c9a3c60c755b35f | 9575c43fa01c261ea1ed573df9b5686b5a6f4211 | refs/heads/master | 2023-06-28T00:35:43.166167 | 2023-06-16T14:45:00 | 2023-06-16T14:45:00 | 203,040,913 | 46 | 65 | WTFPL | 2022-04-09T21:16:39 | 2019-08-18T18:19:32 | Jupyter Notebook | UTF-8 | R | false | false | 829 | r | task4.r | "
Для задания No 4 из Лабораторной работы No 2 написать программы
в которых Пользователь с клавиатуры вводит значения двух переменных разных типов,
которые затем сравниваются между собой. Использовать функции readline(), print(
и функции преобразования типов.
"
{
a1 <- as.integer(readline("Введите число №1 -> "))
... |
a0985077f546058bbe2b47e921141584583a106a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gWidgets2/examples/gframe.Rd.R | a06963b2070bf5b5d944b690525403de34610a15 | [] | 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 | 489 | r | gframe.Rd.R | library(gWidgets2)
### Name: gframe
### Title: Constructor for framed box container with label
### Aliases: .gframe gframe
### ** Examples
## Not run:
##D w <- gwindow("gformlayout", visible=FALSE)
##D f <- gframe("frame", horizontal=FALSE, container=w)
##D glabel("Lorem ipsum dolor sit amet, \nconsectetur adipisc... |
56283c793ae2b1e85890eccf7a4c20bbcc90d6f7 | 80d01fcfe17acdb9953a0910a726e3e70a9d67e7 | /man/geocode_place.Rd | b24ac704728b4235f58d5f89d2fa2f82d32cbfa8 | [] | no_license | bpb824/transportr | d5cb96afd08cc17e6cc078e25fb721852ddb8db1 | 5e9b12aa1f3a8ef539792bbec149c06a8b68b62f | refs/heads/master | 2020-04-06T06:17:34.084717 | 2019-01-29T18:28:47 | 2019-01-29T18:28:47 | 43,518,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 550 | rd | geocode_place.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/googleMapUtils.R
\name{geocode_place}
\alias{geocode_place}
\title{Geocode a place name using Google Places API}
\usage{
geocode_place(placeString, key, output = "loc")
}
\arguments{
\item{placeString}{A string describing the place you'd like... |
1b91b43dc61139abf20f229ac52aa75469e46ae1 | 639cd1de25056e67de6677e0e54698de0a0d5f5c | /man/ATM.Rd | 4265e8bb1bb42be536a10573650a8d43c2cc4178 | [] | no_license | kengustafson/czerlinski1999 | 9d3e11238506633a5f4602078d16d07d57b0465f | 640dcf5206f088739431d6db32d45c98c360a6da | refs/heads/main | 2023-03-17T01:51:01.524455 | 2020-11-22T18:18:40 | 2020-11-22T18:18:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,846 | rd | ATM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/psychology_datasets.R
\docType{data}
\name{ATM}
\alias{ATM}
\title{Attractiveness of Men.}
\format{
A data frame with 32 rows and 7 variables:
\describe{
\item{case_number}{A numeric column with unique number id for each male celebrity.}
\ite... |
30cb44345af80c442fb5b91a7f8cd66ed5e9e672 | 543f60c0dd71a6eb227c0d8176b38d7e3554b00e | /Hyundai.R | 428c2c2f07745c7958d0e91bb30b11d063ef2edc | [] | no_license | ChandanNaik24/Data-Science- | b9e60e31f8b33211708e1f69691b1bdfabfd3714 | f6dcf43d57a37f81bb28b54eb1ebd508943ff941 | refs/heads/master | 2023-04-16T10:12:37.658918 | 2021-04-15T06:01:35 | 2021-04-15T06:01:35 | 287,471,833 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 2,352 | r | Hyundai.R | ##### Installing the packages #####
install.packages("caTools")
install.packages("ggplot2")
install.packages("glmnet")
##### Loading libraries #####
library("caTools")
library("ggplot2")
library("glmnet")
l
##### Loading datasset #####
Hyundai <- read.csv("F:/Huyndai.csv")
View(Hyundai)
summary(... |
207a9320f427b784687aa75bf700c24c0f80a84b | f2a982ef2ad5d0a1086830a59f2700bc7e0c668a | /man/guess_separator.Rd | 76c1fc20c1cbd1d8ac2c68cb18ef44b3bb5b36eb | [] | no_license | jimsforks/cleanser | 6f87363fefd5c0223c17d349ffa19f8d5ff1956c | 1597f2bfcf58a0084c2810fea236e38a51385e43 | refs/heads/master | 2022-03-16T23:49:13.342589 | 2019-09-27T07:43:25 | 2019-09-27T07:43:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 707 | rd | guess_separator.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/guess_separator.R
\name{guess_separator}
\alias{guess_separator}
\title{guess txt file separator}
\usage{
guess_separator(file, separator = c(",", ";", " ", "\\t"),
n_max = 1000)
}
\arguments{
\item{file}{path to csv or txt file}
... |
4d9292a35c5f4bc7b82976527cba03e9dad847c9 | b7808577564924e90aac8ad0bd800b11a078901f | /tests/testthat.R | 3373517587b7bf6ef8d3c19868152b5812d6257d | [] | no_license | congca/einr | c94ad5376a9df0054e87cb4f054237600f95373f | 7e670e9432445d067eae3a3ba526276797c8573b | refs/heads/master | 2021-09-22T17:00:10.187069 | 2018-09-12T10:09:18 | 2018-09-12T10:09:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 52 | r | testthat.R | library(testthat)
library(einr)
test_check("einr")
|
571df06d7a839dcf271f2e3da4d3d10ebc3d7c26 | 49ff0bc7c07087584b907d08e68d398e7293d910 | /mbg/mbg_core_code/mbg_central/LBDCore/R/read_inla_prior_matern.R | 48b5b8e2aec63da2a3b2d795260b7b34e4cf846d | [] | 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 | 1,396 | r | read_inla_prior_matern.R | #' @title Read INLA Matern GP priors for TMB
#'
#' @description Read in a prior specification from config and make
#' it TMB readable.
#'
#' @param prior_string character, character vec of length 1 specifying priors
#'
#' @return List containing (1) spde object and (2) list specifying a TMB prior,
#' containing three e... |
e18bd505269b5872425c54b439609162a26daa93 | da8dae69e597072bc616936d1d72a96f65e4efa0 | /code/currentversion/tools/nictools/R/ComputeDistance2Coast.R | 887fc3609d5b30cdd8608103788725f96ba94a2a | [] | no_license | UCL/provis | 71e82c383cd9414840e57c2a2867826d6b4ee3e6 | 86a287c7bc705d4aeffb9bbcf96747e97e6d688b | refs/heads/master | 2020-08-01T04:08:20.198284 | 2019-11-08T12:09:43 | 2019-11-08T12:09:43 | 210,310,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 527 | r | ComputeDistance2Coast.R | #' Compute distance to coast
#' @param location vector of locations (longitude,latitude)
#' @param mapdir directory containing shape file with england coastline
#' @return distance distance to coast and coordinates of nearest point
#' @export
#' @examples
#' dist <- ComputeDistance2Coast(location,mapdir)
Compute... |
6856f591b0e43efb65db86c3b9bfbf28bb2e39f0 | b7d6ad61d15f2b0dbb81a59ef43ef86c1cac68ff | /usage_cachematrix.R | 920fa41d2dc67bb717314ba66d1e8090f8de6d99 | [] | no_license | maro243/ProgrammingAssignment2 | e663fa26f2c642bd79da25daf73af819d4d34e8c | 0da340798afc27aaf3ecac09e142a5e287c37aa2 | refs/heads/master | 2021-01-18T12:06:19.584144 | 2014-08-24T11:43:40 | 2014-08-24T11:43:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 367 | r | usage_cachematrix.R |
# note to reviewer - this is some example usage, this source call won't work due the the absolute referencing
#couldn't figure out how to get the path to be purely relative
source('ass2/ProgrammingAssignment2/cachematrix.R')
M2x2 = matrix (c(4,3,3,2), nrow=2, ncol=2)
MC2x2 <- makeCacheMatrix(M2x2)
#first call
cach... |
11ac11579f69cad31f58a082a39c7dcf59bff0a9 | 9de75837e81cccb6dcb43f59a2002b774f5753b6 | /LDAcode.R | 05320e1b2df2f490d2a9e07269c9d2026689f586 | [] | no_license | blackaceatzworg/IssueDefinitions | 9383b3e4d3af197259171aa9e4350680c040c652 | 39f4798b30e685109a502fa7c98b3bfa72d27590 | refs/heads/master | 2021-01-21T23:37:51.357641 | 2015-02-17T02:04:33 | 2015-02-17T02:04:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,824 | r | LDAcode.R | # DETERMINE ISSUE DIMENSIONS
install.packages("tm", dependencies=TRUE, repos='http://cran.us.r-project.org')
library(tm)
install.packages("SnowballC", dependencies=TRUE, repos='http://cran.us.r-project.org')
library(SnowballC)
install.packages("topicmodels", dependencies=TRUE, repos='http://cran.us.r-project.org')
... |
3a81298a16a40b7f35fa83925cffb1da69f92439 | f3d4908b3f33681f9c485a7d6f0219ff396608a2 | /WGBSQC/WGBS_QC_ANOVA_ASD.R | c983b14158ee0251ac4aef630292842834fb20a4 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | aciernia/CerebralCortex2019 | 77b8192604e5e65c7024a043180c4842d7177dc1 | ce1a6df53cb8259218a690505c1ce0ac4786b5f2 | refs/heads/master | 2020-05-22T14:47:11.848948 | 2019-05-30T22:56:56 | 2019-05-30T22:56:56 | 186,393,457 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,104 | r | WGBS_QC_ANOVA_ASD.R | #9/5/18
##############################################################################
# MulitQC output from Bismark for alignment data etc
# read in and run ANOVA
#Annie Vogel Ciernia
##############################################################################
#load in and concatinate pm_stats output from wgbs tool... |
ce940c13094e364725e9c8d945512abae8e88522 | d546952d79f8fbbf08942ffcc4fe08450760fae4 | /changebasis.R | 5490c832da71e6fb55e2d4335c57c9c380e00101 | [] | no_license | georgercarder/New-York-City-Taxi-Trip-Duration | 7c555b6e18d2b80bd37ec870397cab5bf49adec7 | ee753e51d016cb2e1a391c4359dde7595eef606e | refs/heads/master | 2021-01-20T22:05:16.884847 | 2017-08-29T19:33:44 | 2017-08-29T19:33:44 | 101,797,939 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 875 | r | changebasis.R | #!/bin/Rscript
orgnshft.x<-function(x1)(x1+73.99363875389099)
orgnshft.y<-function(x2)(x2-40.736031073752805)
#x1,y1 are in rotated basis. of length 0.1 mile
#new origin is E 14th and 5th Ave
p<-pi/5
cp<-cos(p)
sp<-sin(p)
rotate<-function(A,B)c((cp*A-sp*B),(sp*A+cp*B))
#rotation transformation
#shift all points
m<... |
fde05cc999627b23c0bdd4b3922ef585767e5456 | d5626554407c0515919864c622a2841f662bac54 | /man/get_segmentation.Rd | db350f499be3dbf8ad4456f8b261be80ea6bfc68 | [
"MIT"
] | permissive | barefootbiology/heyexr | 45aa456a0b5740a6e926359fe1d20cbceaa4257c | 1f41e9120f7eae02b337d2f5b071f8a4a887d27f | refs/heads/main | 2022-08-13T02:50:24.102333 | 2022-07-08T21:32:31 | 2022-07-08T21:32:31 | 75,117,687 | 4 | 0 | null | null | null | null | UTF-8 | R | false | true | 404 | rd | get_segmentation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_segmentation.R
\name{get_segmentation}
\alias{get_segmentation}
\title{Get the segmentation data from an OCT object}
\usage{
get_segmentation(oct)
}
\arguments{
\item{oct}{OCT list object}
}
\value{
a tbl_df of the segmentation data
}
\de... |
1a9a0f292b4df5ea2c23009710fc39effa394a2c | 5dcacfc095c4eb6afcec840ee8a55e7794e277f9 | /R/00_load-packages.R | b1b92b4d09690531a7106d3eb83d94f6c59db0cb | [] | no_license | skdunnigan/chla-gtm-temp-interference | 15ddf0ce7348e127346e0214757d52d7c956ff9c | a7dcccec56f9b9f23c8457229f1523ad9fbf99b9 | refs/heads/main | 2023-07-02T18:41:54.345749 | 2021-08-03T17:44:50 | 2021-08-03T17:44:50 | 375,429,392 | 0 | 0 | null | 2021-08-03T17:44:51 | 2021-06-09T16:59:05 | R | UTF-8 | R | false | false | 572 | r | 00_load-packages.R |
# 00 install-packages --------------------------------------------------------
# run this code chunk if you need to install the packages into your R console
# packages <- c('tidyverse', 'ggpubr', 'here', 'janitor', 'readxl') # all packages
#
# install.packages(packages) # install all packages
#
# rm(packages) # remov... |
f0009b7035ee746893e3e03f47340a9cf04e9995 | 29585dff702209dd446c0ab52ceea046c58e384e | /TDMR/demo/demo02sonar.r | 0f128caa81d972b7a2ed94783855f9a63ac01ea7 | [] | 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 | 519 | r | demo02sonar.r | #*# This demo shows a level-2 example (SPOT tuning on task SONAR)
## load package and set working directory (dir with .apd, .conf and main_*.r file):
path <- paste(find.package("TDMR"), "demo02sonar",sep="/");
#path <- paste("../inst", "demo02sonar",sep="/");
tdm=list(mainFile="main_sonar.r"
,runList="... |
79505310c46386f0d1ba2e491a506d38d5ee4918 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /paws/R/rekognition_operations.R | 8e0e03518be502617b7ede005550185494c8df58 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | false | 302,781 | r | rekognition_operations.R | # This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include rekognition_service.R
NULL
#' Associates one or more faces with an existing UserID
#'
#' @description
#' Associates one or more faces with an existing UserID. Takes a... |
0cad6090c1be4ec37a3225573d9e820aafbcaae8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phylosim/examples/omegaHist.CodonSequence.Rd.R | ea333bfa778cb4537c7f3ced18f5452b53335227 | [] | 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 | 580 | r | omegaHist.CodonSequence.Rd.R | library(phylosim)
### Name: omegaHist.CodonSequence
### Title: Plot a histogram of omega values from a range
### Aliases: omegaHist.CodonSequence CodonSequence.omegaHist
### omegaHist,CodonSequence-method
### ** Examples
# create a GY94 process
p<-GY94()
# create a CodonSequence object,
# attach a process p
... |
4a8148e7adbea52ac13fecf83c5ec238df562cdf | 15d417ccfacf20314589ed086c0b8daa2245cc1f | /libc/sys/unlink/man.r | f6b353a45d541ea51776d693032dd09777e0ec1c | [] | no_license | paulohrpinheiro/tropix-libs | 17e7faf0715b104fbf6f305074de76bd1cf08fc5 | c41d33a6f95064ec6c0567a801048896be28c626 | refs/heads/master | 2021-12-03T19:21:09.621584 | 2014-11-06T17:47:26 | 2014-11-06T17:47:26 | null | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,110 | r | man.r | .bp
.he 'UNLINK (sys)'TROPIX: Manual de Referência'UNLINK (sys)'
.fo 'Atualizado em 23.08.95'Versão 3.0'Pag. %'
.b NOME
.in 5
.wo "unlink -"
remove uma entrada de um diretório
.br
.in
.sp
.b SINTAXE
.in 5
.(l
#include <sys/syscall.h>
int unlink (const char *path);
.)l
.in
.sp
.b DESCRIÇÃO
.in 5
A chamada ao sistema... |
b70f239ca7d2d35045479f0dfc92d8733a6272e7 | 6f5518ed43cfeb96dd87e9f112aba0058940d0b0 | /Exercise Files/Stats with One Variable/03_04/Ex03_04.R | 86071aee192d360bc0efe0cf3c14b0505a4e88e0 | [] | no_license | Lula27/R_StatisticsEssentialTraining | f0518f3cc6f9f9c2459ac2c6629e9bdb30ce7857 | 7238740374d5e6f115974c2f430009cb7d47cadd | refs/heads/master | 2021-06-27T01:57:11.663965 | 2019-03-22T23:06:03 | 2019-03-22T23:06:03 | 105,818,590 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 952 | r | Ex03_04.R | # R Statistics Essential Training
# Ex03_04
# Single mean: Hypothesis test and confidence interval
# Load data - look at structure
?quakes
str(quakes)
# See the first 5 lines of the data
quakes[1:5, ]
# Just load the magnitude variable
quakes$mag[1:5]
# Select magnitude for entire data set - store in object c... |
4fb5f7b36b19d8c263a794f5a5e9b6b30f8fe1de | 431719d48e8567140216bdfdcd27c76cc335a490 | /man/AgaveCache.Rd | 585be2a26fac24dfdafb8e83f745849230c827b2 | [
"BSD-3-Clause"
] | permissive | agaveplatform/r-sdk | 4f32526da4889b4c6d72905e188ccdbb3452b840 | b09f33d150103e7ef25945e742b8d0e8e9bb640d | refs/heads/master | 2018-10-15T08:34:11.607171 | 2018-09-21T23:40:19 | 2018-09-21T23:40:19 | 118,783,778 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 649 | rd | AgaveCache.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AgaveCache.r
\docType{data}
\name{AgaveCache}
\alias{AgaveCache}
\title{Agave Cache utility class}
\format{An object of class \code{R6ClassGenerator} of length 24.}
\usage{
AgaveCache
}
\description{
rAgave.AgaveCache
}
\details{
AgaveCache C... |
d5a88c585658137b2d629195805da1d3061b02c5 | 335ae8eef3bef300794d25a2e3f70bf5ffd65b0a | /scripts/analise/dadosMar15/usuarios/kendallDistance.R | 93d277f334295a3c5669c8d055b82f216a4c118e | [] | no_license | davidcmm/campinaPulse | 6ba006a13744f2488d650928129b9d2dcdae46aa | 4142bc6e306ff2b95452b2db466a8a48c003555d | refs/heads/master | 2021-01-23T19:44:10.166746 | 2019-09-28T12:56:06 | 2019-09-28T12:56:06 | 20,739,839 | 0 | 0 | null | 2019-10-22T04:56:53 | 2014-06-11T20:20:11 | Jupyter Notebook | UTF-8 | R | false | false | 1,994 | r | kendallDistance.R | # Functions to calculate the kendall tau distance of two rankings
mergeSort <- function(x){
# Sorts a list computing the number of inversions
#
# Args:
# x: list with itens to be sorted
# Returns:
# List with two values:
# "inversions": number of inversions and sorted list
# "sortedVector"... |
c7ead956e4f8e3061bfdd6b8cc4614182f790e83 | 1d14abe82ab2d1eaee21725835b3bde44b44009e | /R/wnd_gen.R | 788a272d50db9ca27ac5c54c8185f2d71c0e6868 | [] | no_license | Dardare/prjct | e055f254f8fc18a7a0c421a89a0ee523d5c7372b | 7903d977894b830e18fe9b4572d4fc99c5a48659 | refs/heads/master | 2021-01-18T23:29:45.754532 | 2016-06-16T20:54:23 | 2016-06-16T20:54:23 | 46,169,800 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 539 | r | wnd_gen.R | windows_gen <- function(wndSize, fname) {
sep_files <- separate_files(fname)
list_eeg <- sep_files[[1]]
list_events <- sep_files[[2]]
target <- list()
nontarget <- list()
for (i in seq(from = 1, to = length(list_eeg), by = 1)) {
#csv_data <- loadCSVdata(list_eeg[1], list_events[1])
tmp <- wnd_creat... |
d99fd8afb2b9006521bd7fc3822aa93e329deedf | 9cdf25555df47da99a719168b24715b45a9630e9 | /cachematrix.R | 16f5f6f1e47b2417a1ff5e4cbbd726f4fd8d6026 | [] | no_license | markraphael/ProgrammingAssignment2 | aecd8d82a4708a8ef2876ee91090955197050c43 | bbe68431bfd28f567e568a37454c4948b01f61b0 | refs/heads/master | 2021-08-30T03:48:39.459783 | 2017-12-15T23:00:05 | 2017-12-15T23:00:05 | 114,416,692 | 0 | 0 | null | 2017-12-15T22:36:09 | 2017-12-15T22:36:08 | null | UTF-8 | R | false | false | 751 | r | cachematrix.R | ## Creates a matrix object which caches its inverse
## Creates the matrix object described above
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
get <- function() x
set <- function(y) {
x <<- y
inv <<- NULL
}
getInverse <- function() inv
setInverse <- function(inverse) {
inv <<- invers... |
10520d2a14168bd46e002a8cdf6c1569b2e8cfb7 | 14b48b4d294d2f5d113ba70821b012f97a609861 | /ctg.r | d8dafb2f98212556f7ef2a9f2fec13a5900352de | [] | no_license | Rutujakenjale/classification- | 99b5c80cd711239036fce4eb6723976a28af8fd1 | db98f8d453832d65d33f65c4d367f3f64b335bbe | refs/heads/master | 2020-03-29T21:35:35.431166 | 2018-09-26T06:27:04 | 2018-09-26T06:27:04 | 150,375,641 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 788 | r | ctg.r | #read file
data<- read.csv("F:/imarticus projects/dataset/data/datasets/CTG.csv")
View(data)
#install.packages("party")
library(party)
#install.packages("rpart")
library(rpart)
data$NSP<- factor(data$NSP)
###spliting data
sam_data<- sample(2,nrow(data), replace = TRUE, prob = c(.9, .1))
train<- data[sam_data... |
4c7024c6a178c243e501407b8b5d68e9f79bced9 | 9e72f2d88e396432a7bdf217c8408f8a1fff02e8 | /181101_ggplot.R | bb13270894e09be048100049b3253a20bed49f9c | [] | no_license | SeokHyeon-Hwang/R_data_analysis | 271cdc33b601d0cc61788e4a0fc1e51795daccbd | 61c4af51e1cac736a0290c6ac6c2dc2a927256f1 | refs/heads/master | 2021-07-11T23:02:26.650102 | 2019-03-05T04:52:33 | 2019-03-05T04:52:33 | 148,569,523 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 236 | r | 181101_ggplot.R | library(ggplot2)
library(ggmap)
library(MASS)
data(Cars93)
str(Cars93)
summary(Cars93)
p1<-ggplot(data=Cars93, aes(x=Manufacturer, y=Horsepower))
p1<-p1+geom_point()
p1<-p1+ggtitle('Plot of Manufacturer vs Horsepower')
p1
|
c0a3825a3253feacc847f8950062fecec6aa1b7f | 4c2e0b1ea2520e3c36414687258525bce7df3e8a | /code/01_install_packages.R | f51184c58c1fb42a0ce38cd0b1da4e41dcba9890 | [] | no_license | czheluo/Teach-Bioinformatics-R-dataviz | d1c313f3f790373133e66ee2a37100cc8b0a7582 | c58e75e7e3d18472616649bcff9aee64c640fdc7 | refs/heads/master | 2021-08-07T20:25:57.608170 | 2020-06-05T01:23:22 | 2020-06-05T01:23:22 | 186,523,260 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 884 | r | 01_install_packages.R | ##########################################
# http://www.majorbio.com/
# Copyright (C) 2019 in Majorbio workshop
# contact: meng.luo@majorbio.com
##########################################
# install R packages
install.packages("name")
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.pac... |
dad639da012008530ed5f20d856cdc98048f1b7d | f73b240bd75b6d72a8af59d6cb8dac6f9fc1eef1 | /run_analysis.R | 2a15233f19d2205e7421a4f4b15b87384f8d2f7a | [] | no_license | whantos/Getting-and-Cleaning-Data-Course-Project | 42b85bee5853eac8e9ab8601244b184a2c746068 | ad82ea77eca035ee68338671c1183d3a8540e1b9 | refs/heads/master | 2021-01-10T02:15:26.257088 | 2016-03-22T10:26:19 | 2016-03-22T10:26:19 | 54,198,537 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,801 | r | run_analysis.R | # This File contains all Function for getting and prepare the Data
# needet fpor the Course Project
library(data.table)
library(httr)
# This function Downloads the raw-Data as zip and extrakt them
url = "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
file = "data/projectfiles.z... |
04bd60d3dcea4c3fadd8c6340acbed2c1e6f7629 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mvprpb/examples/mvprpb-package.Rd.R | 905b0c5e4bfd7b06cc3bfc4e2a7d1246e3ec543f | [] | 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 | 312 | r | mvprpb-package.Rd.R | library(mvprpb)
### Name: mvprpb-package
### Title: mvprpb
### Aliases: mvprpb-package mvprpb
### ** Examples
dim.p <- 8
mu <- c( rep(- 0.5 , dim.p -1) , 3 )
cov <- diag( dim.p ) * 0.5 + 0.5
n.itr <- 800
integ.range <- 10
res.val <- mvorpb( dim.p , mu , cov ,n.itr , integ.range )
print(res.val)
|
16590a371f019fbabed34baa38577571d3776ab5 | a397bbd13ae390b0ad6b14f9162513e5d6a8e11b | /R/print.TextData.R | 964db9156ec3bcccc2abb03afa3081acb765c562 | [] | no_license | cran/Xplortext | 4de9793765cb6fb7f54d201b8193ad641a1c2c42 | 7ff29ca1559610aec4d1982e7b204b5046b7d331 | refs/heads/master | 2023-04-29T02:57:45.909957 | 2023-04-24T08:10:02 | 2023-04-24T08:10:02 | 92,412,290 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,923 | r | print.TextData.R | #' @export
print.TextData <- function (x, file = NULL, sep = ";", ...)
{
options(stringsAsFactors = FALSE)
res.TextData <- x
if (!inherits(res.TextData, "TextData"))
stop("non convenient data")
sink.reset <- function(){
for(i in seq_len(sink.number())){sink()}}
sink.reset
cat... |
4f2fcc6710fe59c263895551b2331bac6c2712f4 | 288c8e62f30fedce6f423bb73cd0c41fce48a421 | /R/utils.R | 7b3e9f6d054367b99474fbd1379f3da4d484034b | [] | no_license | EmilHvitfeldt/ggpage | 7d6ea7a0be00e3c6eab5d5d255b5ebbc7982bc2e | 83821d9dc1fa3639c6a94d4f5e3c9134697060fc | refs/heads/master | 2022-07-13T02:32:44.315719 | 2019-06-13T23:41:07 | 2019-06-13T23:41:07 | 101,570,252 | 332 | 20 | null | 2018-07-30T16:45:03 | 2017-08-27T17:55:37 | R | UTF-8 | R | false | false | 3,132 | r | utils.R | #' Internal function for converting words to lines
#'
#' extents the str_wrap() function from the stringr package to work with longer
#' strings.
#'
#' @param words data.frame. Where each row is a separate word words with the
#' column name text.
#' @param wot_number Numeric. how many words to split whole string by.
... |
b3a796dc259b05233fd3e4d20a40ce3095300e7d | 578ff90fab102b21f283e9e4b624a7fafee4cfdf | /inst/shiny/DiagnosticsExplorer/ui.R | c14045b43fdf283bade411e8238cd1d58eef5607 | [
"Apache-2.0"
] | permissive | gowthamrao/CohortDiagnostics | a97de0cd96b316498edad08944a6ed63e9f5effc | befe0eb0e488fa8a4d308fed861cb03c64b67294 | refs/heads/main | 2023-03-16T09:53:40.348161 | 2022-09-01T20:57:59 | 2022-09-01T20:57:59 | 241,672,665 | 0 | 0 | null | 2020-02-19T16:56:33 | 2020-02-19T16:56:33 | null | UTF-8 | R | false | false | 46,742 | r | ui.R |
addInfo <- function(item, infoId) {
infoTag <- tags$small(
class = "badge pull-right action-button",
style = "padding: 1px 6px 2px 6px; background-color: steelblue;",
type = "button",
id = infoId,
"i"
)
item$children[[1]]$children <-
append(item$children[[1]]$children, list(infoTag))
re... |
0516796f98554663fc73bcd25b4550ae54b8ce7c | 6c739524e36e6847b920574317b9393a4f417fc5 | /man/normalized.ratio.index.Rd | 327fac88e89629f8022bc1b9c809e1bc07fced6e | [] | no_license | cran/hsdar | 8f501bf3006508e86700049e0bec34c159eba802 | 3c5cd851e3dd181b361b23b494d05dbeb02c2018 | refs/heads/master | 2022-04-12T22:08:55.843226 | 2022-02-21T11:20:02 | 2022-02-21T11:20:02 | 31,416,910 | 14 | 10 | null | 2018-08-03T18:30:18 | 2015-02-27T11:44:37 | Fortran | UTF-8 | R | false | false | 2,849 | rd | normalized.ratio.index.Rd | \name{nri}
\alias{nri}
%\alias{print.nri}
%\alias{as.matrix.nri}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Normalised ratio index
}
\description{
Calculate normalised ratio index (nri) for a single given band combination or for all possible band combinations. Calculating nri is a frequently... |
2558022255f69e045f74d2604fbef62b4a483f20 | 19101bca18ae24b13cbb81490fff45d515e4927c | /HW4/tapply_dplyr.R | 1fcd07f9c30ca5132cca7f0481ab8fb97a41a6ee | [] | no_license | zcoeman/PLS900_BBIC | ce48fc80d6f74a8df6ca7453424c96068f5a5027 | 715071d3197bbc2d8f2215c279f748c22ab9d586 | refs/heads/master | 2021-05-05T09:00:38.610972 | 2018-03-22T21:30:53 | 2018-03-22T21:30:53 | 119,143,937 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 851 | r | tapply_dplyr.R |
load("/Users/nickbichay/Desktop/ /aPLS 900/Week 4/polity_dataframe.rda")
#### With tapply
f <- function(x) c(mean = mean(x, na.rm=TRUE), median=median(x, na.rm=TRUE), sd = sd(x, na.rm=TRUE))
bound <- do.call(rbind, sapply(polity[,c('democ','autoc','polity2','xconst')], function(x) tapply(x, polity$year, f)))
st... |
6145ca4bf927ae068a71c3c1653f43747acea279 | 01e473d07ba9e8353a22c39647d78c8eee272ec2 | /data-raw/DATASET.R | 46755317c68f3a9b524541fbbb1445609a0d9a18 | [
"MIT"
] | permissive | bailliem/pharmavisR | 36bc8ca2c79a1ce361a57955aa1e64b6c50422dc | 3d0a1bf63c05543b9757096dc1fce0f4d9850dbe | refs/heads/master | 2023-07-20T21:56:18.811707 | 2022-08-19T06:01:46 | 2022-08-19T06:01:46 | 212,809,031 | 1 | 0 | null | 2019-10-04T12:23:17 | 2019-10-04T12:23:17 | null | UTF-8 | R | false | false | 1,355 | r | DATASET.R | ## code to prepare `DATASET` dataset goes here
usethis::use_data("DATASET")
# Breast survival data
library(dplyr)
library(RTCGA)
library(RTCGA.clinical)
brca_cohort <- survivalTCGA(
BRCA.clinical,
extract.cols = c(
"admin.disease_code",
"patient.breast_carcinoma_estrogen_receptor_status",
"patient.b... |
d0842d2314fc56728e6dbb2538296c2ce1f32264 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkIconSourceCopy.Rd | 06104ea1cf60ff965a0e5d4ce3d1318a3afcbaac | [] | no_license | lawremi/RGtk2 | d2412ccedf2d2bc12888618b42486f7e9cceee43 | eb315232f75c3bed73bae9584510018293ba6b83 | refs/heads/master | 2023-03-05T01:13:14.484107 | 2023-02-25T15:19:06 | 2023-02-25T15:20:41 | 2,554,865 | 14 | 9 | null | 2023-02-06T21:28:56 | 2011-10-11T11:50:22 | R | UTF-8 | R | false | false | 402 | rd | gtkIconSourceCopy.Rd | \alias{gtkIconSourceCopy}
\name{gtkIconSourceCopy}
\title{gtkIconSourceCopy}
\description{Creates a copy of \code{source}; mostly useful for language bindings.}
\usage{gtkIconSourceCopy(object)}
\arguments{\item{\verb{object}}{a \code{\link{GtkIconSource}}}}
\value{[\code{\link{GtkIconSource}}] a new \code{\link{GtkIco... |
7dd429279c29f6b2c1ad64fd5ebdfabbdaa4eff9 | 5cdfe09b136d8c56160b7048733d26d391e770b7 | /Scripts/drafts/master_credito.R | d29f68f17240ea105c955cb2e20158c264a069f5 | [] | no_license | DanielRZapataS/Recommendation_System_Retail_Banking | a7265b731127c60f9233138f492989649f2be3ce | 1de13ca704dfa80ba8e4e374ade481d7ba33ecb9 | refs/heads/master | 2020-11-25T21:28:36.779429 | 2019-12-18T15:09:49 | 2019-12-18T15:09:49 | 228,851,338 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 654 | r | master_credito.R | #' Crear master de cada modelo
# Segun el modelo se toman unas u otras variables
# variables de la base que le sirven al modelo de tarjetas
# aa_vlr_ing_bru_mes
# aa_vlr_egreso_mes
# age
# antiguedad
# aa_vlr_activos
# aa_vlr_pasivos
# aa_estrato
var_interest <- c("aa_vlr_ing_bru_mes", "aa_vlr_egreso_mes",
"aa_vlr... |
b5e8575d2e124c29ce9a989c28c431c7b0599530 | e1cbbf8791b0ac6d40f6d5b397785560105441d9 | /man/quapdq3.Rd | a9e2061137b19b5b7910b516483dcfefd9348473 | [] | no_license | wasquith/lmomco | 96a783dc88b67017a315e51da3326dfc8af0c831 | 8d7cc8497702536f162d7114a4b0a4ad88f72048 | refs/heads/master | 2023-09-02T07:48:53.169644 | 2023-08-30T02:40:09 | 2023-08-30T02:40:09 | 108,880,810 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,584 | rd | quapdq3.Rd | \encoding{utf8}
\name{quapdq3}
\alias{quapdq3}
\title{Quantile Function of the Polynomial Density-Quantile3 Distribution}
\description{
This function computes the quantiles of the Polynomial Density-Quantile3 distribution (PDQ3) given parameters (\eqn{\xi}, \eqn{\alpha}, and \eqn{\kappa}) computed by \code{\link{parpdq... |
209b531d72bb47a837f6898ed2a28f5b88c498c6 | 306758ad07d8287d078bd3b1c18d55aaab086539 | /ProgrammingAssignment2.R | c7c7811c64e44b8a535ed5901364b37f1d7af05f | [] | no_license | JLPherigo/ProgrammingAssignment2 | 9176b0ecad3c9ee6f4c9275996778dfb0f46ffab | f71626bdde1e53af45776393a2ca6a9f71ac41af | refs/heads/master | 2021-09-01T04:23:29.498905 | 2017-12-24T19:19:47 | 2017-12-24T19:19:47 | 115,277,268 | 0 | 0 | null | 2017-12-24T17:13:08 | 2017-12-24T17:13:08 | null | UTF-8 | R | false | false | 1,079 | r | ProgrammingAssignment2.R | ## These two functions are in partial fulfillment of the
## R Programming Programming Assignment 2: Lexical Scoping.
##These two functions will cache the inverse of a matrix
## The function makeCacheMatrix creates a special matrix object
## that can cache its inverse
makeCacheMatrix <- function(x = matrix()) {
inv... |
5ce5559da1a5504543c7370229d02cb7f018ddd7 | 02a204c4ff6a767037e4559d376311eeed50430c | /dm1/lecture/08/centroids.r | e3acc847df70572d226cc26576df02362b62b2ec | [
"MIT"
] | permissive | codeAligned/rw | 6bdecd74388cfc51357b1c48eddc0eb9cbb9042a | 7fb6cf2e0da3fe48a108f391e8d3efb9044e7a03 | refs/heads/master | 2020-06-18T13:40:04.749230 | 2019-06-14T22:48:08 | 2019-06-14T22:48:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,377 | r | centroids.r | x<-c(1,1,2,6,6,7,7)
y<-c(9,10,9.5,1,2,1.5,3)
plot(y~x)
m<-matrix(data=cbind(x,y),nrow=7,ncol=2)
colnames(m)<-c("x","y")
rownames(m)<-c("a","b","c","q","r","s","t")
m
library("lsa")
tm <- t(m)
tm
simMat<-cosine(tm)
simMat
##what's the centroid of a,b,c?
c1.x<-1+1+2
c1.y<-9+10+9.5
##what's the ... |
fd37545a20683cbcf6ae55d6a1af9d2b815b2d70 | 1e36998839f250b75e991bdb27e0d7ec474608e5 | /PracticalML/project/project.R | 08c080d6c8f225a2c7f178aacf29f0eac66a3d7c | [
"MIT"
] | permissive | NatalieTan/R | 7f81e5e8ebbe9ced2db305b78453374d62ce3d62 | 57e493e90250b9667f014a1e30377b044df3aa2d | refs/heads/master | 2021-01-24T05:06:00.061935 | 2015-09-11T14:26:18 | 2015-09-11T14:26:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 882 | r | project.R | library(caret)
set.seed(5152)
# pml.testing <- read.csv("PracticalML/project/pml-testing.csv")
# sapply(pml.testing, class)
pml.training <- read.csv("PracticalML/project/pml-training.csv")
# sapply(pml.training, class)
inTrain <- createDataPartition(y = pml.training$classe,
p = 0.65,
... |
79e0d9dd3d4645b2d0c4faad7192a07af53ba54f | 96dd0f70cfcb97754853ae9279b858133891682c | /man/mvgls.dfa.Rd | abca0f83d030e4f9e9c2823a8a193d16f95b89a5 | [] | no_license | JClavel/mvMORPH | 27e18d6172eefb28e527fde88671275f80afca07 | e75c68a0fece428e5e98d8f9ae7281569b7159c8 | refs/heads/master | 2023-07-10T21:12:01.839493 | 2023-06-30T14:37:11 | 2023-06-30T14:37:11 | 36,449,296 | 17 | 8 | null | 2022-06-22T14:40:37 | 2015-05-28T15:50:01 | R | UTF-8 | R | false | false | 2,902 | rd | mvgls.dfa.Rd | \name{mvgls.dfa}
\alias{mvgls.dfa}
\title{
Discriminant Function Analysis (DFA) - also called Linear Discriminant Analysis (LDA) or Canonical Variate Analysis (CVA) - based on multivariate GLS (or OLS) model fit
}
\description{
Performs a discriminant analysis (DFA) on a regularized variance-covariance matrix obtaine... |
2921e831fa73e8154dc4e8d01628107d7f22285b | 9cc15201bab2a24a4e5a7a9cff49a647d6f97db1 | /man/wabl.Rd | 1074f8c2ad85aadb551dee3b7dbe8c2c12e8b0bc | [] | no_license | cran/FuzzySTs | 486e3bee5be628fcbf95a1e201ed702d74b794d7 | c7c45543baf531f37c64f9082b13aea5532480ea | refs/heads/master | 2023-01-19T15:42:56.661744 | 2020-11-23T12:50:03 | 2020-11-23T12:50:03 | 278,227,869 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,052 | rd | wabl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Distances_17102018.R
\name{wabl}
\alias{wabl}
\title{Calculates a distance by the d_wabl between fuzzy numbers}
\usage{
wabl(X, Y, i = 1, j = 1, theta = 1/3, breakpoints = 100)
}
\arguments{
\item{X}{a fuzzy number.}
\item{Y}{a fuzzy number.... |
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