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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
30eed50f1b4bdebb1feb9476726bea9e917ef0b5 | e788c2cee9b7c0890ebea041c8a192c2af1a83bc | /networks.R | ce188c7e453e318c664a5107ef01d4f5b0082ac8 | [] | no_license | GenevieveRichards/Honours_networks_generate | 755b24126e610ee63b4589a52cc3db14ccc522b5 | 8c421f5c0ce0ac1cf0215df85cca4ac1f1df3470 | refs/heads/master | 2022-05-23T04:29:57.148549 | 2020-04-26T11:30:42 | 2020-04-26T11:30:42 | 256,400,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,641 | r | networks.R | library(igraph)
library(purrr)
library(readr)
MHmakeRandomString <- function(n=1, lenght=12)
{
randomString <- c(1:n) # initialize vector
for (i in 1:n)
{
randomString[i] <- paste(sample(c(0:9, letters, LETTERS),
lenght, replace=TRUE),
... |
b7176ee6c817dcb6a03d5a79bef7c4d3ff6ed457 | 9e87da7892842890e5a5ac50c60d1203112ba257 | /man/geom_plane.Rd | 81fdae594b7e2a85b6001397c52ec06d9a410f4e | [] | no_license | paleolimbot/ggstereo | 2920a21b21f8f0af2111eef391b0734585d37383 | e56129f315058b8eacc710c50865802593efb095 | refs/heads/master | 2020-04-24T20:48:26.566329 | 2019-02-27T02:11:34 | 2019-02-27T02:11:34 | 172,256,451 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 896 | rd | geom_plane.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geom_plane.R
\docType{data}
\name{geom_plane}
\alias{geom_plane}
\alias{GeomPlane}
\title{Draw a plane on a steronet}
\format{An object of class \code{GeomPlane} (inherits from \code{Geom}, \code{ggproto}, \code{gg}) of length 5.}
\usage{
geo... |
4ff4d09c926ad2db7dbdec9d84f31536be28ee0b | d73061d7fcbed9da14de118cef09e9c251c358ed | /code/QA_code/CDEC_daily_BAS_QA.R | d8a68cb913d70879a338e89bec9ffa799259ad81 | [] | no_license | kmabbott/streamtemp_classification | 9bd3534cdcb06034f8699a6f21d054ab6cfecb4e | c5e1157e9421ed3d25f3ac0767d0901d62ceb953 | refs/heads/main | 2023-08-17T11:12:11.484170 | 2021-09-22T20:51:10 | 2021-09-22T20:51:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,420 | r | CDEC_daily_BAS_QA.R |
# Code description --------------------------------------------------------
# Code to review temperature data from gage CDEC_BAS.
library(tidyverse)
library(lubridate)
library(weathermetrics)
library(plotly)
# Get Data ----------------------------------------------------------------
file_list <- list.files("data/d... |
ba7bae5fa18cab409fd8a537072844bdd54f72d8 | 6274edaf656fb18fc9d5eb16be467734f5bc3588 | /tutoriais/forSuzy.r | 7f662ba7d5f7782afa6f0962ee8979e78e75bcd5 | [] | no_license | AlertaDengue/AlertaDengueAnalise | 107ce9ada7aafe84d738cc702acbb97e2189d035 | 167904fc48767a4bbfbd83fe94807f3a50b4d1b2 | refs/heads/master | 2023-03-11T22:33:44.638586 | 2023-02-21T14:46:10 | 2023-02-21T14:46:10 | 21,705,658 | 2 | 1 | null | 2022-04-21T04:33:30 | 2014-07-10T18:32:45 | R | UTF-8 | R | false | false | 1,347 | r | forSuzy.r | # extracao de dados para o estudo com o grupo da Suzy
library(AlertTools)
con<-DenguedbConnect(pass = pw)
# Notification data from geocod Municipality:
geocod<-3304557
query.txt <- paste0("SELECT * from
\"Municipio\".\"Notificacao\"
WHERE municipio_geocodigo = ", geocod)
df ... |
8eaf38977b6cca6797441f0964293819b6ce2250 | 952ded2996c1960f1f3167749e9054df514e065c | /lib/Player_feature/Make_link_playoffs.R | 60221f34ade15fb8eace8c14f0502e81e201a81b | [] | no_license | RuxuePeng/Spr2017-proj5-grp1 | d42b73b726d651a2b619f77a4b66c689dbe3eb38 | 5810275bb4047343a568372408b4db7a9dbc7014 | refs/heads/master | 2021-01-21T11:59:58.430330 | 2018-04-14T19:17:21 | 2018-04-14T19:17:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 950 | r | Make_link_playoffs.R | # The date column changes format for the playoffs data, so we wrote a new function to create links
## The link follows pattern: /date + 0 + nameabbreviation +.html
## eg. http://www.basketball-reference.com/boxscores/201510290NYK.html
# input: Data, a table containing all game one certain team played in a certain ... |
955a9f7917bb5d82a55cc40445d2e08b1671e239 | 7cb61ada8a73c86316182c2ed6eb4685ee3bffe6 | /oldstuff/DBRicerche/ui.R | 68216cb2bf643bb25747862bff343f9eb6571632 | [] | no_license | TMax66/IZSLEReasearch | 4b99b7acbfe61f255e8c5388cc75202201f190d7 | 34c93f6a2d338710efede39187886b20399669b8 | refs/heads/master | 2023-04-08T10:47:06.737184 | 2023-03-26T16:27:38 | 2023-03-26T16:27:38 | 191,943,209 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,669 | r | ui.R |
ui <- dashboardPage(
dashboardHeader(title = "Dbase Progetti di Ricerca IZSLER", titleWidth = 450),
dashboardSidebar(
sidebarMenu(
menuItem("Dashboard", tabName = "risp", icon = icon("dashboard")),
menuItem("Ricerca Corrente", tabName = "corr", icon = icon("th")),
menuItem("Consulta d... |
b7297ee2065bbd7081cb1148365091fad2d34875 | ed28666d9201bf050c305f0740756f7730a66ef3 | /NatureEE-data-archive/Run203071/JAFSdata/JAFSnumPerPatch72588.R | 4553c2123e2d71187b51feca435e9e53db3254e0 | [] | no_license | flaxmans/NatureEE2017 | 7ee3531b08d50b3022d5c23dbcf177156c599f10 | b3183abe6bb70f34b400d0f5ec990cce45e10b33 | refs/heads/master | 2021-01-12T08:02:37.153781 | 2017-01-27T15:48:55 | 2017-01-27T15:48:55 | 77,110,421 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33 | r | JAFSnumPerPatch72588.R | numPerPatch72588 <- c(2568,2432)
|
0d8242c483cecc6096da61c21840be9b83af241c | e52778187dbefd2b04c245b8b591a6608fdcd387 | /src/prazosin_analysis.R | 56f605a7bd28a70e8329b21722b3b57c44d8c92e | [
"MIT"
] | permissive | philipphoman/prazosin | 0768c834c3ad3cfc52ccdb1a1f84b8276dd145f7 | d1aa2b0241c991d432885a52c4d5889bf91c7a07 | refs/heads/master | 2020-03-22T20:35:39.272035 | 2018-07-11T18:30:05 | 2018-07-11T18:30:05 | 140,613,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38,181 | r | prazosin_analysis.R | #
# Prazosin analysis in R
# Started 8/8/16
# PH
#
rm(list = ls())
library('plyr')
library('lme4')
#library('lmerTest')
library('lsmeans')
library('pwr')
# redirect output
#sink("output/R/output.txt")
plotpower <- function(df=NULL, alpha=0.05, power=0.8, n=NULL,
d=NULL, f=NULL)... |
a99af370859c75547f17335fe05aedbd65c27e5b | 6f920ef94c1b690e238e032f9503b67ea589adbd | /code/helper/KaplanScan.R | 5674e25b9ef217c35cd0818331c092b71c13b924 | [] | no_license | PichaiRaman/PDACSurvivalAnalysis | ec1535286e5c369e7c7bfec9c9fc622e61a33cee | 7c4b93e38f7fd5e30d4e1d15692c0b41ab3cdef9 | refs/heads/master | 2020-03-19T02:46:14.990410 | 2018-06-06T15:56:36 | 2018-06-06T15:56:36 | 135,659,254 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,269 | r | KaplanScan.R | ########################################
#Kaplan-meieir based on finding optimal cut-point
########################################
#Call libraries
library("survival");
#1 = alive
#2 = dead
kmScan <- function(x, perc, tVar, eVar)
{
#First sort data frame
timeVar <- x[,as.character(tVar)];
eventVar <... |
a69f30d599b893294c118e252ac6f1855923d11f | a3ff80775cf1ec854dd5c2a8a99d72f2a66a75d6 | /Missing Values.R | c1f05dea8ee8bd4dd76ab15f435292cd14f4122d | [] | no_license | ked66/RepData_PeerAssessment1 | 3731c9ba6c1098a62a6dac4ec9de4e1bf857fd83 | e440e6f72e85c8f83cdb30d26f8462f31493c627 | refs/heads/master | 2022-12-03T01:39:15.353390 | 2020-08-24T17:38:10 | 2020-08-24T17:38:10 | 274,181,797 | 0 | 0 | null | 2020-06-22T15:56:47 | 2020-06-22T15:56:46 | null | UTF-8 | R | false | false | 1,661 | r | Missing Values.R | ## Calculate and report the total number of missing values in the dataset
## (i.e. the total number of rows with NAs)
sum(is.na(data)) ## 2304
## Devise a strategy for filling in all of the missing values in the dataset.
## The strategy does not need to be sophisticated. For example, you could use the
## mean/media... |
c15fd4fb0bf0e83dfbc45ae2de4a29ea359a16f1 | 1b82da8fb3c5ecafd4b63a8a19368b87c64b681b | /R/Lynch_IntroAppliedBayesian/p286.R | 997a907acd731e1c86de7ec55e35737f8298fad4 | [] | no_license | wmmurrah/BayesianAnalysis | 032485eae0df9ff4e95fb691d48e6591ee11982a | b000b99d3f13c274c353d983004f99bb10a60f45 | refs/heads/master | 2021-01-20T21:59:40.988140 | 2020-12-04T01:28:23 | 2020-12-04T01:28:23 | 22,969,346 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 426 | r | p286.R | covmat=diag(2)
covmat[1,2]=covmat[2,1]=.5
z=matrix(0,2000,2)
q=matrix(0,2000,2)
count=0
for(i in 1:2000){
#naive simulation
z[i,]=0
while(z[i,1]<=0 | z[i,2]<=0)
{count=count+1;
z[i,]=rnorm(2,0,1)%*%(chol(covmat))
}
#conditional simulation based on decomposition
q[i,1]=qnorm(runif(1,min=.5,max=1),0,1)... |
4c0365b3436c10fb66f3431298111241587f244a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/nLTT/examples/nLTTstat_exact.Rd.R | ccd76ec11737607c40e1247bd06455cb5f297077 | [] | 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 | 433 | r | nLTTstat_exact.Rd.R | library(nLTT)
### Name: nLTTstat_exact
### Title: Calculate the exact difference between two normalized
### Lineage-Through-Time curves, given two phylogenetic trees.
### Aliases: nLTTstat_exact
### ** Examples
data(exampleTrees)
nltt_plot(exampleTrees[[1]])
nltt_lines(exampleTrees[[2]], lty = 2)
nLTTstat... |
8277d56331a84dcfe9f8e6369573afdce48eab4c | f697cbbb0da988fd43c07652d5955fbb82e25e38 | /David/profileViewer/R/plotGeneDist.r | 8554d9ce32d1de13b1fb38ca7311a9f360261ca6 | [] | no_license | aidanmacnamara/epiView | eec75c81b8c7d6b38c8b41aece3e67ae3053fd1c | b3356f6361fcda6d43bf3acce16b2436840d1047 | refs/heads/master | 2021-05-07T21:22:27.885143 | 2020-06-22T13:08:51 | 2020-06-22T13:08:51 | 109,008,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,757 | r | plotGeneDist.r | #' plots the distribution of the genes
#'
#' @param x the measured gene data
#' @param type the type of plot required: currently only does a simple summary plot
#' @param guides if given, red reference lines are shown for the specified values
#' @param guides2 if given, blue reference lines are shown for the specified ... |
f96d64e8a5df0de98b573fc9ad4a356e3e242c36 | 10f047c7631b3aad90c7410c567c588993bfa647 | /EcuRCode/WeightVsNestSize/PaperCode/Statistics_Output/Old/ModelSelectionPlaying.R | 46f189d279a48e0b25f9796253ba9db9bc5a4239 | [] | no_license | ruthubc/ruthubc | ee5bc4aa2b3509986e8471f049b320e1b93ce1d5 | efa8a29fcff863a2419319b3d156b293a398c3a9 | refs/heads/master | 2021-01-24T08:05:40.590243 | 2017-08-30T01:37:56 | 2017-08-30T01:37:56 | 34,295,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,783 | r | ModelSelectionPlaying.R | # TODO: Add comment
#
# Author: user
###############################################################################
### From here http://rpubs.com/kaz_yos/exhaustive
## Create vectors for outcome and predictors
allModelsAIC <- function(outcome, predictors, dataset) {
list.of.models <- lapply(... |
a01f735fae5ec3846debc337581433699bf5f898 | 29585dff702209dd446c0ab52ceea046c58e384e | /mgpd/R/ml_mix.R | 12869ebaa69ed448c2837b60da3c5ba2334455df | [] | 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 | 1,118 | r | ml_mix.R | ml_mix <-
function(param,dat,mlmax=1e+15,fixed=FALSE,...)
{
loglik = mlmax
hxy = NA
x = dat[,1]
y = dat[,2]
error = FALSE
mux = param[1]; muy = param[4]
sigx = param[2]; sigy = param[5]
gamx = param[3]; gamy = param[6]
alpha ... |
861e24cb0f66b5f1dcfb6d7a94cc7dec054a7c24 | 1d76f7728e21ac36d51ea764b526d82030d1219a | /hard.R | 1d73d34c58f8a232eab7f4c40a88dd3d36bda6cf | [] | no_license | jatinrajani/BiodiversityDataCleaning | ff0c874780da0e1b6241880ba5b91b0019166c59 | 4b95502d8dc5c3b4f974a6ad3e647d786b6418b3 | refs/heads/master | 2020-05-25T08:38:13.946046 | 2017-03-14T09:37:36 | 2017-03-14T09:37:36 | 84,927,531 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,467 | r | hard.R | library(rgbif)
library(spocc)
library(geosphere)
setwd("~/BiodiversityDataCleaning")
# using a csv file for getting coordinates of country's centroid
countries=read.csv("country_centroids_all.csv",stringsAsFactors = FALSE,sep="\t")
# specifying range in kms
nearesttoCentroid<-function(country,range,species) ... |
9bfe27f1a5883a58e0db8a18598b07c9600ceeb0 | e679a30bf9a406ad781f3913735d7ae8f33f78e7 | /R/mdca.R | aec3e2a5ac2f3893bd8bcd13db17650bf47f09da | [
"MIT",
"CC-BY-4.0"
] | permissive | gederajeg/happyr | b40fd1e1008eac240385f7842df397ec49972fa5 | 0ae2fe64a32716905307aa8133eafeccef1f3375 | refs/heads/master | 2021-12-12T22:24:36.112108 | 2021-12-10T23:29:58 | 2021-12-10T23:29:58 | 142,245,688 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,721 | r | mdca.R | #' Multiple distinctive collexeme analysis (MDCA)
#'
#' @description Function to perform \emph{Multiple Distinctive Collexeme Analysis} (MDCA) in Rajeg (2019, Chapter 7).
#' @param df the data frame for the thesis (\code{phd_data_metaphor}) included in the package.
#' @param cxn_var character strings for the column nam... |
9c727c435f861f1d10ed9a5294136291f582ddaf | 450cf51141602b88597d17dc8daa0170f3f1dba2 | /R/data.R | 2d009c1e4d2acdc980cb25751942807a5f93b0be | [] | no_license | jacob-ogre/ecosscraper | 6b16b133738076cb75e1336c28dfc932e1823496 | 63bafcc0213c52a2d2620cc1d17ef290d150d13b | refs/heads/master | 2021-04-30T23:24:42.375356 | 2018-01-23T16:07:03 | 2018-01-23T16:07:03 | 61,709,830 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,602 | r | data.R | #' Data.frame of base information for all ECOS species
#'
#' @details The data is the table returned using the URL:
#'
#' \url{https://goo.gl/tZRu5o}
#'
#' By default, \code{ecosscraper} checks this URL \code{.onAttach} to ensure -
#' assuming the user restarts their sessions regularly - that the source is
#' up-to... |
1c80f6c27ae38ba5a1841035090506cfe8d94d79 | e508870d7b82ca065aff9b7bf33bc34d5a6c0c1c | /pkg/man/GeneralInFlux.Rd | 32309bda31ea4ccdcf47a945a6ce571f024ca33d | [] | no_license | Dong-po/SoilR-exp | 596be0e6c5d291f00c6e08c348952ee23803e15e | c10d34e035deac8af4912c55382012dfc247eedc | refs/heads/master | 2021-09-03T11:12:49.199268 | 2018-01-08T15:52:17 | 2018-01-08T15:52:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 452 | rd | GeneralInFlux.Rd | \description{no Description}
\name{GeneralInFlux}
\alias{GeneralInFlux}
\usage{GeneralInFlux(object)}
\title{GeneralInFlux S4 generic}
\arguments{
\item{object}{see the method arguments for details}
}
\section{Methods}{ \code{\link{GeneralInFlux,function-method}}\cr
\code{\link{GeneralInFlux,InFlux-method}}\cr
\code{... |
56c37e981c7bb4d50134ccb8cd1b49e0064ae741 | f902e30a623b9587cf693a2daf5da83c2bf24204 | /man/remove_before.Rd | 586d54705d7e3a8c65894e2354a6a04546f080a3 | [] | no_license | ormoses/TVscriptsR | e9ba1c378617d50d92b7f8e0bef41cc309055df9 | 075dd38fd28b2d4db40287024b5c1c6c0e4e8196 | refs/heads/master | 2021-01-10T22:37:10.328132 | 2016-10-13T14:16:54 | 2016-10-13T14:16:54 | 70,378,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 600 | rd | remove_before.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/string_helper_functions.R
\name{remove_before}
\alias{remove_before}
\title{Remove everything before a pattern}
\usage{
remove_before(string, pattern, occur = "first")
}
\arguments{
\item{string}{a string to remove pattern from}
\item{patter... |
ef91f25034491b1f77333053d4a8ba6d1010bdb9 | 54a2150db5408c04b39788a23a79843aaa95dbec | /plot2.R | 0c863e4ef97b19e113fe4f0a0d1e66b4d6a301d5 | [] | no_license | kennybob/ExData_Plotting1 | cd697fe67ed0af46d2ba123649ef3915d98e11eb | b535e9d36edc155605d2b86adf0ac92be22c30bc | refs/heads/master | 2021-01-17T18:16:35.522682 | 2014-11-08T16:22:40 | 2014-11-08T16:22:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,114 | r | plot2.R | ## Exploratory_data_analysis_project_week1
## plot2.R
## Usage: source("https://raw.githubusercontent.com/kennybob/ExData_Plotting1/master/plot2.R")
## Local variables
FetchDataAgain <- TRUE
## FUNCTION: getData
## This function downloads the dataset from the web
## ... inserts data into a data frame (hpc)
## ... ... |
a85ea3c170a255ad03e66a8404839e713ced3d9d | 5deadc6dcd320ead5503ae79d4492451aa5eef68 | /tests/testthat/test-fitPM.R | 0f6f208dec02739f55a071b751d4285d73133e71 | [] | no_license | GeoBosh/pcts | 1a739a65efd7486c6f4a5e8702b206825bad27b2 | 8d6d83677e1f1d447680d562991a97eb288529fe | refs/heads/master | 2022-05-28T07:33:58.784092 | 2022-05-18T10:02:16 | 2022-05-18T10:02:16 | 190,918,136 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,615 | r | test-fitPM.R | context("fitPM")
test_that("test fitPM()",
{
set.seed(1234)
x <- arima.sim(list(ar = 0.9), n = 1000)
mx <- matrix(x, nrow = 4)
x_pcts <- pcts(as.numeric(x), nseasons = 4)
expect_error(fitPM(c(1.5, 2, 3, 1), x), "The PAR orders must be non-negative integer numbers")
expect_error(fitPM("dummy",... |
62dae08bfac5533c05dedaeeda70eca3dd43b94f | d481473c7bf59ef07fb2f0f7f6353e0beff5fa48 | /data-raw/ph.R | 29d4965fbe00b1db22edc7ceb95177973e420efd | [] | no_license | crumplecup/muddier | 92e1d4845db3d13e1297060d50d0244d5b00064f | c4d67a17377e45a35426cbb11ace342afaed6806 | refs/heads/master | 2021-11-28T03:41:33.262356 | 2021-08-13T03:11:39 | 2021-08-13T03:11:39 | 175,301,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 624 | r | ph.R | library(data.table)
library(magrittr)
setwd('/home/crumplecup/work/')
ph_cdfs <- fread('inher_ph_cdfs.csv')
df_ph_cdf <- ph_cdfs[1,]
ff_wbl_cdf <- ph_cdfs[2,]
fg_wbl_cdf <- ph_cdfs[3,]
df_ph_pmf <- to_pmf(unlist(df_ph_cdf))
ff_wbl_pmf <- to_pmf(unlist(ff_wbl_cdf))
fg_wbl_pmf <- to_pmf(unlist(fg_wbl_cdf))
setwd('/hom... |
b209c1386759c791e0ed646d1cf44b765953fdee | 29585dff702209dd446c0ab52ceea046c58e384e | /surveillance/inst/doc/glrnb.R | 679f9c25276e55a3ba21001772643bf98b205900 | [] | 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 | 5,764 | r | glrnb.R | ### R code from vignette source 'glrnb.Rnw'
### Encoding: UTF-8
###################################################
### code chunk number 1: setup
###################################################
library("surveillance")
options(SweaveHooks=list(fig=function() par(mar=c(4,4,2,0)+.5)))
options(width=70)
set.seed(247)... |
f5c7d3810b2b1355a66bc5f3d4c3e05653435486 | 31d2d467030565c44f4d28d42c0e4d225dececaa | /R/start.val.ltm.R | 525bb178f110dca5930c6a7a5f4f7e154da7e12c | [] | no_license | cran/ltm | 84fd858915db9fe1506a40628f61e6500a21ed1c | dbbabfa99fa09ad94113856a6a5ae1535e7b817f | refs/heads/master | 2022-02-25T01:10:01.747125 | 2022-02-18T08:40:02 | 2022-02-18T08:40:02 | 17,697,218 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,441 | r | start.val.ltm.R | start.val.ltm <-
function (start.val, data, factors, formula) {
n <- nrow(data)
p <- ncol(data)
cf <- paste(formula[3])
form <- paste("y ~ ", cf)
form <- as.formula(form)
q. <- length(attr(terms(form), "term.labels")) + 1
randStrVal <- length(start.val) == 1 && start.val == "random"
... |
56a3c2c207f38c2299c54e0ea0c45becceb1d1af | 08c48f2627281810fe2a4a37bb1e9bc5c03eeb68 | /Huan_link_all_script/All_result_ICGC/gene_network_merge_repurposing_model/Gene-based_data/side_effect_repo_data/test_repo_cancer_model.R | 8ea1c4b6361cbc2adcbbbb70d5f9fb6d3d81266e | [] | no_license | Lhhuan/drug_repurposing | 48e7ee9a10ef6735ffcdda88b0f2d73d54f3b36c | 4dd42b35e47976cf1e82ba308b8c89fe78f2699f | refs/heads/master | 2020-04-08T11:00:30.392445 | 2019-08-07T08:58:25 | 2019-08-07T08:58:25 | 159,290,095 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,518 | r | test_repo_cancer_model.R | library(ggplot2)
library(Rcpp)
library(readxl)
library(dplyr)
setwd("/f/mulinlab/huan/All_result_ICGC/merge_SV_CNV_repurposing_model_both/Gene-based_drug_R_or_S_optimization/side_effect_repo_data/")
#1. 测试集和训练集3、7分组
#-----------------------------------------------------------------------------------------------------... |
fb6fc76beb20d021d672d919a83e3d1e08bdd77f | 0684d492915ae9997046ede472e23972d68b0889 | /task_reference/svn/trunk/octSugar.r | 26810986c7c9b985394d68ed0695ca8ad0ddeb81 | [] | no_license | githubfun/R | 34206d40375b6c9aacb98780240a38904937cd1b | 48b9c8306968c59b6acdfce5ae4a0910d9d212b6 | refs/heads/master | 2018-05-18T22:33:29.876793 | 2011-06-30T16:40:34 | 2011-06-30T16:40:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,401 | r | octSugar.r | ## getXts:getSection:getManyplots
require(quantmod)
sugar <- as.xts(read.zoo("~/Dropbox/DATA/IDXDATA/$SUGAR.CSV", sep=",", format ="%m/%d/%Y", header=TRUE))
## segment a series of months
sugarOCT1990 <- sugar ["199010"]
sugarOCT1991 <- sugar ["199110"]
sugarOCT1992 <- sugar ["199210"]
sugarOCT1993 <- sugar ["19931... |
04e36323489777418376afe832bdd0d2a7f48280 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/AlphaSimR/man/selIndex.Rd | 9061138e5220d9529e151460ee310254b301ba8f | [] | 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 | 1,213 | rd | selIndex.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{selIndex}
\alias{selIndex}
\title{Selection index}
\usage{
selIndex(Y, b, scale = FALSE)
}
\arguments{
\item{Y}{a matrix of trait values}
\item{b}{a vector of weights}
\item{scale}{should Y be scaled and centered}
... |
6ee9214252ef2854c4e9557af99ad3181a48b5f9 | 9ea8bb629f9f4762ce4737d6e2dbafa9d05b1636 | /Step0_Splicing_junctions_loading.R | 41faf4050452115622ec6e7daa9d7173a25e6b06 | [] | no_license | FlameHuang/ASSC | f85943468888a480cbdcae96f60baaa14c63b56f | fe9436e559f83ef0c18ab44cf8833c2e4cef2d25 | refs/heads/main | 2023-03-23T22:24:39.093756 | 2021-03-25T08:33:43 | 2021-03-25T08:33:43 | 321,023,034 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,215 | r | Step0_Splicing_junctions_loading.R | #### AS site read ---------------------------
# personal function
usePackage <- function(p) {
if (!is.element(p, installed.packages()[,1]))
install.packages(p, dependencies = TRUE)
require(p, character.only = TRUE)
}
# function for replace NA with 0 in data.table
DT.replace.NA <- function(x) {
for (j in se... |
f940ba98959bd3f7bb4708730da7914978d1b096 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/extremis/examples/angdensity.Rd.R | 17300a3a7f24f5352a222c601d324c3042fd2115 | [] | 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 | 335 | r | angdensity.Rd.R | library(extremis)
### Name: angdensity
### Title: Empirical-Likelihood Based Inference for the Angular Density
### Aliases: angdensity angdensity.default
### ** Examples
## de Carvalho et al (2013, Fig. 7)
data(beatenberg)
attach(beatenberg)
fit <- angdensity(beatenberg, tau = 0.98, nu = 163, raw = FALSE)
plot(fit)... |
fba95784b867b4f1f8b18c104a647136ce01b8fd | b3fc678096d3fa31c4289b1e1c414fb0f1b2eeab | /man/growth_parameters.Rd | 7e0e2330b79acb2c951707e8f9bd99b3cd4ea016 | [] | no_license | EricBryantPhD/screenmill | d59402d6fff819b120f2d7b2149cc57eae3e80ec | 4319a2ff90a2b8dbf2bf9a27bb695b443827ef82 | refs/heads/master | 2023-03-05T11:06:16.676866 | 2020-01-11T19:44:06 | 2020-01-11T19:44:06 | 233,282,823 | 0 | 1 | null | 2023-02-26T19:47:15 | 2020-01-11T19:04:06 | R | UTF-8 | R | false | true | 1,323 | rd | growth_parameters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/growth-parameters.R
\name{growth_parameters}
\alias{growth_parameters}
\title{Calculate growth parameters}
\usage{
growth_parameters(time, size, ...)
}
\arguments{
\item{time}{Numeric - time of growth.}
\item{size}{Numeric - size of colony.}... |
000d5b0436c8be436e71f0baa62dcd5589c9bb44 | b35203fec891bdcd1a25ae2e73a444dbb79e6a44 | /R/z%dim0%.R | c2412ca87cc3831ac5bad4ff38102d59a05e54fc | [] | no_license | cran/FactoInvestigate | de865c9e6931b3aadc53edf35ae9b971a0748709 | c0eb72ddaf923d1e75a8350b6de76b218b83ee18 | refs/heads/master | 2022-05-23T15:46:43.763570 | 2022-04-26T19:50:14 | 2022-04-26T19:50:14 | 87,923,417 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 66 | r | z%dim0%.R | `%dim0%` <-
function(e1, e2) if(length(e1) == 0) {e2} else {e1}
|
f942fd0fbcfe1b81a80758d82000d20aae055541 | c9a17e301097741ccf4d4cebd8fb4d0e3ebcaf40 | /shiny.R | 05ec0839abd7037cab976a5fa579e51c3e405554 | [] | no_license | ebfreel/potpourri | 660a3203e42f654d5e2678e8f73acac621e22a5e | 4813e93e4adf35fd31bc43452f3f100465832f81 | refs/heads/master | 2023-08-31T19:44:28.815826 | 2018-04-24T20:49:58 | 2018-04-24T20:49:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,794 | r | shiny.R | ## 4/3: Collaboration mini project I: shiny
## shiny tutorial adapted from various sites, primarily http://shiny.rstudio.com/tutorial/ and https://www.r-bloggers.com/building-shiny-apps-an-interactive-tutorial/
## Package developed by: Winston Chang, Joe Cheng, JJ Allaire, Yihui Xie and Jonathan McPherson (2017).
## ... |
3db4a4813074fa28b221667ab4131b73c2979ce4 | 290a33cabaf69415e9b5137028fdf42b3286b27d | /GO_SpermAtogensis_p.R | 1c8084585f2fa3ef110e287bba02750af490dc4e | [] | no_license | liulihe954/Sheep-Transcriptome0327 | 2b1925d6e68053ad8878c39a0b76bc65aeeccac9 | 33ba5eb9c6b868b79e4eaeecfdf7621f73f91e2b | refs/heads/master | 2021-07-08T08:25:06.904917 | 2020-10-09T07:15:24 | 2020-10-09T07:15:24 | 198,678,712 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 46,788 | r | GO_SpermAtogensis_p.R | ##################################################################################################
### Set Up and Data prep #################
################################################################################################
#setwd("/Users/liulihe95/D... |
6aeed56027e3fda428c1ba9af3d9de220c2b2621 | 22de79245ec10fefc510ae021d39bb4b0e6bfd80 | /code/analysis/lda.R | 9adf5befc66677fe4b46b3bfd49e6f82cd0045d1 | [] | no_license | alex-saez/NYT-project | 9200a176e6b224344ca5f05b7719f8e4c5f53eec | 906f5fae110ecbfe43a2239ad8393a08bcdd3e09 | refs/heads/master | 2020-04-05T21:06:48.219521 | 2019-01-27T16:12:24 | 2019-01-27T16:12:24 | 68,248,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,574 | r | lda.R | library(topicmodels)
setwd("/Users/Alex/Dropbox/NYT project/")
load("./data/dtm.RData")
date()
topicmodel = LDA(dtm, k=50, method = "Gibbs")
date()
terms = terms(topicmodel, 100)
terms = as.data.frame(terms)
write.csv(terms,"./data/topic_models/topic_terms_dtm2_100_Gibbs_burn1000.csv", row.names=FALSE)
# use topic... |
530bdb7d988d6e5403e05d1921f518f7326dbdb8 | a2156e6293cc09bce5cd85d59dc221132fc2c344 | /R/LogLik.phGH.R | 54ecfc500db0caf78dc490f0245fd6a7c5f4429e | [] | no_license | drizopoulos/JM | 5ff1c6f14fb5fd1dbe582ac4fa5375c3bc3e7182 | da53677e5b7b823a75c8f5f3a30738a84778f096 | refs/heads/master | 2022-08-17T07:44:41.133947 | 2022-08-08T13:00:20 | 2022-08-08T13:00:20 | 43,431,362 | 33 | 6 | null | 2018-02-12T19:14:27 | 2015-09-30T12:23:35 | R | UTF-8 | R | false | false | 1,592 | r | LogLik.phGH.R | LogLik.phGH <-
function (thetas, lambda0) {
betas <- thetas[1:ncx]
sigma <- exp(thetas[ncx + 1])
gammas <- thetas[seq(ncx + 2, ncx + 1 + ncww)]
alpha <- thetas[ncx + ncww + 2]
D <- thetas[seq(ncx + ncww + 3, length(thetas))]
D <- if (diag.D) exp(D) else chol.transf(D)
# linear predictors
... |
7dbdb426655ea54a51669ccd56d7ab759fb6a169 | b97ac4bc1929654b92a4c1dbadc2bbe821cfef2a | /gerFc2Yknn.R | 10cabec5ddf0c8525cf32f3bc495327face8cb11 | [] | no_license | tambonbon/German-NPFDA | a97c5f4bf857fa9cee64bcc1df24bbd79eb9e693 | 5e3c358be8b5a7f3e1ca4c78d6774800a95b7023 | refs/heads/master | 2020-06-13T16:49:42.015821 | 2019-07-01T17:57:26 | 2019-07-01T17:57:26 | 194,718,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,576 | r | gerFc2Yknn.R | s2 = 0
gerFc2y.past.learn = germanyFc2y[1:35,]
gerFc2y.past.testing = germanyFc2y[36:41,]
yearEndTest = germanyFc2y[41,]
tGer2y = t(germanyFc2y)
#for(h in 37:46){
for(s in 1:12){
gerFc2y.futur.s = #pred.reg2y
germanyFc2y[39:40,s]
result.pred2y.step.s = funopare.knn(gerFc2y.futur.s,gerFc2y.pa... |
2d3502fe6be230012f267733848d095c8afddb88 | e8667680eba33a0fd63adca9354a7cbead2aa2fd | /man/labelFigure.Rd | cc6eef4e10110413e4bac618f1c8e4b4b8da28ba | [] | no_license | mkoohafkan/figr | 5a831d6dbca923296d94b91e7a39d9effeddd4ef | 1372e1d5b6d632a99e8694ac6c99e4880b862980 | refs/heads/master | 2021-01-17T16:35:03.174702 | 2015-01-17T10:47:31 | 2015-01-17T10:47:31 | 20,353,589 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 462 | rd | labelFigure.Rd | \name{labelFigure}
\alias{labelFigure}
\title{Figure caption}
\description{Place a figure caption}
\usage{labelFigure(key, link, punc)}
\arguments{
\item{key}{identifier for the figure being referenced}
\item{link}{logical: If TRUE, relative link to figure is included}
\item{punc}{separator to use... |
ab725a4c4de1ca0ead310099795d750d61413c6b | b2581e89c08a72208beb7cd01d1d5b3514c0501b | /server.R | 148cf2f8e451050bdbcade8e4bbbbe64ef2fa7f6 | [] | no_license | djdhiraj/Shiny_R | f1fe9cd6d7b262667eb82118cc52592a516a7bb8 | cef5527f3ee41cb4c8032e10171d04ea9c134b1a | refs/heads/master | 2020-04-10T18:02:27.213853 | 2018-12-13T11:05:50 | 2018-12-13T11:05:50 | 161,192,162 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 187 | r | server.R | library(UsingR)
data("mtcars")
shinyServer(
funtion(input,output){
output$newHist<-renderPlot(
{
hist(mtcars$hp,xlab = 'cild height',main = 'hist')
}
)
}
) |
c0e42678681540d3dcce90b39689c9ac788771cc | 1fd16e1779896a3306c11dc36b088acefa179349 | /R/post_stratified_ITS.R | f367f8260ef14885ef35568dc22d5ab6cc651f4e | [] | no_license | cran/simITS | d57144379ad2d68beab5426799b2791c8f16ae5f | 9aa0fd95d9ee8957f0a62203e7bd0f40b7ba4b5b | refs/heads/master | 2022-07-21T19:34:19.293734 | 2020-05-20T12:50:02 | 2020-05-20T12:50:02 | 259,620,598 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,288 | r | post_stratified_ITS.R |
##
## Post-stratified ITS estimator code
##
#' Calculate proportion of subgroups across time
#'
#' Calculate overall proportion of cases in each group that lie within a given
#' interval of time defined by t_min and t_max.
#'
#' @inheritParams aggregate_data
#' @param t_min The start month to aggregate cases over.... |
8f76b992fe73712f4c2f82421374048a54930b9e | 3efe9a7e267d864bc03f560a5475d0a93020b6b5 | /Tasks Collaboration/Nadiia/EDA RQ1.R | 1c18975c9f2021f1b66244f0503b034024ae32a6 | [
"MIT"
] | permissive | dgquintero/Data-Science-with-R | 2230a678ccdd11815a1fb8b6af33869b8f05ccbc | 658a1889fe6865d590b02c13bc5c63ce13c4c21a | refs/heads/master | 2020-05-16T14:31:19.122168 | 2019-01-15T16:49:30 | 2019-01-15T16:49:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,148 | r | EDA RQ1.R | library(dplyr)
library(tidyverse)
library(ggplot2)
library(modelr)
library(gridExtra)
#Splitting the data
get_slice_for_shop <- function(col1, col2){
shop_slice <- shop_ordered_slice3_ext[,col1:col2]
colnames(shop_slice) <- c("distance","price")
return(shop_slice)
}
shop_1_data <- get_slice_for_shop(1,2)
shop_2... |
f72a9a955ca4b0bc844fbf2f09efeed5922898b3 | 799bcc71f47ce548abc0b48b3b2c320017e5d8a1 | /Swirl/Variance_Inflation_Factors.R | af91a40a13441b80e3b6531377ad236e5b20b7a3 | [] | no_license | SivaguruB/Coursera-Regression-Models | add629b508d4bd276fb1858f6bf51d02a0f75365 | 7f9eaec79402517f03b161da1996d6695c24a0d2 | refs/heads/master | 2021-01-10T15:39:19.393801 | 2015-11-01T06:28:36 | 2015-11-01T06:28:36 | 43,863,009 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,803 | r | Variance_Inflation_Factors.R | #Variance_Inflation_Factors
#SivaguruB
- Class: text
Output: "Variance Inflation Factors. (Slides for this and other Data Science courses may be found at github https://github.com/DataScienceSpecialization/courses. If you care to use them, they must be downloaded as a zip file and viewed locally. This lesson corresp... |
8c032a3cfc475eedf712814b86219a28640520c5 | e42e48b9bbfd879f794ec28c803a93b2081fa804 | /tests/testthat/test_input_is_legal.R | e743a479827ab4f970744551e94800d681b9cd6f | [] | no_license | Puriney/knnsmoother | 62c32c01c28448b07153cc269e1b4167a9478d8a | 0408052c758d6660f5cd4d489208ebf79179bf95 | refs/heads/master | 2021-09-04T10:13:40.047598 | 2018-01-09T20:39:02 | 2018-01-09T20:39:02 | 116,868,014 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 43 | r | test_input_is_legal.R | library("testthat")
library("knnsmoother")
|
1231e5c09527dcb2c52ba6d0169c648b0138eab3 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /irt/man/get_cat_administered_items.Rd | 9c4c258e82233e264d85583929f3da39a26c22bd | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,078 | rd | get_cat_administered_items.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cat_sim_helper_functions.R
\name{get_cat_administered_items}
\alias{get_cat_administered_items}
\title{Get administered items from a CAT output}
\usage{
get_cat_administered_items(cat_sim_output)
}
\arguments{
\item{cat_sim_output}{This is a ... |
96c3b5531eb087c69f11c32904a7a05ce5394ea7 | b3bf7b8c56b2f3e8d8594cccce6f65981c9514e5 | /man/plot_cross_anova.Rd | 2b9daf9d3fac7b7f98842072ed29225a01031ac8 | [] | no_license | faustovrz/bugcount | 055ee388bcf9049e5d01cf3ad19898220f7787a2 | f3fbb7e9ed5cecae78fdfaa1035e2a87e072be2d | refs/heads/master | 2021-03-27T15:43:12.992541 | 2018-05-04T22:17:49 | 2018-05-04T22:17:49 | 104,142,648 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 584 | rd | plot_cross_anova.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_cross_anova .R
\name{plot_cross_anova}
\alias{plot_cross_anova}
\title{Plot ANOVA per cross}
\usage{
plot_cross_anova(posthoc, exp_allowed)
}
\arguments{
\item{exp_allowed}{list of vectors containing indices of experiments
to merge in or... |
fa6808f55e39c05f056735ddb0739b12551ecbdc | f134d9004a920a17eb1f092276b68f8721afae10 | /machine_learning/classifier/plot_evaluation.R | 17146953fff3b86137ff73bec9fbab7213290a63 | [] | no_license | jw44lavo/redux | 8c848198b2d8d298e45ecefb65e6c67c42b74ff6 | c14d4d3a8e0e6a9cd9bceb7611c09a37801476a3 | refs/heads/main | 2023-02-23T19:11:12.595226 | 2021-01-27T09:03:11 | 2021-01-27T09:03:11 | 315,052,059 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,683 | r | plot_evaluation.R | library(ggplot2)
library(readr)
library(reshape2)
theme_set(
theme_bw() +
theme(legend.position = "top", legend.justification = "left")
#theme(legend.position = "none")
)
height <- 8
width <- 10
font <- 20
dpi <- 300
args = commandArgs(trailingOnly=TRUE)
data <- read_csv(args[1])
data <- transform(d... |
d6e5a10ac00d528f5448870141645ff8f2f86f03 | 3ffbe0efc8c9de1eed0981188a1bbb6a52506369 | /R/igraphbook.R | 7a2661c2b435e7a894411544a9c52bb1788af0be | [] | no_license | 2wedfrt/igraphbook | 06b0b9855085afdf8979b621ad2654cdd8e2198a | 30e51a774efedf32162f157708819f951f0ff0d2 | refs/heads/master | 2022-04-07T10:48:26.802237 | 2014-12-29T20:00:53 | 2014-12-29T20:00:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 89 | r | igraphbook.R |
#' Companion package to the igraph book
#'
#' @docType package
#' @name igraphbook
NULL
|
3bcd882603bf469ff00342bc67a725bf52d9c276 | 4b7e815c41c1cc7b9e0dca42250e41dfd95e9b1d | /ggThemeAssist.R | 1fc984b068896128624365addb2f47d4f191ed9c | [] | no_license | gonggaobushang/R-Visualization | 93a2fa5a705aaf25498cd6b6e86c8d64a5204ad9 | 44cce4b1fef7d50c15e7d9a6458cd3c70580204f | refs/heads/master | 2020-06-03T20:37:03.943939 | 2020-01-15T06:35:33 | 2020-01-15T06:35:33 | 191,722,173 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 281 | r | ggThemeAssist.R | #用鼠标选择ggplot2的框架
#返回代码
library(ggplot2)
library(ggThemeAssist)
gg <- ggplot(mtcars, aes(x = hp, y = mpg, colour = as.factor(cyl))) + geom_point()
ggThemeAssistGadget(gg)
#右上角的Done,自动输出代码
#中文最好要在代码输出后手动改 |
9ed69074764aa95816b577d1a93617d5030a25ea | 56f4370ece1d225c094692d9b96ee52f07493853 | /simulation_script_3.R | 9c0acd01d1f331c8c1f0e87031387b06c53beb50 | [] | no_license | jrebelo27/Simulation-code-of-persistence | dd3f90efaf33b798e1fd2d89ecead9f63f257092 | 5f6871207d8611343c0650e0d84c1fe7acf2f59d | refs/heads/main | 2023-01-15T10:00:09.124776 | 2020-11-12T23:35:48 | 2020-11-12T23:35:48 | 312,245,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,463 | r | simulation_script_3.R | ###################################################################################
#Persistent population decay according to a power law
#Persister cells do not leave the dormant state as soon as the medium becomes detoxified
###################################################################################
#... |
3ab9fb43173c279a2b0fe20c6f572ebf80456d31 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Ecfun/examples/compareLengths.Rd.R | bf1c2c68a477e71fb72ea158135126447d74adb5 | [] | 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 | 1,612 | r | compareLengths.Rd.R | library(Ecfun)
### Name: compareLengths
### Title: Compare the lengths of two objects
### Aliases: compareLengths
### Keywords: manip
### ** Examples
##
## 1. equal
##
## Don't show:
stopifnot(
## End(Don't show)
all.equal(compareLengths(1:3, 4:6), c("equal", ''))
## Don't show:
)
## End(Don't show)
##
## 2. ... |
04adbc5032a27bb99d3871b6f6cce3acdf5a5ebf | aa78d24940b7d36d7e3a6e1a6e2241b8c20cc5ae | /R/作图基础/pie.R | 225f805d819e08f60b1780b6c1823d3edad5a2f6 | [] | no_license | bancage/program | 4b94af6663cfc537897730cbaab34f4db86a1c58 | 73b6d41d0a7570057dbe877fa82ed5767971ca78 | refs/heads/master | 2021-01-01T19:42:50.132850 | 2013-10-19T13:41:32 | 2013-10-19T13:41:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 381 | r | pie.R | flowers<-c(12,8,6,20,16)
fruits<-c(7,11,17,9,5)
flowers_labels<-round(flowers/sum(flowers)*100,1)
flowers_labels<-paste(flowers_labels,"%",sep="")
#colors<-c("white","grey70","grey90","grey50","black")
colors<-rainbow(length(flowers))
pie(flowers,main="Flowers",col=colors,labels=flowers_labels,cex=0.8)
legend(1.... |
8b111947419509e8c207fdc0fd269bc4e5cb4fc0 | 63d8191d625544acbca97a939d093e61db88a525 | /R/package.R | ed748e64bdaad14b120eecd2d9a67935214755b3 | [] | no_license | jamovi/jmvcore | 80186e1c322024b455bfcacc2b9a4be40cf1534f | 057c97aae936a53981c422891f75b3936a5f463b | refs/heads/master | 2023-08-16T17:34:01.612300 | 2023-08-07T10:38:32 | 2023-08-14T08:14:15 | 70,128,789 | 5 | 8 | null | 2023-08-04T13:31:29 | 2016-10-06T06:33:43 | R | UTF-8 | R | false | false | 20 | r | package.R |
#' @import R6
NULL
|
74341d456408bfbea7702897bc8c6d041300ca71 | 4efbe1d55ea0f650168d1323d1813ea0eaa2ca8b | /misc/boston/bostmoregamfit_int.r | b5ab43b3af7d6b862a3b251cdc4cf478d75816e4 | [] | no_license | panders225/semiparametric-regression | d8e7470576f7d3fd988ba7e677eaceed3216caea | 1fd5a22f4283daf856aad61af2f4abccfa4fc324 | refs/heads/master | 2021-05-11T08:32:43.127850 | 2018-04-24T00:55:53 | 2018-04-24T00:55:53 | 118,055,099 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,831 | r | bostmoregamfit_int.r | ###########################################################
# Clear the workspace. I do this every time
###########################################################
rm(list = ls())
###########################################################
# Set the seed. I also do this every time
################################... |
ec077871f761762bf078ff5d7d3def00a64cbf8b | 190d51eb26b920930ed7033e7dae7f3f1f862733 | /R/updateUKweatherData.R | 278893d9cbcef1b1084186f528d0d1636352b9e9 | [] | no_license | harrysouthworth/mojito | 4415c5c881d4c8c9523dcd92043390cfcd52d811 | 8ff57e1a5316849c9565565a0786aa0a438c37cc | refs/heads/master | 2021-06-27T00:29:25.095510 | 2020-10-13T11:20:44 | 2020-10-13T11:20:44 | 155,350,671 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,613 | r | updateUKweatherData.R | #' Read data obtained from UK Historic Weather Station Data and AZN price data and create data objects
#' @aliases updateAZN
#' @details Use \code{tail(eksdalemuir)} and \code{tail(newtonrigg)} to find
#' out if it worked. You might want to delete the .txt files once you're done.
#' These functions are intended for use... |
5cda80b7645749503d1116a971876fd8b2637343 | 4d2c2e6c274dc94b9b418fbf397cbb5bd06a23ed | /pbdr/tutorial3/tutorial3/11-npbs_for.r | fbe4ad23be8b26017b4774a619bc2fa95b47295c | [] | no_license | snoweye/snoweye.github.io | 5991f8d533e0a0b1428f1c179768925a21bb47b9 | e5d35e49aca7520f97d0719c829df4fc11ff63e1 | refs/heads/master | 2023-07-22T13:35:24.683834 | 2023-07-06T22:09:43 | 2023-07-06T22:09:43 | 64,176,642 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 901 | r | 11-npbs_for.r | ### Non-parametric bootstrap for the confidence interval for the
### GLMM model of Deer Ecervi L1 data set.
rm(list = ls())
source("u0-deer.r") # Load data.
source("u1-npbs.r") # Load library and utility.
### Bootstrap.
n.boot <- 100
set.seed(1234)
ret.sigma <- NULL
ret.a.i <- NULL
for(i.boot in 1:n.boot){
da.new ... |
4d910277bde3407023f7060b357f1b87acbba45c | e9a43e6e25f143ecd59dc17e59158656fd34fb5c | /batch_correction/batch_correction_source.R | a88851d50792323f559ddece3d110bc5ccb649c6 | [
"CC0-1.0"
] | permissive | LeahBriscoe/microbatch_vc | aed3a9bcfc15bc73f4be6e9ac99518f7ab9d3af5 | 1c609724fd9c8efb77a2caf4fbdcc7e57f221aae | refs/heads/master | 2023-06-18T09:37:31.882697 | 2021-07-22T17:38:39 | 2021-07-22T17:38:39 | 233,694,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,742 | r | batch_correction_source.R |
run_ComBat <- function(mat, batch_labels,model_matrix=NULL){
require(sva)
#mat <- data$df_otu_corrected
#range(mat)
# make continuous
combat_predata = mat #log(mat + 1) #mat #
input = ComBat( dat=combat_predata, batch = batch_labels,mod = model_matrix)
return(input)
}
run_ComBat_mle <- function(m... |
0908f2664a7222663c2b4faf16f5298a4085ff7a | 12a8fd342e353257e8f73e9b44ca3cf25ccbbeb0 | /data-BigData/Q56-to-CSV.R | ece2d0ac137b03acfb3f8c5533885cd32869bf9f | [] | no_license | Kundjanasith/data-mini1 | e0d5746c6005ceb1fd6033a311f99b4b1285d308 | 38636aecdba22b60f7bd394d88ffb0503ba82d6e | refs/heads/master | 2020-12-25T15:39:39.608498 | 2016-07-09T17:41:20 | 2016-07-09T17:41:20 | 62,961,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 656 | r | Q56-to-CSV.R |
plotEiei <- function(x){
print("eiei")
print(x['ID'])
}
file <- read.csv('Q56.csv')
res <- c()
for( x in unlist(unique(file['ID']))){
#print(x)
data <- subset(file, ID==x)
ungroupdata <- data[,c("ID","EnrolledYear","Semester","GPA","X2556_GPAX","entry2553_GPAX")]
groupdata <- unique(ungroupdata)
#name... |
9e0fd612e8eddc627b4a9da22520ec837a6c7261 | 5ce3329a63d78e2a86053fd47d9c0fd637cd294d | /plot2.R | d3d958a6ba824868293126717a61880367e711b3 | [] | no_license | sjjbryant/ExData_Plotting1 | b7cff92d8ea1d6aa0f9f69f15e85913676059168 | 9e4f10dfc31abd725b4acb3363e5663281b2315e | refs/heads/master | 2021-01-18T05:34:17.129662 | 2015-08-09T21:01:27 | 2015-08-09T21:01:27 | 40,444,607 | 0 | 0 | null | 2015-08-09T17:31:10 | 2015-08-09T17:31:09 | null | UTF-8 | R | false | false | 805 | r | plot2.R | # read the data into R
energyTable <- read.table("C:\\Users\\Steve\\Documents\\Coursera\\Data Science\\Exploratory Data Analysis\\Week 1\\household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?")
# Covert the Dates
energyTable$Date <- as.Date(energyTable$Date, format = "%d/%m/%Y")
# Select only d... |
36a03fc3cde55f92ce51c5b0e54d9e7086b5ec73 | c6b261da0a15eae8fa89234ce4cad2cbcce93a28 | /.Rprofile | 8d5c56d3a4933f95be5708da8e7b93fcaaecaa66 | [] | no_license | optionalg/dotfiles-60 | e9a1a95e7f8c5afa39ad52874cd44d8fc0e01480 | bdcfe89f25600c5ae449fff0d39dad496fd63f86 | refs/heads/master | 2020-04-01T17:43:05.520301 | 2016-05-25T03:26:56 | 2016-05-25T03:26:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 809 | rprofile | .Rprofile | .First <- function(){
options(
repos = c(CRAN = "https://cran.rstudio.com"),
browserNLdisabled = TRUE,
deparse.max.lines = 2,
help_type = "html",
max.print = 200,
repr.plot.width = 5,
repr.plot.height = 5,
repr.plot.res = 100,
repr.plot.pointsi... |
31edf468ac655fdcd839d0a6a07372604134975a | 02372910c089fad695614717723197e7cc2ac29b | /man/assocTestAggregate.Rd | 0fd84d3d2f7607f0d24d49eb82fa69ebb868215b | [] | no_license | UW-GAC/genesis2 | d1235ce86ea6dc4aff1492d7dc3ce9bb924f530b | 913ad1b84b10c4fa5c9cdd818952dcb17830c2d7 | refs/heads/master | 2021-01-20T11:34:56.788698 | 2018-03-26T17:05:10 | 2018-03-26T17:05:10 | 101,669,905 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,709 | rd | assocTestAggregate.Rd | \name{assocTestAggregate}
\alias{assocTestAggregate}
\alias{assocTestAggregate-methods}
\alias{assocTestAggregate,SeqVarIterator-method}
\title{Aggregate Association Testing}
\description{\code{assocTestAggregate} performs aggregate association tests using the null model fit with \code{\link{fitNullModel}}.}
\usage{
... |
64d9ab8eb03940e5f0fc39643efda6659ea3e551 | 9b72de4f01c77b92ef23cf0433d7f806802bb419 | /SPOJ/ONP_Transform the Expression/Transform the Expression.R | 6c23d125e3aa24192cc8cba8dcc71748c47cebd7 | [] | no_license | GitPistachio/Competitive-programming | ddffdbc447669a2f8ade6118dfe4981bae948669 | f8a73f5152b2016b1603a64b7037602d2ab2c06e | refs/heads/master | 2023-05-01T20:55:18.808645 | 2023-04-21T20:45:08 | 2023-04-21T20:45:08 | 167,733,575 | 8 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,317 | r | Transform the Expression.R | # Project name : SPOJ: Transform the expression
# Author : Wojciech Raszka
# Date created : 2019-02-10
# Description :
# Status : Accepted (23205239)
# Comment :
PNToRPN <- function(exp){
cout <- character(length(exp))
s <- character(length(exp))
j = 1
i = 1
for (e in unlist(strsplit(exp, ... |
c08c7f7842fb63af276a1aceb3672ccb6b13832a | 6cd4cab6f8d181bf19cba90f7ef9c005f9cc016c | /man/install.pkg.Rd | 2a8802aa2af3f278e2bcdd7e0935a5af0bd228ab | [] | no_license | mbojan/mvbutils | 30dbce0664b15d184aa8174bd252fa8bd28941dd | 1c4e7fef56ed27b18a1d01d35cfcf5dbcdfc3098 | refs/heads/master | 2021-01-01T04:55:39.056195 | 2012-02-22T00:00:00 | 2012-02-22T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,573 | rd | install.pkg.Rd | \name{install.pkg}
\alias{install.pkg}
\alias{build.pkg}
\alias{build.pkg.binary}
\alias{check.pkg}
\alias{cull.old.builds}
\alias{set.rcmd.vars}
\title{Package building, distributing, and checking}
\description{These are convenient wrappers for R{}'s package creation and installation tools. They are designed t... |
a522f5a7bc9e1fd19b7cd2cae0446ec9ff62a594 | 29585dff702209dd446c0ab52ceea046c58e384e | /kernDeepStackNet/R/KDSNdirectOpt.R | c13562ec2d1b20995ebec0544637aa37a48797bd | [] | 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 | 18,761 | r | KDSNdirectOpt.R | # General sequential one dimensional optimization of f(x), x \in R^d, f(x) \in R
optimize1dMulti <- function (f_input, interval_matrix, maxRuns=3, repetitions=5,
tol_input=.Machine$double.eps^0.25, x_0=NULL, addInfo=TRUE,
nCores=1, envir=parent.frame(), dire... |
a18bd0617ed982ba5b3bcbd5e55b0161147d6471 | d5bc5d4334969fec87fb2dd85ee03a70f4864caf | /man/ROIVec-class.Rd | 3838e88a1982dc8c26910b5c89ff0d14dd4403c5 | [
"MIT"
] | permissive | bbuchsbaum/neuroim2 | 46e14e10efb2b992e78741b0aa6737bde6971e33 | 2184bfafd75ab51e0c9d67db83076b08bbf3f40b | refs/heads/master | 2023-05-03T11:19:36.844512 | 2023-04-26T00:06:50 | 2023-04-26T00:06:50 | 109,069,233 | 6 | 0 | null | null | null | null | UTF-8 | R | false | true | 967 | rd | ROIVec-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/all_class.R
\docType{class}
\name{ROIVec-class}
\alias{ROIVec-class}
\title{ROIVec}
\description{
A class representing a vector-valued volumetric region of interest (ROI) in a brain image.
}
\section{Slots}{
\describe{
\item{\code{coords}}{A... |
7464a0a8b75952c7f8bed81c8634859b73456d87 | 77572ab0628f675213204e505599a068375da215 | /R/tumor_microenvironment_ui.R | 0f4508f8e7ea700a80b2358fd4608b41a40393dc | [
"MIT"
] | permissive | CRI-iAtlas/iatlas-app | a61d408e504b00126796b9b18132462c5da28355 | 500c31d11dd60110ca70bdc019b599286f695ed5 | refs/heads/staging | 2023-08-23T11:09:16.183823 | 2023-03-20T21:57:41 | 2023-03-20T21:57:41 | 236,083,844 | 10 | 3 | NOASSERTION | 2023-03-21T21:27:26 | 2020-01-24T21:07:54 | R | UTF-8 | R | false | false | 578 | r | tumor_microenvironment_ui.R | tumor_microenvironment_ui <- function(id) {
ns <- shiny::NS(id)
shiny::tagList(
iatlas.modules::titleBox("iAtlas Explorer — Tumor Microenvironment"),
iatlas.modules::textBox(
width = 12,
shiny::includeMarkdown(get_markdown_path("tumor_microenvironment"))
),
iatlas.modules::sectionBox(
... |
80e89e187407d6849e05056edfae907cfa720307 | 7e5c434468d8f97cc4cee95c467967280064c48e | /analyses/Linear/05-convergence-linear.R | ce9283cbb0d4d52e8d17c6aedc5c2ed5e9472010 | [
"MIT"
] | permissive | nhcooper123/NATRICINE-ecomorph | d9fce11d79721ee2aa14f9f997d174598f08076a | 54e67f4a112cc6a7f84945354d3ef870a53ada3e | refs/heads/master | 2023-04-14T23:06:19.814253 | 2022-11-23T16:52:49 | 2022-11-23T16:52:49 | 299,321,129 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,811 | r | 05-convergence-linear.R | # Phylogenetic convergence testing for linear data
# Stayton's distance-based metric
# Modified 2022
#-------------------------------------
# Load libraries
library(ape)
library(geomorph)
library(geiger)
library(phytools)
library(convevol)
library(tidyverse)
#-----------------------------------------------------------... |
19b4bbbd32cf5b7b612e2c400e134ef84f8cc08b | a6566ebc69ed5e7a17e2091bdb10e7b6523eefc9 | /R/test1.r | 68927dd2c00902acf997074c88ef637bd089e42d | [
"MIT"
] | permissive | mabotech/mabo.task | 916e71650b45a24bb3852206a3755a7fd0342e47 | 96752a5ae94349a46e3b6f9369cc0933d5e37be0 | refs/heads/master | 2020-06-05T13:05:02.768838 | 2015-11-29T08:18:10 | 2015-11-29T08:18:10 | 23,750,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 428 | r | test1.r | DF <- read.table(text="Rank F1 F2 F3
1 500 250 50
2 400 100 30
3 300 155 100
4 200 90 10
5 300 100 20", header=TRUE)
print(DF)
library(reshape2)
DF1 <- melt(DF, id.var="Rank")
print(DF1)
library(ggplot2)
ggplot(DF1, aes(x = Rank, y = value, fill = variable)) + ... |
dc317fed63d8c549391cff901d69eaa9e1ff0605 | 1b90387128040d64490c3a27d5423e4c0bf4f0e9 | /R/R Test.r | a71f8a1aaf4e263ea5a3e50cfc070f2db3f9e66a | [] | no_license | michalmar/azure-databricks-projects | 2f30f1228478d3bd5d977aceea684b3a1e6f6fe1 | cc129912e22171f680a8cd18e8e816257035a7f3 | refs/heads/master | 2023-01-23T02:30:23.774756 | 2020-11-18T09:26:20 | 2020-11-18T09:26:20 | 299,838,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,046 | r | R Test.r | # Databricks notebook source
candidates <- c( Sys.getenv("R_PROFILE"),
file.path(Sys.getenv("R_HOME"), "etc", "Rprofile.site"),
Sys.getenv("R_PROFILE_USER"),
file.path(getwd(), ".Rprofile") )
Filter(file.exists, candidates)
# COMMAND ----------
# MAGIC %sh
# MAGIC c... |
147093f231e70af97510df84da2b9d48e078ca61 | bc113c18c979f88158d1e2557efb81bf01f44e35 | /R/ml_mapping_tables.R | c9d06c04fdc2670b593e2b0311efacb6838b212c | [
"Apache-2.0"
] | permissive | awblocker/sparklyr | 6524ce9ac1d9c24392cd9e179ca8836851d3c93f | 18d9df6a6755f8bd10f81721e71c4f818a115084 | refs/heads/master | 2020-04-08T09:27:47.362403 | 2018-11-26T20:02:49 | 2018-11-26T20:02:49 | 159,225,148 | 0 | 0 | Apache-2.0 | 2018-11-26T20:03:28 | 2018-11-26T19:59:20 | R | UTF-8 | R | false | false | 11,628 | r | ml_mapping_tables.R | ml_create_mapping_tables <- function() { # nocov start
param_mapping_list <-
list("input_col" = "inputCol",
"output_col" = "outputCol",
# ml_logistic_regression
"elastic_net_param" = "elasticNetParam",
"features_col" = "featuresCol",
"fit_intercept" = "fitIntercept",
... |
43b84edcf83b60bd8b208295996c3406eaf0e22e | a348c7dab03439d4a07c93be85ab292faf67bc8d | /inst/tests/test_utils.R | ca8380cd6bd4cba72e462bf3227a3e162827cf9e | [] | no_license | jasonzou/papaja | 9aab91b0582a10232a2524c85ed9398d4f73c9f7 | 5d810c548560f31ced7a7712cb1858196c9c526d | refs/heads/master | 2020-12-11T03:27:52.941586 | 2016-02-02T16:45:47 | 2016-02-02T16:45:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,557 | r | test_utils.R | context("Utility functions")
test_that(
"convert_stat_name()"
, {
chis <- c(
convert_stat_name("X-squared")
, convert_stat_name("Chi-squared")
, convert_stat_name("chi-squared")
, convert_stat_name("X^2")
)
expect_that(chis, equals(rep("\\chi^2", length(chis))))
expect_that... |
3981ddc63232671039f8cbe1406e28c6b92cf89c | 4f74e0dfdfce3c41c85b457cf21f10c25b0bcea8 | /WScraping/clean_df.R | d48ada45f013a456b56b72fa8b81cf30e4c25156 | [
"CC-BY-NC-SA-4.0",
"MIT"
] | permissive | anahubel/WScrapingR | c77c87f2da6f1f86b71e27fc978e1430b5e5d051 | d6c8aa02e9b7086ebf5f61e3c71fd9251090946e | refs/heads/main | 2023-01-13T15:24:12.737204 | 2020-11-08T23:18:08 | 2020-11-08T23:18:08 | 310,109,840 | 0 | 0 | MIT | 2020-11-04T20:24:48 | 2020-11-04T20:24:47 | null | UTF-8 | R | false | false | 151 | r | clean_df.R | # Este fichero cargará el dataframe generado por el script "scrap" y lo limpiará.
# Además, generará el csv final listo para la entrega y análisis |
44263e948b5385cfe30db0689721b3b8f6dc8088 | da4f93c6da85ecdf2a3b9936fa72f26982e04875 | /R/blockedReactions.R | 1879c829112e56a98c038cc9710d23dcc1a580ea | [] | no_license | gibbslab/g2f | 11d052b443be50bd7b7ec4a862cff3d99186c3ef | c5f42de12a7e67af1ea83a38e2d9b4c96519de5d | refs/heads/master | 2021-01-16T21:38:43.440135 | 2020-12-05T06:41:27 | 2020-12-05T06:41:27 | 54,038,847 | 7 | 5 | null | 2020-12-05T06:41:28 | 2016-03-16T14:28:15 | R | UTF-8 | R | false | false | 1,561 | r | blockedReactions.R | #' @export blockedReactions
#' @importFrom "sybil" "optimizeProb"
#' @author Andres Pinzon-Velasco <ampinzonv@unal.edu.co> - Mantainer: Daniel Camilo Osorio <dcosorioh@unal.edu.co>
#' @title Identify blocked reactions in a metabolic network
# Bioinformatics and Systems Biology Lab | Universidad Nacional de Colomb... |
e0fb76c56cff07aa065aa726379a0a1297b24af2 | 71db4a78c8a989b58a0d839a77d58d1774dbec5f | /Code/R/Monte Carlo Integration.R | 0753d6baf24863b3296e3a67211ed66981c1724e | [] | no_license | saulmoore1/MSc_CMEE | 906a7bdf09528c39c0daf6e37f2d722b8ad7bd3d | 5bfd0a5f696c59a092aa9df5536169d905d7ab69 | refs/heads/master | 2022-04-30T20:14:59.660442 | 2022-03-30T11:28:15 | 2022-03-30T11:28:15 | 158,312,708 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,798 | r | Monte Carlo Integration.R | #!/usr/bin/env R
###############################################
# Day 2 - Monte Carlo integration & Gene drive
###############################################
x <- runif(20000,0,1) # Sample 20000 numbers from a random uniform distribution of 0 and 1's
mean(sqrt(1-x^2))
pi/4 # Roughly equal
x <- rexp(20000,1) # Random... |
6751f8a9c67a7bfc783b93fcfedbd52982603fdd | 376d771270ef5c36d60b9441a76d96d38529dace | /mudel.R | fc1fcaf6f0188bd8ec51612816030993fef09982 | [] | no_license | AndresVork/koroonajaressursid | 823cbb67227c6876aeb6ea200c0a893fc66543d1 | a3e50b111c146def6b467ee61fa421952f9ad6ee | refs/heads/master | 2021-03-21T02:36:52.433897 | 2020-03-16T07:47:06 | 2020-03-16T07:47:06 | 247,256,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,649 | r | mudel.R | #Markovi mudel ressursside planeerimiseks
#TODO! Prognoosida uute juhtude arvu
#logi
#14.03.2020 - Andres
library(dplyr)
#Sisendparameetrid - input parameters
#seisundid
statenames <- c("homecare", "stationarycare", "intensivecare", "recovered", "deaths")
#initial distribution, it should be possible to be updated ... |
1889029a67d8acd8f7d9fd39442df0f9236b4df5 | c838d151d53af5a9be67b4295a5097f1f84d9aa6 | /QTL_mapping/Ha_QTL_deltaACR15_6_perms.R | 6c075c93be1644d144291e988046ca3575686ca5 | [
"MIT"
] | permissive | ytakemon/1415-Col4a5xDO-Project | 7d3a018042ab31217a00f1f1d383a4cbee710faf | 51c73f57b07ac2c340e623de582755aebd180e21 | refs/heads/master | 2023-06-25T18:58:51.670520 | 2021-07-27T20:47:33 | 2021-07-27T20:47:33 | 76,672,535 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,409 | r | Ha_QTL_deltaACR15_6_perms.R | #Plotting QTL of the delta between ACR at 15 and 6WKs.
library(DOQTL)
setwd("/hpcdata/ytakemon/Col4a5xDO")
#load data
load("./GBRS_reconstruction/reconstruct/best.compiled.genoprob/genoprobs/best.genoprobs.192.Rdata")
load("GBRS_reconstruction/reconstruct/best.compiled.genoprob/GM_snps.Rdata")
load("GBRS_reconstructio... |
4c103148e7ff2ea6bbba8819c0d3c06cdb8416e9 | 5e42a668e417fd55fe28ecee719c759016f963b9 | /tests/testthat/dummy_projects/project/one_start_no_end.R | 0e90c45db425ed8d4f450783257cda5a15a29ab0 | [
"MIT"
] | permissive | cordis-dev/lintr | 2120e22820e8499ca3066fa911572fd89c49d300 | cb694d5e4da927f56c88fa5d8972594a907be59a | refs/heads/main | 2023-08-05T08:50:42.679421 | 2023-07-25T13:21:29 | 2023-07-25T13:21:29 | 225,583,354 | 0 | 0 | NOASSERTION | 2019-12-03T09:41:30 | 2019-12-03T09:41:30 | null | UTF-8 | R | false | false | 24 | r | one_start_no_end.R |
#nolint start
c(1,2)
|
e4ea55044007ef7f0dc37968c8edd3700b2d9c59 | dbcfe68342837b889194a14e9da8173d0415be41 | /man/set_size.Rd | 2c117e554890e4f37d70a739e5c3757b15ed3626 | [] | no_license | zachcp/depict | 3d35bcb10e60d776d183ab60de7bbdb7e1f153c2 | e1f0de362f66ad6a89aa1a6e209c1fbc7d742e65 | refs/heads/master | 2022-11-28T14:16:08.744106 | 2022-03-14T15:11:12 | 2022-03-14T15:11:12 | 78,388,052 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 244 | rd | set_size.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/depict.R
\name{set_size}
\alias{set_size}
\title{set_size}
\usage{
set_size(dg, width, height)
}
\arguments{
\item{a}{Depiction Generator}
}
\description{
set_size
}
|
6c1700cf2e8b5da914ea3e497f23a88997dce075 | bce8156a9e5b39f17f5c4f6fcd4c9fbff4d74897 | /R/utils-methods.R | bd916189810869c6d1f42f125141a28fc4a53bab | [] | no_license | cran/fPortfolio | fb8f26496a32fd8712361a20cbb325c0bfcffe01 | d0189fabdf712c043fb13feb80f47696ac645cef | refs/heads/master | 2023-04-29T14:30:55.700486 | 2023-04-25T06:50:06 | 2023-04-25T06:50:06 | 17,695,954 | 10 | 10 | null | 2015-04-23T18:15:24 | 2014-03-13T04:38:33 | R | UTF-8 | R | false | false | 1,703 | r | utils-methods.R |
# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be use... |
25a5132d77f515b84eccf39328cb8f173c0f30ff | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.security.identity/man/guardduty_get_findings_statistics.Rd | 5f6c9e8dd15bc128e8c94b100b6673ae0dcc2f27 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 912 | rd | guardduty_get_findings_statistics.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/guardduty_operations.R
\name{guardduty_get_findings_statistics}
\alias{guardduty_get_findings_statistics}
\title{Lists Amazon GuardDuty findings statistics for the specified detector ID}
\usage{
guardduty_get_findings_statistics(
DetectorId... |
db141c1adddaa358607ae6d2158b5b3a6774d03b | 6df14ca70004ee08676eef709aa7d2c920b45c5d | /final-script.r | f609548b0e5605ddd45dd867864f05e905be0cac | [] | no_license | LarissaHa/aqm18 | 389f2a0e11e83885d551c6450a82347681e44267 | e03cf7fba7b1d6f39cb7be12a3905038d9495cd1 | refs/heads/master | 2020-04-24T23:23:09.854646 | 2019-02-24T13:52:07 | 2019-02-24T13:52:07 | 172,342,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 55,032 | r | final-script.r | ##################################################################
# Local R-File
# Final Submission for AQM
# Larissa Haas
##################################################################
# Note: The plots and long running code snippets are
# commented out. Please consider if it is really
# nece... |
8b2260335fed572e644a94e64951f1131de50fe0 | f13fbc74fb35e2c2efd3bab429880f56f44cfd33 | /R/blast.pdb.R | e9722d3a8d647256f5489cccc39366245fdc83b5 | [] | no_license | thomasp85/pepmaps | 1a05ba4a1df622cdf23faaa40740a5af4f7c3183 | 86e9b952f38fc1ab198eb526a239f7e620679a54 | refs/heads/master | 2020-12-25T19:14:43.684678 | 2013-04-11T06:17:34 | 2013-04-11T06:17:34 | 5,366,935 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,819 | r | blast.pdb.R | # TODO: Add comment
#
# Author: Thomas
###############################################################################
blast.pdb <- function(seq, database="pdb") {
## Run NCBI blastp on a given 'seq' sequence against a given 'database'
if(!is.vector(seq)) {
stop("Input 'seq' should be a single sequence as a si... |
ca25e97910810ec936b635c5f31f73de1389a830 | 56996f680ba915b6033e444c11f51ba883bfbb9a | /Plot2.R | a1e65023a55d5238b9f7ebd9a2fdbf703614bbba | [] | no_license | csmithmeyer/csmithmeyer-ExData_Plotting1 | dfd5dce1a23e76ff5cb59ff5d64aa94fffcd58b3 | a45df4e73debb98f79c2ed1b3c6b9c6966d999e5 | refs/heads/master | 2020-05-07T09:50:16.429954 | 2019-04-09T23:32:46 | 2019-04-09T23:32:46 | 180,393,809 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 517 | r | Plot2.R | setwd("C:/Users/1257256940.MIL/Desktop/Coursera/Plot/")
library(lubridate)
data<-read.table("household_power_consumption.txt",sep = ";", header = T)
data$Date<-as.character(data$Date)
data$Date<-strptime(data$Date,"%d/%m/%Y")
data$Time<-as.character(data$Time)
datetime <- strptime(paste(data$Date, data$Time,... |
9246ec01c637bd681816c93e57043e44596166a1 | 799468ce526db6f14f2aa5003c601e259e5f0d62 | /man/fundamental.matrix.Rd | 344e949572c2cd24853d1b28228c2d58378e3547 | [] | no_license | kostask84/popbio | 6aa45015bfc1659bd97f2ce51ad5246b8d434fac | 682d3ffb922dfab4fd2c7fc7179af2b0d926edfd | refs/heads/master | 2021-05-09T02:01:42.050755 | 2017-02-09T21:44:20 | 2017-02-09T21:44:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,296 | rd | fundamental.matrix.Rd | \name{fundamental.matrix}
\alias{fundamental.matrix}
\title{ Fundamental matrix and age-specific survival }
\description{
Age-specific survival calculations from stage-classified matrices. Includes the mean, variance and coefficient of variation (cv) of the time spent in each stage class and the mean and variance of... |
a5a8eb50d649e8f1da71441217ca4a77ad8ae06b | 4adb21c499e05df30eb4fc64be68a20d746463b7 | /R/run.eqs.r | c09096d110ace9170a758486f2203444753a2a58 | [] | no_license | cran/REQS | e827f7712a169791b8ca6988bd3b5011ae23d83b | 4a686f98c91c80452ce4a9c35030eed8f9f16d2c | refs/heads/master | 2022-10-02T08:38:50.087618 | 2022-09-28T08:34:02 | 2022-09-28T08:34:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | run.eqs.r | run.eqs <- function(EQSpgm, EQSmodel, serial, Rmatrix = NA, datname = NA, LEN = 2000000)
{
res <- call.eqs(EQSpgm = EQSpgm, EQSmodel = EQSmodel, serial = serial, Rmatrix = Rmatrix, datname = datname, LEN = LEN)
if (!res) warning("EQS estimation not successful!")
filedir.split <- strsplit(EQSmodel, "... |
354784a68542ccc081c964f5e5cb5192d1113221 | 01a13a32f517704d75b93a385634736c0ce43d41 | /statistical_test/statistical_test.R | 670858acb043144b2c52699c257f626af77f6782 | [] | no_license | JudithVerstegen/PLUC_Brazil_stoch | 0dd688a70eb45ac666f3e31c2b3f27ed49841591 | 080102c14bb3d288b7136ce6876631948f3f1df0 | refs/heads/master | 2022-02-23T05:54:40.582481 | 2019-09-20T13:51:05 | 2019-09-20T13:51:05 | 175,794,893 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,069 | r | statistical_test.R | # load libs
if(!require(ggplot2)){install.packages("ggplot2")}
if(!require(gridExtra)){install.packages("gridExtra")}
if(!require(stats)){install.packages("stats")}
if(!require(car)){install.packages("car")}
if(!require(plyr)){install.packages("plyr")}
if(!require(scales)){install.packages("scales")}
if(!require(userfr... |
0d40720202512cd9fb514b053a4e753303a75c96 | 87fdff0de23e7df2862d9c4cbcb4486a9ab80026 | /rscripts/final_analysis/finalDendro.R | 958f687304dda7c94be96a548514096e18796aa6 | [] | no_license | jillbo1000/Data_Expo_2018 | 1aa1586237d39d6564a2353210a5805f7e8036b9 | e79a120c014302117ba877f9f945e93970e1a4eb | refs/heads/master | 2020-11-25T00:22:10.267735 | 2020-10-28T14:45:10 | 2020-10-28T14:45:10 | 228,405,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,154 | r | finalDendro.R | library(dplyr)
library(lubridate)
library(tidyr)
library(cluster)
library(fpc)
library(ggplot2)
library(ggmap)
library(fiftystater)
library(maps)
library(RColorBrewer)
library(gridExtra)
library(grid)
library(ggdendro)
cluster2 <- read.csv("../../data/summary_city.csv")
cluster2$state <- as.character(cluster2$state)
#... |
165d1aa15f82d27d88c5c6a7d437f1c101362fd4 | 69b95cdda3e40b11a7b35a929c54b207c483ea33 | /tree compare v4.R | 8081cf4efbfc42c8f506e15e1f04d7e1ba26e357 | [] | no_license | rulofburger/Machine-learning-examples | 408d57eadf4d6e1f0016d156037c4f960fcc90b2 | c2e4af1beb206e28d4b1d5df267cd2c3c1a99fcf | refs/heads/master | 2020-12-02T11:13:56.815559 | 2020-11-03T11:15:02 | 2020-11-03T11:15:02 | 96,615,204 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,368 | r | tree compare v4.R | #Load libraries
library(leaps)
library(glmnet)
library(class)
library(mlbench)
library(caret)
library(car)
library(rattle)
library(party)
library(dummies)
library(haven)
library(foreach)
library(dplyr)
library(rpart)
library(parallel)
library(doParallel)
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_131')
#... |
53060a022d6e94fd02b4942107ecae31a7396e6b | da53bc94f4a868133940aedf6cf89051b5096f0b | /download.monex.R | a6d88decc4400613453153b2dddfbbc6aaf23d3b | [] | no_license | pvillamichel/Shiny-Application-and-Reproducible-Pitch | c86ed82b0888955949d74dd25aee61ac9f0b4854 | 09b53401cdd13dfa2cb4081944e75c73946efd75 | refs/heads/master | 2020-04-15T13:09:56.570244 | 2019-01-08T18:06:22 | 2019-01-08T18:06:22 | 164,705,158 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,230 | r | download.monex.R | download.monex<- function() {
library(XML)
url<-paste("http://indicadoreseconomicos.bccr.fi.cr/indicadoreseconomicos/Cuadros/frmVerCatCuadro.aspx?CodCuadro=770&Idioma=1&FecInicial=2018/12/29&FecFinal=2100/12/31&Exportar=True", sep="")
DATA <- readHTMLTable(url, strings... |
a9adf3bbb663d1ac154c7fc30276a02d33b82ce7 | 82998c05982ed9ec5cf3b640ac9608df31b4cd0e | /psychmeta.R | 0682adb2941776a6d163ffe451a6114a021d36a4 | [] | no_license | YiLalaWang/Meta-analysis-R-codes | 1a65274c705250da0a82dfda8efd831da6cab64a | 5a61dcd80bd20b8ecceae3a15bff36c8e3df3300 | refs/heads/master | 2021-08-07T20:57:02.390721 | 2020-04-12T07:28:00 | 2020-04-12T07:28:00 | 150,800,006 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,449 | r | psychmeta.R | ######################################################################
########### R code for psychometric meta-analysis main effect ########
## R code that contains the function for:
# (1) doing meta-analysis corrected for measurement errors in X & Y and weighted by sample size;
# (a) Individual correc... |
ac72e0e2728d469ff91147cc25531c6fe49414bc | 3ec87311971341154e85a46e79db410b568e7b37 | /R/tskeyvalparser-package.R | cdefeba81c30caa2146b6f32f55c98fd9f223817 | [
"MIT"
] | permissive | ingonader/tskeyvalparser | a4a6209b3339475dec911b7e9d0cc3cbe2f5d6d5 | 3570df87d020d4cd1b3e33600ea8864f5a2897d8 | refs/heads/master | 2020-03-27T15:48:48.166688 | 2018-09-25T16:59:53 | 2018-09-25T16:59:53 | 146,741,116 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 338 | r | tskeyvalparser-package.R | #' \code{tskeyvalparser} package
#'
#' Parses key-value pairs in a time-series text file.
#'
#' @docType package
#' @name tskeyvalparser
#' @importFrom magrittr %>%
#' @keywords internal
"_PACKAGE"
NULL
## quiets concerns of R CMD check re: the .'s that appear in pipelines
if(getRversion() >= "2.15.1") utils::globalVa... |
1d5582138a629774645a78b018a0b767c9a01d3e | 5b657979777378f26d4de317723c59b11b84cd96 | /R/OldVersions/JaguarPrepDraft.r | 43e15c5894cf7eba6a02fe816076fb2effb0b741 | [] | no_license | piLaboratory/jaguar-codes | 6eb7e73815ec8dbca8b9c2283b08b2d542ba3935 | 4919541548d254eaf8570029a6e9be7f95ac29f5 | refs/heads/master | 2021-07-14T14:43:56.523949 | 2019-04-13T18:31:35 | 2019-04-13T18:31:35 | 145,902,814 | 2 | 4 | null | 2020-05-20T15:11:50 | 2018-08-23T20:18:52 | HTML | UTF-8 | R | false | false | 60,063 | r | JaguarPrepDraft.r | ###########################################################################################################
############# Jaguar Data Preparation ##############
###########################################################################################################
##############... |
df7255ec9e075ba97634cfefe52a92fc88d9ced8 | 6c584706e6eab645e11357bde8f393013c69e4c9 | /Análise Preditiva e Data Mining/Aula 2/Aula 2.R | 5d5dc5207c1e9b9588dc948a3c4b8b093c7699c5 | [] | no_license | charlesartbr/fiap-mba-big-data-data-science | cce1b64c301187a049cd9929d5fafd7e6985503e | de4d8372a7ce26ac8e4556925416e5c9e1932020 | refs/heads/master | 2022-09-05T00:33:21.367281 | 2022-08-09T14:01:28 | 2022-08-09T14:01:28 | 185,289,505 | 0 | 1 | null | 2021-01-15T16:47:00 | 2019-05-07T00:10:35 | Jupyter Notebook | UTF-8 | R | false | false | 1,143 | r | Aula 2.R | arquivo <- read.csv(file = 'Bike_Sharing.csv')
View(arquivo)
attach(arquivo)
modelo1 <- lm(cnt ~ temp)
summary(modelo1)
plot(cnt~temp)
abline(modelo1)
fitted(modelo1)
residuals(modelo1)
modelo2 <- lm(cnt ~ temp + atemp)
summary(modelo2)
modelo3 <- lm(cnt ~ temp * atemp)
summary(modelo3)
#Variáveis preditoras ... |
5432922943418608973cd2a11bb980287b33ab51 | e58cb0a3ce95401501f0f0441a492529632b41f7 | /processes/trinity/createTrinityJob.R | dbc58605bcff316c602fb42cb8d887b529d191e1 | [] | no_license | larsgr/GRewdPipeline | ea451c75b5f4d91d4f92a941e3b2f3461566ee98 | 77a7d5b17373a2139d34327942fcec300b62fb40 | refs/heads/master | 2020-12-29T02:19:46.034273 | 2019-01-15T10:24:33 | 2019-01-15T10:24:33 | 30,870,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,004 | r | createTrinityJob.R | source("R/fillTemplateFile.R")
source("processes/SLURMscript/createSLURMscript.R")
createTrinityJob <- function( leftReadFiles, rightReadFiles, outDir, trinityOutputName,
jobName = "trinity", seqType="fq", SS_lib_type="RF",
CPU=10, max_memory="calculate"){
... |
95b80ac3936b926d0ef7a7af09dfeeb8bedb8b85 | 9284bd4866430a33c9fc5003b76de7ce4858361d | /man/geo_fda.Rd | d4fc74d9a0351a702dc5441994e69c90e9e72221 | [
"MIT"
] | permissive | gilberto-sassi/geoFourierFDA | dd0f8b27120708acf8689c2f5b00c71f1f378599 | 07cffa1366d82636add7fb7d02a49a5f331147fe | refs/heads/main | 2023-03-02T18:33:23.976846 | 2021-02-10T21:46:41 | 2021-02-10T21:46:41 | 276,725,569 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,149 | rd | geo_fda.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geo_fda.R
\name{geo_fda}
\alias{geo_fda}
\title{Geostatistical estimates for function-valued data.}
\usage{
geo_fda(
m_data,
m_coord,
new_coord,
m,
n_quad = 20,
t = seq(from = -pi, to = pi, length.out = 1000)
)
}
\arguments{
\item... |
f9c76ff4d620cc789d4b2443fcc989629635a0a8 | c20ee8390240c178add9f49b497bc3de9a660bba | /R/produce.two.dimensional.noise.R | 51ac484e8b87861ae6b656aa53fbad2eeb08cc05 | [] | no_license | meteoswiss-mdr/NowPrecip | e3c90040f5e5545bfc1808fc2569aee59042c31b | 2ee0521a7cd8b7961bb7eed399f745aa6845c22b | refs/heads/master | 2021-01-22T02:49:03.171561 | 2017-10-16T09:26:06 | 2017-10-16T09:26:06 | 81,074,161 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,612 | r | produce.two.dimensional.noise.R | produce.two.dimensional.noise <-
function( z
,nr.frames = 60
,win.type = 'flat.hanning'
,do.set.seed = FALSE
,on.screen = FALSE
) {
#Produces a 2-dimensional correlated noise
#
#Examples:
#z=Xln0[,,nr.obs]
#At = produce.two.dimensional.noise(z=Xln0[,,nr.obs])
... |
8e7409b9686194d60c74dff14d9b852f67b41f7d | 9ae6b5fa9a3e4ff67901b81702ae1cdf3c6c891b | /inst/dashboard/ui.R | 79b1824c80abf30289f4fbc145d95b3205fc080a | [] | no_license | dimagor/socraticswirlInstructor | ae314942c2882e0d881c44c211d8d000346d228b | b9ebb96d6ed26c00b534627d83353b0bfaf91a89 | refs/heads/master | 2021-01-18T22:32:54.656665 | 2016-07-14T21:31:13 | 2016-07-14T21:31:13 | 33,081,253 | 6 | 5 | null | 2015-12-16T21:04:36 | 2015-03-29T17:02:54 | R | UTF-8 | R | false | false | 4,666 | r | ui.R | library(shiny)
library(shinydashboard)
header <- dashboardHeader(title = "SocraticSwirl",
dropdownMenuOutput("progressMenu"))
sidebar <- dashboardSidebar(
uiOutput("usersessions"),
hr(),
sidebarMenu(
menuItem("Exercise Dashboard", tabName = "exercise_tab", icon = icon("dashboard"))... |
9b37b8b8cc9803a22eec6024e84e402d844e7d9e | 3c5d52b699e6645cbb086b4f45519f898a4b2f64 | /R/read.fh.R | 67fda3994fb344ab659e1131ae5701bb708aa221 | [] | no_license | AndyBunn/dplR | 249427c0be6c68c80c267ad4392a03a331bb056d | 8bb524ae6a024affea2c9ec982b0972fc5175280 | refs/heads/master | 2023-07-06T04:02:45.073034 | 2023-04-12T21:41:31 | 2023-04-12T21:41:31 | 195,873,071 | 31 | 12 | null | 2023-06-23T14:33:40 | 2019-07-08T19:21:39 | R | UTF-8 | R | false | false | 13,582 | r | read.fh.R | read.fh <- function(fname, BC_correction = FALSE) {
inp <- readLines(fname, ok=TRUE, warn=FALSE)
## Get start and end positions of headers and data blocks
header.begin <- grep("^HEADER:$", inp)
header.end <- grep("^DATA:(Tree|Single)$", inp)
n <- length(header.end)
if(n == 0) {
stop('file has no data... |
775636df890424201df004eaef7b7f6b2810ee10 | 4b73241bda37bad4f17fdd53db997c12a59995fd | /man/calc_d18Ow.Rd | ad6ac373c8ee2e13f7df0cfb21cd07d7e01649d0 | [] | no_license | cubessil/isoprocessCUBES | b6c157376f8bfa14b32fab7c1dd689db1d69444c | d9efe3d52b50a2ef96b82c65bc76f2524268a775 | refs/heads/master | 2021-06-07T19:58:59.673194 | 2021-05-26T21:21:48 | 2021-05-26T21:21:48 | 133,712,554 | 0 | 1 | null | 2019-08-05T18:29:26 | 2018-05-16T19:15:33 | R | UTF-8 | R | false | true | 210 | rd | calc_d18Ow.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/custom.functions.R
\name{calc_d18Ow}
\alias{calc_d18Ow}
\title{Calc d18}
\usage{
calc_d18Ow(d18Om, Temp)
}
\description{
Calc d18
}
|
4ae0af12d94314af3c0eb05e23a0d039985594c4 | cbc8bdd64cf3c0eb4bb3b6767d0fb7f2e26280dd | /man/Run_JTree.Rd | 48269157bc0cd730b86651b00f9f1c2818a82373 | [] | no_license | cran/treelet | 6ae35b4c910fb995d9bbdfb0c41b7c7ab441fbd8 | 00c8e56c1910ade023267e10a4bf958c6d7d4242 | refs/heads/master | 2021-01-18T14:02:41.134700 | 2015-02-10T00:00:00 | 2015-02-10T00:00:00 | 17,700,589 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,655 | rd | Run_JTree.Rd | \name{Run_JTree}
\alias{Run_JTree}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
Treelet basis/hierarchical tree construction
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
Returns information on the simultaneous construction o... |
d0f8ac98d021f5f8fb30ff427cd2f1af9f7ad4c6 | 285c603a0782a4806e14278470640d4dfc085b25 | /R/vegInd_RGB.R | a3afd3b9b3baec808a002e5d2be501b55d3b94a9 | [] | no_license | SchoenbergA/LEGION | 6b34ac82132d3e67c5eed274fd688450b0314633 | b01db2c0fa99602578355e246d7b3bde79a1e187 | refs/heads/master | 2023-02-26T15:26:07.459129 | 2021-01-28T15:40:54 | 2021-01-28T15:40:54 | 271,737,635 | 0 | 0 | null | 2020-10-22T09:28:03 | 2020-06-12T07:33:24 | R | UTF-8 | R | false | false | 5,457 | r | vegInd_RGB.R | #' Calculate RGB indices
#' @description computes several indices based on RGB bands
#' @param rgb a RasterStack with RGB bands
#' @param red the band/layer number of band 'red'
#' @param green the band/layer number of band 'green'
#' @param blue the band/layer number of band 'blue'
#' @param indlist comma-separated ch... |
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