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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ef228d7040e78a15e1f38fde1ac519e0dc8d72a1 | c36c96cf50cab02edfbab770c89bdddceed89542 | /ui.R | 177979a3f8c6c5a9c1bfdb00b2754e33523a9d01 | [] | no_license | n3iii/DDP | 99e8f8bac66177b301ae8d69059cae7222bc8fae | edeea6e149ddf9131589e408b33027036192e1b4 | refs/heads/master | 2020-04-05T22:54:03.678344 | 2015-08-19T10:46:53 | 2015-08-19T10:46:53 | 41,006,453 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,033 | r | ui.R |
shinyUI(fluidPage(
titlePanel("Stock Moving Slope"),
sidebarLayout(
sidebarPanel(
selectInput(inputId = "ticker",
label = "Stock Ticker:",
choices =... |
8604f7b7aac97374f2defdf4ed691482e74c7be6 | f45ed0bf62703a21f49cb497e73583eb324c0f77 | /lib/gbs2bed_ames282.R | aa1ee8d82e9b4e66dc170d8f5d60e08ac164a00b | [] | no_license | yangjl/Misc | 1a4271a89751b4f25033c4df4dd304ac78811471 | 2b95f0149c6cd4e90c3d830347a8365887ca9477 | refs/heads/master | 2021-01-16T22:03:36.383306 | 2017-05-23T22:42:23 | 2017-05-23T22:42:23 | 29,626,233 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,654 | r | gbs2bed_ames282.R | ### Jinliang Yang
### April 23th, 2015
###
gbs2bed_ames <- function(gbsfile="/group/jrigrp4/AllZeaGBSv2.7impV5/ZeaGBSv27_Ames282.hmp.txt",
outfile="/group/jrigrp4/AllZeaGBSv2.7impV5/ZeaGBSv27_Ames.bed5"){
### read in GBS file
#library("data.table")
ames <- fread(gbsfile, header=TRU... |
23b5f77b568559c83eb2e511b1a81eff27cb1449 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Linear_Algebra_by_Jim_Hefferon/CH5/EX2.10/Ex5_2_10.R | 63487d6c6731de8ac9d4a866f15d7c87211d3b6d | [] | 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 | 539 | r | Ex5_2_10.R | #Example 2.10,chapter 5,scetion III.2,page 414
#package used matlib v0.9.1
#Github reposiory of matlib :https://github.com/friendly/matlib
#installation and loading library
#install.packages("matlib")
library("matlib")
N <- matrix(c(0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0),ncol=4)
P <- matrix(c(1,0,1,0,0,2,1,0,1,1,1,0... |
1f73c408e8948b1990746c713bb14fae45b3911a | 5072176fd6b49aefdef14049a3d1ba313da95ee3 | /man/reducolor.Rd | 4a1025422b54ff71fa47609c2b0850143a8e6696 | [
"MIT"
] | permissive | UBC-MDS/rimager | c806457feefd0b46488e83e924ebc84970ebd986 | d2323be373f0e065a37e73ff502ab6251989ec6a | refs/heads/master | 2021-01-16T12:43:37.370616 | 2020-03-26T21:49:34 | 2020-03-26T21:49:34 | 243,405,240 | 0 | 4 | NOASSERTION | 2020-03-26T21:49:35 | 2020-02-27T01:40:59 | R | UTF-8 | R | false | true | 878 | rd | reducolor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reducolor.R
\name{reducolor}
\alias{reducolor}
\title{Reduce image color to either 2 or 8 colors for cartoonized effect}
\usage{
reducolor(input_path, style, output_path = NULL)
}
\arguments{
\item{input_path}{character the image file path}
... |
52d155406f363d9e6f7f49d6fad80ba49f49166c | 50e3cbaea158c93651cd0377f6d2e6faa8f5273b | /man/fa_read.Rd | ebe5a8df0a8ab573a8618a6facf4a36b2e11dadf | [] | no_license | cran/seqmagick | b24261d186e15d7c3443770f8e89ace3fefd4def | e27d1022d7e56a033e7e22888de66345689afec0 | refs/heads/master | 2023-07-11T11:55:31.539756 | 2023-06-27T04:10:02 | 2023-06-27T04:10:02 | 236,890,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 474 | rd | fa_read.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read.R
\name{fa_read}
\alias{fa_read}
\title{fa_read}
\usage{
fa_read(file, type = "auto")
}
\arguments{
\item{file}{fasta file}
\item{type}{one of 'DNA', 'RNA', 'AA', 'unknown' or 'auto'}
}
\value{
BStringSet object
}
\descr... |
bfbda0d967e9cf672433840be713142b2924bc2f | 67de204b7f0550def8eea7d6ca605f43aed653fc | /app/lib/analysis/plots/comment.R | 267515b55e8f80e0e15405a3672f5d628ab92405 | [] | no_license | andymeneely/sira-nlp | b1b1bb8a783adac6a69001565d49d8357a4dd8c5 | b027a5d7407043b6541e2aa02704a7239f109485 | refs/heads/master | 2021-01-11T05:29:16.209735 | 2017-12-09T17:13:19 | 2017-12-09T17:13:19 | 69,055,241 | 1 | 1 | null | 2017-06-19T18:42:12 | 2016-09-23T19:36:51 | Python | UTF-8 | R | false | false | 14,169 | r | comment.R | # Initialize Boilerplate ----
source("boilerplate.R")
source("data/comment.R")
InitGlobals()
## Yngve ====
### Query Data
dataset <- GetCommentYngve()
### Plot
metric <- "Comment Yngve (Log Scale)"
title <- "Distribution of Comment Yngve"
plot.dataset <- dataset %>%
inner_join(., COMMENT.TYPE, by = "comment_id")... |
ce8981d949f5927d05723e7109e74181e7361f90 | 503900569f8fe6ff34202e12f6dad9a42bd908d7 | /transpose/app.R | 312da1ca37c1ffcfbc5096ef24ed6bb72de99a1c | [] | no_license | nickriches/transpose | 0663a174bb10e03676832b347b8b644886a82905 | 9a730931f33543c072a601e6b3c22ff6b1fdd319 | refs/heads/master | 2022-09-22T06:12:56.866311 | 2020-06-04T17:20:11 | 2020-06-04T17:20:11 | 268,739,067 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 37,650 | r | app.R |
library(shiny)
library(knitr) # To prepare Rmarkdown instructions
library(tidyverse) # For data manipulation
library(readtext) # Read in .doc and .docx files
library(udpipe) # Part-of-speech-tagger
library(tools) # To get file extension
library(DT) # To create a datatable
library(colourpicker)
library(googleLanguageR... |
f414dbcb34289e7b0869d23770e391cc834ac2e7 | dfa09fcc25994c4c7f33b3fa9a91ba6ce7096547 | /man/resultC.Rd | a95982080688fb24476bb4826651ea9d64a04523 | [] | no_license | chensyustc/SC19027 | a64a8b2137951ae46a814f0389ee06ac849965d8 | 1e291cd7c96cab5d020e471d62a1b3a74a70efe8 | refs/heads/master | 2020-12-03T13:16:13.909137 | 2020-01-02T07:37:25 | 2020-01-02T07:37:25 | 230,364,676 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 444 | rd | resultC.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{resultC}
\alias{resultC}
\title{Bias of estimated sigma and average model size using Rcpp}
\usage{
resultC(hsigma, hbeta)
}
\arguments{
\item{hsigma}{the estimated sigma}
\item{hbeta}{the estimated coefficients}
}
\value{... |
3bc8519b6142df0b89cc63b2d8caa33b6ef000cd | a3f7826863b6b81bc99ccf9c414f8bcf09a335e7 | /R/myKable.R | 6748ad9b15b42aab7f343d7a590bcede1bcd63d4 | [] | no_license | cran/rmdHelpers | 24c9516a15a8d6de20bb92df4df1ceba27786ce1 | b091a8e1ec70f651305074b03ccb38dd0008c599 | refs/heads/master | 2021-01-18T18:09:53.043265 | 2016-07-11T23:09:59 | 2016-07-11T23:09:59 | 55,989,977 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 915 | r | myKable.R | myKable <-
function(x
, row.names = NA
, boldRowNames = TRUE
, boldColNames = TRUE
, ...){
# Function to bold row.names and colnames
# I still need to add explicit handling for things other than markdown
if(boldRowNames){
if(is.na... |
d46a27572a32f7a1e637046deba924c489d26690 | 4a78e4ae68e138abfea88515a101adef410e6bdc | /asc_complaints.R | ae9cfed2ef3390145df4c5dbeabe655a709086a7 | [] | no_license | airsafe/analyses | 8a708d12803d1f93ef54b9907f1be2f86d31b9a8 | 54a6dc14c360312b7f316683797436f84af14727 | refs/heads/master | 2021-01-10T14:57:04.641933 | 2020-01-03T21:25:21 | 2020-01-03T21:25:21 | 46,495,199 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 20,987 | r | asc_complaints.R | # Exploration of complaint file
# ADMINISTRATIVE NOTES
# Note: To describe database at any point, use str(*name*)
# Note: To clear R workspace, use rm(list = ls())
# Note: Searching R help files - RSiteSearch("character string")
# Note: To clear console, use CTRL + L
# PURPOSE
# The goal of this exercise was to take... |
928caca8ee4f39afc46f887288f3c3903df50ad4 | 42d8105ddeb0ab7592b0d634107de240776294f6 | /Class4/elections.R | 53e5a8aeb8513c84f4454756cb877ac3bd8e5a7a | [] | no_license | x0wllaar/MASNA-R-Programming-2020 | af003e70f5bd05134ad132d4b12834272949bf21 | 74452be5dd8edcfc11e0a20b1b9ca22e677e0209 | refs/heads/master | 2023-01-02T19:30:26.411466 | 2020-10-19T23:12:37 | 2020-10-19T23:12:37 | 292,828,300 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,083 | r | elections.R | library(data.table)
library(purrr)
library(stargazer)
library(vioplot)
library(corrplot)
library(MASS)
library(car)
library(nortest)
#Working with data!
##We have a file with 2012 presidential election results in Russia
elec_file <- "47130-8314.csv"
##Load this file into R (data... |
b9a44b33960328f84e179fff47cd207127b90bee | e1eba8f8812ff239d21dd5b1f348ecf62e48ddc9 | /R/utils.R | 1f5e90cd50c03ab9cb6862f64b21ecc661267c4d | [] | no_license | lorenzwalthert/namespaces | e5c60259f5e2f86c032f6da16af76a22b9cd93af | 1d7c95f54bf1202068789b4706a0dcc66d126ef3 | refs/heads/master | 2020-03-09T09:51:50.753911 | 2019-05-06T09:59:17 | 2019-05-06T09:59:17 | 128,722,777 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,041 | r | utils.R | #' Decode base64
#'
#' Decodes base64, which is a common format returned by the GitHub API.
#' @keywords internal
decode <- function(encoded) {
rawToChar(base64enc::base64decode(encoded)) %>%
strsplit("\n") %>%
.[[1]]
}
#' Turn key value pairs into a string
#'
#' @para ... named arguments where the name is... |
2ba4d806c5fc5b7758b344d5de72d08a74ce3f3b | ace90651f890d21104b1f17d55bb5e377402aa55 | /R/ba_describe-methods.R | 7961e953edf76342cafbc446e31db76956f04bbe | [] | no_license | c5sire/brapix | da7959e804c85cb64e952dbe351df82fdd555974 | 58dd8d05553f30c861b6acca8e18ccb13660a219 | refs/heads/master | 2021-05-02T03:00:56.757500 | 2018-02-09T12:46:33 | 2018-02-09T12:46:33 | 120,891,028 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 649 | r | ba_describe-methods.R | #' ba_describe.ba_locations
#'
#' describe method for an object of class brapi_con, which will only display the crop, database address:port and user
#'
#' @param x a brapi_locations object
#' @param ... other print parameters
#' @author Reinhard Simon
#' @example inst/examples/ex-describe.R
#' @family brapiutils
#' @ex... |
d7ca8814731c9a2e42268784d770430557d9d1d3 | f2f213e423ddee153d8c67f725f5be3ed7093c00 | /Statistical Functions/simpleRegression.R | 441d9653209eb8adc006a75127c89c7395a8571d | [] | no_license | dawu29/RStudio-exersices | ab79ddea635b703071f00a35d7f964ce7ca6c669 | e2d1c61e2da0cb3b1633555c7b89f42c0a5b5ee4 | refs/heads/main | 2023-02-08T20:04:07.082051 | 2020-12-30T02:50:29 | 2020-12-30T02:50:29 | 315,168,044 | 0 | 0 | null | 2020-11-23T05:17:51 | 2020-11-23T01:16:15 | R | UTF-8 | R | false | false | 822 | r | simpleRegression.R | #-------------------------------------------------------------------
# SIMPLE REGRESSION
#-------------------------------------------------------------------
x<-c(6,6.3,6.5,6.8,7,7.1,7.5,7.5,7.6)
y<-c(39,58,49,53,80,86,115,124,104)
plot(x,y,main="Simple Linear Regression")
Sxy = sum((x-mean(x))*(y-mean(y))) #... |
18ddfc176602040b6bdd7c758ff163429f39a546 | a29dba249bbd87c29d731a5b794771fda5cf5117 | /R/評估/IG.r | d2780a5304c6e6ed6179ea92528e6ad7401888f5 | [] | no_license | DaYi-TW/Data-science | 30b4f009c074c7fe9a14e9d963dde37c127802c5 | ce8f5dfcf463a25b5a868fe64014d4311c633a18 | refs/heads/main | 2023-06-29T01:32:59.259537 | 2021-07-21T03:36:34 | 2021-07-21T03:36:34 | 370,018,467 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 573 | r | IG.r | #輸入變數:class_lab:類別屬性,fea:欲評估屬性
#輸出變數:eval_value:屬性的IG值
IG=function(class_label,fea){
fea=as.data.frame(fea)
eval_value=as.data.frame(matrix(,ncol(fea),2))
colnames(eval_value)=c("feature","IG")
eval_value[,1]=colnames(fea)
eval_value[,2]=sapply(1:ncol(fea),FUN=function(i,fea,class_label){
eval=cbind(fea[i],cl... |
5fbfb4f5ba43d71878713b4d744c096c48f66ac0 | 16b3d48264d6c78a6258f261543036d9a6284ae0 | /Survival Analysis/Survival Analysis.R | 617358abae4943054254a3a1b4f8c9f43e368f23 | [] | no_license | staciewow/Statistics-in-R | 273a97fb613bed1386960148e4afd30d78804989 | bbadba4c4b19ea73c762eb9cad339ee8d2936c9e | refs/heads/master | 2020-03-06T20:43:26.241540 | 2018-03-28T00:45:47 | 2018-03-28T00:45:47 | 127,060,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,333 | r | Survival Analysis.R | # About Survival Analysis
library(OIsurv) # Includes the "survival" and "KMsurv" packages used for analysis and data sets
#other packages in the market, this isn't the only one for survival analysis
# What is survival analysis? - A set of methods for analyzing data where the outcome variable is the time until the occu... |
c95b296a63ae042edcad428b6808811b41a47ef0 | 3f705d76c0a99c5a41b6722f347b56f981b4df8c | /scripts/Q2.3.r | cf645aea89228c696471d7d0860be358a3da4544 | [] | no_license | cypowers/multivariance | f46cb66f45222e707aa1ff4174d07775a97c853e | 74ca254dde840cd9fcbaf44bd4068148b54e5477 | refs/heads/master | 2020-03-17T05:39:50.523326 | 2018-05-14T13:41:40 | 2018-05-14T13:41:40 | 133,324,623 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 436 | r | Q2.3.r | data <- read.table("data/2.3 data.txt", header = TRUE)
data
data2 <- data[2:6]
data2
summary(data2)
S <- cov(data) # Covarience
R <- cor(data2) # Correlation
R
uniq_root <- eigen(R)
uniq_root
uniq_root$values/sum(uniq_root$values)
p_data <- princomp(data2, cor=TRUE)
summary(p_data)
screeplot(p_data, type="lines", pch=1... |
fa0e72d92d2385bfdd197f7dc1cc5035271ca384 | d167ca17d4649c6122c49696dca4a4187cbdbe9b | /loss.small.evals.R | 1d2cb3d391ed9d30e67bd9eebe0352b3853d34f3 | [] | no_license | tdhock/changepoint-data-structure | ec5e1ba5857862862626f14c33b6288bdf084e65 | e352ced2c313ea1f08e6a92c00422943c21c363d | refs/heads/master | 2021-06-11T14:31:51.893602 | 2021-04-21T22:16:48 | 2021-04-21T22:16:48 | 169,180,248 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 634 | r | loss.small.evals.R | source("packages.R")
loss.small <- readRDS("loss.small.rds")
nb.evals <- loss.small[, {
is.dec <- c(TRUE, diff(loss) < 0)
dt <- data.table(loss, changes)[is.dec]
result <- .C(
"modelSelectionFwd_interface",
loss=as.double(dt$loss),
complexity=as.double(dt$changes),
N=as.integer(nrow(dt)),
mod... |
436a3b39643391b74e830e4de36e3347b7a58579 | e81f55d813e5cbd4ec78a62aed26cf9c26bda877 | /scripts/at2masterdownloads.R | f19cc386594680c1490cf50244872fc7306cedeb | [] | no_license | ewiik/lac | 47065852aef8a057c1426645ac268dcb1018e08a | dbe5601f76ff7f609b8a32022fc519e35d72d30b | refs/heads/master | 2021-01-10T11:50:50.447189 | 2016-03-07T01:31:11 | 2016-03-07T01:31:11 | 45,217,985 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,224 | r | at2masterdownloads.R | ## read in all supporting data for AT2 and get it organised
## using master file for pigs and C/N stuff....
## FIXME: no actual diatom counts in Dropbox????
## read in files
master <- read.csv("data/private/AT2_MasterSpreadsheet_15-12-15.csv") # rundepth is topdepth
cladorel <- read.csv("data/private/AT2-Cladocera-cou... |
f4bc7b0d892f8d51bc156b200d5b4d1d8ec2d59b | b761234cdc3b07e81dbc05da5ec1f726650ee7bd | /R/read_officer.R | 3e17415522f8083d9275350bab76a6c5792c3df1 | [
"MIT"
] | permissive | elipousson/officerExtras | 1d76ee389f2d649cf397199d00fb6894fd42eaa0 | f491277b69e659bb65f65f258878516b2c997e78 | refs/heads/main | 2023-08-27T01:32:07.879195 | 2023-08-26T16:51:15 | 2023-08-26T16:51:15 | 606,570,447 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,100 | r | read_officer.R | #' Read a docx, pptx, potx, or xlsx file or use an existing object from officer
#' if provided
#'
#' [read_officer()] is a variant of [officer::read_docx()],
#' [officer::read_pptx()], and [officer::read_xlsx()] that allows users to read
#' different Microsoft Office file types with a single function.
#' [read_docx_ext... |
ffcf0d0879bd57cce3654b169bce71fe171265e4 | abdf3380f36b8fd63a6390aa54e73730417570bc | /tests/testthat.R | 2b5f9fb96764396bd133bcb4a710c14481a8cb98 | [] | no_license | dpique/oncomix | a2f25d1cffc3415de07799f4c6b831d9242d0bba | ec0a61f8249bf9b36f633206d479b01289410031 | refs/heads/master | 2021-05-23T06:08:24.259863 | 2017-12-17T17:06:08 | 2017-12-17T17:06:08 | 94,810,609 | 2 | 1 | null | 2017-08-15T17:50:33 | 2017-06-19T18:57:16 | HTML | UTF-8 | R | false | false | 62 | r | testthat.R | library(testthat)
library(oncomix)
test_check("oncomix")
|
0fa1c4236de2079b954c3c977d9f2f9663ddf387 | a518c2ca0ac4edb94ccbf144e7cd58f13b512bc6 | /man/nzmaths.Rd | 3be048c50f2f89f15366f154ee313ac6321b1f1f | [] | no_license | tslumley/svylme | e6f5dd0fab582c4cfd35b5ecd5e6f272e029cdf9 | 2a1305ec0f1c1b0959146569c28d899431fcc939 | refs/heads/master | 2023-08-10T09:42:56.243181 | 2023-07-21T00:37:47 | 2023-07-21T00:37:47 | 127,377,020 | 26 | 6 | null | null | null | null | UTF-8 | R | false | false | 2,592 | rd | nzmaths.Rd | \name{nzmaths}
\alias{nzmaths}
\docType{data}
\title{
Maths Performance Data from the PISA 2012 survey in New Zealand
}
\description{
Data on maths performance, gender, some problem-solving variables and some school resource variables.
}
\usage{data("nzmaths")}
\format{
A data frame with 4291 observations on the foll... |
8012180937aa933f154baee2f66a56ab8a4ef7f8 | bfbdfd00872efbec5ac8f449dcb058792baec3a0 | /R/dic.R | b239487d75d37d70482ae77c6f608bbc7403b42e | [] | no_license | jags/rjags | ad35dda50e96b11ac79af985b1e0a77b89fa28c8 | e1c94aa8e2e73e4345c3e35abbdd32f72a34045f | refs/heads/master | 2020-04-13T19:37:32.208375 | 2018-10-19T17:02:30 | 2018-10-19T17:02:30 | 163,408,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,073 | r | dic.R | # R package rjags file R/dic.R
# Copyright (C) 2009-2013 Martyn Plummer
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License version
# 2 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will... |
b8c9a0e06222fc5bcaa8d3f6c8b81fccfe262a8e | bc714def3a27f812bf00c5b89c3e64687594ff23 | /R/l3.R | 0803faded7f5af614d910b5b2bf126d3503e04b7 | [] | no_license | devillemereuil/RAFM | af4846c78e00a9d0fd3be19eeb905e6ed7c4abdd | ca2fb6d3ddc47a1f3dfdd6a02027502b5cdbb30e | refs/heads/master | 2021-04-03T07:28:41.408826 | 2018-03-13T09:31:59 | 2018-03-13T09:31:59 | 125,025,235 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 123 | r | l3.R | l3 <-
function(logalpha_, prioralpha_){
return(dnorm(logalpha_, prioralpha_[1], sqrt(prioralpha_[2]), log=TRUE))
}
|
4f5db7f516097f9a8ad56f809af3d6ac9cdd596b | 9a1277a635b73c72472ae40442994d6c301ca1b4 | /R/separate_img.R | fb4d4606d972fdc133e9a6590e9a2f4418b0b512 | [] | no_license | muschellij2/neurobase | eaf8632de4659cd857bb5a864bf3a60f83333a89 | 375101bab5a546bd8c8a092c21190b48b36f9a13 | refs/heads/master | 2022-10-25T16:00:24.322516 | 2022-10-23T16:07:05 | 2022-10-23T16:07:05 | 68,750,968 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,535 | r | separate_img.R |
.separate_img = function(img,
levels = NULL,
drop_zero = TRUE){
if (is.null(levels)) {
levels = unique(c(img))
} else {
levels = unique(levels)
}
if (drop_zero) {
levels = setdiff(levels, 0)
}
if (length(levels) == 0) {
stop("No non-zero va... |
355f3fdfa613593835badcd4c9ad79ae3d03775c | 771502151a4e152ecb69c075703ff35756a0b35b | /PlotFit3dPeople/server.R | e4b84299182e89f265479dfa1fd7a5150ebb1273 | [] | no_license | hinto033/radar_chart | 89337ff1170df75947d7c9d6fe7b59d04a49497f | 8d642ab0513df00bec1b5e49b7a1a00a1809f43b | refs/heads/master | 2021-01-17T01:54:21.222806 | 2017-03-07T20:53:15 | 2017-03-07T20:53:15 | 39,858,084 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,023 | r | server.R | # server.R
###Need to:
##Convert to LMI and FMI
#finish the final calculations
#Produce the radar charts.
library(fmsb)
maxmin <- data.frame(
Z_TR=c(2, -2),
Z_LA=c(2, -2),
Z_LL=c(2, -2),
Z_RL=c(2, -2),
Z_RA=c(2, -2))
chartDim <- c(1,1)
#setwd('X:\\bhinton\\radar_chart\\Plot-From-DXA')
blackData <- read... |
07ee1bb6f5f0ec9596ca7bdea2531a3dd9ae565e | 76beb7e70f9381a5bded37834ba8783e16cc8b9a | /ipmbook-code/c2/Diagnose Monocarp Growth Kernel.R | 9d7f90a2f14aafc41095844bbfaddb60b3c69fd0 | [] | no_license | aekendig/population-modeling-techniques | 6521b1d5e5d50f5f3c156821ca5d4942be5a1fc9 | 713a5529dcbe7534817f2df139fbadbd659c4a0c | refs/heads/master | 2022-12-29T20:54:51.146095 | 2020-10-07T12:18:23 | 2020-10-07T12:18:23 | 302,026,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,900 | r | Diagnose Monocarp Growth Kernel.R | ### This script assumes that you have just source'd the Monocarp model
### using MonocarpSimulateIBM.R
# or, load an .Rdata file with saved simulation results
load("MonocarpSimData.Rdata")
require(car)
require(mgcv)
source("../utilities/Standard Graphical Pars.R")
## Construct a data set of plausible size
pick.... |
961e530374604709c4e79e905215d163e2ff08a2 | cb9adc2ebaecde6169e6261cc52cb78029b2061b | /exhaustion.r | c7c5367b022f7f7b7469bc85f4ecb8318bd7592b | [] | no_license | zxzx310310/DSL_paper | 97e5cef1c50bd1158b77898259e2ff6f6b34a58d | 4d38df01f915cb4e256dde38ebec5c731f225ea5 | refs/heads/master | 2021-05-02T14:12:42.331994 | 2019-08-22T13:49:01 | 2019-08-22T13:49:01 | 120,715,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,448 | r | exhaustion.r | #----時間紀錄(開始)----
startTime <- Sys.time()
#----資料初始化(本地端)----
sourceData <- read.csv(file = "assets/商品資料庫_s.csv") #讀取原始資料
preferenceTable <- read.csv(file = "assets/preferenceTable_s.csv") #讀取商品偏好表
sourceData <- sourceData[c(-1, -13)] #移除不必要的資料欄位
names(sourceData)[11] <- "重量" #重新命名欄位名稱
goodData <- sourceData #將原始資料複製一... |
4bc8a653728b91b8c150420839c96cb0ae73f646 | 15b5a30b17ce3b1dea0ed27ac6b436047c27150e | /shiny/ui.R | 7ce6901dd36ac1b2fb079aed35ac47b16fc738bc | [] | no_license | wwkong/UW-Course-Evals-Shiny | e1bbf5c501e19f595112b1057918dc2375e1b2d4 | 367e51cadbe6c6d70d37cb0917d6139d698ec48f | refs/heads/master | 2016-09-11T03:01:18.913369 | 2015-04-19T02:29:52 | 2015-04-19T02:29:56 | 33,841,207 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,732 | r | ui.R | shinyUI(fluidPage(
# Header:
titlePanel("Shiny - UW Course Evaluations",
title="Analysis of UW Course Evaluations"),
# Sub-header
fluidRow(column(12,p("Coded by William Kong. All rights reserved."))),
# Input in sidepanel:
sidebarPanel(
#------------------------------ Input D... |
0e79d782a012343072e5ecca1bd03bdc31791cf1 | aa26052173994c5ce2363f11340f771d83d380a4 | /man/showcues.Rd | 2be689959580e3e0f06f14a34e28a8a98a8baa67 | [] | no_license | ronypik/FFTrees | ff92103e0c7d3105d9da96580da66d311e5a71ff | 21421d9e7a48db3508bc721cd5b2ed9e60b0b19b | refs/heads/master | 2021-01-11T16:31:20.673528 | 2017-01-26T08:04:48 | 2017-01-26T08:04:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 806 | rd | showcues.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/showcues_function.R
\name{showcues}
\alias{showcues}
\title{Visualizes cue accuracies from an FFTrees object in a ROC space}
\usage{
showcues(x = NULL, data = "train", main = NULL, top = 5,
palette = c("#0C5BB07F", "#EE00117F", "#15983D7F",... |
5072bde09203fa5b59b9fdf973ba737646d61167 | ad24e05bb17df332554fe592d8f4070ad709db3a | /RStudio - text lessons/Run-shiny-apps.R | 8354878d6ebeba3ff514567c84e5d80b66008118 | [] | no_license | jyuill/proj-r-shiny | 19d28ba43d6091dff319968eeec8f2846b0c58e0 | 74bf261352ac53c1969b8a41c035701499e1ed3b | refs/heads/master | 2023-01-11T23:45:25.619212 | 2023-01-09T05:53:34 | 2023-01-09T05:53:34 | 79,695,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 766 | r | Run-shiny-apps.R | ## R file to run shiny apps - using examples from R Studio text lessons
library(shiny)
## need to highlight desired code and use 'ctrl+enter' to run
## use path from project working directory
## Lesson 1: Basic Histogram
runApp("RStudio - text lessons/Lesson1-histogram")
## Lesson 2: HTML
runApp("RStudio - text less... |
eadbdb68fad3eb9a3d93068d79e39f4e642ba01c | 5350321bf95b9b836140cdadf0ad1108c140ee76 | /R/convert_date.R | 42acbcf74f594943c12a21029d9fe5596963816c | [
"MIT"
] | permissive | barrenWuffet/convPkg | b439c4c954fa73be30b1ee5e1617b76cfe1ecf0b | 483a6267da7a52bf02833bd18771173ee584cada | refs/heads/master | 2021-07-04T12:51:16.565108 | 2021-06-04T23:54:26 | 2021-06-04T23:54:26 | 24,740,982 | 7 | 3 | NOASSERTION | 2019-04-01T14:52:17 | 2014-10-02T23:42:45 | R | UTF-8 | R | false | false | 1,595 | r | convert_date.R | #' Converts all columns of class POSIXct or POSIXt in a data.frame to Date class.
#'
#' @param xx A data.frame containing columns of class POSIXct or POSIXt
#'
#' @return data.frame with any columns of class POSIXct or POSIXt converted to Dates
#' @export
#'
#' @examples
#' z <- seq(1472562988, 1472563988, 100)
#' df1 ... |
78ab49c4613c6cea7b0493628e50392dc99b606b | 7d9627e3973c43a820b4a0819d69563f4f4eadb4 | /PCA/pca.r | 43d2e4fd63ff91998110a9a83bedcebeb4be20fd | [] | no_license | Kinsman-Road/rcode | 48dbd102de59108f3c457fce866775fb49bfc691 | b07e066fbc6819aec57703af029fa30fe20838d3 | refs/heads/master | 2021-08-07T21:44:36.902168 | 2021-01-19T22:58:15 | 2021-01-19T22:58:15 | 241,498,378 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,296 | r | pca.r | #Resources
#https://www.datacamp.com/community/tutorials/pca-analysis-r
#http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/
#https://www.climate.gov/maps-data/dataset/past-weather-zip-code-data-table
#::::: Import :::::
... |
6157e3b6988305d7d7d6130882c8e87143a438c1 | 15e6816528dfd35bb10c2c87897812e9c416fd3a | /man/readBlast.Rd | a12ad1c49ddb5f1b347f07564becaa0372e95928 | [] | no_license | jackgisby/packFinder | 0e038fd8529ac43e47c9adbfdcfb017b6122c178 | 068bad218f049e389608dba10c348b581daa9449 | refs/heads/master | 2022-08-14T23:50:21.026807 | 2022-07-18T10:19:53 | 2022-07-18T10:19:53 | 201,337,387 | 6 | 1 | null | 2019-10-28T12:15:12 | 2019-08-08T21:04:47 | R | UTF-8 | R | false | true | 3,103 | rd | readBlast.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readBlast.R
\name{readBlast}
\alias{readBlast}
\title{Convert NCBI BLAST+ Files to Dataframe}
\usage{
readBlast(
file,
minE = 1,
length = 0,
identity = 0,
removeExactMatches = FALSE,
scope = NULL,
packMatches = NULL
)
}
\argumen... |
f139fd6afd2d00c8d64f39d1c4de690b0caf2791 | dae88885e447582fa3f6f0c31ba0a7a5e4b96a32 | /R/qqplots.R | 2a36c6cc2761020bc3ef15d2609b4861ec6a08ce | [] | no_license | jergosh/cluster | 078cc62b3af11a36c93a5e64482f63a2eec77f16 | 0cd07bedf8386d4b32c021c5613ce74cac23557d | refs/heads/master | 2021-05-01T17:28:16.393079 | 2016-12-07T11:28:04 | 2016-12-07T11:28:04 | 44,182,290 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,366 | r | qqplots.R | ggd.qqplot = function(pvector, main=NULL, ...) {
o = -log10(sort(pvector,decreasing=F))
e = -log10( 1:length(o)/length(o) )
plot(e,o,pch=19,cex=1, main=main, ...,
xlim=c(0,max(e)), ylim=c(0,max(o)),
ann=FALSE)
mtext(expression(Expected~~-log[10](italic(p))), side=1, line=2.5, cex=0.7)
mtext(e... |
1e3a84507d0b2accca914cbdfa7e35b653fb0a4a | fefd0ae2c6ce3ef6230091b1fa437631a8c72e1f | /W2/w2part3rassignment.R | 6570080f34203920dd0422f3e604b0d530b21410 | [] | no_license | praveenkandasamy/johnhopkinscourse2 | c54512e9b8072946b818b398cce38929269b994f | f745bbee2fb768e53db79ef55b36fff477fc2670 | refs/heads/master | 2020-06-16T12:42:32.944465 | 2019-07-06T20:34:00 | 2019-07-06T20:34:00 | 195,578,731 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 548 | r | w2part3rassignment.R | corr <- function(directory, threshold = 0){ #function
filelist <- list.files(path = directory, pattern = "*.csv", full.names = TRUE) # create a list of files vector
id <- 1:332
for (i in id){
data <- read.csv(filelist[i]) #loop through all the files and read them
threshold <- sum(complet... |
a86a7afe5a015104a87886d770866d8e54bc12e3 | e3c0607809caa6e35ffb2af5ac890678936a7704 | /namelist.general.post.r | 29be745c7246a0c73fc819e0957d924027742664 | [] | no_license | chrisdane/echam | 30988375fb51b99caca355693b5c3817e32ad178 | 2f3b4df106b6549744d9ea5547acfc6fe90ec772 | refs/heads/master | 2023-07-11T10:39:11.091753 | 2023-06-26T07:20:23 | 2023-06-26T07:20:23 | 207,476,216 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,093 | r | namelist.general.post.r | # r
# input for post_echam.r
message("###################### namelist.general.post.r start ##########################")
graphics.off()
options(show.error.locations=T)
options(warn=2) # stop on warnings
#options(warn=0) # back to default
# clear work space
if (T) {
message("\nclear work space ...")
ws <- ls(... |
8993fb647cec87ad4fc93385eeeb6e19c05df508 | a442f04a26b881d93318911a2d14f5b91189fdef | /R/hf_diabetes_meds.R | 4d7fa5a946f41333dc1cae261853702ce06d1ae2 | [] | no_license | unmtransinfo/cerner-tools | fb45b7d347e17ea444a794ad276e958966390864 | 93ada80c97a28f405007d7178631cec674a21f76 | refs/heads/master | 2023-06-23T00:36:59.047465 | 2023-06-09T17:13:20 | 2023-06-09T17:13:20 | 157,255,596 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,124 | r | hf_diabetes_meds.R | library(vioplot)
hf <- read.delim("data/hf_diabetes+labs+meds.csv", stringsAsFactors=F)
print(sprintf("total input data rows: %d", nrow(hf)))
hf$lab_date <- as.Date(hf$lab_date, "%Y-%m-%d")
hf$med_date <- as.Date(hf$med_date, "%Y-%m-%d")
hf <- hf[hf$lab_date >= hf$med_date,]
#hf <- hf[hf$numeric_result>3,]
print(spr... |
88ad95b5d1ed89f14a2914143e992b797ce6ac08 | 2dcb9d91668917be46c25549b6e42ecde77fcd33 | /man/xml_parse.Rd | 77f8e41d741c8365c4a63244586950b619788959 | [] | no_license | arturochian/xml2 | 03cdd3a5135ad74ad1519daef6271fdb84d68071 | 9754f9fc69f5d77f0a4098012a304c340cbeec12 | refs/heads/master | 2020-12-29T03:19:31.547580 | 2015-02-12T20:56:17 | 2015-02-12T20:56:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 254 | rd | xml_parse.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/hello.R
\name{xml_parse}
\alias{xml_parse}
\title{Parse XML string}
\usage{
xml_parse(x)
}
\description{
Parse XML string
}
\examples{
xml_parse("<foo> 123 </foo>")
}
|
46f1227be03c38780ee45ff394e812a78c327c75 | 1dda9df405a23ab8dea17648051cec68f8ec3196 | /shiny/source/GUI/asm_GUI_LB.R | 67f214e82fe2dd5cb9c6e3cd31e1d8094b053e72 | [
"NIST-PD"
] | permissive | asm3-nist/DART-MS-DST | 57dd0b2b8c39120f769396d1dfda07ea4d36b96c | 966a5b4ba5d1cd8498431d951986e515eb40980d | refs/heads/master | 2023-05-03T11:22:44.520330 | 2021-05-19T15:14:05 | 2021-05-19T15:14:05 | 297,452,624 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 100 | r | asm_GUI_LB.R | asm_GUI_LB <- tabPanel(
"Library Builder (offline)",
DisclaimerMessage,
EmailMessage
) |
ee0da9c16f5413b93bcb794d9b686f18ee9bb55e | f09df42ce7959b701bc73e0f0f09778070751d37 | /ROC-AUC.R | dbf7bdf7b8a70e72666e8c4a6cc6f8a6d50cead0 | [] | no_license | saldh/R | f4e30c22a16e6a0eadadd267892deb121f345d0f | 1ac1490b9a8bc1c5dace04eb1528a5556152b8f3 | refs/heads/master | 2020-03-26T07:25:37.960188 | 2019-04-12T08:32:36 | 2019-04-12T08:32:36 | 144,653,834 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,595 | r | ROC-AUC.R | library(pROC) # install with install.packages("pROC")
library(randomForest) # install with install.packages("randomForest")
## Generate weight and obesity datasets.
set.seed(420) # this will make my results match yours
num.samples <- 100
## genereate 100 values from a normal distribution with
## mean 172 and stan... |
6dc542378068fb99193c8ca836b8e05d07b40599 | 9c79f8d1e89ee5adf7b93115ccc741d3303404f1 | /Scripts_Curso_R/Tarea_3_The_Office.R | 7b889e40c537debf026b7d8c13e90fd9bea8b9c9 | [] | no_license | derek-corcoran-barrios/derek-corcoran-barrios.github.io | e1631feef111cfc9bc693df1853e02818435071a | ccb8f21c053fd41559082eb58ccb7f64cc7fcf86 | refs/heads/master | 2023-07-17T13:11:43.739914 | 2023-07-03T07:24:21 | 2023-07-03T07:24:21 | 107,616,762 | 33 | 33 | null | 2020-06-18T19:25:50 | 2017-10-20T01:23:44 | HTML | UTF-8 | R | false | false | 1,477 | r | Tarea_3_The_Office.R | library(tidyverse)
Episodes <- read_csv("https://raw.githubusercontent.com/derek-corcoran-barrios/The_office/master/The_Office_Episodes_per_Character.csv")
words <- read_csv("https://raw.githubusercontent.com/derek-corcoran-barrios/The_office/master/The_office_Words.csv")
stop_words <- read_csv("https://raw.githubus... |
233e9140d80f00ef29190404d3c1b54c03be8c21 | c10c3e569ee4581269295f40d977ef1783202793 | /R/imp_import.R | b1fa8f7e2929b542a2c10a69a49ee769bf61675d | [] | no_license | zoltankovacs/EThu | 6de4a14885d3e980c0bd9d75347fef54892dbffd | d72f65d2bed9003fceafa6c70be9c3fbcca2129d | refs/heads/master | 2021-08-23T09:05:35.997209 | 2017-11-18T10:29:21 | 2017-11-18T10:29:21 | 111,195,054 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,028 | r | imp_import.R |
impD <- function(NrFile = 1) { # import the raw txt file(s), NrFile: which file to import
files <- list.files(paste0(getwd(), "/rawdata"), full.names = TRUE, pattern = "*.txt")
filesShort <- list.files(paste0(getwd(), "/rawdata"), full.names = FALSE, pattern = "*.txt")
print(paste0("The file: '", filesShort[NrFi... |
cfc30b756c0ee0d8df0f5b31c036e7812556656e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pterrace/examples/muscle_fiber_dat.Rd.R | 9d54d4d28ec861cdbed8225418de5d9ecdf0bfd0 | [] | 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 | 831 | r | muscle_fiber_dat.Rd.R | library(pterrace)
### Name: muscle_fiber_dat
### Title: Point cloud sampled from the muscle tissue cross-sectional image
### Aliases: muscle_fiber_dat
### Keywords: datasets
### ** Examples
# load muscle fiber data
data(muscle_fiber_dat)
# input variables
Xlim <- c(-50,350)
Ylim <- c(-50,250)
lim <- cbind(Xlim, Yl... |
81f44e718c051eed97c0d8a50b50584e3bc8baa6 | 50221ba3c8d502486f21e11946aca054a96e04f9 | /run.py | 297b5183b15d98cb6f808b25b5836687d8dbbf00 | [] | no_license | HilarioCuervo/first_commit | 84307b2888504a06676e70c047c52c06ba94c950 | 393b49aaf07a74a6497c1c8e69aabe65a4a7bd11 | refs/heads/master | 2023-04-29T15:32:33.073800 | 2021-04-28T13:46:33 | 2021-04-28T13:46:33 | 349,608,329 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 30 | py | run.py | a = 2
b = 3
c = a + b
print(c) |
c2db22cd303f4b8e9ff75475aeb3fbdedf08c92b | 6b40427744ca122897f25eda12504d4239870437 | /run_analysis.R | 7947798235b037187e30d1262317def78db8054e | [] | no_license | henzi23/datacleanproject | 614286315c9e11198e8fb5489ef88abb33b5e527 | ae0562e106bd41b893771909dc507fdecd506501 | refs/heads/master | 2021-01-01T15:44:35.379542 | 2014-10-26T14:02:07 | 2014-10-26T14:02:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,511 | r | run_analysis.R | ## This is the R script to create a tidy dataset from wearble computing dataset found at
## https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
## The script assumes this dataset has been extracted into your working directory
## The lines below read the data into R
features<-read.tab... |
da3085dd6475c93d95049ac363cb6dd2478935e9 | e9e5a348573f0099d8a6c03ab90ca93d7e6df9ca | /bDiscrim.R | 93cbc4c04359beaf1f5bafa3d38583007a0c9603 | [] | no_license | nxskok/stad29-notes | a39f73502e18f92b12024a910a3e4f83b3929c15 | a8a887e621b84fdadb974bf50c384ba65d2a8383 | refs/heads/master | 2021-06-08T11:21:53.709889 | 2021-04-26T23:02:15 | 2021-04-26T23:02:15 | 161,848,845 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,808 | r | bDiscrim.R | ### R code from vignette source '/home/ken/teaching/d29/notes/bDiscrim.Rnw'
###################################################
### code chunk number 1: berzani
###################################################
hilo=read.table("manova1.txt",header=T)
attach(hilo)
fno=as.integer(fertilizer)
plot(yield,weight,pch=fno,... |
bd2ce9c05d13508354bf2a1dbc2cdf07b9c8b9b2 | 208fe844817df6e34f869afb60cd69d2cc1e2ba8 | /main.R | c123a1f5717aac54b19b18577e17e90b92c10b6d | [] | no_license | jyjek/pasha_pdf | fefa9b94fc0797e1f5f9d413ff77ff2c65a63c3f | 58e5613b3d0f882d2ca9a15726b60ade82cab6c2 | refs/heads/master | 2020-06-17T09:19:54.815502 | 2019-07-08T20:11:46 | 2019-07-08T20:11:46 | 195,878,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,652 | r | main.R | library(tidyverse)
library(tabulizer)
library(textclean)
library(stringr)
f <- "data/documentView_retrieveStatementPdf07.pdf"
f1 <- "data/documentView_retrieveStatementPdf07 (2).pdf"
hawaii_telecom <- function(f){
local_df <- pdf_text(f) %>% # читаємо pdf
.[[1]] %>% str_split(., "\n", simplify = TRUE) %>%... |
b47154109872d33fb71ecf9d7d921edcebe57f31 | 5ea19ffbb17c4f943de4b9e3047f7a7fa8bfa605 | /R_Code_and_Analysis/distance_decay/old/distance_decay.R | b6f6aed2e24c4cdba4da3ce35c90c647a50c3a4e | [] | no_license | mawhal/Calvert_O-Connor_eelgrass | c0dfbc02a8ea8c217512e1389be709649dfdde85 | fad8a7be27ce79a99ebb5744043318984c5cb42d | refs/heads/master | 2023-02-13T17:54:02.839659 | 2020-12-19T00:41:46 | 2020-12-19T00:41:46 | 183,318,061 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 76,826 | r | distance_decay.R | ################Preliminary Analyses to explore the impact of distance between sites on grazer dissimilarity
##Started by Coreen April 2020
##This script makes distance matrices between sites and plots Bray-Curtis grazer dissimilarity between each pair of sites against dstance between each pair of sites for 2014-207
#... |
1434b2f21f3562bc6343cbfc9b3fc17cbfa4cd4d | 073892c868e40d709be048603cee7c5ed549dd6d | /code/paper/figures/1/main.r | ea73181b298733cbdb48f7af7e72aee3c68c2316 | [] | no_license | Ran485/TFbenchmark | 9d7d0a3372841080f53ec1beeca9a65a6f1c510a | 1c7b9f11c5ba2aa7afdeda768e3c99e2bde18607 | refs/heads/master | 2021-10-28T10:12:49.078369 | 2019-04-23T10:58:32 | 2019-04-23T10:58:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,980 | r | main.r | rm(list = ls())
home = '/Volumes/GoogleDrive/My Drive/projects/TFbenchmark/'
setwd(home)
source('code/lib/utils.r')
# Load network
network = read.csv(file = 'data/TF_target_sources/omnipath_scores/database_20180915.csv', stringsAsFactors = F)
names(network)[5:8] = c('curated', 'ChIPseq', 'TF binding motif', 'inferre... |
3cc1b2a616fbc75c95827afd0e16074006f7f34a | bc42c76a961ef56d4d08a714c0eaabb4366a36a1 | /R/NHFaux.R | ac4f10546dd66d319ec9f895496e5fb9fdd527a7 | [] | no_license | cran/IndTestPP | 593ab1dc0ddb6addd008e80aed948d88058a240c | a628d5be9c314513541656d6e2ea28dd9bc91cee | refs/heads/master | 2021-06-28T21:12:36.085070 | 2020-08-28T18:00:03 | 2020-08-28T18:00:03 | 64,703,962 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 248 | r | NHFaux.R | NHFaux <-
function(r,L, lambdaD,posD,typeD, T)
{
posLW<-L[(L>=r)&(L<=(T-r))]
L1D<-(1-min(lambdaD)/lambdaD)
L1L0<-sapply(posLW, FUN = prodN2, r=r,L1D=L1D,posD=posD, typeD=typeD)
NHF<-sum(L1L0)/length(posLW)
return(NHF)
}
|
d1c15af287610caaf9c0d1c82ef69cff1bdd4e02 | cc0254622f705d4049af62b843dcab0a3e393de1 | /man/plotICC.Rd | fccedc3ff3642aa0cd92cb5ff99937c1dc0d9c1b | [] | no_license | cran/eRm | 88c4ff62cc445f4e8ad90a4fdffc00de4246716e | b54bd5930675dcfab50a10ec401b4eefa2990c91 | refs/heads/master | 2021-07-20T03:19:44.904031 | 2021-02-15T10:03:06 | 2021-02-15T10:03:06 | 17,695,687 | 4 | 4 | null | null | null | null | UTF-8 | R | false | false | 6,241 | rd | plotICC.Rd | \encoding{UTF-8}
\name{plotICC}
\alias{plotICC}
\alias{plotICC.Rm}
\alias{plotjointICC}
\alias{plotjointICC.dRm}
\title{ICC Plots}
\description{Plot functions for visualizing the item characteristic curves}
\usage{
\method{plotICC}{Rm}(object, item.subset = "all", empICC = NULL, empCI = NULL,
mplot = NULL, xlim = c... |
ab2d8bf1b17ee5021885c98fe2ad980a8c177298 | c65dac3d7161db24db2c963b2448c20339c421be | /example.r | 5a5359f18fa5bcd16207a6d79cc89936331fbe47 | [] | no_license | strug-lab/RVS | aa19bb5db48d11b144c6768b89716700022fe538 | 3265ff03e413ffc73d8bbfa8057813ea1e01640c | refs/heads/master | 2016-09-06T03:14:59.984165 | 2014-10-28T04:27:17 | 2014-10-28T04:27:17 | 19,017,632 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 797 | r | example.r | #
# Read vcf file
#
a = 'C:/chr11_113low_56high/1g115low_1g56exomehigh_filtered.hg19.chr11.vcf'
#
# Read vcf helper functions
#
source('likelihood_vcf.r')
filen = a
filecon = file(filen, open='r')
#
# Skip header of vcf file.
# n = may be changed until reach header that contains list of samples
tt2 =... |
a29945d8157550f7d64ae1505547a5222cae6ca9 | 93427de297e8ef8232ea2874b4f9fec5e0ecbdab | /R/haplo.bin.R | 2a78e72d84a3fb210eac13a7999835dfbe8387e5 | [] | no_license | cran/SimHap | 03f5402bdd68f3ca6b6f139db631b217c9d6cf2b | dd834d94c954662ee49c3c50799166557de1c72d | refs/heads/master | 2020-05-18T07:48:55.312886 | 2012-04-14T00:00:00 | 2012-04-14T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,103 | r | haplo.bin.R | `haplo.bin` <-
function(formula1, formula2, pheno, haplo, sim, effect="add", sub=NULL, adjust=FALSE) {
library(stats)
call <- match.call()
hapFreqs <- haplo$hapObject$final.freq
haplo <- haplo$hapData
if(!identical(as.character(unique(pheno$ID)), as.character(unique(haplo$ID)))) stop("Phenotype data and Hap... |
9293fd7b4fb5637f12631625775cd56bdef1ede8 | f45dd2f2c39445c70f89874025b5fc9eb0e42929 | /demo/SimSeq.R | 9a8b9cca36cf8f5b49670b0b4db877ebce82346d | [] | no_license | sbenidt/SimSeq | 84858e529303e96491648d015e8449b1c978db45 | 2ae1518ab759da3a7554f867f31d95d3a9f90460 | refs/heads/master | 2021-01-20T12:04:45.988720 | 2015-03-07T06:23:14 | 2015-03-07T06:23:14 | 12,185,093 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,704 | r | SimSeq.R | data(kidney)
counts <- kidney$counts # Matrix of read counts from KIRC dataset
replic <- kidney$replic # Replic vector indicating paired columns
treatment <- kidney$treatment # Treatment vector indicating Non-Tumor or Tumor columns
nf <- apply(counts, 2, quantile, 0.75)
require(fdrtool)
### Example 1: Simulate Ma... |
d62e4c10388fe938321bba5ba287f8afa4f327fb | c7b4ef7427031fd72755c1aedbcb41a2a8b4abd7 | /K-means US_Arrests.R | a2c35a4d55a45b6c4350821ac82768dd37f451bd | [] | no_license | edkambeu/K-Means-Clustering | 0b2d8edf19853c89722131cadb49cbc8b0a7f1e2 | 19eca4e5cb6e41c994fc01ff1771d5a4d664cb68 | refs/heads/master | 2023-08-19T03:19:05.178421 | 2021-10-02T20:40:43 | 2021-10-02T20:40:43 | 412,883,407 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,775 | r | K-means US_Arrests.R | #Importing data
data("USArrests")
str(USArrests)
#Looking at the data
head(USArrests)
tail(USArrests)
str(USArrests)
#Is there any missing value in the dataset?
any(is.na(USArrests))
#Any errors in the data set
summary(USArrests)
#Scaling the data
USArrests_scaled <- scale(USArrests)
head(USArrests_scaled)
#K-means c... |
ac1d3a69dd7148b49cb5d33f572219470a2dc1c7 | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /paws/man/iotanalytics_describe_logging_options.Rd | f72b998dc94643dad451cf5fc0a8946cdc4f65e0 | [
"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 | 509 | rd | iotanalytics_describe_logging_options.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iotanalytics_operations.R
\name{iotanalytics_describe_logging_options}
\alias{iotanalytics_describe_logging_options}
\title{Retrieves the current settings of the AWS IoT Analytics logging options}
\usage{
iotanalytics_describe_logging_options... |
0a9cdbeb7f104f7bc355ce8071c80847b8c7a232 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/9071_0/rinput.R | 641b9eb72b598b16558f1fc5a19bed7005f86fd6 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("9071_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="9071_0_unrooted.txt") |
6c82e858e807a8431745eaa1756815ab02c0b3d5 | 4ebfa1f80041836d40c9b23bc0c44cd9a40a48e5 | /Rcode.R | 00724ecd2cf6b8adc81e00fe3e030bc113e0cffb | [] | no_license | ar3781/MayInstitute-Example | f78a982e28c2633aebf2ee9dc0552b1187b58a41 | 365718a14994df54b9de7734090bfb8299786867 | refs/heads/master | 2020-05-18T14:32:40.306209 | 2019-05-01T20:19:53 | 2019-05-01T20:19:53 | 184,474,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | Rcode.R | data = iris
plot(x=iris$Sepal-Length), y=iris$Septal.Width)
|
7d936cdcbe9d8de4411576452d860d4635db3513 | abad318b342c41d0f73f9d5491c2f05fce216430 | /cachematrix.R | 073ef5d077f25f861599e17e29fb0b8b451ac381 | [] | no_license | JoieGiArdT/ProgrammingAssignment2 | cd649ce11ffbb8037e59eddfdaa4a33ad1cbc9d8 | 61f67f45357249d8da341326e2d58af96d2b07c6 | refs/heads/master | 2022-11-26T18:33:14.139151 | 2020-08-03T22:26:34 | 2020-08-03T22:26:34 | 284,796,627 | 0 | 0 | null | 2020-08-03T20:08:31 | 2020-08-03T20:08:30 | null | UTF-8 | R | false | false | 2,015 | r | cachematrix.R | ## Put comments here that give an overall description of
## what your functions do
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function(inverse) inv <<- inverse... |
57d6ff5a376b27e723e2e9535400c5c647fdd450 | 00b21e537d2150cd44d1783b660de09208f75978 | /R/viewHashes.R | 9b53e6ce281540ba617f5aa0328426374c6edd09 | [] | no_license | wdwatkins/gdpAnalytics | 6f16db6fa1d55cb30c9b45cbc39f1aa49887ff3e | 6c2a29aa65d7de60c5b84620314f9161c7306d8a | refs/heads/master | 2021-01-23T06:25:19.768023 | 2019-06-07T23:40:31 | 2019-06-07T23:40:31 | 86,365,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 654 | r | viewHashes.R | library(dplyr)
library(data.table)
library(lubridate)
jobsDF <- fread('data/uniqueDF_4_21.csv', stringsAsFactors = FALSE,
colClasses = "character")
xmlDF <- fread('data/GDP_XML_4_21.csv')
joinedDF_noAgent <- left_join(jobsDF, xmlDF, by = "requestLink")
successJobs <- filter(joinedDF_noAgent, status == ... |
f4db505b4744f4548d8ebb7f7fbb6837c14b3c8d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/polspline/examples/predict.polymars.Rd.R | 1b3c4a25dedd84f60d9a45a7c4b6f246fa6bb53f | [] | 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 | 369 | r | predict.polymars.Rd.R | library(polspline)
### Name: predict.polymars
### Title: Polymars: multivariate adaptive polynomial spline regression
### Aliases: predict.polymars
### Keywords: smooth nonlinear
### ** Examples
data(state)
state.pm <- polymars(state.region, state.x77, knots = 15, classify = TRUE, gcv = 1)
table(predict(state.pm, x... |
98652a6942cd03280ee04950ccae00e1df5827ad | b926f0ac08bfe1b7c0feb654849cbdc70330d462 | /man/functiontable.Rd | f2810de2c820b232918db4360541bef635fead45 | [
"CC0-1.0"
] | permissive | hpiwowar/knitcitations | 2157e0c94c376dc5a539996c1b472310d0ae0a9d | 97456fe4fa138eac68dc4e242500bf9fe8c4012c | refs/heads/master | 2021-01-17T22:50:40.657655 | 2013-02-11T19:51:22 | 2013-02-11T19:51:22 | 8,145,133 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 919 | rd | functiontable.Rd | \name{functiontable}
\alias{functiontable}
\title{a table of functions in a package}
\usage{
functiontable(pkg, ...)
}
\arguments{
\item{pkg}{a string specifying the name of a package,}
\item{...}{additional arguments to xtable}
}
\value{
the output of xtable (as html, or specify type="latex")
}
\description{
... |
9f5ebce92924da7844c3745e72ff0b955f39f69a | bdd86fde8ecc268a08ab787ae295c0175164f556 | /man/plot_ci.Rd | 8728dbb62a7870661ebdd5818faa905b0265e756 | [] | no_license | mauriziopaul/litterDiallel | 448c94e7fb42ba823fda54c3ef7a698959e97625 | dba0c8383f6baf0dc20a2136243db208f2af33fc | refs/heads/master | 2022-06-19T05:45:25.506450 | 2022-05-30T22:16:54 | 2022-05-30T22:16:54 | 124,441,857 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,021 | rd | plot_ci.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/litterDiallel.R
\name{plot_ci}
\alias{plot_ci}
\title{plot_ci}
\usage{
plot_ci(
midvals,
narrow.intervals,
wide.intervals,
names = 1:length(midvals),
add = FALSE,
main = "",
main.line = 2,
xlab = "Estimate",
xlab.line = 2.5,... |
42d2e2efa68ac2994b5026925116a9a28733ea29 | 9cce1788a21acd01c9deab2bb25f3733a356736c | /man/related_artists.Rd | fa79407fda8502a94d0d2c6bac823909bd8c4fbb | [
"MIT"
] | permissive | raffrica/spotifyremoji | 30fd90fa270943627ec3f270b770923ba8e917cc | 629df278794d586df550a32c93780c0c9d9ac76d | refs/heads/master | 2020-03-09T14:11:43.800853 | 2018-04-14T19:21:30 | 2018-04-14T19:21:30 | 128,828,857 | 0 | 0 | null | 2018-04-09T20:18:41 | 2018-04-09T20:18:41 | null | UTF-8 | R | false | true | 559 | rd | related_artists.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/related_artists.R
\name{related_artists}
\alias{related_artists}
\title{Prints dataframe of artist's related artists.}
\usage{
related_artists(user_auth_token, artistName)
}
\arguments{
\item{user_auth_token:}{String containing the users auth... |
39b5b36a186e0525b9f507c774a7b70dd3398d93 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/forecast/examples/thetaf.Rd.R | 3670fe5824eeefe874b7b6449c04ffa4edaaec9e | [] | 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 | 173 | r | thetaf.Rd.R | library(forecast)
### Name: thetaf
### Title: Theta method forecast
### Aliases: thetaf
### Keywords: ts
### ** Examples
nile.fcast <- thetaf(Nile)
plot(nile.fcast)
|
2f0e806576349c37ee71b8cd6443c038b6bdb198 | 36628243c050cc012243cce16d55e6d24c95b1cf | /man/client_slack.Rd | c139217cfa3f61fe2795549cdcfc66b7cf1dc516 | [
"MIT"
] | permissive | TymekDev/sendeR | e5bf9ca406dd130b8003f54c00050de16fedae7a | 32142f3ee24ad0c1b674102848e41c461a5107d0 | refs/heads/master | 2022-11-07T07:07:13.054088 | 2020-06-26T16:48:17 | 2020-06-26T16:48:17 | 213,371,734 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,120 | rd | client_slack.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/client_slack.R
\name{client_slack}
\alias{client_slack}
\title{Slack client}
\usage{
client_slack(slack_webhook, ...)
}
\arguments{
\item{slack_webhook}{a webhook obtained from the Slack API settings.}
\item{...}{named arguments with additio... |
8b995b68aa21d0940f863fed139785010da5c6bf | 1cf864651a3cad23eb3c7f25aecda77b9d51c7e5 | /man/createstartvalues.Rd | a3252d36b3fb6f16c68bdb78bee3d88a1b0ce995 | [] | no_license | gobbios/EloRating | 98eec32ae178db6bca95d55691c5d66b525bce9a | ebb4957676b3ff5638e5eb9ca34464a480138902 | refs/heads/master | 2023-06-08T00:58:34.065438 | 2023-06-02T10:12:35 | 2023-06-02T10:12:35 | 79,722,236 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,406 | rd | createstartvalues.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createstartvalues.R
\name{createstartvalues}
\alias{createstartvalues}
\title{calculate start values from prior knowledge}
\usage{
createstartvalues(
ranks = NULL,
rankclasses = NULL,
shape = 0.3,
startvalue = 1000,
k = 100
)
}
\arg... |
b590f6c782e4f574391ded0a610008dcc473eb98 | bdeb6048c3fbaf04e916f1f6a0f341ac4d47f088 | /LAPDcalls2.R | b9f2860702b04b2023034da42df793d8f0cf4a17 | [] | no_license | RexWoon/blog-files | 0657162c05e2592aa1761e06418be49d9e6afe6e | 55c032fe134c34b56de44601755b81de5a66a8b7 | refs/heads/master | 2021-01-10T16:13:52.413049 | 2017-05-25T03:04:06 | 2017-05-25T03:04:06 | 50,158,861 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,475 | r | LAPDcalls2.R | ##### LAPD Calls Part Deux##################
library(ggplot2)
library(dplyr)
library(scales)
############## Time series of number of calls per day#####################
lapd.data <- read.csv("LAPD_Calls_for_Service_YTD_2015.csv")
Day <- unique(lapd.data$Dispatch.Date)
daytotal <- vector()
for (i in 1:length(Day)){
... |
0f9ee0d5affe279a9f8efcb748b87f353a3f7459 | 745d585395acad1376d84f8ca1284c13f2db70f0 | /R/calcCumulatedDiscount.R | dbe6d6110f57fe332c1ce71ad25dbc3705805191 | [] | no_license | pik-piam/quitte | 50e2ddace0b0e2cbfabf8539a0e08efe6bb68a0b | 4f5330695bd3d0e05d70160c1af64f0e436f89ea | refs/heads/master | 2023-08-20T04:15:16.472271 | 2023-08-09T08:14:32 | 2023-08-09T08:14:32 | 206,053,101 | 0 | 8 | null | 2023-08-09T08:14:34 | 2019-09-03T10:39:07 | R | UTF-8 | R | false | false | 5,366 | r | calcCumulatedDiscount.R | #' Calculates the cumulated discounted time series
#'
#' Discount and cumulated a times series - gives the time series of the net
#' present value (NPV). Baseyear for the NPV is the first period.
#'
#'
#' @param data a quitte object containing consumption values - consumption has
#' to be named "Consumption"
#' @param ... |
863076ad06f555062485816dde070e0aa5679aa6 | ca2de03ce862c0bf549de4fea51817600793084e | /SW2 Midterm/Seatwork 2 Midterm/SW Midterm Angelo Ricohermozo.R | c329c6f0cfcdf7bac384caafefd00fb8aa97dcae | [] | no_license | Ranzelle06/Midterm_Repo | 1fff8373182abc336c7dbb915f86a127c7721e72 | 99f93152106ecde4d7ed80815f51134970144731 | refs/heads/master | 2020-03-22T05:08:25.033802 | 2018-09-18T18:39:49 | 2018-09-18T18:39:49 | 139,544,789 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,283 | r | SW Midterm Angelo Ricohermozo.R | data <- read.csv("Seatwork 2 Midterm/midetrmseatwork_data.csv")
MeanFunction <- function(data, removeNA = TRUE){
col_num <- ncol(data)
means_per_col <- numeric(col_num)
for(element in 1:col_num){
means_per_col[element] <- mean(data[ ,element], na.rm = removeNA)
}
means_per_col
}
MeanFunction(data)
subse... |
d4fffea3888a83e2c99a74dfd4bfed40ce31f567 | cbe529bda1ca9624c7d89e9beea75c6202787d64 | /R/team_functions.R | 350da534f486bf1b354f15392a3937380f25ad48 | [
"MIT"
] | permissive | JamesDalrymple/cmhmisc | bc5b29a182d5816f204b008e7cce77b8f5fb5312 | 6590092cb43fe9778799fec2ae33adea5f711c85 | refs/heads/master | 2021-10-15T23:35:54.128023 | 2019-02-06T21:25:16 | 2019-02-06T21:25:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,960 | r | team_functions.R | #' @title WCCMH team functions
#' @description
#' cmh_recode recodes Washtenaw CMH team names to a standardized
#' team format.
#' recode_team_prog recodes Washtenaw CMH team/program names to a standardized
#' program format.
#' cmh_teams_f factors (ordered is an option) teams.
#' cmh_priority_dt assigns a priority to ... |
20a43f92bc3dcc77b845d7f43ed15ea40ca982b6 | a4e7ce9ece9ab83b6ca5ef06b22f7b8b2c043362 | /RDeco/demo/testClustering.R | 3b466872f54124cac6fecdb3f5a072f8f1e7a319 | [] | no_license | giuliomorina/DECO | fb89fc2ffa94e70aefa85bc2f699ebdf3ce40e90 | 05a5565cf0bf8900248efd05d462c6cfa3e99b13 | refs/heads/master | 2021-06-10T19:33:14.319654 | 2016-12-01T11:13:26 | 2016-12-01T11:13:26 | 74,596,605 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 280 | r | testClustering.R | library(parallel)
clust <- makePSOCKcluster(c("greywagtail",
"greyheron",
"greypartridge",
"greyplover"))
x <- list(X=5,Y=4,Z=8,T=9)
lambda <- clusterApplyLB(clust, x, sqrt)
stopCluster(clust)
|
752e32a02b41f00d2ebb8219457d17691268700b | f75ca2ee0877514a8728dfca44a30bc2fe2da74d | /R/group_rates.R | 7fc58d10bf979f5014db8a3844f14ded41a4daed | [] | no_license | rafalab/smallcount | f5858cc5ec51f89037b1f7d867a78554840a63d0 | 98f500684c8df958fa6eef91310c4583d9a2f6ca | refs/heads/main | 2023-06-16T21:09:39.832958 | 2021-07-13T02:33:40 | 2021-07-13T02:33:40 | 365,328,599 | 9 | 2 | null | 2021-05-26T15:00:53 | 2021-05-07T19:03:02 | R | UTF-8 | R | false | false | 789 | r | group_rates.R | #' Rowwise rates for groups
#'
#' @param y A tgCMatrix sparse Matrix.
#' @param g A factor defining the group for each column.
#'
#' @export
#'
group_rates <- function(y, g){
if(!is(y, "dgCMatrix")) stop("y must be class dgCMatrix")
if(!is.factor(g)){
warning("Coercing g into a factor")
g <- as.factor(g)
... |
850ad14564199fb30b313e5fa112a13140f61bda | d8f643de8f7d1bc3af1478e8f934e4c41ddbc6f1 | /man/try_catch_error_as_na.Rd | a3097d1803754851350ed6e58c94043a24bb0758 | [] | no_license | meerapatelmd/police | d0aff7be9a95a3928c6884675f3cef0b587f11b9 | 7f4f440a0e21de0af10a027c38573af51b059601 | refs/heads/master | 2023-01-13T12:59:48.668697 | 2020-11-29T21:45:57 | 2020-11-29T21:45:57 | 258,654,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 407 | rd | try_catch_error_as_na.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/try_catch_error_as_na.R
\name{try_catch_error_as_na}
\alias{try_catch_error_as_na}
\title{Skip error messages, records NA, and continues to loop on the expression}
\usage{
try_catch_error_as_na(expr)
}
\arguments{
\item{expr}{expression}
}
\d... |
6a3da295d9bddc0a40e97c010f58f1051d4459f8 | bf67c57a29eeb452a32bd77f820a274f7fe11bee | /tests/testthat/test_integration_builtin_templates.r | 4b9320f9144339ea55edeb3e7735ce1122e4c387 | [] | no_license | Display-Lab/pictoralist | 231ac2c3ad82b5b362c61aadf3dd1519b20a8ad7 | 7c4dacab17390bad1e49c4e9cf9a366e8a0fbee9 | refs/heads/master | 2021-06-19T14:28:34.803546 | 2020-03-23T18:53:05 | 2020-03-23T18:53:05 | 159,402,840 | 1 | 0 | null | 2020-03-23T17:20:58 | 2018-11-27T21:39:36 | R | UTF-8 | R | false | false | 10,576 | r | test_integration_builtin_templates.r | context("Integration test of baked in templates")
test_that("Baked in templates with single time points work with mtx data",{
mtx_data <- read_data(spekex::get_data_path("mtx"))
mtx_spek <- spekex::read_spek(spekex::get_spek_path("mtx"))
templates <- load_templates()
mtx_templates <- c(templates$ComparisonBar... |
30e75fde891cd59fd8b0e6f43fe1070d639efa96 | b6bd266b6b10290665231f1cc9bc892b51cf6716 | /man/sample_2006.Rd | d7e54e1beb9d5c3efd267f15cd9619d8dc8e4df6 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | tereom/estcomp | 9a95e9a0be674d1f029801d3818a8aee8cf3f718 | 817f7e20ab82bffd064db4ccd68f5303a72844e5 | refs/heads/master | 2020-06-30T15:26:14.627799 | 2019-11-05T16:17:34 | 2019-11-05T16:17:34 | 200,871,105 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,250 | rd | sample_2006.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sample_2006.R
\docType{data}
\name{sample_2006}
\alias{sample_2006}
\title{Sample of 2006 presidential elections.}
\format{election_2006: A data frame with 7200 rows and 10 columns:
\describe{
\item{state_code, state_name, state_abbr}{Chara... |
b697e412781c9fef4c8dc03ce1a579b9e5ebc66b | cfacbfb653f0662be0c70d2c6659c3d1d3305b71 | /Data-Mining/Lab/XGBoost/XGBoost-Tutorial.R | 597bb8cc0dac79891294b8520c10a6e6561e1cbc | [] | no_license | ihaawesome/Graduate | 37327af1acd4b2f2bf56648485e5a8378a2bbddd | a0ee4b8863b2cd03855685d17cab802e2b5898d3 | refs/heads/master | 2020-05-03T07:46:48.563738 | 2019-09-17T05:49:38 | 2019-09-17T05:49:38 | 178,507,439 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,981 | r | XGBoost-Tutorial.R | setwd('C:/Users/HK/Desktop/GitHub/Graduate/DataMining/XGBoost')
##### XGBoost Tutorial #####
# how to use Xgboost to build a model and make predictions
# gradient boosting framework: linear & tree learning
# Input Type: matrix, dgCMatrix, xgb.DMatrix (recommended)
# 1.2 Installation
library(xgboost)
# 1.3 Learning
... |
a94efc63fa9e89c8f8fcb744989e5bff54f16b82 | f72a6bc75fd994afd900dd72d0d03e6ecd875191 | /credit card.R | d3930d8df70f496a8bfb59a664cfbe51f2c0130d | [] | no_license | belenamita/namita | 503134d2ee7900c35d287eee54bf5e9277bb76c7 | 7a73383f6a0df687c20215a33bd129e7228faf45 | refs/heads/master | 2021-05-26T01:06:26.010517 | 2020-09-03T06:55:07 | 2020-09-03T06:55:07 | 253,994,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 540 | r | credit card.R | #Logistic Regression
#Credit Card Problem
Crcard <- read.csv("//Users//smitshah//Desktop//Assignments//Logistic Regression//creditcard.csv")
attach(Crcard)
str(Crcard)
Crcard.omit=na.omit(Crcard)
Crcard.omit
#Model Building
Model1 <- glm(factor(card)~reports+age+income+share+expenditure+factor(owner)+factor(selfemp)... |
69c38c4cde6135da7341b0acf093b8314f553d0c | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/oppr/man/plot_phylo_persistence.Rd | 491a34d65ae599e2dca811e39b6f781e29a28e17 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 5,357 | rd | plot_phylo_persistence.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_phylo_persistence.R
\name{plot_phylo_persistence}
\alias{plot_phylo_persistence}
\title{Plot a phylogram to visualize a project prioritization}
\usage{
plot_phylo_persistence(
x,
solution,
n = 1,
symbol_hjust = 0.007,
return_da... |
8d6cfd6a5516325996190afed84749a52778cd60 | 13f0b3f37544339d5821b2a416a9b31a53f674b1 | /man/find_group_match.Rd | c96c164be3713835931244611784e1f1fb953c8b | [
"MIT"
] | permissive | hejtmy/eyer | 1f8a90fd7a8af0a4c4c73790633589dc624edda2 | 0b49566c76ab659184d62e1cdd658b45b0d33247 | refs/heads/master | 2020-04-24T11:17:25.414641 | 2019-09-17T22:44:52 | 2019-09-17T22:44:52 | 171,920,561 | 0 | 0 | MIT | 2019-09-10T21:54:40 | 2019-02-21T18:08:07 | R | UTF-8 | R | false | true | 577 | rd | find_group_match.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eyer-synchronisation.R
\name{find_group_match}
\alias{find_group_match}
\title{tries to find a sequency of N elements in eye_durations that correspond to the synchro durations
returns index of first matchin eye event}
\usage{
find_group_match... |
0cac176d3bf4776545fd83766b78cd0f5dbc343a | c03b75d4c6cd199a6a252799b4382b061e7c53e6 | /figure/plot3.R | 23bd4628e61905567c86e4d45008332f7c35172c | [] | no_license | ravinderpratap/ExData_Plotting1 | bac39c6b24359bfea19f6625a71154eb3d5b0be0 | 6977e8f5d0d981259562538e0c150eb4928cc26e | refs/heads/master | 2020-05-07T22:31:27.850894 | 2019-04-14T08:03:02 | 2019-04-14T08:03:02 | 180,948,734 | 0 | 0 | null | 2019-04-12T06:54:58 | 2019-04-12T06:54:57 | null | UTF-8 | R | false | false | 1,467 | r | plot3.R | # Loading required Packages
library(dplyr)
#Set Working Directory for reading dataset
setwd("C:/Users/r.pratap.singh/Desktop/JohnHopkins/exdata_data_household_power_consumption")
#Read the file
power_data <- read.table("household_power_consumption.txt", header=T, sep = ";", na.strings = "?")
head(power_data)
str(... |
6cdca795c39a5ee779efde5972e8724d4a60bced | 5c7e7dce5d0b75b2299f0710393ecf29e768e342 | /man/recalc_snowextent_scene.Rd | 33e404b9d6c8b17ca90a0e2d8ab16ad915f3ff27 | [] | no_license | SebEagle/snowcoveR | 995c860ec05fe456b6c8914c48f37af532f50316 | 39d21758976bb697068c84e64aad84b86fddc05d | refs/heads/master | 2020-03-09T08:22:35.168913 | 2018-05-25T21:29:21 | 2018-05-25T21:29:21 | 128,687,597 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,287 | rd | recalc_snowextent_scene.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/recalc_snowextent_scene.R
\name{recalc_snowextent_scene}
\alias{recalc_snowextent_scene}
\title{recalc_snowextent_scene}
\usage{
recalc_snowextent_scene(store_directory, dem_subdir, height_zone_size,
threshold1_snow, threshold2_cloud)
}
\ar... |
8ab93fd7b242e9fcb0f49b4bda1f80fcd81b4207 | af243341d1c806d2c67e9a7101f92ab508d4f05e | /analysis/Fig_S_trankplots.R | 3ba8e691b0806c055eff7160961763ac5fed076a | [] | no_license | michaelchimento/acquisition_production_abm | 5fb103e1528785d703899695ae15fe247ecd8763 | a3d74aafc7a16b93373651a203eb11a9432dd389 | refs/heads/master | 2023-05-25T14:45:29.223353 | 2023-05-18T05:24:25 | 2023-05-18T05:24:25 | 285,799,633 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,063 | r | Fig_S_trankplots.R | library(tidyverse)
library(rethinking)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
library(grid)
library(gridGraphics)
library(gridExtra)
loadRData <- function(fileName){
#loads an RData file, and returns it
load(fileName)
get(ls()[ls() != "fileName"])
}
load(file="../model_outputs/Rda_files/df_... |
36e01c37a2635837609fed2e28b19ab158199537 | d99e3989183cddfac8a2011e91929ca104192b29 | /plot1.R | ee6638bd0c0fe203223381db8fc964ad485f085a | [] | no_license | Diegoscn/ExData_Plotting1 | 86e7d290678b68e3529a9d35c9ba95eb2c9c97df | 11efb17516c97517c53b9d7b84cc2c8b37d910e7 | refs/heads/master | 2021-01-24T05:15:41.027509 | 2014-06-08T23:43:46 | 2014-06-08T23:43:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 561 | r | plot1.R | ### Read data and plot a histogram
data <- read.table("household_power_consumption.txt", sep=";",header=TRUE)
data$DateTime <- strptime(paste(data$Date, data$Time), "%d/%m/%Y %H:%M:%S")
data <- subset(data, as.Date(DateTime) >= as.Date("2007-02-01") &
as.Date(DateTime) <= as.Date("2007-02-02"))
data$... |
d346e12704e486d399c6b9bcae2ce5ef53478592 | 595aa005d1a9d84b03c54b6049453b1e1495b424 | /man/run_multiple_iscam.Rd | e403e53e5d426975d30a46af6e38e26c8300303f | [] | no_license | pbs-assess/gfiscamutils | 6e67c316c0a91d0e639dff8a46eeb4c22d5dd194 | 815275ca470bd086f28ccab752cba91c4c5dbfb7 | refs/heads/master | 2023-09-03T22:54:08.246801 | 2023-03-10T09:12:24 | 2023-03-10T09:14:10 | 198,681,740 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 441 | rd | run_multiple_iscam.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/run-iscam.R
\name{run_multiple_iscam}
\alias{run_multiple_iscam}
\title{Run multiple iscam models in parallel}
\usage{
run_multiple_iscam(model_dirs, ...)
}
\arguments{
\item{model_dirs}{A vector of model directories}
\item{...}{Arguments pa... |
6e0ad8036ab05b0949d32fd509726f01a25d112d | 4d6cb9288727a510475fc1e9ebcf247653486580 | /2021/day03.R | aaac0523aa38ed2642ad5480ae22c1b3a49f948d | [] | no_license | rrrlw/advent-of-code | b9ac82442d7c6164ca49c4fb3107fa323810680a | 66c26a723717bfd7d95e2cb4e690735ec0f66005 | refs/heads/main | 2021-12-28T02:36:51.593985 | 2021-12-15T19:40:24 | 2021-12-15T19:40:24 | 226,371,569 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,208 | r | day03.R | library(magrittr)
library(dplyr)
#####UTILITY#####
binvec_to_b10 <- function(bin_vec) {
bin_vec %>%
as.character %>%
paste(collapse = "") %>%
strtoi(base = 2L)
}
filter_step_o2 <- function(df, pos) {
df %>%
filter(.[[pos]] == as.integer(median(.[[pos]] + 0.5)))
}
filter_step_co2 <- function(df, p... |
38a44241df92a89870d4b31b153f57cd6b725423 | 4434c2a0f03d1cf8ca0ee8abc3cda21ce82cbc64 | /dircheck.r | b5b2834ac650e44e544018508a0cfe53ec1385be | [] | no_license | churchlabUT/ldrc | 7c3b3498d438642a617494a30abe86d1394c5b09 | 4f67751fa709aab8515c189b183ccd71fdbbff12 | refs/heads/master | 2020-12-31T07:55:02.382876 | 2016-02-05T21:00:06 | 2016-02-05T21:00:06 | 49,591,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,249 | r | dircheck.r | #This script is useful if you need an easy way to see what's in everyone's directory.
#To run this script type 'Rscript dircheck.r ______ __' into the terminal,
#The first blank after the name has five options: runs, check, fs, all, allfiles, subfiles. Each are explained below
#The second blank is the subject index yo... |
8ee691db0194085537a728bb01c0c4ebaafc2c20 | adc72eff51513f076338e0f591277bdec5dc5295 | /TwitterProject/forest.R | ff248238f4c66f21a5fcec670245e171f74fd6df | [] | no_license | BryceRobinette/MATH4400Project | fa1448f081b556048b23d83a0d7eab9506cdb94b | 259de04c113b755271381fe9e7c3d88b92813641 | refs/heads/main | 2023-01-11T22:17:49.051676 | 2020-11-03T17:48:58 | 2020-11-03T17:48:58 | 303,511,271 | 0 | 0 | null | 2020-11-02T22:02:24 | 2020-10-12T20:56:50 | R | UTF-8 | R | false | false | 1,188 | r | forest.R | library(RMariaDB)
library(tm)
library(syuzhet)
library(wordcloud)
library(randomForest)
library(plyr)
#Run random forest algorigthm.
Random_Forest = function(){
source("helpers.R")
query <- "SELECT DISTINCT tweet, person FROM candidates;"
df = dbGetQuery(con, query)
df$tweet = clean(df$twee... |
8b2bf532039f63ddcc7913f7cdac77e8aecd0475 | a05e541c30580b2091f05bba7bcc373d23333290 | /data_aggregation.r | 1ff9a7ec18fe5282e51ba0f98a47f9c3b7454283 | [] | no_license | krmaas/software_carpentry_2014_12_5 | 8370a733eae8bd96702a606e69894956978702f9 | 7aab499ad7cf4af49e66ab8306bf8fa93ab05cd2 | refs/heads/master | 2016-09-03T07:30:46.856236 | 2014-05-13T23:22:38 | 2014-05-13T23:22:38 | 19,756,220 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,232 | r | data_aggregation.r | ### apply, built in works with rows/columns of matrices and arrays
### aggregate, built in for groups by factors in single vector
### read 2011 wickham, jorunal of statistical software http://www.jstatsoft.org/v40/i01/paper
### plyr::ddply, groups by factor in data.frame
### plyr::l*ply
### dplyr optimized for large da... |
083dce6ee76a40394deae2347369f9dd8b580d61 | 4f13d728eaa1d82f6cfca9f943e5ddda2c654c2d | /Sample.R | ef3c6c60ead2becf4e0874e305057d3faefe38a8 | [] | no_license | GITAshRose/Edx_course | 08575ff6cfd3c33d4706211d6070a33fee3e9b16 | c8b747cd0be7c332ef7b3d58bd6ea08cc0c8ce14 | refs/heads/master | 2023-03-03T22:50:52.801639 | 2021-02-17T12:57:23 | 2021-02-17T12:57:23 | 339,721,205 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26 | r | Sample.R | data(mtcars)
head(mtcars)
|
201d62e9f6c710a19f0591d3e50409a51c10386c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mltools/examples/exponential_weight.Rd.R | a3f7705e7ff29534de05345d98a38fc7cecbed1e | [] | 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 | 233 | r | exponential_weight.Rd.R | library(mltools)
### Name: exponential_weight
### Title: Exponential Weight
### Aliases: exponential_weight
### ** Examples
exponential_weight(1:3, slope=.1)
exponential_weight(1:3, slope=1)
exponential_weight(1:3, slope=10)
|
8b6576f36d717a6db1f6cba9b91c4325f2bdab9d | fc680f24d60a8bf68e144e367e454d0183379ed8 | /R_codesnippets_usefull.R | b0d6103c53520543579f3cf520025c4f48f8a3e0 | [] | no_license | lv601/Phosphoenrichment | 696da90d08362def85df517dc46dfc25432542e8 | 098300f9861ac6338f6e6086579f649bde3bec32 | refs/heads/master | 2022-10-18T17:32:06.096310 | 2020-06-12T15:44:09 | 2020-06-12T15:44:09 | 271,834,008 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,609 | r | R_codesnippets_usefull.R | ##Code Snippets Usefull
readFun <- function( filename ) {
# read in the data
data <- read.csv( filename,
header = FALSE,
col.names = c( "Name", "Gender", "Count" ) )
# add a "Year" column by removing both "yob" and ".txt" from file name
data$Year <- gsub(... |
5849057d8a565735d6d231e346d208c9da2374d4 | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleaiplatformv1.auto/man/GoogleCloudAiplatformV1BatchPredictionJobOutputInfo.Rd | 1d93152a8b3d4cd1d923a0e8d3881c0a8d66c281 | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 709 | rd | GoogleCloudAiplatformV1BatchPredictionJobOutputInfo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aiplatform_objects.R
\name{GoogleCloudAiplatformV1BatchPredictionJobOutputInfo}
\alias{GoogleCloudAiplatformV1BatchPredictionJobOutputInfo}
\title{GoogleCloudAiplatformV1BatchPredictionJobOutputInfo Object}
\usage{
GoogleCloudAiplatformV1Batc... |
cb3b98e4d46cd3dad2a819fe522d7e8090433a73 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mtk/examples/getDistributionParameters-methods.Rd.R | 1fba4536367a68787c24e1bbd4e1b7e677a5158d | [] | 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 | 739 | r | getDistributionParameters-methods.Rd.R | library(mtk)
### Name: getDistributionParameters-methods
### Title: The 'getDistributionParameters' method
### Aliases: getDistributionParameters-methods getDistributionParameters
### ** Examples
# Define three factors
x1 <- make.mtkFactor(name="x1", distribName="unif",
distribPara=list(min=-pi, max=pi))
x2 <-... |
9861c8c2ff4e11d21878fa30f0f17425d9d656b6 | de9df77e3b35f0b9cd77693a815b14e903cb9dce | /Emission_Lines/Extinction/Calzetti_Base_Fluxes.r | 2cc85c45cb15e3870eea9e4aa692b82aa793288c | [] | no_license | Gargoloso/Skyfall | f16d37449291dd37bf4dffd1e344953ec9accf1d | a80b7293ae04875a386629b1094ed61e350666c9 | refs/heads/master | 2020-03-18T03:11:20.881201 | 2018-07-19T03:18:51 | 2018-07-19T03:18:51 | 134,220,033 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,164 | r | Calzetti_Base_Fluxes.r | #############################################################################
#############################################################################
## This calculates extinction corrected fluxes using the Calzetti et al. ##
## (200) extinction law. ##
##... |
4b44397fc60066d39c04aebe0dff62171944b4b5 | e25af04a06ef87eb9fc0c3c8a580b8ca4e663c9b | /man/Sobolev.Rd | f01e87e9f9f49c091de52907f4dd4f4eade1d9a0 | [] | no_license | cran/sphunif | c049569cf09115bb9d4a47333b85c5b7522e7fd8 | 4dafb9d08e3ac8843e8e961defcf11abe2efa534 | refs/heads/master | 2023-07-16T01:12:47.852866 | 2021-09-02T06:40:02 | 2021-09-02T06:40:02 | 402,474,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 7,668 | rd | Sobolev.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Sobolev.R
\name{Sobolev}
\alias{Sobolev}
\alias{d_p_k}
\alias{weights_dfs_Sobolev}
\alias{d_Sobolev}
\alias{p_Sobolev}
\alias{q_Sobolev}
\title{Asymptotic distributions of Sobolev statistics of spherical uniformity}
\usage{
d_p_k(p, k, log = ... |
765b2bcfea8995478a9cd5facafbdd249da87d12 | d81b9067f72bcc60dca62e3552768015cfa4eab6 | /complete_code/05 - SVM.R | 340beff5f3fb919b3cbb61da18b14182ece136cd | [] | no_license | mrverde/msc_dissertation_santander | f626aa414b34ce95366cd11f74e61e6cd2b8b943 | 6a3045363be0bca9281424fb77a4f83d7bdbf415 | refs/heads/master | 2021-08-19T14:50:09.965744 | 2017-11-26T18:50:23 | 2017-11-26T18:50:23 | 112,000,564 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,119 | r | 05 - SVM.R | ######################### 02 - SVM #########################
#Cargamos las librerías
library(doMC)
library(caret)
library(Boruta)
library(ggplot2)
library(ggthemes)
library(reshape2)
library(gridExtra)
library(DMwR)
library(caret)
#Establecemos los núcleos usados a 8
registerDoMC(cores=7)
#Establezco el directorio d... |
a2213ac6dd036a505b69fbf4c9d086a1cf8c97bd | ef5d2a392a111815e932a4ec758bab5cb3e073cf | /R/include_tweet.R | 79fd66f787336a38108cd43f15a1ac1990972cd6 | [
"MIT"
] | permissive | gadenbuie/tweetrmd | 19b6d74c295e289c14e9950946c29d2eaec4c280 | c683b537a4a5234ee750fff234d21e4e9c201ba8 | refs/heads/main | 2023-02-07T05:50:25.881377 | 2023-02-03T02:27:22 | 2023-02-03T02:27:22 | 230,986,374 | 104 | 16 | null | null | null | null | UTF-8 | R | false | false | 1,805 | r | include_tweet.R | #' Include A Tweet in All R Markdown Formats
#'
#' Similar to [knitr::include_graphics()], but for tweets. In HTML documents,
#' the tweet is embedded using [tweet_embed()] and for all other documents types
#' a screen shot of the tweet is rendered and used [tweet_screenshot()]. If you
#' would rather that just the tex... |
84c98e6e752b7c637e0b93f4a946fbd067831f69 | 4640be0f41a18abd7453670d944e094a36e4181d | /R/to_phylo.R | e15875a44a8f38ab88662b49eb67588561636d9a | [] | no_license | gitter-badger/datelife | 534059d493b186030f0c2507ce8b35027d120dec | 94d93bb4e6cecd0884afe99571bf96b291454899 | refs/heads/master | 2020-06-16T22:49:58.916722 | 2019-06-20T16:25:11 | 2019-06-20T16:25:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,871 | r | to_phylo.R |
#' Convert patristic matrix to a phylo object. Used inside: summarize_datelife_result, CongruiyTree.
#' @param patristic_matrix A patristic matrix
#' @param clustering_method A character vector indicating the method to construct the tree. Options are
#' \describe{
#' \item{nj}{Neighbor-Joining method applied with ape:... |
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