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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
fe2271c0adee5366ffeeb3b53ee4eb8f85512f3b | f9229ecfef3b8ce90dad05cfef395c442cc90e23 | /script/question3.r | d672bdaaabf498bad88f1736c7d8ee94396a7f37 | [] | no_license | lga37/mc2-trabalhofinal | ee452fa32499b294e0446207e25b5d29c84b4162 | fc027d48082bc31bfdf0390d5ee97ddfaff8470e | refs/heads/main | 2023-03-09T09:40:19.332620 | 2021-02-23T22:02:56 | 2021-02-23T22:02:56 | 342,687,141 | 0 | 0 | null | 2021-02-26T20:01:15 | 2021-02-26T20:01:14 | null | UTF-8 | R | false | false | 896 | r | question3.r | library(tidyverse)
rm(list = ls())
# SETANDO A HOME COMO WORK DIR
setwd("~/Mestrado/TrabalhoMC2");
data <- read.table("data/data_t3-t4.txt", header = TRUE);
configs <- unique(as.character(data$config));
instances <- unique(as.character(data$inst));
# qualidade dos dados
ic <- matrix(nrow=length(instances), ncol=length... |
2ba599784c4338f576966af78ffcd029da039978 | c2c35d0c4e9fc33b9efae54ca98f76e9e216bbbd | /project_code/Download MICS from json.R | 6213f4b3329dda99772139a8c073e56abdf1b6d8 | [] | no_license | danjwalton/MPI | c4bcb83037dd3b176eb0c3a1a30ecf9a80288b61 | 930d759ad0f12ed5fead7e560b04feccd54733f8 | refs/heads/master | 2020-07-04T14:50:05.227444 | 2019-08-19T16:19:55 | 2019-08-19T16:19:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,941 | r | Download MICS from json.R | required.packages <- c("reshape2","ggplot2","data.table","jsonlite","RCurl","XML","xml2","RStata","stringr","foreign")
lapply(required.packages, require, character.only=T)
wd <- "G:/My Drive/Work/GitHub/MPI/"
setwd(wd)
basename.url=function(path){
path_sep=strsplit(path,split="/")[[1]]
path_len=length(path_sep)
... |
1aaf1d6fdd400481319c6356820a9bf1b57ce9ba | ea492f927e78f9eef5e805bb1b884830c1a76f68 | /mctd_nc | b5d3bf6afdc44aa709ea628bed527d1f2ffc03ca | [] | no_license | EOGrady21/netCDF | b1f2d7041fb3e556919953b9b54963ac812e00d3 | 742615c0b8422ca4c260065d7118fc216fe40c46 | refs/heads/master | 2022-04-27T16:23:19.705034 | 2019-04-17T16:46:40 | 2019-04-17T16:46:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 25,253 | mctd_nc | ####mctd NC template####
# obj <- read.odf('C:/Users/ChisholmE/Documents/sample files/mctd/MCTD_HUD2015006_1897_11688_1800.ODF', header = 'list')
# metadata <- ('C:/Users/ChisholmE/Documents/sample files/metadata/MCTD_SAMPLE_METADATA.csv')
source('asP01.R')
#' Moored CTD netCDF template
#'
#' @param obj an odf obje... | |
7b3d1e00ba465c321a71527ba13e1b7be18760ec | ba14c315f4ed435384c5b48185a5707dcf1ce093 | /SidebarUi.R | c9e6ff771681fcdac0d99eb6326499c2c8c2a547 | [] | no_license | antgers/Project_AquaMiner_Periodic | 0e318e381f1e244ba6858407f22d8900a78d7f6f | 7e81781d607e83833e1bd2fd60f93bd5995b8497 | refs/heads/master | 2021-01-17T19:20:10.620858 | 2016-10-23T20:57:37 | 2016-10-23T20:57:37 | 71,663,263 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,615 | r | SidebarUi.R | sidebarUni <- sidebarPanel(
#fixed responsive img #added class img
img(src="menfishing21.png", class = "img-responsive", align = 'middle'),
hr(),
bsCollapse(id = "collapseSidebar" , open = "Upload Data", multiple = FALSE,
bsCollapsePanel("Upload Data", style = "primary",
radi... |
9fe0d902dff5c71a1edad97eb4f483713b339270 | 2cb802c7e9bb18670769604cb289b03192661d5a | /COPS code/6b create pregnancy level file.R | 1b66a315e5c145b3ecf6dfafb256ea60d15990d9 | [] | no_license | Public-Health-Scotland/COPS-public | a18d36d8a69479e34c1ddd31f23a15b5b7a6eba6 | b4c4df18020712fbae08a979226d0a382d6aeda9 | refs/heads/main | 2023-07-29T17:41:26.677028 | 2023-07-11T12:40:32 | 2023-07-11T12:40:32 | 362,821,738 | 0 | 2 | null | 2021-12-07T12:55:46 | 2021-04-29T13:11:02 | R | UTF-8 | R | false | false | 8,080 | r | 6b create pregnancy level file.R |
fetuslevel <- read_rds(paste0(folder_temp_data, "script6_baby_level_record_infection.rds"))
#quick fixes to names to let the cohort run with extra data.
#needs changes in 6aa to retain names without "_value_", or a decision to change names below in the long run
fetuslevel <-fetuslevel %>%
rename(tests_mother_has_h... |
f556a9157829fa6cfda53a6615c5069ad3a1c038 | b6abcd32866919c5330394ec171a09d6a9085930 | /R/topicModel.terms.R | ba8535e1e6ebeb1f176f77d5ad895683bf352f2d | [] | no_license | aidanoneill/textmining | b1c35e3cff9de1de392b90ba99c99ebde242464b | 1699dddb76d305c33799af5bd3f976031db2438a | refs/heads/master | 2016-09-10T19:40:03.450673 | 2015-03-19T19:38:19 | 2015-03-19T19:38:19 | 32,542,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 276 | r | topicModel.terms.R |
# Models which terms belong to which topic, based on a passed number of topics
topicModel.terms <-
function(data, k){
require("topicmodels")
lda = LDA(x = data, k = k, method = "VEM")
return(terms(lda, 10)) # which documents belong to which topic
} |
eb5f791115caabce15649bc5b3dd49767fd739a7 | 4fea06a47c87fec7905b553c7f2184664abec48f | /R/create-events.R | dbc4e0ae48bb2bbde429311df368c41a83afee42 | [
"CC-BY-4.0",
"MIT"
] | permissive | au-cru/site | 982792332b309e72cb8417415f2d1b9422fe78ae | c92349af621d62efeb58d4b11e81c85c8328ece8 | refs/heads/master | 2021-07-24T05:52:37.834147 | 2021-07-07T15:17:31 | 2021-07-07T15:17:31 | 207,162,840 | 0 | 3 | NOASSERTION | 2020-02-28T09:31:43 | 2019-09-08T19:30:07 | CSS | UTF-8 | R | false | false | 3,632 | r | create-events.R | library(tidyverse)
# Also use glue and datapasta.
# Code-along --------------------------------------------------------------
# Paste from Google Sheets into Excel/Calc using datapaste package.
events <- tibble::tribble(
~Date, ~Topic, ~Level,
... |
60ade196f0694b1383c5d764590f131f7a051542 | 247168dd727c19cef2ce885476d3e4102d2ca7de | /man/auth_put.Rd | 65f87e05642520320a83038338cfdaacf7616d91 | [
"Apache-2.0"
] | permissive | DataONEorg/rdataone | cdb0a3a7b8c3f66ce5b2af41505d89d2201cce90 | 97ef173bce6e4cb3bf09698324185964299a8df1 | refs/heads/main | 2022-06-15T08:31:18.102298 | 2022-06-09T21:07:26 | 2022-06-09T21:07:26 | 14,430,641 | 27 | 19 | null | 2022-06-01T14:48:02 | 2013-11-15T17:27:47 | R | UTF-8 | R | false | true | 846 | rd | auth_put.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth_request.R
\name{auth_put}
\alias{auth_put}
\title{PUT a resource with authenticated credentials.}
\usage{
auth_put(url, encode = "multipart", body = NULL, node)
}
\arguments{
\item{url}{The URL to be accessed via authenticated PUT}
\ite... |
fb19c08d3c7e0d715f02a346ba9b6a1ea641cd93 | 4add4f324b954c7dc2e53fc040108dd5d200ce2f | /R/code.R | 1721b76db09003b8cf631adc891be1212091c509 | [] | no_license | tdhock/requireGitHub | c12b211875be47bb878d2d05f5b9f0211dc592ed | f36a95a1542bbab0614ba20c610bfe4e6f497332 | refs/heads/master | 2020-12-29T02:19:36.062504 | 2019-05-17T16:03:10 | 2019-05-17T16:03:10 | 18,137,680 | 1 | 0 | null | 2017-03-16T20:02:13 | 2014-03-26T12:46:55 | R | UTF-8 | R | false | false | 893 | r | code.R | ##' Print a requireGitHub declaration.
##' @param ... unquoted package names.
##' @return An invisible character vector of repository/package
##' version codes.
##' @author Toby Dylan Hocking
##' @export
##' @examples
##' if(FALSE){
##' requireGitHub_code(requireGitHub)
##' }
requireGitHub_code <- function(...){
pkgs... |
5fbd9b6eecd0577b8e3147592ea6b85e8e422fd5 | 7bae5569fd5509263b0cdd20fc1c6c14436410f9 | /packages/RNASeq/summary/RCODE/readFiles.R | f06d9cb5bf058391f813bf99aa8eb3c090c64ed2 | [] | no_license | cfbuenabadn/YosefCode2 | fe578ac0e9d0ff5ce724209dde1379acae6ab0ad | 35bd4e749301b728ad502d6327b88c01de71cbd3 | refs/heads/master | 2021-07-05T03:40:51.753643 | 2017-06-23T10:50:08 | 2017-06-23T10:50:08 | 105,191,082 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,768 | r | readFiles.R | #for debug
rm(list=ls())
setwd("/data/yosef/users/allonwag//YosefCode//packages//RNASeq//summary//RCODE")
source("loadProcessedRNASeq_NG.R")
collect_dir="~/archive/users/allonwag/temp/big_pipe_out/collect"
collectedRNASeqStudy = loadProcessedRNASeq_NG(collect_dir=collect_dir,
... |
eb283f196984440799e98bbf106e166efff4e19d | 2ae1c90fd6beefbf099342fbc48e079ca904b979 | /legacy/dhfr/alt_rank_plot.R | fc9b82e30e278a1d3337e0a7192a6f91a78a8041 | [] | no_license | SamStudio8/gretel-test | 62bab2e1719d5cb20467549fe2ce429cb4b6d55d | 9d168f64ab5485416c58f1b7976356ad89eea903 | refs/heads/master | 2021-10-26T03:26:47.408040 | 2021-10-18T13:19:46 | 2021-10-18T13:19:46 | 64,262,553 | 2 | 2 | null | 2019-06-24T10:54:50 | 2016-07-26T23:54:56 | Python | UTF-8 | R | false | false | 2,068 | r | alt_rank_plot.R | library("ggplot2");
#d <- read.table(TABLE_P, header=T);
#d <- read.table("fbc_hamming_wpd_wmeta.txt", header=T);
d <- read.table("fbc_hamming_wpd_wmeta.withbestworstlikl.withabslikl.txt", header=T);
#d <- d[d$cov == 10,]
#p <- ggplot(d, aes(recovery, rank_w, colour=factor(in_hname))) + facet_grid(readsize~cov) + sc... |
3cdb84092d446505f3e53e7765cfabc047ce8278 | 1817920a05d0282936b6bd88fcbc5eeb9cbbcaf0 | /run_analysis.R | 2acb8eddde9a1be508892fceb3ed3212279c016b | [] | no_license | sallytian/Getting-and-Cleaning-Data-Project | 0114324db338871b3e3583c7827852f6d2dcd148 | 0d805b555fddf086ac9e8cff20800697c95afd81 | refs/heads/master | 2020-05-19T18:52:56.983970 | 2015-02-22T22:09:39 | 2015-02-22T22:09:39 | 31,182,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,690 | r | run_analysis.R | ## Get features names
feature <- read.table("./UCI HAR Dataset/features.txt")
colnames(feature) <- c("No", "Names")
## Extracts mean and std related features
index <- grep("mean|std", feature$Names)
## Get test and train data with desired features
testAll <- read.table("./UCI HAR Dataset/test/X_test.txt")
test <- tes... |
306170c36139a0e68014b913e4dbdd1f959919c0 | 2281cdb6065a06304e9ed82649d546e1e64edce3 | /man/print_class_id.Rd | f57b099793ba67ddbe378d7be91cb35ab0c1842d | [
"Apache-2.0"
] | permissive | hhoeflin/hdf5r | 142d391e60d97b57a4fcba76a407ef9bc0e08984 | 450d483364e3bf84db19df7eb330633952ec31ae | refs/heads/master | 2023-08-17T07:04:15.470093 | 2023-01-21T15:57:59 | 2023-01-21T15:57:59 | 70,118,016 | 77 | 29 | NOASSERTION | 2023-06-22T13:49:27 | 2016-10-06T02:58:42 | C | UTF-8 | R | false | true | 472 | rd | print_class_id.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Helper_functions.R
\name{print_class_id}
\alias{print_class_id}
\title{Print the class and ID}
\usage{
print_class_id(obj, is_valid)
}
\arguments{
\item{obj}{The object for which to print the class and id}
\item{is_valid}{is the object valid... |
35c04fc06edc51d5e74490d7e36108a5e5c30aa1 | b9c2609f7ba23410bb549383a7edc1faefd47c3c | /R_scripts/021_PeriodicProcessTemplate.R | 5753c009de080be894cc3ede0e6231bf63955712 | [] | no_license | stochastictalk/msc_statistics_thesis | 8faa6f4ce3a9384e6f1728e5e431508de89c88d6 | fb584d8b34c33e527106b369983ec56d3b10e833 | refs/heads/main | 2023-01-12T04:23:59.325525 | 2020-11-20T13:37:38 | 2020-11-20T13:37:38 | 313,441,237 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 318 | r | 021_PeriodicProcessTemplate.R | rm(list=ls())
T_ <- 10
j <- seq(1, T_)
sigma_P <- 0.1
sigma_P <- sqrt(1/(1 - mean(sin(2*pi*j/T_)^2)))
N <- 1000
X_t <- rnorm(N, mean=0, sd=1)
X_t <- c(X_t, rnorm(N, mean=sin(2*pi*seq(1, N)/T_), sd=1/sigma_P))
plot(X_t)
EX <- c(rep(0, N), sin(2*pi*seq(1, N)/T_))
lines(EX)
mean(X_t[(N+1):(2*N)]^2)
mean(X_t[1:N]^2)
|
e23551d3e6f4833e80d4da33ffc78b9e24f40e16 | a07c8e474c1f44d69ed862bea9f571e48c61dfc9 | /missing-values/missing-values-poisson.R | aea371192b12c22f5eee75c479a18fe1f00dd725 | [] | no_license | zlliang/statistical-computing-experiments | 11fa050af5ad29895b730fe33847c69547676089 | e67a6e1927c372962fcb4f6b28df73db7ecf8487 | refs/heads/master | 2020-03-17T14:28:03.972110 | 2018-05-24T10:37:07 | 2018-05-24T10:37:07 | 133,673,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | missing-values-poisson.R | # -----------------------------------------------------------
# Statistical Computing Experiments
# -----------------------------------------------------------
# EM Algorithm for Poisson Mixture Distribution
# Author: Zilong Liang
# Date: 2018-04-04
# -----------------------------------------------------------
# -----... |
8b77c0fa374b68afe2988bf2ee9886251dba4d7d | 5c2350f172e1a7b7f61e1047d515357735e5895e | /man/christmas_stats_participants.Rd | 5ea87ef46ea44ddc4c4d67fe6fbe843569c2603b | [
"CC-BY-4.0",
"MIT",
"LicenseRef-scancode-public-domain",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | richarddmorey/Morey_Hoekstra_StatCognition | 4da5b3f205d1038b850fa701354bd59b62a05eed | 373b9ac75219d84d7b5a6454296e80aa4f34ea54 | refs/heads/master | 2022-12-06T14:50:55.198542 | 2022-11-30T18:23:58 | 2022-11-30T18:23:58 | 189,821,493 | 4 | 1 | null | null | null | null | UTF-8 | R | false | true | 5,889 | rd | christmas_stats_participants.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/christmas_stats_public-data.R
\docType{data}
\name{christmas_stats_participants}
\alias{christmas_stats_participants}
\title{Cleaned, participant-level data}
\description{
Participant-level data for the Christmas statistical
cognition experim... |
cad557fbd9bc866b9876047f72e264235ef4d62e | 45fcec2ad46e80b31ab2cd855feb2a0b6a7d7431 | /plot3.R | d3e1239518782abcbea41f69efd60dde1ed48370 | [] | no_license | lamwai/ExData_Plotting1 | 371c76090b91c039e8083f3b5d00ba1cdf7dbc6c | 289345adeaab504323a055107bb62fddb592460d | refs/heads/master | 2021-01-21T17:50:21.513968 | 2014-09-05T12:15:10 | 2014-09-05T12:15:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,183 | r | plot3.R | ## read the complete file and read each column as character type
fulldata<-read.csv("household_power_consumption.txt", sep=";", colClasses=c(rep("character",9)))
## filter only the first 2 days in Feb 2007, and output the first three columns,
## comprising the date, time and Global Active Power
tmpdata <- subset(full... |
2addfa87785fe4ca46e8b77332a5b46d119ef907 | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /23-functions/49c-replicate.R | 236c9742267ddddf47c5d0419d84576b9a2ef343 | [] | no_license | DUanalytics/rAnalytics | f98d34d324e1611c8c0924fbd499a5fdac0e0911 | 07242250a702631c0d6a31d3ad8568daf9256099 | refs/heads/master | 2023-08-08T14:48:13.210501 | 2023-07-30T12:27:26 | 2023-07-30T12:27:26 | 201,704,509 | 203 | 29 | null | null | null | null | UTF-8 | R | false | false | 250 | r | 49c-replicate.R | #replicate
#
?replicate
replicate(n, expr, simplify = "array")
replicate(4, rnorm(5))
my.fun = function() {
for (i in 1:1000) {
...
for (j in 1:20) {
...
}
}
return(output)
}
rep(1:4,len=20)
replicate(1:4,len=20)
|
acbb011f14079e7c8ffafee6d83a86b1a778ed6c | 6e9abf08a2d2728495c89611c9e8a1517ff329d8 | /man/gwc_parse_args.Rd | 4ef5daaae50fac22f038a4ecb3a6969181d64765 | [] | no_license | ebi-gene-expression-group/workflowscriptscommon | ef307c615d07599e65dc00bed3334acd291d1956 | 59d978c4c27df5e6bd1a3fbea443305cff3312b2 | refs/heads/develop | 2022-03-15T12:34:14.491968 | 2022-02-13T10:41:00 | 2022-02-13T10:41:00 | 141,469,535 | 0 | 1 | null | 2022-02-09T22:43:13 | 2018-07-18T17:43:06 | R | UTF-8 | R | false | true | 542 | rd | gwc_parse_args.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/arguments.R
\name{gwc_parse_args}
\alias{gwc_parse_args}
\title{Wrap optparse's parse_args() to add support for mandatory arguments}
\usage{
gwc_parse_args(option_list, mandatory = c())
}
\arguments{
\item{option_list}{List of OptionParserOpt... |
da7f9ad219787598b1a5c7ecfb087913bdf1c00f | f8588995f20739d4ffadd60baa48dafc85a3d1fa | /perceptron_pocket.r | d241912456f81b48d04bae351c5d95c680a7fbcf | [] | no_license | gauravshelangia/ai-lab | 90ef8bf6d76d7f2e2373c51ec027ae6da25888b0 | 3d19c8e5ce849f0a974620e0d1080688b380991c | refs/heads/master | 2020-12-01T01:17:52.696436 | 2016-02-11T09:25:35 | 2016-02-11T09:25:35 | 51,135,556 | 0 | 0 | null | 2016-02-05T08:42:03 | 2016-02-05T08:42:02 | null | UTF-8 | R | false | false | 1,780 | r | perceptron_pocket.r | # By Gaurav Yadav
# reading file and storing that in matrix form
train = as.matrix(read.table("Iris_data_norm_train.txt",sep=","))
test <- as.matrix(read.table("iris_data_norm_test.txt",sep=","))
H<- function(x){
if(x > 0){
r <- 1
}
else{
r <- -1
}
return (r)
}
# initial random weight are runif(len,in... |
0f61410a08bcf76d911904b428b35ce7f1253507 | 3727eb350c9f10d1f835314115e401e76ea8e913 | /EDA_VisualizationScripts.R | dce4e0e588acc2e03635c8550f4da8aafc2200a2 | [] | no_license | TaneishaArora/Pfft | 950b6497811fb712ccb6f30f14a618d30b95e81a | 0582369efa952be0a4ae7539e01ce84a0a9f5ccd | refs/heads/master | 2022-08-19T08:04:24.023120 | 2020-05-24T12:43:44 | 2020-05-24T12:43:44 | 265,209,275 | 0 | 0 | null | 2020-05-24T12:43:45 | 2020-05-19T09:53:26 | R | UTF-8 | R | false | false | 7,135 | r | EDA_VisualizationScripts.R | library(tidyverse)
library(ggplot2)
# Dividing the data up by visit month
baseline <- amyloid %>% filter(month == 0)
# Test score Progression at baseline, by different demographic traits
# Sex
baseline %>% gather("test_number", "score", c(t1sum, t2sum, t3sum, t4sum, t5sum, t6sum, t7sum)) %>%
ggplot(aes(x = test_num... |
9e0c13ae6640826919acd8a9d936a4e4c4d15fde | b47aa2e09add49ab85ec3b04c3e3279f28706c1c | /man/FitGP_MLE.Rd | fb9cffcf02db34a058cf52cd0d0d71f06721434f | [] | no_license | ceesfdevalk/EVTools | db232bc94b0a22b1a0fdfbd8ba5e6e9e94e8ad3c | 0e3440f031b6a8abcfd6fc00d981d0656710d93e | refs/heads/master | 2022-09-30T06:25:51.784236 | 2022-08-22T07:48:01 | 2022-08-22T07:48:01 | 130,972,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,162 | rd | FitGP_MLE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FitGP_MLE.R
\name{FitGP_MLE}
\alias{FitGP_MLE}
\title{FitGP_MLE}
\usage{
Value <- FitGP_MLE(X, p, N= 0, r11= 1, fixedpar= NULL, l0= NULL, metadata= NULL)
}
\arguments{
\item{X}{data sample (double(n))}
\item{p}{probabilities of exceedance of... |
da5b724c0aa24a5b30b6286bb51016c7d3d55402 | 0fb3d61813752e6134b4dc4d88876d380e31c60b | /code/RunMosaicStates.R | 7364d2de40e37a8dfa79d3e53155a1388a068654 | [] | no_license | melaniekamm/MergeLANDFIREandCDL | ec96612568a0e090ee4df0b5ec127e3b2cd6cf42 | 1df8767ef662631d115f3c99e068b6615aa0603a | refs/heads/main | 2023-04-08T15:15:13.270691 | 2023-02-24T19:20:39 | 2023-02-24T19:20:39 | 351,850,305 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 582 | r | RunMosaicStates.R |
args <- commandArgs(trailingOnly = T)
message(args)
# specify input parameters
CDLYear <- args[2] # year of NASS Cropland Data Layer
tier <- unlist(stringr::str_split(args[3], pattern=":")) # which hierarchy of mosaic states to process
message(tier)
#outdir <- 'D:/MergeLANDFIRECDL_Rasters/2017MergeCDL_LANDFIRE/' #fi... |
7d2ae9f29c19d5a512916fb66d7a458954fbbe0e | f5f6069fc04306383a2b1e015dc9925d57543442 | /R_Programming/R_Programming_Coursera/Assignment_Week_4/rankall.R | e6a27cd26adda8b28cfa50f776b5c85325fe37f9 | [] | no_license | grizztastic/projects | 42b408b2321a27610691629451a014d982533c6c | 03019a82b16a23264a8eb1394ea30bc7dc7beb4a | refs/heads/master | 2023-01-27T17:33:01.962227 | 2020-12-02T03:05:32 | 2020-12-02T03:05:32 | 267,395,406 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,592 | r | rankall.R | rankall <- function(outcome, num = "best") {
## Read outcome data
outcome1 <- read.csv("rprog_data_ProgAssignment3-data/outcome-of-care-measures.csv", colClasses = "character")
## Check that the outcomes are valid
unique_outcomes <- c("heart attack", "heart failure", "pneumonia")
if(!outcome %in% un... |
6b8278bebce650dd21b1f211009d9042dc3813df | 40051d5f9e1fe85adbbb5cebf42bb541012e5237 | /R/calculatePosteriors.R | c42edd2b6040872e085b4118b7076ad050e86e47 | [] | no_license | ndukler/tkSim | e5f1edce59a746c61a71e7bb22b55a9f097cd9e0 | 0520ba0467dda9a68f786943237c85ec04760e38 | refs/heads/master | 2021-05-11T15:35:58.096459 | 2018-05-01T18:33:40 | 2018-05-01T18:33:40 | 117,736,436 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,233 | r | calculatePosteriors.R | setGeneric("calculatePosteriors", function(object,...) standardGeneric("calculatePosteriors"))
#' Calculate Posterior Probabilities for Infered Parameters
#'
#' Uses numeric methods to estimate posteriors for the infered parameters \code{alpha} (synthesis rate) and \code{beta} (degredation rate).
#' Currently uses a f... |
6dea293ef1db0cb3ceabf7d656cf98c732244b67 | a2cfda897fad97d76a3b4c4be986eb63ef8046cc | /exercises/c5.R | 84e4cd9fc00076a7f5f1d6cd9e988e38f2952fd4 | [] | no_license | ssh352/islr_stats | 1815ff8731b6d460216898d02bfc88f26ed16cee | 9eae37ca99ca6762e67eb9c99e459ef01a9ee9ea | refs/heads/master | 2021-09-10T12:26:59.658428 | 2018-03-26T09:04:30 | 2018-03-26T09:04:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,471 | r | c5.R | require(data.table)
require(ISLR)
## Question 05 ----
dt <- data.table(Default)
glm.fit <- glm(default ~ income+balance,
data = dt,
family = binomial)
# (b)
dt <- sample(dt)
train <- dt[1:6667]
test <- dt[6668:10000]
glm.fit <- glm(default ~ income+balance,
data = train,
fam... |
275eab1e6f15eb9e828f0c471fded97ce275ac66 | 3e9052c3badc3b2363456142b53a552cf3bffdde | /R/create_equal_alignment.R | 37a58c3e7c0d40273da0241c2de8e7d9eeb24d8d | [] | no_license | thijsjanzen/nodeSub | ad0a73acfc99241302d2c8307e90dcc4ac302306 | a85bb1a6251a1b15cd6add635b721247b508f896 | refs/heads/master | 2023-05-25T17:28:39.099142 | 2023-05-15T08:21:56 | 2023-05-15T08:21:56 | 180,762,207 | 1 | 2 | null | 2020-01-08T15:03:51 | 2019-04-11T09:45:02 | R | UTF-8 | R | false | false | 3,324 | r | create_equal_alignment.R | #' function create an alignment with identical information content
#' @param input_tree phylogeny for which to generate alignment
#' @param sub_rate substitution rate used in the original phylogeny
#' @param alignment_result result of sim_normal, sim_linked or sim_unlinked
#' @param sim_function function that accepts a... |
c9bca76cdcbd46799921b9e678750402c51ac1c4 | 0cc863fed706b96df0c44afe7d466cff23228049 | /man/suff_stat.Bernoulli.Rd | 38246f9c8b56de0fabf29fbf79f6f7cdffc88198 | [
"MIT"
] | permissive | alexpghayes/distributions3 | 80a96665b4dabe2300908d569cb74de3cc75b151 | 67d27df128c86d80fe0c903b5b2c8af1fb9b0643 | refs/heads/main | 2023-01-27T14:49:47.588553 | 2023-01-18T18:12:22 | 2023-01-18T18:12:22 | 185,505,802 | 52 | 11 | NOASSERTION | 2023-01-18T18:12:24 | 2019-05-08T01:38:24 | R | UTF-8 | R | false | true | 723 | rd | suff_stat.Bernoulli.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Bernoulli.R
\name{suff_stat.Bernoulli}
\alias{suff_stat.Bernoulli}
\title{Compute the sufficient statistics for a Bernoulli distribution from data}
\usage{
\method{suff_stat}{Bernoulli}(d, x, ...)
}
\arguments{
\item{d}{A \code{Bernoulli} obj... |
71621656d85ed2309815089d948c717e5fe7578d | 6129a47af94390370862748e1cb00104309766d6 | /Human_Cultural_Boundaries/R/Source_Plotting.R | eeca2634cc7d57f9190db141341edbf5304a2682 | [] | no_license | NeuroBio/HCB_R_Package | ea06605d1420499044e0478b2719bca2be2e4bb3 | a7458d1d6e4ce9a130b741e0b05ce7b17750b3c5 | refs/heads/master | 2023-08-28T04:35:33.451306 | 2021-11-08T11:07:07 | 2021-11-08T11:07:07 | 191,992,585 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,633 | r | Source_Plotting.R | #Plotting
#THIS NEEDS TO BE CLEANED UP
#' Get Groups
#'
#' Returns the territories descended from each seed. Includes detailed ancestory data. Ony works when Uproot and Death are FALSE.
#' @param P A list of parameters.
#' @param Data The Pre or Post output from an HBC simulation.
#' @keywords Plotting
#' @export
#... |
1b5b25a413380dd6d4c536c6fc4edb414704b564 | 3f36e3afc25870cf6e9429de4a5b0604d52dc03a | /inst/shiny/VisualisingTrajectories/app.R | 442568430b2e2dc7173b8a07ff434821469bfd62 | [] | no_license | Patricklomp/VisualisingHealthTrajectories | 4077a62b7da7b92ad2c7aa99a918aaf15585788e | 98e69c50d354a693f0e9e8a3d76c81e3e5088a7a | refs/heads/master | 2023-05-31T16:21:50.435675 | 2021-06-04T07:43:42 | 2021-06-04T07:43:42 | 317,466,951 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 61 | r | app.R | #Starts shiny application
shinyApp(ui = ui, server = server)
|
a0fac1bf948521d541cfb800c6b277116db27c10 | 1b2646afcc7c602243e1025c6653d91a8aa313e9 | /R/resample.CoxBoost.R | 596103ebeccd8b5179debe6e63698e884beea5a2 | [] | no_license | kaixinhuaihuai/CoxBoost | b171c8d28b0ff7f302acf3310845f53ae40b04e4 | e7fe9d6a30a8e77d9516539f2c21d377c83ea8f2 | refs/heads/master | 2023-01-15T12:08:06.490468 | 2020-11-18T22:06:48 | 2020-11-18T22:06:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,491 | r | resample.CoxBoost.R | resample.CoxBoost<- function(time,status,x,rep=100,maxstepno=200,multicore=TRUE,
mix.list=c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
stratum,stratnotinfocus=0,
penalty=sum(status)*(1/0.02-1),criterion="hscore",unpen.index=NULL)
{
re... |
c24cd8d4b8af37410765953d0822d8b811ad8366 | f2643256c6611d7de0db96d162f594388c2c2c50 | /analyses/Trial 2/satstudy_recruitment.R | 35f703f9a1c90ddcee69aade70c5e66b3e285789 | [] | no_license | raubreywhite/trial_dofiles | e06a5b3b39e9195eda79dd33856d67c918ec4053 | eface3b83b107cf7e621b3c654e65b5cbd45b711 | refs/heads/master | 2022-06-14T03:26:17.492945 | 2022-06-02T07:27:04 | 2022-06-02T07:27:04 | 114,857,557 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,408 | r | satstudy_recruitment.R | ### not to be run on server ###
# set working directory
setwd("C:/Users/Mervett_Isbeih/sat_study")
getwd()
#setting up folders
FOLDER_SAT_RESULTS <<-file.path("C:/Users/Mervett_Isbeih/sat_study/sat_results")
FOLDER_SAT_DATA_CLEAN <<-file.path("C:/Users/Mervett_Isbeih/sat_study/sat_data_clean")
#idenfifying packa... |
3ea3b28206deb089a1cb8b58c02fa3ecf5e135c5 | 9e77527c480d453d64b317f1261f842f260efa6c | /code/06_variables.R | a68552a89c1d6ee3bf7b890752abc198e2dc92b3 | [] | no_license | AnnikaErtel/CropDiversity_NutritionalSupply | e636663df614002d2c25c643317b8787388b9d81 | 34bdcd6582eca1984f031ecd84480d1d5b7e7aba | refs/heads/main | 2023-06-27T03:45:18.198520 | 2021-07-28T09:41:15 | 2021-07-28T09:41:15 | 360,645,197 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,243 | r | 06_variables.R | #####Affiliance
#Annika Ertel
#Universität Leipzig/ Institut für Geographie
#Matrikelnummer: 3710313
#SKRIPT 6: Preparation of other variables
####Setting up####
setwd("~/data/MAS-group-share/04_personal/Annika/CropDiversity_NutritionalStability_new")
rm(list=ls())
library(tidyverse)
library(readxl)
library(count... |
09f451959e2e9b2ed0115d4bb10150651f8d98e9 | fbc244647eaf602abb4637c43641d3cdb1d178a8 | /xts.processing.R | e4921a2a99bd443201441131990a3e97231bc551 | [] | no_license | patchdynamics/ct-river-R | 9111dcebe232db3f6aa8e9c4edbcbe60a468cecc | bf3d4384a225aa7db705d483cb61d16ce3bbcbe2 | refs/heads/master | 2021-05-04T11:33:15.144478 | 2016-09-24T01:43:15 | 2016-09-24T01:43:15 | 50,894,715 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 773 | r | xts.processing.R |
# calculate warming series from xts
yearly.hval = function(ts,col) {
processed = ts[,col]
minimum = min(ts[,col])
highest = minimum
for(i in 1:nrow(ts)){
if(.indexyday(ts[i,col]) == 0) {
highest = minimum
}
if(as.numeric(ts[i,col]) > as.numeric(highest)){
highest = ts[i,col]
}
p... |
1850b4ff27b028cd7da3bbabcd70c77554f11818 | 98614a140562bebd7a6dde6df7d3fec149159e0b | /R/HKCSS - service utilization 191207.r | 0e71299651357cd1a58b1419398d9705cd74c6bd | [] | no_license | chenshuangzhou/programming101 | 07628a4fc797eb564531aa405b6e896c3c349da7 | c09dbdb4777afa237a989fa67d9b7766079c821c | refs/heads/master | 2020-03-29T16:30:36.184733 | 2020-03-08T10:53:57 | 2020-03-08T10:53:57 | 150,116,457 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,977 | r | HKCSS - service utilization 191207.r | ### Notes
# dementia, non-dementia
# male, female
# met/unmet needs
# generations
### HKCSS data on Unmet Need of Caregivers ###
library(xlsx);
library(outreg);
library(plyr);
library(psych);
library(stargazer);
library(interplot);
library(Hmisc);
# library(VIM) # visualization of missing data
# aggr(d,prop=F,... |
f3d3bc997679bf29a8e37e64139bb441daedafc5 | 2d1a8db7061ceda55e5f37990f764317d4c193d8 | /LOLA Enrichments/LOLA_mmarge_4_30_19.R | 4bcb600080c809cdd2856795f9c32acd11c6aff7 | [] | no_license | aciernia/BTBR-BMDM-Endotoxin-Tolerance | 643a007011b7a273eed111480a57c7b957579f9c | be119492b3d1208dc59f578ca2e1f53bbef30afa | refs/heads/master | 2022-12-27T07:25:31.580457 | 2020-10-09T14:42:38 | 2020-10-09T14:42:38 | 258,902,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,206 | r | LOLA_mmarge_4_30_19.R | #author: Annie Vogel Ciernia
#a.ciernia@gmail.com
#10/9/2018
##############################################################################################################
library(dplyr)
library(tidyr)
library(cowplot)
library(gplots)
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocMana... |
3ea323ca0b8191427c28a9204923ed0194bd30d7 | c12d52663ecd6f7088337fe371e77e2f82398758 | /man/colCumprods.Rd | 0657457c085919eedfb57f3e899e0d3bb0f740a2 | [] | no_license | federicomarini/sparseMatrixStats | 8d581ad4db29583c4f9d56f18fbdfdcebc3cf1d0 | b5c036095d3aac4be00096f793b199aebf4d1fcd | refs/heads/master | 2020-08-05T03:24:15.701873 | 2019-10-02T14:37:51 | 2019-10-02T14:37:51 | 212,375,029 | 0 | 0 | null | 2019-10-02T15:22:15 | 2019-10-02T15:22:14 | null | UTF-8 | R | false | true | 1,683 | rd | colCumprods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/methods.R, R/methods_row.R
\docType{methods}
\name{colCumprods}
\alias{colCumprods}
\alias{colCumprods,dgCMatrix-method}
\alias{rowCumprods}
\alias{rowCumprods,dgCMatrix-method}
\title{Cumulative sums, products, minima and maxima for each row... |
acc1ebcb4ba5200928f3921f1032624565f69f6e | 9cfdec25ad3ec65679a4cca555422bfef54e73ab | /hw/hw1/test.r | ae5bcb829cbfd48e1f4165baae5c362083895282 | [] | no_license | huberf/matlab-class | a5e186f558cf3bbdd42340ee4fb134a723cf26a0 | 202e30ec217bf6e50ff48757e4a4fedc9598987b | refs/heads/master | 2021-01-17T16:46:47.765418 | 2016-07-01T05:35:27 | 2016-07-01T05:35:27 | 62,062,404 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 248 | r | test.r | total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07) - 3 * (pi * 0.015^2 * 0.07)) * 8050
total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07)) * 8050
total <- ((pi * 0.04^2 * 0.07) + (0.25 * 0.08 * 0.07) - 3 * (pi * 0.02779327^2 * 0.07)) * 8050 |
acd97d4dca644eea97cbcd14b375cb8b59c47633 | 538b909ebc208800939ee38d479e19f34e033123 | /cachematrix.R | 704ee2eba5b1a8b0e6453c9c49cecbc29e76c056 | [] | no_license | mwirth7070/ProgrammingAssignment2 | 0e4b61610eba71462c92a8ac40cb3d36fed02a78 | d7d3adb67c6c03963bd5b88f3db4c8c2a2af9e6c | refs/heads/master | 2021-01-18T04:35:00.814275 | 2014-07-25T01:48:10 | 2014-07-25T01:48:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 993 | r | cachematrix.R | #makeCacheMatrix: This function creates a special "matrix" object that caches its inverse.
#cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix.
makeCacheMatrix <- function(x = matrix()) {
m<-NULL # Set the value of the vector
set<-function(y){
x<<-y
m<<-NUL... |
ac7d6e0230b835e2f9823101f9deb82360236543 | fadd25738df09516aedb88a53579e7e121ad51f4 | /R/signalInfo.R | 38ce552e112d47f10fbe8ed9d65e3819f4b83c1d | [] | no_license | JangSeonghoon/maintcivil | 2630dee5df3512c5f9ea39b71169b590138e4ddc | 7a5c61eedfdd4bb3b10f506b8e11aac11b475f30 | refs/heads/master | 2021-09-06T21:48:32.855105 | 2018-02-12T05:23:12 | 2018-02-12T05:23:12 | 103,596,243 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,203 | r | signalInfo.R | #'
#' signal information
#'
#' @param workspace_no, startT,lastT,direction, order, kind
#' @return km of the signal
devtools::use_package("stringr")
#' @importFrom stringr str_c
#' @importFrom stringr str_detect
#' @importFrom compiler cmpfun
#' @export
signal=function(workspace_no,startT,lastT,direction,order,kind){
... |
bcbb2fe83d7872ebab19e0a4f150a3047b37b399 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/gaston/R/bm_vcf.r | 58f833053d8accfd2207f9559e2c14b3022b4aa1 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,622 | r | bm_vcf.r | read.vcf <- function(file, max.snps, get.info = FALSE, convert.chr = TRUE, verbose = getOption("gaston.verbose",TRUE)) {
xx <- NULL;
filename <- path.expand(file)
if(missing(max.snps)) max.snps = -1L;
L <- .Call("gg_read_vcf2", PACKAGE = "gaston", filename, max.snps, get.info)
snp <- data.frame(chr = L$chr... |
9c51a3575f9d3bd376f51a56927ca818bf8e2c80 | 4a6b5be2d735c8d6c3caa4ba2c47803dd386d546 | /R/centrality.R | 25e798696ef5541ceab33fd2f9afefd3e4184de8 | [] | no_license | jonmcalder/tidygraph | 8e19df9c90c696a24878d8bcdd4a1d3762b7faa4 | fba663d33b1ac4dfc18b30488b4f5ea24a0d079a | refs/heads/master | 2020-12-02T22:17:46.282865 | 2017-07-03T12:08:28 | 2017-07-03T12:08:28 | 96,108,967 | 0 | 0 | null | 2017-07-03T12:35:01 | 2017-07-03T12:35:01 | null | UTF-8 | R | false | false | 4,411 | r | centrality.R | #' Calculate node and edge centrality
#'
#' The centrality of a node measures the importance of node in the network. As
#' the concept of importance is ill-defined and dependent on the network and
#' the questions under consideration, many centrality measures exist.
#' `tidygraph` provides a consistent set of wrappers ... |
9d9ea2a547a338dac268ba05d2e348d4788e0ca8 | d33e98129206021371f50e4d74c44486a5a0a5a1 | /install_load.R | 3a8e4f8b38a423feddecf3bcda3a1524e4f5fa15 | [] | no_license | therealcrowder/Case_Study_2 | 312367728bdee372feedf29a6365caca4b58447d | 1746696b4d0f39da3c3277edb7b04096dd0c9e03 | refs/heads/master | 2021-01-20T00:46:03.247362 | 2017-04-24T22:43:29 | 2017-04-24T22:43:29 | 89,184,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 596 | r | install_load.R | install.packages("weathermetrics", repos = 'http://cran.us.r-project.org')
install.packages("knitr", repos='http://cran.us.r-project.org')
install.packages("markdown", repos='http://cran.us.r-project.org')
install.packages("ggplot2", repos='http://cran.us.r-project.org')
install.packages("plyr", repos='http://cran.us.r... |
4a643bcf4bbf6140e42def32c75c5f0931150198 | 91f977492d1e2757c0fabc52e3ade6680c5dec30 | /tests/testthat/test_helsinki.R | 87c8cfc3e83b0716c433b7fd6ea4ba5628a5959e | [] | no_license | cran/helsinki | 1fa8b241c639f87446ebced598ab59ec0e9a754b | 13d68daba1321e156f77ae47d2c5e89a235d1669 | refs/heads/master | 2022-12-15T08:13:36.309621 | 2022-12-02T08:30:05 | 2022-12-02T08:30:05 | 18,805,161 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 892 | r | test_helsinki.R | test_that("wfs_api() works correctly", {
expect_error(wfs_api(base.url = NULL))
expect_error(wfs_api(base.url = "gopher://gopher.quux.org"))
suppressMessages(expect_message(wfs_api(base.url = "https://httpstat.us/404", queries = "search")))
suppressMessages(expect_message(wfs_api(base.url = "https://httpstat.us... |
7bedde392ef9b7c4a9c32c44c63c45ebb9e98738 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.analytics/man/mturk_list_workers_with_qualification_type.Rd | 59a2f19022f6f534e48750a6b38ddbe7a308b618 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 1,616 | rd | mturk_list_workers_with_qualification_type.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mturk_operations.R
\name{mturk_list_workers_with_qualification_type}
\alias{mturk_list_workers_with_qualification_type}
\title{The ListWorkersWithQualificationType operation returns all of the
Workers that have been associated with a given Qu... |
3c08c46dee2fa0ee110936e2e4736754cf46c388 | f7018991debe81fc53a55e9bf125e6514932379d | /NegBinModel.R | b0aa3e447aba8657e93d2d915600968945ccfb8d | [] | no_license | nguyenty/stat544 | 66b583bbab1ea3a7ce858cacfff5be4ae22a7634 | 01c82e02c4ac72f77719a02a8c50f989d9a0028b | refs/heads/master | 2016-08-04T17:46:33.683260 | 2014-06-02T17:32:25 | 2014-06-02T17:32:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,798 | r | NegBinModel.R | library(rjags)
library(xtable)
#############modelm - using point mass mixture prior for signals###############
modelm <- "
model{
# likelihood
for (i in 1:length(y)){
y[i] ~ dnegbin((1/omega[gene[i]])/(lambda[i] + 1/omega[gene[i]]), 1/omega[gene[i]])
log(lambda[i]) <- alpha[gene[i]] + (-1)^line[i]*tau[gene[i]] + beta[... |
51620b1f440a0cf57ae651a1722362b0ac40e9c0 | 599e6d59345ba36cbfb297de29a61243cc728e4d | /learn lattice.R | 0b94a960cabb4dc2f4f2eef97f4ae75d0a902d70 | [] | no_license | abhatia2014/practice-R-Models | a0433e11ea49dd0598cac4648b8bb9779b71f130 | c2e6c639e5565c2ccb22116bde562d60ed4c52f8 | refs/heads/master | 2021-01-11T00:06:47.639003 | 2016-10-13T01:21:29 | 2016-10-13T01:21:29 | 69,142,991 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,441 | r | learn lattice.R | getwd()
#y~x|A*B means display relationship between numeric variables x&y separately for every combination of factors A,B
library(lattice)
attach(mtcars)
# create factors with value labels
str(mtcars)
head(mtcars,3)
gearf=factor(gear,levels=c(3,4,5),labels=c("3gears","4gears","5gears"))
table(gearf)
summary(cy... |
fe620ec168b86a5e56342d4d0db7983505bb4074 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1/Database/Miller-Marin/trafficlight-controller/tlc02-nonuniform-depth-48/tlc02-nonuniform-depth-48.R | 2fb97624991ddbf68d1007c88a1a69b6b1938944 | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 78 | r | tlc02-nonuniform-depth-48.R | 1e0c557ad954c2663f2c790d1f284f4b tlc02-nonuniform-depth-48.qdimacs 11222 29588 |
b742a166ca5b12280121cd112e1de34e05811854 | b2d074c532e4077987d1452d79622eeda753d158 | /kNNImputationNonRandom.R | d1000a5be0df6443de61f6850bc6fbf77f17d8c6 | [] | no_license | Alex-Nguyen/CS5331R | f7c477b68acc96f7ab0fbc6d3ef5a256d1627c3f | c2fb18ac0520e2e2a5d595df41338a49535e1d9b | refs/heads/master | 2021-09-06T17:13:14.148598 | 2018-02-08T20:45:51 | 2018-02-08T20:45:51 | 104,676,043 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,915 | r | kNNImputationNonRandom.R | original_data <-iris
set.seed(104)
####### Set initial parameters
portion <-0.2 # percent of missing values to occupy the data. 0.02 = 2 %
training_size <-0.7 # percent of data for training
data_length <-nrow(original_data)
missing_data <-original_data
id <-portion*data_length
missing_data[1:id,'Petal.Length'] <-NA
... |
a6678e50d2bb8ae9c1b37dacdd4dd70bf08ed6bb | 4af4d40aaf9ce8311c75774d41be1256bb5730c7 | /R/data.R | 4d1fb663ea911f82c08af9f8b4cdcbbdff9af3fb | [] | no_license | zhgarfield/violationsandpunishmentsdata | 69376879d3dd0932ff40eedfabc14963e4436014 | 5bb456f8bd52298fe1772760498473470f8c7a4b | refs/heads/master | 2023-04-12T06:27:17.070753 | 2023-03-15T09:38:12 | 2023-03-15T09:38:12 | 552,007,083 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,863 | r | data.R | #' @title violations and punishments data
#' @description Primary data set of researcher-coded punishment types, SCCS socioecological predictor variables, and phylogenetic tree.
#' @format A data frame with 131 rows and 14 variables:
#' \describe{
#' \item{\code{SCCS_NAME}}{character SCCS culture name associated with... |
9078d60c0c43dafd6186c1e61bb51b45def2c90e | 198aafbe613df9a2cad68e70329b4cb133572018 | /R_passion_tool2.R | 7726a13835291661351256ad985465b4b0b3a81a | [] | no_license | josemtnzjmnz/PASSION_WDM_planner | 332972968e8b6713f59d357c57cd42fe7403a01d | 8e27457d96946e5c8c4b6cdf845caae755a6da18 | refs/heads/main | 2023-06-01T14:43:00.083688 | 2021-06-16T08:51:47 | 2021-06-16T08:51:47 | 377,280,190 | 0 | 0 | null | 2021-06-15T20:01:38 | 2021-06-15T20:01:37 | null | UTF-8 | R | false | false | 36,999 | r | R_passion_tool2.R |
# EU H2020 PASSION
# Planning tool
# Jose Alberto Hernandez
# May 2021
# Inputs:
# Network topology and traffic (nodesLabeling and crossmatrix)
# Passion OSNR characterisation for lightpaths
# Passion cost values
# Output:
# Lightpaths, both primary and secondary, and their allocation in the fibre/wavelengths (Fi... |
4128b90c3e8575be447f152bdc6f0d9128a562df | d121f587f7e0678030d33a4c5428e594c5978dad | /R/quant_txrevise.R | 965adf4dc8c166628958eca01c439db4bfbfe4fc | [
"Apache-2.0"
] | permissive | kauralasoo/eQTLUtils | fcf0907721b3a8f19fe68e611cecb4f16d7a0c9d | 26242562a4e244334fd9691d03bc1ef4d2d6c1d9 | refs/heads/master | 2023-03-05T19:10:45.247191 | 2023-03-03T13:33:08 | 2023-03-03T13:33:08 | 149,779,618 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 758 | r | quant_txrevise.R | constructTxreviseRowData <- function(phenotype_ids, transcript_meta){
#Split phenotype ids into components
event_metadata = dplyr::data_frame(phenotype_id = phenotype_ids) %>%
tidyr::separate(phenotype_id, c("gene_id", "txrevise_grp", "txrevise_pos", "transcript_id"), sep = "\\.", remove = FALSE) %>%
dplyr... |
d67e4bbb18bd81370f47e0fff20cdcdb9f65aa31 | 15011c6bec5eff7ab07b14b423879b022851c5a6 | /TUCUMAN/SMT/Circuitos/grid.circuitos.smt.R | b5410ff0000afdd00954f9da18306c6607a7702b | [
"MIT"
] | permissive | shirosweets/geofacet_ARG | 97ae4ee815ccf0cfa7c1a1acf7eb4ece64861e79 | 48685f8535ae628eeacff97e1a79511b8e139b65 | refs/heads/master | 2023-03-18T21:54:58.972109 | 2019-04-22T16:26:58 | 2019-04-22T16:26:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 773 | r | grid.circuitos.smt.R |
SMT.circuitos <- data.frame(
name = c("B15", "A15", "A13", "A12", "A14", "A16", "16", "15", "13", "12", "14", "A17", "17", "7A", "6", "5", "11", "18", "10", "B18", "7", "2", "1", "8", "2A", "1A", "A18", "A10", "8A", "3", "4", "9A", "19", "9", "20", "21", "22"),
code = c("B15", "A15", "A13", "A12", "A14", "A16", "1... |
cd3d7ee8fd2866ef2d10a31e9d530ee4b07ef47c | beca8e699a02bf123aa98ee5eaacba9ad245aa8c | /T_32_Tables.R | b981e1321a414879647fc66d9d52bbe3f3deb730 | [] | no_license | brandtn/R_Code_Misc | d7bd4539a07684c88a032836fc563842628e1426 | cc7cdbef22dd75084892717361dd6240d924c7c0 | refs/heads/master | 2021-09-26T00:32:47.320529 | 2018-10-26T15:24:21 | 2018-10-26T15:24:21 | 120,939,745 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 555 | r | T_32_Tables.R | #Load libraries
library("tidyverse")
library("googlesheets")
student_data <- gs_read(ss = gs_title("T_32"))
glimpse(student_data)
#function to convert date
#take in x which is a 4 digit number
convertdate {
#convert x to a character
#first charater is either 0 or 1
#0 = 19
#1 = 20
#Second two characte... |
4bfb9b4705862e221c4c62d2a7efe0c6df62cd11 | ac84d0a57c45731f36048895b604ea2721d5c8ba | /src/figure-cum-mul.R | 7e05072f928316444dc805ca7aefbbf36d289dc3 | [] | no_license | yshin12/llss-rz | de2a9e84a463243e8627e2936e21c940fc696c9f | d561bdbacd21a27f3ee34ebc1728ff5205fe5d80 | refs/heads/master | 2020-05-03T10:30:56.360791 | 2019-11-01T13:22:34 | 2019-11-01T13:22:34 | 178,581,261 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,058 | r | figure-cum-mul.R | library('ggplot2')
junkmultse.original = data.frame(read.csv(file='../output/junkmultse-original.csv', header=T))
junkmultse.original = junkmultse.original[1:20,]
junkmultse.original[,'h'] = c(1:20)
junkmultse = data.frame(read.csv(file='../output/junkmultse-newsy.csv', header=T))
junkmultse = junkmultse[1:20,]
junkmu... |
222168fe47da460398f21fa2dda40c7dc5c4e05c | 7f9f945c8a02dfd5f38d30abfcbbfa20d24a4391 | /man/print.fixest_multi.Rd | 1fb8f62071ee5d4ce5ccb472ff1f830a7dacaa5d | [] | no_license | lrberge/fixest | 96428663b68c3701f1063f0fb76a87b68333b7d4 | 6b852fa277b947cea0bad8630986225ddb2d6f1b | refs/heads/master | 2023-08-19T22:36:19.299625 | 2023-04-24T08:25:17 | 2023-04-24T08:25:17 | 200,205,405 | 309 | 64 | null | 2023-09-13T09:51:03 | 2019-08-02T09:19:18 | R | UTF-8 | R | false | true | 1,123 | rd | print.fixest_multi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fixest_multi.R
\name{print.fixest_multi}
\alias{print.fixest_multi}
\title{Print method for fixest_multi objects}
\usage{
\method{print}{fixest_multi}(x, ...)
}
\arguments{
\item{x}{A \code{fixest_multi} object, obtained from a \code{fixest} ... |
d2ce81b8350fa52993103282ed08aaf4f28c1fe3 | 37665649d838e477d74d48888be750d15bfeb651 | /man/tree_idx.Rd | 21411aea8a2a17ed08f57382dd9db7f91993e298 | [] | no_license | manueleleonelli/stagedtrees | 880c9ecf5b9ec8ba68fd62d2320e8239c803cb5c | f0ebb7ca2f1fa05ccda5558baed2fe086625d7da | refs/heads/master | 2020-05-21T00:08:39.125640 | 2019-03-06T15:50:34 | 2019-03-06T15:50:34 | 185,819,192 | 0 | 0 | null | 2019-05-09T14:50:05 | 2019-05-09T14:50:05 | null | UTF-8 | R | false | true | 536 | rd | tree_idx.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/0-util-functions.R
\name{tree_idx}
\alias{tree_idx}
\title{return path index}
\usage{
tree_idx(path, tree)
}
\arguments{
\item{path}{a path from root in the tree}
\item{tree}{a symmetric tree given as a list of levels
This function return ... |
eafa270ef41ae4c9ed16853608337ac47e5a029c | 58facb39c3292cbfd100b5adae942f313f9e682e | /src/pkgSetup.R | 47321bac6ffe77c72fea974a445e91da75c36bca | [] | no_license | amitpatil21/page-2018-mrgsolve | c86f023bccc863b3041808ff62ef1a5be9585a6d | 1d3c8227e95d5244976b4d770fce528adee64030 | refs/heads/master | 2020-07-30T07:19:21.767448 | 2018-09-18T15:05:25 | 2018-09-18T15:05:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,006 | r | pkgSetup.R | author <- c("*")
pkgs <- c("tidyverse", "mrgsolve", "knitr", "rmarkdown", "data.table",
"caTools", "bitops", "formatR", "git2r")
pkgRoot <- "/data/page-packages"
pkgDir <- file.path(pkgRoot, "src", "contrib")
pkgDir <- normalizePath(pkgDir)
libDir <- "/data/page-Rlibs"
if(!dir.exists(pkgDir)) dir.create(... |
eddced5385bd1dd7e1a5e28bf5c36da0c7036158 | 969711eebedba44718b75ef6ad2a2a39a070ca08 | /ui.R | a4a67d41547b1a00341209401329ec79b8f740c5 | [
"MIT"
] | permissive | SubramaniamLab/DEGenR | 1f88fee026d6a26f40b63a9bc507824584876495 | 5f5cf58a46e11f20cd692e920edffdc56edbe158 | refs/heads/main | 2023-04-07T00:37:02.381321 | 2021-10-08T15:11:39 | 2021-10-08T15:11:39 | 339,816,245 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 52,377 | r | ui.R |
source(paste(getwd(),'global.R',sep="/"))
header <- dashboardHeader(
title = "DEGenR"
#titleWidth = 250
)
header$children[[3]]$children[[3]] <- div(tags$img(src='', align="right", height='50px'))
sidebar <- dashboardSidebar(
width =250,
sidebarMenu(id = "sidebarmenu",
menuItem("DEGenR Introd... |
f512374c6dae0825b6b15a0695b4648f4be9d210 | 4cee6dec70875ca85f20dd738932be86f361a63e | /pkg/tests/testthat/test-ci.R | 3049404f27ec88de239ab86e2f5db38c74d2f6e7 | [] | no_license | dieterich-lab/pulseR | 9b7114769b48a305ba0a11357226e8f774b73a20 | 1323b378e95b483c8bda99d6c71befccd45c810f | refs/heads/master | 2021-01-18T20:40:00.474158 | 2018-10-26T10:45:32 | 2018-10-26T10:45:32 | 72,013,067 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,458 | r | test-ci.R | context("Confidence intervals")
set.seed(259)
formulas <- MeanFormulas(X = mu, Y = nu)
formulaIndexes <- list(
EX = 'X',
EXandY = c('X', 'Y'))
normFactors <- list(
EX = c(1),
EXandY = c(1, .1)
)
nTime <- 1
nReplicates <- 4
conditions <- data.frame(condition = rep(names(formulaIndexes), each = nTime),
... |
7a03d797ceddba1aaeaa34dcac0741b0e31da057 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tangram/examples/table_builder.Rd.R | 51a33b43f43385016eb8a56b090d5f625acccfe6 | [] | 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 | 1,077 | r | table_builder.Rd.R | library(tangram)
### Name: table_builder
### Title: Table Construction Toolset
### Aliases: table_builder col_header row_header write_cell home cursor_up
### cursor_down cursor_left cursor_right cursor_pos carriage_return
### line_feed new_line new_row new_col table_builder_apply add_col
### add_row
### ** Exa... |
ee78e6c58da9897236571c83da25a21cebe58de4 | 948b78fc214a1b9981790c83abb6284758dbfa89 | /r-library/man/locfitGrowthEstimate.Rd | aa1ce924d42b0597c62fdea0ec965936be6454fc | [
"MIT"
] | permissive | terminological/jepidemic | 4ea81235273649b21cf11108c5e78dd7612fdf6e | f73cc26b0d0c431ecc31fcb03838e83d925bce7a | refs/heads/main | 2023-04-14T10:13:56.372983 | 2022-05-24T22:07:10 | 2022-05-24T22:07:10 | 309,675,032 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 900 | rd | locfitGrowthEstimate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/estimators.R
\name{locfitGrowthEstimate}
\alias{locfitGrowthEstimate}
\title{Generate a smoothed estimate of the absolute growth rate of cases using a poisson model.}
\usage{
locfitGrowthEstimate(
simpleTimeseries,
degree = 2,
window = ... |
99d4bac7d4b24acd7e4faec33478c5d50b3598d4 | bf6eeabe8154eb0c192c1f27603dbd53fca4bdec | /R/class.R | 7f1acfa1bb5f9e9c8f510d2c147ed8908d45ba9b | [] | no_license | gokmenzararsiz/MLSeq | a81484c77bc77cc43a9317a5cc71ec10eb751a63 | f58cf5297d97b1f338d1748cc38df75f2e2accd3 | refs/heads/master | 2021-01-17T09:21:45.177052 | 2016-12-22T07:42:47 | 2016-12-22T07:42:47 | 18,428,996 | 1 | 1 | null | 2016-04-19T13:10:50 | 2014-04-04T05:58:29 | R | UTF-8 | R | false | false | 3,387 | r | class.R | setOldClass(c("confusionMatrix","train"))
#' \code{MLSeq} object
#'
#' For classification, this is the main class for the \code{MLSeq} package.
#'
#' Objects can be created by calls of the form \code{new("MLSeq", ...)}. This type
#' of objects is created as a result of \code{classify} function of \code{MLSeq} package.... |
fe6c6bb235b114dd84c17b6c5e246700037bdeaa | 548f28065c18662debd5b6514fc634913a77b49c | /medium_case_animate.R | 72b2467944c25b0926ca917558f536c463484b5d | [
"MIT"
] | permissive | EngyMa/animated-case | 34a5f6ab669d008af3d473961ac08b57b7c59d80 | cb762351ac6ecc7d218babe4b9a9c46e01558d18 | refs/heads/master | 2020-04-25T17:11:30.399169 | 2018-12-04T12:35:46 | 2018-12-04T12:35:46 | 172,938,451 | 1 | 0 | MIT | 2019-02-27T15:15:01 | 2019-02-27T15:15:00 | null | UTF-8 | R | false | false | 6,451 | r | medium_case_animate.R | # ggplot2 theme to use later
theme_chris <- function (base_size = 12, base_family = "serif", ticks = TRUE)
{
ret <- theme_bw(base_family = base_family, base_size = base_size) +
theme(legend.background = element_blank(), legend.key = element_blank(),
panel.border = element_blank(),
strip.b... |
cea4f1e96f40a1c560cae6bf13fcdae1c6895e44 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.storage/man/backup_create_backup_vault.Rd | 83e644fc1c102fc6d87ea7aa771a1800b8fbf9d5 | [
"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 | 1,609 | rd | backup_create_backup_vault.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/backup_operations.R
\name{backup_create_backup_vault}
\alias{backup_create_backup_vault}
\title{Creates a logical container where backups are stored}
\usage{
backup_create_backup_vault(
BackupVaultName,
BackupVaultTags = NULL,
Encryptio... |
2a5dd8493029ecad3647acc8f67fef555fec4628 | 3f0498c8f6463302b1a8ca3ea5d357f41cd08e60 | /Composite.R | 601773e41093ee63684eaf75fa32dd10e3033a63 | [] | no_license | YTTom/R | 3f30f29597e8d41724678b670e674948753b569a | 38fa42889ff1abc856eefe086e225962e07c6062 | refs/heads/master | 2022-11-06T15:50:29.478437 | 2020-06-27T06:16:47 | 2020-06-27T06:16:47 | 273,939,345 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | Composite.R | #讀取檔案(路徑要改成自己的)
data <- read.csv('~/Downloads/data.csv')
#抓取106和106年度的資料
year106<-data[年度=='106',]
year107<-data[年度=='107',]
#抓取總人數的資料
year106_people <- year106[,c(4)]
year107_people <- year107[,c(4)]
#將總人數轉換為matrix(向量)
matrix106<-matrix(year106_people)
matrix107<-matrix(year107_people)
#合併兩年的matrix
merge_matrix<-c... |
444efeea20bd9a73b9c2aa86708aa506aedc0142 | d2c7b6f677eb501b6f08c54fce7aebaf4119ae15 | /man/plot.ssgraph.Rd | b62403c6c493c09e29afc4b460b8d0f2b36129de | [] | no_license | cran/ssgraph | 9b792a284ee5ca70c24bbeeaf998fe769ef323db | 15e27003a9ef1bf99ccc881f255853e309e17914 | refs/heads/master | 2023-01-12T03:58:58.043221 | 2022-12-24T12:30:02 | 2022-12-24T12:30:02 | 130,663,048 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,583 | rd | plot.ssgraph.Rd | \name{plot.ssgraph}
\alias{plot.ssgraph}
\title{ Plot function for \code{S3} class \code{"ssgraph"} }
\description{ Visualizes structure of the selected graphs which could be a graph with links for which their estimated posterior probabilities are greater than 0.5
or graph with the highest posterior probability. }
\... |
01d4a7aaddbf9e56e4582b228f734bf4033eba3d | bd454c45d38cc48f6247d9dec829de0533793549 | /man/piat.feedback.no_score.Rd | 242e74f2aad06c5a6ef4c5bd284b9d50ff5d6064 | [
"MIT"
] | permissive | pmcharrison/piat | f445431e6d59cbf63228619547ad4e078af58c2f | 73c77acf379c233480819738214187cd9b1ba3f7 | refs/heads/master | 2023-08-14T17:02:04.665315 | 2023-07-26T21:27:39 | 2023-07-26T21:27:39 | 131,727,383 | 2 | 3 | NOASSERTION | 2022-12-21T10:09:03 | 2018-05-01T15:09:06 | R | UTF-8 | R | false | true | 406 | rd | piat.feedback.no_score.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/feedback.R
\name{piat.feedback.no_score}
\alias{piat.feedback.no_score}
\title{PIAT feedback (no score)}
\usage{
piat.feedback.no_score(dict = piat::piat_dict)
}
\arguments{
\item{dict}{The psychTestR dictionary used for internationalisation.... |
876aa0e93b35623f6b4d50db30e6392b133124df | 1522b308afd42bc80bf4b5192c2d1670f8579c26 | /man/Fragman-package.Rd | 4de27f2a5cee6ffb60d2a65dd7431b30bf8cead1 | [] | no_license | covaruber/Fragman | 2c1830036ccd968c1d4df82983c0cb74d7c84651 | 55fd3627d9f6699ad97f1643883ce93387b382c3 | refs/heads/master | 2020-04-11T21:44:30.948814 | 2018-12-17T10:45:13 | 2018-12-17T10:45:13 | 162,114,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,194 | rd | Fragman-package.Rd | \name{Fragman-package}
\alias{Fragman}
\docType{package}
\author{
Giovanny Covarrubias-Pazaran, Luis Diaz-Garcia, Brandon Schlautman, Walter Salazar, Juan Zalapa.
}
\title{Fragment analysis and automatic scoring}
\description{Fragman is a package designed for Fragment analysis and automatic scoring of biparental popula... |
e45e5c9ae5fa18baaaaeacfe138207c9c584a6c6 | 0e6d8c50bd6c0ef5e3c97b17626bb42c9e3d8eff | /R/RcppExports.R | 834ce8db68665acb5c17090dbe87c748e4492e6b | [] | no_license | tobiasmuetze/gscounts | e04903db1993df538065cc427c45f01d2904796f | 1c614a3fd36be86a5608b83df91df040fbf0d98d | refs/heads/master | 2021-11-23T23:36:17.203662 | 2021-11-01T16:14:35 | 2021-11-01T16:14:35 | 92,069,741 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 503 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
cpp_calc_critical <- function(r, lower, upper, error_spend, information, theta, side) {
.Call('_gscounts_cpp_calc_critical', PACKAGE = 'gscounts', r, lower, upper, error_spend, information,... |
f9f1bc3dd54383ff64b71930259786ac326b7109 | 9580717f9f09fe026dee8224b35f3f72c9f78675 | /man/create_net_animate.Rd | 516314018fae56531e704ad4bdaf72c2282197d8 | [] | no_license | sctyner/netvizinf | dc0fed9791ae7c29255ff133b010ed5f6bc39a17 | d9cd0e249ad6b351b59b9478d238dbaf0a8762ce | refs/heads/master | 2021-05-03T22:30:58.889245 | 2017-10-26T19:52:35 | 2017-10-26T19:52:35 | 71,607,611 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 372 | rd | create_net_animate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/crete-net-animate.R
\name{create_net_animate}
\alias{create_net_animate}
\title{Construct plots to be animated.}
\usage{
create_net_animate(dat)
}
\arguments{
\item{dat}{\code{data.frame} The output of a call to \code{\link{tween_microsteps}}... |
3f87ab69eb4dfe1532b38316ecaf047610ebed19 | b8aed4a0a0f267d15c97ddc8957949999b9f5004 | /R/fsf_query.R | b0151eb27f60cac5ecd00670603d577317d8860e | [] | no_license | jtbradt/firstStreetAPI | af0d721bd9c76c8055a407043bf79ff33902a356 | 7870fc59dfb0e7d9e630a486ba356723ea9bdd0f | refs/heads/master | 2023-06-12T14:01:03.068212 | 2021-07-13T15:27:50 | 2021-07-13T15:27:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 718 | r | fsf_query.R | #' FSF query function
#'
#' This function constructs a fsf API request:
#' @param api.cat is one of FSF's 7 API categories
#' @param api is one of FSF's 18 APIs
#' @param arg is a query argument
#' @keywords fsf.query
#' @export
fsf.query <- function(api.cat, api, arg) {
# Create path:
path <- paste(pkg.env$a... |
73e3f96948fee51fb6f06e96037aa95aa32f324b | 05884bd8afb3222aec86c6a2b363e67ed3c64590 | /toolbox/examples/ecoex.R | d1171d45a50f9684f63aa3a1fb5dbde781072cc7 | [] | no_license | nmarticorena/mineria_datos | bcfbea31e6de6f292e4404068b360638ab8a3cbb | 6e3f22c2fb79fe551a5d8c94136f495638088813 | refs/heads/master | 2020-03-09T00:36:28.806062 | 2018-06-14T03:12:35 | 2018-06-14T03:12:35 | 128,492,056 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 118 | r | ecoex.R | # eco example 1
x=1:10
z=eco(x,3)
#x_1=(0,1,2,3...)
#x_2=(0,0,1,2,...)
#x_3=(0,0,0,1,2...)
#z=x_1+x_2+x_3
z
|
738f7243f740f9e22f605a88da362a4f9ee50987 | 5a3e9cad940ab62c63177618397f4f7fa91069cc | /Destructive_harvest_2018_BRA_LAU_GAL_WUU.R | 6cbcfe8c2da0db18db49a16cc0725f5697ecdaf7 | [] | no_license | mirodemol/destr_valid_BGLW | ade749e5aca48937ed4b38cf974fa4f5abfd1287 | 312461570b48ee7301c36471e00754758bb9d983 | refs/heads/master | 2020-03-15T11:34:34.753516 | 2018-05-15T13:52:05 | 2018-05-15T13:52:05 | 132,123,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,925 | r | Destructive_harvest_2018_BRA_LAU_GAL_WUU.R | ####
# This code is to analyse the 2018 field data from destructive harvests
# written by Miro
####
remove(list=ls())
# load packages and functions ----
delete.na <- function(DF, n=0) {
DF[rowSums(is.na(DF)) <= n,]}
# read data, check data ----
setwd('C:/Users/midemol/Dropbox/Doctoraat/fun_in_R/destr_va... |
fd77c009690f53f04b653ddeaef5046ed9c6a99a | b71ce56fa3133ad7040e493a525cfe7ca0b07b2f | /man/metroTilesGrid.Rd | ebbe396c09dc2f2561e1458a7cc66806e471417e | [] | no_license | bright-spark/shinyMetroUi | 2a22ede6e35b91fe5780e4f2bebb83d57332d1fb | 2c2acfe9abd3f1d444d3f3d95c99d441532f4d8a | refs/heads/master | 2023-03-19T03:42:01.896849 | 2019-12-20T16:04:10 | 2019-12-20T16:04:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,948 | rd | metroTilesGrid.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/metro-tiles.R
\name{metroTilesGrid}
\alias{metroTilesGrid}
\title{Create a Metro 4 Tiles Grid}
\usage{
metroTilesGrid(..., group = FALSE, title = NULL, size = 2)
}
\arguments{
\item{...}{Insert metroTile inside.}
\item{group}{Whether tiles a... |
76b3f7b269a9d39bb81acef8af72bad24010d85d | 9f89cc309f9ddf8765f43605409de498d5e8f0e3 | /Assignment4/Assignment4.R | d61482a4d208286425df02ab9ce9bfa243b26822 | [] | no_license | htdrajiv/r_programming | 386f9266c04778d59a335ab5c0b1f4918259dd80 | bb780777069df22958aafdaa203a813be90cd54c | refs/heads/master | 2021-01-20T18:27:21.465794 | 2016-08-17T02:09:35 | 2016-08-17T02:09:35 | 65,594,777 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,384 | r | Assignment4.R | data1 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q1Q2/Divvy_Trips_2014_Q1Q2.csv")
data2 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q3Q4/Divvy_Trips_2014-Q3-07.csv")
data3 <- read.csv("E:/Projects/R_Programming/Data/Divvy_Stations_Trips_2014_Q3Q4/Divvy_Trips_2014-Q3-... |
9282d5297e11663bfaf6f87ee3ebadaab9403ae1 | c3f09d043409d3f30cc1de6732a55d4a91d6a0a7 | /scripts/script_0.R | 336d20cdbe7fbf8f15a7cb86b6a40a1c4efe67bf | [] | no_license | mark-andrews/repdemoproj | 77afdbeab6e56bc02fb26b88b0321070bd571a41 | f51871f1b22347f39decf708a21c617da10bc3c6 | refs/heads/master | 2022-11-11T14:20:28.881053 | 2020-07-04T15:22:44 | 2020-07-04T15:28:35 | 276,920,438 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 139 | r | script_0.R | library(here)
library(bayeslmm)
result <- lmm(rt ~ day + (1|subject), data = sleepstudy_df)
saveRDS(result, file=here('tmp/model_0.rds')) |
b6ead726b3b371b0617b94fa377c1af584f168de | b98ece6254219513180cc730f7e26f7f9a277124 | /plot3.R | c38b2b6a76673b8bce2e6acb0865ce2e47cc68b4 | [] | no_license | OlgaRusyaeva/ExData_Plotting1 | 543fdf16bba72a3cac2d60170f56325b362a3e63 | 0c0988359a8be37155b5c0142b09eb3a9a26f6c5 | refs/heads/master | 2021-01-14T10:23:38.695401 | 2015-06-07T14:41:30 | 2015-06-07T14:41:30 | 35,160,355 | 0 | 0 | null | 2015-05-06T13:33:03 | 2015-05-06T13:33:03 | null | UTF-8 | R | false | false | 920 | r | plot3.R | #read data from a file with the dates 2007-02-01 and 2007-02-02
library(sqldf)
fileName <- "household_power_consumption.txt"
df <- read.csv.sql(fileName, sql='select * from file where Date="1/2/2007" OR Date="2/2/2007"',sep=";",header=T)
closeAllConnections()
#create new column out of Date and Time columns
df$DateTime ... |
06cf0ad90de9811f7e889ae93d8c0014e442490d | 0f709b508989fc77d8f1d62ad97ef050e1bbed48 | /Week_06_HypothesisTesting/exercises.week.06.hypothesis.testing.applied.R | 3742be067332f33af1180cd1a7795d88298989d7 | [] | no_license | alekssro/DataScienceBioinf | b1bcf5afa4e0edb38d6cc25cc694bc948d47a54f | 968dba2e87af764223e6d15a7a1e510581176a8b | refs/heads/master | 2021-08-29T01:43:21.753379 | 2017-12-13T09:56:16 | 2017-12-13T09:56:16 | 112,108,656 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,485 | r | exercises.week.06.hypothesis.testing.applied.R | library(tidyverse)
#### Hypothesis testing ####
# Our null hypothesis is: minor allele frequencies are the same everywhere on chromosome 6
# Our alternative hypothesis: some places have higher minor allele frequencies than expected (balancing selection)
# Load data and calculate maf
d = read_delim(file="1000g.alle... |
871c330a074ba89c695e7142b8fe0694ebc916f3 | b98c5cbe6ab6887097e0337376fc56b6cec15996 | /man/methyvolc.Rd | 375f88455f04ed25e312ac3e9c68c1d869356a00 | [
"MIT"
] | permissive | nhejazi/methyvim | bebd0758f8aff2ad06430aac43b82cf0ec4f45b1 | 7b4ee9f83aa7d2cfd11645fcb0658de2ea7a0df7 | refs/heads/master | 2021-03-24T13:29:34.450771 | 2020-04-27T19:11:00 | 2020-04-27T19:11:00 | 79,256,902 | 1 | 1 | MIT | 2020-02-06T01:09:32 | 2017-01-17T18:15:07 | TeX | UTF-8 | R | false | true | 1,512 | rd | methyvolc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plots.R
\name{methyvolc}
\alias{methyvolc}
\title{Volcano plot for methytmle objects}
\usage{
methyvolc(x, param_bound = 2, pval_bound = 0.2)
}
\arguments{
\item{x}{Object of class \code{methytmle} as produced by an appropriate call
to \code{... |
316bd99da2b5173f5fc5951b2891de6ac84c0699 | 745d585395acad1376d84f8ca1284c13f2db70f0 | /man/make.ISOyear.Rd | 5837e68752a5193145a0f08625b8db9ce73bcec4 | [] | no_license | pik-piam/quitte | 50e2ddace0b0e2cbfabf8539a0e08efe6bb68a0b | 4f5330695bd3d0e05d70160c1af64f0e436f89ea | refs/heads/master | 2023-08-20T04:15:16.472271 | 2023-08-09T08:14:32 | 2023-08-09T08:14:32 | 206,053,101 | 0 | 8 | null | 2023-08-09T08:14:34 | 2019-09-03T10:39:07 | R | UTF-8 | R | false | true | 748 | rd | make.ISOyear.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make.ISOyear.R
\name{make.ISOyear}
\alias{make.ISOyear}
\title{speedily converting years to POSIXct values}
\usage{
make.ISOyear(years)
}
\arguments{
\item{years}{ignored.}
}
\value{
The \code{\link[=ISOyear]{ISOyear()}} function.
}
\descript... |
ba2dde2cda9c740ea85db49eff513c31406aa4a1 | 1bd99e7010d5314765a4fa482176ee2963e460d5 | /tests/testthat/test-expectation.R | d19c12563cda42191971647fbc9e5f536a534820 | [] | no_license | hadley/rv2 | 6470147c77b7ffacd2ea5d4546c27113aed9ab24 | a56d359026f9fd8d57fa47aa81430c84af922c89 | refs/heads/master | 2021-01-24T03:58:07.773158 | 2017-01-12T02:08:37 | 2017-01-12T02:08:37 | 15,084,986 | 7 | 4 | null | null | null | null | UTF-8 | R | false | false | 736 | r | test-expectation.R | context("Expectation")
dice <- rv(1:6)
coin <- rv(c(-1, 1))
test_that("expectation correct for known cases", {
expect_equal(E(dice), 3.5)
expect_equal(E(coin), 0)
})
test_that("expectation is additive", {
expect_equal(E(dice + coin), E(dice) + E(coin))
expect_equal(E(dice + dice), 2 * E(dice))
expect_equa... |
d03e0b1eac3a7b66d792f25ab586fc815b161e76 | cbdfc6b1ee1121090a538a74a9408fa5c206a4f8 | /R/prev.R | dbc462f6341ca94287f429bae0edd4ca865c03eb | [] | no_license | mplex/multiplex | 9d1eb7e1289f2fce4181094f831f6f020f1526d9 | 4153f723ac0c8d1e42c78fce91c26977d955f329 | refs/heads/master | 2023-07-21T17:23:08.732663 | 2023-07-10T12:10:46 | 2023-07-10T12:10:46 | 65,552,701 | 23 | 4 | null | null | null | null | UTF-8 | R | false | false | 4,249 | r | prev.R | prev <-
function (x)
{
if (is.array(x) == FALSE)
stop("Data must be a stacked array of square matrices.")
if (is.na(dim(x)[3]) == TRUE) {
s0 <- data.frame(matrix(ncol = 1L, nrow = 1L))
if (isTRUE(all.equal(replace(x %*% x, x %*% x >= 1L,
1L), x) == TRUE))
... |
39e8763bd1cfd9ac71e7333d1e9507853f851e47 | 236f960cf07b0b68034821234dc6ae45c1bf2e79 | /Bayesian Statistics/multiparameter models,HW5-1.R | 7a4489ccf4435d36a7b335e23c549af4ea7b9198 | [] | no_license | xiaojianzhang/R_life | b001b65eeab429a394f841f62d1c76e6676d2db1 | 62ad4072079b0fe814f3c5ba57bc3db57358cf6d | refs/heads/master | 2021-01-01T04:11:51.997116 | 2016-05-17T04:12:39 | 2016-05-17T04:12:39 | 58,959,582 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,886 | r | multiparameter models,HW5-1.R | #Chapter 5 Exercise 5
#setup
library(geoR)
ydata <- c(10, 10, 12, 11, 9)
n = 5
y_bar = 10.4
s_square = 1.3
#(c) How do the incorrect and correct posterior
# distributions differ?
#(1)Consider incorrect posterior
#draw sigma_square from inverse chi square(n-1, s^2)
sample_sigma_square <- rinvchisq(1000, n-1, s_square)... |
850d20a2b39db6b1820fb55060ac69129bc20e89 | d9112b28db3cdc905fa4ee5abb223b969da81579 | /man/PerformPeakAnnotation.Rd | 2713d3b7bc6501bd4b3857aee145324b82fa42ca | [] | no_license | wangyongdalt/OptiLCMS | 323d4a483fc01ae5ffede01e474640bca31285f4 | 2ed8b90f7cd54cd38275240c6801d779b00398c0 | refs/heads/master | 2023-07-27T01:45:54.421922 | 2021-09-02T13:28:08 | 2021-09-02T13:28:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,033 | rd | PerformPeakAnnotation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Perform_functions.R
\name{PerformPeakAnnotation}
\alias{PerformPeakAnnotation}
\title{Perform peak annotation}
\usage{
PerformPeakAnnotation(mSet, annotaParam, ncore = 1, running.controller = NULL)
}
\arguments{
\item{mSet}{mSet object, usual... |
12350b16a7859436c7676b5714ace1a108826f5f | 4d3672136d43264176fe42ea42196f113532138d | /man/Rehab.Rd | 5b8691b589fafd59ae224175eb582eb15332d637 | [] | no_license | alanarnholt/BSDA | 43c851749a402c6fe73213c31d42c26fa968303e | 2098ae86a552d69e4af0287c8b1828f7fa0ee325 | refs/heads/master | 2022-06-10T10:52:15.879117 | 2022-05-14T23:58:15 | 2022-05-14T23:58:15 | 52,566,969 | 5 | 13 | null | 2017-07-27T02:06:33 | 2016-02-26T00:28:07 | R | UTF-8 | R | false | true | 939 | rd | Rehab.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BSDA-package.R
\docType{data}
\name{Rehab}
\alias{Rehab}
\title{Rehabilitative potential of 20 prison inmates as judged by two psychiatrists}
\format{
A data frame/tibble with 20 observations on four variables
\describe{
\item{inmate}{inmate... |
971f4407e94b3a1ee886d555c0f4a50c8c5c9d81 | 062355817e0682b8eaaefaf0e7477c031895e02f | /test_web_scraping.R | a75394dc903cb26d35e7b54299a6478e0ce20c7d | [] | no_license | informationista/wikiscraping | 5f8b7b6e2b5f1211fbbca9805e7e2a53d8a000c8 | 3fda53a5a91efcc71ca6f90d2dc85ec9cef95946 | refs/heads/master | 2021-01-12T07:26:47.316999 | 2017-01-04T20:48:47 | 2017-01-04T20:48:47 | 76,962,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,600 | r | test_web_scraping.R | library(rvest)
library(RCurl)
library(plyr)
library(tidyr)
library(ggplot2)
library(httr)
library(dplyr)
##create a list of URLs to retrieve
##create the list of years of interest
year_list <- as.list(c(2006:2016))
url_df <- function(year){
url <- paste("https://en.wikipedia.org/wiki/Deaths_in_", month.name, "_", y... |
58c18088279abc304368b5464c9ff4471711a07c | 8ab151cc5bfb154cc4ae4b1d97ddd6b2bedc95fa | /R/filter.date.R | 4919c2821e4eb20b7159cec9598b6f0dbec4f709 | [] | no_license | arturochian/MetFns | 5eafd4bc404edbbdefd27223c5b8a99d32cd048d | 5ce9fc52efdac3c2a12aa18282ab71e53aacf115 | refs/heads/master | 2020-04-06T04:20:15.871591 | 2014-09-16T00:00:00 | 2014-09-16T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 425 | r | filter.date.R | filter.date<-function(data,year,month, day.beg,day.end=day.beg)
{
if(!is.data.frame(data) || !is.numeric(c(year,month,day.beg,day.end)) || year<1984
|| (month<1) || (month>12) || (any(c(day.beg,day.end)<1)) || any(c(day.beg,day.end)>31))
stop("invalid input parameter(s) specification")
day<-day.... |
ace5b9f69c36119e52d9e0c60e198f8ed2c11ab3 | 36b14b336e0efdda255fa3f163af65127e88105f | /man/Problem2.33.Rd | 62417be1c52bea0442a5820d811c9f75a3bb5f6d | [] | no_license | ehassler/MontgomeryDAE | 31fcc5b46ae165255446e13beee9540ab51d98b3 | 43a750f092410208b6d1694367633a104726bc83 | refs/heads/master | 2021-06-24T13:46:19.817322 | 2021-03-11T16:36:37 | 2021-03-11T17:13:18 | 199,803,056 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 388 | rd | Problem2.33.Rd | \name{Problem2.33}
\alias{Problem2.33}
\docType{data}
\title{Exercise 2.33}
\usage{data("Problem2.33")}
\format{A data frame with 20 observations on the following variable(s).\describe{
\item{\code{Uniformity}}{a numeric vector}
}}
\references{Montgomery, D.C.(2017, 10th ed.) \emph{Design and Analysis of Experiments}, ... |
cf9c260afef9aa5ee359dea14bd8fde0d43ddb4c | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed_and_cleaned/10304_2/rinput.R | 416b7b832a32829cf8369914c1225bd00dbfdca9 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | rinput.R | library(ape)
testtree <- read.tree("10304_2.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10304_2_unrooted.txt") |
449c26ee542d0ee91b917b3679fb1feb3e841e3b | 7d125cf7b30e9be0ef1f02e24ad13495b4481f4e | /src/Library/filterGeneExpSamples.R | 03f628624b8199ed3cc3ec42fccf7c6af481f605 | [] | no_license | DToxS/Differential-Comparison | 0616004e275cfa17d605505cecc6842a0baa4b2a | d6b3d4cc7c4ef2bdb21527655fb927c146453942 | refs/heads/master | 2022-04-06T22:12:37.767298 | 2020-02-27T22:19:56 | 2020-02-27T22:19:56 | 105,199,221 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,661 | r | filterGeneExpSamples.R | # Filter outlier samples by removing outlier samples.
filterGeneExpSamples <- function(exprt_design_merged, read_counts_merged, drug_names, dist_cutoffs, subset_field_name=NULL, dist_cutoff_outlier=0.01, dist_cutoff_group=0.015, min_samples=3, filter_outlier=TRUE, keep_under_samples=FALSE, plot_orig_clust=FALSE, plot_... |
05d6e4fc665c68b0fce9a9d40098b9ca605600ec | 202684be012c3153a9791a6430a8f7eae997a036 | /data_handling.R | d6b46d9ac821c9c5c415d3f483245d82e9ee9965 | [] | no_license | fbaffie/NVE_API_readR | cfc2090df2a9e8e737a85278486059ff6b38790f | b8dc5b1bb25f6374bfe3ee6f1ddf23584220a4b5 | refs/heads/master | 2020-03-21T12:48:16.703179 | 2018-07-03T09:16:00 | 2018-07-03T09:16:00 | 138,572,644 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,787 | r | data_handling.R |
# Construct metadata table from data list
metadata_for_app <- function(data_main) {
df_meta <- c()
for (i in 1:length(data_main)) {
df_meta <- rbind(df_meta, data_main[[i]]$metadata)
}
df_meta$prec_mean <- sapply(data_main, function(x) x$prec_mean)
df_meta$runoff_mean <- sapply(d... |
687ed98d7f3390038dfb73757cfc1bbdc83cdb95 | 20a9435ef4586a43a4e55502d0f0ac40aa185821 | /tests/testthat/test_binary_single.R | 2d140af5d1cce490b5ea74adea763cd3b70e4dbe | [] | no_license | cran/hmi | a9df9353e459bfe45d9952370a962fa879c8f5a1 | 6d1edb0d025c182cedb325fa9826f4ba00e988d1 | refs/heads/master | 2021-01-23T06:20:51.264453 | 2020-10-01T22:20:02 | 2020-10-01T22:20:02 | 86,358,162 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 672 | r | test_binary_single.R | context("binary_single")
library(testthat)
library(hmi)
library(mice)
set.seed(123)
y_imp <- sample(c(0, 1, NA), size = 150, replace = TRUE)
y_imp2 <- sample(c("A", "B", NA), size = 150, replace = TRUE)
X_imp <- cbind(1, iris[, 1:4])
#test_check("hmi")
test_that("binary_single returns plausible values", {
... |
5a306bb094f0a9483b86faae18f0446d2356e1f5 | 184180d341d2928ab7c5a626d94f2a9863726c65 | /issuestests/SpatialEpi/man/binomialLogLkhd.Rd | 51099b4faaff8ebf088a74d98fc2d920ff6c8782 | [] | no_license | akhikolla/RcppDeepStateTest | f102ddf03a22b0fc05e02239d53405c8977cbc2b | 97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5 | refs/heads/master | 2023-03-03T12:19:31.725234 | 2021-02-12T21:50:12 | 2021-02-12T21:50:12 | 254,214,504 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 687 | rd | binomialLogLkhd.Rd | \name{binomialLogLkhd}
\alias{binomialLogLkhd}
\title{Compute Binomial Likelihoods}
\description{Compute binomial likelihood ratio test statistic for Kulldorff method}
\usage{binomialLogLkhd(cz, nz, N, C)}
\arguments{
\item{cz}{count inside zone}
\item{nz}{expected count inside zone}
\item{N}{total expected count... |
5678eedf2d3ee288e53c1b24919ae308952191b1 | 29585dff702209dd446c0ab52ceea046c58e384e | /s2dverification/R/ConfigApplyMatchingEntries.R | ae976662923794df22ef1ffcbf9d0b14d4fe2349 | [] | 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 | 5,647 | r | ConfigApplyMatchingEntries.R | ConfigApplyMatchingEntries <- function(configuration, var, exp = NULL, obs = NULL, show_entries = FALSE, show_result = TRUE) {
## Function to tell if a regexpr() match is a complete match to a specified name
isFullMatch <- function(x, name) {
ifelse(x > 0 && attributes(x)$match.length == nchar(name), TRUE, FALS... |
a19f31d7c42618633cf4da9c30927048770be22a | efa60dd053fbeb2c176315ee269eae9f2ecb1a58 | /enseignements/rcode/xgboost.R | 014767664f1bb3ab7ae70b688cd0d9cd235766ef | [
"MIT"
] | permissive | masedki/masedki.github.io | b5e5f5ac2fc9d2241b1b022560fb1b63fe067f26 | ee79edc83016714151ebf2cddcf951e1da214f2e | refs/heads/master | 2023-07-10T05:19:27.703327 | 2023-07-07T07:35:32 | 2023-07-07T07:35:32 | 89,248,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,698 | r | xgboost.R | setwd("~/Dropbox/enseignement/M1/supports/Rscripts")
rm(list=ls())
load("insurance.rda")
require(rpart)
require(rpart.plot)
require(caret)
require(doParallel)
require(xgboost)
# lecture du jeu de données
summary(insurance)
set.seed(11)
train = sample(1:nrow(insurance), round(0.75*nrow(insurance)))
insurance.tr = insur... |
a20424ef3e73f44cb485f17581ff47eb2cf31404 | 9e1d5eaa04362bd5c2669b62cef8da6e5f99586f | /project-prototype/dataprocessing.R | 2ca2c51f1091668b01c8d2a038ab76be4cf57989 | [] | no_license | deekshachugh/msan622 | 6e4f672bad23d28ebc5716e9ad59fead9a36aea4 | 49ba336831cd3d75845c0504b39bc2d67eaf307e | refs/heads/master | 2021-01-21T03:50:37.782150 | 2014-05-16T06:03:58 | 2014-05-16T06:03:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,064 | r | dataprocessing.R | data <- read.csv("/home/deeksha/github/msan622/project-dataset/COmpleteweatherdata.csv")
head(data)
colnames(data) <- c("Date", "Temperature", "Dew Point Temperature","Precipitation","Humidity","Wind Speed","Percent Cloud Cover", "City")
latlongdata <- read.csv("/home/deeksha/Desktop/airports/airports_fsx_icao_lat_lon... |
155f75e448a195e0aef9e2220fb93a52bef4d63a | 01b8fa708e8e0318871d0ee7b4155ae35d64dd9a | /R/rd_sens_cutoff.R | 25ab22e4d0ef98b3f8f6df95ef06ced38e9123be | [] | no_license | felixthoemmes/rddapp | b313f32dd89248de26b173be077ac28ea90bf022 | f81091ab1978c1ee0a50f7608a4a42ae56f6e4b1 | refs/heads/main | 2023-04-12T23:46:30.030859 | 2023-04-07T01:49:56 | 2023-04-07T01:49:56 | 119,074,922 | 10 | 4 | null | 2022-01-31T14:50:54 | 2018-01-26T16:24:45 | HTML | UTF-8 | R | false | false | 2,423 | r | rd_sens_cutoff.R | #' Cutoff Sensitivity Simulation for Regression Discontinuity
#'
#' \code{rd_sens_cutoff} refits the supplied model with varying cutoff(s).
#' All other aspects of the model, such as the automatically calculated bandwidth, are held constant.
#'
#' @param object An object returned by \code{rd_est} or \code{rd_impute}.... |
bb8ba4055b3d45e6a187e43d5e93cdef6be15354 | a56e7a0ce097b8da6ae95f750b5bf1a6bbb251c5 | /r/tests/testthat/test_trading_api.R | 128205dcff8814edb09fb75b8f28bfd1e4bab268 | [] | no_license | harshabakku/deribit_options | d08f7a61386f6a047ec0c4726d882fead8fbb7a7 | 4f344f2fbf0b761cc5378d852a38d33849223b53 | refs/heads/master | 2022-12-08T20:58:47.873520 | 2020-09-01T16:38:04 | 2020-09-01T16:38:04 | 292,051,793 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 18,327 | r | test_trading_api.R | # Automatically generated by openapi-generator (https://openapi-generator.tech)
# Please update as you see appropriate
context("Test TradingApi")
api.instance <- TradingApi$new()
test_that("PrivateBuyGet", {
# tests for PrivateBuyGet
# base path: https://www.deribit.com/api/v2
# Places a buy order for an instr... |
7eb174d9f8f5ddd70fe2506da2b89f7ff030f10b | c104b6569f1bc152b2e05c8cbbd91a5d88962be2 | /man-roxygen/file-plural.R | 90c69e609614bb579febe2dc133c42c20fb8c12f | [
"MIT"
] | permissive | tidyverse/googledrive | 46057f3ea96ae0fc47da30fd5d38d35e01a67d2a | 74a69a3a1fd66e930802ce6e461538c9e15f7c86 | refs/heads/main | 2023-09-01T18:13:56.084465 | 2023-06-27T15:59:01 | 2023-06-27T15:59:01 | 89,535,184 | 317 | 60 | NOASSERTION | 2023-06-27T06:13:13 | 2017-04-26T23:22:32 | R | UTF-8 | R | false | false | 219 | r | file-plural.R | #' @param file Something that identifies the file(s) of interest on your Google
#' Drive. Can be a character vector of names/paths, a character vector of file
#' ids or URLs marked with [as_id()], or a [`dribble`].
|
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