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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b1d9f8bcd6774f79dc1d85c50778658e5e0a95e0 | e953c138d3808d92fcc9848824985be5bc42f034 | /r/list.r | 6433ce3dd8409354d4aa435dd26cb4d630e57abb | [] | no_license | hotoku/samples | 1cf3f7006ae8ba9bae3a52113cdce6d1e1d32c5a | ce0d95d87e08386d9eb83d7983bd2eaff0682793 | refs/heads/main | 2023-08-09T09:05:15.185012 | 2023-08-04T09:29:06 | 2023-08-04T09:29:06 | 222,609,036 | 0 | 0 | null | 2022-03-30T01:44:03 | 2019-11-19T04:35:27 | Jupyter Notebook | UTF-8 | R | false | false | 52 | r | list.r | x <- list()
x[1] <- 100
y <- list()
y[[1]] <- 100
|
e6190b2b72b9bb3d1c333ca5f0dc177a6fdcbc2a | ad5b9d6a560f8b023e1aa3391ebb072f7d40afb5 | /plot_mq_dist.R | 77c24769a278995f1abf8049ca9d5e8366cf9423 | [] | no_license | ebete/MC_HiC | 1d0138652881cf59cdc8438901d51ec11addf7ac | 09b48db36623b2e187d7b73070a0d231a1812d8e | refs/heads/master | 2020-04-08T00:11:09.794448 | 2019-04-11T14:08:12 | 2019-04-11T14:08:12 | 158,840,919 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | r | plot_mq_dist.R | #!/usr/bin/env Rscript
suppressPackageStartupMessages({
library(ggplot2)
library(reshape2)
library(scales)
})
# load MAPQ matrix
mq <- read.csv("/home/thom/mc_hic/mc_4c/mq_dist.txt", header = F, sep = ";", as.is = T)
mq <- t(mq)
colnames(mq) <- mq[1,]
mq <- mq[- 1,]
class(mq) <- "numeric"
mq.melt <- melt(mq)
#... |
bed33e0ed7c3bd13c9a0965ad78552694743c59e | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.customer.engagement/man/connect_list_prompts.Rd | 84ef98e0fb2c7aeb91146c6309cfbbd8452badc0 | [
"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 | true | 890 | rd | connect_list_prompts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/connect_operations.R
\name{connect_list_prompts}
\alias{connect_list_prompts}
\title{Provides information about the prompts for the specified Amazon Connect
instance}
\usage{
connect_list_prompts(InstanceId, NextToken = NULL, MaxResults = NUL... |
1f28c86632ecc26180ffe4dc27a0e6389ddebde1 | c4910920221d62ec83a9345dcc918e708351eae1 | /tests/testthat/test_chisquare_extractor.R | 0111f4555e00d15f79d5489c576f49a9da3f39df | [
"MIT"
] | permissive | fsingletonthorn/EffectSizeScraping | 7e0a7b1a9b1c9eea25b91a0d8390a67934929b67 | bcc3abfe088ddaf16439b772d9bcb316f41c52b9 | refs/heads/master | 2021-06-01T22:40:05.114137 | 2020-03-25T03:04:09 | 2020-03-25T03:04:09 | 148,737,572 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,566 | r | test_chisquare_extractor.R | context("chisquare_extractor")
testChi <- c("chi square = 12.32",
"chi2 = 123.32",
"χ2(1234) = 1232.23, p < .05",
"χ2 = 122.23,p = .13",
"chi2(12345) = 123.2, p < .001",
"χ2(1, N = 320) = 22.31, p < 0.001",
"χ2(n = 320, df =12) = 23.31, p ... |
5bd08ce6ceee500a004e7956447328c1aa305c59 | 344513a699b3aa6c15ce2e9c3b7c7a913a4f4a1f | /man/rport.db.cache.save.Rd | f8426912fc02293c923a0e87e1f77bc57b21689c | [
"MIT"
] | permissive | logufu/rport | df1d2a3f2c0dfa58c91d4ebd170c98cd5c5d2c83 | a3ae88ae2fe60a8a44545824482433ceb6cd237e | refs/heads/master | 2020-12-28T23:35:38.957165 | 2014-06-01T11:04:39 | 2014-06-01T11:44:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 390 | rd | rport.db.cache.save.Rd | \name{rport.db.cache.save}
\alias{rport.db.cache.save}
\title{Upsert an entry in the .Rportcache file with a hash(query)}
\usage{
rport.db.cache.save(query, conn, dat)
}
\arguments{
\item{query}{sql query string}
\item{conn}{string connection name}
\item{dat}{data.table with the results to save.}
}
\descripti... |
a59009d4abdd4086b4a010bdd33d8db56dd09e81 | 9fd42fbf2ec96f73a4a64f9bc21884c9cafb4a11 | /bigdata/N2H4.R | 799239fdb773532b1d6ec08672fc21b3d0e10c70 | [] | no_license | ParkNuri/R-Study | 11742a0647f87ec9c395b78d9fd3567a9c933ff3 | 8837f01fc7edf17cb4156b306535d1c0b62abc2a | refs/heads/master | 2022-03-30T17:12:17.187885 | 2020-05-01T09:06:18 | 2020-05-01T09:06:18 | 260,416,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 266 | r | N2H4.R | install.packages("N2H4")
library(N2H4)
library(stringr)
library(dplyr)
url <- "https://news.naver.com/main/read.nhn?mode=LSD&mid=shm&sid1=100&oid=020&aid=0003276790"
mydata <- getAllComment(url) %>%
select(userName, contents)
mydata
# ctrl + shift + A : 줄맞춤 |
0699a8b4edfbcc267ce6fa3d923794fc64061e55 | ae576fcfb2f1da232ec4345c2f207ff5e2f1a8b7 | /pollscrape.R | 17a0d69dffc9b3119af6fa3c2214d3550c7bb1c5 | [] | no_license | fghjorth/danishpolls | 2827ca124f04aa613d1076cda7f6454ccf36e589 | 148c8d683d194f1a40c8c8e3c453b0e30f76c1a4 | refs/heads/master | 2016-09-02T05:54:40.314602 | 2015-05-28T12:28:22 | 2015-05-28T12:28:22 | 22,598,248 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,814 | r | pollscrape.R |
library(RCurl)
#read in data
polls<-read.csv("https://raw.github.com/fghjorth/danish-polls/master/all-polls.csv")
#clean up
polls <- polls[,1:11]
names(polls) <- c("house", "date","Venstre","Socialdemokraterne","DF","Radikale","SF","Enhedslisten","LA","Konservative","Kristendemokraterne")
polls$house<-gsub("Rambøll... |
e18257b10970b4fa3b269013e70efd508564373a | a1a25e620a92a30f7fcff613cfc005fed63fd0e0 | /R/faoswsEnsure-package.R | 1b5bc6eee5bc0a7f01f3fcf5c5bfcc2d6baafb13 | [] | no_license | SWS-Methodology/faoswsEnsure | 12614f7efa803490743184766beb26b01e59f881 | 40a6d73fa2bbbc997511f82ad2d91bc480b1fe22 | refs/heads/master | 2021-01-21T14:44:06.030613 | 2018-08-07T07:40:10 | 2018-08-07T07:40:10 | 59,017,938 | 0 | 0 | null | 2016-05-17T15:19:55 | 2016-05-17T11:52:34 | R | UTF-8 | R | false | false | 343 | r | faoswsEnsure-package.R | ##' Package to ensure data and input quality
##'
##' @name faoswsEnsure-package
##' @aliases faoswsEnsure
##' @docType package
##' @title The package host standard check function for the Statistical Working
##' System (SWS).
##'
##' @author Michael. C. J. Kao \email{michael.kao@@fao.org}
##' @keywords package
##' @... |
a791cbe37d764aa6a32e9d97bccdbf1f6b3e771d | 15f8232b8a574ae94266927e4df5182cfc99f517 | /man/get_parent_id.Rd | f166adb29e1433a257c362cb931813ebc7d108cd | [] | no_license | cran/autoharp | a2c6d51ad22354a276145098200e92aecd7bc3fd | d2efbb0a76285ba95ed6950a61e05f1398b5e656 | refs/heads/master | 2023-09-06T05:29:22.580401 | 2021-11-12T21:50:02 | 2021-11-12T21:50:02 | 334,082,346 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,041 | rd | get_parent_id.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/th_getter-length.R
\name{get_parent_id}
\alias{get_parent_id}
\alias{get_parent_id,TreeHarp-method}
\alias{get_parent_id,list-method}
\title{Generic for Getting Parent Node Id.}
\usage{
get_parent_id(x, node_num)
\S4method{get_parent_id}{Tre... |
f87db766bfadf5e863a6aa90b108d94dc72c582d | f836ed096d28f86e86c9704903840ff969cbf9b5 | /src/assignment3/exercise1.R | a814f468c2a188eeea7ab390dd4b486fe9d039be | [] | no_license | charx7/statisticalGenomics | bf7556de89e20db5a0796a6e4b8f1748d6301470 | 45466758c9e9f127b121e4e32b732ab2fd307332 | refs/heads/master | 2020-07-22T18:01:41.178986 | 2019-10-21T11:38:14 | 2019-10-21T11:38:14 | 207,283,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,209 | r | exercise1.R | rm(list=ls()) # clean R environment
cat("\f") # clear the console
source('./assignment3.R') # source pre-processing script
# Exercise 1
true_Net <- make_true_Net() # get the true net value
true_Net <- true_Net + t(true_Net) # get the simmetric true network
# Compute the AUCROC for each data set on the data set list... |
a2f8df8fa6597f03f6f1b2b52135fe8a8db62641 | 56cf8abe4f8a8bdf0bf903f87020d73e8d882261 | /rankhospital.R | d9c098ce3433952fdd0185d580ae85a73a56d667 | [] | no_license | ebratt/ProgrammingAssignment3 | 5a658fcff2e9c0fb52af4bd53b77f344aef9ae6c | 5eb3c5770e2fc1555fb5d05a5f94d038f80c4413 | refs/heads/master | 2020-05-17T03:01:34.574885 | 2014-08-23T13:09:29 | 2014-08-23T13:09:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,492 | r | rankhospital.R | ## 2014-08-22
## Eric Bratt
## Coursera rprog-006
## https://github.com/ebratt/ProgrammingAssignment3
## data from http://hospitalcompare.hhs.gov
## Assignment 3
## exercise 3
## function that takes a state, an outcome type, and a ranking number
## The function reads the outcome-of-care-measures.csv file and returns a... |
fadb4dc459b6e905dae84e4fe6a89b8a191f828f | 533848bd6eee73a18b9995e23c8f7667233a6226 | /materiales/prope/programacion_r/Script-Introduccion-R.R | 17f5c0f3b672fb9ca67a3ca513339219aa52e253 | [] | no_license | ramegon76/maec | 839b60bdbc54bb86cf48bdc3db41dfc0fc2a580f | 0b9d46be0a53f9b94e83e1f43b8563d044e240c2 | refs/heads/master | 2021-11-27T13:57:04.541059 | 2021-08-16T02:41:53 | 2021-08-16T02:41:53 | 216,220,925 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,518 | r | Script-Introduccion-R.R | #-----------------------------------------------------
#
# Maestria MAEC INEGI
# Propedeutico
# Introduccion a la Programación
# Nociones Basicas de R
#
#-----------------------------------------------------
#-----------------------------------------------------
# Librerías necesarias
library(M... |
2d4bdf5c44ab08ad9aef9a71d0f8ed5d88e3686b | 7f0b89090cbbf264ec3084573795c8a28df19cd8 | /run_analysis.R | 00e7475386c83e0d212fecb6dbb10218d9a161d9 | [] | no_license | ssmandyam/GettingandCleaningData | 14a49cd12cca6a2faa629b42bb8a978580f68520 | 6633b6e32e8006f06d79ad736e0854e22104e217 | refs/heads/master | 2021-01-01T05:26:23.673714 | 2016-04-12T20:50:25 | 2016-04-12T20:50:25 | 56,013,311 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,715 | r | run_analysis.R | #run_analysis.R
#consolidates the training and test data sets into a single tidy data set
#Step 1: read the training and test sets
x_traindf <- read.table("./train/X_train.txt", stringsAsFactors=FALSE)
y_traindf <- read.table("./train/y_train.txt", stringsAsFactors=FALSE)
x_testdf <- read.table("./test/X_test.txt", st... |
2e49e6b8420575fddb5a7d68d601ff2e1ee4cbf8 | 8a045230ab4809ea6d28cf4a8d1105e9977a4076 | /code/ml1.r | 3884c4c710a74d17ccb37e46405c04f4afa185a3 | [
"BSD-4-Clause"
] | permissive | jaredlander/odscwest2020 | d69db3163305f8dd62d7739c969d3fcf53b1e9c0 | 06c3afe93939cfe6b437fb9b304fbfd57a3b55e0 | refs/heads/main | 2023-03-05T15:49:57.428459 | 2021-02-17T21:52:10 | 2021-02-17T21:52:10 | 306,472,524 | 5 | 10 | null | null | null | null | UTF-8 | R | false | false | 11,311 | r | ml1.r | # Packages ####
# resampling, splitting and validation
library(rsample)
# feature engineering or preprocessing
library(recipes)
# specifying models
library(parsnip)
# tuning
library(tune)
# tuning parameters
library(dials)
# performance measurement
library(yardstick)
# variable importance plots
library(vip)
# combing ... |
2f2d60d5b4abf5845bc39374d5c7b5db5292fb20 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gtkCTreeFind.Rd | 35cfb6ab0d1a5e325ddcba67a6e856aa67bdd0eb | [] | 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 | 626 | rd | gtkCTreeFind.Rd | \alias{gtkCTreeFind}
\name{gtkCTreeFind}
\title{gtkCTreeFind}
\description{
\strong{WARNING: \code{gtk_ctree_find} is deprecated and should not be used in newly-written code.}
}
\usage{gtkCTreeFind(object, node, child)}
\arguments{
\item{\verb{object}}{The node to start searching from. May be \code{NULL}.}
\item{\verb... |
7a57e21ace0a63c17d9f45d6c01381993ad9146b | 694d8ed931a130b5b03900c250444e3eae494293 | /metropolis_prod_geom.R | 2aae1c43755d4f61b29beb73920a98281d56f0ae | [] | no_license | angieshen6/anisotropy | de0e8d3fdb4fbf1885aefbca587cfb68992188da | b7ced5d90ab0ac72b52d79f683cbc112e87a0d12 | refs/heads/master | 2020-05-22T10:05:09.440237 | 2019-05-23T21:16:02 | 2019-05-23T21:16:02 | 186,303,618 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,814 | r | metropolis_prod_geom.R |
##############################################
#This script compares isotropy with geometric
# anisotropy with matern covariance and product
# covariance
###############################################
# product anisotropy
library(MASS)
library(mvtnorm)
library(invgamma)
library(truncnorm)
library(spBayes)
l... |
a6e4c2a63fe1027b805c7dd69ef197d651b7bad6 | 5ae7cdb3e0f6cdd172b4b9c2f4605859b389ce0a | /scripts/data-carpentry/generate-tidy-data.R | 80697d9c2bd824865ca1e0d4832c1d19df51ad32 | [] | no_license | mikoontz/ppp-establishment | 2d2573d5669f2740c6b6374d8a034684e1ac11a3 | eefc8a27cd669293ff4fd3737464bf60d2f62143 | refs/heads/master | 2018-10-31T18:13:22.108599 | 2018-03-26T15:43:10 | 2018-03-26T15:43:10 | 74,297,185 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,338 | r | generate-tidy-data.R | # Title: generate tidy data
#
# Author: Michael Koontz
# Email: mikoontz@gmail.com
#
# Date Created: 20150330
# Last Updated: 20150331
# This function takes the entered Tribolium flour beetle data from the "Eco-evolutionary consequences of multiple introductions" main experiment (which is in long form), and puts it i... |
2cf6e3a9d7ba1147bf3d34d3cbf881b9d407d00c | 7e4603d96817a188ff0f4e45a94054668d1ea49b | /R/summarise.glm.R | 49826ff8545c24cd1990a166e6502e0ba29fc7f6 | [] | no_license | c97sr/SRileyIDD | 5ff45e19804b7ec0b86d959ee48e38765ce75a99 | ddd9d0f18fa5af6633da47fb25b780c5cbc4d017 | refs/heads/master | 2020-05-18T10:49:00.615332 | 2015-07-01T22:02:00 | 2015-07-01T22:02:00 | 22,818,900 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,937 | r | summarise.glm.R | summarise.glm <- function(
lstModels,
outfunc=exp,
writetab=TRUE,
file="modsum.csv",
sigdigits=3,
transpose=FALSE) {
# Figure out the number of models
nomods <- length(lstModels)
# Make a vector of all coefficients
allCoeffs <- c()
for (i in 1:nomods) {
# Select current model resul... |
db0f442424733eae7fc7c7a96337e910ce37946c | 9cc7423f4a94698df5173188b63c313a7df99b0e | /man/crawford.test.freq.Rd | de422645aed3f72fc0386e062d9d4875b9eb7e7c | [
"MIT"
] | permissive | HugoNjb/psycho.R | 71a16406654b11007f0d2f84b8d36587c5c8caec | 601eef008ec463040c68bf72ac1ed8d4a8f7751f | refs/heads/master | 2020-03-27T01:24:23.389884 | 2018-07-19T13:08:53 | 2018-07-19T13:08:53 | 145,707,311 | 1 | 0 | null | 2018-08-22T12:39:27 | 2018-08-22T12:39:27 | null | UTF-8 | R | false | true | 1,142 | rd | crawford.test.freq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/crawford.test.R
\name{crawford.test.freq}
\alias{crawford.test.freq}
\title{Crawford-Howell (1998) frequentist t-test for single-case analysis.}
\usage{
crawford.test.freq(patient, controls)
}
\arguments{
\item{patient}{Single value (patient'... |
2ba48e62ab904cc878659f543ff9e4e47a7245e9 | 0031c07492e0878b9add94356c112ece169ff2dc | /assignment5/question3.R | 9982d8e8128f0fca3960aeceeb0e5220c8a5d700 | [] | no_license | jonkeane/datasci_course_materials | 6c13965827220650dafee76536740ac1781851c9 | 1dde97ef1a947a8d868aaab2d7b063bb22e9a965 | refs/heads/master | 2020-12-27T12:02:51.673899 | 2015-11-28T18:12:05 | 2015-11-28T18:12:05 | 47,002,383 | 0 | 0 | null | 2015-11-27T22:58:53 | 2015-11-27T22:58:53 | null | UTF-8 | R | false | false | 66 | r | question3.R | source("quiz.R")
# question 3
summary(flow$fsc_small)["3rd Qu."]
|
f5ce16dbb6170862ce28acb7404bae18f2d9befe | 2c1561e4467b6664a2791df07827cbf91f48b97a | /Part_05_Linear_Model_Examplepedit.R | 34686dff846d1dfe5029044e2f87ead6da323e44 | [] | no_license | anhnguyendepocen/R_Book | 38ea878d4843df063adf719215bb65a83179d29c | e3935d740f6094b10f0a75b896810eb365144ac3 | refs/heads/master | 2020-03-18T02:46:29.153118 | 2017-08-20T20:39:27 | 2017-08-20T20:39:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,635 | r | Part_05_Linear_Model_Examplepedit.R | #
# SCS 2011: Statistical Analysis and Programming with R
# September-October 2011
#
# Linear Models in R
# -- much of the code is borrowed from Fox,
# Introduction to the R Statistical Computing Environment
# ICPSR Summer Program, 2010-2011
"
== Install packages (if needed) ==
"
install.pack... |
1b970f163c6ae5e7eac6f512aa56d45c6e5bb607 | 25bb3d517b8b5b847b184aac8b0e12320cb265b0 | /somatic_germline_overview/1_somatic_germline_overlap_Figure1D.R | 67d108503e8073e792b70e513447562c3cbb78c0 | [] | no_license | tao-qing/DDRImmune | 922128fac03f9be34f0ea30cdf4db5d73a36e4a8 | b6294541ce5402b8bf77eb7cb057019b7e4d0316 | refs/heads/master | 2023-07-15T05:53:12.508020 | 2021-08-27T01:29:08 | 2021-08-27T01:29:08 | 346,764,696 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,106 | r | 1_somatic_germline_overlap_Figure1D.R | ##### somatic_germline_overlap.R #####
# Find overlap of genes/variants for somatic/germline variants
setwd("/Users/qingtao/Box Sync/GermlineSomatic/analysis/somatic_germline_overlap/")
#epig<-data.frame(readxl::read_xlsx("/Users/qingtao/Box Sync/GermlineSomatic/Huang_lab_data/TCGA_PanCanAtlas_2018/DDR_Knijnenburg_Ce... |
aba277234ef057233ef1ba6dfa299ea75f8ceae8 | abea0b5d000d7c01d390eeb615427bc0322aa30f | /src/merge/R_finish.R | fbd3495e17ce7d3d957d4858b374cf40eafd7279 | [] | no_license | janmandel/firewx-evaluation | 5e176d8762f34b4e88a9446f1d898b3698abc5e5 | 51ca3c4a1c63d8c6ba00e910a87f4c87c2c0ac53 | refs/heads/master | 2020-05-05T01:10:49.662013 | 2017-08-24T17:40:06 | 2017-08-24T17:40:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,432 | r | R_finish.R | ############# FINISH SOLAR/FM/MERGING RAWS DATA
### STEP #1 / Finish solar radiation
## Function to correct max solar to station solar
cloudFun = function(cloud_percent,maxsolar) {
try1 = ifelse(cloud_percent < 10, (0.93*maxsolar),ifelse(cloud_percent >= 10&
(cloud_percent < 50), (0.8*maxsolar),ifelse((cloud_pe... |
4ccc7bf4b4b1e763d64436917e6989bc1c96a8dc | 228055717bb12cd31410cf0723be428b050602bb | /R/lines.regression.circular.R | c7414b610487282825da18e5da8c56120654cbf9 | [] | no_license | cran/NPCirc | a2e0f329b9e48969940e29f5dd4fb387de8e401d | 3717249094d79c668c2b9fe53b39bab5a23521b2 | refs/heads/master | 2022-11-19T16:49:47.532395 | 2022-11-10T12:00:11 | 2022-11-10T12:00:11 | 17,681,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,313 | r | lines.regression.circular.R | lines.regression.circular<-function(x, plot.type=c("circle", "line"), points.plot=FALSE, rp.type="p", type="l",
line.col=1, points.col="grey", points.pch=1, units=NULL, zero=NULL, clockwise=NULL, radial.lim=NULL, plot.info=NULL, ...){
xcircularp <- attr(x$x, "circularp")
ycircularp <- attr(x$y, "circularp")
... |
56cd9470d6608dddda9def5764246359315b92dd | 9e03756d86ee78175357c87c936a7716dcf5b6f7 | /man/gsdim.Rd | 7706e74238b96be130413e33ae053cc9cdb822e3 | [] | no_license | anishsingh20/imager | 8659e760682697bc1f44a226eb63b4d6d9d9754e | c9c6f17364411aae3add9160f93775d8a2d077c1 | refs/heads/master | 2021-01-16T19:07:01.381026 | 2017-06-21T13:47:18 | 2017-06-21T13:47:18 | 100,137,455 | 1 | 0 | null | 2017-08-12T20:57:27 | 2017-08-12T20:57:27 | null | UTF-8 | R | false | true | 408 | rd | gsdim.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cimg_class.R
\name{gsdim}
\alias{gsdim}
\title{Grayscale dimensions of image}
\usage{
gsdim(im)
}
\arguments{
\item{im}{an image}
}
\value{
returns c(dim(im)[1:3],1)
}
\description{
Shortcut, returns the dimensions of an image if it had only ... |
3874fbf9bac74e857f3e7392c98dea06fd366c34 | afeb43c3b8828758886a5f32c828249e44daf811 | /R/run.R | 35833f3fbc2831a84370a3a9cc1e7de42d1f36cf | [] | no_license | parksw3/observation | ff74fede75c31e9c677fd317b10a304aaeff9102 | f6011df2b769b72c50fec11bc3c41302f682027f | refs/heads/master | 2020-05-19T14:21:13.152800 | 2020-02-13T19:50:52 | 2020-02-13T19:50:52 | 185,058,936 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 436 | r | run.R | run_sir <- function(param=c(R0=2, gamma=1, N=1e5, rho=0.5),
yini=c(S=1e5-10, I=10, R=0),
tmax=20,
tlength=0.1) {
param[["beta"]] <- param[["R0"]] * param[["gamma"]]
tvec <- seq(0, tmax, by=tlength)
dd <- ode(yini, tvec, sir, param)
data.frame(
time=tail(tvec, -1),
incidence=-diff(dd[,"S"]) ... |
49975224593cd811bfa4442d1575ade8826b15c7 | 3105237755f3ef7ba5ead8b87ee88afe00dbfb6a | /man/vcov_outcome.CBPSContinuous.Rd | 3569d6fdef6f7aed16d38c0dbd9fb1e4fa3afdd5 | [] | no_license | kosukeimai/CBPS | 4237517a2c9a09f230ec60e8f31a6d5930a6cb05 | c6695181b44f494ea335548b4060271c727ebd52 | refs/heads/master | 2022-01-21T15:51:57.714571 | 2022-01-18T16:49:48 | 2022-01-18T16:49:48 | 72,245,166 | 24 | 10 | null | 2022-01-18T03:31:59 | 2016-10-28T21:50:18 | R | UTF-8 | R | false | true | 936 | rd | vcov_outcome.CBPSContinuous.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/analytic_vcov.R
\name{vcov_outcome.CBPSContinuous}
\alias{vcov_outcome.CBPSContinuous}
\title{vcov_outcome}
\usage{
\method{vcov_outcome}{CBPSContinuous}(object, Y, Z, delta, tol = 10^(-5), lambda = 0.01)
}
\arguments{
\item{object}{A fitted ... |
5d7d33f18dfe6624e8755acbd5638a519ae23e82 | 6365ca059c0ba5ab9ef70d6ec143c28d85e550c0 | /Homework/HW03_C++_code_from_R/bios735/R/RcppExports.R | 0be171235c4b7c0b677431293abcea44b7e6d96a | [] | no_license | leoleosuperdope/UNC_BIOS735 | 140102a38135880ea513bbe9daebf7585188fc51 | 2bc28899698b492361f50e7087aefaf42452a526 | refs/heads/main | 2023-05-09T11:07:49.775659 | 2021-05-30T02:23:21 | 2021-05-30T02:23:21 | 372,105,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 572 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
one_or_exp <- function(x) {
.Call('_bios735_one_or_exp', PACKAGE = 'bios735', x)
}
randomWalk2Rcpp <- function(niter, lambda) {
.Call('_bios735_randomWalk2Rcpp', PACKAGE = 'bio... |
389610d39ff70c1dc0b51ac4bea2a5ea5509eb26 | 3282d51ed8f89ead3d9f16af1e843501f5fbe8cb | /man/gmdh.combi.Rd | 895fde671dd214b4fdfd26f4eef13008fcfaa2c7 | [] | no_license | cran/GMDHreg | 7d69b110f57df5e1220c007a88b6d3f0c695013b | 0104cbc52becf0515e3ea6007b77c66b625325ab | refs/heads/master | 2021-07-09T12:34:51.724176 | 2021-07-05T11:30:02 | 2021-07-05T11:30:02 | 174,552,055 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 3,161 | rd | gmdh.combi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/combi.R
\name{gmdh.combi}
\alias{gmdh.combi}
\title{GMDH Combinatorial}
\usage{
gmdh.combi(
X,
y,
G = 2,
criteria = c("PRESS", "test", "ICOMP"),
x.test = NULL,
y.test = NULL
)
}
\arguments{
\item{X}{matrix with N>1 columns and M r... |
61a0312a985f501e5fa9d377edb488b79a5ef976 | c84951af9d248e3e2cf0f8f265a5649c31a08320 | /man/coef_xtune.Rd | 894db9f9e12bd00ef079d13c5b9b8de18d648b0a | [
"MIT"
] | permissive | JingxuanH/xtune | f250f64a947ccb4eb3ea3a31c2ea032a16109479 | 33d88886b6f66decb262fd90eb8e688e14d073bc | refs/heads/main | 2023-06-28T18:54:59.826328 | 2023-06-18T23:02:32 | 2023-06-18T23:02:32 | 653,879,167 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 786 | rd | coef_xtune.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/coef.xtune.R
\name{coef_xtune}
\alias{coef_xtune}
\title{Extract model coefficients from fitted \code{xtune} object}
\usage{
coef_xtune(object, ...)
}
\arguments{
\item{object}{Fitted 'xtune' model object.}
\item{...}{Not used}
}
\value{
Coe... |
4c4622d1e88cddec65d42f9dff03e0ee84a6d15d | 05a249bd9d45f691df5599816b0929770fb47bf7 | /scripts/methods/04-sim_data-dyngen.R | 98445e924be34241cdc8ea8d0c76962524d3257e | [] | no_license | jligm-hash/simulation-comparison | 457dbdfae7c09e7e4aef74af3639858b4f6566fc | 0724439875168fb497bf9ada0742a6082a77b5ac | refs/heads/master | 2023-04-16T08:04:59.746920 | 2021-04-30T08:21:22 | 2021-04-30T08:21:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 230 | r | 04-sim_data-dyngen.R | suppressPackageStartupMessages({
library(dyngen)
library(SingleCellExperiment)
})
fun <- function(x) {
sink(tempfile())
y <- generate_dataset(x,
format = "sce",
make_plots = FALSE)
sink()
return(y$dataset)
} |
de3f377da73a65d0bd3b4cf20bdff007e5ca8bfc | bebda466cc6b3c0772b8b64c0e706b2478c1ebaa | /plot3.R | 956a07a65b6fa825e684d0bfe6255fde0ba6ae22 | [] | no_license | xiaoq007/ExData_Plotting1 | 438e6ae60e970d0ab3323bc9c3d121962c8f29df | 42d55aa251666b420f5ec477debe849c20f3f22b | refs/heads/master | 2021-01-17T23:59:54.184567 | 2016-04-30T02:01:37 | 2016-04-30T02:01:37 | 57,347,616 | 0 | 0 | null | 2016-04-29T02:12:44 | 2016-04-29T02:12:43 | null | UTF-8 | R | false | false | 1,024 | r | plot3.R | setwd("ExData_Plotting1")
## download zip file
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url,"household_power_consumption.zip",method="curl")
## load data and subset data then clean up R objects
hpc <- read.csv("household_power_consumption.txt",sep=";... |
3c8f318fe8583a1c5660c0555cd564271417fb63 | 2e940271c21be18f391ebaeab2079e03728eecb9 | /man/prob_distribution_2.Rd | c66e974b425195750feee61fa56032b3739a866e | [] | no_license | pspc-data-science/branchsim | 3fe1a98b4f219dd6258f82868f36718c2e344ef5 | d49ab68e071e91ac8f46b074ff23cb728a147c64 | refs/heads/master | 2023-02-13T18:56:06.240842 | 2021-01-12T17:48:36 | 2021-01-12T17:48:36 | 273,470,739 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 852 | rd | prob_distribution_2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CMJ-functions.R
\name{prob_distribution_2}
\alias{prob_distribution_2}
\title{The probability distribution of births from a single mother in the branching process.}
\usage{
prob_distribution_2(tbar, kappa, lambda, p, n_samp = 300000L, min_cou... |
aa9743f97796c943f8eb47aea7fcf4231b09a459 | 5f0cfcec5194f11137db76056ef2b3836ab80ff8 | /R/abcmodels.intrinsic.R | 5c303163e66db0df3d82639e38416ce04767aac6 | [] | no_license | JakeJing/treevo | 54d341655f1e6ddac5ab73df38c890be557e7d17 | 3429ba37e8dc7c79cf441361d07c000f07423b6e | refs/heads/master | 2021-01-12T01:20:10.296046 | 2016-10-03T01:09:15 | 2016-10-03T01:09:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,808 | r | abcmodels.intrinsic.R | #intrinsic models
#note that these work for univariate, but need to be generalized for multivariate
#otherstates has one row per taxon, one column per state
#states is a vector for each taxon, with length=nchar
#' Intrinsic Character Evolution Models
#'
#' This function describes a model of no intrinsic character ch... |
7de3e3e2b1f27b63e28e9a2ea609dbbdcec96225 | 7e0d67d6676662e15f26a8b9aafa4ace40fa3e28 | /TimeSeries_TD1_FirstAnalysis.R | 9d04b5afb7441a160b9a2390a03fbd640c9d8e6b | [] | no_license | ettabib/TimeSeries | 09754d83465c4e13903f1b30c779a652212a74e0 | b18ab034a293f03176336e8353b8a106b1a694ce | refs/heads/master | 2020-06-03T13:59:14.912686 | 2015-01-09T21:04:34 | 2015-01-09T21:04:34 | 24,608,232 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | TimeSeries_TD1_FirstAnalysis.R | # Time Series
# TD1: First Analysis
data()
EuStockMarkets
data.class(EuStockMarkets)
summary(EuStockMarkets)
plot(EuStockMarkets)
cac.ts=EuStockMarkets[,"CAC"]
plot(cac.ts)
# Differenciation
dcac40=diff(cac.ts)
rcac40=diff(log(cac.ts))*100
par(mfrow=c(2,1))
plot(dcac40)
plot(rcac40)
# 1) Underly... |
94c13f8cf785762758b379f183564cb310b30250 | 1cdd0be21213738bc8429f136f2188346709436b | /kappa_score.R | e2fbd1f31b632f2cf9616451772fdf6021937c98 | [] | no_license | vitkl/imex_vs_uniprot | abdebdb80cc60cd3797ae9b29c00610d7bcc8a4c | 6fb090c9ae15d05cf6d31bd1829f86f43d160ef1 | refs/heads/master | 2020-06-15T09:26:41.622769 | 2017-11-23T11:14:49 | 2017-11-23T11:14:49 | 75,309,520 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,641 | r | kappa_score.R | # GO term similarity - kappa score
#####################################
##' @author Vitalii Kleshchevnikov
# function to calculate kappa score between two categories describing a set of elements(GO terms, KEGG pathways, genes)
# defined as described in this article:
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2375... |
31f6c213d0d08136036cfe19d898831bf4774945 | 49f06ff67c5a8723f0158985850f5758500e941d | /man/logsum_matrix.Rd | 62fb5e5f08d14f9a64daa4e52a5f4da550eb0e53 | [
"MIT"
] | permissive | annahutch/corrcoverage | 518e7356cab27c6e1860bfc9dea14f72abaaab9c | 3b610d1450c7936db6ab2664de37543165ac6c0e | refs/heads/master | 2021-12-15T03:52:12.104240 | 2021-11-26T13:16:39 | 2021-11-26T13:16:39 | 169,068,733 | 8 | 3 | NOASSERTION | 2019-08-28T14:27:44 | 2019-02-04T11:29:57 | R | UTF-8 | R | false | true | 352 | rd | logsum_matrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/func_utils.R
\name{logsum_matrix}
\alias{logsum_matrix}
\title{logsum rows of a matrix}
\usage{
logsum_matrix(x)
}
\arguments{
\item{x}{numeric matrix}
}
\value{
rowwise sums
}
\description{
matrix-ified version of logsum to avoid needing app... |
082fd52653d97786f28aca6e04b38ac970cbdd73 | 2b1899fd505ee7382a88e218d652852b493fce78 | /man/clustA.Rd | 0d45551f6e1e41d4e381248fa5f70d79b2a7fe74 | [] | no_license | anwaarms/package-clustA | 726d2bcc99dd787c90701782cf6fcf251d472813 | cdd600b6ab810991fe1f25312592d44d8065b48f | refs/heads/master | 2020-04-10T19:06:37.423467 | 2018-12-10T19:07:36 | 2018-12-10T19:07:36 | 161,223,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 802 | rd | clustA.Rd | \name{clustA}
\alias{clustA}
\title{Joining Clustering methods}
\usage{
clustA(data,kmeanclust,fit)}
\description{
this function prints the plot of a kmeans clustering as well as the Ward Hierarchical Clustering along with the optimal number of clusters proposed by kmeans
}
\arguments{
\item{data}{The dataset ... |
90143c66dfd3bf91f87f13ba5282a63bc97bfc37 | a02959f6a5e0df6666722eb7c78d74b047b588ae | /man/ds_plot_histogram.Rd | 9265c2215c80b33aee437c877b6abe852c241900 | [] | no_license | cran/descriptr | ea2e24f865f84589ad03c25ffb8d35eb201cb922 | 793dbc743532c9e243801dcf9286c20cb32208b8 | refs/heads/master | 2021-01-01T05:20:10.888615 | 2020-12-09T16:10:02 | 2020-12-09T16:10:02 | 77,553,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 742 | rd | ds_plot_histogram.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ds-plots.R
\name{ds_plot_histogram}
\alias{ds_plot_histogram}
\title{Generate histograms}
\usage{
ds_plot_histogram(data, ..., bins = 5, fill = "blue", print_plot = TRUE)
}
\arguments{
\item{data}{A \code{data.frame} or \code{tibble}... |
ebf018ec92e0b310f155fab1e519f8c44ec5aede | 76b2e98418bfdc467292653e7aada9fea8f81c89 | /R/postcrawling.R | b2119bb0b35cbac7e0729e503c84da656719d9d6 | [] | no_license | swoos91/TIL | 8d7b4b32d819fe266835f5c3e99cf167cc8a89b6 | 292ff24a33cc8ac3ba513ffb6a1a7575b37804f3 | refs/heads/master | 2022-12-10T06:15:09.046562 | 2020-09-17T22:21:10 | 2020-09-17T22:21:10 | 226,819,185 | 0 | 0 | null | 2022-12-08T03:17:51 | 2019-12-09T08:14:53 | Jupyter Notebook | UTF-8 | R | false | false | 212 | r | postcrawling.R | unico<-POST('http://unico2013.dothome.co.kr/crawling/post.php',
encode='form', body=list(name='R',
age='27'))
a<-html_nodes(read_html(unico), 'h1')
b<-html_text(a) |
a448f5fa375023f030c98e792dd3881a501fe21a | 82836f3fd15546df37fbaaa1946dd0f2342f83c7 | /myFun.NP.R | c02494067c72a1f1773a21720b56d4c5d7078a96 | [] | no_license | fugapku/NPscan | 358c184a334a8d9500257693cf400e19ef59efa9 | ab705560ebda89ca69435991402fec0a15d35932 | refs/heads/master | 2020-09-28T05:55:34.466999 | 2016-09-12T04:55:43 | 2016-09-12T04:55:43 | 67,960,279 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 365,344 | r | myFun.NP.R | # myFun.NP.R
read.antiSMASH <- function(file='fungalAntismashClusters.txt', root=NULL){ #'/Users/yongli/Dropbox/Galaxy/Project_Current/t.NPbioinformatics'){
setwd(root)
a = read.table(file, header=F, sep='\t', comment='', quote="")
# Description Genbank ID Cluster num antismash cluster type cluster length ... |
4c6d819a5b50b4a302285d2956b1285104b5915a | d7c90718e8ddbad5f4b5be582f05177dfabc7c61 | /scripts/global.R | d84da40d6cc36ab366d50580d8f1a3b14192ba86 | [
"MIT"
] | permissive | BPSTechServices/ARPA-Shootings-and-COVID-19 | 93cb56236e1e7d41887204d37f95e07d362f77d6 | 720926f72e8d15f1e9dc9e846baa12a8a9b0d975 | refs/heads/main | 2023-05-26T06:22:42.220017 | 2021-06-11T18:46:35 | 2021-06-11T18:46:35 | 375,158,384 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 675 | r | global.R | library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(tigris)
library(geojsonsf)
library(areal)
library(effectsize)
library(corrplot)
library(GGally)
library(factoextra)
library(biscale)
library(cowplot)
library(extrafont) #; font_import()
library(viridis)
library(tmaptools)
library(tmap)
tmap_mo... |
e5fd68c514c31fc01e1b65c0d32d7bbdb7c94d9e | e34e41af9dbaf18c572961627bef058672c8a785 | /R/figures-maturity-ogive.R | f67eb625d3a5b44fda793efd3c7c84172a5986c5 | [] | no_license | aaronmberger-nwfsc/hake-assessment | a14bcaef0babafe7c20ab81a17100c799a39bc3c | ae469a25f7394cad97757e98b70dfa4fab35fc73 | refs/heads/master | 2022-02-19T15:06:04.240995 | 2022-02-12T13:43:13 | 2022-02-12T13:43:36 | 79,608,293 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,441 | r | figures-maturity-ogive.R | maturity.ogive.figure <- function(model, useyears = 1975:(assess.yr - 1)){
# maturity.samples is created by data-tables.r
# which reads
# maturity.samples.file <- "hake-maturity-data.csv"
mat1 <- maturity.samples
Amax <- 15 # plus group used in calculations below
# subset for North and South of Point Con... |
fddfad51babb697c42b16d5e1e766b364637739c | 6557f5c17490476c54eff0ebf1270773060afed9 | /man/createCutoffsDF.Rd | 9f5c5767156cdafa99604df4a96283e218a94064 | [] | no_license | cran/QuantileGradeR | 8db9be8aad76e905ad7ecf669bc2ab719cc216f1 | 5a1903963062f97084baad15ddf549ba8b15516a | refs/heads/master | 2021-01-09T06:18:00.827284 | 2017-02-06T20:22:48 | 2017-02-06T20:22:48 | 80,955,265 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,256 | rd | createCutoffsDF.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createCutoffsDF.R
\name{createCutoffsDF}
\alias{createCutoffsDF}
\title{Create Cutoffs Dataframe}
\usage{
createCutoffsDF(X, z, gamma, type)
}
\arguments{
\item{X}{Numeric matrix of size \code{n} x \code{p}, where \code{n} is the
number is re... |
e3d0e4d27b1382dd75fd331e985c0ef1a84efb9b | 18b55cde3cc1050831d7567dd7bee9e5c1a12b82 | /R/hello.R | 7fb1822bef9318f5d311ff01f01e40e00c86dd8a | [] | no_license | stla/AOV2F | 3ddbfc558a6843ff2a6259d9a7a4a0444728cc63 | 6c3b3433264805d0f668404bf8b2cd7e38971780 | refs/heads/master | 2020-04-30T22:08:20.712223 | 2019-03-22T09:33:24 | 2019-03-22T09:33:24 | 177,110,998 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,288 | r | hello.R | #' Run the two-way ANOVA child.
#'
#' @description Run knitr child for two-way ANOVA; to be used as inline code in
#' a knitr document.
#'
#' @param dat dataframe
#' @param factors the column names or indices of the factors
#' @param resp the column name or index of the response
#' @param factor.names the names of the ... |
26352ac2b53f8c7b9802a459ad8abf13b30fa29c | 69d690455a1a86f500efbd7815eb6b415c4c64ee | /aula1_.r | aa6111c0f6900f58c3df6f76f885d676aa215336 | [] | no_license | RAmluiz/compR_aulas | e56e99802861589a1338eb44bcd029eec7f378af | 33ce59a7b3e2096c02723a71637aae6a4171f4f7 | refs/heads/main | 2023-04-07T13:08:42.215169 | 2021-04-18T23:22:52 | 2021-04-18T23:22:52 | 359,262,483 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 117 | r | aula1_.r | library(tidyverse)
teste = function(x,y){
a = x^(25*y)
return(a)
}
b = teste(2,5)
soma = b*25
soma + b
|
0465c2570fa890c1195a9fe26894de9e388b3736 | 51f64cbf05bb169808e618a9054619ff9d25bf7f | /R/4Factors.R | 54b23e0d0ccb86442ede8e452029339378be1cb8 | [] | no_license | Vijayoswalia/R-R | 8143865fc2dee900e60f9b0cd0f2e5ab567475fb | d15ae45f3d6baa0e061d9cec8c6c3352f6de22ae | refs/heads/master | 2021-08-18T18:30:48.848192 | 2017-11-23T14:04:01 | 2017-11-23T14:04:01 | 105,735,681 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,062 | r | 4Factors.R | ## Factor might be viewed as a vector but they spectify a discrete classification of the components of other vectors
##################################################################################
## Method 1 converting vector to factor
##############################################################################... |
76d2f916fa6b7e806317cddc48c4dec1433aca9a | 02ad97dab34af39120a5fa3882090e2ccc0039cb | /Risk/src - DP/8.4 - Gating Rules - 2021.R | 10df17b02bd3d48ffffee2b6b2af8fbf69dbdf0d | [] | no_license | DineshPamanji/Python | 7c674c274e728d79037d910a5bf857b28467b360 | e0580ba41a2fa79fc6ccbf2789a86ffc20e2a503 | refs/heads/master | 2022-05-12T12:58:49.564546 | 2022-05-06T13:16:17 | 2022-05-06T13:16:17 | 170,642,127 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 39,451 | r | 8.4 - Gating Rules - 2021.R | ############################################################################################
################## 8 - Gating Rules ######################################################
############################################################################################
## 0. Load helper functions & libraries ... |
dd5d09ac093e0c0f990aa29b7103868f5337b4c7 | f247158fb166901454b8e4e6c4afe3340eed663b | /R/Distribution_DeletEffects.R | 116ef9a35a5b9e01be32dfbb6cbfbee7c755b261 | [] | no_license | kjgilbert/aNEMOne | 3126382f7588c4bfb998c1f23fa6b4a9718eea88 | 51701f2991c5693e71e19b1147c0c9a43599a86f | refs/heads/master | 2020-06-02T18:18:31.577221 | 2017-04-05T08:41:09 | 2017-04-05T08:41:09 | 34,544,653 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,765 | r | Distribution_DeletEffects.R | #'
#' Takes an output file from Nemo of deleterious loci genotypes, and plots the distribution of both the homozygous and heterozygous effects of these loci.
#'
#' @title Examine effect size distribution of deleterious loci from Nemo
#'
#'
#' @param file The file containing deleterious loci output from Nemo.
#'
#' @p... |
8a4d0e8d48d5f6c2fbd0fd7fc3711b530378bb13 | 7f691f36fe8c40efc2a01bf4dae10086140182fe | /qPCR expression graphs.R | c162f74fc3d2c15106e352499e407e42e9641061 | [] | no_license | nunngm/RNA-sequencing | 5594400818b8e428bc36c33d0caf2d4cb342c578 | 4056982e449cce7812ba9b611ab9ef2cbe280f24 | refs/heads/master | 2023-07-19T22:03:02.746290 | 2023-07-18T16:22:50 | 2023-07-18T16:22:50 | 236,590,229 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 16,777 | r | qPCR expression graphs.R | ## Plotting qPCR data
library(tidyr)
library(ggplot2)
library(dplyr)
library(svglite)
library(agricolae)
library(car)
library(ggthemes)
setwd("C:\\Users\\garre\\OneDrive\\Documents\\Cameron Lab- McMaster University\\Data\\Data-ARR RNA-seq\\Exp-qRT-PCR\\Graphs")
df.avg = read.table("clipboard", sep = "\t", row.names ... |
66fa3904510614753208a93a61a2ab9ed351d8a3 | da71dedb5877dfb0807cf33cea66ac0817317d58 | /ISRIC2CropSyst/src/CalculateSlope.R | 03384f5006cd6e4c14eab0ba731456ac0430cfe5 | [] | no_license | sonthuybacha/misc_R | 359bcc53612ac208c88c00641ead1102c5e1de45 | 03541711817eff8ad08be71aff690eea86ecaee7 | refs/heads/master | 2020-09-24T00:50:41.296265 | 2019-10-31T16:10:31 | 2019-10-31T16:10:31 | 225,622,353 | 1 | 0 | null | 2019-12-03T13:06:52 | 2019-12-03T13:06:51 | null | UTF-8 | R | false | false | 432 | r | CalculateSlope.R | #calculate slope
#author: John Mutua
#load packages
require(raster)
require(rgdal)
#set working directory
setwd("D:\\ToBackup\\Projects\\SWAT\\ArcSWAT_Projects\\Sasumua_data\\ISRIC2Cropsyst_Sasumua")
layers<-list.files(".", pattern='tif')
dem<-raster("DEM.tif")
plot(dem)
#calculate slope
slp <- terrain(dem, "slop... |
1e74d7c1ca433392a41993e71451eae5398cc339 | 895b3548b2dc255e0f544fd08e61973b5bde6e84 | /BasicUnitTest.R | c7a5ed39f2462461e2a024539f9a2d2aa79b5f79 | [] | no_license | SuzanElbadry/Hamlet | e054eee13beed38112587ca4df195c81417fdcc1 | 89e1420824810633efdf19b15135876085706597 | refs/heads/master | 2020-09-13T03:28:39.156840 | 2020-01-15T05:41:33 | 2020-01-15T05:41:33 | 222,644,080 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,022 | r | BasicUnitTest.R | library(testthat)
test_that('First 2 Scenes Speakers', {
Sub <- substr(Hamlet,0,21039)
SubHamletTest <- data.table(text=Sub)
SpeakersPart <- setorder(Task1(SubHamletTest),-Total)
RightSpeakersCount <- data.table(Speakers=c("HORATIO","HAMLET","KING CLAUDIUS","MARCELLUS","BERNARDO","FRANCISCO","QUEEN GERTR... |
ce49c996c760e8e4c11124dd4459deccca827ad6 | 6b4fe2baa84e74af637f319ea5d887cb2fd6f9a2 | /kevin/rimod-analysis/ENA_data_upload_renaming.R | ee9e07c1e3f95defd418007cce11e7841baaf38b | [] | no_license | dznetubingen/analysis_scripts | 1e27ca43a89e7ad6f8c222507549f72b1c4efc20 | 4fcac8a3851414c390e88b4ef4ac461887e47096 | refs/heads/master | 2021-06-25T10:47:40.562438 | 2021-01-04T16:02:34 | 2021-01-04T16:02:34 | 187,789,014 | 1 | 0 | null | 2020-09-03T11:37:25 | 2019-05-21T07:55:17 | Jupyter Notebook | UTF-8 | R | false | false | 3,026 | r | ENA_data_upload_renaming.R |
library(stringr)
setwd("/media/kevin/89a56127-927e-42c0-80de-e8a834dc81e8/data_upload/")
# Load the master sample table
md <- read.table("RiMod_master_sample_file.txt", sep="\t", header=T)
number <- as.numeric(gsub("rimod", "", md$Sample_UID))
md <- md[order(number),]
write.table(md, "ordered_master_table.txt", sep... |
85c9ac37cb70357b164b90170496b8a116ca4c72 | a069699b0d96c6083a202e5b82eb9026e10f5833 | /R/helpers.R | b113501ef4bf0f9142541862b51b279ccc92811e | [] | no_license | JonasMoss/SPQR | 46c795b79648a7485b69eb246a5f7f660d88316b | aa669e7b42919310a985c291d007208928686971 | refs/heads/master | 2020-07-19T04:49:22.188854 | 2019-09-04T19:59:33 | 2019-09-04T19:59:33 | 206,376,891 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,367 | r | helpers.R | #' Checks if a call references a name.
#'
#' @param call A call.
#' @param name A name.
call_uses_name = function(call, name) {
args = as.list(call)[-1]
for(i in which(sapply(args, is.name)))
if(name == args[[i]])
return(TRUE)
val = FALSE
for(i in which(sapply(args, is.call)))
val = val | call... |
cc77dc86b641415eb65b47650802bd856714f1b3 | 8c5e07af84c21e5b252069d1232b269c44b4ca69 | /core.R | 8709e9bc00798b7fefbc45c2495d92c584e6c1fe | [
"CC-BY-4.0",
"MIT"
] | permissive | mysociety/councillor_participation_research | 514356443efd30c0fab8293a21b7f64d3074bf77 | 8a5b71486d406b278ebe1e204f99e1fde9738972 | refs/heads/master | 2023-03-08T15:46:52.387388 | 2021-02-22T21:21:23 | 2021-02-22T21:21:23 | 332,840,452 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,328 | r | core.R |
require(MASS)
require(pscl)
require(car)
require(broom)
require(ggplot2)
require(weights)
rm(list=ls())
weighted_t_test <- function(x,y,w){
ysplit <- split(x, y)
wsplit <- split(w, y)
wtd.t.test(ysplit[[1]], ysplit[[2]], wsplit[[1]], wsplit[[2]])
}
df = read.csv("data//survey_with_weights.csv",header=TRUE)
#... |
0f8141176d95573b2eeeed34b54d6b1eb7d2f4a7 | 01bce092104ec1b0ae0eaa7daf3aa03e18d3d56d | /R/read_smf.R | 357afb3926cc3b7edb8c4f03edc7d021179a7815 | [] | no_license | niszet/rmusicwork | a557d13afdf82e16e60c3aac365cade956aa7449 | de1e4c8a5b5ab27e45199e8ff14051dd30e18a21 | refs/heads/master | 2020-04-05T14:33:56.825678 | 2017-08-26T00:49:21 | 2017-08-26T00:49:21 | 94,679,935 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,392 | r | read_smf.R | #' SMF read funcition
#'
#' @name read_smf
#' @param file input file path
#' @export
#'
read_smf <- function(file){
con <- file(file, "rb")
on.exit(close(con))
file_size <- file.info(file)[["size"]]
# smf_header <- data.frame(stringsAsFactors=FALSE)
# smf_header <- rbind(smf_header, c("fileSize",... |
a6ff8a35bcec2e8db78befc88a79a255af687034 | d6dc738b26970938ab2311983d5f0cb32fb4c277 | /saymyname/tests/testthat/test-saymyname.R | 9099525f6c6b6ddd59c828960237802b2840bdfc | [] | no_license | CavinWard/My_Name_Is | 26d7a50522107bd28f8ae11c820a5f9102139f1d | b7f1217c90b656afad66c321277353cfa67c41e1 | refs/heads/master | 2021-05-04T08:37:44.226606 | 2016-10-11T18:03:29 | 2016-10-11T18:16:51 | 70,406,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 423 | r | test-saymyname.R | context("say my name")
name <- "Fred"
test_that("name correct", {
expect_equal(saymyname(test_name="John",my_name="John"), "My name is John")
})
test_that("integer: name is what?", {
expect_equal(saymyname(1), "What?")
})
test_that("NULL: name is what?", {
expect_equal(saymyname(NULL), "What?")
})
test... |
be5c4d970ab2f0264ec8753af3c437a35775e69a | 2c8c644d446e9ed0fb885ba28cf793b35ee4257a | /man/draw_legend.Rd | 6526389f1294939acc9a9c19d35c54ea140ba67b | [] | no_license | EngeLab/CIMseq | 7a195fd6c400740ff30d417f223284bba9a09981 | 65adabfc662491a9e00c39ad8a443b9da95760ca | refs/heads/master | 2023-04-17T05:02:11.676987 | 2022-01-20T12:24:05 | 2022-01-20T12:24:05 | 59,555,300 | 9 | 1 | null | null | null | null | UTF-8 | R | false | true | 308 | rd | draw_legend.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotExtras.R
\name{draw_legend}
\alias{draw_legend}
\title{draw_legend}
\usage{
draw_legend(l)
}
\arguments{
\item{l}{A grob containing a legend.}
}
\description{
Helper function for plotSwarmCircos.
}
\author{
Jason T. Serviss
}
|
5dab97e7c5f1229c732de6fd46d9bc7aae311598 | 308d107fd0cfffb6f13b9101f77bb6ed2f3fe9ae | /01 - Environmental data/02 - SoilVeg/Soil and fecundity/01 - Analysis.soil.R | 33403d459e7491073f884949d040bfba80661e53 | [] | no_license | MarcoAndrello/Stoch_Demogr_Comp_Arabis | 08a5a241c76550aed1e70fb2aecd2b56d4724fba | d327e434e3a7634f28f7efa4acc27de7e4f2f25d | refs/heads/master | 2020-08-26T18:22:08.247883 | 2020-02-18T10:23:11 | 2020-02-18T10:23:11 | 217,101,255 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,998 | r | 01 - Analysis.soil.R | rm(list=ls())
library(MCMCglmm)
dataCN <- read.csv("CN.csv",h=T,sep=",")
# Nitrogen
hist(dataCN$N)
summary(dataCN$N)
m1 <- MCMCglmm(N ~ 1,data=dataCN,random= ~site + SiteQuad,
nitt=101000, thin=50, burnin=1000,verbose=F)
summary(m1)
autocorr.diag(m1$VCV)
plot(m1$VCV)
vSite <- m1$VCV[, "site"]/(m1$VCV... |
0a29654f2d0c9617c98cba6467c5077f5ff50104 | b4eb6cc4124477bd884e2558bd6f9ac63aaab531 | /gDNA_correction/test_imbalance.R | e1d6cf0ebbc5e4d3a9c83478be45b84a09990c6d | [] | no_license | dagousket/cisreg | 8a0e2af3be81216858a560ca163aa039fbfb61fb | ec351d0734a734442e71107ea0f7191ef0e24591 | refs/heads/master | 2021-05-24T10:17:18.807140 | 2020-04-10T17:54:25 | 2020-04-10T17:54:25 | 253,514,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,043 | r | test_imbalance.R | #!/usr/bin/env Rscript
library("optparse")
library("tools")
library("reshape2")
option_list = list(
make_option(c("-i", "--input"), type="character", default=NULL,
help="list of count files to use in a txt file", metavar="character"),
make_option(c("-o", "--out"), type="character", default="binom_te... |
79dfbcde066598065f28acb71e7cd8aa4fcda766 | 531086b7ef1d45aea9f7d7de065891a39ebd5c35 | /jhu/regmod/manipulate.R | f76bc11d45e593213f9cbfa3d985fa8ea97bf596 | [] | no_license | githubfun/coursera | 5d06463b646bf1ca4b4b6aac307945e93f34eada | 209361035ac384eb4a9e31174fe3a26c92754ec8 | refs/heads/master | 2017-05-29T00:11:21.768914 | 2016-02-23T21:53:04 | 2016-02-23T21:53:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 310 | r | manipulate.R | library(ggplot2)
library(manipulate)
x <- c(0.8, 0.47, 0.51, 0.73, 0.36, 0.58, 0.57, 0.85, 0.44, 0.42)
y <- c(1.39, 0.72, 1.55, 0.48, 1.19, -1.59, 1.23, -0.65, 1.49, 0.05)
myHist <- function(beta){
lse <- sum((y-beta*x)^2)
plot(x, y, main=lse)
}
manipulate(myHist(beta), beta=slider(0, 2, step=0.1))
|
8ca78726944faf79b74cedc11e6a09f8e0531b70 | c4ee384cbb2a7071832a87c27e19bdb9f1662d66 | /alessandra_eda.R | 62bf19ddca21b0df197ecc9819dd5d996ca1918f | [] | no_license | naterowan00/cloth_filter | eb836ec1b0e22a910bd7ecadf26d8627dfe94843 | e57eee69027d15172118dac80c50f597454cd4a1 | refs/heads/master | 2022-10-07T12:10:38.467989 | 2020-06-10T05:46:23 | 2020-06-10T05:46:23 | 269,202,652 | 2 | 1 | null | 2020-06-10T05:46:24 | 2020-06-03T21:55:41 | Rich Text Format | UTF-8 | R | false | false | 1,893 | r | alessandra_eda.R |
#######################
##set working directory
#######################
setwd("/Users/alessandrarodriguez/Desktop/AQUACLOTH")
################
##load libraries
################
library("tidyverse"); theme_set(theme_minimal())
theme_update(panel.grid.minor = element_blank())
library(xts)
library(tidyverse)
#########... |
a64a868150b56c29da79b176926ac8b424ed4f6b | d3dd96dc9a8d6ee708ef3430b384cf633d09bf32 | /tests/test_rfci.R | cd78b72f2a6c4e3e75f1c8b9257992ab477158d3 | [] | no_license | cran/pcalg | 3f0609f316139a29cae839cd798251e92b96d5ee | 032fd6d1c51579a784f893a4c4838b0381dc9830 | refs/heads/master | 2023-01-06T08:18:03.758296 | 2022-12-20T23:20:05 | 2022-12-20T23:20:05 | 17,723,261 | 27 | 25 | null | 2021-12-15T22:20:59 | 2014-03-13T19:30:38 | R | UTF-8 | R | false | false | 4,992 | r | test_rfci.R | library(pcalg)
doExtras <- pcalg:::doExtras()
source(system.file(package="Matrix", "test-tools-1.R", mustWork=TRUE))
##--> showProc.time(), assertError(), relErrV(), ...
R.home(); sessionInfo() # helping package maintainers to debug ...
.libPaths()
packageDescription("pcalg")
packageDescription("Matrix")
## load the ... |
1ff80344021a33dcb5c2eb0775162c3b5626e516 | a36271f5008e6178473337db948649a7fdce2027 | /R/tex2rmd.r | 33f34d56997e2035a200dc01bfbe1f6140b8b2e2 | [] | no_license | sctyner/tex2rmd | 655638b6b1bfea7f40b763b759b2b33e984d1dc3 | 085ce4f3fbce1124518d60cc3a3fc6c2f0e3f5d7 | refs/heads/master | 2020-05-23T07:18:36.368874 | 2018-03-01T20:52:07 | 2018-03-01T20:52:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,057 | r | tex2rmd.r | #' @title tex2rmd
#'
#' @description Converts a raw LaTex file to RMarkdown format, then to Word
#' format. Can optionally convert to any format supported by RMarkdown
#'
#' @param infile Full path to the input Latex file.
#'
#' @return The markdown code is written to a file named <root>.Rmd,
#' where \code{inFile} is... |
ec99b1185331327691242a512379a6086b92d04d | 032f396221e412ae04fb013e15fc310a17cc3e68 | /climate/CWD_Hist.R | de57ef2df6719b3b56eb9381a65d138d5066cbec | [] | no_license | tvpenha/sismoi | 6a2f7fde2106c45f256a44cef158aa790f98a41f | 1e6267b74faf7daf6f0de064c59cf230f945714e | refs/heads/master | 2020-04-20T21:34:15.199323 | 2019-03-20T18:25:27 | 2019-03-20T18:25:27 | 169,112,900 | 0 | 0 | null | 2019-02-04T16:51:30 | 2019-02-04T16:51:29 | null | ISO-8859-1 | R | false | false | 15,460 | r | CWD_Hist.R | require(ncdf4)
require(ncdf4.helpers)
require(ncdf4.tools)
require(ggplot2)
require(raster)
require(rgdal)
require(spatial.tools)
################################################################################
setwd("C:/Users/inpe-eba/SISMOI/CWD/Historical")
# Abrir shapefile
brasil = readOGR("C:/Users... |
c3c91a6c1fd08e685b25ff49c5e2f465463c6177 | fb35ed59baa26c8945b4c8253c59cfb92db706c4 | /cgatpipelines/Rtools/filtercounts.R | 00ff92ef9e7527a294c83edabb359025137b5aab | [
"MIT"
] | permissive | cgat-developers/cgat-flow | 483f3c582e7ec72efab8440cfefed967cb521e79 | 7ae2e893a41f952c07f35b5cebb4c3c408d8477b | refs/heads/master | 2023-04-13T22:47:19.627132 | 2022-04-27T08:57:44 | 2022-04-27T08:57:44 | 120,881,178 | 13 | 9 | MIT | 2022-04-27T08:57:45 | 2018-02-09T08:50:08 | Jupyter Notebook | UTF-8 | R | false | false | 8,223 | r | filtercounts.R | #' Basic filtering analysis Script
#'
#'
#' Example usage:
#'
#' Rscript PATH/TO/filtercounts.R
#'
#' input: directory containing read count files or tsv file containing reads
#' additional input variables: method used to generate file, model
#' output: `experiment_out.rds` is an experiment object after filtering
#... |
62c5de1ee7e20cc08f3f58b2042eb7024dd32a02 | c36626e74b54e0c748f1da46904a58198415641e | /man/eco.2genepop.Rd | d8e91132e6432da311af6e1288b6a8924add05e2 | [] | no_license | jcassiojr/EcoGenetics | a3f3e5586bee15f2f87fc284b4ad8f3243db061e | 9256797efd715f3eb3de960dcec03aa31e53510f | refs/heads/master | 2021-01-24T08:32:49.115341 | 2016-07-15T20:02:10 | 2016-07-15T20:02:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,078 | rd | eco.2genepop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eco.2genepop.R
\name{eco.2genepop}
\alias{eco.2genepop}
\title{Exporting an ecogen genetic data frame into Genepop format}
\usage{
eco.2genepop(eco, name = "infile.genepop.txt", grp = NULL, nout = 3,
sep = "")
}
\arguments{
\item{eco}{Objec... |
7d4ebc1e5f5894e8289fdcbbb3735ec7fe066aaa | f25c5405790cf17a2b6e78b4ef58654810c8bb7b | /man/label_tooltip.Rd | 48f0c2983eac82b88add1f7fed50284adfd922ab | [] | no_license | moturoa/shintodashboard | 15ad881ea4c72549b616a3021852a0db8c25f6fd | 80385da221d370a563eb1cfe8946964acfacfe15 | refs/heads/master | 2023-05-31T07:05:11.026309 | 2021-06-28T12:55:32 | 2021-06-28T12:55:32 | 312,505,839 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 244 | rd | label_tooltip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tooltip.R
\name{label_tooltip}
\alias{label_tooltip}
\title{Handy tooltip for Shiny}
\usage{
label_tooltip(label, helptext)
}
\description{
Handy tooltip for Shiny
}
|
cd9cb5ef14488f215e3cf0142236e666aeb2cef8 | b39cbdaadbc53903b9cd19cfe5d80002dab69c3b | /demo/convertGraph.R | 3f1ead256e4e89cd152a58a238d88804adfd7fbd | [] | no_license | cran/GGMselect | 19b460696fbcd1a7553f7b8e299260c77e374b6d | c985831fbcb5f1e66f10ef5dd5fed15474057d79 | refs/heads/master | 2023-06-20T00:22:49.184652 | 2023-05-24T15:10:04 | 2023-05-24T15:10:04 | 17,679,431 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 437 | r | convertGraph.R | p=30
n=30
# simulate graph
eta=0.11
Gr <- simulateGraph(p,eta)
X <- rmvnorm(n, mean=rep(0,p), sigma=Gr$C)
# estimate graph
GRest <- selectFast(X, family="C01")
# Neighb and G are 2 forms of the same result
a <- convertGraph(GRest$C01$Neighb)
cat("Is G equal to Neighb?\n")
print(all.equal(a, GRest$C01$G)) # TRUE
# recal... |
f4171ede963eb96e9fce185760932976cf1bc397 | 4a4a4dedf17b593c0ec206f0b6681d06d42ff62e | /tests/testthat/test_and.R | 9ca3316513e70cd352aaba877f6cbfe2d4382a1f | [
"MIT"
] | permissive | ellisvalentiner/DeepOperators | c2ffb56159fff1f82f87bc6e416090d854e47545 | 7043817af00492b7ea5ed820475df93828549141 | refs/heads/master | 2020-04-23T00:10:38.524510 | 2019-10-15T13:48:09 | 2019-10-15T13:48:09 | 170,769,482 | 1 | 1 | NOASSERTION | 2019-10-15T15:07:28 | 2019-02-14T22:55:25 | R | UTF-8 | R | false | false | 298 | r | test_and.R | context("AND operator")
library(DeepOperators)
test_that("TRUE and TRUE is TRUE", {
expect_true(TRUE %&% TRUE)
})
test_that("TRUE and FALSE is FALSE", {
expect_false(TRUE %&% FALSE)
expect_false(FALSE %&% TRUE)
})
test_that("FALSE and FALSE is FALSE", {
expect_false(FALSE %&% FALSE)
})
|
c08df4d779c28d4d3ee094ac31f1cf31476e6f39 | 0f8ead4a8550b858634e62a85af4b2d2d999038f | /man/summarize_vdj.Rd | 0e4833b4fadc5bbd4de6f655916cbf4419f073c5 | [
"MIT"
] | permissive | rnabioco/djvdj | b6d39e678d130986aa4c0c9944097512ab615ea0 | 4f27269103a3ed32d5e9149560b9ff38ddc9a047 | refs/heads/master | 2023-06-27T23:42:22.757221 | 2023-05-08T15:39:12 | 2023-05-08T15:39:12 | 249,536,177 | 23 | 3 | NOASSERTION | 2023-09-09T21:09:39 | 2020-03-23T20:23:59 | R | UTF-8 | R | false | true | 3,420 | rd | summarize_vdj.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mutate-vdj.R
\name{summarize_vdj}
\alias{summarize_vdj}
\title{Summarize V(D)J data for each cell}
\usage{
summarize_vdj(
input,
data_cols,
fn = NULL,
...,
chain = NULL,
chain_col = global$chain_col,
col_names = "{.col}",
retu... |
2cbfe45211a3fa82714435fac63c14a84b4c0a2c | af5228afb1a0b9edcd46025e0fb60b2434a7298a | /vresidual.r | ccd5ddcf97538de0cca34f1e2d02a88896490111 | [
"MIT"
] | permissive | yuting1214/2021_Fall_RA | 3fdff4c0f79ad1b1b83a3e8977b125e2639a6adb | 62bfa7d90f1330be5020eca551e1312130543fc8 | refs/heads/master | 2023-08-25T09:59:04.359051 | 2021-11-08T19:57:58 | 2021-11-08T19:57:58 | 425,974,278 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,476 | r | vresidual.r | function(y,yfit,family=binomial(),variance=NULL)
{
# Calculate the residual for given observed y and its fitted value yfit:
# the length between y and yfit along the quardratic variance function:
# V(mu) = v2*mu^2+v1*mu+v0
qvresidual<-function(y,yfit,v2,v1)
{
vpa <- 2*v2*yfit+v1
svpa2 <- sq... |
4f8af8f9bb7b511a726c9d80b583ae14540299d9 | 166745a0a997ccf5a6aa5d6fb0d3ff7dcf322ac9 | /man/dcem_cluster_uv.Rd | 7f42bb6b41105b486183f64c4021cd06f0b371b8 | [] | no_license | parichit/DCEM | 21498e70e4fd03c04e5160370afa1677a091cc93 | 9c88ddaf031d3c572491f1d5952eca44ed8a5a36 | refs/heads/master | 2022-01-29T18:48:36.529380 | 2022-01-15T22:54:22 | 2022-01-15T22:54:22 | 149,658,612 | 5 | 4 | null | null | null | null | UTF-8 | R | false | true | 2,524 | rd | dcem_cluster_uv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dcem_cluster_uv.R
\name{dcem_cluster_uv}
\alias{dcem_cluster_uv}
\title{dcem_cluster_uv (univariate data): Part of DCEM package.}
\usage{
dcem_cluster_uv(data, meu, sigma, prior, num_clusters, iteration_count,
threshold, num_data, numcols)
}
... |
cfcb9722f5487a4ed4427a85c12bba742bba7364 | 63625ddab551f84243149bb89e9686801a893f66 | /R/etkpf_util_f90.R | 4479fe1dd1578a994a18f6772b1a414b9e81246a | [] | no_license | robertsy/ETKPF | 6602d81b79f57017ed766563cb40478acf34787e | a3d9404e95834058a4a0ed4f13d6ff6e76721edc | refs/heads/master | 2021-01-20T14:41:03.481210 | 2017-05-08T16:29:40 | 2017-05-08T16:29:40 | 90,648,252 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,069 | r | etkpf_util_f90.R | ## fortran implementation of basic etkpf functions
## see etkpf_util_R.R for the documentation
# Wmu part ---------------------------------------------------------------
get_Wmu_f90 <- function(R_evc, R_evl, n, gam){
wmu <- matrix(0,n,n)
output <- .Fortran('get_Wmu',
R_evc = as.double(R_evc... |
8371484a9a32033fec87bfc1ca2d56deb7c225ec | 13457e168e5628a931e3dd3ab696a865e05327e5 | /man/TestModularity.Rd | 339e591109fa757e13ae3e7721fbae91486010c7 | [
"MIT"
] | permissive | aivuk/Morphometrics | 3c74f652295796384b08becdca82452d074013b1 | 4371a964cf3dd52573560abded1e0f0861c2bf30 | refs/heads/master | 2021-04-12T08:59:55.965063 | 2015-02-15T03:06:57 | 2015-02-15T03:06:57 | 30,614,868 | 0 | 0 | null | 2015-02-10T21:14:12 | 2015-02-10T21:14:12 | null | UTF-8 | R | false | false | 1,450 | rd | TestModularity.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/TestModularity.R
\name{TestModularity}
\alias{CreateHipotMatrix}
\alias{TestModularity}
\title{Test modularity hypothesis}
\usage{
TestModularity(cor.matrix, modularity.hipot, iterations = 100)
CreateHipotMatrix(modularity.hipot)
}
\... |
7ef601249e0652dc7059dec6fcbabd1ea875a0f6 | 24570c916b873579da36f0405789f8b374f8d1d4 | /SNPtoAA.r | 380fadbeec856aeb6a6b9076a264b6e4a1ff9d2f | [
"MIT"
] | permissive | RILAB/siftmappR | 4010d7cd8d27ee23226f26644baa76721a04d401 | 22ec698151533359c7c05b006f769733dd4dea93 | refs/heads/master | 2020-04-06T18:32:24.060731 | 2013-11-13T01:34:33 | 2013-11-13T01:34:33 | 13,351,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,873 | r | SNPtoAA.r | #-----------------------------------------------------------------------------------
#XOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOXOX
#-----------------------------------------------------------------------------------
# This script gives the amino acid position (first transcript) of... |
dcfebe86196fe5bf1d5b672dca00791f05a2ee24 | 17cd1bc89ddf0a7567dbc386329585b91e76f1fc | /data-raw/clean-clev.R | 818d3876fb1ad6d2035490d74e25fea1476344bf | [
"MIT"
] | permissive | VaishnavMenon/geodaData | be7fab8ac2c2f24f56de98a05ff6a8a503b7010f | 9e7bba1d879e45c475cea52fb76715d06bf8c9c5 | refs/heads/master | 2020-08-17T20:17:18.386421 | 2019-10-17T05:08:29 | 2019-10-17T05:08:29 | 215,707,472 | 0 | 0 | null | 2019-10-17T05:06:40 | 2019-10-17T05:06:39 | null | UTF-8 | R | false | false | 131 | r | clean-clev.R | library(sf)
library(usethis)
clev_pts <- st_read("data-raw/clev_sls_154_core.shp")
usethis::use_data(clev_pts, overwrite = TRUE)
|
6dc1ec1589f58650036162c7240f9842c3cf07d3 | ecbf1722b5a6a8d100126b2995afa4b80e9b34f1 | /docs/Dropout/AnalysisBasicsDataPrepetc.R | 693f15cab314704a5039d7bff4d377ca097b4b90 | [] | no_license | bclavio/stats-on-grades | 86e9aeeb7778c36e342804b8650f13adf7e25437 | 029d14a635a584833aa65f5f61b5c41d995fa5bc | refs/heads/master | 2021-04-30T05:49:57.406611 | 2019-08-26T12:32:05 | 2019-08-26T12:32:05 | 121,425,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,213 | r | AnalysisBasicsDataPrepetc.R |
myWD<-if(grepl("BiancaClavio", getwd())){'C:/Users/BiancaClavio/Documents/stats-on-grades'} else {"~/git/AAU/DropOutProject/analysis/"}
setwd(myWD)
#when downloading from Qlikview remember to remove last three lines and upload download as cvs from google docs
source('importDataAndgetInShape.R')
### Comment: I get a w... |
5a128dcd7c6fe5babb601be189c0edabaf656a3d | e34b03c2bca6573c00dccf3906302371c5fae0bd | /man/dewpoint.Rd | 0bcdfaf8409dd7dd08a15aa782de795a3a85d5bc | [] | no_license | SnowHydrology/humidity | f3c274ce0e2aa2b0dfec851b2d19c6a38a7708a9 | e1b144519340fc6499a288df581df55a59986cca | refs/heads/master | 2020-06-26T19:12:05.447860 | 2019-07-30T21:40:19 | 2019-07-30T21:40:19 | 199,727,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 746 | rd | dewpoint.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/humidity.R
\name{dewpoint}
\alias{dewpoint}
\title{Dew point temperature conversion}
\usage{
dewpoint(TAIR, RH)
}
\arguments{
\item{TAIR}{The air temperature in degrees Celsius}
\item{RH}{The relative humidity in percent}
}
\value{
The dew p... |
65b102c4b3f4ae76aa83bc796edb9aa3d7629704 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/geophys/examples/DoMohrFig1.Rd.R | 81f67a8994ffe8ef2eb3f2bf9c30e95174fe30fe | [] | 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 | 222 | r | DoMohrFig1.Rd.R | library(geophys)
### Name: DoMohrFig1
### Title: Annotated Stress Tensor
### Aliases: DoMohrFig1
### Keywords: misc
### ** Examples
Stensor =matrix(
c(50, 40,
40, 10), ncol=2)
DoMohrFig1(Stensor)
|
e5f4e3c15f5f9bcba0e6ef7f301d402643f67302 | 20646e416b48befc8d8152d1b262b6886d19fed1 | /R/8.1.f.metrics.R | 0db8bc305af33da5f1af0de8a5dc5042da1a524f | [] | no_license | lizhizhong1992/ENMwizard | d0bce677d0957564d22170572f65c8ad0889440e | a4d7e16e85f038ca2d86166b772314e81439083f | refs/heads/master | 2023-01-31T07:13:14.074555 | 2020-12-11T17:41:05 | 2020-12-11T17:41:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,440 | r | 8.1.f.metrics.R | # # ##### 5. METRICS
# TO DO - get_tsa;
# In area.occ.spp[[sp]][] <- array(aperm(ar.mods.t.p, c(3, 2, 1))) :
# number of items to replace is not a multiple of replacement length
# TO DO - get_fpa
# Error in `[<-.data.frame`(`*tmp*`, , ncol(areas), value = c(0.526, 0.461, :
# replacement has 6 rows, data has 8
# ... |
d135eb12f18320326e20dd2532e408a49803703c | 25102e1b8cc367b03be176800fbfea9e3e6eeed6 | /R/print.adf.r | d80091ee658033e28a5337f89f3bc8809a493797 | [] | no_license | kaneplusplus/adf | b63731157990a5ffc8b1928370e730d7dfb42977 | 16bbc3072da2effcb9faabf6cc1ab24e1f360cca | refs/heads/master | 2021-05-02T08:45:44.939640 | 2018-03-23T14:39:46 | 2018-03-23T14:39:46 | 120,813,401 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 285 | r | print.adf.r | print.adf <- function (x, ...) {
w = min(max(nchar(x$colNames), 10L), 40L)
cat(sprintf(" An abstract data frame with %d columns:\n\n",
length(x$colClasses)))
cat(sprintf(paste0(" %-", w, "s %-10s"), names(x$colClasses),
x$colClasses), sep = "\n")
}
|
6380375ac7fe87ec5f18b6b389ba077024c4e3d3 | de0c51e2035c743f8855b0c7041a34139eb1e4fe | /man/cambioInterAnual.Rd | 9c0ff28bb8e49cd2162e238a160e8df726cbec81 | [] | no_license | hugoallan9/funcionesINE | 6c163b55ccb445ecb0427c9bd93d252560a7946a | 0031f6236b40a6c04adc7c7481f8a0525c79f380 | refs/heads/master | 2021-01-19T02:11:56.808791 | 2018-01-15T13:13:21 | 2018-01-15T13:13:21 | 29,080,328 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 559 | rd | cambioInterAnual.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/funcionesINE.R
\name{cambioInterAnual}
\alias{cambioInterAnual}
\title{Función que calcula el cambio interanual en porcentaje para un data frame dado}
\usage{
cambioInterAnual(data, primeraPos = 5, ultimaPos = 9)
}
\arguments{
\item{d... |
84a699e32483eec065c8a75970808819a22f2a9b | f8a8acd017d7a3cf0891ab51b64cb67750805da0 | /plot3.R | 2b1c34f1ebade04e21f516418bee06ce1852db73 | [] | no_license | jrosenbl/NEI_data | 414a61e94849adcfe910b8a68e4e479b30b2e674 | b676bff42f6f2a954ec9328eeaf32705cf6de6ea | refs/heads/master | 2016-09-06T07:22:21.034915 | 2014-07-27T22:41:13 | 2014-07-27T22:41:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,686 | r | plot3.R | # plot 3
# Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad) variable,
# which of these four sources have seen decreases in emissions from 1999–2008 for Baltimore City?
# Which have seen increases in emissions from 1999–2008? Use the ggplot2 plotting system to make
# a plo... |
0318a31ce0ab8d2e17714b72e683bb301ef2ee52 | d7ddcb1505b3df46d22578a704634e47a4f41155 | /man/MTDrh.Rd | fcd2244690e1a9c8a064548dd4c8325f756d9fcc | [] | no_license | cran/MTDrh | 04474f82b24b0de69c9f1c6f5862fdba8d6b9d44 | 54b8d92ca1317c2fc72fe5b53fb6560408ac93ae | refs/heads/master | 2021-01-12T08:47:44.164591 | 2016-12-16T23:31:52 | 2016-12-16T23:31:52 | 76,692,689 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,155 | rd | MTDrh.Rd | \name{MTDrh}
\alias{MTDrh}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Construct Mass Transportation Distance Rank Histogram
}
\description{
Constructs a mass transportation distance rank histogram to assess the reliability of probabilistic scenarios using observations for a set of ins... |
c12c444a9259972f79381bf6fac3d2dcded4626c | b247d884a1508bc5862d05c920e37b7cf4c805f0 | /TransientSymmetry/PAA2018/Poster/ExampleBrute.R | 0dd2401ca1cf91e09e5dd8ffab67b6fd2874299a | [] | no_license | timriffe/TransientSymmetry | 8967d7b66396b9e0c717a78709c45251da71fac4 | 9457c24767b1cae65c74c9b50d0c7ca2ec9e9805 | refs/heads/master | 2021-01-25T12:13:45.425307 | 2020-02-24T08:33:01 | 2020-02-24T08:33:01 | 123,459,141 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 12,104 | r | ExampleBrute.R | # Author: tim
###############################################################################
if (system("hostname",intern=TRUE) %in% c("triffe-N80Vm", "tim-ThinkPad-L440")){
# if I'm on the laptop
setwd("/home/tim/git/TransientSymmetry/TransientSymmetry")
}
# -----------------
# gets all possible trajectories assum... |
a7be1a80054e1eb0c44d8e31eec5396415bdca62 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/mcmc/examples/foo.Rd.R | f96e782c49e467c3d58517d7fd0e8635c36514de | [] | 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 | 235 | r | foo.Rd.R | library(mcmc)
### Name: foo
### Title: Simulated logistic regression data.
### Aliases: foo
### Keywords: datasets
### ** Examples
library(mcmc)
data(foo)
out <- glm(y ~ x1 + x2 + x3, family = binomial, data = foo)
summary(out)
|
371ebb5b692123e2063517ccc60d8b1c011dadcf | 67af11952ff7ef35d9cdbf490351abfb020b34da | /man/pm_eloRunTourneyELO.Rd | ea3f9165e096b710c72da47f9669278256a85a51 | [] | no_license | quietsnooze/pmpackage | 1c68d1f1aa70c53a81fc1abc2e0182dec6ce30b0 | 45bf5a3694cfb2c162f65855a2b8a827649198c4 | refs/heads/master | 2021-04-15T09:26:15.434185 | 2021-02-28T17:57:34 | 2021-02-28T17:57:34 | 126,635,991 | 1 | 1 | null | 2018-12-29T13:57:30 | 2018-03-24T20:05:07 | R | UTF-8 | R | false | true | 1,001 | rd | pm_eloRunTourneyELO.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/eloFunctions.R
\name{pm_eloRunTourneyELO}
\alias{pm_eloRunTourneyELO}
\title{pm_eloRunTourneyELO}
\usage{
pm_eloRunTourneyELO(
tournamentSetup,
keyCols = c("roundNum", "player_name", "opponent_name", "match_date", "Tournament"),
simCols... |
0cab03a890dda112ef0b0aebd816ad597f42808d | 4a73d57edc5ef1ea7798549ef74210eb7be51883 | /shotspotter.R | cb1c46b0a2c56e4e2bf4567d9a04e2c417c29b11 | [] | no_license | AlistairGluck/shotspotter | bd2be72c4fa90a951f651b74a5fa436cfac3c74f | e8f01782cd328229fc59b86a581a4106131376a6 | refs/heads/master | 2020-05-15T06:53:17.434225 | 2019-04-23T04:50:48 | 2019-04-23T04:50:48 | 182,131,656 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,847 | r | shotspotter.R | # Loading packages
library(tigris)
library(tidyverse)
library(maps)
library(readr)
library(fs)
library(sf)
library(lubridate)
library(gganimate)
library(transformr)
library(ggthemes)
# Reading in the CSV file with data from Fresno
fresno = read_csv("http://justicetechlab.org/wp-content/uploads/2018/09/fresno_sst.csv... |
ea0998fc20fb12c4f0995949d11aae7947943027 | b6af1fe1c1ed1b3d2b56779f29644fd8d30fc4f2 | /man/rpsblast.Rd | 76038a6516f31a7bd2451dbda53e14f69efab56e | [] | no_license | 418704194/blastr | 8f59a25f8433ad94afe585aa28a8472aa5e146a6 | f4acc87bf0e082e8d58d7c6f254af90b08dc2583 | refs/heads/master | 2020-05-02T10:10:54.162009 | 2019-03-27T00:52:01 | 2019-03-27T00:52:01 | 177,890,346 | 1 | 0 | null | 2019-03-27T00:38:08 | 2019-03-27T00:38:08 | null | UTF-8 | R | false | true | 1,431 | rd | rpsblast.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/blast.r
\name{rpsblast}
\alias{rpsblast}
\title{Wrapper for the NCBI Reversed Position Specific Blast}
\usage{
rpsblast(query, db = "Cdd", out = NULL, outfmt = "xml", max_hits = 20,
evalue = 10, remote = FALSE, ...)
}
\arguments{
\item{...}{A... |
87950f0e42621b89c5b82b0bf9bbc4d3242d19c9 | be5a158c7571df8faa94c50ce174ea0d41c29c67 | /TwoProps/ui.R | 2b0a657d4036b98f05804d209b8f42de714af359 | [] | no_license | nxknuepp/ShinyPrograms | 602dd136fbca71e88b16c4cea2a5cde717da25ae | 7124aff1df776e95ba27e6cec0aebbf919173559 | refs/heads/master | 2020-07-21T12:41:16.303168 | 2019-09-08T20:54:57 | 2019-09-08T20:54:57 | 206,867,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 899 | r | ui.R |
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Simulation: Difference of Two Proportions"),
# Sidebar with a slider input ... |
5bc5f97cdde8e1ea1ebb22192096f209edffd384 | 388d7a62bbbd144f243438f9e6a5a456eb2cce3c | /R/tCol.R | feca2cf5428f83bc4ee79552dcbb1afd33558033 | [] | no_license | aspillaga/fishtrack3d | 64c7dcb2a97a833ef830d845e8bfbc3aaf387827 | 2be695e0f88d97e095f074acd17240cb8878dbbc | refs/heads/master | 2022-01-18T10:50:53.776454 | 2019-05-23T15:09:18 | 2019-05-23T15:09:18 | 118,634,135 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,261 | r | tCol.R | #' Create colors with transparency
#'
#' This function easily creates transparent colors providing a color name and
#' the desired proportion of transparency
#'
#' @param color character vector with the names or codes of the original
#' colors.
#' @param trans percentage of transparency to apply to the colors (0 to... |
e9eead68ec579dde2d7826a891f767711dd3ef42 | 2975fba6bf359214c55e7d936f896a5a4be3d8f5 | /tests/testthat/test-plotRisk.R | c54359967040299977f37178ac3bb264870b4158 | [] | no_license | tagteam/riskRegression | 6bf6166f098bbdc25135f77de60122e75e54e103 | fde7de8ca8d4224d3a92dffeccf590a786b16941 | refs/heads/master | 2023-08-08T03:11:29.465567 | 2023-07-26T12:58:04 | 2023-07-26T12:58:04 | 36,596,081 | 38 | 14 | null | 2023-05-17T13:36:27 | 2015-05-31T09:22:16 | R | UTF-8 | R | false | false | 2,186 | r | test-plotRisk.R | ### test-plotRisk.R ---
#----------------------------------------------------------------------
## Author: Thomas Alexander Gerds
## Created: Sep 15 2022 (16:04)
## Version:
## Last-Updated: Sep 16 2022 (12:56)
## By: Thomas Alexander Gerds
## Update #: 4
#---------------------------------------------... |
a23922d3d29aecbe7c0183c7dbb6c700c62a98ed | 857aa1256af30137c47baeed06bc45b412a5a27c | /google_schoolar_scrapping/scrape.R | 3779a8d8b4e71508467e906f772933d73c9c91e8 | [] | no_license | vinayvamshirr/r-blogs-examples | c2a367ecd0358cdbaffbdbf23c4341a627476fe6 | fa7dfd9c0fc26479e8a475098cef67767289bfe0 | refs/heads/master | 2021-06-05T01:05:54.669298 | 2016-07-23T23:48:47 | 2016-07-23T23:48:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,900 | r | scrape.R | library(rvest)
library(ggplot2)
# -----
page <- read_html("https://scholar.google.com/citations?user=sTR9SIQAAAAJ&hl=en&oi=ao")
# http://selectorgadget.com/
citations <- page %>%
html_nodes("#gsc_a_b .gsc_a_c") %>%
html_text() %>%
as.numeric()
citations
barplot(citations, main="How many times has each pape... |
3ec0c09b92c8c6ab026883a7e73501f003bcc515 | 885c4683202c5af87698f5ffbdb19a1905303737 | /code/accuracy_vs_time/produce_plots.r | 362b2a829652017e7367a2ff00bb17b14f431a7d | [] | no_license | mauriziofilippone/preconditioned_GPs | ebc50cb85f06f59700257f25860ceaddc14a9775 | d7bc09b6804ef002cc3fc6bbf936517578d7436e | refs/heads/master | 2021-01-19T03:32:35.745442 | 2016-06-09T09:05:26 | 2016-06-09T09:05:26 | 51,930,826 | 14 | 6 | null | null | null | null | UTF-8 | R | false | false | 7,561 | r | produce_plots.r | ## Code to produce plots of error versus time for GPs trained using CG and preconditioned CG
DATASET = "concrete"
DATASET = "powerplant"
DATASET = "protein"
DATASET = "credit"
DATASET = "spam"
DATASET = "eeg"
## KERNEL_TYPE = "RBF"
KERNEL_TYPE = "ARD"
ps.options(width=10, height=8, paper="special", horizontal=F, p... |
3f74fbc5f6a266b5867a88f054a546e536744808 | 8c98d9d669743136fa8a11bc3751162a45518bb2 | /R/01_early_examples/json_file.R | 3eba92bee8e3501b06915c9b5f6323af7d9d57b0 | [] | no_license | retodomax/Bauland | e9e2c4b62c433ce3f6540f55f9e90d2613cfb34d | 0a7d7a3285b4f97e123e363f354780b549ee950b | refs/heads/master | 2022-03-09T05:11:32.876186 | 2019-10-26T21:16:43 | 2019-10-26T21:16:43 | 212,530,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,141 | r | json_file.R | ## Try to flip the y coordinate
# 1) import LV03 (CH1903) coordinates of municipals
## --> flip coordinate
# 2) trasform to WGS84
# 3) plot with leaflet
# 1) ----------------------------------------------------------------------
ch_cant <- geojsonio::geojson_read("swiss-maps/topo/ch-cantons.json",
... |
0d1c6d4f355e3c913921a93c60d52968a58208b6 | 92e979e0e55cf88078795becc261a20850004acb | /man/calc_ratio.Rd | 1b332ea8b5071b54dcd6f5e2469363b6d85f5d07 | [] | no_license | rjnell/digitalPCRsimulations | c47becb1e19e2a3fb8f5056fc02f78d8c25e3f86 | b341cf024b262cbc0cb77a6745c05b71247fd70d | refs/heads/master | 2023-02-07T07:47:51.514474 | 2023-02-04T14:00:03 | 2023-02-04T14:00:03 | 295,371,471 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,156 | rd | calc_ratio.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/calc_ratio.R
\name{calc_ratio}
\alias{calc_ratio}
\title{Calculate ratio with confidence intervals of two values with known confidence intervals.}
\usage{
calc_ratio(input_a, input_b)
}
\arguments{
\item{input_a}{A vector specifying the value... |
a27994645690cea8f693f8d234427a63bb54f8b0 | 1ea35aa8adc3131f178d873800c1c818343b9dec | /src/R/shiny/ROMOPOmics_demo/ROMOPOmics/R/readInputFiles.R | be4a45b1225e37b7cc1a3622821b4cba6854104c | [
"MIT"
] | permissive | NCBI-Codeathons/OMOPOmics | 9afa7abd4f59baa48248b73a823d5e50d0197663 | c6f0293f99189cc682d04aef9f40e43a8878ca8b | refs/heads/master | 2020-12-06T04:54:42.723704 | 2020-06-04T16:45:14 | 2020-06-04T16:45:14 | 232,348,286 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,813 | r | readInputFiles.R | #' readInputFiles
#'
#' Function reads in TSV files designed with a given mask in mind, with rows
#' for each field and table combination and columns for input data entries.
#' Output is an "exhaustive" table including all fields and tables from the
#' specified data model, including unused tables and fields.
#'
#' @pa... |
7ab34c98c33e5b7989777103a5d6a45019b35521 | b56eee2ac6a95d0e0eb9bdbdda40679de795de10 | /microEMAResponseSummaries/plotParticipantResponsebehavior.R | c280d1d1600c3380659016323d75c1f950b9e00b | [] | no_license | adityaponnada/microEMA-Preprocessing | 1c658a46582753c1dae2fd1349a339a34e2868a0 | d254a4fcd8b088399108261994b56c6cfe6b3424 | refs/heads/master | 2020-03-10T14:36:24.369331 | 2018-10-30T16:17:44 | 2018-10-30T16:17:44 | 129,430,763 | 0 | 0 | null | 2018-10-30T16:17:46 | 2018-04-13T16:57:48 | R | UTF-8 | R | false | false | 567 | r | plotParticipantResponsebehavior.R | #### Include libraires
library(psych)
library(MASS)
library(ggplot2)
library(plotly)
library(reshape2)
library(dplyr)
#### Plot stacked histograms of response rates
RRSet <- c("USER_ID", "W1_COMPLIANCE", "TOTAL_COMPLIANCE", "W1_COMPLETION", "TOTAL_COMPLETION")
uEMARRSubset <- uEMAResponseRate[RRSet]
meltRRDataFrame... |
170a10d4c262203519f1855118c5ca0193994bc5 | 926b4a96b4a68250e1e44ab332bd5a80d050b5ec | /R/print.summary.lognlm.R | a00275167fc4ca4776d7e020c3e24af477e9530a | [] | no_license | cran/logNormReg | 037c51fea1ab7a72fa6ad6b577c7a9e5832aff2b | a5c9af006d98a3778090f4478ba8cdc470494853 | refs/heads/master | 2021-11-26T18:04:05.306516 | 2021-11-08T16:00:02 | 2021-11-08T16:00:02 | 153,307,292 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,411 | r | print.summary.lognlm.R | print.summary.lognlm <-
function(x, digits = max(3L, getOption("digits") - 3L), signif.stars = getOption("show.signif.stars"), ...) {
cat("\nCall:\n", paste(deparse(x$call), sep = "\n", collapse = "\n"),
"\n", sep = "")
# cat("Deviance Residuals: \n")
# if (x$df.residual > 5) {
# x$devi... |
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