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53ee41f928f076b9b2b887b7e54f6c91b1780bf6 | 5bfe5326df355d9014046646eda81ec5c9ea0b0e | /man/ez.combat.Rd | 15f134929323f390daa2357c427ebbf37b1addee | [
"Artistic-2.0"
] | permissive | TKoscik/ez.combat | c015f293d093d2115ee989906b914fb28f54bfa5 | 41d00cbc3d73f7ffc6d0f8bc131ea1b85a160a76 | refs/heads/master | 2023-01-13T22:52:49.239829 | 2023-01-05T19:23:14 | 2023-01-05T19:23:14 | 139,920,229 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,579 | rd | ez.combat.Rd | \name{ez.combat}
\alias{ez.combat}
\title{ComBat Harmonization for Dataframe Objects}
\description{A easy to use function for applying ComBat Harmonization for batch effects on dataframe objects.}
\usage{
ez.combat(df,
batch.var,
adjust.var = "all",
exclude.var = NULL,
... |
c04147be6a210969453dd8e1e1aa0af576c64b94 | 6afccd04e65601f1af177ec273b223e85fadf5f3 | /etrib.R | c05e3224c6930d62cbb79f777c4802138992d0ca | [] | no_license | patdab90/etrib | fccaa5433f70a3e70157cfceb9a8ad489c0b37ed | 13901547b633cd9851457b2d80273fe2be01a854 | refs/heads/master | 2016-09-02T00:39:44.921882 | 2014-08-05T10:59:02 | 2014-08-05T10:59:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,005 | r | etrib.R | M <- 1E+10
MINEPS <- 1E-10
source('etriutils.R')
source('etribbase.R')
source('etribcardinalities.R')
source('etribpairwisecomp.R')
etrib.init <- function(performances, profiles, assignments, monotonicity, th, cardinalities, pCk, pCl) {
stopifnot(ncol(performances) == ncol(profiles))
stopifnot(is.null(assignment... |
e01407643079f9252b8645c8b3498c0aa8ebfcfe | 2b88050b540cc67759ad9722be2ae69b93466b4f | /man/check_stop_criteria.Rd | 9b5dbace471623fd6623c1524e5fed2ba26e756f | [] | no_license | fcampelo/MOEADr | 632ff9dd2e6755ea5e9d5f389064688aee0878b4 | 0ac9b1962ef0c76eb1eedfa63756eeeec147135a | refs/heads/master | 2023-06-30T11:01:23.181052 | 2023-01-06T12:39:42 | 2023-01-06T12:39:42 | 61,828,989 | 15 | 9 | null | 2023-06-10T13:58:44 | 2016-06-23T18:50:55 | R | UTF-8 | R | false | true | 1,131 | rd | check_stop_criteria.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_stop_criteria.R
\name{check_stop_criteria}
\alias{check_stop_criteria}
\title{Stop criteria for MOEA/D}
\usage{
check_stop_criteria(stopcrit, call.env)
}
\arguments{
\item{stopcrit}{list containing the parameters defining the stop
handl... |
86da7c8f27c12f458f88799be74160023167ab6e | 83f40f224a0f935338010fde3398a2f3fef746e9 | /TDS3301 - Data Mining/Assignment/Part 2/shiny/ui.R | 7a786971abd881b868533ead2e016351da85364d | [
"Apache-2.0"
] | permissive | jackwong95/MMURandomStuff | 4cf78b5784cdf65b0302e568178121aa983b2003 | ce0bedbba97344da8cd1d12411d47c3a31af09b1 | refs/heads/master | 2023-02-13T13:19:38.083830 | 2023-01-29T15:57:08 | 2023-01-29T15:57:08 | 49,407,629 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,354 | r | ui.R | library(shiny)
# Define UI for application that draws a histogram
shinyUI(fluidPage(
tags$head(
tags$style(HTML("
@import url('//fonts.googleapis.com/css?family=Lobster|Cabin:400,700');
h1 {
... |
7e925c2918ebb6ff6c6eb857cb57b1017920225b | 3a5b24af385e8bd09526d4742c81bc3a2e01be4e | /man/mergeCellChat.Rd | 058ef33cbe98347d8fe0612db606c7737a5fbd28 | [] | no_license | teryanarmen/CellChat | 019aa5099f53518aef45b3c1bf8a7cdc8370b2a2 | 56ac7b92718517ab5f9cddb80ca859d6ae29bf30 | refs/heads/master | 2023-03-29T19:42:54.226398 | 2021-04-08T19:05:21 | 2021-04-08T19:05:21 | 356,020,334 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 565 | rd | mergeCellChat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CellChat_class.R
\name{mergeCellChat}
\alias{mergeCellChat}
\title{Merge CellChat objects}
\usage{
mergeCellChat(
object.list,
add.names = NULL,
merge.data = FALSE,
cell.prefix = FALSE
)
}
\arguments{
\item{object.list}{A list of mult... |
2226091a5316b1a0f1e80d0bf425a506fb1c998c | ff478f2f7793123759f25fa62976f275d593fb10 | /code/fncs/11_confusion_scheme.R | b4e4cbd89a34d31e38dbe87f74c55086515c8eb5 | [] | no_license | EUROMAMMALS/RP016 | a74896138b024143ffcd00c5305ee4462b7a61a1 | 862e7672e175eb18c77054ae0bc6e701ac93fb2d | refs/heads/main | 2023-04-13T19:11:48.430917 | 2023-02-09T16:24:11 | 2023-02-09T16:24:11 | 586,227,085 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 865 | r | 11_confusion_scheme.R | confusion_scheme <- function(xleft=4.160,ybottom=0.630,xright=4.660,ytop=0.680,
col=c('darkgreen','darkblue','green','lightblue'), xpd=NA){
# Plots a 2x2 confusion matrix scheme in your plot window. x and y values are based on
# axis values. You can specify the colors from upper left, ... |
eb9ea019b64c5b847566babc18ab1c385aef045d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/aprean3/examples/dst192.Rd.R | 5d883a8bf678a2a3cf55153544e58816533d869e | [] | 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 | 142 | r | dst192.Rd.R | library(aprean3)
### Name: dst192
### Title: Dataset from Table 19.2
### Aliases: dst192
### Keywords: datasets
### ** Examples
dst192
|
96922b857ec389bcd74d6e0983ea9f27afbce003 | 35bace0a2490d9e15f021f752c854748c9ff378c | /man/od_fatal_alcohol_tox.Rd | d015c983403708c6c946197aa0a472a08349d878 | [] | no_license | doh-FXX0303/overdoser | 077c0c1eebdf8a3f3a32a0ef2439aee61e546243 | d0314c87545b5fd66026f3bd3d956afc7ce6de38 | refs/heads/master | 2023-03-15T20:48:32.451747 | 2018-10-29T22:47:03 | 2018-10-29T22:47:03 | 569,497,723 | 1 | 0 | null | 2022-11-23T00:46:34 | 2022-11-23T00:46:33 | null | UTF-8 | R | false | true | 492 | rd | od_fatal_alcohol_tox.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/od_fatal_alchohol_tox.R
\name{od_fatal_alcohol_tox}
\alias{od_fatal_alcohol_tox}
\title{Find any Alcohol toxicity}
\usage{
od_fatal_alcohol_tox(data, underly_col, mult_col)
}
\arguments{
\item{data}{input data}
\item{underly_col}{... |
0c093673210ce842b46b17be07fd9963f6a3a837 | 0ef0c56da75c5fd15e1a95d7454b3bcfb2afa218 | /PA1_template.R | 9c1316d6ea272bd55d19800b423694c37d982cca | [] | no_license | bvsrini/RepData_PeerAssessment1 | 1fe66fc4a7126b37f3ceddf6dda2874fec0fab4d | 59b9ec530ebcbaaba9c4f178ee2c468b7b8a2468 | refs/heads/master | 2021-01-16T20:51:30.777567 | 2015-09-20T20:18:06 | 2015-09-20T20:18:06 | 42,819,303 | 0 | 0 | null | 2015-09-20T16:04:34 | 2015-09-20T16:04:33 | null | UTF-8 | R | false | false | 4,231 | r | PA1_template.R |
## Loading and preprocessing the data
library(curl)
library(dplyr)
library(date)
library(lubridate)
library(ggplot2)
####Load the data
#Load the data and unzip
curl_download(url= 'https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip',destfile='repdata_activity.zip')
####Process/transform the data ... |
bfcb8f44c9e650bc65d26896ba7f2e64e2da8b82 | 6638c6fe683991e37d16e1da65c7307b2efb856b | /lib/KNN.R | 4b96341e06943db7a3b1694f43883ee9a8e23514 | [] | no_license | TZstatsADS/Spring2020-Project4-group5 | 4d960c5aa7ad157ebb8dc44e1739455be4ff4cff | 156664645e9fecbf9345da1d389d1f981cc43012 | refs/heads/master | 2022-04-25T11:01:01.033150 | 2020-04-22T15:12:05 | 2020-04-22T15:12:05 | 252,766,431 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,486 | r | KNN.R | KNN.post <- function(data, result){
Q <- result$q #movie
P <- result$p #user
R <- t(P) %*% Q # 32*2427
User_all <- colnames(P)
Movie_all <- colnames(Q)
U<-User_all %>% length()
I<-Movie_all %>% length()
distance_movie <- cosine(Q) %>% as.matrix() # use similarity matrix
... |
d87a827292c579da0a372df3cc86df31f13ce26f | 79b935ef556d5b9748b69690275d929503a90cf6 | /man/Kdot.Rd | 47273c7ff0e5616273069f5439cdf49be93ff39c | [] | no_license | spatstat/spatstat.core | d0b94ed4f86a10fb0c9893b2d6d497183ece5708 | 6c80ceb9572d03f9046bc95c02d0ad53b6ff7f70 | refs/heads/master | 2022-06-26T21:58:46.194519 | 2022-05-24T05:37:16 | 2022-05-24T05:37:16 | 77,811,657 | 6 | 10 | null | 2022-03-09T02:53:21 | 2017-01-02T04:54:22 | R | UTF-8 | R | false | false | 7,644 | rd | Kdot.Rd | \name{Kdot}
\alias{Kdot}
\title{
Multitype K Function (i-to-any)
}
\description{
For a multitype point pattern,
estimate the multitype \eqn{K} function
which counts the expected number of other points of the process
within a given distance of a point of type \eqn{i}.
}
\usage{
Kdot(X, i, r=NULL, breaks=NULL,... |
43ca86648b14d49053022d2bd95d86401a275d59 | 5562044b1aa4b0c147e871a0eca25668644f8e2a | /R/upd.prob.R | 73bd5d3425a324bceee547c4ffa5ffb70258299b | [] | no_license | cran/RankAggreg | 94e9018c8130792627aa9421d57b9e234957cace | 70e56b13ba4e718e0cf461aa90551c755ef88419 | refs/heads/master | 2021-06-04T10:53:01.236658 | 2020-05-09T19:10:03 | 2020-05-09T19:10:03 | 17,693,066 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 304 | r | upd.prob.R | `upd.prob` <-
function(samples, v, weight, comp.list)
{
p <- matrix(0, nrow=nrow(v), ncol=ncol(v))
s <- nrow(samples)
p <- apply(samples,2,function(x)
table(x)[match(as.character(comp.list), dimnames(table(x))[[1]])]/s)
p[is.na(p)] <- 0
(1-weight)*v + weight*p
}
|
89b855522525450c2367335560cc29d56a182395 | a34c06ae520679dd370c0bd75aa9a5b83d0e622f | /svmAir.R | 4be401b1aed9a7532f0339005dac1780e330eedb | [] | no_license | WaughB/LIS4761_Data_Mining | f1f0fb6246ff9d81a55accc6149a0db902d23227 | e1d6ad351eaf960c1e1c86b5bb908e47392a57aa | refs/heads/master | 2020-06-11T14:37:15.378050 | 2019-07-29T16:31:23 | 2019-07-29T16:31:23 | 194,000,379 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,625 | r | svmAir.R | # Brett W.
# LIS4761 - Data Mining
# Lesson 8: Support Vector Machines
# SVM Homework -- Using SVM on an Air Quality Dataset
# Necessary libraries.
require(dplyr)
require(kernlab)
require(ggplot2)
require(caret)
require(e1071)
require(gridExtra)
# Figure out averages of Ozone and Solar.R.
ozone_mean <- airquality ... |
40eb02472b58a4d5d81bb7ccf7a5af59c76c1d82 | 6ba493ca9129518a3a9d52826beb6d3404b140da | /R/CAAMoonPhases_MeanPhase.R | da75e3ed600c17ccf13cefac0ba28c03d3b1aa44 | [] | no_license | helixcn/skycalc | a298e7e87a46a19ba2ef6826d611bd9db18e8ee2 | 2d338b461e44f872ceee13525ba19e17926b2a82 | refs/heads/master | 2021-06-16T08:54:46.457982 | 2021-03-25T02:15:38 | 2021-03-25T02:15:38 | 35,885,876 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 82 | r | CAAMoonPhases_MeanPhase.R | CAAMoonPhases_MeanPhase <-
function(k){
.Call("CAAMoonPhases_MeanPhase", k)
}
|
966697e204a671415710cfdd46319ecdfe6ca2e2 | 2851571531c96d50b4257aeef037b5742011284b | /man/g.sib.det.Rd | 0b3f1e56f60821ef7aed818a216914fb7d9ed441 | [
"Apache-2.0",
"CC-BY-4.0"
] | permissive | wadpac/GGIR | b1771d5be02bdad7905514ad09909d0edc501e35 | b596ca209cd97e32149e60621dcf7e8675a20628 | refs/heads/master | 2023-08-21T06:08:15.190626 | 2023-08-09T18:00:32 | 2023-08-09T18:00:32 | 88,166,964 | 87 | 73 | Apache-2.0 | 2023-09-14T10:54:48 | 2017-04-13T13:20:23 | R | UTF-8 | R | false | false | 1,900 | rd | g.sib.det.Rd | \name{g.sib.det}
\alias{g.sib.det}
\title{
sustiained inactivty bouts detection
}
\description{
Detects sustiained inactivty bouts. Function not intended
for direct use by package user
}
\usage{
g.sib.det(M, IMP, I, twd = c(-12, 12),
acc.metric = "ENMO", desiredtz = "",
myfun=c(), sens... |
3a0edebcf820ada96e377b817218c17ce3ffeeec | 646ff7456fb2c84b5bb8af6ef4448f71b2a730f7 | /man/appointments.Rd | d9a15c4b2de0fce728f2446ad170646355ea8b76 | [
"MIT"
] | permissive | Rkabacoff/qacData | edb6efdc04bb1814f579f89a24cf28373fe39bf6 | 3fa95cf72003972c98f035735ae3367aa69b0d0c | refs/heads/main | 2022-03-26T01:35:36.463038 | 2022-02-23T19:06:40 | 2022-02-23T19:06:40 | 157,761,849 | 0 | 10 | MIT | 2018-11-30T01:03:33 | 2018-11-15T19:29:44 | R | UTF-8 | R | false | true | 1,862 | rd | appointments.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/appointments.R
\docType{data}
\name{appointments}
\alias{appointments}
\title{Medical Appointment No Shows}
\format{
A data frame with 110527 rows and 14 variables:
\describe{
\item{\code{PatientId}}{double. Identification of a patient.}
... |
35cf3e91f685ff5c262af7f59387e26f1649ecb0 | 1b2f7c0ed5a4e06b580510cfa5a925c6c5d1c82f | /R Implementation/debseal_HW2.R | d264dcfa110b99d02bf51d532cab425765d4f236 | [] | no_license | debseal/DataMiningRep | ec176866494f3b2b92cdf29d9630b97ca24b4487 | 048a32a210e35e478a40c2fd1830910d48d2a995 | refs/heads/master | 2020-12-24T14:27:31.447609 | 2014-11-07T09:01:00 | 2014-11-07T09:01:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 84 | r | debseal_HW2.R | ### R code from vignette source 'C:/Users/debpriya.seal/Documents/debseal_HW2.Rnw'
|
797c3c075f3e4b941126cfd78c65b26e6d9b5962 | c597f0e84f86372fa72d3cefe140480a824cf605 | /lib/RGB_GBM/RGB_feature.R | 21e45eb050d58ea214793ad5a0d42b8f1d37a519 | [] | no_license | TZstatsADS/Spring2018-Project3-Group8 | 51c0745e3371372d4082985269af5bc2fb380060 | 814bc21b7e921ef440156f44c0c028f4728a21b4 | refs/heads/master | 2021-03-27T09:51:24.698459 | 2018-03-28T23:19:09 | 2018-03-28T23:19:09 | 123,044,634 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,287 | r | RGB_feature.R | #This function is used to extract RGB color histogram features
RGB_feature <- function(path, export=T){
library(EBImage)
library(plyr)
#define bins of color histogram for each channel
nR <- 8
nG <- 8
nB <- 8
Rbin <- seq(0, 1, length.out=nR)
Gbin <- seq(0, 1, length.out=nG)
Bbin <- seq(0, 1, length.o... |
c54c430d7d067b1960d72d0a53df2d9e4035855b | 3058305c903e8406843cec0a69b27e67d8ebe5c0 | /Functionality.R | cc189a23077fa9a0563432c1a7b69a3db4b4fc95 | [] | no_license | lc19940813/CapstoneProject | 39a564af2664bb3f9a79814de6c207158c39533a | 7fa848c59dc9628c3b19b7c1ccfd89cbfa077bda | refs/heads/master | 2021-01-17T16:02:39.611226 | 2016-06-10T02:03:03 | 2016-06-10T02:03:03 | 60,817,917 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,709 | r | Functionality.R | # This R file contains all the definition of functions of the main R file
# Chao Liu
# 06/2016
library(tm)
library(dplyr)
library(SnowballC)
library(wordcloud)
library(ngram)
training <- read.csv("train.csv",stringsAsFactors = FALSE)
wordtable <- read.csv("bigram.csv",stringsAsFactors = FALSE)
wordtable3 <- read.csv("... |
da4b00b758f6a8bf3ff830bccd3c6ffb9b29f1de | 60fc8a667347948b196fae19f4a50e8cd663577b | /TimeSeries/R_cisco1.R | 746f651992dd5fd4e39f53ddf7c0297ce996481e | [] | no_license | kumarivin/GitHub | 85f9bb12d4c1be6cf193b77dc361d92281f8f7ae | 5b118b161a612eacedc5338d984a53d5b833a1ba | refs/heads/master | 2021-01-17T07:40:09.810792 | 2016-10-04T03:07:18 | 2016-10-04T03:07:18 | 37,492,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,861 | r | R_cisco1.R | # LOAD LIBRARIES
# you may need to install packages, if this is the first time you # use them. Select Packages > Install Packages in R/RStudio)
setwd("C:/Users/TG/Documents/CSC 425/GNP_60_12_q")
library(tseries)
library(zoo)
#create new R dataframe
cisco = read.table('unemp_rate_m2005_2015.csv', header=T, sep=',')
# c... |
70de598038a8fce07c32a367fb22bbf01675b90b | 5b153389e67e59a30aebf6a0d84b69fd89f805d4 | /quantutils/man/is.missing.Rd | 8b9dd93e837717cdcb3daf4cd872a305ffef01d2 | [] | no_license | dengyishuo/dengyishuo.github.com | 480d9b5911851e56eca89c347b7dc5d83ea7e07d | 86e88bbe0dc11b3acc2470206613bf6d579f5442 | refs/heads/master | 2021-03-12T23:17:10.808381 | 2019-08-01T08:13:15 | 2019-08-01T08:13:15 | 8,969,857 | 41 | 35 | null | null | null | null | UTF-8 | R | false | false | 260 | rd | is.missing.Rd | \name{is.missing}
\alias{is.missing}
\title{Is an object missing}
\usage{
is.missing(x)
}
\arguments{
\item{x}{any object}
}
\value{
Logical
}
\description{
Is an object missing
}
\details{
Is an object missing
}
\author{
Weilin Lin
}
|
45bb9280a6906dc5265ba356a846aed3929659bc | 5d232ae6dc8bedba3bc3869b280c45c30a9064a5 | /simulations/independenceTestChiSqMultinomial.R | f5e5178fa9be2ce5a750358874aacd45f0943312 | [] | no_license | jfiksel/compregpaper | 727502d50fdf64359b97be823ccbaa00d40431cf | d79a54d035a3be7c0f90f6fd84bbf07596f940a6 | refs/heads/master | 2023-03-26T07:30:32.012691 | 2021-03-24T14:24:43 | 2021-03-24T14:24:43 | 258,839,237 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,698 | r | independenceTestChiSqMultinomial.R | ### set seed for simulations
set.seed(123)
seeds <- sample(-1e6:1e6, 10000, replace = F)
C <- 3
#########################
p_list <- lapply(1:10000, function(index) {
pvals <- lapply(c(100, 250, 500, 1000), function(n) {
set.seed(seeds[index])
### Sample x from uniform dirichlet
x <- t(rmulti... |
efe0356dbb05479edbdf441c2dc227eb331fe485 | 8c1834e19513a9d6f86808548fd473a9c0bfe0cf | /Infnet-Analytics/MBA Big Data - Analytics com R (Aulas 07 e 08)/Arquivos Etapa 04b/Etapa_04d_(Geolocalização 0).R | 7df8db30a26d54434263bb05693b963d6c17229d | [] | no_license | xBarbosa/Data-Analytics | d8f63137a0ea3e4e4677939e1e466673746f5a8a | d6bdf4cff3804312a00c1022c3ccb2ee5583ad52 | refs/heads/master | 2020-05-01T18:55:36.169279 | 2019-04-06T12:12:29 | 2019-04-06T12:12:29 | 177,635,035 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,372 | r | Etapa_04d_(Geolocalização 0).R | # Disciplina: Big Data Analytics com R
# -------------------------
# EXEMPLO DE DIAGRAMA DE VORONOI
# DATASET: U.S.A AIRPORT LOCATIONS
# FONTE: http://flowingdata.com/2016/04/12/voronoi-diagram-and-delaunay-triangulation-in-r/
# D3.js SIMILAR: http://bl.ocks.org/mbostock/4360892
# -------------------------
# Instalan... |
d232281c6a1e9e96f28456394449b217c7ba8ab3 | 41d472fd9969b74cef4577ce946125fe8948d5ad | /LmFit326maxwell.R | 77afb51708a49635dd102a2535a91ef7ac80caa7 | [] | no_license | processis/learnML | 787905a762e6f403135a00f2a67db31fb5be44d1 | 903bd304ead3301ed2138ce0a5e01e0a954ebd08 | refs/heads/master | 2023-02-08T19:45:14.992164 | 2023-01-31T12:49:20 | 2023-01-31T12:49:20 | 236,637,831 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,248 | r | LmFit326maxwell.R | ## read minitab processed Maxwell67 data 2020.3.26
SwMainCost=read.csv("Maxwell326es.CSV")
# simple linear regression , Gareth 3.6.2
lm.fit=lm(ln.acorreff. ~ ln.totfp. , data=SwMainCost)
lm.fit
summary(lm.fit)
names(lm.fit)
coef(lm.fit)
confint(lm.fit)
#predict func to produce confid intervals and predict intervals, ... |
da3afc7bb3e0478a446e9856b276e6c51708e7bd | f581a1cec7e634730dc759dafe2c59caa6fc064d | /R/lpa.R | 6158279654a0018b2c3c0eab13b5c631f6afd44f | [] | no_license | ebmtnprof/rties | c02919b4ce78a5ac1342d2a52e8baaec427f2390 | fae56523593318ded0d7d38c8533f39515711dfe | refs/heads/master | 2022-09-17T08:06:59.139907 | 2022-08-23T00:41:03 | 2022-08-23T00:41:03 | 127,973,424 | 10 | 2 | null | null | null | null | UTF-8 | R | false | false | 6,375 | r | lpa.R |
######## This file includes the function needed to estimate the latent profiles based on IC or CLO model parameters
#################### inspectProfiles
#' Provides information to help decide how many profiles to use for subsequent rties analyses.
#'
#' The function prints out the number of dyads in each profile f... |
a41c888123e6adeca3943b9f1a904bd5e2395bc9 | 3e63a021d9f7ee9cd23da2246eacb68572fe3822 | /R/cluster.by.distribution.R | 56ffbe8b33e46fcb981024bab113a27b0465d27f | [] | no_license | vegart/R-pkg-clusterd | fc9cdeaff02158bff68469887cbc9aaed9d71058 | 88c1f82554c8de3541887ad28934e478a81de034 | refs/heads/master | 2020-07-18T17:09:20.588981 | 2019-09-10T08:16:57 | 2019-09-10T08:16:57 | 206,279,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,983 | r | cluster.by.distribution.R | #' @title cluster.by.distribution
#' @description The objective of clustering is to find how similar the distribution of data is, among different groups.
#' @param x data frame with a grouping variable, and at least one numeric variable to be used for comparing distribution
#' @param group_column column name of x which... |
b462235d739cfd2b7f4122f9e73539793f10b2fb | ea481968a765f97210e370b0a08f13d60a11f969 | /R/lm_meta.R | 0da2651017d7c8a29c73478a03fb84f7b0aedc64 | [] | no_license | ssmufer/MMUPHin | e77ded165ab5180136d34563cc5f791e75565b60 | ca24f992850a877a4558b83515987dc6da403008 | refs/heads/master | 2023-05-07T21:37:04.250896 | 2021-05-26T17:13:10 | 2021-05-26T17:13:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,723 | r | lm_meta.R | #' Covariate adjusted meta-analytical differential abundance testing
#'
#' \code{lm_meta} runs differential abundance models on microbial profiles
#' within individual studies/batches, and aggregates per-batch effect sizes with
#' a meta-analysis fixed/random effects model. It takes as input a
#' feature-by-sample m... |
1f594259806d25cf69df0d7c5f82c4729b6d4bf4 | 1fb0d37c7ba1afd1777097922def88c50a4b3117 | /R/params_list.R | 140aa57d88eecb52106d0375ef1e8f0247dc3c6a | [
"MIT"
] | permissive | ZhangJieYeahBuddy/knowboxr | 0dd51c0203b0d5dc1c07d1a6938b8f7b31577801 | 11bba8361ea545f96c48df404068131acbc8980e | refs/heads/master | 2020-09-03T07:46:16.428585 | 2019-03-29T03:16:50 | 2019-03-29T03:34:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 900 | r | params_list.R | # -------------------------------------------------------------------------
# Consul Parameters (Keys) Lookup Table for Functions in Package
# -------------------------------------------------------------------------
#' @keywords internal
func_list <- list(
# conn to MySQL database
est_mysql_conn = c(
"userna... |
3f5be8859b6ffcc51a19e3c171e4180a4e90cc19 | 9f353a8a69942f3f6fea818b9361a95693af22b2 | /R/analysis1.R | fd17e7fd08f21d1bbc977d97cad37f9c4c40ae88 | [] | no_license | josephsdavid/teachR | f11d1fb19b5c15e8071085c5954e54cea7c7a8cf | f84b8e180e7e9d5444bdcee84faafc8d9e72b9f4 | refs/heads/master | 2020-06-03T14:43:17.148632 | 2020-03-29T20:06:53 | 2020-03-29T20:06:53 | 191,610,354 | 12 | 10 | null | null | null | null | UTF-8 | R | false | false | 8,034 | r | analysis1.R | library(tswgewrapped)
library(ggthemes)
library(ggplot2)
library(cowplot)
source("../R/preprocessing.R", echo = TRUE)
source("../R/helpers.R", echo = TRUE)
# data import
# imports the data as a hash table
fine_china <- preprocess("../data/")
names(fine_china)
# [1] "ChengduPM_" "ShenyangPM_" "ShanghaiPM_" "Beijin... |
f42ae3debbafde90b47d3b288fe0e20aedc877ab | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/7109_0/rinput.R | f671f085f517c1bba1ffc3e91fb649509c975e95 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("7109_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="7109_0_unrooted.txt") |
db378ad5e762955d8b116a50876a3fb67805e2cc | f15a0236a88c02e44933ebc7aa133fba72c1db55 | /analysis/contact_analysis.R | bf270f9fb5ec41ec18f7b8e4c3f004631c26c3e4 | [
"BSD-2-Clause"
] | permissive | YTomTJ/edge4d | ffbe34cad8b5c785b784b4d223ee2f149f57e92f | e21a308d619c03db83ef68cf63b1637685bd2139 | refs/heads/master | 2023-03-16T08:37:22.507256 | 2017-05-08T20:51:24 | 2017-05-08T20:51:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,076 | r | contact_analysis.R | library(plyr)
contact <- read.delim("live_data_output/072112_02_t11_50_contact_analysis.txt", stringsAsFactors=F)
contact$time <- contact$time - 850 ## time0 is 850
contact <- contact[which(contact$neighbor.trajectory.id >= 0),]
contact$pcent <- contact$neighbor.contact.sa / contact$total.contact.sa
PF.basal.cells.... |
af069ac1f5d7fe7931f06192c9720fc5d19aea06 | 4b48647555feaac4cbb9bb4864db20e6e40a8980 | /man/yside.Rd | 1158107c4682a4849ac6d1b0f28717bb0a3cf30c | [
"MIT"
] | permissive | seifudd/ggside | 8d9fdca5b042f9528c5dc4ef5ce0d7f64537f730 | 442c83db4cca57bc9cc962be563fbd7df0463d86 | refs/heads/master | 2023-07-12T20:29:20.936007 | 2021-08-16T19:30:55 | 2021-08-16T19:30:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,736 | rd | yside.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggside.R
\name{yside}
\alias{yside}
\title{The yside geometries}
\value{
geom_yside* return a YLayer object to be added to a ggplot
}
\description{
\code{yside} refers to the api of ggside. Any \code{geom_} with
\code{yside} will plot its res... |
df18babc88df58cf7a8cb96373b6f6e0d7cb51ba | 314bba245605c2e3abe598b0a99e932c6f346185 | /Run.r | b7d8141b23cdc7c19205d3c1f23c3e1a763ff478 | [] | no_license | hajime0105/IRT-ICC | d5a71495a78259dbb67df77d5067e6d24c0d1496 | fefb78ad371ce2ac8a969395dd87ccddf782d053 | refs/heads/master | 2021-01-10T11:32:31.391486 | 2015-11-17T04:51:14 | 2015-11-17T04:51:14 | 45,888,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 325 | r | Run.r | library(ltm)
if (param == 1) {
source("makeData1PL.r")
} else {
source("makeData2PL.r")
}
source("changeData.r")
data <- read.csv("dataLogic.csv", header = FALSE)
mod <- rasch(data, IRT.param = TRUE)
print(mod)
plot(mod, lwd = 3, cex.axis = 2, cex.lab = 1.5, xlab = "θ", ylab = "P(θ)", main = "")
print(summary(mod... |
7b992e2c05770447c986f4dc23836321b9940129 | 727b8af88b6d32bb1f4087537779ea269cf9cd07 | /fast_sampler.R | 886125272d82140fe8ff73b3576e7283b05fb7d4 | [] | no_license | Rene-Gutierrez/boom_project | 3b13848465484f52fcafd2ecc740ee14029c26e3 | cd6af9024ed2b504918f6baa82d5a5205f975396 | refs/heads/main | 2023-06-18T04:28:11.370121 | 2021-07-17T17:16:18 | 2021-07-17T17:16:18 | 360,395,925 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 387 | r | fast_sampler.R | ### Fast Sampler
fast_sampler <- function(Phi, D, a){
# Dimensions
n <- dim(Phi)[1]
p <- dim(Phi)[2]
# Step 1
u <- rnorm(n = p, mean = 0, sd = sqrt(D))
d <- rnorm(n = n, mean = 0, sd = 1)
# Step 2
v <- Phi %*% u + d
# Step 3
w <- solve(Phi %*% (D * t(Phi)) + diag(n), a - v)
# Step 4
... |
ab1207d0a45f44d78cc8d6a2f114889740abd1ca | cbb79420b0e2ba0fa560aa6f4c793e5b43ad0519 | /R/figure_03_S3.R | d647512adfe6325c90a9c8a28b1f6ec721afdb61 | [
"Apache-2.0"
] | permissive | csbl-usp/evolution_of_knowledge | b477f328a1fb8e9b07abb07e26b17de68d5a818e | 81ab3511f5ea3c4e26e374ce8a67d0e67a38dc59 | refs/heads/main | 2023-04-17T07:08:37.080859 | 2021-06-16T14:33:16 | 2021-06-16T14:33:16 | 377,523,481 | 0 | 0 | NOASSERTION | 2021-06-16T14:25:17 | 2021-06-16T14:25:16 | null | UTF-8 | R | false | false | 14,251 | r | figure_03_S3.R | #With this scritp, you can reproduce the analysis and plot the images that
#compose panel A-C in figure 03 of the paper and figure S3
#load necessary packages
#file management
library(data.table)
#data manipulation
library(dplyr)
library(tidyverse)
library(reshape2)
library(stringr)
#network/gene analysis
library(igra... |
4c33ca7a83788642706213c5ccb2154df1f88bc2 | d8173649c1613b14bde4cf72317d5d56f8ba7b88 | /man/BUSexample_data.Rd | 79366192fdc451bdd32de3783c1a080589d30def | [] | no_license | XiangyuLuo/BUScorrect | 477be4a08de6be8484bc4ec79ccdf77a488a0be3 | 1a3fc2b0d65c4a9d1421347a4f8ab5486e12ecda | refs/heads/master | 2020-03-21T15:48:34.198860 | 2019-06-14T09:40:05 | 2019-06-14T09:40:05 | 138,570,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,597 | rd | BUSexample_data.Rd | \name{BUSexample_data}
\alias{BUSexample_data}
\title{
A simulated data set
}
\description{A simulated data set for demonstrating how to use the BUScorrect package}
\examples{
\dontrun{
#This data set is simulated according to the following R code
rm(list = ls(all = TRUE))
set.seed(123456)
B <- 3 ... |
588790de7c14f1e47d0c0a47e3813bad3ee7e4c3 | c6e2b67fd3c237175098c1be621541dba2170ace | /tests/testthat/test-keyed-df.R | 0ffd78276592112ca0e9304a67c93435d96b7b61 | [] | no_license | echasnovski/keyholder | 666ff734edcb953dcb3161e5aab6294a46c5a4a1 | f950226fe4692e15e9960a7d55a179b9e8ad9e4a | refs/heads/master | 2023-03-16T14:20:15.738601 | 2023-03-12T09:59:02 | 2023-03-12T09:59:02 | 96,685,600 | 7 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,050 | r | test-keyed-df.R | context("keyed-df")
# Input data --------------------------------------------------------------
df <- mtcars
df_keyed <- df %>% key_by(vs, am)
keys_df <- keys(df_keyed)
# is_keyed_df -------------------------------------------------------------
test_that("is_keyed_df works", {
expect_true(is_keyed_df(df_keyed))
... |
5843768b6d2afeb9be7f9627e71dbb9b79f5bdc0 | 21642d86c73f6307380dead06681040f73b89971 | /scripts/historical_lakeMAGs_reassembly_metadata_organization.R | 7ebd7c897eb03f16c58dc83a0d1585bd25fde151 | [] | no_license | elizabethmcd/Lake-MAGs | a89f85ff078344b061239886ba393e5fc2d3d0e6 | ec745695148c55d497e043a91c38be745faec540 | refs/heads/master | 2022-02-03T02:23:17.929130 | 2022-01-24T04:03:26 | 2022-01-24T04:03:26 | 200,298,745 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,189 | r | historical_lakeMAGs_reassembly_metadata_organization.R | # Organizing re-assembly of Lake MAGs
library(tidyverse)
# Trout Bog epi
tb_epi_checkm <- read.delim("results/troutBog_epi_historical/troutBog-epi-checkm-stats.tsv", sep = "\t", header = FALSE)
tb_epi_gtdb <- read.delim("results/troutBog_epi_historical/troutBog-epi-gtdbtk.tsv", sep="\t")
tb_epi_bins <- read.table("... |
0d9eb9bce84ce83200bdde5a6e6886260eba44cc | 0602fe83b9dbbb78abf83bb64eca577684a89b81 | /ProjektR/extractRandC1Patches.r | cb7b7e0a6ee45b1ed270bac4a80cab929b9bfa69 | [] | no_license | Barteboj/WKIRO_ZWIERZETA | 2ed62c1ff98a999cd98e98db4e12ba8838e65666 | a6545b52b31b551e81bfc10b98ca0541d9bca7b1 | refs/heads/master | 2021-01-20T15:45:01.705984 | 2017-06-16T18:03:21 | 2017-06-16T18:03:21 | 90,793,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,345 | r | extractRandC1Patches.r | extractRandC1Patches = function(cItrainingOnly, numPatchSizes, numPatchesPerSize, patchSizes) {
#extracts random prototypes as part of the training of the C2 classification
#system.
#Note: we extract only from BAND 2. Extracting from all bands might help
#cPatches the returned prototypes
#cItrainingOnly the... |
29dbbaf97eb66f4498e77e25991022357fcb7bbf | a3adc89eb1fcceb2d209dae732cde0c8a50183a5 | /run_analysis.R | 0d02bbe4a1e2ee2ba7472d8e5ec6cc3c310f475e | [] | no_license | adshank/GCDproject | c7fb72924732fa4340907c0012a1509cbd9ae25a | 2b786f93cac7e76412f51b7dcfe0b688159f5920 | refs/heads/master | 2021-01-12T12:07:37.330535 | 2016-10-29T23:15:02 | 2016-10-29T23:15:02 | 72,310,412 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,140 | r | run_analysis.R | # run_analysis.R script, created by Adam Shankland, 10/29/2016
# Completes steps 1-4 in Getting and Cleaning Data course project, Coursera
# See README.md for more information about this script
# Set working directory (wd_path must be changed to run script on a different machine)
wd_path <- "C:/Users/adshan/Documents/... |
d153505120044aef410b17355da9ef0a1cdba389 | 9a23d27ef39a865c3ebc521624cb7ab7c2e107d3 | /R/rstudio_addins.R | 88b8c18a8448e9f26313a611b4a7bb83b929c30b | [] | no_license | conradbm/knitrdata | 8c86cb83ff049b66fb07db8ab36e0d94e94a8f17 | dccfe4922d4f4c386544cca6e15923717cd20c9e | refs/heads/master | 2022-11-13T09:36:16.258977 | 2020-07-18T20:46:56 | 2020-07-18T20:46:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,003 | r | rstudio_addins.R | # Functions meant to be used as Rstudio addins ---------------------------------------
# Some minor helper functions
isemp = function(x) is.null(x) || (x=="")
firstword = function(x,split=" ") sapply(strsplit(x,split=split),function(y) y[1])
# Function for creating a data chunk --------------------
#' Invoke Shiny g... |
0db9a7e5e4bf76e594a805481db48a2ff4ec0571 | 8d8470305ca859cce51f3fe462c2b3eec7452a06 | /RCode/textaug.R | a9ae7d5a1d690c5111268f4f28e6bdddb62ade7f | [] | no_license | kmcalist682336/SimultaneousScaling | 256555c4a6218dbf4bb8f8527f06b105291d6a00 | 30931c387f0579ceedddeba3aad00312680881a3 | refs/heads/master | 2020-03-21T08:15:31.568215 | 2018-06-22T21:11:35 | 2018-06-22T21:11:35 | 138,330,954 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 999 | r | textaug.R | augment.text.data <- function(aa,bb,mstar,p,d){
pred.mat <- bb%*%t(aa)
aug.mat <- function(j){
pm <- pred.mat[j,]
ms <- mstar[j,]
pmms <- data.table(cbind(seq(1,p),pm,ms))
setnames(pmms,c("id","pm","ms"))
setorder(pmms,"ms")
new.augs <- c()
um <- sort(unique(pmms$ms))
min.vals <- -In... |
4aa4b6779abe51c27bf9ef487de875595b0d5f9e | 203486d84e6759bdd402c17b4c40a8a36edce293 | /WorkInProgress/Rolling_Attrition.R | c014da0d56aafa12c56996c3fcb93052b33ad4c3 | [] | no_license | ndeprey/MPX | 596384e0f019c64df31f3fa7c6b2707e1b849802 | 10c633500f0f7721aa5baa4cd2cb2e077b71f803 | refs/heads/master | 2021-01-19T15:35:04.201252 | 2017-01-12T19:57:33 | 2017-01-12T19:57:33 | 21,352,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 645 | r | Rolling_Attrition.R | users <- read.csv("Last_Listen_Date_10_03.csv")
users$LastDayActive <- as.POSIXct(users$LastDayActive)
users$FirstDayActive <- as.POSIXct(users$FirstDayActive)
users$FirstDayActive <- as.Date(users$FirstDayActive)
users$LastDayActive <- as.Date(users$LastDayActive)
users$ActiveDateRange <- as.numeric(users$LastDayActi... |
1b432a7001e86ea07c6147aea119dff54849d0a9 | fc7279546bff47fbc30953a9976c368ddc9daffe | /tase_index_selenium.r | 0210905e7cc09bac0f7adcf25187df862c290585 | [] | no_license | githubfun/taseR | 28a043df04202076e3128bbce2fd583b0f0d1522 | d3ee1cd43434d18c5d0806a8b2d3271b9007e6fa | refs/heads/master | 2017-12-01T11:27:15.859936 | 2015-07-07T03:33:03 | 2015-07-07T03:33:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,551 | r | tase_index_selenium.r | library(lubridate)
library(rvest)
library(XML)
library(plyr)
library(dplyr)
library(RSelenium)
library(stringr)
setwd("C:\\Users\\yoni\\Documents\\GitHub\\tase")
RSelenium::startServer()
remDr <- remoteDriver()
remDr$open(silent = F)
df.in=data.frame(Name=c("TA25","TA100"),indexID=c(142,137))
df.in$from.date=rep(for... |
da970e9e4dbba8e13d8438e48b8c0a2b7cf72386 | 00cc47dde2afd5a66293af0b8b07f380f993d2af | /rprojectSPL/lib/invalid.R | 407687544df92771b5fcf74ca17e52fb62731f6e | [
"MIT"
] | permissive | UTexas80/gitSPL | 277ea78a08559115b15ff3e9046e1b0e18938280 | 212fe631d8162dabf4593768f7ff6aacc0457026 | refs/heads/master | 2021-01-25T09:04:31.709072 | 2019-04-30T14:35:47 | 2019-04-30T14:35:47 | 93,775,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24 | r | invalid.R | invalid <-
character(0)
|
a8f3c91deedcc2dee28d08197e0a0199d4b8256c | 0ebf0950d351f32a25dadb64b4a256a8a9022039 | /man/parseRegion.Rd | 871efd71a6d0ac17cd39a4ccb4416141ef5415dc | [] | no_license | HenrikBengtsson/aroma.cn.eval | de02b8ef0ae30da40e32f9473d810e44b59213ec | 0462706483101b74ac47057db4e36e2f7275763c | refs/heads/master | 2020-04-26T16:09:27.712170 | 2019-01-06T20:41:30 | 2019-01-06T20:41:30 | 20,847,824 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,433 | rd | parseRegion.Rd | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% parseRegion.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{parseRegion}
\alias{pa... |
28578615b56339579a031d47bace0de92799a991 | f8f3d53abf579dfbf6d49cfb59295b1c3ddc3fb2 | /man/code2office.Rd | 2a9e7bf911d6df6c75daaa93ad55f3708be6be4e | [] | no_license | cardiomoon/rrtable | 9010574549a6fc41015f89638a708c691c7975cf | 8346fca2bb0dc86df949fb31738e1af90eeb5a70 | refs/heads/master | 2023-03-15T20:43:07.685721 | 2023-03-12T11:36:34 | 2023-03-12T11:36:34 | 127,721,282 | 3 | 2 | null | 2021-11-17T01:08:31 | 2018-04-02T07:32:08 | R | UTF-8 | R | false | true | 1,332 | rd | code2office.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/code2pptx.R
\name{code2office}
\alias{code2office}
\title{Save plot/ggplot code to Microsoft Powerpoint format}
\usage{
code2office(
...,
ggobj = NULL,
target = "Report",
append = FALSE,
title = "",
type = "pptx",
preprocessing ... |
84957d953119fe1863aeccbee9dffa21a844d934 | 40234ef2ad5efa4c566ff501f3972ab03b181bd9 | /data/charm/Step4_apply_thredds_bias_correction_model.R | e59780d8454d22f3f07795796e00580c23059636 | [] | no_license | cfree14/domoic_acid | 63fefd3c577d0cd277747254aa50f425401c438f | dfe6f4d9b94ad7a71c092c92bf63100a46cb3d0c | refs/heads/master | 2023-07-15T10:28:49.815164 | 2021-08-25T22:31:47 | 2021-08-25T22:31:47 | 279,933,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,358 | r | Step4_apply_thredds_bias_correction_model.R |
# Clear workspace
rm(list = ls())
# Setup
################################################################################
# Packages
library(mgcv)
library(ncdf4)
library(raster)
library(tidyverse)
library(lubridate)
library(betareg)
# Directories
plotdir <- "data/charm/figures"
datadir <- "data/charm/processed"
gi... |
6d637b27bc6c49e0f3f62abec2890c0848e322ff | 4a2c6f223ff6063640475840209927bf85a9f33b | /lostruct/man/getMaxRad.Rd | 9a011a2be525319809724f7cca5ff79cb18dc81b | [] | no_license | petrelharp/local_pca | d69cc4122c381bf981af65a8beb8914fabede4d5 | abf0c31da5cd74a1de62083580d482f5bd08d7de | refs/heads/master | 2023-06-25T18:12:39.355780 | 2023-06-14T04:39:12 | 2023-06-14T04:39:12 | 47,361,457 | 61 | 13 | null | 2021-02-25T17:20:18 | 2015-12-03T21:23:41 | HTML | UTF-8 | R | false | true | 296 | rd | getMaxRad.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/enclosing_circle.R
\name{getMaxRad}
\alias{getMaxRad}
\title{Vertex that produces the circle with the maximum radius}
\usage{
getMaxRad(xy, S)
}
\description{
Vertex that produces the circle with the maximum radius
}
|
b5c071458dbe48e909cbf90aca2c3d7bf5335171 | 543d87fd0201639b00e72950e80ecd98389d6de2 | /R/functions.R | eb563f5f0ea4a94dc76a419aa11297ac9c818eb7 | [
"MIT"
] | permissive | mahdiprs/Group-Sampling | d9d3b72d061876ce1f4bea51ade01ec6331caa08 | 20fc0a22eb2f37926d7753518087364700b3031e | refs/heads/master | 2023-05-10T18:20:07.478444 | 2023-05-10T00:51:26 | 2023-05-10T00:51:26 | 416,548,185 | 0 | 0 | null | 2021-10-13T01:28:34 | 2021-10-13T01:18:30 | R | UTF-8 | R | false | false | 2,359 | r | functions.R | ##########################################################################
# Functions to estimate statistics introduced in
# Sections 3.1 - 3.3 of journal paper
##########################################################################
library(dplyr)
library(actuar)
library(extraDistr)
library(sqldf)
library(kSamples... |
86c8426fed7a16404797ba5af8f6633ce2eb4cf7 | cd94ae361315380160c53aba76e55bad57c1ccdb | /man/date_to_fy.Rd | 63238c6d9f03a9a8f3a36aa7cfe4b3b36c27e78f | [] | no_license | rcgentzler/ojodb | c931836ff88b8ece481143c0752c16149d9851c1 | 7ba3700458023c8d8e39f6f6194692c277072df1 | refs/heads/master | 2023-01-20T20:48:12.975911 | 2020-11-20T19:48:53 | 2020-11-20T19:48:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 490 | rd | date_to_fy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/date_to_fy.R
\name{date_to_fy}
\alias{date_to_fy}
\title{Determine the Oklahoma state fiscal year that a date falls in}
\usage{
date_to_fy(date)
}
\value{
Fiscal year of a date as an integer
}
\description{
Returns the Oklahoma state fiscal y... |
152e5404037fe63cf953b0bf29e13025556a35d6 | 61f27287fda5c604c2bc2dfd5a2aa6db87cb293b | /R/plot.TipologiaRodizio.R | 8cd7903ca0972c937b1c96674a690e57d455c483 | [] | no_license | brunomssmelo/RcextTools | 5effdea589d88fbe83e3a15f99490baf7d45af14 | c9081f45b383ad57bdaea023830ef6c1e20a1aef | refs/heads/master | 2020-12-03T15:33:40.675627 | 2017-06-16T04:29:39 | 2017-06-16T04:29:39 | 66,433,016 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,661 | r | plot.TipologiaRodizio.R | #' Metodo S3 que plota na tela uma representacao visual do grafo do tipo `igraph` contido no objeto da classe `TipologiaRodizio`
#' @author Bruno M. S. S. Melo
#' @description Os diferentes agrupamentos representam empresas suspeitas de praticarem alguma acao colusiva num determinado mercado. As arestas apontam na dire... |
a11fb1ef214bca4b41baa8a15cd887985af6a92b | 8575c4ce854151973bb8f58b8a124f7b1816df45 | /Rafael_R_scripts/ExClVal.R | dd496316f52050c6b6499cd5f147c004e50581ee | [] | no_license | mlldantas/Gal_classification | e0a3ce375d0661ca1933b4d36ff20f6fb4d469cc | 81c392ec828709d30dea351a2fe27ec81bc6e69d | refs/heads/master | 2022-03-30T14:24:18.340900 | 2020-02-21T17:24:25 | 2020-02-21T17:24:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,478 | r | ExClVal.R | fancy_scientific <- function(l) {
# turn in to character string in scientific notation
l <- format(l, scientific = TRUE)
# quote the part before the exponent to keep all the digits
l <- gsub("^(.*)e", "'\\1'e", l)
# turn the 'e+' into plotmath format
l <- gsub("e", "%*%10^", l)
# return this as an exp... |
9a5aec6f03c118ff2c0e77143ab6deb54c38777f | f2373d4d8ad3fbd1735f280a21aec7eb0f666ffb | /man/uniqueBy.Rd | 07ecda211c8f9bb8f1f85ddb3675e857dd1d984e | [] | no_license | ahmczwg/psichomics | 0c37b23fc06096309f9de71e4c8d4a3a15b982ab | cad23d89b8f07420b6cd90067b9ede60a4651881 | refs/heads/master | 2020-04-15T20:18:02.301980 | 2018-12-03T14:59:22 | 2018-12-03T14:59:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 442 | rd | uniqueBy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{uniqueBy}
\alias{uniqueBy}
\title{Check unique rows of a data frame based on a set of its columns}
\usage{
uniqueBy(data, ...)
}
\arguments{
\item{data}{Data frame or matrix}
\item{...}{Name of columns}
}
\value{
Data frame wit... |
8b67447a943f07f9b02806fc563c5db656c138ff | 55bdc9a36d8564216db073f19fffd931ffeaa9ae | /R/tests/testthat/test-crs-transform.R | 1c65e12abb209410301a72457897302b91060d8c | [
"BSD-3-Clause",
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0",
"BSD-2-Clause"
] | permissive | awadhesh14/GeoSpark | 2362d691d8e84397f9cee33692a609ee218faf9e | 86b90fc41a342088d20429ebcd61a95b2f757903 | refs/heads/master | 2023-04-09T07:02:03.610169 | 2023-04-01T07:30:02 | 2023-04-01T07:30:02 | 202,829,602 | 0 | 0 | Apache-2.0 | 2022-12-21T21:28:50 | 2019-08-17T03:20:13 | Java | UTF-8 | R | false | false | 1,646 | r | test-crs-transform.R | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may... |
0779a351cbc2c7506af37cbe5f466a2dd278592b | 277dbb992966a549176e2b7f526715574b421440 | /R_training/실습제출/이정환/20191029/movie2.R | 31561ba3b388d0eed8993854fde21b2a5a99918e | [] | no_license | BaeYS-marketing/R | 58bc7f448d7486510218035a3e09d1dd562bca4b | 03b500cb428eded36d7c65bd8b2ee3437a7f5ef1 | refs/heads/master | 2020-12-11T04:30:28.034460 | 2020-01-17T08:47:38 | 2020-01-17T08:47:38 | 227,819,378 | 0 | 0 | null | 2019-12-13T12:06:33 | 2019-12-13T10:56:18 | C++ | UTF-8 | R | false | false | 653 | r | movie2.R | # 실습2
site = "https://movie.daum.net/moviedb/grade?movieId=121137&type=netizen&page="
daummovie2 = NULL
for (i in 1:20) {
url = paste(site, i, sep='')
text = read_html(url)
grade = html_nodes(text, '#mArticle > div.detail_movie.detail_rating > div.movie_detail > div.main_detail > ul > li > div > div.raking_grade ... |
822f6b31eb20358382cff415d56c31e531e23ed1 | 5397b2f52030662f0e55f23f82e45faa165b8346 | /man/j_get_index.Rd | ce8c52878e4ccfda1a22b40df5890d55771a6229 | [
"MIT"
] | permissive | data-science-made-easy/james-old | 8569dcc8ce74c68bcbb81106127da4b903103fcd | 201cc8e527123a62a00d27cd45d365c463fc1411 | refs/heads/master | 2023-01-12T21:16:23.231628 | 2020-11-19T13:46:17 | 2020-11-19T13:46:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 583 | rd | j_get_index.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/j_get_index.R
\name{j_get_index}
\alias{j_get_index}
\title{Get index}
\usage{
j_get_index(type, version, scenario = james.env$j_root$scenario,
project = james.env$j_root$project)
}
\arguments{
\item{type}{data type (default "")}
\item{ver... |
1fa82fd7ca3f1310931225c53b750b07e73e4251 | 753d940b683819bf96d3fbabb9f23e631ed061a3 | /man/topsort.Rd | 068a2c5e6576c28d7f172d2cf48bec5e37dccaa0 | [] | no_license | ralmond/Peanut | d2762206dd396ac2d3b31d6105ea9f9c03901e6a | 9f5ca85b8d2a48a6d4f1f34705f0e97351cacdf3 | refs/heads/master | 2023-06-29T02:01:18.055310 | 2023-04-14T15:59:31 | 2023-04-14T15:59:31 | 239,856,304 | 1 | 1 | null | 2023-04-14T15:50:54 | 2020-02-11T20:20:10 | R | UTF-8 | R | false | false | 1,527 | rd | topsort.Rd | \name{topsort}
\alias{topsort}
\title{Topologically sorts the rows and columns of an Omega matrix}
\description{
The structural part of the \eqn{\Omega}-matrix is an incidence matrix
where the entry is 1 if the node represented by the column is a parent
of the node represented by the child. This sorts the rows ... |
c61ffc332bc0e29f57d9b9d5fea6ca6eacc0ef66 | 91a685393b3d9633f2cfab0d6e15c9134b10eddc | /man/tParams.Rd | 39ea2042d368611bd1b1c53620b8953304cb91aa | [] | no_license | ablejec/animatoR | c40ffeb05a79104ff1da81d04a8f0c5a44aaa7ea | 841bbd7f0ed3eef50a1818964d5e9c0a7d5b8f3a | refs/heads/master | 2021-01-19T00:58:01.980054 | 2020-02-07T09:09:53 | 2020-02-07T09:09:53 | 62,981,413 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 341 | rd | tParams.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/animatoR-functions-knitr.R
\name{tParams}
\alias{tParams}
\title{Argument t}
\arguments{
\item{t}{numeric, homotopy parameter, limited between 0 and 1.
This parameter can be considered as fraction of animation duration time.}
}
\description{
... |
53b8720998137637ad1a6a265a13347500ebb38a | 0b1e3b297ab4034e66150e1782a0fc25de009bc4 | /1_Data_for_pooled.R | e73a42265d014a16f81fa8939da5e2f358d06873 | [] | no_license | Alicja1990/Panel_analysis | b6bed08bded2db96c8dae099973ee3729200f6fd | f575b8ab2fb73cd6da6b872cc4716f08153d3ce7 | refs/heads/master | 2020-03-24T12:11:35.813577 | 2018-08-16T19:10:15 | 2018-08-16T19:10:15 | 142,706,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,512 | r | 1_Data_for_pooled.R | options(scipen = 100)
setwd("C:/Users/Alicja/Documents/Doktorat/Rozprawa doktorska/Panel_analysis/Data")
data_analizy_17 <- read.csv("data_analizy_17.csv", as.is = T)
data_analizy_18 <- read.csv("data_analizy_18.csv", as.is = T)
data_stooq <- read.csv2("stooq_17_18.csv", as.is = T)
data_18 <- merge(data_analizy_18, ... |
ecc376de54c745683d1178169edef54a7a3fada1 | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/revision_scripts/revisions2/2-Liger_full_data_repressive_k9me3_cluster_specific_bins_keeptop_bymark_with_TSS.R | ccfa345d9975220129ec60d348a44622c9286ee7 | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,620 | r | 2-Liger_full_data_repressive_k9me3_cluster_specific_bins_keeptop_bymark_with_TSS.R | # Jake Yeung
# Date of Creation: 2022-07-22
# File: ~/projects/scchic/scripts/revision_scripts/revisions2/2-CCA_full_data_repressive_k9me3_cluster_specific_bins_keeptop_bymark.R
#
rm(list=ls())
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(Seurat)
library(hash)
library(i... |
2bef1fddab286ce8c1fdc4dbcda7feb55e66a66d | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /cran/paws.internet.of.things/man/iot_describe_domain_configuration.Rd | 34cf0dd09c923787272a80f5b0444ff14e700d34 | [
"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 | 705 | rd | iot_describe_domain_configuration.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iot_operations.R
\name{iot_describe_domain_configuration}
\alias{iot_describe_domain_configuration}
\title{Gets summary information about a domain configuration}
\usage{
iot_describe_domain_configuration(domainConfigurationName)
}
\arguments{... |
632258220448513ba658425ed3710c06791f8e72 | 904022448a1599c2e6dcb2a438beb4b64efb65bc | /adr_cc_numbers.R | ed13b6c1118d58cc7250cad956a15ad7ef09dade | [] | no_license | GKild/neuroblastoma | 1bb9ec3df959482d6d7e9a718fc98bb813033a8f | a17d359b8ad915ce3eb831739254c951df1719e4 | refs/heads/master | 2023-03-04T04:24:57.783627 | 2021-02-16T11:12:00 | 2021-02-16T11:12:00 | 217,528,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,203 | r | adr_cc_numbers.R | adr_all <- readRDS("/lustre/scratch117/casm/team274/my4/oldScratch/ProjectsExtras/SCP/Results/preProcess/fAdrenal/processedSeurat.RDS")
adr_all@meta.data$new_clust=as.character(adr_all@meta.data$seurat_clusters)
adr_all@meta.data$new_clust[which(adr_all@meta.data$new_clust%in%c("25"))]="SCPs"
adr_all@meta.data$new_clus... |
a18d61a1776f02c82c3f57d537a3f26fae7d777e | 7bc2a4cfd299263c8b9b1b4d1ea3afc8d598163f | /src/R/makedata_200930.R | b91be34d95591b82a25357939a91aceb1e08f722 | [] | no_license | mrmtshmp/predict.valid | 740821a54f09da18eb2af1327de9b15391c62e04 | 9bc64dfa207d512d8e8d95f984134556b94c67c8 | refs/heads/main | 2022-12-20T06:25:36.794688 | 2020-10-14T11:51:55 | 2020-10-14T11:51:55 | 303,998,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 757 | r | makedata_200930.R | #' Make data from dataset received 2020/09/29
#'
#' proj. predictCandida
#' PI: Taiga Miyazaki
#' author: Shimpei Morimoto
#'
dir.sub <- "./sub"
for(i in 1:length(list.files(dir.sub)))
source(
file = sprintf(
"./sub/%s",
list.files(dir.sub)[i]
)
)
df.col_info <-
readxl::read_excel... |
d3aca292082654497f25f7e8e3567305114149cd | d69e8c2403c7aed57c1401fbdaf90373daacf98b | /Code/P60_SmartSeq2_Batch_Corrected/02-Analyses.R | 89aa9020cb6f475b12ebbab57e3b4ccd2e672376 | [
"MIT"
] | permissive | suterlab/SNAT-code-used-for-primary-data-analysis | b9ff67b64dbb87c6cdd2be774e455cbabbfe3bbb | 60199934be21b74f8c90291dc441684503e25487 | refs/heads/main | 2021-08-17T15:42:07.680292 | 2021-04-20T15:33:35 | 2021-04-20T15:33:35 | 307,694,631 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,383 | r | 02-Analyses.R | library(Seurat)
library(tidyverse)
library(here)
library(cowplot)
library(stringr)
source(here("Code", "utils.R"))
resDir <- here("Results", "SS2_P60_BatchCorrection_woFilter50k", "02-Analyses")
dir.create(resDir, recursive = TRUE)
figNumer <- 1
# Upgrade Seurat v2 object to v3 object
scData <- readRDS(here("Results"... |
5a49ebba70fc0d09e23c7c86c3bb30915c864b09 | f130be9a29145c213abd2d833e203618ecd03ae5 | /processData.R | a773061b282a6a5aa22ad203b5e91c9c6c16402c | [] | no_license | rebekahlow-jy/nm3239-data-project-r | b4f5cbe62e1b7a427c6bd4919c9c021e795e9ab1 | 01e87a4ef001d4a36f54f59df9bfaaa7c4a81888 | refs/heads/master | 2021-05-07T01:46:18.355180 | 2017-11-12T11:10:42 | 2017-11-12T11:10:42 | 110,425,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,275 | r | processData.R | library(dplyr)
library(tidyverse)
library(rvest)
library(stringr)
url_grants <- "http://www.hdb.gov.sg/cs/infoweb/residential/buying-a-flat/new/first-timer-and-second-timer-couple-applicants"
webpage_grants <- read_html(url_grants)
grant_amount <- html_nodes(webpage_grants, 'table')[2] %>% html_node("tbody") %>% html_... |
b15da626da8f6406070aad056c2217954e413c6e | 86014f58758b030b8bc54e88ac5f66078b18d07b | /R/GetInfoVisstat.r | 5bf247cfff891b39fb9fdb1561a17f9414f27504 | [] | no_license | nielshintzen/visstat-extraction | f04e657e302fc37eb1639c548e6ca8131d2af63a | 7073a8abaffc8f8ade469cea1154f4698e0d8675 | refs/heads/master | 2021-01-06T20:38:26.085103 | 2015-06-10T09:42:11 | 2015-06-10T09:42:11 | 37,180,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,162 | r | GetInfoVisstat.r |
GetInfoVisstat <- function(user=user,passwd=passwd)
{
visstat <- dBConnect(which.database="visstat",user=user,passwd=passwd)
#Get CATCHES
qca <- paste("select * FROM CATCHES WHERE rownum < 5")
print(sqlQuery(visstat,qca))
print("CATCHES")
# Get GEAR PROPERTIES
qgp <- paste("selec... |
485ea14760715bc234436b17f11b2327d21dcb11 | 19dfd82cc612e5f40fe680270d24cf90cf547300 | /man/Rfam.Rd | be9343428fa468021b21e82269c79c450f04d649 | [
"Artistic-2.0"
] | permissive | YuLab-SMU/ggmsa | 300d049a3470be301788119c4cf261aa1ef06958 | f1e62e07daa1a97cc209baa909a56af0b398222a | refs/heads/master | 2022-09-10T20:03:47.473418 | 2022-08-22T01:35:30 | 2022-08-22T01:35:30 | 131,572,300 | 160 | 30 | null | 2022-08-03T15:24:05 | 2018-04-30T08:28:53 | R | UTF-8 | R | false | true | 609 | rd | Rfam.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Rfam}
\alias{Rfam}
\title{Rfam}
\format{
a folder
}
\source{
\url{https://rfam.xfam.org/}
}
\description{
A folder containing seed alignment sequences and
corresponding consensus RNA secondary structure.
}
\detail... |
1521fea05494d7d96235f3b1d8f3b3e644146a83 | 29585dff702209dd446c0ab52ceea046c58e384e | /TDCor/R/ssq.delay.R | 979ffc501a829b24b9b96a66a476234804b3bf45 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 515 | r | ssq.delay.R | ssq.delay <-
function(delay,hr,ht,time_l,time_u,time_step,type,deriv)
{
u=seq(time_l,time_u,time_step)
if (deriv)
{
if (delay>=0)
{
k1=hr(u-delay,deriv=1)
k2=ht(u,deriv=1)
}else
{
k1=hr(u,deriv=1)
k2=ht(u+delay,deriv=1)
}
}else
{
if (delay>=0)
{
k1=hr(u-delay)
k2=ht(u)
}else
{
k1=hr(u)
k2=ht(u+de... |
2778982d944ce8f0ef55386f51cd1a5ef87513fb | dcb60332de2bf412c6ae0d0028337bd8cfc68f76 | /misc/get_sn_atac.R | f698c2959fcb482fd3f3de5e6f5d58589473e91e | [] | no_license | Jeremy37/ot | 2037a10d38451d4d1321a81fbea3191dbcf0e7b7 | 5d04775f23af51ab8dc1d83c972b94c42138ddc8 | refs/heads/master | 2023-04-29T06:32:37.372719 | 2021-05-25T16:15:21 | 2021-05-25T16:15:21 | 112,335,122 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,424 | r | get_sn_atac.R | #!/usr/bin/env Rscript
# This script is to get candidate causal ATAC QTL SNPs, which can then be
# used for Sarah's CRISPR assays for ATAC-altering variants
library(tidyverse)
#setwd("/Users/jeremys/work/opentargets/sensoryneurons/GRCh38/ATAC/")
args <- commandArgs(trailingOnly = TRUE)
leadSnpFile = args[1] #rasqual.... |
edce2581e399546ff4d016da29e5f1dda0961bec | f745c898aab20fa173bd75eb959e982b6e99ef0b | /betfair/Functions/twitter_graphs.R | c19adb6f8ec286b66028f833f643ed8499072cda | [] | no_license | NimaHRaja/WorldCup2018 | 3a95fca0aac5cdf615e3ccf6f3af86764d235186 | 93f50d4cc2e982c29985be279fdaf8c8f0229643 | refs/heads/master | 2020-03-19T02:11:27.323390 | 2018-07-02T20:27:19 | 2018-07-02T20:27:19 | 135,607,539 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 656 | r | twitter_graphs.R | #to be documented
# jpeg("betfair/outputs/twitter_BetfairWinnerTotalMatched_20180606.jpg", width = 800, height = 600)
ggplot(subset(all_data_odds, marketName == "Winner 2018"),
aes(x = as.POSIXct(time), y = totalMatched)) +
geom_point(colour = "blue") +
# ylim(c(0, max(subset(all_data_odds, marketName ... |
506015115f1e3783d52f524a33a5d7a4bc436325 | afd52451e8845963de4ad1243005834fa0958beb | /data_handling.R | aa4e6039a7c282c4451589fdc6897540a25e7e72 | [] | no_license | plus4u/R | 7c0d867767ae948b24a15322df11b500abcfd920 | c8c25313567bd8bcf5142a04187a24e0d5ad12d1 | refs/heads/master | 2021-09-19T13:52:40.115595 | 2021-08-11T06:47:22 | 2021-08-11T06:47:22 | 155,179,952 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,689 | r | data_handling.R | ## data handling
rm(list = ls())
# vector
x1 <- c(1,2,3)
x <- 1:9
x
x1 <- c(1: 9)
n1 <- rep(10,3)
n1 <- rep(1:3, 3)
n1
x1.n1 <- data.frame(x1, n1)
x1.n1
d1 <- data.frame(A=c(1,2),B=c(3,4),C=c(5,6),D=c(7,7),E=c(8,8),F=c(9,9))
d1
subset(d1, select=c("A", "B"))
# Generate a random number : ?runif / ?sample / ?.... |
286b180efbc143efe8997dad0c2fad4fccc750a6 | 180be150463963373eadd36bbaa735061dc7f0b1 | /man/visual2pw.Rd | e575c4bc283f56970c298cd6f581d948bcac76fc | [
"AFL-3.0"
] | permissive | fskeo/GESTIA | 4cf7b19d969acdf2078ca15b1dc8d5498987e5d1 | d25976609fb9b5fc1eedb26cbc342744a41f8cea | refs/heads/master | 2023-01-20T23:43:49.384722 | 2020-08-19T02:38:51 | 2020-08-19T02:38:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 839 | rd | visual2pw.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/source_GESTIA.R
\name{visual2pw}
\alias{visual2pw}
\title{Visualize two pathways' components}
\usage{
visual2pw(ig.all, genesa, genesb, colorset = c("yellowgreen", "tomato",
"gold"), isolated = FALSE, ly = c("kk", "fr", "random", "gr... |
b32aede55098c6d5418fa90d62f4af62b5fba3a1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/m2r/examples/factor_poly.Rd.R | 8f5dc15f8b1af5466284b88183e0bbddfda717e3 | [] | 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 | 957 | r | factor_poly.Rd.R | library(m2r)
### Name: factor_poly
### Title: Factor a polynomial
### Aliases: factor_poly factor_poly.
### ** Examples
## Not run:
##D requires Macaulay2 be installed and an interactive session
##D
##D ##### basic usage
##D ########################################
##D
##D ring("x", "y", coefring = "QQ")
##D f... |
e902bd3cc3e85be310a8bab7828cf03f44565b78 | b051db434b8ec8e30ec4264181ba6bf86b539ce9 | /man/validMassSpectraObject.Rd | 38055a53fb138a247ace054ac22be28a95ed05d9 | [] | no_license | lorenzgerber/tofsims | 6484b58a532385bcbc906fe2921190d2221cc77f | cf0791d3324638d604dea5a111729b507f5b2192 | refs/heads/master | 2021-06-25T23:27:27.148518 | 2020-10-15T06:32:08 | 2020-10-15T06:32:08 | 73,091,887 | 1 | 1 | null | 2016-11-07T15:24:28 | 2016-11-07T15:24:28 | null | UTF-8 | R | false | true | 430 | rd | validMassSpectraObject.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllClassDefinition.R
\name{validMassSpectraObject}
\alias{validMassSpectraObject}
\title{Validation method function for class MassImage objects}
\usage{
validMassSpectraObject(object)
}
\arguments{
\item{object}{object of class MassSpectra}
}... |
b50fa61429a8c4a7d5ef03486807787c54e87004 | 98a79efaca28ba04ba44c8a18c03b0549d1549c7 | /R/makeVAR.R | 5cb6bf20d8627fd3e00ab0f1c1eb255a0ed3c901 | [] | no_license | joshoberman/backtest | 81b2e5e14c6dfb300a7a54aacc5c9468362e2ca1 | 0c9f64977a98a0239800cd73c9d3619cf1559082 | refs/heads/master | 2021-01-01T18:46:00.947093 | 2017-07-26T14:27:40 | 2017-07-26T14:27:40 | 98,430,911 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,555 | r | makeVAR.R | ##Function to make VAR model with a given number of predictors and an n-Ahead projection
makeVARandPredict<-function(x,nAhead=24){
#Subset to remove month counter
x.noMnthCnt<-subset(x,select=-c(monthCounter))
x.noMnthCnt.ts<-ts(x.noMnthCnt,frequency=12)
#year on year difference
x.noMnthCnt.ts.diff<-diff(x.... |
642505f608ca7881114bdd0ac293fe7f2a7afe48 | 77157987168fc6a0827df2ecdd55104813be77b1 | /palm/inst/testfiles/pbc_distances/libFuzzer_pbc_distances/pbc_distances_valgrind_files/1612987992-test.R | bdd725ae50fc325842d635f7fc39158479d34b91 | [] | 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 | 589 | r | 1612987992-test.R | testlist <- list(lims = structure(c(2.37636445786895e-212, -Inf, 2.37636445786895e-212, 0), .Dim = c(2L, 2L)), points = structure(c(-Inf, NaN, -Inf, 3.50129302979654e-312, 5.82833713021365e+303, 0, 6.00939702786469e-307, Inf, 7.2911220195564e-304, 1.08650122118086e-310, 4.94065645841247e-324, 7.06327380456815e-304,... |
5607dc662fde26152b16d32ed772a9f13390e44c | 311ae82a6efaf9c9cac8d1b517ba992815c88128 | /Production/DSM/pH/digital_soil_mapping/model_fitting/variogram/d1_residual_variogram.R | 7559bc5833b29aeba4ce8ea47b64fceb69ba7f19 | [
"CC0-1.0"
] | permissive | AusSoilsDSM/SLGA | 8c77f0ad24a49e05f00c8a71b452214e401d6a3f | 41d8e2c009c1595c87bdd805a8ba6c4a3f45cbd1 | refs/heads/main | 2023-03-18T13:18:33.073555 | 2023-03-07T23:54:51 | 2023-03-07T23:54:51 | 555,090,777 | 7 | 2 | null | 2023-03-07T21:55:39 | 2022-10-20T23:51:23 | R | UTF-8 | R | false | false | 3,210 | r | d1_residual_variogram.R | ### TERN LANDSCAPES
# Soil pH model model fitting
# Author: Brendan Malone
# Email: brendan.malone@csiro.au
# created: 18.5.21
# modified: 18.5.21
# CODE PURPOSE
# # Apply model fits to all available data [excluding external data.
# need to estimate model residuals
# fixed parameters
vart<- "pH"
depth<- "d1"
# roo... |
2bd0f0b473e2452cd857690fdaf36a49e6f86215 | 390b8ecc5591fe3562c5af26f5cd444442d07616 | /R/interpolateData.R | 6802ba3e7d19131135ea151e2e93844dc7fb33c4 | [] | no_license | selu220/weatheR | d86e05c5e17579d0d601145c4bc9c08e35c03527 | 18a0fd66ef17e907959cc99643b8edb0771c0f1e | refs/heads/master | 2021-01-18T09:28:00.072649 | 2016-02-18T08:48:51 | 2016-02-18T08:48:51 | 51,706,012 | 0 | 0 | null | 2016-02-14T17:58:53 | 2016-02-14T17:58:53 | null | UTF-8 | R | false | false | 3,975 | r | interpolateData.R | #' Interpolate Weather Station Data
#'
#' This function takes in a list object of one or more data frames and will return
#' a data frame with hourly observations, with missing observations linearly interpolated
#'
#' @param station_data List object of weather data
#' @param type Type of data structure to return the we... |
b380f2755c7e78e003ffa196e998ae27eba84934 | 88ab69d4cd76460be75494157528f0a4c829b27e | /R/Permafrost/Mapping/PermafrostCompara/CalDemKappa.R | dcba9acc8d70db08d68f0f682c85f87d22906195 | [] | no_license | smallwave/Noah-Tibet | 92706f1932de81f9c6e0e5a057c0ab542353a0a0 | 8c2469608368375673075aa806e06eeeae1b379a | refs/heads/master | 2020-12-24T15:23:31.060111 | 2016-10-09T02:24:59 | 2016-10-09T02:24:59 | 42,633,145 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,560 | r | CalDemKappa.R |
##########################################################################################################
# NAME
#
# PURPOSE
#
#
# PROGRAMMER(S)
# wuxb
# REVISION HISTORY
# 20160921 -- Initial version created and posted online
#
# REFERENCES
################################################################... |
ec915835005c6545313c0ef6a996136147f671a9 | 16d916a7a309a224fb038f294ac2926abfe5a23a | /R/rotavirus_functions.r | 842a99ce0790f4ea5e1f5473cf4fa163f661a136 | [] | no_license | chrishedw/rotavirus | ed6fc72b8e7ab2d0472e554bff2c8fe39a6aeca8 | ec72f2ecc1c60d06808bde08568d005ebea28482 | refs/heads/master | 2020-05-20T08:35:44.901117 | 2015-03-26T12:44:33 | 2015-03-26T12:44:33 | 32,926,524 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 409 | r | rotavirus_functions.r |
adds.one <- function(x){
if(!hasArg(x)){
stop("missing argument")
}
if(is.na(x) | is.null(x) | !is.numeric(x)){
stop("argument not numeric")
}
return(x+1)
}
calculate_NMB <- function(benefits,costs){
if(!hasArg(benefits) | !hasArg(costs)){
stop("missing argument")
}
retval <- benefits-cost... |
329cba1d10d75617996e6d5c900397a85d4ebc1b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/generalCorr/examples/bootSign.Rd.R | 93af86faee81475af3c98e30b19c0e032c49ff34 | [] | 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 | 980 | r | bootSign.Rd.R | library(generalCorr)
### Name: bootSign
### Title: Probability of unambiguously correct (+ or -) sign from
### bootPairs output
### Aliases: bootSign
### Keywords: bootstrap, comparisons kernel meboot, pairwise regression,
### ** Examples
## Not run:
##D options(np.messages = FALSE)
##D set.seed(34);x=sample(1:1... |
16a7c22fa1bf4b22ee41c2a8cae31d2f71ca532b | 7a9a8fb85481a80124bb1004eb3f4cfb46cdbede | /program12.R | 1a6415871c42d6c11fa55a558d24b10af6065ac0 | [] | no_license | xinyizhao123/Predicting-Future-Ambient-Ozone | 6459a9eef144bbf68416522f1987cf60f87af6bd | 1b682e4fcc16f443b4d3d8c9216cb5f823ac2986 | refs/heads/master | 2020-05-25T14:58:23.133324 | 2016-10-06T00:52:51 | 2016-10-06T00:52:51 | 69,671,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,231 | r | program12.R |
setwd("C:/Users/Hitomi/Dropbox/Ozone project/data")
st <- read.csv("fulldata.csv", stringsAsFactors = FALSE)
st <- st[-c(1)]
st$date <- as.Date(as.character(st$date), format("%Y-%m-%d"))
simu$date <- as.Date(as.character(simu$date), format("%m/%d/%Y"))
str(st)
str(simu)
st <- st[order(st$siteID, st$date)... |
e0a419e5140eee20c7d4383fbb2e370d309304b3 | 56f9a8a7475ea8e81c88e7b4ddaffc75ff82d940 | /plot.R | 94392e09d170d6f87c03d3cd61f2be5905dd36b9 | [
"MIT"
] | permissive | OtakuSenpai/trace | 5d8c04295c0a85b9865a0a36af7f6db9abfb097b | ca10fd16bfbcf840f0a9041e1aa4b5fe859b7668 | refs/heads/master | 2020-12-03T03:44:42.831222 | 2017-04-15T20:38:02 | 2017-04-15T20:38:02 | 95,768,677 | 1 | 0 | null | 2017-06-29T10:56:27 | 2017-06-29T10:56:27 | null | UTF-8 | R | false | false | 870 | r | plot.R | png(filename='chrome.png',width=728,height=400)
trace <- read.table('trace.tsv',header=T)
time <- trace$ms/1000
cpu <- trace$cpu_load_perc
ram <- trace$res_mem_kb/2^10
reads <- trace$read_b/2^20
writes <- trace$write_b/2^20
colors <- c('#0099cc','#9933cc','#669900','#ff8800')
plot.new()
title('Chrome cold-start')
p... |
a782def1f1ad78117787e5842cdc347c29d076f5 | 392b2626516030de72268f9c6f2a622cc08601ce | /man/Replace_ex.Rd | 8b5fe3e12cee3751fa8c97326a0019435c433b6d | [] | no_license | minghao2016/do | c047e262d3a87df5b82283e6599f50855f2bf917 | f38962795f860c464fe99c8872f6e56632140d92 | refs/heads/master | 2022-04-05T06:37:35.108769 | 2019-12-16T23:43:40 | 2019-12-16T23:43:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 768 | rd | Replace_ex.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Replace_ex.R
\name{Replace_ex}
\alias{Replace_ex}
\title{Replace Exactly}
\usage{
Replace_ex(x, from, to, pattern)
}
\arguments{
\item{x}{vector, dataframe or matrix}
\item{from}{replaced stings}
\item{to}{replacements}
\ite... |
bd0d0be471b54a3bbf7785961456e1b839d6983a | 5c979309940cbb6458deac553ef3620d02e55f36 | /man/double.Rd | 48a66cc629676928a8945723598a2bfbcd8e07bc | [
"MIT"
] | permissive | evanamiesgalonski/speciesdemo | ca65af7d2d4dbf9f32375aa69bb8950d0690249d | f49ede58c960c9b7d59b51956ef179e430ba1aef | refs/heads/master | 2022-04-26T10:12:37.080098 | 2020-04-18T21:06:24 | 2020-04-18T21:06:24 | 255,143,434 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 288 | rd | double.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/demo.R
\name{double}
\alias{double}
\title{double}
\usage{
double(num)
}
\arguments{
\item{num}{The number to be doubles}
}
\value{
The value for 'num', doubled
}
\description{
double
}
\examples{
double(2)
}
|
dccb96622827efe7371c27edb3efa51864b9786b | ce4a1573d7aeec032138536684918865ff765e85 | /古松/server.R | bfd512a7123de612ec3bd7425f698d34d97ab3cf | [] | no_license | wenjing14bjtu/railwayIdx | 6f4d810a674483898ca60fd015bd6f84dab03bd7 | ecfd82928cac2f1565c78e9506c3938c7e17bb73 | refs/heads/master | 2021-01-17T20:37:45.253728 | 2016-05-20T09:40:17 | 2016-05-20T09:40:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,552 | r | server.R | shinyServer(function(input, output) {
#------------------------------
#货运量预测
#------------------------------
require(ggplot2)
require(DT)
require(e1071)
require(randomForest)
df<-read.csv("freight.csv",head=T)
df$tm<-as.Date.POSIXct(df$tm,"%Y-%m-%d",tz=Sys.timezone(location = TRUE)) #转化为... |
faf908887c30fb4b6020110b6e996e20e1530450 | d6ebbd011285a838e3ac2bfd7efc1070c7bc2d7f | /shinyApps/ui.R | 6b4c69522a594962f2e18e84d0785ad46c706201 | [] | no_license | jpzhangvincent/ucdrocksgg | e85a9c3006e58186ecb803f6cbbb62515ce47556 | 7c252823b1e912a3d6f0f13ecf9ae3950d28059f | refs/heads/master | 2021-01-10T09:22:55.609439 | 2015-10-24T23:08:29 | 2015-10-24T23:08:40 | 44,872,822 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,219 | r | ui.R | # ui.R for green gov shinyApp
library(shiny)
library(shinydashboard)
library(rCharts)
library(graphics)
dashboardPage(
dashboardHeader(),
dashboardSidebar(
absolutePanel(
class = "main-sidebar",
strong(h1("Version 1.1"))
)
),
dashboardBody(
fluidRow({
column(
widt... |
6d49f89ec551d84dfa56fcc7c81f4c8f3eb03f0c | 93039300d230662ecfc357ea73417467f27d97eb | /EPDr-HowToExportToWordPress.R | cb6d2f87c643d47e82d5792222cfe5730eb336a4 | [] | no_license | dinilu/EPD-workflow | a480a2578d50192a8edff0a6dc2bc668a51be666 | 0260924b5d0d694baf63b9261f975f9f44d88e38 | refs/heads/master | 2020-04-06T23:15:07.745126 | 2017-01-27T14:23:36 | 2017-01-27T14:23:36 | 50,992,481 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 915 | r | EPDr-HowToExportToWordPress.R | # Install RWordpress
#install_github(c("duncantl/XMLRPC", "duncantl/RWordPress"))
library(RWordPress)
# Set login parameters (replace admin,password and blog_url!)
options(WordpressLogin = c(dnietolugilde = 'g6yX:#!x'), WordpressURL = 'https://dnietolugilde.wordpress.com/xmlrpc.php')
# Include toc (comment out if not... |
7640d8dbfa9830b83660932e40e9bd4314fa63d8 | 0b7b0502560aa79d813224c62929095539427a89 | /R/on_load.R | 1edb35b5803a411ed92b2d9770dc4aa0f2839df3 | [
"MIT"
] | permissive | kant/rerddap | c1f890eca53719502d9eb8d94dbdcd4f03747b92 | 42329b752415b52eb6c219036724a4f139c0fd52 | refs/heads/master | 2020-12-02T20:17:56.251512 | 2019-12-31T01:11:53 | 2019-12-31T01:11:53 | 231,108,519 | 0 | 0 | NOASSERTION | 2019-12-31T15:12:39 | 2019-12-31T15:12:38 | null | UTF-8 | R | false | false | 153 | r | on_load.R | rrcache <- NULL # nocov start
.onLoad <- function(libname, pkgname) {
x <- hoardr::hoard()
x$cache_path_set('rerddap')
rrcache <<- x
} # nocov end
|
0a90fca5cf548528d0ad091d06d11e8b530002b5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/wpp2010/examples/e0.Rd.R | ba8eb1c01485a66612fb447af8994c155a1f326d | [] | 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 | 277 | r | e0.Rd.R | library(wpp2010)
### Name: e0
### Title: United Nations Time Series of Life Expectancy
### Aliases: e0 e0_supplemental e0F e0Fproj e0M e0Mproj e0F_supplemental
### e0M_supplemental
### Keywords: datasets
### ** Examples
data(e0M)
head(e0M)
data(e0Fproj)
str(e0Fproj)
|
56b526fa7e4bbc102e6ccbbc912629744de2dd36 | 35fdff8e2f540586e264f9ec8931b73957848192 | /conifers_empirical_analysis/run_scripts/conifer_run_analyses.R | 6b58d1a8bcd0d077ba3078cba1961359cad82120 | [] | no_license | hoehna/CoMET | a33fbb76eb81b04a128fd74fc6bf679e7524fd46 | 1e701bdfd7c598812d67a252936eeb4019eec224 | refs/heads/master | 2021-01-10T10:06:30.037115 | 2016-03-03T15:23:40 | 2016-03-03T15:23:40 | 51,861,998 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,535 | r | conifer_run_analyses.R | # Data and priors used for the analysis
extinction_rate <- c('empirical')
diversification_shift_rate <- c(0.1,log(2),2,5)
mass_extinction_rate <- c(0.1,log(2),2,5)
runs <- 1:4
# Change this to the number of processors mclapply will use
num_cores <- 4
# Change this to the directory containing the dryad package
dir <- ... |
414d3ba80cf4589a19973cbd46ad8383e58a5c85 | 1bda17508c8734f8aa1132d4f8f80b91ba332ef7 | /R/0.preparation/old/1.create_raw/create_rawdata_population_kebeles.R | 970bf3aca011c1394de60cc7f2bd22d11434cace | [] | no_license | araupontones/ETH_IC | b08b12bee201905859292e53de36ced454bd5dba | 681c6d7ae98926a111ed1b887974f0741519e2d4 | refs/heads/main | 2023-08-03T11:59:55.612161 | 2021-09-27T06:42:58 | 2021-09-27T06:42:58 | 329,865,799 | 0 | 0 | null | 2021-09-27T06:42:59 | 2021-01-15T09:25:24 | R | UTF-8 | R | false | false | 2,682 | r | create_rawdata_population_kebeles.R | #to read pdf files in R
library(tabulizer)
source("set_up.R")
dir_kebeles_download = file.path(dir_data_reference_downloads, "Kebeles_population")
#lisf of all pdf files
#list.files(dir_kebeles_download)
#extract_areas(file.path(dir_data_reference_downloads, "Kebeles_population", "Oromiya.pdf"), 985)
#tabulizer::lo... |
e7fba641048c5ddfa8e45497431d6bdba1c91f4c | 2d7dd1f3ab97fc89538dfab09b8b0acc1187c472 | /R/data.R | 6e53e9a8ace0ea3bf31341789aa16997c8a460b8 | [
"MIT"
] | permissive | inbo/inborutils | 642e36bdbd514ce2b3b937bdcb387531859d93d0 | fd1174a95770144024ad2e2e8938f40f5e542b2d | refs/heads/main | 2023-05-23T07:18:40.570529 | 2023-03-24T15:46:48 | 2023-03-24T15:46:48 | 69,332,829 | 9 | 7 | MIT | 2023-03-24T15:46:50 | 2016-09-27T07:54:51 | R | UTF-8 | R | false | false | 1,795 | r | data.R | #' Example `data.frame` with species name column
#'
#' A dataset containing 3 taxa to be matched with GBIF Taxonomy Backbone. The
#' variable are as follows:
#'
#' @format A data frame with 3 rows and 3 variables
#' \itemize{
#' \item {`speciesName`: name of the species}
#' \item {`kingdom`: kingdom to which the sp... |
7dd428c528ab42634730d095ff1cba07a2da3769 | 394b0b27a68e590165d0dfb9243e7b2d5deaf4d5 | /R/turnTaking.R | fc09942cea98e4b8c7d278ace098decd7879f206 | [
"MIT"
] | permissive | NastashaVelasco1987/zoomGroupStats | 5b414b28e794eecbb9227d4b1cd81d46b00576e4 | 8f4975f36b5250a72e5075173caa875e8f9f368d | refs/heads/main | 2023-05-05T18:23:17.777533 | 2021-05-24T16:08:23 | 2021-05-24T16:08:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,610 | r | turnTaking.R | #' Simple conversational turn-taking analysis
#'
#' Generate a very basic analysis of the conversational turntaking in
#' either a Zoom transcript or a Zoom chat file.
#'
#' @param inputData data.frame output from either processZoomChat or processZoomTranscript
#' @param inputType string of either 'chat' or 'transcri... |
a3e6a46367190609b9cfc66d2075b99a00e001c8 | aa1da12305bb9a442f1c1fa4389b92d601afdb50 | /man/strat_metrics.Rd | eb87e9b29b1a29ad0c05fe59aff40c6173b0ccc0 | [
"MIT"
] | permissive | rubenvalpue/sgsR | 845814721d1e9834d589cf7ac4bf8d5430c554cd | 20edde477d2287214467ae27b1649371dabdd471 | refs/heads/main | 2023-04-17T06:21:42.364584 | 2021-04-26T17:44:16 | 2021-04-26T17:44:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,491 | rd | strat_metrics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/strat_metrics.R
\name{strat_metrics}
\alias{strat_metrics}
\title{Stratify metric raster using metric quantiles.}
\usage{
strat_metrics(
mraster,
metric = NULL,
metric2 = NULL,
nstrata,
nstrata2 = NULL,
plot = FALSE,
samp = 1,
... |
4ee5313afd447a0cf2356184e0692c6c79ab21ca | ad522819f54aa659c951ff39fff1dda0fff0f89f | /man/functional_db_to_amplitude.Rd | 206230c6d5fa6008bb678000e5df6b0d18311906 | [
"MIT"
] | permissive | davidbrae/torchaudio | 4dbc4e12067b14dedd8fa785a6b753719e39b0d3 | d20ccc237a8eff58e77bb8e3f08ef24150a4fc4e | refs/heads/master | 2023-07-20T16:06:59.791249 | 2021-08-29T19:16:50 | 2021-08-29T19:16:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 712 | rd | functional_db_to_amplitude.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functional.R
\name{functional_db_to_amplitude}
\alias{functional_db_to_amplitude}
\title{DB to Amplitude (functional)}
\usage{
functional_db_to_amplitude(x, ref, power)
}
\arguments{
\item{x}{(Tensor): Input tensor before being converted to p... |
ac48b148c579eec053fc19f6f69a73cf411501e6 | 56ceeb0f231c60f2af78c66fc9c74d49ce398777 | /man/simPlusMinus.Rd | 1a73a296f957f2b0c54d7c08dc8afd8951c048f3 | [
"MIT"
] | permissive | ajrominger/RarePlusComMinus | 741bab1eb6771232f758eb68095008b0009d460a | 902476ad3998f19fb82592785127eac05843ca25 | refs/heads/master | 2021-06-29T19:33:00.592586 | 2021-06-25T19:32:06 | 2021-06-25T19:32:06 | 228,719,169 | 0 | 1 | MIT | 2021-06-07T14:09:01 | 2019-12-17T23:28:17 | TeX | UTF-8 | R | false | true | 2,438 | rd | simPlusMinus.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/indSwapTest.R, R/simPlusMinus.R
\name{indSwapTest}
\alias{indSwapTest}
\alias{simPlusMinus}
\alias{simpleSim}
\title{Explore the effect of using the independent swap algorithm on inference
of connection between abundance and positive or negat... |
1bd2a2b511910d0ba8839e0c4eba401b284d448d | 98383fc7513540b66d98bf74abfe9bd9dc3f1a52 | /data_preprocessing_2.R | de79afdb530f16de3f2874c1f425e20e958a54a9 | [] | no_license | AbdullahMakhdoom/Movie-Recommendation-System | 4a6e279df3169a523359309778f6b089a3f95fd1 | 7fc2d520d4500161ee58a1f2ce89718df1531f46 | refs/heads/master | 2022-12-04T04:44:31.023096 | 2020-08-23T20:04:56 | 2020-08-23T20:04:56 | 289,512,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,402 | r | data_preprocessing_2.R | # Selecting useful data
movie_ratings <- ratingMatrix[rowCounts(ratingMatrix) > 50,
colCounts(ratingMatrix) > 50]
movie_ratings
minimum_movies<- quantile(rowCounts(movie_ratings), 0.98)
minimum_users <- quantile(colCounts(movie_ratings), 0.98)
image(movie_ratings[rowCounts(movie_ratings) ... |
1aaa9703af3509623c99e453e4669669a45794b4 | cba10b84d2cc708dd66148a4511451d77a92a7c5 | /tests/testthat/test-plotCI.R | e2e3463363034d0a9502bb02d57e152cfc2bede2 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | r4ss/r4ss | 03e626ae535ab959ff8109a1de37e3e8b44fe7ad | 0ef80c1a57e4a05e6172338ddcb0cda49530fa93 | refs/heads/main | 2023-08-17T08:36:58.041402 | 2023-08-15T21:42:05 | 2023-08-15T21:42:05 | 19,840,143 | 35 | 57 | null | 2023-07-24T20:28:49 | 2014-05-16T00:51:48 | R | UTF-8 | R | false | false | 301 | r | test-plotCI.R | test_that("plotCI function works", {
# Note: could look into using snapshot test to verify output
set.seed(123)
x <- 1:10
y <- rnorm(10)
uiw <- 1
liw <- 2
output <- plotCI(x = x, y = y, uiw = uiw, liw = liw)
expect_equivalent(x, output[["x"]])
expect_equivalent(y, output[["y"]])
})
|
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