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fe4c601e3c393ee94d72b0cd652b3662c28254f9 | 5f179d46db7e5e6c93e8d54b29170e55d57f6513 | /R/RBaseX.R | 50e1c83104e6dda0af0a6f61bb178ccdf11eaf4c | [] | no_license | BenEngbers/RBaseX | 727538fab9a10ad72496c2e2eb1d3d6fe81f8be0 | 64a3de8bd3d0e31cb9789a8bf30fa89a50f4729c | refs/heads/master | 2022-11-09T03:55:29.343796 | 2022-11-05T00:25:37 | 2022-11-05T00:25:37 | 243,994,987 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,509 | r | RBaseX.R | #' @title RBaseX
#' @docType package
#' @name RBaseX
#' @description 'BaseX' is a robust, high-performance XML database engine and a highly compliant XQuery 3.1 processor
#' with full support of the W3C Update and Full Text extensions.
#'
#' @importFrom magrittr %>% %<>%
#' @import dplyr
#' @import utils
#' @import... |
444f5814b163dd5d85c72e749d839a57625472d2 | 4e90257f2a8644c7fa3b7f2312c72edd2f2b0e06 | /project/tidytuesday/tidytuesday_10-19.R | 55dc1175545350858643a737f9ad85ef201a1fb3 | [
"CC-BY-4.0"
] | permissive | sjspielman/datascience_for_biologists | 292b9f0c17a1abf28ebba4e088c53ec0aeea2b70 | 46caf48ba3e0ab2d3e8bdfa222004f0db2b9839e | refs/heads/master | 2023-01-09T15:37:41.490683 | 2022-12-27T01:00:15 | 2022-12-27T01:00:15 | 230,664,262 | 27 | 12 | null | 2022-05-24T23:12:22 | 2019-12-28T20:39:45 | HTML | UTF-8 | R | false | false | 253 | r | tidytuesday_10-19.R | library(tidyverse)
pumpkins <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-19/pumpkins.csv')
pumpkins %>%
mutate(weight_lbs = as.numeric(weight_lbs) ) -> pump_num
ggsave("whatever.png") |
d13b820eb6036bea8884431c5f91ee320f120137 | d6302bdd07645e0da8ad4430a261d3ebe2149435 | /man/clusterSummary.Rd | 7ab4eaf155eb10b8f639cdf842ff400a296aa137 | [] | no_license | cran/RclusTool | 3a8fec24edeaedee42ef0f255f32dfc4be107dfe | 7ed428f6c896889a9b291a279e1e82f8f6d9cd3b | refs/heads/master | 2022-09-04T15:47:33.547991 | 2022-08-29T07:40:08 | 2022-08-29T07:40:08 | 236,879,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,421 | rd | clusterSummary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sampleClustering.R
\name{clusterSummary}
\alias{clusterSummary}
\title{Clusters summaries computation}
\usage{
clusterSummary(
data.sample,
label,
features.to.keep = colnames(data.sample$features[["preprocessed"]]$x),
summary.function... |
c00e5daa91085fa3698eb461b241448dc5ade944 | 0368ef1544151da85ebb4f52d1d366abe51ca82a | /inst/examples/im.convert-ex.R | 80d208a3ca3fc4db2f692cbebb86f936b5d73a36 | [] | no_license | yihui/animation | 6ccd25a6e71c34cbabbe4f7151e5d7d80646b092 | 30cb6f3859383c251d8d91cb59822341e420d351 | refs/heads/main | 2023-04-08T00:12:33.697435 | 2023-03-24T15:41:48 | 2023-03-24T15:41:48 | 1,086,080 | 182 | 65 | null | 2022-08-22T15:39:12 | 2010-11-16T19:13:38 | R | UTF-8 | R | false | false | 555 | r | im.convert-ex.R | ## generate some images
owd = setwd(tempdir())
oopt = ani.options(interval = 0.05, nmax = 20)
png('bm%03d.png')
brownian.motion(pch = 21, cex = 5, col = 'red', bg = 'yellow',
main = 'Demonstration of Brownian Motion')
dev.off()
## filenames with a wildcard *
im.convert('bm*.png', output = 'bm-animation... |
b352511e45693ed247ca01326ce672ecb557605f | 7f22f51805020814cb1140323df1423d9d913a39 | /R/extractAudioFeatures.R | 0c960cf7d81a10c627a6a982320eef8a7ba4378a | [] | no_license | jhinds/communication | 44da7c61a80e8a319fa1d947cdabf84413c39d08 | 3619bcd7ecdc2679a4c6d1fc81684abb3a380d47 | refs/heads/master | 2023-03-12T01:52:09.686031 | 2021-02-25T08:20:02 | 2021-02-25T08:20:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,347 | r | extractAudioFeatures.R | #' Title
#'
#' @param wav.dir Directory of wav files for featurization
#' @param wav.fnames If wav.dir = NULL, a list of wav files for featurization
#' @param windowSize Size of window in milliseconds
#' @param windowShift Amoung to shift window in milliseconds
#' @param windowType Window type
#' @param derivatives Inc... |
7d4ea6c3e2eac4113a1829e02af0717a9f27af91 | cda60c696922a3b20f688b41c51b95f92e24813f | /workflow_fresh/plot.r | 876d653744fe7b9f0a0430c9d655e850c2c960ca | [] | no_license | cmkobel/gBGC | bdd2df9fb7b0c8155cf80d239b9bb928c63212b2 | 7c10850f323df3caa8d784ec93e9759cc96b2d1c | refs/heads/master | 2021-01-14T20:28:24.151670 | 2020-06-13T14:00:21 | 2020-06-13T14:00:21 | 242,748,258 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,435 | r | plot.r | library(tidyverse)
library(zoo)
library(gganimate)
library(ggpmisc)
#library(ggpubr)
library(cowplot)
setwd("c:/users/carl/urecomb/lokal")
height = 5; width = 8
# ClonalFrameML files
CF_raw = read_delim("G:/gBGC/carl/workflow_fresh/output/CF_collected.tab",
"\t", escape_doubl... |
5cf3e1665b284b0374e5815332905f6f91ae635a | 37eddbfbf657b02849ba56bd3aaba1654fbca04e | /code/intermediates.R | 1f90a703c322725732751981b26b89ac0511d809 | [] | no_license | jGaboardi/pmedmize | f370dca31dae6428b512750c3cfac1cc3cfe3513 | 93828b1c5b1c6610e514a7f7bfb46b49add820e2 | refs/heads/master | 2023-01-02T02:02:28.538709 | 2020-10-27T13:41:42 | 2020-10-27T13:41:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,359 | r | intermediates.R | "
Definitions for intermediate variables to build individual-level
P-MEDM constraints.
"
assign_householder_item_level <- function(pums, v){
"
Helper function. Assigns the item level of the
householder to all members of the household
(for building household-level intermediates).
"
v <- lapply(split(v, pu... |
d92fa232a006e36f44f81a79c672dae3f9243307 | e69d3476863aa2f73cdf3787b6f078618f133bbb | /week_2/week_2/RCrawler_example/CaseStudies/Case6PttGossiping/test.R | fe777cd3aff65fad299dab78cd8eeec6c2053cc1 | [] | no_license | pumpkinlinlin/CSX_RProject_Spring_2018 | 08d714d8ed9318c2c4aafefeba5ce5a5416a92f5 | ef3d5abe1ba3351c0a9daef7a71fb3ceb1c725b5 | refs/heads/master | 2020-04-24T07:21:47.641885 | 2018-10-01T09:45:53 | 2018-10-01T09:45:53 | 171,796,950 | 1 | 0 | null | 2019-02-21T03:57:13 | 2019-02-21T03:57:13 | null | UTF-8 | R | false | false | 474 | r | test.R |
rm(list=ls(all.names = TRUE))
source("packIntoFunction.R")
listPageUrls = getListPageUrls("Gossiping")[1:5]
listPageUrls
postUrls = unlist(lapply(listPageUrls,getPostUrls))
postUrls
getPostData("https://www.ptt.cc/bbs/Gossiping/M.1431338763.A.1BF.html")
getPostData(postUrls[2])
postData = lapply(postUrls[3:5],ge... |
aa5d0e7e67a0bbe310ee986b34dfc0a40b813f11 | a845ad855b81eec06afe792f5f18a82a8e1c89d8 | /Week 10/R/SpatialExerciseWickers.R | 7081df297b9746458bae0a7d4187701a60417a71 | [] | no_license | mwickers1/Statistical-Machine-Learning | a0542d7e8951ba636ef5b48de25deeb6b7a60b42 | 7af2138a4aaece6e28974132eb3e829e2e31ae09 | refs/heads/master | 2020-08-08T01:47:10.596237 | 2019-12-11T16:34:41 | 2019-12-11T16:34:41 | 213,664,455 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 589 | r | SpatialExerciseWickers.R | library(dplyr)
library(spatstat)
data <- read.csv('Data/311_Cases.csv', quote = "\"", comment = "", stringsAsFactors = F)
shapes <- read.csv('Data/sf_polygon.csv', stringsAsFactors = F)
data <- data %>%
filter(Latitude != 0)
dataPE <- data %>%
filter(Category == 'Parking Enforcement')
window <- owin(xrange = c(... |
898fb9eef14c268e7ed736281688d35c87dd3e2c | bce7dad4675f5edd62b2e74431a2dfc41d028f7a | /R/ex/rjmcmc.R | 9501022a0a33648475ef28f99615dd7f545183eb | [] | no_license | volkerschmid/bayeskurs | fb06d0feab461dbd4fbbb1323a8f22cd9210c98e | 601246bc2fe4a4850dc1550e7d92e1cac90277c0 | refs/heads/main | 2023-07-12T16:15:14.185395 | 2021-08-19T18:01:51 | 2021-08-19T18:01:51 | 398,031,370 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,104 | r | rjmcmc.R |
g.predict<-function(Mod, X)
{
j = Mod[2]
beta0 = Mod[3]
beta1 = Mod[4]
beta2 = Mod[5]
P = 0*X + beta0
if (j >= 2) {P = P + X*beta1}
if (j >= 3) {P = P + (X^2)*beta2}
return(P)
}
g.perterb<-function(M=c(-Inf, 3, 0, 0, 0), Qsd=c(0, 0, 0.1, 0.01, 0.001), LB = c(0, 1, -10, -2, -1 ), UB = c(0,... |
fd86d50fed01f247f325039cb5319621e69b12a7 | 67de204b7f0550def8eea7d6ca605f43aed653fc | /app/lib/analysis/tests/normality/comment.R | 581e37c48d5173ea5c46dee4e7fbedb84b60e708 | [] | 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 | 926 | r | comment.R | # Initialize Boilerplate ----
source("boilerplate.R")
source("data/comment.R")
## Test: Continuous-values Metrics ====
metrics <- names(COMMENT.CV.METRIC.VARIANTS)
test.outcomes <- data.frame()
for (i in 1:length(metrics)) {
metric <- metrics[i]
metric.label <- COMMENT.METRIC.LABELS[[metric]]
cat("[", i, "/", l... |
ab3cfa11c47285af02dd231a67d83cd325d37c01 | dbdd0281bfdd1fa07542054d4b5c2dbf9fba83f6 | /R/unmarked/man/formatMult.Rd | fb10d86a98a45ab0494bb154744f6c4c8f12f596 | [] | no_license | kwinner/latentcountmodels | 0eaeff2750877129da9f1f9d2b0ae9c3af9c32e0 | d4400cc28453dbf1c4e00e9da7a8ffe880518584 | refs/heads/master | 2020-09-11T07:56:15.155998 | 2019-11-14T14:30:38 | 2019-11-14T14:30:38 | 221,996,225 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 836 | rd | formatMult.Rd | \name{formatMult}
\alias{formatMult}
\title{Create unmarkedMultFrame from Long Format Data Frame}
\usage{formatMult(df.in)}
\description{This convenience function converts multi-year data in long format to unmarkedMultFrame Object. See Details for more information.}
\details{\code{df.in} is a data frame with colu... |
b53529e99c5266cf0a555119e90a441f46a7e1f6 | f81cb7ae4215f20e6537f79bfb1bb96a23019f0f | /Codes/peak_wordcloud_1.R | 6c5ec10f3cdf29dc5dbe6674f6bf99ee8dcf7b5f | [] | no_license | Sonull/Analysis-of-Tweets-made-during-NBA-2018-Finals | 53c92efee39502b3b0888408734bb1e6a09ea59b | e9a5179cb45ab2704003f71915fefe46745ec5d6 | refs/heads/master | 2021-02-26T02:51:12.235768 | 2020-05-10T17:44:00 | 2020-05-10T17:44:00 | 245,489,873 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,375 | r | peak_wordcloud_1.R | library(readr)
library(ggplot2)
library(tidyr)
library(dplyr)
library(tidyverse)
library(scales)
library(tm)
library(SnowballC)
library(wordcloud)
library(tidytext)
library(reshape2)
library(gridExtra)
library(corrplot)
library(ggmap)
library(igraph)
library(leaflet)
library(knitr)
library(htmlwidgets)
library(htmltool... |
86a047a998a1800b9f1d4964b02766ab5ea5661f | de7ffad3cdc6923f1e98f21887b9fb433403875e | /PEpCenteias/man/dengue2013.Rd | 90a7b6367b6b2f814a8e61a5ae3e25dc40036b44 | [] | no_license | acgabriel3/EpCenteias | cc2c45d15fe8dc1f0dd4b5ebfa4c8dc769c1f010 | 9b122bdd4da25ad902b3c08248a2a4617e3557a5 | refs/heads/master | 2020-04-06T23:43:42.046543 | 2020-02-10T20:13:21 | 2020-02-10T20:13:21 | 157,879,323 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 393 | rd | dengue2013.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dados.R
\docType{data}
\name{dengue2013}
\alias{dengue2013}
\title{dengue2013}
\format{Um data frame com 788 linhas e 43 variaveis:}
\source{
MS (Ministério da Saúde)
}
\usage{
dengue2013
}
\description{
dados coletados acerca da situacao epi... |
57b680c0936bdad7c76f7255109283e39bb25ec1 | 1dd6a9098c42ae910ff9d59b932bfe5a33261995 | /statistical_testing.R | b789f6ac7b0f753e5b83ab7d54334e7ea3ea9351 | [] | no_license | nanjunbaka/prettyderby | 4c7ccd9891d741809c8ef098a8abbc855c9fd69b | 80c2141a573809a5d031defce1ac52ba6e86ed0b | refs/heads/master | 2023-07-01T18:16:13.601815 | 2021-07-31T10:00:55 | 2021-07-31T10:00:55 | null | 0 | 0 | null | null | null | null | SHIFT_JIS | R | false | false | 1,246 | r | statistical_testing.R | #visualize 2nd error
rm(list=ls())
z_alpha <- 1.282 #標準正規分布の上側alpha%点(alphaは第１種の過誤の確率)
p_0 <- 0.05
nlist <- c(10,100,1000)
for(n in nlist){
A <- function(x) {sqrt(p_0*(1-p_0)/(x*(1-x)))}
B <- function(x) {(x-p_0)/sqrt(x*(1-x)/n)}
p_2nderr <- function(x) {pnorm(z_alpha*A(x)-B(x))}
if(n==10){
pl... |
52bcee921c7f32be28a07db297335f0655b5faab | 671097044dcd383018e5b332aa0eff90bb61894a | /R/run_dauer_stan.R | bd8bd6bba949ed1fc6b8be3c8a1cd02a76003613 | [] | no_license | mikeod38/dauergutTest | 37106579174b4a1dcbb65c606dd543a7004db177 | d694dbf16835ad2147c40fe11d1f4b1682fdd6d5 | refs/heads/master | 2021-01-23T14:15:37.912388 | 2017-09-07T04:09:03 | 2017-09-07T04:09:03 | 102,681,186 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,638 | r | run_dauer_stan.R | #' run_dauer_stan
#'
#' Function runs a stan glmm for dauer data
#'
#' @param df input dataset. Requires a "genotype" column. See type for data column types
#' @param type "dauer" (default),"dauer-grouped". For dauer data, needs to have raw counts, with "dauer" column and "n" column.
#' For grouped data, must have gro... |
0484b91f33b7d6d579bf1587f6bab557fa612d4b | 76e8cb6c69438cde64462fe27d9aa71bc7488712 | /Procesamientodatos/hist_app_simple.R | 83d9641cf5f484968c7a141ba005deb70d01d26b | [
"MIT"
] | permissive | diazshejo/Friday_IA | 978cd6d81649d0c1cb6415e725d6143e38140015 | 2d6cd2f54fda7b2935eaacc84df2cf4277eac762 | refs/heads/master | 2020-04-01T03:50:34.027085 | 2018-10-13T12:44:01 | 2018-10-13T12:44:01 | 152,837,661 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 125 | r | hist_app_simple.R | library(shiny)
port <- 8086
runApp(appDir = "hist", port = port, launch.browser = TRUE, quiet = FALSE, host = '127.0.0.1')
|
a6db13f2fd84f0fb4261835f7e8f5abe37066926 | 046d9aaaef9a4f5e607c583c1e876083569d116d | /lib/rate_plot.r | e0a1dfc4dba8a7dff72078849e5d7bf5cc5a4f3e | [] | no_license | JamesWong6VA/goals-of-care | fde220fa244ad21e8b7fa4c184df97212a95c671 | e1e160e6f1784103b6592829540e116a061c512a | refs/heads/master | 2021-09-06T10:47:49.203974 | 2018-01-05T19:54:49 | 2018-01-05T19:54:49 | 120,348,504 | 0 | 0 | null | 2018-02-05T19:05:15 | 2018-02-05T19:05:15 | null | UTF-8 | R | false | false | 3,383 | r | rate_plot.r | library(tidyr)
library(dplyr, warn.conflicts = FALSE)
library(ggplot2)
library(ggthemes)
library(scales)
# Generate the plot for a rate type performance metric
# This script contains two utility functions:
# * Transform data for plotting convenience
# * Produce plot
# Hueristic for determining the lower limit of... |
009cf2a96c3497ba63ecff025c031c4f36e9d71f | e74f214e229556aa6395a3f4da75e0d97291748b | /mepstrends/hc_pmed/json/code/r/totPOP__TC1name__ind__.r | dd7f79f2097127bcd46262cd3c98ed7be5d27d55 | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain"
] | permissive | HHS-AHRQ/MEPS-summary-tables | a0bc5069eeab97da04446283ff3a6133f97df595 | c705b91c2e5918447cb738848d7ea6b0c6481507 | refs/heads/master | 2023-06-10T14:56:34.711734 | 2021-04-01T14:16:57 | 2021-04-01T14:16:57 | 115,756,417 | 11 | 6 | null | 2018-02-14T14:32:46 | 2017-12-29T21:52:22 | SAS | UTF-8 | R | false | false | 1,353 | r | totPOP__TC1name__ind__.r | # Install and load packages
package_names <- c("survey","dplyr","foreign","devtools")
lapply(package_names, function(x) if(!x %in% installed.packages()) install.packages(x))
lapply(package_names, require, character.only=T)
install_github("e-mitchell/meps_r_pkg/MEPS")
library(MEPS)
options(survey.lonely.ps... |
2b1a3678417371a364c7ed5ae21eaaa84e47f93b | 0e76443b6de1312c8d3988d2538263db0cd7385b | /分析及画图/0. 文献_书籍代码/数量生态学/数量生态学-R语言应用 数据和代码/赖江山/NumEcolR_scripts/chap3_web_PC.R | 83713027061749592d4b50ca3f68d374464b322d | [] | no_license | mrzhangqjankun/R-code-for-myself | 0c34c9ed90016c18f149948f84503643f0f893b7 | 56f387b2e3b56f8ee4e8d83fcb1afda3d79088de | refs/heads/master | 2022-12-30T08:56:58.880007 | 2020-10-23T03:20:17 | 2020-10-23T03:20:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,046 | r | chap3_web_PC.R | ### CHAPTER 3: ASSOCIATION MEASURES ###
# Load required libraries
library(ade4)
library(vegan) # should be loaded after ade4 to avoid some conflicts
library(gclus)
library(cluster)
library(FD)
# Import the data from CSV files
spe <- read.csv("DoubsSpe.csv", row.names=1)
env <- read.csv("DoubsEnv.csv", row.... |
dc5f38de15d866d118617812b784be911a3298de | ef3c4696a0ca1ed2f5188c0be4374ef49fd92da8 | /500494994 Extra Script.R | 3032143735e85497cc38b99a127cf2608d51cff1 | [] | no_license | MHuang2001/Financial-Econometrics | 041872b91dfcf9ce7164f374951eeddf37d32b29 | 32284287be2f41737d55f21a811d5ff515a832bb | refs/heads/main | 2023-04-17T18:13:29.833200 | 2021-04-30T01:47:39 | 2021-04-30T01:47:39 | 362,995,732 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,348 | r | 500494994 Extra Script.R | # SID 500494994
#INPUT DATA
##RANK THE INDIVIDUAL STOCK BY THEIR AVERAGE MONTHLY EXCESS RETURNS OVER THE RISK FREE RATE
xsReturns <- stockPriceReturnsData
for (i in stockPriceNames) {
xsReturns[, i] <- averageMonthlyStockReturns - averageRF
}
xsReturns
averageExcess <- colMeans(xsReturns)
averageExcessStdDev <- St... |
b8574114bca41013b8654b34f297abfefaaac575 | 3286534741f7bf2a5bd19e3bb036bcaaf5320091 | /str2vec.R | 13c346b7ea304ff8e6e66598cabcdf6697dc75dd | [] | no_license | junyzhou10/OptTrialDesign_ShinyApp | 26c628fa22ee22f9db144abd1aa6d4a4efec0542 | 34841354bba730fdefd3b9881ed057debe4712ed | refs/heads/main | 2023-05-31T07:50:15.773263 | 2021-06-20T10:07:29 | 2021-06-20T10:07:29 | 362,327,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 448 | r | str2vec.R | str2vec <- function(input){
output = NULL
if (input == "") {
output = 0
} else {
str1 = unlist(strsplit(input, ','))
for ( i in 1:length(str1) ) {
if ( grepl('-', str1[i], fixed = T) ) {
output = c( output, seq(as.numeric( unlist(strsplit(str1[i], '-')) )[1], as.numeric( unlist(strsplit(... |
e7d33fd0970aadb0fd838bf5fc611906f728293a | 41776e29d270961456f222772125c0046933be67 | /Word_cloud_ggplot2.R | 1366a9dfb6e02f18eba344bb469e281f6a8953c3 | [] | no_license | vishnusankar55/R | 1f557d8fa800f942e078da87fe49b5fa1c8a5cb7 | 3ba38398b4fea72daba847db2e58a80935f18ee8 | refs/heads/master | 2022-12-12T07:07:07.809225 | 2020-09-05T05:05:58 | 2020-09-05T05:05:58 | 293,005,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,372 | r | Word_cloud_ggplot2.R | ###########Word Cloud #######
install.packages("tm")
install.packages("wordcloud")
install.packages("RColorBrewer")
library(tm)
library(wordcloud)
#data= file.choose()
setwd("C:/Users/Asus/Desktop/Priyanka/Simpli learn/June_July_2020/Day5_4July")
getwd()
data=read.csv("Word_cloud_Day4.csv",header = T,strings... |
369495f0b2f1e2809b16223668056957252d27a8 | 8ac3b63c2fd288c9d56a7efaf8c3caf04333f6e7 | /logging_in_r.R | 3d4bff77c969a50fa2572011b7d94f5abc0e2e89 | [] | no_license | OlivierNDO/stat_funcs_r | e89c11cedd117659393711a470a66638393ad7b2 | b7c6ab3771392292f30d28151c91952f233c3c5a | refs/heads/master | 2020-03-06T21:57:09.043363 | 2020-02-24T14:15:20 | 2020-02-24T14:15:20 | 127,090,602 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 703 | r | logging_in_r.R | # Script config
library(logging)
config_log_folder = 'C:/.../.../.../.../'
config_log_file = 'abc_model.log'
config_logger_name = 'abc_model'
# Setting up logger
logging::basicConfig()
logging::addHandler(logging::writeToFile,
logger = config_logger_name,
file = pa... |
323a6a804fb254e2524de6ea620deb17513ab5eb | 12ba6afc7836493cd03f51321d3fcb88db8feb4a | /R/rts_process.R | fca938fbef4f2cb88a4e40488645f078695537dc | [
"Apache-2.0"
] | permissive | wangbo2020/CTPReutersComparison | ff159a6b6c99aa7d121fe4156b67d7799c66bb19 | 7ac7fea8baecd0fe6cd0a5b9bf7e1870f9ad3182 | refs/heads/master | 2021-01-16T18:18:38.437975 | 2014-10-20T15:08:33 | 2014-10-20T15:08:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,522 | r | rts_process.R | library(pipeR)
library(data.table)
library(dplyr)
library(foreach)
library(doParallel)
rts.files <- paste0("data/Reuters/", list.files("data/Reuters", "gz", recursive=TRUE) )
# check.names <- sapply(1:file.num, FUN=function(i){
# data.i <- read.csv( rts.files[i] )
# return( names(data.i) )
# })
#
# check.names <... |
1d63b54c52988352566759d8fee88ff83cbb7c60 | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /paws/man/networkmanager_untag_resource.Rd | f9ef05b8a5c06508c2bf48c24a2d90ae2f4a179e | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | 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 | 666 | rd | networkmanager_untag_resource.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networkmanager_operations.R
\name{networkmanager_untag_resource}
\alias{networkmanager_untag_resource}
\title{Removes tags from a specified resource}
\usage{
networkmanager_untag_resource(ResourceArn, TagKeys)
}
\arguments{
\item{ResourceArn}... |
da7938016b84bb87f4de93a7374bf2f6b25e205b | d2f7e45413cbbfcea96617824908f954175198da | /plot5.R | f8a668c0f8ec05431c47370ce195db85c05f9955 | [] | no_license | cmarieelder/particulate_analysis | 5d3c656e933a8d9affc4c89641205f618762d1e1 | edab2bcf65d2eec2b21869e06f2632b367046dc4 | refs/heads/master | 2022-12-01T11:43:01.926958 | 2020-08-10T09:57:45 | 2020-08-10T09:57:45 | 284,366,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,570 | r | plot5.R | # Author: Cynthia Elder
# Date: 8/2/2020
# Creates a plot to show how emissions from motor vehicle sources have changed
# from 1999–2008 in Baltimore City.
plot5 <- function() {
# Load the required packages -----------------------------------------------
list_of_packages <- c("dplyr", "ggplot2", "ggpmisc")
... |
b823c5d30244a6f5152c6e88fed5db32f2410e77 | 7f72ac13d08fa64bfd8ac00f44784fef6060fec3 | /RGtk2/man/gEmblemedIconGetEmblems.Rd | b64619e475f46d269464f25adfeeb51d4dbc0898 | [] | 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 | 351 | rd | gEmblemedIconGetEmblems.Rd | \alias{gEmblemedIconGetEmblems}
\name{gEmblemedIconGetEmblems}
\title{gEmblemedIconGetEmblems}
\description{Gets the list of emblems for the \code{icon}.}
\usage{gEmblemedIconGetEmblems(object)}
\arguments{\item{\verb{object}}{a \code{\link{GEmblemedIcon}}}}
\details{Since 2.18}
\author{Derived by RGtkGen from GTK+ doc... |
c704d6c2fb5646500e5c957e71c49d1abdccd098 | 60fe11cdf42e4dbfa7c5b96c89e31f8001f06a75 | /Plot2.R | 098829ebbc214cbab9a734ea8d563ee77e4d1813 | [] | no_license | rajpaz/ExData_Plotting1 | 8e6daea1324b6b8588d4b334ee65545771c25619 | 0aa7fb46bdd8c92442a2c5d6636b9e90048e8a17 | refs/heads/master | 2021-01-22T00:59:26.855566 | 2016-01-11T00:41:13 | 2016-01-11T00:41:13 | 49,376,538 | 0 | 0 | null | 2016-01-10T17:09:26 | 2016-01-10T17:09:24 | null | UTF-8 | R | false | false | 259 | r | Plot2.R | # Plot
source("./getFebData.R")
# plot 2
if (!exists("febData"))
febData = getFebData()
png("./Plot2.png", width = 480, height = 480)
plot(febData$date_time,febData$Global_active_power,
ylab="Global Active Power (kilowatts)",type="l",xlab="")
dev.off() |
b95126c9c9e48715e85a5af9a47ff93221aa4461 | 1aee971dc7c8407cc9a50f59bde7b7b6998a6489 | /tests/testthat/test-pin-dataframe.R | 6834fb974a3e84327a371f11768b80cee3321981 | [
"Apache-2.0"
] | permissive | jduckles/pins | c9290eba050214db6823cfe0fbb426af09bf4c80 | 199b8b1b0ba3da3449158a60a17b7321fcd06ee5 | refs/heads/master | 2020-09-09T00:51:36.383883 | 2019-11-11T19:50:33 | 2019-11-11T19:50:33 | 221,294,526 | 0 | 0 | Apache-2.0 | 2019-11-12T19:23:07 | 2019-11-12T19:23:06 | null | UTF-8 | R | false | false | 402 | r | test-pin-dataframe.R | context("pin dataframe")
test_that("can pin() data frame", {
roundtrip <- pin(iris, "iris")
expect_equal(as.data.frame(roundtrip), iris)
expect_equal(as.data.frame(pin_get("iris")), iris)
})
test_that("can sanitize data frame names", {
name <- "___sdf ds32___42342 dsf dsf dsfds____"
expect_equal(
p... |
aa5bbdef85bcb568e5dd24ff2c0797c038a9583c | fedc48dd7f973fa7e44e4c7fba79b72c3c40c9f3 | /FRED_GDP_COMPONENTS.R | bd12d58f454338fedd209748d28c52352f13e595 | [] | no_license | yanlesin/FRED_GRAPHS | b759f4b4a8a04ed894722d9ae8abbb382c488da2 | d0ff985bae97462a94e7227e226d112d6ddfac78 | refs/heads/master | 2020-04-07T07:07:14.201349 | 2018-11-20T19:11:53 | 2018-11-20T19:11:53 | 158,164,438 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,737 | r | FRED_GDP_COMPONENTS.R | #Components of GDP: Compare their contributions from 1947 to 2018
library(fredr)
library(tidyverse)
library(dygraphs)
library(xts)
library(rvest)
# FRED Key ----------------------------------------------------------------
# fredr_set_key() sets a key as an environment variable for use with
# the fredr package in the... |
f362b90ef76e489ae2c5454dbcacc3878d3ed46a | 2e12507002a5c68f8fba51995879cbc07b5160ad | /project.R | c4a7246d657c033515dda5b7143a6b2b36c22f55 | [] | no_license | shobacherian/datamungingcoursera | a28843a3c29f0dcdbb9ade7cf0fa6f46c8089ecb | 859bcaffb400c9d6b442895777a9fa431989deb1 | refs/heads/master | 2021-01-15T12:14:09.086744 | 2015-05-05T11:32:36 | 2015-05-05T11:32:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,830 | r | project.R | # Project Work
simulatorCentral = function(rfun, dfun, ssize=40, runs=1000, title="Course Project: Central Limit Theorem",...) {
# Initialize
# m: stores the means of samples
# runs: no. of simulations
# ssize: sample size
m <- double(0);
for (i in 1:runs) m[i] = mean(rfun(ssize, ...)); # Coll... |
38fc5b61ccf2d206168113270867429a07c2a93f | 570abc2b93f05cbce92d95f6b9bffbe48708bb6c | /R_source/analise_dados.R | d1fbf9821440df3b11bbd0e33bc51c932dd492e6 | [] | no_license | Danhisco/artigo_mestrado | ed871edab0e89e28a5569668a670ea43d3f548e3 | 1ff761608ea7312c533ef216b51fd67fb8268b00 | refs/heads/master | 2023-02-03T00:15:42.720220 | 2023-01-30T02:13:25 | 2023-01-30T02:13:25 | 121,779,367 | 0 | 1 | null | 2018-11-01T11:05:29 | 2018-02-16T17:28:00 | HTML | UTF-8 | R | false | false | 5,284 | r | analise_dados.R | ###A ideia é ter dois data frames: i) refID+range+U_est+riqueza+KS+tree_cover; ii) refID+range+abundance
##
require(plyr)
require(dgof)
require(magrittr)
require(reshape2)
require(dplyr)
source("~/Documents/dissertacao/R_source/sad_media.R")
##Leitura e arranjo dos dados simulados
#o primeiro elemento de cada element... |
cbfe871c6dce91b4264956bdccaf58e656b34d5a | 051880099402393c9249d41526a5ac162f822f8d | /tests/testthat.R | 49b250972d642fb7f411309b2cb957367bd4a345 | [
"MIT"
] | permissive | bbTomas/rPraat | cd2b309e39e0ee784be4d83a980da60946f4c822 | 4c516e1309377e370c7d05245f6a396b6d4d4b03 | refs/heads/master | 2021-12-13T19:32:38.439214 | 2021-12-09T18:42:48 | 2021-12-09T18:42:48 | 54,803,225 | 21 | 7 | null | null | null | null | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(rPraat)
test_check("rPraat")
|
487f0e51e25863cdf93885ef65205a97d7135715 | 01a614cc641ab1c650a874ee679b6cc2ab1f4c0e | /R/mo_engen_gr.R | 1eace0f99f6a5b0f13d39d9d6a9ba6b068beafda | [] | no_license | kfullzz/590_final | ce7c6b5bd7c320e0a94002bf4096f9ca4bbd0525 | fd89cad80b265bd579f2cffdaaae1b9bedc5ea31 | refs/heads/master | 2021-01-23T01:07:56.117943 | 2017-05-02T22:48:00 | 2017-05-02T22:48:00 | 85,877,006 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 261 | r | mo_engen_gr.R | mo_engen_gr <- function(df, col, plant, x, y, z) {
df_gr <- df[df[ , col] == plant, ]
ggplot(df_gr, aes(df_gr[ , x], df_gr[ , y])) +
geom_point() +
xlab(x) +
ylab(y) +
scale_x_continuous(breaks=seq(1,12,1)) +
facet_wrap(~ df_gr[ , z])
} |
72116e7a7a35a6e7f39a9e932280bfec6914fd86 | 77157987168fc6a0827df2ecdd55104813be77b1 | /palm/inst/testfiles/pbc_distances/libFuzzer_pbc_distances/pbc_distances_valgrind_files/1612988480-test.R | de8e231d82e30f18f2f95febc9f789e39743e83b | [] | no_license | akhikolla/updatedatatype-list2 | e8758b374f9a18fd3ef07664f1150e14a2e4c3d8 | a3a519440e02d89640c75207c73c1456cf86487d | refs/heads/master | 2023-03-21T13:17:13.762823 | 2021-03-20T15:46:49 | 2021-03-20T15:46:49 | 349,766,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 272 | r | 1612988480-test.R | testlist <- list(lims = structure(0, .Dim = c(1L, 1L)), points = structure(c(1.61035410759544e-317, 1.44131612505985e-83, 7.30643551760841e-309, 3.9934513075558e-305, 5.42219658867978e-312), .Dim = c(5L, 1L)))
result <- do.call(palm:::pbc_distances,testlist)
str(result) |
36f0cbb835ad8c4e7869211809b59c0326ade1c7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/quanteda/examples/corpus_sample.Rd.R | 054520e1bbe9ff37204708358c8e43fe9015e4d1 | [] | 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 | 640 | r | corpus_sample.Rd.R | library(quanteda)
### Name: corpus_sample
### Title: Randomly sample documents from a corpus
### Aliases: corpus_sample
### Keywords: corpus
### ** Examples
# sampling from a corpus
summary(corpus_sample(data_corpus_inaugural, 5))
summary(corpus_sample(data_corpus_inaugural, 10, replace = TRUE))
# sampling senten... |
2e7eff824415caf16d8e7b726fc6a9c69ca58154 | 1ebd3a1132c11fc4c8848381d5fcb7eb7d6dfebd | /man/SSVS.Rd | 0401b34ad55c35e521a2f5d742eda0159fc92bab | [] | no_license | SValv/BayesianEstimation | 7e4caf5e717eb3f64e891d48433bac1caf1108c5 | ffb871c1994cb4bf62247bec4bff696f745a50d0 | refs/heads/main | 2023-07-08T17:08:38.269286 | 2021-08-04T14:21:37 | 2021-08-04T14:21:37 | 380,739,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 601 | rd | SSVS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/UserFunctions.R
\name{SSVS}
\alias{SSVS}
\title{Load a Matrix}
\usage{
SSVS(
y,
x,
nsave = 1000,
nburn = 1000,
tau0 = 0.01,
tau1 = 10,
S0 = 0.01,
PriorSemiScaling = F,
scaling = T
)
}
\arguments{
\item{y}{Path to the input f... |
292b2f92e6dae3914832141f5228c92efbcea7dd | 37794cfdab196879e67c3826bae27d44dc86d7f7 | /Math/Poly.System.S5.Ht.Formulas.Derivation.R | d78c0f9d3685605c1d993f54e60569473593279b | [] | no_license | discoleo/R | 0bbd53a54af392ef53a6e24af85cec4f21133d17 | e9db8008fb66fb4e6e17ff6f301babde0b2fc1ff | refs/heads/master | 2023-09-05T00:43:32.381031 | 2023-08-31T23:03:27 | 2023-08-31T23:03:27 | 213,750,865 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 13,494 | r | Poly.System.S5.Ht.Formulas.Derivation.R | ########################
###
### Leonard Mada
### [the one and only]
###
### Polynomial Systems
### S5: Hetero-Symmetric
### Useful Formulas
###
### draft v.0.1j
### Derivations
# - Basic derivations;
# - Numerical approaches: basic intuition;
####################
### Helper Functions
### Sol... |
993c7665c61391389de11889e862d81efb8838a3 | 90c5c8a79cb01f1a2475f01f8c0e4ba539492956 | /Scripts/R_Scripts/Data_setup_scripts/add_chromatin_state_annotation_to_genotypes.R | 445d986c5bb95ff755c1d38a4158075f03740c13 | [] | no_license | JacobBergstedt/MIMETH | 626725179fb37adf3853adafd19ccf33c4c1623a | c475440ee5bb3389fae72f1684d270641884ce0a | refs/heads/main | 2023-04-15T03:18:12.731765 | 2022-08-23T13:36:50 | 2022-08-23T13:36:50 | 527,968,587 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 593 | r | add_chromatin_state_annotation_to_genotypes.R | library(tidyverse)
library(glue)
library(vroom)
library(GenomicRanges)
source("./Scripts/R_scripts/Libraries/functions_for_annotation.R")
map_labex <- readRDS("./Data/RData/Genotypes/LabExMI_imputation_1000x5699237_annotated_map_with_ancestral.rds") %>%
mutate(SNP_chr = paste0("chr", SNP_chr))
map_labex_roadmap <-... |
5118894202e519571c366cc669e9af0ec37ee9ee | 94b199a2541bf48ecd5bdeacf3ac7f3cb96613fd | /man/MegaLMM_control.Rd | 76432165c144b59d379e26d684318944c7973145 | [
"MIT"
] | permissive | deruncie/MegaLMM | e83c23851ef7dfdb0fbc9db702a854ea0fbd5fc0 | e9e93c542949213597d3adb02c1bdf52ce4633ea | refs/heads/master | 2023-06-09T10:28:46.851382 | 2023-05-15T18:53:30 | 2023-05-15T18:53:30 | 260,344,286 | 32 | 14 | MIT | 2022-12-07T20:07:24 | 2020-05-01T00:01:28 | R | UTF-8 | R | false | true | 4,071 | rd | MegaLMM_control.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MegaLMM_master.R
\name{MegaLMM_control}
\alias{MegaLMM_control}
\title{Set MegaLMM run parameters}
\usage{
MegaLMM_control(
which_sampler = list(Y = 1, F = 1),
run_sampler_times = 1,
scale_Y = c(T, F),
K = 20,
h2_divisions = 100,
... |
da889f2b2004f9af9592d40bda17126657d376be | 806d4c20f16475c054050a483ddb5d0f9683d94a | /simsem/inst/tests/test_adjust-methods.R | 8ca9a96bb485776997ef8605937baeeb37de273b | [] | no_license | yczh/simsem | b0ffb75da611962164c9de2b855c154c8864c202 | 7262ebab6695178641424e4f30cd97b744799fce | refs/heads/master | 2021-03-10T15:23:51.143229 | 2020-02-12T16:16:58 | 2020-02-12T16:16:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 929 | r | test_adjust-methods.R | source("../../R/AllClass.R")
source("../../R/AllGenerics.R")
source("../../R/simMatrix.R")
source("../../R/simUnif.R")
source("../../R/simVector.R")
source("../../R/adjust-methods.R")
context("adjust-methods: SimMatrix")
loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LX <- simMatrix(loading, 0.... |
d2793cf2ef66394b2177c441672e8ddf313cecad | 6b7eac94cab95036dfcb8f49f992524947aa40ca | /man/sympt_risk.Rd | 95875be7961be8b4b774d62fc2de4a208aa7bd9d | [
"MIT"
] | permissive | Urban-Analytics/rampuaR | d9e4a7b4acfbf06cccc0b25a68dfafebc1836256 | 4a73131228b872a517916e964ac732ff3b25d519 | refs/heads/master | 2023-01-14T11:27:10.922266 | 2020-11-24T15:20:49 | 2020-11-24T15:20:49 | 280,127,722 | 2 | 0 | MIT | 2020-11-05T10:31:09 | 2020-07-16T10:44:09 | R | UTF-8 | R | false | true | 902 | rd | sympt_risk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/covid_status_functions.R
\name{sympt_risk}
\alias{sympt_risk}
\title{Calculating risk of being symptomatic mortality rate based on age
and health risks}
\usage{
sympt_risk(
df,
overweight_sympt_mplier = 1.46,
cvd = NULL,
diabetes = N... |
4c576b50e7aa0d2fdc635d41582cacff6c8b091a | 145b352ce415133220b875ae59cc4cd849c57d7b | /Models_WolfRec_IPM.R | 2c9831f0596741b6fce5a173fce5f215540c75b8 | [] | no_license | akeever2/PhD-WolfProject | c4a9f42564001e03f5372f3e27fae4650e1399ab | f59b818bd1110d705abac4edaae3a3f2ebac8b3d | refs/heads/master | 2021-01-09T20:18:54.651390 | 2019-04-17T18:46:35 | 2019-04-17T18:46:35 | 60,861,089 | 0 | 0 | null | 2017-02-14T17:49:09 | 2016-06-10T16:14:20 | R | UTF-8 | R | false | false | 111,567 | r | Models_WolfRec_IPM.R | ####################################################################I
# A priori models using IPM to estimate recruitment of wolves in Montana
# For more details regarding the IPM see the rscript IPM_WolfRec_V2
# For running models see the rscript Analyses_WolfRec_IPM
# Allison C. Keever
# akeever1122@gmail.com
# ... |
462f06a6ab36210362dda0cd9131ecfa196b2abe | ab39ad07bbbb65c1e61315076a43bce6cfa688f3 | /R_Codes/3-PLS/spls_scores/man/yeast.Rd | 460fcee90f7ec534797572bd6f80666f8fe94765 | [] | no_license | ManonMartin/thesisMaterial | 771e7fdcd0ff3ceba7607f2abaa7d05e20366d61 | 950b79588f224c649ef12a3a3c0fa7c3280e9806 | refs/heads/master | 2021-11-06T23:15:01.741098 | 2021-11-04T13:34:46 | 2021-11-04T13:34:46 | 208,266,675 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,914 | rd | yeast.Rd | \name{yeast}
\docType{data}
\alias{yeast}
\title{Yeast Cell Cycle Dataset}
\description{
This is the Yeast Cell Cycle dataset used in Chun and Keles (2010).
}
\usage{ data(yeast) }
\format{
A list with two components:
\describe{
\item{x}{ ChIP-chip data. A matrix with 542 rows and 106 columns.}
\i... |
3647496a23a4328229edac1135baa728c3d986e8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/WeibullR/examples/weibayes.Rd.R | 64ff1777486926573da909c8485dc3b505989bc6 | [] | 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 | 198 | r | weibayes.Rd.R | library(WeibullR)
### Name: weibayes
### Title: Fitting for Minimal Failure Datasets
### Aliases: weibayes
### ** Examples
fail<-5
susp<-rweibull(10, 1, 10)
eta<-weibayes(fail, susp, beta=1)
|
540738146201eee1b935e82a4d14778d3c367d33 | 1d9f05ac52835004f1f54e890cd9e6b11ea7bc0c | /R/utils.R | bc53ec43628dd57f8efb3009384d6fdb9672421b | [] | no_license | NickPTaylor/simple | 492b423cb00afeef83b3f58652d67bc2623eddc3 | 7988d1d006febb4f120ffa1f09e5789e7a8fcc4c | refs/heads/master | 2021-06-07T04:29:22.456303 | 2016-10-31T10:02:38 | 2016-10-31T12:05:36 | 72,423,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 732 | r | utils.R | #' Launch \code{reverse_string} shiny application.
#'
#' This is a wrapper function for launching the 'application' shiny app in this
#' package.
#'
#' @param ... Arugments to \code{shiny::runApp()}.
#'
#' @export
#'
#' @examples
#' \dontrun{
#' launch_application()
#' }
launch_application <- function(...) {
shiny:... |
7aef9bdfc1e47af031221fc6b8a2c6cf2ff3cea4 | f207a065215b4fa52a4c6e8056f071ec8af28c38 | /man/ler_noticias_g1.Rd | 35df108f5ef3e0e2480523af141a9f19a9a6e1e4 | [
"MIT"
] | permissive | jjesusfilho/g1 | eba458ad5bb859cadf53276c1a06d6fb51b15cda | f8f603a29560deabced4a7f4ca6bd70dc03776fd | refs/heads/master | 2020-08-30T07:25:59.514378 | 2019-10-29T17:56:57 | 2019-10-29T17:56:57 | 218,304,559 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 552 | rd | ler_noticias_g1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ler_noticias_g1.R
\name{ler_noticias_g1}
\alias{ler_noticias_g1}
\title{Lê as notícias baixadas pela função baixar_noticias_g1}
\usage{
ler_noticias_g1(arquivos = NULL, diretorio = ".")
}
\arguments{
\item{arquivos}{Vetor de arquivos. Se NULL... |
f558ce41b634e8ab862860b4b3fb98f0eb78ff91 | f7fe3a7dd980fe1199df3de3708f778745ca116d | /scripts/PGLS.funcs.R | 7d466b1976092c0d2747eee8892b1fb478e1c672 | [
"MIT"
] | permissive | flw88/mut_sex_bias_amniotes | 2597b175cff38ce9069514d5ee1b73428c514288 | 37da9bdbc2c7cb839de15aadb554cf6c98128add | refs/heads/main | 2022-08-23T10:05:29.039202 | 2022-08-17T21:04:45 | 2022-08-17T21:04:45 | 455,734,577 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,206 | r | PGLS.funcs.R | #!/usr/bin/env Rscript
suppressMessages( library(getopt) )
suppressMessages( library(ape) )
suppressMessages( library(ggplot2) )
suppressMessages( library(data.table) )
suppressMessages( library(caper) )
suppressMessages( library(stringr) )
suppressMessages( library(Cairo) )
suppressMessages( library(factoextra) )
supp... |
3a250753f2942bf96e2c325d6288a7275bf22511 | db6b7886398c4602d858fbb1855ce6d8c91b3e68 | /man/agg_vline.Rd | 493127f1f1dca74eae9d8ce6150a83ed6f2113c2 | [
"MIT"
] | permissive | angusmoore/arphit | 08c1bec58bf22d29999204480e962547c1486add | 389efbf00b0775d1e1ec6b0f8f2311eff72bb1b0 | refs/heads/master | 2023-02-08T06:20:35.082649 | 2021-02-09T05:27:35 | 2021-02-09T05:27:35 | 104,701,875 | 3 | 4 | MIT | 2021-02-09T05:27:36 | 2017-09-25T04:02:59 | R | UTF-8 | R | false | true | 753 | rd | agg_vline.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gg-constructors.R
\name{agg_vline}
\alias{agg_vline}
\title{Add a vertical line to your graph}
\usage{
agg_vline(x, colour = "black", panel, lwd = 1, lty = 1)
}
\arguments{
\item{x}{The x coordinate to draw the vertical line at}
\item{colour... |
8b4b64096dc3338c2590830b0e178c0ac084e4b3 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/TESS/R/tess.likelihood.rateshift.R | eab842233a2db2957d8f1d35eeb9ba41dca48aa1 | [] | 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 | false | 12,147 | r | tess.likelihood.rateshift.R | ################################################################################
#
# tess.likelihood.rateshift.R
#
# Copyright (c) 2012- Sebastian Hoehna
#
# This file is part of TESS.
# See the NOTICE file distributed with this work for additional
# information regarding copyright ownership and licensing.
#
# TESS is ... |
028a9b2dde42ed64c2411c923ee0493d2a7e7131 | 571ded08fd6515e01ebeea788688cbd238df7803 | /R/benchplot.r | f322842259109374bad718d6e0b5e8532f1253d7 | [] | no_license | soh-i/Benchmarking | 3391caf01d6738d525f4290eccc002841d4fc878 | 7f613545fcd10a652b47b6dca295036850af40c0 | refs/heads/master | 2020-05-20T10:07:05.950456 | 2013-11-11T15:53:01 | 2013-11-11T15:53:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 869 | r | benchplot.r | library('ggplot2')
# Add data as data.frame
Zhu2013 <- data.frame(precision=0.6178, recall=0.0042, Label="Zhu2013")
PooledSamplesAlu <- data.frame(precision=0.3227, recall=0.0635, Label="PooledSamplesAlu")
PooledSamplesRepetitiveNonAlu <- data.frame(precision=0.4168, recall=0.0032 , Label="PooledSamplesRepetitiveNonAl... |
3a2b44ee9383a2ff5b8d4e7dddf2febf021752b2 | 91470b7555082ff514224bf775d2ecb3379fb2cd | /macro.R | a71aa41b8845d4413b5f7913fce325f5a30f788f | [] | no_license | ChrisComiskey/Misc | 5bbda1f7a784198045a6a0dc4bdbfe934e604468 | 8d218c45ce4734f080fbfe181f60f350c3b0535e | refs/heads/master | 2020-03-28T02:40:31.797862 | 2018-08-28T22:00:54 | 2018-08-28T22:00:54 | 147,588,803 | 0 | 0 | null | 2018-09-05T22:57:24 | 2018-09-05T22:57:24 | null | UTF-8 | R | false | false | 14,718 | r | macro.R | devtools::load_all("/Users/cwcomiskey/Desktop/ODG/Macro-models/macro")
macro::depends() # load all dependencies
# Regression: lm(CPI ~ Top25, ...) =======
lin_reg <- lm(CPI ~ ., data = select(reg_dat, -date))
summary(lin_reg) # R^2: 0.9865
sum_na <- function(x) sum(as.numeric(is.na(x)))
apply(reg_dat, FUN = sum_na... |
52afa7c7385b71686d66f892b85d3f7f0b1c8456 | 1aa92f850ce632811aaa74d769527a8037d8c484 | /tests/check_set_threshold_type.R | 2d67ac8e60c8fdc8012b9e4deed29ab265c08b76 | [] | no_license | cran/mvord | 253c6e7deaf07bf5ac111571b6db307219f1597c | 6699126154748d7510647afc7bda27066aad3549 | refs/heads/master | 2021-06-02T15:11:40.519370 | 2021-03-17T12:20:12 | 2021-03-17T12:20:12 | 102,715,261 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,279 | r | check_set_threshold_type.R | library(mvord)
rho <- list()
rho$ndim <- 5
rho$error.structure$type <- "correlation"
rho$intercept.type = "fixed"
#rho$ntheta <- 1:5
#rho$threshold.values <- lapply(1:rho$ndim, function(j) rep(NA,rho$ntheta[j]))
rho$threshold.values <- list(c(NA),
c(NA,NA),
... |
6672f7c740baaf038d1b9089d90ef7d623f3e16b | 8e5f9b92af6688c11fe1f6e285869a2a6314b3ed | /R/cal_ORA_cluster_similarity.R | f78fe6e9b7ad470c55b8e1efb0209b4668e4066b | [] | no_license | huzhenyu115/LEGO | c9e6d68f698577399843520a8c5d0600dde66cb1 | 92353393807ab7eb3e25ac61c4f4a75e8a41479a | refs/heads/master | 2022-06-23T02:41:57.650298 | 2020-05-08T02:42:18 | 2020-05-08T02:42:18 | 259,661,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,688 | r | cal_ORA_cluster_similarity.R | # this function aims to calculate similarity between cluster results
cal_ORA_cluster_similarity <- function(enrich_file, geneset_over_file){
#enrich_file = ARGV[0] ## output of ORA_filter.pl
#geneset_over_file = $ARGV[1]
#$geneset_over_file = "demo/GeneSet_human.txt_FC2_human_overlap_union.txt"
geneset_over_file_i... |
68db491727b87cbd7bc17c3209f19a1426f73f22 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/patentsview/vignettes/getting-started.R | 18c2ca1094948e64486998f2bc723cef0011d49c | [] | 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 | 3,701 | r | getting-started.R | ## ---- echo = FALSE, message = FALSE--------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ------------------------------------------------------------------------
library(patentsview)
search_pv(
query = '{"_gte":{"patent_date":"2007-01-01"}}',
endpoint ... |
b3a880249e7a18eb42af25e67576126995b855c0 | 6feaf86d6b090b2bfce36ded29df837e2355a65c | /plot6.R | 1f4ce1b960f071ded97cdaff7378c8a6dacbff30 | [] | no_license | Arijit-Nath/ExData_Plotting2 | e20c5d01976942bf183688d923a94d1d6247ab21 | 11463ad9c0f0b12cafce3e2a62c86f5c30381d07 | refs/heads/master | 2020-03-31T17:57:04.099289 | 2018-10-10T16:11:08 | 2018-10-10T16:11:08 | 152,440,170 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,138 | r | plot6.R | #Load libraries
library(ggplot2)
library(stringr)
#Data files are stored in /DATA/ folder
#No need to read if it has already been read
if (!exists("NEI")) {
NEI <- readRDS("./data/summarySCC_PM25.rds")
}
if (!exists("SCC")) {
SCC <- readRDS("./data/Source_Classification_Code.rds")
}
#Comparison ... |
ebf48c871f4027449f2969820b0a6d0a200e7eee | 5149701f86a0cfcc16724af6d3133d404e61cf23 | /SPP by IP (pointer).R | 49f444e89e2706316336530b70e2e72aec71866c | [] | no_license | takanari-seito/R-codes | bc39880dc2b2a0977bc587b0a1be9970a9b0830c | 14e90e70bca2e858245248f2b18fb59a62e30e92 | refs/heads/master | 2020-12-28T22:46:46.195011 | 2015-09-29T08:56:57 | 2015-09-29T08:56:57 | 37,835,712 | 0 | 0 | null | null | null | null | SHIFT_JIS | R | false | false | 1,727 | r | SPP by IP (pointer).R | #@@@グラフデータ(ポインタ)から整数計画法の入力形式への変換,最短距離問題の整数計画法を用いた解法@@@
###データフレームw読み込み(辺v->u;重みw)### Excel上であらかじめソートしておく
data <-read.table("graph3(pointer).txt") #要変更
names(data) <-c("v","u","w")
#頂点数n
n <-max(data$v,data$u)
#辺数ne
ne <-nrow(data)
#始点s
s <-1 #要変更
#終点
t <-18 #要変更
###LP行列m###
#objective function
d <-matrix(0:0,ncol... |
bc1c1949900233fc2da5c2d44c4209ecfa76c809 | 1d9019bf76931acd5a25ff83af59fadf49a41767 | /R/0_functions.R | b7ef89e0cb8c114301560dff09ad8cdbef3b4177 | [] | no_license | GatesDupont/scr_design_sims | 55b0c8a6f1fab770f143252c6cfd4253cd56c318 | 6e620d229b17dba95520b0dc495d228441a4c377 | refs/heads/master | 2023-01-20T08:24:09.016921 | 2020-11-19T21:39:50 | 2020-11-19T21:39:50 | 249,788,566 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,773 | r | 0_functions.R | # Gates Dupont #
# gdupont@umass.edu #
# Sept. '19 - July '20 #
# # # # # # # # # # # #
require(oSCR)
require(dplyr)
require(NLMR)
require(raster)
require(viridis)
require(stringr)
require(landscapetools)
select = dplyr::select
"RIGHT"
right = function(text, num_char) {
substr(text, nchar(text) - (num_c... |
e7e51af64ceeea3d4e541b9e586fddfaa990f5d2 | 7d821006d3c3664c5426e6a488eeb445f587a591 | /code/create-exons-ercc.R | 1beb07f1f79e4f5fd43aab7ae1f2d45f8b55a2a3 | [
"MIT",
"CC-BY-4.0",
"LicenseRef-scancode-unknown-license-reference",
"LicenseRef-scancode-public-domain"
] | permissive | jdblischak/singlecell-qtl | 04e82fcadeecbb2edc1cb6c4181078ccd0becbc4 | 34fcec0eec5ada409c725c56634517903d6a8818 | refs/heads/master | 2022-01-24T00:57:35.897712 | 2022-01-06T20:32:30 | 2022-01-06T20:32:30 | 99,834,237 | 10 | 5 | NOASSERTION | 2019-10-21T15:26:19 | 2017-08-09T17:18:38 | Python | UTF-8 | R | false | false | 2,379 | r | create-exons-ercc.R | #!/usr/bin/env Rscript
# Create exons file of ERCC spike-ins for mapping reads to genes with featureCounts.
#
# Usage:
# Rscript create-exons-ercc.R path/to/ERCC92.gtf > file.saf
#
# where ERCC92.gtf is downloaded from Invitrogen:
# http://media.invitrogen.com.edgesuite.net/softwares/ERCC92.gtf
#
# Notes:
# + Output... |
361f19906982a05e5a4f3ec7fa01c03431b0e0e4 | ba2845eadc8880147e906ab727d322d875226efa | /Analyses/expdesigns.R | 5ae1bc28716c3bca2a7518ab982c5d9995e2ec8c | [] | no_license | AileneKane/radcliffe | 80e52e7260195a237646e499bf4e3dad4af55330 | 182cd194814e46785d38230027610ea9a499b7e8 | refs/heads/master | 2023-04-27T19:55:13.285880 | 2023-04-19T15:15:02 | 2023-04-19T15:15:02 | 49,010,639 | 5 | 2 | null | null | null | null | UTF-8 | R | false | false | 9,442 | r | expdesigns.R | ### Started 28 June 2016 ###
### By Lizzie (so far) ###
## Looking at simple ANOVA results of warming experiments ##
## Given different estimates of warming and given exact design (e.g., blocks) ##
################################
## Read me, notes from Lizzie ##
################################
# Merge in a file tha... |
4ec33d3048cb52918da80dfc801877e127bd5d55 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MSPRT/examples/ump.match.ber.Rd.R | c539b5940ce4034696307419fd5fed67e8c9ee66 | [] | 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 | 195 | r | ump.match.ber.Rd.R | library(MSPRT)
### Name: ump.match.ber
### Title: Finding the "evidence threshold (delta)" in a proportion test
### Aliases: ump.match.ber
### ** Examples
ump.match.ber(n.obs= 60, p0= .2)
|
4405ae0fe5a0a7920aa4a5df20b46206fbc2bdf8 | 4458625f2049529b04909571b0cd7477ab988964 | /tests/testthat/test_cache_rfsrc_datasets.R | 6c3c006137c128758677dced3da5b0c0351063c6 | [] | no_license | ehrlinger/ggRFVignette | 22622b90d728cf2985cb2b60d62d4e32c65a8b27 | 501fbba6c2ad33625cad1732481cf48961f1296c | refs/heads/master | 2020-05-22T01:33:27.901068 | 2018-06-12T18:03:36 | 2018-06-12T18:03:36 | 61,134,330 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 523 | r | test_cache_rfsrc_datasets.R | # testthat for gg_error function
context("cache_rfsrc_dataset tests")
test_that("cache_rfsrc_dataset",{
# # Check the default set of data
# expect_output(cache_rfsrc_datasets(test=TRUE),
# "iris: randomForest")
#
# # If we have a bad path...
# expect_error(cache_rfsrc_datasets(pth="nothin... |
58cea4e51e90820998f7458707d57442713a0907 | 0beecb08c0462c9ef02076d811fc37a185af18c3 | /sim1/epiHistory2tree0.R | 82a24b831d844d0c1646ba411af8866c443abafc | [
"MIT"
] | permissive | emvolz/PhyDyn-simulations | 08fc23e66c19c3136a6396bd35ec2426556771c8 | 694e5cddae08aa7962ce462b91c9dcb6e79be39f | refs/heads/master | 2021-01-22T12:35:27.752379 | 2018-07-04T09:16:40 | 2018-07-04T09:16:40 | 102,350,223 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,705 | r | epiHistory2tree0.R | require(ape)
epi2tree <- function( transmission_table, removal_table )
{
print(date())
donor = transmission_table$donor
recip = transmission_table$recip
t = transmission_table$t
donor[is.na(donor)] <- 'src'
# ~ pids, tremoval, tiplab
pids = removal_table$pids
tremoval = removal_table$tremoval
tiplab = remo... |
7dd2a49aace89c623abcc46a336c1e2adf788d7f | 86a282f2e03d0d8e64127bfe2aa4be6d968d24b4 | /man/puffin.Rd | 9cefa2d054d29018253e7902878dd246eb157ea5 | [] | no_license | u44027388/LearnBayes | fc57e5689c9619de966f4b9e0210bb3aa078ec8f | f7722076d01768bb845bfe9bed78c365fcf292df | refs/heads/master | 2021-09-14T19:23:22.283849 | 2018-05-17T21:04:10 | 2018-05-17T21:04:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 728 | rd | puffin.Rd | \name{puffin}
\alias{puffin}
\docType{data}
\title{Bird measurements from British islands}
\description{
Measurements on breedings of the common puffin on different
habits at Great Island, Newfoundland.
}
\usage{
puffin
}
\format{
A data frame with 38 observations on the following 5 variables.
\describe... |
b15c3436a442ccabe1603aa2a4d3da01176dd4f8 | a2099edf0795624d588faae2095f6104944977ab | /R/utils.R | 5487b5316b8af411b060abfa9d63a24b4cc56bfa | [] | no_license | ijlyttle/shinychord | 00343338bb7e0fdf33ee703201bc89ecf82e4726 | 71c6abd6b05d012f1fb54a227f6ca24adb760911 | refs/heads/master | 2020-05-17T03:58:11.967469 | 2016-01-29T23:52:00 | 2016-01-29T23:52:00 | 40,910,215 | 14 | 2 | null | 2016-01-29T23:52:00 | 2015-08-17T16:27:03 | HTML | UTF-8 | R | false | false | 3,869 | r | utils.R | #' Closure for file-downloading
#'
#' A closure to provide a function to download a file,
#' by copying it from the specified place. Useful for
#' \code{shiny::downloadHandler()}.
#'
#' @param file_source character describing path to file to be copied
#'
#' @return function
#' @export
#
cl_file_copy <- function(file_s... |
50250fb7d49c57875efcdf5ceecc98f253022b52 | fbacfe68bd4fd04b2d1e980679b8521bdc6d2fa1 | /man/tweet.Rd | 9a6c4e7dbdb753c0985d4f8e58d964a62ce6de1f | [] | no_license | cran/seismic | 107f8486f0f3aaaa8e17a899783c9e432021e8a4 | b1bc6c9f978369f0b11f91c89c2c5b62a87b038e | refs/heads/master | 2022-06-18T09:18:16.150815 | 2022-05-20T20:30:02 | 2022-05-20T20:30:02 | 36,954,497 | 3 | 2 | null | null | null | null | UTF-8 | R | false | true | 490 | rd | tweet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tweetfunctions.R
\name{tweet}
\alias{tweet}
\title{An example information cascade}
\format{
A data frame with 15563 rows and 2 columns
}
\description{
A dataset containing all the (relative) resharing time and node degree of a tweet. The orig... |
e1fae9ccdb8dc1985cf44656aa4a6625cdfc896a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/UsingR/examples/sp500.excess.Rd.R | 616dc9a9b42ee4ee4451913531e33f58e9139d02 | [] | 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 | 186 | r | sp500.excess.Rd.R | library(UsingR)
### Name: sp500.excess
### Title: Excess returns of S&P 500
### Aliases: sp500.excess
### Keywords: datasets
### ** Examples
data(sp500.excess)
plot(sp500.excess)
|
2e7a8e8453d7cbf6444bafa46ca147bf10cc46dc | 87fdb51b3b0e92f42a3e33dbf07d0c01628d2aaa | /man/possol.Rd | 7c6a59dda58c7f926bd3804a917ee82d5e6a8901 | [] | no_license | belasi01/Cops | 0b5e0f04c46a639dfe5b8716199abf32757732f4 | 5cd0fa2f5fedb338cc063d1af21cf22ead0f9c5f | refs/heads/master | 2023-07-25T16:07:36.614152 | 2023-07-12T01:08:54 | 2023-07-12T01:08:54 | 72,561,413 | 9 | 8 | null | 2022-08-31T16:13:21 | 2016-11-01T17:49:49 | R | UTF-8 | R | false | true | 750 | rd | possol.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/possol.R
\name{possol}
\alias{possol}
\title{Solar position as a function of date and time UTC and position (lat, lon)}
\usage{
possol(month, day, tu, xlon, xlat)
}
\arguments{
\item{month}{is the month (1 to 12)}
\item{day}{is the day of th... |
5ecde356695f1bd7cd6babf96803661a3c2219b0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SimCorrMix/examples/calc_mixmoments.Rd.R | c7a02511978289efdfa74d9c56e1f7b42bb94604 | [] | 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 | 450 | r | calc_mixmoments.Rd.R | library(SimCorrMix)
### Name: calc_mixmoments
### Title: Find Standardized Cumulants of a Continuous Mixture Distribution
### by Method of Moments
### Aliases: calc_mixmoments
### Keywords: cumulants mixture
### ** Examples
# Mixture of Normal(-2, 1) and Normal(2, 1)
calc_mixmoments(mix_pis = c(0.4, 0.6), mix_mus... |
130aad06679ef0a0db53ba6f3a20fd1d92251eae | 16ba38ef11b82e93d3b581bbff2c21e099e014c4 | /haohaninfo/交易實務案例/63.R | b6859e49497292f458f7b24e7798d708b4d13e71 | [] | no_license | penguinwang96825/Auto-Trading | cb7a5addfec71f611bdd82534b90e5219d0602dd | a031a921dbc036681c5054f2c035f94499b95d2e | refs/heads/master | 2022-12-24T21:25:34.835436 | 2020-09-22T09:59:56 | 2020-09-22T09:59:56 | 292,052,986 | 2 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,038 | r | 63.R | #取得即時報價，詳細在技巧51
source("function.R")
#設定初始的資料格式
Qty <- matrix(, nrow = 0, ncol = 2)
MAnum <- 5
QMA <- NA
while(TRUE){
#取得成交資訊
Mdata<-GetMatchData(DataPath,Date)
MatchAmount <- as.numeric(Mdata[[1]][4])
HMTime <- as.numeric(paste0(substr(Mdata[[1]][1],1,2),substr(Mdata[[1]][1],4,5)))
#若為初始值，即更新最新一... |
4ded814ba2965e49184077dd42e38acb595e8792 | be7f65793d79b77b520739cf7aa24008829ab92f | /hw3/My code and submission/hw3.R | 04e7b797f89f824f2a8d759ccdf91fd7e550e283 | [] | no_license | aten2001/ISYE-6503 | 5506a8ba5d3c6ef4357f23e65db3acac10f7cd42 | 18824dc41b8ac381b551db041ba24e0fedaf2aad | refs/heads/master | 2022-01-27T17:07:50.912732 | 2019-07-01T20:51:32 | 2019-07-01T20:51:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,347 | r | hw3.R | library(data.table)
library("smooth")
setwd("//cdc.gov/private/L137/yks5/OMSA/ISYE6501/hw3")
setwd("G:/a-XiaoWang/OMSA/ISYE-6501/hw2")
#Question 7.2
Using the 20 years of daily high temperature data for Atlanta (July through October) from Question 6.2
(file temps.txt), build and use an exponential smoothing mod... |
55b8150ddb89f16e8396aa1e3fb17a20c2f03353 | afe4c3518d472c82479f9ed646491545e1ee6718 | /2.scripts/4.all.figures.R | dbab795be889d1b82049f1cfb70252a4ca496e0f | [] | no_license | gmkov/microclimate.thermal.tolerance.Heliconius-JEB-2020 | fdeec8271e2e623138d646c55bec7d1b9221dc40 | 795646343b2f8a082628e1221017f1e62b5be0d2 | refs/heads/master | 2020-12-27T08:45:34.752930 | 2020-02-02T21:25:30 | 2020-02-02T21:25:30 | 237,835,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 73,168 | r | 4.all.figures.R | ### Montejo-Kovacevich, G., Martin, S.H., Meier, J.I., Bacquet, C.N., Monllor, M., Jiggins, C.D. and Nadeau, N.J., 2020. ###
### Microclimate buffering and thermal tolerance across elevations in a tropical butterfly. ###
### Journal of Experimental Biology. ######
##################### ALL FIGURES PREP ################... |
e9300e84c3155605c22bb0dbbeac10cc74419518 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/glamlasso/man/glamlassoRR.Rd | 17ed7c5de034bb55246898a754241a2bfc1df86d | [] | 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 | 9,262 | rd | glamlassoRR.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/glamlassoRR.R
\name{glamlassoRR}
\alias{glamlassoRR}
\title{Penalized reduced rank regression in a GLAM}
\usage{
glamlassoRR(X,
Y,
Z = NULL,
family = "gaussian",
penalty = "lasso",
... |
fb9134c553977d5752630c7b51696fa553d5a50d | fc7a76422c87efc0b48ee58aa0bcb881f1511cbe | /R Code/Classification Model/Parkinsons Dataset/AllMethods.R | 8558a33f8137d6cc60835558e38b371217329998 | [] | no_license | Sam-Malpass/Data-Analytics-and-Mining | 9d551da5325ef9f17d05dc54f1370a20da0206a2 | 62a627bc39f4a959e6881f1a49056791e9a36cb7 | refs/heads/master | 2022-03-23T14:08:25.454576 | 2019-12-03T20:44:38 | 2019-12-03T20:44:38 | 225,706,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,900 | r | AllMethods.R | # Resubstitution
library(rpart)
all_accuracies<-c()
all_devs<-c()
input_data<-read.csv("C:/Users/sam/Desktop/DM Coursework Data/parkinsons.data", header=TRUE)
input_data<-subset(input_data, select=-c(name))
numRecords<-length(input_data[[1]])
numTrials<-20
accuracies<-c()
for(i in 1:numTrials)
{
training_set<-inpu... |
e556ebd8115d83c95f58ded54a274e1a39b24e3f | fb6416b9ded37fb3a00709280e5577605b3a1f7d | /R/utils_classify.R | e46877a230358348c11a67e6eb99a788e16b0f78 | [
"Artistic-2.0"
] | permissive | nlawlor/powsimR | 5c6d31bd3e2fdbad9cfc61fa6f253cb5c5d27f96 | 326cd1cc3d1b885c5ad3cbaaf3c2a3c28ebd7cf8 | refs/heads/master | 2020-03-20T15:42:26.677961 | 2018-06-15T18:31:08 | 2018-06-15T18:31:08 | 137,519,673 | 0 | 0 | null | 2018-06-15T18:22:44 | 2018-06-15T18:22:43 | null | UTF-8 | R | false | false | 2,202 | r | utils_classify.R |
# WRAPPER FOR CELL GROUP CLASSIFICATION -----------------------------------
## NOTE: minclust has adjusted rand index implemented
# TODO: put a counter of how many HVG were actually found (absolute and relative to total # genes!)
# NULL always means skip this step
.classify.calc <- function(Pipeline,
... |
51ace54d6cec713a99a818d1d3e82dabe2a8ed08 | b7e1d6a5ffc9f5e80614ec6eb987cde629765c49 | /HeatMapProject.R | d798d120cd8f32a8038da8fb1bb7c66a5a970920 | [] | no_license | tristanjkaiser/Heatmap_rWorldMap | 2cc64001f7e4d87c3fe9e31615d8b14c4c0bd3f2 | 13e55632c714f58547a1063a83c89e59815181f9 | refs/heads/master | 2021-09-16T14:04:51.472735 | 2018-06-21T16:55:28 | 2018-06-21T16:55:28 | 67,171,815 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,604 | r | HeatMapProject.R | # First HeatMap exploration
# By: Tristan Kaiser
# 09/01/2016
library(readr)
library(rworldmap)
library(RColorBrewer)
library(classInt)
# Load data ---------------------------
df <- read.csv('./Data/Metal_Data.csv', header = TRUE, na.strings=c("", NA))
# Clean data -------------------------
# Convert Percentage col... |
a4fe18a88d57043eb41269bcc28d72ca33e97594 | f697cbbb0da988fd43c07652d5955fbb82e25e38 | /GoViewer/R/wideDirectionFormat.r | 994c56bd35efd7bba160f87d112895678acc2d89 | [] | no_license | aidanmacnamara/epiView | eec75c81b8c7d6b38c8b41aece3e67ae3053fd1c | b3356f6361fcda6d43bf3acce16b2436840d1047 | refs/heads/master | 2021-05-07T21:22:27.885143 | 2020-06-22T13:08:51 | 2020-06-22T13:08:51 | 109,008,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,124 | r | wideDirectionFormat.r | #' prepares projected direction observations to reconstruct a direction vector
#'
#' In an attempt to guess the direction in a higher dimensional PCA space, the
#' user selects the direction in one or combinations of PCA components. This
#' code takes the output captured from the GUI and coverts it in to a simpler form... |
25eb5c9b73f0373843a936ca9b64f2d3cff3554c | 1d2e96ba9f11671e0f422ad56932d4e101941cf4 | /man/topo2.Rd | b4dd110c8481e15c35a9668baa3b71d7ae96b3b3 | [] | no_license | cran/ocedata | 65642b611b59cea252d59f45950cf084b2d60a09 | 7f152f7b7a357900f720239cbb0dcca93045808c | refs/heads/master | 2022-08-23T19:45:28.151629 | 2022-08-19T08:40:02 | 2022-08-19T08:40:02 | 19,524,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 928 | rd | topo2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/topo2.R
\docType{data}
\name{topo2}
\alias{topo2}
\title{World topography on a 2-degree grid}
\usage{
data(topo2, package="ocedata")
}
\description{
\code{topo2} is a matrix containing world topography data, on a 2-degree grid.
This is provi... |
bb9dd54a2b950328706f08ac6e986ebe20bce492 | 89f09607d51b2552f05465d7325e235fe376ba19 | /finestructure/Weighted_parameters.R | e6d9b08dfea8686f5d7dc30afabedacaae854144 | [] | no_license | weigelworld/north_american_arabidopsi | d0e68bcd749ffa4634c154e884d49e05d807b777 | 00cb7750fe04e1246d2bcb3bd058cca23b3d8f03 | refs/heads/master | 2023-05-30T12:37:11.772765 | 2021-06-10T05:00:36 | 2021-06-10T05:00:36 | 375,575,979 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 472 | r | Weighted_parameters.R | setwd('/ebio/abt6_projects9/Methylome_variation_HPG1/origins/post_vcf1/shapeit_noQD30/combinePops/EurasianOnly')
parameters <- read.table('EurasianOnly.parameter_summary.txt', header = T)
head(parameters)
parameters$sum_chr_Ne <- parameters$N_SNPs*parameters$Ne
parameters$sum_chr_Mu <- parameters$N_SNPs*parameters$Mu
... |
83ee165734b3c43e6dc95d13c9147d4aaa3fb7b8 | 84936791d8a9f5686ea68ac96efdf851dc7b5fcf | /R/bpw_wg1.R | 8bbf55d4c8bdb00c4e8f98ff2d71801f7523fc81 | [] | no_license | petzi53/austRia | 3cf128648831332a57a3e4d4812bc64dd473b3e4 | c8300bd9d2348166d51ca1df9fb39a86c95a43e7 | refs/heads/master | 2021-01-11T22:30:54.103082 | 2016-10-30T16:04:45 | 2016-10-30T16:04:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,035 | r | bpw_wg1.R | #' Bundespräsidentenwahl Österreich 2016 (Austrian presidential election)
#'
#' This dataset contains the number of votes for each candidate per municipality
#' for the first round of voting of the Austrian presidential election 2016.
#'
#' @source \url{http://www.bmi.gv.at/cms/BMI_wahlen/bundespraes/bpw_2016/Ergebni... |
d90b14d196155e698c69b9e590ff648904c2874c | 81fe9027382cf52464ec0bc66ed54e9b27ba3c8e | /cachematrix.R | 2cff2117eac17b3a73777ed247e4a2d46de8fa9e | [] | no_license | minnatwang/ProgrammingAssignment2 | 3a52bc5136a1d1da0e2c8b9c962ca4f4a85a53b4 | 6040cec935dd3bb06283fceea6293042421031b9 | refs/heads/master | 2021-01-15T16:51:44.359340 | 2017-08-08T20:19:57 | 2017-08-08T20:19:57 | 99,729,596 | 0 | 0 | null | 2017-08-08T19:39:14 | 2017-08-08T19:39:13 | null | UTF-8 | R | false | false | 809 | r | cachematrix.R | ## Cache the inverse of a matrix for faster inverse retrieval in the future
## Create functions to set and get both the matrix and its inverse
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
get <- function() x
setinv <- function... |
1275e62a549fa89c18260232bcf2284ff923f6e6 | 1a4db22d3a7702c38b61c9d4700c6a3b94d145ba | /PER/plm.R | 9b4c4f3917fae9e977e640b4e39d7193a8d15950 | [] | no_license | koki25ando/hatenablog_code | 316e99eff210169e70773d62d355fb7ca8b7eea0 | 464309485a3fe21ad21335c264b9baa9597bb354 | refs/heads/master | 2020-12-08T10:27:19.638923 | 2020-07-12T13:45:35 | 2020-07-12T13:45:35 | 232,957,511 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 676 | r | plm.R | # https://nigimitama.hatenablog.jp/entry/2018/11/05/021516
setwd("/Users/KokiAndo/Desktop/R/hatenablog/PER")
pacman::p_load(tidyverse, data.table, naniar, plm)
stats = fread("Seasons_Stats.csv")
per_dat = stats %>%
filter(Pos %in% c("PG", "SG", "SF", "PF", "C")) %>%
filter(Year > 2000 & G > 20 & Age < 40 & Age > ... |
229be14da74f570bc8be6cecc699f2f83434d404 | 753e3ba2b9c0cf41ed6fc6fb1c6d583af7b017ed | /service/paws.cloudwatchlogs/man/test_metric_filter.Rd | dfa000b5028ab79f3fc8face670f3c370f96cb03 | [
"Apache-2.0"
] | permissive | CR-Mercado/paws | 9b3902370f752fe84d818c1cda9f4344d9e06a48 | cabc7c3ab02a7a75fe1ac91f6fa256ce13d14983 | refs/heads/master | 2020-04-24T06:52:44.839393 | 2019-02-17T18:18:20 | 2019-02-17T18:18:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 760 | rd | test_metric_filter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/paws.cloudwatchlogs_operations.R
\name{test_metric_filter}
\alias{test_metric_filter}
\title{Tests the filter pattern of a metric filter against a sample of log event messages}
\usage{
test_metric_filter(filterPattern, logEventMessages)
}
\ar... |
e826aa39ab70e70e95db234743fa06482cb4ca4d | c9d5c137e1a3c11175f002c1e159a5e091ea6efc | /dataanalysis/analysis/topic-modeling/04bstmSelectModelsK20.R | 959099be3e9b1a4ef574d172664484e50bd0b6c0 | [] | no_license | margaretfoster/WTO | 955cb80ee70e0fc527c89dd20da68db006452078 | 476ba4a2821b2929c8310f1058ac847a904a30ce | refs/heads/master | 2022-06-22T11:38:02.944583 | 2022-06-16T12:40:58 | 2022-06-16T12:40:58 | 139,746,359 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,516 | r | 04bstmSelectModelsK20.R | #########
### Model select K20
#########
rm(list=ls())
loadPkg=function(toLoad){
for(lib in toLoad){
if(! lib %in% installed.packages()[,1])
{ install.packages(lib, repos='http://cran.rstudio.com/') }
suppressMessages( library(lib, character.only=TRUE) ) }}
packs <- c('tm', 'stm', 'p... |
b68f693e7bcf0529578254a05149abd962c1dd10 | 1c7ed6ecdf6101a1d68e8106309283e7bb325af3 | /man/commSimul.Rd | a6e9be36fe87c8472710283178e93e860757e77e | [] | no_license | guiblanchet/countComm | 5aec6c61ad32ff75c9a63b8178c252fa14d78542 | b338009feba8789f898c800963229822e8401e6a | refs/heads/master | 2022-09-11T16:10:14.346773 | 2020-06-02T19:33:38 | 2020-06-02T19:33:38 | 268,888,496 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,491 | rd | commSimul.Rd | \name{commSimul}
\alias{commSimul}
\title{
Community simulation function
}
\description{
Simulate a community following the procedure proposed in Blanchet et al. (in press).
}
\usage{
commSimul(nquad,nsp, nind,patchrad=0.2,SAD="lognormal",sdlog=5)
}
\arguments{
\item{nquad}{Number of quadrates in which to divide th... |
eab3f2532633e27f07736498e03393e373a4df91 | 14161ca2db029ea5850d916b6ca7fb959e2c3f3d | /wine.R | 9da70d0abad38abdc255376338b27a905150c4d4 | [] | no_license | pochuan/stats315a | dea23679c76893155b3e336097e7208ab5a7ecff | c50f3377b9009a0dedd1778d2dbfdc1cbbb893f1 | refs/heads/master | 2016-09-06T02:22:58.617929 | 2014-03-12T07:39:42 | 2014-03-12T07:39:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,719 | r | wine.R | # load data with: wine.train <- read.csv("wine.train.csv", header=TRUE)
cv.error <- function(model.fn, formula, num.folds, train.data) {
N = nrow(train.data);
fold.size <- floor(N/num.folds);
error <- 0.0
fold.begin <- 1; fold.end <- floor(N/num.folds);
for (i in 1:num.folds) {
# train model on all-bu... |
a8271087e9ef1543dc0ab62ff45f68e179683390 | 22a5979ae04326d13c4e4b13f4f1d72748ab412e | /R/rlognormal.R | 65e7323a3b590967ec44c3e1c4c83e0751b0e6f8 | [] | no_license | guochunshen/sce | 7c719bb0ccf58d73310935f0650a6e0a92d0e037 | 206cd176ef23eebb9daec3380f5a9f65531a65d4 | refs/heads/master | 2021-05-04T14:04:16.579562 | 2013-06-26T07:14:06 | 2013-06-26T07:14:06 | 6,288,742 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 465 | r | rlognormal.R | #'
#' generate a lognormal species abundance distribution
#'
#' @param N total number of individual
#' @param S total number of species
#' @param meanlog the expect mean value of lognormal distribution
#' @param sdlog the expect standard deviation of the lognormal distribution
#'
#'
#'
rlognormal=function(N,S,me... |
5bf4d6448679bc41c08046f0ef2041c99ccd16a4 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /hetGP/inst/doc/hetGP_vignette.R | 9883761cfc361635457c9fb6224d0e47f1f9527d | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 20,872 | r | hetGP_vignette.R | ## ----include=FALSE-------------------------------------------------------
library("knitr")
## cache can be set to TRUE
opts_chunk$set(
engine='R', tidy=FALSE, cache=FALSE, autodep=TRUE
)
render_sweave() # For JSS when using knitr
knitr::opts_chunk$set(fig.pos = 'ht!')
## ----preliminaries, echo=FALSE, results='hi... |
a1ac91e691b48882bc3dcf2dd0b46da04ecf1027 | 2abd33ed5fb7048bde5f7715c2d404bdb31406d0 | /Week 11/Studio11/perm.log.reg.R.txt | aac6d7e97d163517346692a2476d6f9235cbe926 | [] | no_license | theRealAndyYang/FIT2086-Modelling-for-Data-Analysis | ba609c3b7a63f414d5e19968f9d6650864590e5c | 81fa3a4a2ffe47dadb9702ae77115203766094a0 | refs/heads/master | 2022-11-26T03:57:22.673076 | 2020-08-05T11:13:53 | 2020-08-05T11:13:53 | 285,262,206 | 9 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,594 | txt | perm.log.reg.R.txt | perm.log.reg = function(formula, data, R)
{
rv = list()
n = nrow(data)
# Fit the initial logistic regression model to the original data
rv$fit = glm(formula, data, family=binomial)
# Figure out what the target variable is, and how many coefficients there are
target = as.character(rv$fit$t... |
7124180c89e0d5e4ceb127840322e545260f288a | 36fe24142a262ab40d5b0c8f1f5ceecf8fd6b8cf | /man/IVoverid.Rd | 85ea3c40c33dbcc97db531519ec4df55a55523f3 | [] | no_license | shizelong1985/ManyIV | 2619a18d304a8ad4b65935aa652b271aa7f068f6 | 7b5c3e957b7488f72ddd12d2221f288665bb730c | refs/heads/master | 2023-03-17T15:07:42.329751 | 2021-02-22T18:38:26 | 2021-02-22T18:38:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,415 | rd | IVoverid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/manyiv.R
\name{IVoverid}
\alias{IVoverid}
\title{Test of overidentifying restrictions}
\usage{
IVoverid(r)
}
\arguments{
\item{r}{An object of class \code{RDResults}}
}
\description{
Report the Sargan and modified Cragg-Donald test statistics... |
855a34b2bf9163b0ad3ce19c85f0125f4653fa89 | ab79177ad95b0e89d70210a3478b91f98cdb6b30 | /man/unit_contrast.Rd | a5d9584fe0754c7e2cbe1b54e3a6f11876205ddf | [] | no_license | bbuchsbaum/fmrireg | 93e69866fe8afb655596aa23c6f9e3ca4004a81c | 2dd004018b3b7997e70759fc1652c8d51e0398d7 | refs/heads/master | 2023-05-10T17:01:56.484913 | 2023-05-09T14:38:24 | 2023-05-09T14:38:24 | 18,412,463 | 6 | 1 | null | null | null | null | UTF-8 | R | false | true | 685 | rd | unit_contrast.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/contrast.R
\name{unit_contrast}
\alias{unit_contrast}
\title{Unit Contrast}
\usage{
unit_contrast(A, name, where = NULL)
}
\arguments{
\item{A}{A formula representing the contrast expression.}
\item{name}{A character string specifying the na... |
933dfa83d04e5cbabd762680a352992bd064e36d | 9430e5cd40071a7a0a5e92a3a17ee4706538f0d3 | /man/getSimWang.Rd | da9095107bd0ca614bc2abcc47b681874d556b9c | [] | no_license | MoudFassad/HPOSim | 63314cf60d420dc402fb42411f9557af82687f61 | 03a2559b5d0cedc8db6a6e207236a1cd220a6763 | refs/heads/master | 2022-03-30T07:25:05.146874 | 2020-01-09T12:38:10 | 2020-01-09T12:38:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 704 | rd | getSimWang.Rd | \name{getSimWang}
\alias{getSimWang}
\title{ Semantic Similarity Between Two HPO Terms by Wang's Method }
\description{
Given two HPO terms, this function will calculate the Wang's Semantic Similarity between them
}
\usage{
getSimWang(term1, term2)
}
\arguments{
\item{term1}{ one HPO term }
\item{t... |
7d49ea3581567b89fa9d942c0d51cd64f26a9955 | 768a5e8713ed0751fdea1fc0512dc5e87c1c06b0 | /R/EvapHeat.R | 99d7c04baae5e1049a72774e6b65743156891bc8 | [] | no_license | cran/EcoHydRology | c854757a7f70f91b3d33d6f7c5313752bf2819e3 | d152909c12e6bb0c1f16b987aa7f49737cdcf3d3 | refs/heads/master | 2020-05-16T21:01:18.881492 | 2018-09-24T11:52:33 | 2018-09-24T11:52:33 | 17,691,749 | 6 | 6 | null | 2018-08-29T19:54:05 | 2014-03-13T02:26:21 | R | UTF-8 | R | false | false | 819 | r | EvapHeat.R | EvapHeat <- function (surftemp, airtemp, relativehumidity=NULL, Tn=NULL, wind=2) {
## surftemp: Temperature of surface [degrees C]
## airtemp: Temperature of air [degrees C]
## relativehumidity: between 0 - 1 [-]
## Tn minimum dailiy air temperature, assumed to be the dewpoint tempera... |
c62c092c07f19dc80d0215b26e3d3592682a4af2 | 42312ee43cf5ce7c629ea43841ca4a2c7f4e176f | /inst/shiny/server.R | a21436930b9d2bf6e6e7b296e224dac84b60ff42 | [] | no_license | marieannevibet/ArchaeoPhases | dca4175f9e9655caa702442d3779bb178aa304da | d6094e9335a23d78e5fe11ddb3cf10c8ad95cd49 | refs/heads/master | 2020-12-02T08:02:28.833445 | 2017-09-28T11:59:23 | 2017-09-28T11:59:23 | 96,762,386 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,018 | r | server.R |
shinyServer(function(input, output, clientData, session) {
# By default, Shiny limits file uploads to 5MB per file. You can modify this limit by using the shiny.maxRequestSize option.
# For example, adding options(shiny.maxRequestSize=30*1024^2) to the top of server.R would increase the limit to 30MB.
option... |
329a04fed4bd6457480f70a84182c1993481b6ae | 8585dd8814d82d9a0870804d8a5acf9ad650d0ed | /tests/testthat/test-hypothesis.r | fe7aa5f95a0e29e3626a4c4d11cd102747e026bb | [] | no_license | brentonk/coefbounds | 7500d38188c87b41c2b6ebdbef5f1d5f04517dce | 7c7b65a7d34ecec01ac6a6f1062c4eeab24cab08 | refs/heads/master | 2021-01-17T19:17:25.817055 | 2016-06-28T21:33:03 | 2016-06-28T21:33:03 | 59,677,826 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,179 | r | test-hypothesis.r | context("Interval hypothesis testing")
set.seed(1776)
x1 <- rnorm(100)
x2 <- rnorm(100)
yl <- rnorm(100)
yu <- yl + rexp(100)
fit <- coefbounds(yl + yu ~ x1 + x2, boot = 111)
test_that("interval_hypothesis() fails on bad input", {
fit_bad <- coefbounds(yl + yu ~ x1 + x2, boot = 0)
expect_error(interval_hypot... |
15c861130e46da659292062c3946f1748898ba3a | ce4e06b44516ffa6028b3c7046537ec1e36a384d | /man/replace_null.Rd | 571a2dfb147b36560ad448d2fd35f45ee7a36883 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | DiseaseOntology/DO.utils | 6bdac0491225559d104e652289e1cfc1e3768cc8 | 64d8b7c272228f10a9277b380b6864125fd2377f | refs/heads/main | 2023-08-17T18:54:02.968998 | 2023-08-11T18:29:44 | 2023-08-11T18:29:44 | 379,923,811 | 1 | 0 | CC0-1.0 | 2023-07-05T19:37:42 | 2021-06-24T12:52:22 | R | UTF-8 | R | false | true | 670 | rd | replace_null.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/replace-methods.R
\name{replace_null}
\alias{replace_null}
\title{Replace NULLs with specified value}
\usage{
replace_null(data, replace)
}
\arguments{
\item{data}{A list (or list column in a data frame).}
\item{replace}{A single value to us... |
54ecdc074abb8cb106bd3f44fb446d4f087a8568 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/dynamichazard/examples/ddhazard.Rd.R | f15c0abc5bb7f97e17332cb5fa5ec85bfb44788e | [] | 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 | 610 | r | ddhazard.Rd.R | library(dynamichazard)
### Name: ddhazard
### Title: Fitting Dynamic Hazard Models
### Aliases: ddhazard
### ** Examples
# example with first order model
library(dynamichazard)
fit <- ddhazard(
Surv(time, status == 2) ~ log(bili), pbc, id = pbc$id, max_T = 3600,
Q_0 = diag(1, 2), Q = diag(1e-4, 2), by = 50,
cont... |
c7be0881a249fa53cd6e5b42f8a83840e048c925 | 2afc7ea170926ca71969e9177beadf69e3e7b25b | /man/data_package_shiny_handler.Rd | eff1b63cedffdfea35a0280e8f8a9da3520f7334 | [
"MIT"
] | permissive | mobb/datapie | 7ffdbb806cc95cfbfcc08ed12987caa0f3a25750 | 23be1331e2bbb84b37693aa8317282a8e681ef21 | refs/heads/master | 2022-04-13T03:36:01.536365 | 2020-02-21T23:47:06 | 2020-02-21T23:47:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,312 | rd | data_package_shiny_handler.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_package_shiny_handler.R
\name{data_package_shiny_handler}
\alias{data_package_shiny_handler}
\title{A data_package_* handler for the shiny app}
\usage{
data_package_shiny_handler(data.pkg.doi = NA, current.data = NULL,
download.dir = N... |
747cf8ecab5dae840c932055ce92b221275614f2 | 9540aa50a146f51f564c01276ded066b5fa834fe | /fun/plot_sim_intv.R | b360c86a3714fdbf979cee1f245464e69a0b6ec9 | [] | no_license | kklot/india-subnational-tbmodel | feebfe2c15661d53c7d6e89c3a4f00538d2d6dd3 | c365a681624d6c2cba6bbd7b287c4f61c726472b | refs/heads/master | 2023-04-06T03:54:47.961703 | 2019-12-24T15:27:27 | 2019-12-24T15:27:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,971 | r | plot_sim_intv.R | plot_sim_intv <- function (
itvs # Takes object with intervention results
) {
library(ggplot2)
library(gridExtra)
x <- seq(2026 - length(itvs$inc), 2025, by = 1)
fields <- c('inc', 'mort')
titles <- c('Incidence', 'Mortality', 'Incremental Cost')
if (size(itvs$inc, 1) > 1) {
... |
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