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176125b3a58bcbf156c7eae4e34fe7ba2278dc9f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/semantic.dashboard/examples/notification_item.Rd.R | 56a2bf8c958566bc3473cf04793b7f621ccdb05a | [] | 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 | 223 | r | notification_item.Rd.R | library(semantic.dashboard)
### Name: notification_item
### Title: Create a notification item.
### Aliases: notification_item notificationItem
### ** Examples
notificationItem("This is notification!", color = "red")
|
022c60a347e20b3b7e734ae698b99aa3c5ce910f | 3e643d92b967dca3f43517920681b5ea7285c9a1 | /R/searcherAddin.R | afebac259928f663e874a1d8c19d46556c50a82f | [] | no_license | will-r-chase/searcher2 | e74891d09cc5649a1488a8758d11d6fda92749ce | 82ee823097d6e34f63d1cab1b3d67027d353bbcb | refs/heads/master | 2020-08-29T18:54:31.173255 | 2019-10-28T20:22:38 | 2019-10-28T20:22:38 | 218,138,064 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 304 | r | searcherAddin.R | #' Search google addin
#'
#' Search google for the selected word w/ the `searcher` package
#'
#' @return
#' @import searcher rstudioapi
#' @export
#'
#' @examples
searcherAddin <- function() {
doc <- rstudioapi::getActiveDocumentContext()
searcher::search_google(query = doc$selection[[1]]$text)
}
|
0b1e128a62b1a94e24eb28e9a20d09ba2cec4fc3 | 0913ef989631d5fbb6461667563bb13c102d22c8 | /man/import.info.Rd | f24e84935cd4b56b393768b9ae9dc601b892dddb | [] | no_license | oucru-biostats/C306 | 57c8b501a106384f101d20c54bdcbc54d99c8bdf | 7d30d14b081ba64b32fc47ac985bc45ad2672f70 | refs/heads/master | 2022-05-01T09:33:44.355345 | 2022-04-28T11:42:23 | 2022-04-28T11:42:23 | 203,103,489 | 0 | 2 | null | 2019-11-07T05:46:23 | 2019-08-19T05:21:54 | R | UTF-8 | R | false | true | 694 | rd | import.info.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/OUCRU_data_dictionary.R
\name{import.info}
\alias{import.info}
\title{Import OUCRU's data dictionary into R}
\usage{
import.info(table_name, input, output)
}
\arguments{
\item{table_name}{a character vector specifies names of Excel sheets (in... |
8fe7be0f41cdb28822fbb107762b24d5b243ecb5 | 167e2d9523f4fe3b0752654a5f26259f36bcc214 | /script/IOT1.R | eddab58b49d2587f04ea054cccaf20775c7f50e2 | [] | no_license | UbiqumCodeAcademy/IOT1 | 5b391b0734519c4d8315fb661a4e64b996b1b477 | 8f6f1fdc76588c2cb506172b9eab2797a3c12730 | refs/heads/master | 2020-05-25T13:14:33.402906 | 2019-05-21T10:48:26 | 2019-05-21T10:48:26 | 187,817,113 | 0 | 0 | null | 2019-05-21T10:39:16 | 2019-05-21T10:39:16 | null | UTF-8 | R | false | false | 18,395 | r | IOT1.R | # General comments
# take care with variable names
#
# Settings -----
pacman::p_load(chron, dplyr, plyr, RMySQL, lubridate, ggplot2, reshape2,
quantmod, scales, RColorBrewer, sqldf, ggfortify, tidyr,
compareDF, reshape, rstudioapi, stringi, plotly, padr,
DescTools, anyt... |
da79cc3ea403121e0516c364f8169a94ee49b998 | 1a9ef448017a28bfffdfb78887022b46a6169507 | /man/text2times.Rd | bd282dc8638e31fbd57eea388fdc6992ac9c6c66 | [
"BSD-3-Clause",
"BSD-2-Clause"
] | permissive | rtelmore/RDSTK | 4ae28abbb12c937141c5a834dc46a010799d0f15 | cdddc4d3647281155067bb434f54f12c27fdd3aa | refs/heads/master | 2021-01-21T11:45:00.044495 | 2017-11-20T20:47:30 | 2017-11-20T20:47:30 | 1,686,614 | 19 | 12 | null | 2017-03-16T18:52:10 | 2011-05-01T02:08:32 | R | UTF-8 | R | false | true | 1,426 | rd | text2times.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/text-to-times.R
\name{text2times}
\alias{text2times}
\title{Parses a text string for time information.}
\usage{
text2times(text, session = RCurl::getCurlHandle())
}
\arguments{
\item{text}{A text string containing possible time information.}
... |
73f03c5e6b15f57a70a9e13f3c381c5fd16f7624 | 22d9614d91d5cac0a14512eabbf477aa30e2acd7 | /_episodes_rmd/tests/test_fail.R | dd6b3c84e6c9c908763abc213b1ef62a7b899d6c | [
"MIT",
"CC-BY-4.0",
"LicenseRef-scancode-public-domain"
] | permissive | mawds/r-programming-intro | 78890568a2ac29e04e10d83ed1f94f6a79cb816c | afe759c8974644ece3af1ef943e9895e9bc18c09 | refs/heads/gh-pages | 2021-09-14T09:40:21.439999 | 2018-05-11T13:15:29 | 2018-05-11T13:15:29 | 110,844,421 | 0 | 1 | null | 2018-02-01T13:01:08 | 2017-11-15T14:32:46 | Python | UTF-8 | R | false | false | 155 | r | test_fail.R | context("Cleaning fields")
test_that("Can clean a field", {
testvector <- c(1,2,-999.99)
expect_equal(c(2,3,NA), cleanfield(testvector))
})
|
f933d22f5a3766a176cc7c3cc3a8885de82f6f14 | c0bc3866a2b44320138e6dd8f26f775620f43851 | /Q1.R | aa73ebb3edcfffe4f750ccee0a4168384dd41ffc | [] | no_license | navaneethreddymatta/Defect_Data_Analysis_for_Defect_Projection | 580be44d1186533b9c73e3457a76c6e00ff421a8 | 38f0da3756c45f0a79795068548118a64e045f3d | refs/heads/master | 2021-01-21T18:10:40.443154 | 2016-09-16T06:12:28 | 2016-09-16T06:12:28 | 68,357,112 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,817 | r | Q1.R | currdir <- getSrcDirectory(function(x) {x})
maindir <- paste(currdir, "/Datasets", sep="")
subdir <- list.files(maindir)
jsd = function(x, y) {
maxlen = max(length(x), length(y))
p = rep(0, maxlen)
for (i in 1:length(x)) p[i] = x[i]
p[p==0] = 0.1
p = p/sum(p)
q = rep(0, maxlen)
for (i in 1:length(y)) q[i] = ... |
a20fc69fd8b01e8c4dec4ee68700be943994fa4e | caf6f15fe311eb9e8a70465054b2f47d6729cfc0 | /man/kappa4nlsBoot.Rd | cc2caab6b124a262b6eef07fdc84564d07382a37 | [] | no_license | cran/alR | 8f4f84656fc2bbed4381444a70f6b442da903d59 | c9d857d9bfdea576eb3bbe7ca5dcb67ed7a5728b | refs/heads/master | 2021-01-21T12:46:29.775345 | 2017-12-07T11:02:24 | 2017-12-07T11:02:24 | 102,097,008 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,531 | rd | kappa4nlsBoot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kappa4nlsBoot.R
\name{kappa4nlsBoot}
\alias{kappa4nlsBoot}
\alias{kappa4nlsBoot.default}
\alias{print.kappa4nlsBoot}
\alias{summary.kappa4nlsBoot}
\alias{print.summary.kappa4nlsBoot}
\alias{kappa4nlsBoot.formula}
\alias{predict.kappa... |
d910c76defa8305e8af1113be38ab015f8eb9216 | 99bc8c37eb2a40dd7b2dfa3b7215b908fb508b1b | /delivery time.R | 324765c4a6107c497786983579b3bc667c2a0771 | [] | no_license | karthi-25/Tutorials-on-R-codes | 1310d418f32e1c08cb81ef52c953cb64d0161e96 | ba6014cbe47f5645c88dc09a6c0e2b9c4ffca00b | refs/heads/main | 2023-04-19T06:01:56.577593 | 2021-05-06T11:36:18 | 2021-05-06T11:36:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 267 | r | delivery time.R | #load data
hi<-delivery_time
#create regression model
model=lm(hi$`Delivery Time`~hi$`Sorting Time`)
summary(model)
#predict for the data set
pred=predict(model,hi)
pred
#load the pred values into final data
final_data=data.frame(pred,hi[,-2])
final_data
plot(model)
|
e60f1e66c740d12c419c02fa5af7c95f1aeba9e5 | e37f4f64b615dd7871893cc8fc2d6aac0040b828 | /man/coxdual.strata.Rd | 9b6bbe706c6130532bf84537d01e7a7235c4a5a2 | [] | no_license | aboruvka/coxinterval | 3b6b0ba6b4ad864daa28551b6569ecad72f5c48c | de93fd1f12bb16550c8b0d62a16359f8fa96c3b7 | refs/heads/master | 2021-01-21T04:54:38.901728 | 2016-06-03T01:24:53 | 2016-06-03T01:24:53 | 17,570,288 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,184 | rd | coxdual.strata.Rd | \name{coxdual.strata}
\alias{coxdual.strata}
\title{Identify transition type in model terms}
\description{
A utility function for \code{\link{coxdual}} that identifies the
state-transition types.
}
\usage{coxdual.strata(from, to)}
\arguments{
\item{from}{
a variable representing the originating state.
}... |
438137d5c3af03edf6a196b27914ab6b3e2dd6f5 | 2b73cb9ae681bc43be9c1d53eee9e6116a1af173 | /man/addwmfs.Rd | 580d8b05288f1400b56bf1027713e21792390ab1 | [] | no_license | cran/wsyn | 8a161b1c239875cddb0a802e8393b4a1993725e9 | 72e97f83500ebc44fb1a15d968c426ad478d4f9f | refs/heads/master | 2021-08-22T22:28:55.339113 | 2021-06-18T20:10:02 | 2021-06-18T20:10:02 | 167,043,420 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,842 | rd | addwmfs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/addwmfs.R
\name{addwmfs}
\alias{addwmfs}
\title{Adds wavelet mean field information to a \code{clust} object}
\usage{
addwmfs(obj)
}
\arguments{
\item{obj}{An object of class \code{clust}}
}
\value{
\code{addwmfs} returns another \code{clust}... |
d5b29e510bc2433692823d5b9b38f9a0117cf042 | 4b0f029aa00f9d1d7499cc11c26c6898cc08e4d1 | /R/validate-tactics.R | 757a094c5eae267236568d9422e99ae07d2ea913 | [
"Apache-2.0"
] | permissive | Quinn-Yan/attckr | 6d2d37eb5371f6c48b154706f718a71f9fb0316e | 1fc2fd066fd54f4edfb380c716f2dc5b719f5bf6 | refs/heads/master | 2022-12-13T01:10:51.141220 | 2020-08-11T14:59:49 | 2020-08-11T14:59:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,714 | r | validate-tactics.R | #' Validate Tactics strings against MITRE authoritative source
#'
#' @param tactics a character vector of tactic strings to validate. This will be
#' converted to lower-case, left/right spaces will be trimmed and
#' internal spaces will be converted to a single `-`
#' @param matrix which matrix to use whe... |
3172f3eedfc70f040db81f50434d2c6a1d2670bf | a9c83b44c60b998a390c905163387aae9c0c0543 | /src/main/scripts/R/dataManipulation.R | 724c0c3d82c0ff4bb57c4cb64d16f011816988fa | [] | no_license | MengsiLu/population-linkage-master | 7c91e527deff32470fa25913e0f5f472904afcb5 | 77fa04f2ef1ab64af12e834cc46a3765d387ba71 | refs/heads/master | 2022-12-04T14:11:48.396656 | 2020-08-21T14:49:55 | 2020-08-21T14:49:55 | 289,294,051 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,052 | r | dataManipulation.R | ## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summarized
## groupvars: a vector containing names of columns that contain grouping variables... |
79bb1f036e5c86bce822709bd294a5240eaedb4f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmomco/examples/parTLgld.Rd.R | 4362f0bfdea4616c9e1e4a698ad66dc8d5d9f140 | [] | 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 | 2,624 | r | parTLgld.Rd.R | library(lmomco)
### Name: parTLgld
### Title: Estimate the Parameters of the Generalized Lambda Distribution
### using Trimmed L-moments (t=1)
### Aliases: parTLgld
### Keywords: distribution (parameters) Distribution: Generalized Lambda
### ** Examples
# As of version 1.6.2, it is felt that in spirit of CRAN CPU... |
ee03355df6660dd70da3190204f1b0419672c434 | 54ead174cfc1d2f8e8246ec0f25c3ba026b5fe39 | /man/satisfy.Rd | 06caf5c9d0b6e2830ee0a896d6a360ac122ab64b | [
"MIT"
] | permissive | edlee123/Ramble | ebe3398825265c1d68f86bacbb0ea84c2ea49178 | 1081ae736929551be201b48c0c4aa16f8df4d4ef | refs/heads/master | 2020-12-30T15:54:39.942970 | 2017-04-06T11:57:18 | 2017-04-06T11:57:18 | 91,184,310 | 1 | 0 | null | 2017-05-13T15:35:45 | 2017-05-13T15:35:44 | null | UTF-8 | R | false | true | 438 | rd | satisfy.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parser.R
\name{satisfy}
\alias{satisfy}
\title{\code{satisfy} is a function which allows us to make parsers that recognise single symbols.}
\usage{
satisfy(p)
}
\arguments{
\item{p}{is the predicate to determine if the arbitrary symbol is a m... |
71e0ec80c3efe9d260468f5fa7263f1f02427678 | 6b286ff42ae9135bcaeb1d8d537460f532ebab45 | /R/citations.R | 0f76f27d3ac36ee0107cc8bc53e5615af4ad5554 | [] | no_license | cran/move | 6864db092eba41580170d4a09c5124758986b3ea | 559c7a0ff40bd070373b82b43b880a862d4a33e2 | refs/heads/master | 2023-07-21T16:48:30.636533 | 2023-07-06T22:10:02 | 2023-07-06T22:10:02 | 17,697,651 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 502 | r | citations.R | setGeneric("citations", function(obj) standardGeneric("citations"))
setMethod("citations", ".MoveGeneral", function(obj) {
return(obj@citation)
})
setGeneric("citations<-", function(obj, value) standardGeneric("citations<-"))
setReplaceMethod("citations", ".MoveGeneral", function(obj, value) {
if (length(value) !=... |
bed0951c6195915b6c02b53aaf3919333a85ab7d | 1b5cb6a23dd47e9b0ff9171721e67251afe7dbc8 | /cachematrix.R | f82c18c024a099d1dc21b649826a88b69290f6e2 | [] | no_license | narishman/ProgrammingAssignment2 | a2bf312e4130c3634360b635922074aa66d0275d | 54e26406dcf22dfdf58bffe7d4f9c6138b7bfbd6 | refs/heads/master | 2020-12-25T08:00:03.911712 | 2015-02-22T21:52:09 | 2015-02-22T21:52:09 | 31,178,833 | 0 | 0 | null | 2015-02-22T20:24:46 | 2015-02-22T20:24:44 | null | UTF-8 | R | false | false | 1,580 | r | cachematrix.R | # These functions help optimize the runtime of creating inverse matrices by caching
# the computed inverse matrix and reuse them if the base matrix does not change.
## Function : makeCacheMatrix
## This function creates an object with the following attributes
## x - holds the matrix
## invmatrix - h... |
2f6ffe477cbedb638df7fa3d3d19484ab9bb6068 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/yhat/examples/commonalityCoefficients.Rd.R | 8436e5e0c642a2e8f2124e8611dbd4ae5e840129 | [] | 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 | 711 | r | commonalityCoefficients.Rd.R | library(yhat)
### Name: commonalityCoefficients
### Title: Commonality Coefficents
### Aliases: commonalityCoefficients
### Keywords: models regression
### ** Examples
## Predict miles per gallon based on vehicle weight, type of
## carborator, & number of engine cylinders
commonalityCoefficients(mtcars,"m... |
3a6e2b6221cdd32dbed005cb2e81b1e259178400 | 7ec3de26bcd47df4ed3f5aa227cd0dda0125e9ba | /PA1/corr.R | 4d870290322ae86281858fc6194e92021e38528a | [] | no_license | 505515/RProgramming | ab02fe50a8d51255c0c56542eada1e23c5dfca58 | da6ad0a02ef7c00be54f02c1db19d57ff69cca0c | refs/heads/master | 2021-01-25T06:00:50.831567 | 2015-05-04T19:29:19 | 2015-05-04T19:29:19 | 33,884,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | r | corr.R | corr <- function(directory, threshold = 0) {
files_list <- list.files(directory, full.names=TRUE)
dat <- data.frame()
#final <- vector(mode="integer", length=332)
final <- numeric()
for (i in 1:332) {
temp_cor <- vector()
dat <- read.csv(files_list[i])
dat_na <- dat[complete.cases(dat),]... |
dbdc6542f20e450efb400721aa7bb8d62cfbdc22 | 3063fbc948249327a3f5c6a4e834bec8443eecb0 | /20180726_PieChartTXNDecreased.R | 3c24cc1069c196a9f63f6ff1e702c026bfa0f15e | [] | no_license | olgabane/10XGenomics | 98737a2791e483417c3f33c8839bfa7e52cb79f1 | ed908f5bd3e289d40cdf7032d9953433863ededb | refs/heads/master | 2022-05-26T03:46:49.774053 | 2018-08-23T19:00:06 | 2018-08-23T19:00:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 653 | r | 20180726_PieChartTXNDecreased.R | #Pie chart downregulated genes
#20180726_PieChart_TXNDecreased.R
#For poster
#July 26, 2018
a<-c(8,5,3,20,13,11,23,9,41,8,72,43,62,4,16,104,45,58)
b<-c("ATP", "Calcium binding", "Cell adhesion", "Chromatin regulation", "Cytoskeleton and ECM", "GTPase and GPCR"
,"Kinases and phosphatases"
,"lncRNA"
,"Metabolic enzyme"
... |
5eea1f266e90d629ebad19e0b527fc4dd897c67a | 7485b5784c0a27a0dc480f87296493ea3fe557f9 | /scripts/parciais/analise.r | ffe07c1d1cdf9184dd9922ccd3e3e8cc2cc04408 | [] | no_license | marciobarros/VisualNRP | 041e5abe6153a036118b7c2482f7b1d592b4b934 | cda23e9e173e1ecbb5a13ff415b80a445f90c2bb | refs/heads/master | 2020-12-26T02:39:51.224688 | 2017-03-16T22:27:18 | 2017-03-16T22:27:18 | 53,698,777 | 1 | 0 | null | 2016-03-11T21:49:11 | 2016-03-11T21:49:11 | null | UTF-8 | R | false | false | 798 | r | analise.r | rm(list=ls())
# basedir <- "/Users/marcio"
# basedir <- "/Users/marcio.barros"
basedir <- "~"
zipfile <- paste(basedir, "/Desktop/Codigos/VisualNRP/results/analysis/resultados - bsgreedy.zip", sep="")
data <- read.table(unz(zipfile, "saida.txt"), sep=";", header=FALSE)
library("data.table")
dt <- data.table(data)
m... |
bc54edc8436fc92d286dac24a9306b1aa4266b76 | 0e8328adf2b9eb4af93f93d1e3bb209787bf5c07 | /Calculation.R | 8d9128d05327f59815d8561e4adffa97a5c89bb9 | [] | no_license | ganluannj/weights_Holm_procedure | e73d34bdec4009330179329b3d94419837776ea2 | 229e9ec0c6caa2ac387a54654f1ca984e09f3050 | refs/heads/master | 2022-06-09T23:29:46.316269 | 2020-05-06T21:46:24 | 2020-05-06T21:46:24 | 260,344,761 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,403 | r | Calculation.R |
Calculation<-function(w1list,mu1, mu2=0, mu3, mu4=0, sigma1=1, sigma2=1,
sigma3=1, sigma4=1, N, alpha=0.05, Method='OF')
{
library(gsDesign)
library(mvtnorm)
LEN=length(w1list)
# generate a dataframe to store the result
# value in Result represent the probability
Result<-data.frame('w1'=... |
620637b0f3b54c22e92af692efd4f097efdb6c8b | e56247c094ad626694e2d187930f774362616d2d | /R/state.R | 5e53fd7b0be7c3ed7b9b56dcff7ec5dda828c067 | [] | no_license | pmur002/rdataviewer | 2996fc981e84d77f4f205e2a0162bdd25675317a | 31459cf81ae9b28a86f9d18e3c0f09b26d46f011 | refs/heads/master | 2021-01-02T09:15:27.076865 | 2011-11-29T22:34:55 | 2011-11-29T22:34:55 | 32,362,192 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,340 | r | state.R |
# Just an S4 object
# Other options might include a closure to avoid copying?
setClass("ViewerStateSimple",
representation(lrmode="character",
udmode="character",
fontsize="numeric"),
prototype(lrmode="left-to-right",
udmode="top-to-b... |
2c2935981c18cb6399f7aef5c44c7e95e1c0e852 | 119b181488acae0e7d49a5d35ee7decf527ebe44 | /man/removeMissingSectors.Rd | e28ab353e1682aadff649396d4f57fb6fdd2e43d | [
"MIT"
] | permissive | USEPA/useeior | 0d46f1ca9ca1756e1760b153be620a234fddda03 | 169ae5a16c4e367a3c39ceabff3c85f0b4e187a1 | refs/heads/master | 2023-08-06T19:03:28.121338 | 2023-07-14T18:39:13 | 2023-07-14T18:39:13 | 221,473,707 | 30 | 24 | MIT | 2023-09-06T15:47:55 | 2019-11-13T14:07:05 | R | UTF-8 | R | false | true | 517 | rd | removeMissingSectors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SatelliteFunctions.R
\name{removeMissingSectors}
\alias{removeMissingSectors}
\title{Removes flow data where sectors are NA after mapping. Should only be used after checkSatelliteFlowLoss}
\usage{
removeMissingSectors(tbs)
}
\arguments{
\item... |
05a1fee9c61f0581a727816e664445d10780bd3a | ee45d5d568f9911ae50f049a0d4f2408677fa8ec | /man/Attributes.Rd | 8f4104f7d8f69cf7bfa1493b072c319e9e3097eb | [] | no_license | cran/MullerPlot | b0fe7d9f2e1e6193dfce72bb77b81c36f21346b1 | 9df71feb53ecb8163452ed727f7fa19aa5c92459 | refs/heads/master | 2022-05-17T20:42:45.055716 | 2022-04-27T10:40:02 | 2022-04-27T10:40:02 | 71,505,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 654 | rd | Attributes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Attributes}
\alias{Attributes}
\title{Attributes of OTUs}
\format{
A matrix with 3 columns and 8 rows.
}
\usage{
data(Attributes)
}
\description{
A matrix with 3 columns and 8 rows.
}
\details{
The first column con... |
75cf9d52c7040d01628986f240ab0b2a1a8f7865 | f2e83e4afb99d5779e11702f518cb76c645c9982 | /man/block.map.matrix.Rd | 196c5dbf801c556ed549efab39d2980a83ad4e8f | [] | no_license | cran/ldlasso | c9503fd9a65fb3488f1ebd2d2caca9a4c30dba28 | ed253e1f1271d7e0173e1ef733effeff281112b1 | refs/heads/master | 2020-06-06T05:12:21.935945 | 2013-01-02T00:00:00 | 2013-01-02T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | rd | block.map.matrix.Rd | \name{block.map.matrix}
\alias{block.map.matrix}
\title{ Creates an indicator matrix for haplotype block boundaries, for
use in ld_lasso.
}
\description{
Simple function that maps the block boundary vector to an indicator
matrix for use in the definition of constraint matrix. This matrix
ensures that only within blo... |
8e98dd95d9d92a63a2cd1fb834219adc0a8ea61f | ab00bc7e17121d2dcf3741dc9f650a4e76ed4a44 | /tests/testthat/test-mutate.R | b631efa6ba6ab2766e01596d9c20155ac4b02e83 | [
"MIT"
] | permissive | tidyverse/dplyr | 9b7fdc07e6a70bc8e802094e2e2a127af22bcc02 | cf8031d00f406c6dc5d483d7e9e34639df797b81 | refs/heads/main | 2023-09-01T03:52:50.608019 | 2023-08-25T13:42:29 | 2023-08-25T13:42:29 | 6,427,813 | 3,290 | 1,982 | NOASSERTION | 2023-09-09T20:14:25 | 2012-10-28T13:39:17 | R | UTF-8 | R | false | false | 25,145 | r | test-mutate.R | test_that("empty mutate returns input", {
df <- tibble(x = 1)
gf <- group_by(df, x)
expect_equal(mutate(df), df)
expect_equal(mutate(df, .by = x), df)
expect_equal(mutate(gf), gf)
expect_equal(mutate(df, !!!list()), df)
expect_equal(mutate(df, !!!list(), .by = x), df)
expect_equal(mutate(gf, !!!list()... |
3748ae2114d9edbf7e57da46b5d9957f10db586b | 3578d3e6b04ff37d299980d648a64dcf26e95e09 | /R/hmm.R | aaf00468055e645b588df77340017f697b3459aa | [] | no_license | eduardoscopel/util | ae6342d9c30a7d0cec55b160301ec9e7bb0db26d | f306a2dc4589522d0d5d5b130fdcf21c98cd1db9 | refs/heads/master | 2021-05-30T00:25:48.120182 | 2015-12-30T20:26:48 | 2015-12-30T20:26:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,896 | r | hmm.R | ## --- hmm.R --- ##
## Date: 10 March 2015
## Purpose: Finally implement a general HMM for myself in pure R, allowing time-varying transition and emission probabilities.
rescale <- function(x, y = 1) {
if (sum(x) > 0)
y*x/sum(x)
else
rep(0, length(x))
}
## initialize an HMM object with known transition and emis... |
d57634470ba90f7e59f23e570057d375a0d770d9 | af77cc9ccadb9cf4d451831fdd07abe13503a879 | /yelp/wekafiles/packages/RPlugin/mlr/mlr/R/control.seq.r | 2b559497dd6bd92a936c8c9dc3f10adf99de3712 | [] | no_license | tummykung/yelp-dataset-challenge | 7eed6a4d38b6c9c90011fd09317c5fa40f9bc75c | 84f12682cba75fa4f10b5b3484ce9f6b6c8dad4a | refs/heads/master | 2021-01-18T14:10:55.722349 | 2013-05-21T09:30:37 | 2013-05-21T09:30:37 | 9,527,545 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,437 | r | control.seq.r | #' @include control.varsel.r
roxygen()
#' @exportClass sequential.control
#' @rdname sequential.control
setClass(
"sequential.control",
contains = c("varsel.control"),
representation = representation(
method = "character",
alpha = "numeric",
beta = "numeric"
)
)
#' Constructor.
setMethod(
f... |
1ef460b9755df2c694a48de170a263576b94265d | d60b8cbc13369977ad63ec7d4600a8caafbc5b57 | /shiny/server copy.R | 62b3ae781a03888b99da72724dc6b70d557855f7 | [] | no_license | aokay/projects | 8aa7b4d7bac162b8b76d854dbcf50830633b4c73 | 46b67adab3d53d847c2448fd65ff962472052fcd | refs/heads/master | 2021-01-22T07:27:42.453285 | 2015-03-02T11:45:03 | 2015-03-02T11:45:03 | 30,963,254 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,172 | r | server copy.R | library(shiny)
# Rely on the 'WorldPhones' dataset in the datasets
# package (which generally comes preloaded).
library(datasets)
# Define a server for the Shiny app
shinyServer(function(input, output) {
# Fill in the spot we created for a plot
output$bikesharePlot <- renderPlot({
train_full$day <- w... |
17f9ca53a0293ed707fe31220956f4a93147f624 | 7ca3dfeff09d362063e18a458bce38e1983247fe | /man/reproduce_emeans_effect_size_COMPLETENESS.Rd | fa0956d90498bccdb1d973353996e64694fb1377 | [] | no_license | karacitir/reproducerTaskGra | e390b5d56cae74f78c088cca17506a4a279c867a | 9d81ebe1e312e4f74dbf615511ecc91bba276852 | refs/heads/master | 2020-04-12T05:13:44.721531 | 2019-01-30T17:04:03 | 2019-01-30T17:04:03 | 161,769,783 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 758 | rd | reproduce_emeans_effect_size_COMPLETENESS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lmm_funcions.R
\name{reproduce_emeans_effect_size_COMPLETENESS}
\alias{reproduce_emeans_effect_size_COMPLETENESS}
\title{Reproduce effect sizes based on estimated marginal means}
\usage{
reproduce_emeans_effect_size_COMPLETENESS()
}
\value{
a... |
6ca241bc5a048b0cd3708c7102378f48173729d8 | 77c4f4dd27b8d7497e66a7a5a87ad7ea83f2c4be | /dev/tasks/conda-recipes/r-arrow/install.libs.R | 005bbe16b9984b773ce4c72b03f9c31d53cfafa3 | [
"Apache-2.0",
"MIT",
"BSD-3-Clause",
"BSD-2-Clause",
"ZPL-2.1",
"BSL-1.0",
"LicenseRef-scancode-public-domain",
"NTP",
"OpenSSL",
"CC-BY-4.0",
"LLVM-exception",
"Python-2.0",
"CC0-1.0",
"LicenseRef-scancode-protobuf",
"JSON",
"Zlib",
"CC-BY-3.0",
"LicenseRef-scancode-unknown-licens... | permissive | apache/arrow | 0714bfbf6fd491e1f4ed4acf838845ce4b94ec3e | 59954225d4615f9b3bd7a3c266fb68761794229a | refs/heads/main | 2023-08-24T09:04:22.253199 | 2023-08-24T07:21:51 | 2023-08-24T07:21:51 | 51,905,353 | 12,955 | 3,585 | Apache-2.0 | 2023-09-14T20:45:56 | 2016-02-17T08:00:23 | C++ | UTF-8 | R | false | false | 346 | r | install.libs.R | src_dir <- file.path(R_PACKAGE_SOURCE, "src", fsep = "/")
dest_dir <- file.path(R_PACKAGE_DIR, paste0("libs", R_ARCH), fsep="/")
dir.create(file.path(R_PACKAGE_DIR, paste0("libs", R_ARCH), fsep="/"), recursive = TRUE, showWarnings = FALSE)
file.copy(file.path(src_dir, "arrow.dll", fsep = "/"), file.path(dest_dir, "lib... |
f664971a64b0a0dd5f42a01f6b56cd708df10edc | 317ab2d664c90292aaa3c98d55e3970c5b9089e1 | /FS12b_16S.R | d4679adaf777a459ca9396e3350200ae6331bd93 | [] | no_license | Jtrachsel/FS12 | a35dd5d190d3ccd78c057e9028de2fd90022afde | 2cbbad7db6583592928ab98391d7925aca92c1c5 | refs/heads/master | 2021-07-09T08:28:18.223628 | 2020-07-30T17:44:41 | 2020-07-30T17:44:41 | 173,172,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 91,209 | r | FS12b_16S.R | library(phyloseq)
library(tidyverse)
library(funfuns)
# library(pairwiseAdonis)
library(DESeq2)
library(vegan)
library(funfuns)
meta <- read.csv('./data/FS12_final_meta.csv', header = TRUE, stringsAsFactors = FALSE)
shared <- read_delim('./data/FS12.shared', delim = '\t') %>% as.data.frame()
taxa <- extract_mothur_tax... |
ebab9efe0de3f618818cb6aed3badcec13a81d9e | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/anomaly/man/show-methods.Rd | 2d9a8053ab499f06499c7c54dcb66b986fd9469b | [] | 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 | 1,393 | rd | show-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bard.R, R/capa.R, R/capa.mv.R, R/capa.uv.R,
% R/pass.class.R
\docType{methods}
\name{show}
\alias{show}
\alias{show,bard.class-method}
\alias{show,bard.sampler.class-method}
\alias{show,capa.class-method}
\alias{show,capa.mv.class-method}
\... |
bbd1fe1ddef2a9f78325f4fbc5231da7b9e58382 | 5103964f10540aa2aa78df58cd943fcc14295d08 | /analysis scripts/stratification_analysis.r | 6d2744b257090f6e5479009764795c3cfe6aaa42 | [
"MIT"
] | permissive | javipus/mcrds_public | 216917afc0f8afe348b7f0bff7f5fdea419c119f | 43bb44cc8b6b79536e4b2afd0a7c724e90f137f7 | refs/heads/master | 2023-01-29T22:01:21.302921 | 2020-12-14T14:48:38 | 2020-12-14T14:48:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,109 | r | stratification_analysis.r | # Stratiifaction analysis of acute outcomes
#first load libraries
library(lme4)
library(lmerTest)
library(emmeans)
pacutes = read.csv("[PATH TO FILE]/pacutes.csv")
# to reproduce the stratification analysis, define the same model as before (acute_analysis.r)
# and add the guess + guess*condition terms (the... |
d20750ee0f2aaf0bfc9e55c40c914e0741743a39 | 6de90602b0d82a5e5b08f00a7305ba5f1e61ed33 | /Main/K35/LoadSubjK35.R | db38564bfd180d9bbc995e13dd842d029cbb9710 | [
"MIT"
] | permissive | NeuroStat/PaperStudyCharCBMA | b2bb32af7d2088ce0c2aad84fa851d209434eeeb | 67b140af59eb415964749bf2e2cf7f9fd4528f3d | refs/heads/master | 2021-03-29T16:53:24.139381 | 2017-12-22T12:34:33 | 2017-12-22T12:34:33 | 90,959,743 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,974 | r | LoadSubjK35.R | ####################
#### TITLE: Load the data frames with information about the sampled subjects: sampling without replacement in studies.
#### Contents:
####
#### Source Files: //Meta\ Analyis/R\ Code/Studie_CBMA/PaperStudyCharCBMA.git/
#### First Modified: 12/05/2016
#### Notes:
#################
##
###########... |
53fa26445186c5f5dc3979b6d56a7c1f813dbdc1 | 6eb6be10dfb00975aa041b19b47ef2511808096d | /ExData_Plotting1-master/plot4.R | b25f8dab11fbcee726e18cc50f72b85fca9598f7 | [] | no_license | yashika-sindhu/datasciencecoursera | 5e72af030f83d7ba90433da32af1bc2940b50b54 | 971ceb4526935374250fa646d8722b7e94bb0ed6 | refs/heads/master | 2022-01-22T04:59:26.645366 | 2019-07-22T09:35:49 | 2019-07-22T09:35:49 | 116,703,715 | 0 | 1 | null | 2018-01-09T05:04:23 | 2018-01-08T16:57:01 | null | UTF-8 | R | false | false | 2,342 | r | plot4.R | ## Download the dataset
download.file(
"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip",
destfile="Electric_Power_dataset.zip"
)
## Unzip the data
unzip("Electric_Power_dataset.zip")
## Read the relevant data into R
install.packages("sqldf")
library(sqldf)
my_data<-read.csv.s... |
bfa5c5bb6c72c1cdf938eb7f2bed5de1c87c1229 | 7547f30e8151d75850182ab76f6a76714f4bc90d | /Theoretischer Teil/Skript_zur_VL.R | 3ca9cb354218578e09c0bc9bec0c6c1e2401d580 | [] | no_license | KerstinPierick/RWorkshop | 80d292ed8faf3e9cd0abe64d76baaef409a35e4d | e8e378c24da238400d8df3b2b4ea968106c6c8fb | refs/heads/master | 2022-07-21T13:11:58.471113 | 2022-07-13T16:43:13 | 2022-07-13T16:43:13 | 202,128,172 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,843 | r | Skript_zur_VL.R | ###################################################################
######## "Statistik und Programmieren mit R" ######################
###################################################################
########### Workshop von Kerstin Pierick ##########################
################### Campus 2019 ################... |
10fcc07aacc43bd9efd6ca9bd72e4e1ff3dbebd7 | 547f84f7397b7fc0ac91b5e680c4554d6b5dff72 | /rcpp-code/MultivarTV/man/predict.mvtv.Rd | b04e6a40921461c32faa313f3e722afc451254a3 | [] | no_license | brayano/MultivarTV | 461a6992dfea8c4fe37d2fff1ad17b527d1607ce | 89cf66a0cf7fa2e087574e9df9f688ea89f4defc | refs/heads/master | 2021-03-24T12:42:13.330960 | 2018-04-30T19:13:52 | 2018-04-30T19:13:52 | 120,711,985 | 0 | 0 | null | 2018-04-30T19:13:53 | 2018-02-08T04:44:14 | HTML | UTF-8 | R | false | true | 1,004 | rd | predict.mvtv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MultivarTV.R
\name{predict.mvtv}
\alias{predict.mvtv}
\title{MVTV Predict for Fitting Observed/New Data}
\usage{
\method{predict}{mvtv}(object, data = NULL, mesh = NULL, ...)
}
\arguments{
\item{object}{object produced by mvtv.default}
\item... |
1f4f3ca1b74e9a896dae24ac8204d63d2776ce7c | c6076132c2740f2abbf3504eda9bfdd3c62a7969 | /man/maximize_spline_metric.Rd | 4836881626f742146931603fa0b94b2d303d6471 | [] | no_license | Thie1e/cutpointr | ae5866d8bd685bc5679352f8960c22ef99f3b93e | b84a39cc88bdeee788123c647d5cae50e5ee42e1 | refs/heads/master | 2022-04-30T01:28:35.752777 | 2022-04-13T17:32:19 | 2022-04-13T17:32:19 | 74,686,042 | 80 | 20 | null | 2022-01-18T11:46:51 | 2016-11-24T15:41:00 | R | UTF-8 | R | false | true | 4,836 | rd | maximize_spline_metric.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/optimize_metric.R
\name{maximize_spline_metric}
\alias{maximize_spline_metric}
\alias{minimize_spline_metric}
\title{Optimize a metric function in binary classification after spline smoothing}
\usage{
maximize_spline_metric(
data,
x,
cl... |
2c441c3c3a94802bc35332b8d836fc0516254e7a | e68e99f52f3869c60d6488f0492905af4165aa64 | /man/torch_linspace.Rd | e6031ffc522023a5b62df5142454e64f432d276a | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | true | 1,879 | rd | torch_linspace.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/creation-ops.R, R/gen-namespace-docs.R,
% R/gen-namespace-examples.R
\name{torch_linspace}
\alias{torch_linspace}
\title{Linspace}
\usage{
torch_linspace(
start,
end,
steps = 100,
dtype = NULL,
layout = NULL,
device = NULL,
re... |
1e1bf82368a75d60279e120f954af1364d46fb73 | 5a9fad5bf2b3f91ee6802d342546408217eeff14 | /R/prepare_dictionary_ngram.R | 8a8968820992e6499716a25a64a6821c8f403554 | [] | no_license | phileas-condemine/bodily_injury_atp | c02c6dae8046fa26b0fca814567522dce08817fb | 8bb594e5e757cedea080f3da476c9e8a1ad682a6 | refs/heads/master | 2021-05-07T06:05:29.691425 | 2017-12-12T09:22:36 | 2017-12-12T09:22:36 | 111,701,372 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 810 | r | prepare_dictionary_ngram.R | load("Documents/bodily_injury_atp/data/CAPP_1ST_2ND_INSTANCES/CAPP_text_extraction.RData")
library(text2vec)
library(magrittr)
pattern="corporel"
ngram=3L
n_cores=28
token <- CAPP_docs %>%word_tokenizer
itokenized <- text2vec::itoken(token,ids = 1:length(CAPP_docs), progressbar = FALSE)
dictionary <- create_vocabula... |
640629aa8a5b548a55f7b5622bae48179941f5ff | 2e280dbf7411ea0c1b485e2587fd2b94c0be875b | /tp.r | 32a891252ec356ec5e5ab11b305a1d2d04c639bd | [] | no_license | badbayard/tp_R | 49229cb05a2a3cf6e6c6459df991e9a7e5a9d037 | 3a720b43031a344f0af5d55979b1f9efd504cf43 | refs/heads/master | 2020-03-22T05:09:13.311432 | 2018-07-03T07:37:26 | 2018-07-03T07:37:26 | 139,545,849 | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 211 | r | tp.r | exo 1 ša marche c'est cool :)
exo 2 x<-c(0,7,8)
y<-c(5,6,x[2],x[3],10,11,12,0,x[2],x[3])
y[3] y[5] y[8] y[9]
y[y[]<=8]
[1] 5 6 7 8 0 7 8
> y[-2]
[1] 5 7 8 10 11 12 0 7 8
|
de1ef17832dab5b2514a24f7d1b42bd1fd051653 | 902037115141ead7b315e7b63e437ec61c01c2c1 | /R/ia.samp.R | 323f5476e2208b7c92e260c7601bd1e8dc97680d | [] | no_license | cran/scrime | 4bdc7e989ba9e648d004ca47cd2d10bb5e78a717 | cf0033dbfe2a6fa807593a460ef4bcb0931db96a | refs/heads/master | 2021-06-02T21:50:17.706604 | 2018-12-01T10:00:03 | 2018-12-01T10:00:03 | 17,699,500 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 169 | r | ia.samp.R | `ia.samp` <-
function(n.pair,conj=0){
mat<-matrix(0,2^n.pair,n.pair)
for(i in 1:n.pair)
mat[,i]<-rep(rep(c(1,conj),e=2^(n.pair-i)),2^(i-1))
mat
}
|
bb49a2a14e004d8bfa057e5376f2a77cf13a4d9a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pathological/tests/test_decompose_path.R | 3402cd54bc13fa5386717559b9bb4a57ce05b7b3 | [] | 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 | 16,375 | r | test_decompose_path.R | create_expected_decomposed_path <- function(dirname, filename, extension, row.names)
{
structure(
data.frame(
dirname = dirname,
filename = filename,
extension = extension,
row.names = row.names,
stringsAsFactors = FALSE
),
class = c("decompose... |
8fe19749c9631d1ee07dd783a07c9acd461168d1 | 9b50e27c9b97e4693a2a98040157f74f8c7c6525 | /man/rotate.somites.Rd | 046c17707faccc0b45328fff7520b98ac8c7f662 | [] | no_license | erinboyleanderson/CellTrackingEBA | ece73ea8930478ecb4dd3085889f518ad5c26f9b | f8640cf4fbf8c3f2849b379e02c4be9d88ed7a28 | refs/heads/master | 2020-03-21T16:40:50.732377 | 2018-12-11T21:30:50 | 2018-12-11T21:30:50 | 138,785,925 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 998 | rd | rotate.somites.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rotate.somites.R
\name{rotate.somites}
\alias{rotate.somites}
\title{Function for rotating the somites}
\usage{
rotate.somites(somiteDF, bros, side)
}
\arguments{
\item{somiteDF}{Dataframe containing the boundary information. must include the... |
3f20e0d13c7a28122e1e3a354de0594fe1c27c61 | 0df826d83af76bec2e82c823beeca216057dac38 | /R/langmuirTrans.R | d981c434fa994e84a2ef07a74cbcf300bde31784 | [] | no_license | hdraisma/quantroSim | 07caebf668a7973c0d8d5c95563fb87516da82c2 | ef4720f1c1bb41dccad5deddbc876c157e3e0bb2 | refs/heads/master | 2020-08-01T17:54:54.215757 | 2018-08-22T13:26:15 | 2018-08-22T13:26:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 600 | r | langmuirTrans.R | #' @title Langmuir adsorption transformation
#'
#' @description To model DNA methylation and gene expression from arrays
#' this function represents the saturation reached in the arrays.
#'
#' @param x expected number of methylated molecules after PCR and
#' bisulfite-sequencing
#' @param a intensity from scanner
#'... |
b3bc92d3740aa015fab4211edb444a057988efd6 | 9bd88feb5cd6ab8bc54a443c2f0037cf09c0c299 | /analysis/_fl/xx_twitter_follower_summary.R | e995820c6c28300e29ec4c660494c533be4b172c | [] | no_license | gmaubach/of-dollars-and-data | 9fc404f391f4cf3b840e76ca79660cb62873780a | b1b50c3aa132e6b8e4a045c5c136c08760d587f2 | refs/heads/master | 2023-07-09T04:17:02.890021 | 2021-08-18T01:18:35 | 2021-08-18T01:18:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,553 | r | xx_twitter_follower_summary.R | cat("\014") # Clear your console
rm(list = ls()) #clear your environment
########################## Load in header file ######################## #
setwd("~/git/of_dollars_and_data")
source(file.path(paste0(getwd(),"/header.R")))
########################## Load in Libraries ########################## #
library(rtweet... |
42015c61d0903f447a143cae1fdedf7049b730eb | ba87a73a22600087a4bb8ea21b15b391c98c9579 | /Scripts/ROC.R | c3a894b0ce6f6a7056e00689b2be5d4a656b7fbc | [] | no_license | rajkorde/RTestCode | d32b5f87b122b06d5cb7ce71ec4b4823a02669a4 | 4b07937128acd3e73bb6489d341e859df737ff6b | refs/heads/master | 2021-01-16T23:57:45.330753 | 2019-01-31T05:26:20 | 2019-01-31T05:26:20 | 58,427,571 | 5 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,665 | r | ROC.R | simple_roc <- function(labels, scores) {
labels <- labels[order(scores, decreasing = TRUE)]
data.frame(TPR = cumsum(labels)/sum(labels),
FPR = cumsum(!labels)/sum(!labels),
labels)
}
set.seed(1)
sim_widget_data <- function(N, noise = 100) {
x <- runif(N, min = 0, max = 100)
y <- 122... |
5b8cd9340216c965da0eb1567f737104bbe4f407 | 56a262e561b5d13b2aa47f710a1b04ea385db33d | /R/LiDARForestStand.R | af2cc62fd62b9e85b7d62cf0fb8328614a4df667 | [] | no_license | carlos-alberto-silva/rLiDAR | f94053414eeebafddd40f5404de32b2de9630488 | 956431248635ef04bc31fa0c1eff4f7b972c5d88 | refs/heads/master | 2023-01-22T02:25:27.999142 | 2021-10-04T19:38:20 | 2021-10-04T19:38:20 | 169,238,645 | 10 | 5 | null | 2023-01-11T18:50:33 | 2019-02-05T12:37:11 | R | UTF-8 | R | false | false | 11,802 | r | LiDARForestStand.R | #'3D stand visualization of LiDAR-derived individual trees
#'
#'@description Draws a 3D scatterplot for individual trees detected from Lidar data.
#'
#'@usage LiDARForestStand(crownshape = c("cone", "ellipsoid", "halfellipsoid",
#' "paraboloid", "cylinder"), CL = 4, CW = 8, HCB = 10,
#' ... |
39bec8f3f49e5b5c8016c669fc3fc2885cf367bd | af9ab6ba9d4f4d33d68cd47ec2dfb4e178deb517 | /similate_RW_no_migr_range.R | 5dd73cca2d9e08067ede582d931008d9c9de5370 | [] | no_license | diego-ellis-soto/Simulation_the_spread_of_poop | acb8aff71859f9d6030cc1ef923d595419314b61 | 6da3c9f406adb1334369686bd765280f30e8e781 | refs/heads/master | 2020-04-05T16:53:43.434239 | 2018-11-13T03:16:45 | 2018-11-13T03:16:45 | 157,033,340 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,059 | r | similate_RW_no_migr_range.R | # Start at 1, then 9, then 17 ...
# Every nth event (discrete timestep) a pooping event happens
# Random walk within winter/summerrange
# average of 624 guava seeds
# Simulate random walks within wintering and breedingg range:
rw_within_homerange = function(ndays, daily_distance_moved, range_shp, avg_gut_retention_tim... |
05322b7ee58e8233ec048bf49517373743929e13 | 32082d5417b956c162cc37f81c86557cdf085900 | /PreliminaryCalcsFunction.R | 44de36bcc3834365746979fdbfedf8189fcc3190 | [] | no_license | vshanks/ghg_emissions_predictions | dcf2923244bb341ed75a7c5b5d4efb443246d0ad | 162f96d688ec4ca0655a97eeda94632987eb0475 | refs/heads/main | 2022-12-14T13:31:52.492644 | 2020-09-10T19:03:08 | 2020-09-10T19:03:08 | 294,498,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,154 | r | PreliminaryCalcsFunction.R | PreliminaryCalcsFunction <- function(CurrentDirectory, carbon_data){
raw <- carbon_data
# See the variables
# colnames(raw)
# Select some for analysis
Subsector <- as.character(raw$RBICS.subsector.Code)
TotalGHG <- raw$Total.GHG.emssion..1..2.
Scope1 <- raw$GHG.Scope.1
Scope2 <... |
25ec5fa601b85efa3b709d31d06139ea2437674c | 36c806d7529594cf933b19278d2741b83509fd96 | /MToolBox_config_files/Mtoolbox.R | fbfac3231172063c4059e0a506f8de3d8bac1816 | [
"MIT"
] | permissive | Phillip-a-richmond/AnnotateVariants | 98c33b361ca8ee1bf2ac82b7c8d02188ec41efd8 | dddbb245f7348b119460607d59ca6cba4afa72f3 | refs/heads/master | 2023-04-13T13:14:02.664815 | 2023-03-20T23:35:21 | 2023-03-20T23:35:21 | 103,580,004 | 16 | 4 | MIT | 2021-05-17T23:58:27 | 2017-09-14T20:46:56 | Shell | UTF-8 | R | false | false | 9,157 | r | Mtoolbox.R | #!/usr/bin/env Rscript
## Script to filter and prioritize MT variant identified with MToolBox.
## Based on Maddie Couse Variant Prioritization : https://team.bcchr.ca/display/TGA/MToolbox+Mitochondrial+Analysis
## Developped by Solenne Correard on March 29, 2019
##Last update: SC, April 3rd, 2019
library(plyr)
#Open... |
ad245dd089ef9ee9bfdfc17a2200522d71e0569a | 5b173d65c0efe16c3c0a58dc6e49e2f3ac26d001 | /segRNAcountings/R/seg_criteria.R | 4823e5bbf73c782734b26869285d258391d27b0c | [] | no_license | danilodurs/newsegcrit | 2a5bdb35a565b29242c216572ccb3cf5948c6da9 | 22c4a5bb39f44b7d5216771d72fd6fcb3c98a21a | refs/heads/master | 2020-03-26T21:46:29.033866 | 2018-08-20T11:21:32 | 2018-08-20T11:21:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,319 | r | seg_criteria.R | #' log-transformation function for segmentation
#'
#' @param rna.data : a vector of counts from RNA-sequencing
#'
#' @return a vector of log-transformed data
#'
#'
#' @examples
#' log.data <- log.transform(dataset1)
#' plot(dataset1, type="l")
#' plot(log.data, type="l")
log.transform <- function(rna.data)
{
log.data... |
3b4770d75bf74871b9b63ec822bd715aead90ec8 | 2161e2c9b1463f3f0b8d27a9447c136e5e08d2b9 | /man/getDesign.Rd | 64d1c1b3c2b7742a07b9deb26e8f4e3cf3d118c3 | [] | no_license | NCRN/NCRNbirds | 14a258e8182849bb0434eb4368fa291105d56a7c | 5a512b736d674d9308c27667e7a99b142aebfcef | refs/heads/master | 2023-08-16T13:00:26.367713 | 2023-07-11T15:54:50 | 2023-07-11T15:54:50 | 32,335,489 | 5 | 12 | null | 2023-08-17T15:09:47 | 2015-03-16T15:44:44 | R | UTF-8 | R | false | true | 1,277 | rd | getDesign.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getDesign.R
\name{getDesign}
\alias{getDesign}
\title{getDesign}
\usage{
getDesign(object, info)
}
\arguments{
\item{object}{An \code{NCRNbirds} object or a list of such objects.}
\item{info}{A length one chararcter vector. Indicates which a... |
5aec6af13d02ad6a33e5667781ff7903312bfd6e | bad08314942d890670cb8186827e93387f8242cb | /R/HLgofTest.R | 37ad7bf070b684bf4f0546eba8f3696175d89fb1 | [] | no_license | stamats/MKmisc | faaa5a4bc04d015143fcd2d468bc11aa12ef5633 | e738e1f1b18899af42c1149335c6ee063e9de80c | refs/heads/master | 2022-11-25T06:06:56.692986 | 2022-11-19T15:35:13 | 2022-11-19T15:35:13 | 33,780,395 | 10 | 2 | null | 2015-06-29T18:02:53 | 2015-04-11T15:13:48 | R | UTF-8 | R | false | false | 2,875 | r | HLgofTest.R | HLgof.test <- function (fit, obs, ngr = 10, X, verbose = FALSE){
ngr1 <- ngr
## Hosmer-Lemeshow C statistic
brks <- unique(quantile(fit, probs = seq(0, 1, by = 1/ngr)))
cutfit <- cut(fit, breaks = brks, include.lowest = TRUE)
if(length(brks) < ngr+1){
warning("Found only ", length(brks)-1, "... |
06444122bbb114511fbfcab1ba26276b6d29643e | edee4a9c4cf3c35a52dfc99ac53279ab23e069ab | /examples/FeatureCollection/vector_symbology.R | 88a391a89cbc14c13ff36c36ce4eaa3334c86e92 | [
"Apache-2.0"
] | permissive | benardonyango/rgee | a8dd22a72f2c77a0d1e88f6177c740942fe2cfbc | e9e0f2fa7065e79c1c794bd7387fd0af633031ff | refs/heads/master | 2022-04-09T18:10:23.689798 | 2020-03-31T10:56:00 | 2020-03-31T10:56:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 357 | r | vector_symbology.R | library(rgee)
# ee_reattach() # reattach ee as a reserved word
ee_Initialize()
fc = ee$FeatureCollection('TIGER/2018/States')
image = ee$Image()$paint(
featureCollection = fc,
color = 1,
width = 3
)
Map$setCenter(-99.844, 37.649, zoom = 5)
Map$addLayer(
eeObject = image,
visParams = list(palette = 'FF00... |
77548822eea1c8452f63229fea111417450f0db3 | 25524de30d715f1464789860405e6d98d4e71159 | /hw1.R | 9d9c4abc9073b3173d8a1a0dc858d38318451219 | [] | no_license | sunnyhyo/Multivariate-Statistical-Analysis | 1d3e5f46418de4dcadd2240f383d35aa51fc8648 | 4ebfd44670ab8b1f30a2f238603b1515b0877e33 | refs/heads/master | 2021-04-26T23:02:13.698323 | 2018-05-28T01:45:19 | 2018-05-28T01:45:19 | 123,919,384 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 657 | r | hw1.R | #hw1
A <- matrix(c(1,2,1,2,3,2,1,2,6),
ncol=3, byrow=T)
A
#1 rank(A)
library(Matrix)
rankMatrix(A)
#2 eigenvalues, eigenvectors
eigen(A)
#3 spectral decomposition
P <-eigen(A)$vectors
E <-diag(eigen(A)$values)
P%*%E%*%t(P)
#4 trace(A)
sum(diag(A))
#5 A inverse
A.inv<-solve(A)
A.inv
#6 eigenvalues, eigenv... |
5f511b1047e1beb097b6114e719ecadb168607fb | abdccf6134a4a9bac2ebe27e4fc1c449eab8b1f1 | /newdateversion.R | 12b8c71015c483f8a858aed6b91ae152afb68234 | [] | no_license | carolinemsinclair/OMFSBillingDynamics | f0de69cefce97aaf5434f2d5aef08446268a9501 | 8b037fcf31872a1d4ca925c73cd73f6d495d6597 | refs/heads/main | 2023-03-21T16:28:04.822302 | 2021-03-09T18:08:26 | 2021-03-09T18:08:26 | 346,099,148 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,918 | r | newdateversion.R | #attemptwithxls
library(readxl)
library(lubridate)
library(dbplyr)
library(dplyr)
library(tidyr)
library(readr)
drugs=read.csv("updateddrugs.csv")
blood=read.csv("blood.csv",header = FALSE)
wcprice=read.csv("WCprice.csv")
weird2007codes<-read.csv("weird2007codes.csv",header=FALSE)
OMFS2003<-read.csv("RVU2003.csv")
OMF... |
3a683e3531ca71c1385684613e16e19651938107 | 750cacb8a12d2ef36c343072f979be48a01e3209 | /rCode/rawCode/regexTrials.R | f0507cba1c5692a75a4f3dfdd47b9e57ddb7a2c6 | [] | no_license | mterion/capstoneProj | b192de37c7128677d6bfa5dbf8247f92bf1646f3 | 219f13700db7ced0b0a6331e9a59d217a1847e66 | refs/heads/master | 2023-03-02T09:37:19.990896 | 2021-01-28T06:47:51 | 2021-01-28T06:47:51 | 319,615,507 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,924 | r | regexTrials.R |
domainNamesDf <- read.table("./data/rawData/domainName.txt", sep = "\n", quote = "", header = F, colClasses = "character", encoding="UTF-8")
domainNamesDf <- domainNamesDf %>%
rename(names = V1) %>%
mutate(names = tolower(names)) %>%
mutate(regEx = paste... |
a79a8a4164d2efde7abba6c169e8f8360c7f9bb7 | 5558e08bfe36159684f95a0782f92e30156f5c1d | /plot1.R | 043927c0e5705e68950942fee00adfa1743228da | [] | no_license | glenbert/Modul4Week1Assignment | 2c879dea4a6b3563e34db65f3f8831f93cc4c6f1 | b1da967cd9568be342f948adaf39827b1984d209 | refs/heads/master | 2021-01-25T13:18:01.498152 | 2018-03-02T08:05:11 | 2018-03-02T08:05:11 | 123,549,562 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 693 | r | plot1.R |
## Download the zip file and unzip
## Make sure to save the unzip file in your working directory
## Rename the txt file to data.txt
dt <- read.table('./data.txt', header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
## Reformat or conver the date
dt$Date <- as.Date(dt$Date, format="%d/%m/%Y")
## Select the data ... |
ab9162d1c839b8447812629ee5cfd7929ef2ce98 | 5db3703c9817250f1a245107adac883f48b03b53 | /bootstrap/had.27.46a20.R | 198371f7b4d450d620cd00f89a0f18ea4d06f5c4 | [] | no_license | ices-taf/2020_4029-29_SpecialRequest | 656ff69f76be57e67af0c17d96cb507fdc896d78 | abedb79202600c6ddf7299988cdd8265d16e7d91 | refs/heads/master | 2022-12-23T22:49:11.188668 | 2020-10-09T07:32:07 | 2020-10-09T07:56:01 | 289,861,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 998 | r | had.27.46a20.R | #' Data from had.27.46a20
#'
#' @name had.27.46a20
#' @format csv file
#' @tafOriginator ICES, WGCSE
#' @tafYear 2020
#' @tafAccess Public
#' @tafSource script
library(icesTAF)
taf.library(icesSharePoint)
spgetfile(
"Documents/Preliminary documents/bootstrap_initial_data/had.27.46a20/f-at-age.csv",
"/admin/Reques... |
7ead8a185725055285f31ac3604c955cc1ae1b46 | 9b835eb60ed6c453ad224a95b6c2e1776d164194 | /plot4.R | cbb542ecd78dfddc86465369592a96910e17e3ed | [] | no_license | Wondamike7/ExData_Plotting1 | c30d8dfd4142634ebd6857de797351b47188ef36 | 453183de8907cf4ac942fe180cae1badb8ba30a9 | refs/heads/master | 2020-12-07T00:47:00.299143 | 2015-04-09T07:55:22 | 2015-04-09T07:55:22 | 33,649,271 | 0 | 0 | null | 2015-04-09T04:58:28 | 2015-04-09T04:58:28 | null | UTF-8 | R | false | false | 1,697 | r | plot4.R | ## look for the program to load data, if not found, change directory
if(!"LoadData.R" %in% list.files()) {
setwd("C:/Users/fieldrep/Documents/Coursera/Repositories/ExData_Plotting1/")
}
## with LoadData in directory, source the program to load and tidy the dataset
## note: this will error out if the data set (or the ... |
695537cfd26fa6406d514a044a0e607bdf62a44c | 934f82c4a6f7b3364c97834bab6a7514bca79853 | /man/automateDataPreparation.Rd | 462061326d2cf6035a5b3dbd04378dcbc62f7287 | [] | no_license | sachseka/eatPrep | 164c3b007355b6f366d2bc9ee9fbcb300d44ff57 | 7f3cfcad9ac0479dafed0ddd9ba2ad3db50238ea | refs/heads/master | 2023-09-05T17:54:15.570238 | 2023-02-21T11:11:38 | 2023-02-21T11:11:38 | 151,248,346 | 0 | 1 | null | 2023-08-23T18:02:00 | 2018-10-02T12:09:19 | R | UTF-8 | R | false | false | 5,756 | rd | automateDataPreparation.Rd | \name{automateDataPreparation}
\alias{automateDataPreparation}
\title{Automate Data Preparation using Functions from Package eatPrep}
\description{
This function facilitates automated data preparation and wraps most functions from the \code{eatPrep} package.
}
\usage{automateDataPreparation(datList = NULL, inputList, ... |
27db6e0f0b13ef57a8ea7587dbf32101f68b8994 | a70ed9684eeb7a6b50dbba62e7979e7ab865b969 | /expert_mendel_sheridan.R | 796f968197797fd2ba5ed2e32df442222f2950c9 | [] | no_license | miller00315/estudos_r | b95ecbcd297c6b1e6cd9d30f659df002994de8f1 | 3ade710540393b6d1e5116191a45efc4a4440e51 | refs/heads/master | 2022-12-18T16:12:16.944044 | 2020-09-26T15:20:54 | 2020-09-26T15:20:54 | 297,691,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 992 | r | expert_mendel_sheridan.R | install.packages("expert")
library(expert)
x <-list(
EXP1 <-list(
SEM1 <- c(75, 80, 85),
SEM2 <- c(10,15,20),
INT <- c(650, 800,850)
),
EXP2 <-list(
SEM1 <- c(80, 90, 95),
SEM2 <- c(25,30,35),
INT <- c(500, 600,700)
),
EXP3 <-list(
SEM1 <- c(65, 70, 80),
SEM2 <- c(20,25,30),
... |
61bbaec3c75cfd57e9ca024cdb223bdd5762c548 | 552d495af4b801425da2b2b82df006ea4819184a | /phoneme/phoneme.R | 2c1bc55e15435f9ccc445e28970e85acfc28c3d7 | [] | no_license | nihaoHX/Logistic_subsampling | c85fe986bfcd263b9e82bf55a607b1eb2acb3af8 | 52f024678d54321151d0eddf1df3f1f8bda768cb | refs/heads/main | 2023-02-07T12:09:08.348456 | 2021-01-04T16:16:18 | 2021-01-04T16:16:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,214 | r | phoneme.R | ################ Real data: phoneme ################
library(nabor)
library(mined)
library(mvtnorm)
library(OSMAC)
library(ggplot2)
library(foreach)
library(doParallel)
library(readr)
library(splines)
source("more_efficient")
#source("MED")
source("IBOSSL")
source("Leverage")
source("DKDO")
### data treatment
phon... |
4faa81a0354f24949f9657c3f065fc77dd2c4b33 | 44cf65e7ab4c487535d8ba91086b66b0b9523af6 | /data/Newspapers/1999.12.06.editorial.19652.0206.r | c2f9b9c1b800cf33e3d1166faa55306ab7a582e0 | [] | no_license | narcis96/decrypting-alpha | f14a746ca47088ec3182d610bfb68d0d4d3b504e | 5c665107017922d0f74106c13d097bfca0516e66 | refs/heads/master | 2021-08-22T07:27:31.764027 | 2017-11-29T12:00:20 | 2017-11-29T12:00:20 | 111,142,761 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,245 | r | 1999.12.06.editorial.19652.0206.r | Atrocitatile si agresiunile sexuale de la Suceava au socat Romania .
oamenii sint consternati .
de citeva decenii , romanii n - au mai fost pusi in fata unui asemenea caz .
de la psihozele cu Rimaru ( in Bucuresti ) si cu ciocanarul ( la Cluj ) , opinia publica n - a mai trait sentimente atit de puternice .
cu ceva... |
9e6f6427deb23ad68a459b0ed9f3b34975e1fba2 | e507b9f3094ff40cfc4359d2647a9dcdeb3bf712 | /src/00_01_date processing.R | 8e39624860ae554c126540f1d3bc3a66796f95bd | [] | no_license | DomHenry/sdm-pipeline | 2bb8ef25f730f4fb51bbbc5435356989b54c63a9 | 0049728a68053d156c52753ce58decabdd48f192 | refs/heads/master | 2020-06-12T14:03:31.577533 | 2019-08-08T10:43:01 | 2019-08-08T10:43:01 | 194,322,619 | 0 | 0 | null | 2019-07-18T09:27:03 | 2019-06-28T19:12:35 | R | UTF-8 | R | false | false | 7,849 | r | 00_01_date processing.R | # Description -------------------------------------------------------------
## Thu Jan 10 10:47:30 2019
## Import the original amphibian database, subset to include only RL, NT and DD
## species. Remove duplicate records then work on dates. Create 4 DFs with
## different date combinations (full date, month & year, y... |
27baba6493aa1cdd38a073c1fcb5da2c28c25f78 | 6a7f09929503ff62efad8bf049119d5fabd5b6d6 | /man/nonet_plot.Rd | d8514a12b8b2ef3bff207fc320f9136a210a1e91 | [] | no_license | GSLabDev/nonet | 514aa1dd1fdae7eb123b88a7a8524ac3e41b753f | 66b416211abbde4455e029e22aea1e879cec358b | refs/heads/master | 2020-04-12T10:05:19.679235 | 2019-01-03T11:26:02 | 2019-01-03T11:26:02 | 162,418,570 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,847 | rd | nonet_plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nonet_plot.R
\name{nonet_plot}
\alias{nonet_plot}
\title{Plot the predictions or results of nonet_ensemble}
\usage{
nonet_plot(x, y, dataframe, plot_type = NULL, nonet_size = 20,
nonet_alpha = 0.3, nonet_bins = 25)
}
\arguments{
\item{x}{x ... |
f87a3dbef3a02222ebabe28f369d570c82e93f5a | 7590a2ceba0efdc130c5d7631617e4d829016d5c | /man/ggHorizBar.Rd | 109b2957b416c34337f38e1f8f1f1d5076d2e289 | [] | no_license | andymckenzie/bayesbio | e52b8bfb46d32d04373a3161f6a9722b47af8e32 | 1389283ba9ac8e1778dd7930af35e719a3baf540 | refs/heads/master | 2021-01-17T12:44:10.735034 | 2019-06-11T16:24:17 | 2019-06-11T16:24:17 | 59,575,223 | 2 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,013 | rd | ggHorizBar.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ggHorizBar.R
\name{ggHorizBar}
\alias{ggHorizBar}
\title{Create a color-labeled horizontal bar plot in ggplot2.}
\usage{
ggHorizBar(data_df, dataCol, namesCol, labelsCol, decreasing = TRUE)
}
\arguments{
\item{data_df}{Data frame with columns... |
11747e59e510c99277d5c96b5b7d255befbb61bb | 950030f19c1368f889700299bc36ecf7104f56b8 | /tests/testthat/test_is_authorized.R | 1e4e0d602cb63eab224f304aa237a47e57abd3cf | [
"MIT"
] | permissive | ropensci/EDIutils | 0cadce6b8139417fcfa65194e7caf8c77ea087af | b1f59cccee3791a04d7702bcb37f76995ae2fcbe | refs/heads/main | 2023-05-22T09:49:03.633710 | 2022-09-09T16:12:30 | 2022-09-09T16:12:30 | 159,572,464 | 2 | 1 | NOASSERTION | 2022-11-21T16:22:08 | 2018-11-28T22:13:59 | R | UTF-8 | R | false | false | 267 | r | test_is_authorized.R | context("Is authorized")
testthat::test_that("is_authorized() works", {
url <- "https://pasta.lternet.edu/package/report/eml/knb-lter-sbc/6006/3"
vcr::use_cassette("is_authorized", {
res <- is_authorized(url)
})
expect_true(class(res) %in% "logical")
})
|
7755d7ef1f83a6b3ad975b6a6ae8c19da2459fe7 | 295ab607a406c3c4d0c24e3b162ddfa066086cbd | /R/aniplotevents.R | e7cef72ae285ed08e20fce0feaa1775c25408ead | [] | no_license | ykang/TED | 34b5e528d43af774df9b3812b851b21aeff3425e | 6384c692d131817ec60243fa256feb85c6d86de4 | refs/heads/master | 2016-09-16T13:13:15.144382 | 2014-05-10T10:23:26 | 2014-05-10T10:23:26 | 19,638,723 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,187 | r | aniplotevents.R | #' Generate a gif to visualise the event detection process
#'
#' This function generates a gif file demonstrating how the event detection process is implemented.
#'
#' @param x a vector or a time series.
#' @param w a scalar specifying the size of the sliding window.
#' @param noiseType background noise type assumed ... |
64b4eaf1eb10c4886ecc238a77be1bdc5b93a910 | dcdf83c33f192db3d6783cf8d9f2bf947e2cd1c9 | /man/optimizeUnits.Rd | 1b5d361e1d7010b25b291d6522ab861341d4a5b0 | [] | no_license | edvinf/aaSimulator | a6461917287b05f1d48170462540a81b978252d8 | 6a5cb890388dfd0a6e9e54c5d91aebedba7da4d9 | refs/heads/master | 2020-12-04T03:03:33.587876 | 2020-05-27T22:58:11 | 2020-05-27T22:58:11 | 231,583,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,813 | rd | optimizeUnits.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simulator.R
\name{optimizeUnits}
\alias{optimizeUnits}
\title{Optimize units}
\usage{
optimizeUnits(
cost,
iterations = c(5, 30, 100, 2000),
replications = c(25, 10, 3, 1),
rank = c("overlap", "overlap", 10, "all"),
attacker = NULL,... |
9dc8b7a6c6b76e7e8befd62ed98fc644b38eca7a | 5e605fdb3bd68987f776b0121f950a7aee1ccbb9 | /R/log.likelihood.R | 3ec10380f5ca8487f2a948df8f46d0f66b04e9e4 | [] | no_license | diystat/NBPSeq | f150da798677a3c27dc27cee916f960f66af149d | 358f095f4846476c7c9ffe720b502899ea18affb | refs/heads/master | 2021-01-01T16:19:01.230766 | 2014-05-18T00:19:07 | 2014-05-18T00:19:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 617 | r | log.likelihood.R | ##' The log likelihood of the NB model under the mean shape parameterization
##'
##' This function call dnbinom to compute the log likelihood from each data point and sum the results over all data points.
##' kappa, mu and y should have compatible dimensions.
##'
##' @title (private) The Log Likelihood of a NB Mo... |
699b741b8f1904c2e8d06f2ede48ae26fddb3820 | cc61c862afca41b011a8af626812543010b1453d | /man/view.Rd | 58f6a23f4ff98edd3818dde52a53900c50bbdbdb | [
"MIT"
] | permissive | JohnCoene/aframer | 2dcfc2d4349a24258e1ceee07d122a0a46cdc8f3 | 290e43ba2f1ec21c9458ed5e6e32353e10b491b3 | refs/heads/master | 2020-03-26T16:28:28.346296 | 2020-03-08T21:17:28 | 2020-03-08T21:17:28 | 145,104,198 | 11 | 1 | null | null | null | null | UTF-8 | R | false | true | 685 | rd | view.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/helpers.R
\name{browse_aframe}
\alias{browse_aframe}
\alias{serve_aframe}
\alias{embed_aframe}
\title{Browse & Embed}
\usage{
browse_aframe(a)
serve_aframe(a)
embed_aframe(a, width = "100\%", height = "400px")
}
\arguments{
\item{a}{An afra... |
1213d3dced5a04c61c142b82a1ed3b3b6c298234 | 126cc53af71b48594bb904c658a021da95ea6441 | /man/survivor_age_mean.Rd | 4a6bb1e2ec4482dfcc9a56556f41496e1a0280ca | [] | no_license | unimi-dse/a2b3bab6 | 5e9cc0739db4f5526412e0483661fc877f7ae790 | d10891cdd6703d62bf08ee556a91e6fa8cd24027 | refs/heads/master | 2020-12-19T23:55:24.921670 | 2020-02-16T21:27:17 | 2020-02-16T21:27:17 | 235,889,837 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 889 | rd | survivor_age_mean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/survivor_age_mean.R
\name{survivor_age_mean}
\alias{survivor_age_mean}
\title{Survivor Age Mean}
\usage{
survivor_age_mean()
}
\arguments{
\item{df:}{The titanic train data set which we imported and converted to df dataframe.}
\item{sboa}{Va... |
2ac0b9bdfee2347f3a838e75bb6df6b4f3d1afbf | e64f29c44c383284e244d6f42f0fa9b3ceea73ba | /IDS_freq.R | 30f986692dce457edc42b9d48fc5e7001ced92b0 | [] | no_license | peterpnorwood/WrittenPrelim | a6ba7b2e354d682c8fbb80c2a5bccce650a6d70f | 876bc82d55a19b7beaf5953a0cb35c4f2e07557d | refs/heads/main | 2023-02-04T14:39:36.265394 | 2020-12-19T18:14:25 | 2020-12-19T18:14:25 | 307,384,756 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,835 | r | IDS_freq.R | ## ----------------------------------------------------------------- ##
## IDS_freq.R ------------------------------------------------------ ##
## Author: Peter Norwood, NC State University ---------------------- ##
## Purpose: run an experiment with a frequentist analog of IDS ----- ##
## -----------------------------... |
147865a56e24aa2093f59779f3551a07945f11b3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/astrolibR/vignettes/astrolibR.R | 2a0665bb65c75be73c491ed7d2009e1daf434922 | [] | 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 | 49 | r | astrolibR.R | ### R code from vignette source 'astrolibR.Rnw'
|
6df4b9492cc2fd6a4788e2d4cea7a26d73d1baab | e04dddfe950a587d802bb18b44ee33de0890be12 | /cross-species/4.DE.R | 8c7ba4356f38e17cb5704a0f00aa62b731ca4222 | [] | no_license | xscapintime/crspecies_RNAseq-analysis | aacaa55b468573701709020c88c9569312102ee5 | cff864a924d5619fe770d79c570ea2dfb23ec41d | refs/heads/master | 2023-07-18T20:27:57.241427 | 2021-09-18T15:12:09 | 2021-09-18T15:12:09 | 379,606,798 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,138 | r | 4.DE.R | rm(list = ls())
#options(scipen = 999)
library(tidyverse)
library(DESeq2)
## read count (genes below cutoff removed)
# huamn
human_exp_mat <- read.table("human_select_rdcounts.tsv")
# mouse
mouse_exp_mat <- read.table("mouse_select_rdcounts.tsv")
## only keep the homology/orthology? PAIRS
human_sy... |
8eb162b8201870ed45792e29d897823a51792fe8 | becf66d452c52a4ebafbfb061c11f3c95347ddcf | /stats/non-merge.R | 48e3fb967b9a7397a2f199170525a6176733af99 | [] | no_license | caiusb/MergeConflictAnalysis | 55b40734c71e9618eae14e921782b69a79ce841e | e41863670505bff853c85572ccf6414b23a0a0af | refs/heads/master | 2021-09-05T17:52:18.254826 | 2018-01-30T02:14:44 | 2018-01-30T02:14:44 | 29,747,203 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 507 | r | non-merge.R | source("common.R")
resultsFolder <<- "../../results/merge-data"
loadNonMerge <- function(folder) {
files <- listCSVFiles(folder)
data <- data.frame(SHA = character(0),
TIME = integer(0),
AUTHOR = character(0))
data <- readCSVFiles(files, data)
return(data)
}
n... |
e29fc9098f9201f35a65467b0fe889fe2d16ede8 | f5feacda6bcf986bf61cdfa57f5387ed7e651918 | /man/compute_fit_stats.Rd | f29c0b70df9e9129ba43b12d5476844d3dbd3960 | [] | no_license | cran/functClust | 3386c3179bdf9e255bfec00ed8f39b6c3c696da1 | f7415612fbc0fd749a1da01e822b6217e2b8bb0e | refs/heads/master | 2023-01-20T01:30:18.270906 | 2020-12-02T09:30:02 | 2020-12-02T09:30:02 | 318,755,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,060 | rd | compute_fit_stats.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/validating.R
\name{compute_fit_stats}
\alias{compute_fit_stats}
\title{Statistics of model goodness-of-fit}
\usage{
compute_fit_stats(mCal, mPrd, fobs, xpr, nbK)
}
\arguments{
\item{mCal}{a numeric matrix.
This matrix is the matrix ... |
ef0d8369832ade356b92e45db63c746893a41bd3 | f1721111e077d9e5d14b4fe8f40f6baa33308fcb | /man/motion_readfd.Rd | 82887a9edb7c7e4aa3a8a68a98716f8280c274b3 | [] | no_license | LabNeuroCogDevel/LNCDR | 6d71d98c36a42ebef3479b9acc183680d0d0bb8d | f9944ce2ca03c38476975b59b0edb458d65ee227 | refs/heads/master | 2023-04-27T05:01:26.259112 | 2023-04-18T19:12:33 | 2023-04-18T19:12:33 | 41,372,116 | 3 | 1 | null | null | null | null | UTF-8 | R | false | true | 268 | rd | motion_readfd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/motion.R
\name{motion_readfd}
\alias{motion_readfd}
\title{extract FD from fd file}
\usage{
motion_readfd(f)
}
\arguments{
\item{f}{- 'fd.1D' file}
}
\description{
extract FD from fd file
}
|
3650fcf99097d2f3c6bddee711b67b0aa2879789 | 2624780e9ac235d2b08aa69b191033fe35cdc915 | /tests/testthat/test_summary.R | c0db0e4c81531d1a87f0f8723c7186def1381d6f | [] | no_license | Sandy4321/janus | 81c01fdc783252cff1d16cdd506bcaf1d0f22ca8 | 8dc36385a063de0e1efc0ed76bb00dccccd78012 | refs/heads/master | 2021-01-14T14:07:50.902289 | 2015-09-13T23:06:58 | 2015-09-13T23:06:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 197 | r | test_summary.R | library(janus)
context("summarize fitted model")
test_that("error is thrown if non-janus object is given as parameter", {
mod <- lm(mpg ~ ., data = mtcars)
expect_error(summary.janus(mod))
})
|
ffc63fb9a74a3bcd8664f3f4fd223abb4605b04e | e67140b7633e8a45fc2662866e2a2dcd4ed904b8 | /R/parseFactorLevels.R | 7d672fedbdb90f1f98a8d61246ded96d9222737a | [] | no_license | jakobbossek/farff | 7a21de6df12ed8a5249e3d2cd68a4ce2b7193297 | 5090b3bcc4a927be80ad29091d508d57090a5718 | refs/heads/master | 2021-01-14T13:22:13.241728 | 2015-10-27T14:27:45 | 2015-10-27T14:27:45 | 41,908,306 | 0 | 0 | null | 2015-09-04T09:39:00 | 2015-09-04T09:38:59 | R | UTF-8 | R | false | false | 1,257 | r | parseFactorLevels.R | parseFactorLevels = function(x, line = "LINE NOT GIVEN") {
consume = function(s, r, no.match.error = FALSE) {
m = stri_match_first_regex(s, r)[1L, ]
if (is.na(m[1L])) {
if (no.match.error)
stopf("Error while parsing factor levels in line:\n%s", line)
else
return(NULL)
} else {... |
5fc12c6b0b22c9c4cd97f4c8898f7b4f0bd929b2 | 2b3cbc05953d0502cfd03db9cc8818ceff5783c2 | /80bb2a25-ac5d-47d0-abfc-b3f3811f0936/R/Temp/axEMDqiV4SCHo.R | 07c888e4888d12e27244ce6feedd964e2cf38d14 | [] | no_license | ayanmanna8/test | 89124aa702fba93a0b6a02dbe6914e9bc41c0d60 | 4f49ec6cc86d2b3d981940a39e07c0aeae064559 | refs/heads/master | 2023-03-11T19:23:17.704838 | 2021-02-22T18:46:13 | 2021-02-22T18:46:13 | 341,302,242 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 850 | r | axEMDqiV4SCHo.R | with(a29fa0dbeda864e2ab65658eef89b70c2, {ROOT <- 'D:/SEMOSS_v4.0.0_x64/SEMOSS_v4.0.0_x64/semosshome/db/Atadata2__3b3e4a3b-d382-4e98-9950-9b4e8b308c1c/version/80bb2a25-ac5d-47d0-abfc-b3f3811f0936';source("D:/SEMOSS_v4.0.0_x64/SEMOSS_v4.0.0_x64/semosshome/R/Recommendations/advanced_federation_blend.r");a2Hrpdwy3col1<- as... |
dacc5e3ebc841b1ee8379c78e070319780a8308e | 322f61a1b25bff4adbe818d6b823257e246a3640 | /1_6.R | ba1bdd67f501f73e6dca45a9f53551c419eec13d | [] | no_license | TShibano/class_data_1 | 1b25bcda427be4467df6832d87fb6e1a473822d5 | c80eb0f7a1f2c34ba326a0c4d59b91063dbb6ae4 | refs/heads/master | 2020-08-04T18:51:14.488627 | 2019-11-26T03:13:12 | 2019-11-26T03:13:12 | 212,243,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 175 | r | 1_6.R | # 1
a = 2
b = 8
print(a^b)
# 2
do = pi/180
c = 67
print(sin(c * do))
# 3
d = -23.456
print(abs(d))
# 4
e = 7
f = 3
print(e%%f)
# 5
set.seed(0)
runif(10, min = 0, max = 1)
|
eb53633bae6ff6f342e79cb270f75044fb17630d | 140fd86a6c3954128e3f3781240af79e9156430c | /R/ModelVarPlote.R | 4d036d3c7ee699020c14c4c4d329da23ca955ae0 | [] | no_license | gfalbery/ggregplot | b69aaf0eda216795c991bc28b88cc9d5943f35ca | b3aeb51756f305b00ab17653793b72291935fa4b | refs/heads/master | 2023-03-30T08:16:22.918679 | 2023-03-16T13:17:37 | 2023-03-16T13:17:37 | 163,702,155 | 6 | 5 | null | 2019-09-24T17:39:24 | 2018-12-31T23:08:20 | R | UTF-8 | R | false | false | 458 | r | ModelVarPlote.R | ##### ModelVarPlote
ModelVarPlote<-function(model){
graph<-unique(data.frame(rbind(summary(model)$Gcovariances,summary(model)$Rcovariances)))
graph$Factor<-0
graph$Factor<-factor(rownames(graph),levels=rownames(graph))
ggplot(as.data.frame(graph),aes(x=Factor,y=post.mean))+geom_point()+geom_errorbar(aes(ymin=gr... |
4ba8c21ca3d5c069eb115437908deb5ddb7cb763 | 352f976e18a570948d915aba7d4b3a32bf147186 | /Practicals/Practical_B/Photosynthesis.Rcheck/00_pkg_src/Photosynthesis/R/get.es.R | c3de3a9d4ba3096df1223bf483384bef6aa5f014 | [] | no_license | femeunier/VegMod_course | 04109dd66990be845f7a2f856d9a2212a4397ae2 | 436e66c909b260c0bc7d36dd0ad038a0d7c63b12 | refs/heads/master | 2023-04-23T16:46:41.346969 | 2021-05-10T10:21:23 | 2021-05-10T10:21:23 | 263,193,597 | 0 | 3 | null | 2021-05-07T10:25:49 | 2020-05-12T00:45:48 | HTML | UTF-8 | R | false | false | 358 | r | get.es.R | #' @description Calculate saturation vapor pressure
#'
#' @title get es
#' @param temp temperature in degrees K
#' @return saturation vapor pressure in mb
#' @export
#' @author David LeBauer
#' @examples
#' temp <- -30:30
#' plot(temp, get.es(temp))
get.es <- function(temp) {
return(6.11 * exp((2500000/461... |
270a0c548b039a7f5eff803e23fbb6254092df59 | c9f3369c749e5a3cfebaa96dc1b484e72a3dd7d0 | /man/nogenV.Rd | 014739334dbefb8ffa0671a6e61663e19be53949 | [] | no_license | amoloudi/R-PKG-Distributions | 128ff992a10a0da915f27aa941d2f9d476ad9e9a | 20daa9657d6833cb7aff2ac9194340504c1f0270 | refs/heads/master | 2021-09-02T19:18:04.772144 | 2018-01-03T19:13:48 | 2018-01-03T19:13:48 | 115,752,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,008 | rd | nogenV.Rd | \name{nogenV}
\alias{nogenV}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Normal Generator Visualization
}
\description{
Improved version of nogen, Can generate n random numbers.
}
\usage{
nogenV(n, u, s)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{n,u,s}{
n ... |
4b86bc1771a1e2006c09a7c4d7e08e7aba227e96 | d628e0d2fe6e3124784c5b9b4d6901311c5f6aac | /Journal_figs/single_locus_selection/Culex_resistance/culex_resistance.R | 385ad5b29175e024b8475c0230bb35e2e60f2af7 | [
"MIT",
"CC-BY-3.0"
] | permissive | cooplab/popgen-notes | ac5f53efc75aa8e40702990116b8a4cfb35d791d | 8c1f59f3aaed2da5e037be6bf032e5505f27d606 | refs/heads/master | 2023-09-03T17:39:48.129549 | 2023-05-02T04:17:19 | 2023-05-02T04:17:19 | 9,527,828 | 558 | 125 | MIT | 2023-06-01T04:13:47 | 2013-04-18T17:42:24 | PostScript | UTF-8 | R | false | false | 1,095 | r | culex_resistance.R |
culex<-read.csv("~/Dropbox/Courses/Popgen_teaching_Notes/Journal_figs/single_locus_selection/Culex_resistance/culex_resistance.csv")
layout(t(1:2))
plot(culex[,c("dist_km_Ace1","Ace1_freq")],cex=1.2,pch=19,xlab="Distance from coast (km)",ylab="Allele frequency",ylim=c(.1,1),cex.axis=1.2,cex.lab=1.4,main="Ace 1",cex.... |
d43bd97f0966f5b4e33050af3fd754fb13049a4e | b572344dacbc26cd6ccf746b75ec8ceb943cbcd6 | /plot3.R | 4abeaee5ad381488b5ca33b72bfef2874094ca92 | [] | no_license | amete/ExploratoryDataAnalysisProject | b22fe49f37e85378ff69cb468993039dee4e8128 | a4084916097c247fd777d9354580e29212aaf9a4 | refs/heads/master | 2021-01-20T02:07:47.837827 | 2017-04-26T15:44:14 | 2017-04-26T15:44:14 | 89,377,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,476 | r | plot3.R | # Download the data
emission.data.file.name <- "summarySCC_PM25.rds"
classification.data.file.name <- "Source_Classification_Code.rds"
if(!file.exists(emission.data.file.name) || !file.exists(classification.data.file.name)) {
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip",
... |
c24a8da5cdc4e439b057282a086ffa6636699aa8 | 302f30032ecf57300d4947f458b58132418d5d57 | /code/DiM_PG_example-calls.r | 8ed01752990df6337ef0f8eacf018f487882aa44 | [] | no_license | m-clark/bretthorst | 3fe22d5b1e3a2ce13cb68752bbd028e3596ea81e | 615287b04e03d0505f151f482daa4ec37fbdf790 | refs/heads/master | 2022-09-17T13:19:55.855914 | 2020-06-05T15:56:48 | 2020-06-05T15:56:48 | 256,238,635 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,480 | r | DiM_PG_example-calls.r | rm(list=ls())
source("DiM_PG.r")
################################################################################
# Phil Gregory
inputvalues <- list(snames = c("riverB.1","riverB.2"),
# sample 1
d1 = c(13.2,13.8,8.7,9,8.6,9.9,14.2,9.7,10.7,8.3,8.5,9.2),
# sample 2
d2 = c(8.9,9.1,8.3,6,7.7,9.9,9.9,8.9),
#... |
2d23972940e2d2a5485559e45f65f2ec09cf9bac | ec03eebabc6dfb26731404b5263a40c79c13cfbc | /Functions_Fonctions/PlotScripts/InfoPlotsFUN/MonthlyNtables.2xls.r | 6a437d08a8a25caffcb5e334e1a00c8e4c4895dd | [] | no_license | martinjeanphd/CABIN_vv_RCBA | 3887137f09bbce7b7d2dc63e83a68ae23fbeace0 | 05d5efa98406130139008e169e53a8bb65bc6b13 | refs/heads/master | 2023-02-09T16:10:04.601531 | 2020-06-23T19:38:55 | 2020-06-23T19:38:55 | 244,471,324 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,377 | r | MonthlyNtables.2xls.r | ######################################################################################
# FUNCTION TO WRITE LIST OF MONTHLY SUMMARY TABLES to XLS
# -written for FreqTables.FUN.R
# -input=list of dataframes (1 df/summary table per group)
# - writes each Parameter list to a different sheet
########################... |
2f378b140fd357455f80ba9f25669bbc3bf08693 | 590d0ac331da7be6398818df8c4c2b50fb83d072 | /cachematrix.R | 718f4f49f121017a0f541c8c42a4953507d7ca01 | [] | no_license | Coconuthack/ProgrammingAssignment2 | 48b98549b58ab71246815f38da1d7529de473144 | 72238aab20dc7da8b1327f6898584fe9fc1c5f85 | refs/heads/master | 2021-01-24T00:09:24.502180 | 2014-05-25T20:14:55 | 2014-05-25T20:14:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,901 | r | cachematrix.R | ## The two functions below make matrix computations more efficient by
## providing a way to create a matrix, compute its inverse and cache it for
## future computation
## This function creates a special matrix with space to set and get its cached
## inverse once computed
makeCacheMatrix <- function(x = matrix(), .... |
ec9d95217a2224cb7426b958031afe7a0a72d4f8 | d1667295bdadc16eed11901d79a4d06cca62fb13 | /results/Untitled.R | cb20b86da328bd9b7b6c5810f8a62c8b417aad60 | [] | no_license | loneharoon/KDD2017 | de9c5dd8eafed74aa89622b6041b669a43984ec2 | a2399f02c23f00c34bfdc9f172e18410b5e780c1 | refs/heads/master | 2021-03-27T16:34:33.512063 | 2018-11-02T12:09:05 | 2018-11-02T12:09:05 | 92,056,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,785 | r | Untitled.R | neuralnetwork_procedure_withoutweather <- function(train_data,test_data,hourwindow,daywindow){
#days <- length(unique(lubridate::day(index(test_data))))
days <- as.numeric( last(as.Date(index(test_data))) - first(as.Date(index(test_data))) )
result <- list()
for (i in 1:days) {
# browser()
result[[i]] <... |
c83b2a44d5d074f269c6940e3a0b0902d522ce80 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/languageR/examples/xylowess.fnc.Rd.R | 70fbcfec87b432bbca3626b988d688369f72ba04 | [] | 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 | 342 | r | xylowess.fnc.Rd.R | library(languageR)
### Name: xylowess.fnc
### Title: Trellis scatterplot with smoothers
### Aliases: xylowess.fnc
### Keywords: regression
### ** Examples
## Not run:
##D data(weightRatings)
##D xylowess.fnc(Rating ~ Frequency | Subject, data = weightRatings,
##D xlab = "log Frequency", ylab = "Weight Ratin... |
8291eba2bc0f286afe88d6076e97a4ba5c2473a3 | 070dceea65adaa4b5b14f0eea4f1839b519acedf | /functions/calc_LUE_smith.R | 922105853893420aa7ce7aeca2b78a77b7981db7 | [] | no_license | Shirleycwj/LUE_CO2 | c9f2023edb345eec64b3a4d060b19cb4498ef575 | 78edb4d39481773604936253cf8a2266c4f3f37b | refs/heads/master | 2023-04-12T21:36:34.505940 | 2021-04-21T20:10:51 | 2021-04-21T20:10:51 | 358,309,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | r | calc_LUE_smith.R | # this script simulate LUE as in Smith et al. 2019
calc_LUE_smith <- function(cao, temp, vpd, z, theta,c_s) {
patm <- cal_patm(z)
vpd_pa <- vpd * 1e3
ca <- cao * 1e-6 * patm
K <- cal_K_pa(temp,z) #Pa
gamma_star <- cal_gammastar_pa(temp,z)
chi <- cal_chi(temp,z,vpd,cao)
eta_star <- cal_etastar(temp,... |
aa1551995e9f1f8bddc0d29588bfe35d18b69c95 | aa3c74e4fd4c3865dc102e8b1324d6d9aa43a26d | /R/rtwibble.R | 5f733f5c663258e5b82dc45c2e93ac1123631cfb | [] | no_license | mkearney/rtw | 9b38f4282b4f61e4347cbcdbe53ac20ae5fb63cc | 2e113bd17a5fe100cac9b39b0b7d7fde4484807c | refs/heads/main | 2023-08-25T14:40:24.091921 | 2021-11-05T18:06:02 | 2021-11-05T18:06:02 | 420,239,950 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,353 | r | rtwibble.R | add_class <- function(x, ...) unique(c(..., class(x)))
as_rtwibble <- function(x = NULL) {
if (is.null(x) || NROW(x) == 0) {
x <- data.frame()
}
x <- as.data.frame(x, row.names = NULL, stringsAsFactors = FALSE)
structure(x, class = add_class(x, "rtwibble"))
}
hd <- function(x, n = 10) {
if (!is.data.fra... |
a99340051d38425e6c4ac6fddab82093c9bf8a96 | 96d1e3eb80e81f6a29eac4df65151f3b17c0ccdd | /R/decon.primary.R | b3d407e634691c6ba9fcaec930d884d58bd2048f | [] | no_license | donaldtmcknight/microDecon | 83ba8e9fd722169214398fc2cd2a499bbd3c0091 | 85cb9ab41124cad1fca311ce9e2b603218b73ba6 | refs/heads/master | 2021-06-15T03:18:32.684712 | 2021-02-19T01:44:26 | 2021-02-19T01:44:26 | 162,779,976 | 12 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,604 | r | decon.primary.R | #decontaminates sample based on regression 1
#Takes a data frame of only three columns (ID, blank, sample)
decon.regress1<- function(contamination){
colnames(contamination)[1:2] <- c("OTU","blank")
#removes the OTU column and limits the sample to just the ones that amplified in the blank
cont <- subset(cont... |
26e75b2d3d6c3060c7af09aada564abb74dd1038 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/gofCopula/examples/tests11.Rd.R | 98f9d70cb1289ba8149feceb224ed279c765f059 | [] | 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 | 253 | r | tests11.Rd.R | library(gofCopula)
### Name: gofPIOSRn
### Title: 2 and 3 dimensional gof test based on the in-and-out-of-sample
### approach
### Aliases: gofPIOSRn
### ** Examples
data(IndexReturns)
gofPIOSRn("normal", IndexReturns[c(1:100),c(1:2)], M = 10)
|
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