blob_id stringlengths 40 40 | directory_id stringlengths 40 40 | path stringlengths 2 327 | content_id stringlengths 40 40 | detected_licenses listlengths 0 91 | license_type stringclasses 2
values | repo_name stringlengths 5 134 | snapshot_id stringlengths 40 40 | revision_id stringlengths 40 40 | branch_name stringclasses 46
values | visit_date timestamp[us]date 2016-08-02 22:44:29 2023-09-06 08:39:28 | revision_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | committer_date timestamp[us]date 1977-08-08 00:00:00 2023-09-05 12:13:49 | github_id int64 19.4k 671M ⌀ | star_events_count int64 0 40k | fork_events_count int64 0 32.4k | gha_license_id stringclasses 14
values | gha_event_created_at timestamp[us]date 2012-06-21 16:39:19 2023-09-14 21:52:42 ⌀ | gha_created_at timestamp[us]date 2008-05-25 01:21:32 2023-06-28 13:19:12 ⌀ | gha_language stringclasses 60
values | src_encoding stringclasses 24
values | language stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 7 9.18M | extension stringclasses 20
values | filename stringlengths 1 141 | content stringlengths 7 9.18M |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
64a5c171340d2432277c711c584783977f10d102 | f30911417d39d5e539579ffb8c17e9e17e069a8f | /scripts/6 código para gerar o design.R | 23e0596618b21b713aa32f96dae83b3fc9d0d247 | [
"MIT"
] | permissive | LucasLBrandao/Efeitos-redistributivos-dos-impostos-estaduais-MG | d06760ac813dd77a1ff3960a1b0d5b3e0b0838b1 | bcb20cfb03d1e823ff9661ddcb2da5dd0dc776b7 | refs/heads/main | 2023-09-02T04:44:07.668461 | 2021-11-18T01:07:27 | 2021-11-18T01:07:27 | 400,895,240 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 147 | r | 6 código para gerar o design.R |
files <- paste0("./scripts/",list.files("./scripts"))
for (i in 1:9) {
source(files[i], encoding = "UTF-8")
}
rm(files,lista)
|
266bb4287a30919a79e564c09786d4a934f407db | de49df10f28de69ff2220746a00c459ef80ec62b | /Phenotypic_Stats.R | c4aaf1e6bc481724b5061bfe783f562568262962 | [] | no_license | ars26/Phenotypic-Selection | 64cf124a39348538494c056d6fd7a65c5dedcfe1 | 32290a41d7a65150371ae77313c4913a286a1a8a | refs/heads/main | 2023-02-27T14:24:15.507023 | 2021-02-01T04:19:02 | 2021-02-01T04:19:02 | 334,529,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,042 | r | Phenotypic_Stats.R | ---
title: "BIO395 Lab Notebook on Phenotypic Selection"
author: "Ars Ghani"
date: "9/11/2019"
output: pdf_document
---
# __BIO395 Lab Notebook__
# *Arranged chronologically from 9/6/19 to the end of semester.*
#_________________________________________________________________________________
# Friday, 6 September ... |
dd3afb8b9b1f947793e9816c15e6583585ec99ec | 722b1b22e1c2cf99d79d23dbe1181c103236b3b2 | /Scripts/Data/ForStan/create_data_issue.R | 1d08b94212d01952d1f07ae3c42fdd6e2bba171f | [] | no_license | GFJudd33/UNHRCprocedures | 4bd15c1a2410f64cf0a08386499260965b885859 | 8af19f845c4460535476cf579fa3b3eba71825ae | refs/heads/master | 2020-12-24T16:07:33.194168 | 2016-03-10T13:49:28 | 2016-03-10T13:49:28 | 40,205,597 | 0 | 0 | null | 2015-08-04T19:57:55 | 2015-08-04T19:45:42 | null | UTF-8 | R | false | false | 2,286 | r | create_data_issue.R | ################################################################################
###
###
### Code to create data object for use in Stan code.
### Authors: Gleason Judd, Brad Smith
###
### Created: 11/12/2014
### Last modified: 11/12/2014
###
###
### Purpose: This script creates a data object that estimates... |
26ea42eb12c25ab9cc1483ed61ba63fe8b2244ab | 42887bb4d93c96125f492852176283469dcb771e | /R/BCG.Metric.Membership.R | 61860b8d2bbba977233f0024ce002ea3ee6baa9e | [
"MIT"
] | permissive | leppott/BCGcalc | a9fb1aa7d686fc53b9833dc1e3b12c6751e5d3ba | e4e226581c79c3603f7e65a7b523c61a6b48cbca | refs/heads/main | 2023-09-01T20:52:27.555241 | 2023-08-29T20:29:44 | 2023-08-29T20:29:44 | 121,774,105 | 4 | 0 | MIT | 2021-12-01T21:54:21 | 2018-02-16T16:36:58 | R | UTF-8 | R | false | false | 9,039 | r | BCG.Metric.Membership.R | #' @title BCG Metric Membership
#'
#' @description Biological Condition Gradient fuzzy membership for metrics.
#'
#' @details Converts metric values into BCG membership values.
#' Uses a rules table to define the metrics, scoring range, and direction for
#' each named index.
#'
#' Deprecated col_SITE_TYPE for co... |
df2912fd783ee283f10fe1e8cef55db9e3251710 | a474308e2677232a30a60c2604627aa9b7a23f7a | /R/loom.R | 7d0c7a2184d8b19b75fcfbe7216c79976eed6099 | [] | no_license | antortjim/sleepapp | 3dcea8ddf1a8bbc65fc3419bf9ecc0a32368d744 | 1ad9ac5e5d5d6034c2f49b3a4b7fd6c8cbbc27f6 | refs/heads/master | 2023-02-04T14:50:27.497147 | 2020-12-23T19:16:37 | 2020-12-23T19:16:37 | 316,051,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,614 | r | loom.R | #' Load a Scope loomfile into R
#'
#' Open a Scope loomfile and load its data
#' into R as a SingleCellExperiment object
#' @param path Path to a Scope loomfile
#' @import rhdf5
#' @import HDF5Array
#' @import SingleCellExperiment
#' @details A scope loomfile is an HDF5 file with the following structure:
#' \itemize{
#... |
57b08ab79da65126158c3a7c96fab71d95d9e7c4 | ee0689132c92cf0ea3e82c65b20f85a2d6127bb8 | /06-DS/15a-objects.R | 924d23ca3e27c8e789439fc006630f4823dcf0ed | [] | 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 | 1,018 | r | 15a-objects.R | # Objects
#
m1 = matrix(c(10:1, rep(5,10), rep(c(5,6),5),seq_len(length.out=10)), byrow=F, ncol =4)
colnames(m1) = c('sub1','sub2','sub3','sub4')
rownames(m1) = paste('R',1:10,sep='')
a1= array(1:24, dim=c(4,3,2), dimnames = list( c(paste('c',1:4,sep='')), c('d1','d2','d3'),c('s1','s2')) )
a1
df1 = data.frame(sub1=... |
01b491b796e1532253e1c4a4e013625c9adaf59f | fad0b084f2b9bd157e27cb642f137fad3d14d8b8 | /man/get_grades_f2.Rd | 4fcb594f236d53af9c5b1456e445c07d2d933da2 | [] | no_license | thelayc/laycReportCards | 7414a5754dd44cf8789cff6313809388a347b772 | ee1ed75d13a41eed0a06df581ac4451efd62d46a | refs/heads/master | 2016-09-06T12:41:09.314066 | 2016-03-09T03:07:48 | 2016-03-09T03:07:48 | 32,465,167 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 613 | rd | get_grades_f2.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/11_get_grades_f2.R
\name{get_grades_f2}
\alias{get_grades_f2}
\title{get_grades_f2()}
\usage{
get_grades_f2(student_rcard)
}
\arguments{
\item{student_rcard}{A vector containing a student's report card information}
}
\description{
Thi... |
6fc8fbd1b29fcd42c14fdbe73f38cf892644175e | 9a4518c0ac57cfaffd4069a39fcdccd6a8173949 | /R/geom-spatial.R | 8c4223249e1f19d9ca713fcfb496a9a9122cfdc6 | [] | no_license | paleolimbot/ggspatial | 78c1047e344ec658d092851ce6fb2a83d10c5db3 | 5c4c903a0785702d83acfe6d9753294882ed676c | refs/heads/master | 2023-08-19T22:53:31.102638 | 2023-08-18T00:27:19 | 2023-08-18T00:27:19 | 63,102,201 | 357 | 37 | null | 2023-08-18T00:27:20 | 2016-07-11T21:06:12 | R | UTF-8 | R | false | false | 6,748 | r | geom-spatial.R |
#' Spatial-aware ggplot2 layers
#'
#' These layers are much like their counterparts, [stat_identity][ggplot2::stat_identity],
#' [geom_point][ggplot2::geom_point], [geom_path][ggplot2::geom_path],
#' and [geom_polygon][ggplot2::geom_polygon], except they have a `crs` argument that
#' ensures they are projected when us... |
f865633ebb5b228c41ef158770d500a0b43fae9c | cb53f7e3d95513a4ef7ac086b5b4c34ba62ed1b3 | /man/options_locales.Rd | fa6138f5a5454638f680fd20eb9672e99c09bc7e | [] | no_license | 24p11/dimRactivite | c381a4b273dc7b9694169612a45c08bd1d0841f8 | 619bd12fa334cfac4fd9ea32b04b9fd777a34f83 | refs/heads/master | 2021-06-29T20:48:44.231817 | 2021-02-15T08:06:39 | 2021-02-15T08:06:39 | 198,663,596 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 961 | rd | options_locales.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fonctions_complementaires.R
\name{options_locales}
\alias{options_locales}
\title{Mise en oeuvre des options locales pour le décompte des séjours dans les données d'activité}
\usage{
options_locales(DF, val = NULL, niveau = NULL)
}
\arguments... |
554440fc92a37456d8b73caf80cb819f5a17e366 | e7d40077078eae86b06770e95474d245b33472a1 | /tests/testthat.R | 806430a816b241c631e11b1f278da0eb9d78b541 | [
"MIT"
] | permissive | lpantano/DEGreport | 1f90ac81886da7b96c024dfc8dbfe4831cf20469 | 0e961bfc129aab8b70e50892cb017f6668002e1a | refs/heads/main | 2023-01-31T23:33:51.568775 | 2022-11-22T14:40:17 | 2022-11-22T14:40:17 | 17,710,312 | 20 | 14 | MIT | 2023-01-20T13:55:22 | 2014-03-13T13:06:49 | R | UTF-8 | R | false | false | 93 | r | testthat.R | library(testthat)
library(edgeR)
library(DESeq2)
library(DEGreport)
test_check("DEGreport")
|
1d625a638e3d8fb1e0a912dc6e501ee6453c9c2c | affee151ef20940e52eea1473635c8f4e35b65de | /man/allen.string.from.result.Rd | 1ef6dab94c8c7b0dc20f41dcbbb0d8834b5dfc13 | [] | no_license | tsdye/allen.archaeology | d433c346b6ae93935cb369a8dd917e267aee2cb0 | ae1e3806df684ffa27fbf2ec1645f178ba101e18 | refs/heads/master | 2023-04-11T14:12:16.751804 | 2023-03-25T12:59:26 | 2023-03-25T12:59:26 | 245,044,592 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 503 | rd | allen.string.from.result.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/set.R
\name{allen.string.from.result}
\alias{allen.string.from.result}
\title{String representation of an Allen set from a result vector}
\usage{
allen.string.from.result(result.vector)
}
\arguments{
\item{result.vector}{A result vector}
}
\v... |
294acf2091659ea25deea120858c57834e322e95 | 256c40c02a738a59541a8699491b08b21f458991 | /Code/mapping.R | d8bbce9057ebdd696355648e572558a57e314d77 | [] | no_license | dmorison/GIS-in-R-spatial-crime-data-analysis | 5d874c22926476b4e5d0f64a3555bb1cd6a7ca9e | 60963d56b7d0de5be9f90f3a96fcba2e6e71fccf | refs/heads/master | 2021-01-22T17:49:08.045108 | 2017-04-27T07:22:22 | 2017-04-27T07:22:22 | 85,037,504 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,384 | r | mapping.R | libs <- c("ggplot2", "rgeos", "rgdal", "maps", "mapdata", "mapproj", "maptools", "sp", "ggmap")
lapply(libs, library, character.only = TRUE)
### Variables to keep
retain <- c("Month", "Longitude", "Latitude", "LSOA.code", "LSOA.name", "Crime.type")
### Import the City of London dataset
city.init <- read.csv("Data/city... |
527d3db23c331adb80d7c12c9c4184fc342021d1 | 006666eece54ebcbfc1f29b56f8146f808c781a1 | /method/0_simulateonelargePLINK.R | 475b9f1be250ce6140599e9e1bfc574989385265 | [] | no_license | MoisesExpositoAlonso/nap | d56d278bcc4c7e49fb3a8d4497df962a0ed94004 | 8fcbe7aa89f97bcf615943318fd1daa931e7a34f | refs/heads/master | 2020-03-28T09:11:16.076766 | 2019-05-13T00:16:13 | 2019-05-13T00:16:13 | 148,019,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | 0_simulateonelargePLINK.R | library(bigmemory)
library(data.table)
library(devtools)
library(dplyr)
library(moiR)
load_all('.')
set.seed(1)
################################################################################
#### SIMULATE GENOME MATRIX ####
totsnps=1e4
totinds=1.5e3
# x<-XBMsimulate(n=1.5e3,m=1e4,force=T)
# MAPwrite(x,path="../dat... |
227929db0f9452b303db8a1aac76cccc21f134c0 | 84dd0562ebd14dab4913a27313c3e82b792e3d76 | /R/year_survRate.R | 46fd5febb609e858c68b45283598a66adbb546b9 | [
"Apache-2.0"
] | permissive | yikeshu0611/TCGAimmunelncRNA | daaca3623943b63b83ad98ef357c179ad6048c2f | 9c86b20caa6674a976aca6ea3111755fd34bb5b5 | refs/heads/main | 2023-06-11T09:01:11.701450 | 2021-07-03T07:10:31 | 2021-07-03T07:10:31 | 381,676,631 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | year_survRate.R | year_survRate <- function(survfit,times){
x <- summary(fit,times = times)
data.frame(model = deparse(substitute(survfit)),
strata = x$strata,
times = x$time,
surv = x$surv,
lower = x$lower,
upper = x$upper,
n.risk = x$n.ri... |
4edb976b5d80c5ee2628c2f6aa4833173cb852ac | 3c76c3b1e9a91043a0d872cf2f5ad1e07538f6e0 | /plot4.R | 72e032c8f8fae2b07773b559fa5812c28e98e6ef | [] | no_license | dpeka/Exploratory_Graphs_Coursera | b095b3f9b66f75526bf3aebca0ad598854c40c6f | b00e37f76fcb91cd696d07a29d4adbf238a1d83f | refs/heads/master | 2020-04-13T03:15:26.646360 | 2018-12-23T21:58:54 | 2018-12-23T21:58:54 | 162,926,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,016 | r | plot4.R | # This will plot the first graph
# which is a histogram
library(lubridate)
library(dplyr)
energy <- read.csv("household_power_consumption.txt", sep=";", header=T)
# concatenating Data and Time into a new field, which I then process
# with lubridate to convert into R date format. I am using
# timezone of Par... |
f6119afe9757673fb14c51bc6a45d542c4aa3c02 | 5c67b449677b90e98bca928c2948ae15b4f34a49 | /www/Internationallandings/2017/RectangleLand - 2016.R | a8035d480e547a4549a5726389a0c7af2710e32b | [] | no_license | isabelle08/digital-stockbook | 9dc6e86d859b1e264bed2b801ad44c0f5438524d | 2c1b91ac4a3184a23d62a1a41b6b976ca2564686 | refs/heads/master | 2021-12-13T06:46:57.941478 | 2021-12-09T16:49:37 | 2021-12-09T16:49:37 | 218,807,888 | 1 | 0 | null | 2021-09-01T15:24:17 | 2019-10-31T16:15:34 | R | WINDOWS-1252 | R | false | false | 3,357 | r | RectangleLand - 2016.R | library(mapplots)
library(shapefiles)
library(RColorBrewer)
library(splancs)
library(rgdal)
coast <- read.shapefile('//galwayfs03/FishData/Mapping/Shapefiles/Europe')
#eez <- read.csv('F:\\Mapping\\Shapefiles\\Maritime Boundaries\\eez.csv')
eezWorld <- readOGR("//Galwayfs03/FishData/StockBooks/_StockBook2017/M... |
480dd7ea470eb2fdd2e84c1b4c6ce22517fd7db2 | 4248965331d139de1baa1039333a1147c1bb483e | /data-raw/add_internal_data.R | d133122f034c453c0c89e544e224d567a99a96fe | [] | no_license | tonyfujs/eextoddh | 7de9b3678577e9a4a779cb65ad9eb3f747fc9bd8 | a08daad5cf6c498d13edbaa9d88989ca33ba1dbc | refs/heads/master | 2021-06-19T17:08:26.820856 | 2017-07-20T14:09:15 | 2017-07-20T14:09:15 | 97,842,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,547 | r | add_internal_data.R | library(dplyr)
library(jsonlite)
root_url <- ddhconnect:::stg_root_url
# CHECK ASSUMPTIONS -------------------------------------------------------
# taxonomy_stg <- ddhconnect::get_lovs(root_url = ddhconnect:::stg_root_url)%>%
# rename(ddh_machine_name = machine_name, field_lovs = list_value_name)
# names(taxonom... |
e37c25632e3ee7ff0a5dba2a5d30ea71107d7872 | ad522819f54aa659c951ff39fff1dda0fff0f89f | /man/functional__find_max_per_frame.Rd | 63c13b536d761318c8ca6cd5c2189880ed00b765 | [
"MIT"
] | permissive | davidbrae/torchaudio | 4dbc4e12067b14dedd8fa785a6b753719e39b0d3 | d20ccc237a8eff58e77bb8e3f08ef24150a4fc4e | refs/heads/master | 2023-07-20T16:06:59.791249 | 2021-08-29T19:16:50 | 2021-08-29T19:16:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 786 | rd | functional__find_max_per_frame.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functional.R
\name{functional__find_max_per_frame}
\alias{functional__find_max_per_frame}
\title{Find Max Per Frame (functional)}
\usage{
functional__find_max_per_frame(nccf, sample_rate, freq_high)
}
\arguments{
\item{nccf}{(tensor): Usually... |
f6df1a65773845d2de6d186a6b56998d40dcd7b0 | 9bbde9df5f4fe193f234512cf019fbb244f3814e | /RCode/02_DataManipulation.R | 788a0318d01a4f70a27562352ee69276ad08e411 | [] | no_license | alanchalk/SL_Intro_1_TODO | fb9681e99da4ff8de4b32ffe3f15d14d305334e0 | 3477bdd93b7f15d7f0661be2ec21ba1c3bb53558 | refs/heads/master | 2021-05-11T19:55:42.526197 | 2018-01-14T12:36:37 | 2018-01-14T12:36:37 | 117,426,877 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,419 | r | 02_DataManipulation.R | # 02_ManipulateData.R
# Author: Alan Chalk
# Date: 10 Jan 2018
# Purpose: Carry out any data manipulation needed
# Contents
# _. Load data
# 1. Add an exposure variable
# 2. Add folds
# Notes
#--------------------------------------------------------------------------------
# _. Load data
load(file.path(dirRDa... |
89d093f293c840294c11f641eb0a58e04799ed3c | 616e8ba5e7356a3b493062cd8095fa98455d12f1 | /SensitivityAnalysis/utils/sensitivity.to.model.parameters.plots.R | 06f1447dc5e422a95c58fe4cc440514942108de7 | [] | no_license | Breakend/RIBSS_tax_evasion_ABM | 0813ecc2ac964d0d68a12fb5b4559f26d25b502d | f64c4b8ab1e02a95fa3e032fbcb3b37647eeb017 | refs/heads/master | 2022-02-21T14:40:09.271146 | 2019-09-03T20:39:18 | 2019-09-03T20:39:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,484 | r | sensitivity.to.model.parameters.plots.R |
#This function returns the plots of sensitivity of field of interest to model parameters
sensitivity.to.model.parameters.plots <- function(field, dataset, full.name = NULL, params=NULL)
{
all.names <- get.full.names()
if(is.null(full.name)) full.name <- all.names[field]
o.title <- paste("Overall Sensitivity ... |
6f99db92bc0020f14bcb4626134d82cbfccb8260 | cd82731e5755625d0f65151430b47d8d86737530 | /man/slopeshatprime.Rd | f714aabf17ac748a0740109896e334dccf1bc97c | [
"MIT"
] | permissive | ArefinMizan/jeksterslabRlinreg | 31a2d8f9201bf084b385a52e8788b7d7a5225307 | 21b2ed9dcae3b6c275b573b4a71438558c35d08d | refs/heads/master | 2023-03-19T10:08:30.303897 | 2020-12-30T22:31:36 | 2020-12-30T22:31:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,495 | rd | slopeshatprime.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/betahat_matrix.R
\name{slopeshatprime}
\alias{slopeshatprime}
\title{Estimates of Regression Standardized Slopes \eqn{\boldsymbol{\hat{\beta}}_{2, \cdots, k}^{\prime}}}
\usage{
slopeshatprime(X, y)
}
\arguments{
\item{X}{\code{n} by \code{k} ... |
6e41a7c1a591316fe3d27720c1861199988c623a | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/anyOutside/libFuzzer_anyOutside/anyOutside_valgrind_files/1610388068-test.R | 6469c5ed85e9cee5a1299d729938902a0e7c9af4 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 864 | r | 1610388068-test.R | testlist <- list(a = -1694498817L, b = -128L, x = c(-7010049L, -1694498817L, -27L, -218L, -6625793L, -43415L, 1702308136L, 1397053520L, 543387502L, 1936993379L, 1869509492L, 536870912L, 0L, 197504L, 0L, 402915079L, 0L, 0L, 771L, NA, 1573632L, NA, 4200451L, 8388608L, 16392L, -1140130048L, 65490L, 741134714L, -738260... |
3937ff048378a413144976a2000822adf28b2e10 | c1fba56a73eea1ed8ff817b0a86e57a67ba4ad44 | /app.R | e40d707778c1164d75e1df9fd75738fddbfe02de | [] | no_license | mjclemen/Water_Features_Dashboard | f38dedec28fe40f41e8895087a77af99905ad7df | 0bb0518db9ca98b2598d299fac36653ebdc527f2 | refs/heads/master | 2020-11-29T16:49:15.715137 | 2019-12-26T01:12:53 | 2019-12-26T01:12:53 | 230,172,007 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,790 | r | app.R | library(shiny)
library(shinydashboard)
library(reshape2)
library(dplyr)
library(plotly)
library(shinythemes)
library(DT)
library(stringr)
library(tools)
library(rlist)
library(scales)
library(data.table)
library(rgdal)
library(httr)
library(jsonlite)
library(leaflet.extras)
# FIRST API CALL: Grabbing ... |
c151cfa39631191cd134300fc2fe9cf57ad7e2b1 | eb370df99e4af3976213eb2fc3c786b765120fd8 | /Q1/HW03/PartitionTests.R | ae0a982a5819752944375a03151d745062fd3df2 | [] | no_license | LeoSalemann/UW_DataScience | 55692d19b6bb25b52310477ac6af5a396ad1ee3c | 1cd43bc342c56eea2b065e3edc3e45550c289295 | refs/heads/master | 2021-09-15T02:05:00.473068 | 2018-05-24T01:37:32 | 2018-05-24T01:37:32 | 106,077,578 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,400 | r | PartitionTests.R | # PartitionTests.R
# Copyright 2016 by Ernst Henle
# To use this script:
# 1 Place CollegeStudentsDataset.R in your working directory
# 2 Source this script
# Clear objects from Memory
rm(list=ls())
# Clear Console:
cat("\014")
source("CollegeStudentsDataset.R")
getDataToPartition <- function(numberOf... |
2f3fcb5ad0fa584132c09f3f801a1976fb1444a2 | 184ddf699ff3a35b91691a2a8c2589c279b80494 | /a35.r | 81b81cbfa61698092c2e4e940b8bf9d2b321c227 | [] | no_license | majidaldo/tsa | 870ac07ec914ce2185469928164491601e842b6e | b5ec7c254c33ac942aacf146fb46e4a887325c89 | refs/heads/master | 2021-01-01T18:29:38.602269 | 2014-11-21T14:37:42 | 2014-11-21T14:37:42 | 16,283,197 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 191 | r | a35.r |
ibmdata=read.csv("ibmd.csv")
closings=xts(ibmdata$Adj.Close ,as.Date(ibmdata$Date))
sr=(abs(diff(closings))[-1] #simple return
/as.vector(closings[-length(closings)]))
#acf(sr,100)
|
bd2457ad8c60b3361c1759ce95391ec190edae86 | 0934f0bef7587d90c47dfb405fcececb91eae01b | /P2_studies/comp_case_study/graclus_cocit_fig.R | 817d9f370e2efcd20758b53945f4a7fb01e51b23 | [
"MIT"
] | permissive | Djamil17/ERNIE | 2def22ad0848cb1e9985ee32f3e4e917f9da5199 | 454518f28b39a6f37ad8dde4f3be15d4dccc6f61 | refs/heads/master | 2022-07-27T11:09:03.620862 | 2021-04-20T15:41:38 | 2021-04-20T15:41:38 | 186,028,656 | 0 | 1 | MIT | 2019-05-10T17:32:49 | 2019-05-10T17:32:49 | null | UTF-8 | R | false | false | 1,025 | r | graclus_cocit_fig.R | # script to reconcile dc with co-c
# for S&B paper
rm(list=ls())
setwd('~/Desktop/dblp')
library(data.table)
x <- fread('graclus_cocitation_clusters.csv')
x1 <- x[order(graclus_cluster,-co_citation_nodes)]
nl <- fread('nl_scp_top20.csv')
nl1 <- nl[,.(cocit_cluster_total=length(source_id)),by='cluster_no']
merged <- me... |
ac71f6f8e91518545b496b8c185b99d8993c036b | e54e7a8f0140a33da41e420f4149c5c737175a89 | /R/create_master_raw_data.R | f2179afa829f86119ae4506909891d137f761e1a | [] | no_license | one-acre-fund/arc2weather | 0d80547adca12a0bcdd1a299d9886ce192dc16cd | 25b6ab102f8cf74e6e60b48dac760ba07d198e86 | refs/heads/master | 2020-03-28T04:33:39.962589 | 2019-02-04T16:49:59 | 2019-02-04T16:49:59 | 147,722,497 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 806 | r | create_master_raw_data.R | #' consolidate raw data into single file to simplify storage and loading
#'
#' @param dir directory where raw data lives. Dir is the raw_data folder from
#' inhereted from the get_raw_data
#' @param pattern the pattern that identifies the raw data files
#' @inheritParams get_raw_data()
#' @return nothing. It saves a ... |
53a72e162f1857ce5387040c5da6f7448e9a02af | 9721b7e97328faf3e4dafaa24d70310129d52b01 | /man/plsR.dof.Rd | 4d918c02fdb78e695f28d32a9306ed05cf15a0fe | [] | no_license | kongdd/plsRglm | 77dd10e804ec3606d914aae22a863a497497cd18 | dfa4e54ea02bca8bf04d29bb65dc7dba611927c9 | refs/heads/master | 2022-02-19T20:26:29.672362 | 2019-10-01T10:41:55 | 2019-10-01T10:41:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,097 | rd | plsR.dof.Rd | \name{plsR.dof}
\alias{plsR.dof}
\title{
Computation of the Degrees of Freedom
}
\description{
This function computes the Degrees of Freedom using the Krylov representation of PLS and other quantities that are used to get information criteria values. For the time present, it only works with complete datasets.
}
\usage{... |
03fe495f89c31f83fd4f584afb45dcfb334717dd | 52a6cea02ee8ac8c53e1049a1df8c31494aaadd0 | /model.R | 70fba2f0a87ca3e8fdf6d5fc6d017eddd3e878f6 | [
"MIT"
] | permissive | ControlNet/ml-algorithms | ccd8ed592e8dfa90ca0a15b9f4aa7b1843e07cf0 | 16e37eae032250ecda7a12d84839d5ad72753635 | refs/heads/main | 2023-04-14T14:59:06.459439 | 2021-04-16T17:58:02 | 2021-04-16T17:58:02 | 358,675,339 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,348 | r | model.R | Model <- setRefClass("Model", fields = list(), methods = list(
intialize = function() { },
fit = function(x_train = NULL, y_train = NULL) { },
predict = function(x_test) { },
evaluate = function(y_pred, y_test) { }
))
SupervisedModel <- setRefClass("SupervisedModel", contains = "Model", fields = list(), method... |
0d7c0b7a0c12055532837ea72f52039aa9b10aa8 | b75c76365c852202db6616287f9163ce0e704202 | /man/Armitage.Rd | efe08dc4ae6f02b2f12be0845deae3f6d6a15be6 | [] | no_license | cran/MCPerm | 848a919f3d28d176c63cb979e51138c492e4d0f4 | 7501eb560d0b2c8809fa6f22407141a59a3dcb4f | refs/heads/master | 2020-05-27T04:16:30.278126 | 2013-06-17T00:00:00 | 2013-06-17T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,559 | rd | Armitage.Rd | \name{Armitage}
\alias{Armitage}
\title{
Armitage's trend test for the 2x3 genotype table
}
\description{
Armitage's trend test for the 2x3 genotype table.
}
\usage{
Armitage(case_11, case_12, case_22, control_11, control_12, control_22)
}
\arguments{
\item{case_11}{
a non-negative integer, the frequency... |
e1177290a70574af8f6af37d9df187b9f27d10f5 | 7d51f78a1a58e67aa5302e0cac35e2142246c1ae | /permutation.R | 08f70d9bf708aef7196d61e7ef732d5b384939bc | [] | no_license | richasdy/HelloR | 8a35b4ad32403f61df8b8f17b03a21cfea819e97 | 384bab9cf879a4176d7c131a66cee178d5e9e8ed | refs/heads/master | 2021-06-10T14:31:49.126870 | 2016-12-08T04:45:54 | 2016-12-08T04:45:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 490 | r | permutation.R |
# permutation, combination
# load library
library(gtools)
#urn with 3 balls
x <- c('red', 'blue', 'black')
#pick 2 balls from the urn with replacement
#get all permutations
permutations(n=3,r=2,v=x,repeats.allowed=T)
#pick 2 balls from the urn with replacement
#get all permutations
permutations(n=3,r=2,v=x)
#nu... |
332044d8ec942887e03be0f58cba0bf19032b8f6 | 9034dfa4936f52dff9b31f2dee5515c848a3d9bb | /man/GenfrailtyPenal.Rd | 3923c5ca27e4e9b39b213e3ad407278d54011309 | [] | no_license | cran/frailtypack | 41c6fa2c6a860c9b1e2feff84814e29d32b56091 | dfbdc53920a754f8529829f1f0fccc8718948cca | refs/heads/master | 2022-01-01T02:07:40.270666 | 2021-12-20T09:30:02 | 2021-12-20T09:30:02 | 17,696,141 | 9 | 4 | null | null | null | null | UTF-8 | R | false | true | 44,502 | rd | GenfrailtyPenal.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/GenfrailtyPenal.R
\name{GenfrailtyPenal}
\alias{GenfrailtyPenal}
\title{Fit a Shared or a Joint Frailty Generalized Survival Model}
\usage{
GenfrailtyPenal(formula, formula.terminalEvent, data, recurrentAG = FALSE,
family, hazard = "Sp... |
ebefd1fb7d2a3b8e5c8f189358416ab63b6c486c | 9c475c4e74df2f07218f947ab7e2f64adbcdd0a8 | /man/pe_avg_cv.Rd | 30e0ba2f37d0ea82a6ce9f9e1be294688c68f4b2 | [] | no_license | lilyzzhao/ecofolio | 9133794f6b5b54a3c2040b158d318a853880ab47 | de3f887018c0cf9b53889ea21cdd1582fa193a9b | refs/heads/master | 2020-04-27T03:06:40.701337 | 2014-12-10T21:37:59 | 2014-12-10T21:37:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,297 | rd | pe_avg_cv.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{pe_avg_cv}
\alias{pe_avg_cv}
\title{Estimate the average-CV portfolio effect}
\usage{
pe_avg_cv(x, detrending = c("not_detrended", "linear_detrended",
"loess_detrended"), ci = FALSE, boot_reps = 500, na.rm = FALSE)
}
\arguments{
\item{x}{A matrix or dataframe... |
90be12993bd9ab47410c89b9cf4f1091553cfe6d | a189183555031f077a0b52ac55176c808b272e9d | /GIS_2.R | e1d5999fb5bba574e2f9a05737a76e6d01cef7c1 | [] | no_license | TomoyaOzawa-DA/GIS | 030d427c13e1009178d3c92f21273fa78811469a | 07321e584d43859d9d40a647bcfad5d728bbaa8c | refs/heads/main | 2023-01-06T21:49:55.093138 | 2020-10-30T12:22:52 | 2020-10-30T12:22:52 | 308,618,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | r | GIS_2.R | #################################
### Rでのジオコーディング入門 ###
#################################
### データの読み込み
# Windowsの人はfileEncoding = "utf8"とする必要があるかもしれません。
gas_naha <- read.csv("gas_naha_new.csv", fileEncoding = "shift-jis")
## データを整える（エクセルで作業可能です）
library(tidyverse)
gas_naha <- gas_naha %>%
select(store, address,c... |
0af98707aa07e3d24e2820f239e018d280956c53 | d94a9c6d27f64d020241dd7c6bf5d48e256b090d | /Scripts/test 1. determine what is removing all the soil observations.r | 45f88b24c73421d6a01061f3ab6ad90db35dbd78 | [] | no_license | colinaverill/FIA-ecto.growth | eb737e4487dd303efe43077d3546cda9a2f2abb4 | 7fa6b8bb94d366a6316bd58e7e5856646d970ed3 | refs/heads/master | 2016-08-11T15:36:33.993055 | 2016-02-04T19:56:44 | 2016-02-04T19:56:44 | 47,923,316 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,083 | r | test 1. determine what is removing all the soil observations.r | ##test what is narrowing ~3.5k soil observations to ~300
rm(list=ls())
library(data.table) #note, version 1.9.4 or higher must be installed, otherwise you will have trouble running particular commands.
library(RPostgreSQL)
library(bit64)
source('required_products_utilities/PSQL_utils.R') #this will give you the tools ... |
759ca513f3c3c840ddad0e8091b43b8dccc6e020 | a6965cf239160fd1586d1fdb6ed16df8604cebeb | /tests/testthat/test-Contours.R | 230edb1ecb9732c1c884aa2c84b75e7e8ecff33f | [] | no_license | ms609/Ternary | 3d4be17ca91cca8f48323a2ce7819ca233f7a6b2 | db7451ca733878f499f08a22997bf0f26aa42e21 | refs/heads/master | 2023-07-06T12:41:57.171117 | 2023-06-29T15:34:08 | 2023-06-29T15:34:08 | 111,806,977 | 34 | 3 | null | 2023-04-20T19:53:55 | 2017-11-23T12:30:42 | R | UTF-8 | R | false | false | 8,821 | r | test-Contours.R | test_that("Densities are correctly calculated", {
coordinates <- list(
middle = c(1, 1, 1),
top = c(1, 0, 0),
belowTop = c(2, 1, 1),
leftSideSolid = c(9, 2, 9),
leftSideSolid2 = c(9, 2, 9) / 2,
right3way = c(1, 2, 0),
rightEdge = c(2.5, 0.5, 0),
leftBorder = c(1, 1, 4),
topBorder =... |
f54dc6fb80b1e3ef1ba4e21a0b7cc1fdb8d6ad09 | 9baaf555daf5ef32086a80b0d5094bf0cba183dd | /man/H.diff.small.nopopvariance.Rd | 0ddbd432bede3edf9afc8673d2603a85607d4994 | [] | no_license | edeaguiar/springstatsexam2 | 0a780ab38d2ad21bd6567e380dfe78aedd842cc1 | 37a8c01a1deac56b4b1ffa654d2f6f144178c99b | refs/heads/master | 2022-04-21T16:47:25.846371 | 2020-04-17T14:04:59 | 2020-04-17T14:04:59 | 255,984,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 518 | rd | H.diff.small.nopopvariance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HypothesisTesting.R
\name{H.diff.small.nopopvariance}
\alias{H.diff.small.nopopvariance}
\title{Hypothesis test for a VECTOR that is population mean difference. Outputs values needed to put into pt}
\usage{
H.diff.small.nopopvariance(x, mu)
}... |
7a92d9a69c048cedb3d02260ddc9e82e62e3b6a1 | 3726e1e995dd037da7e97a244e8cef8a03ad4421 | /plot3.R | 7628bd26d5b4ac688ac1dc9dbbccd6ec27898c54 | [] | no_license | yiyanghu/ExData_Assignment2 | 153faec4ec9a9ce99a6b23ee5db9fadf4436ac8e | d543c15f720967b4b38e5059d57e1ba2d9ed3940 | refs/heads/master | 2021-01-18T18:45:15.223112 | 2016-08-13T19:11:56 | 2016-08-13T19:11:56 | 65,632,872 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 639 | r | plot3.R | if (!exists("NEI")) {
NEI <-readRDS("summarySCC_PM25.rds")
}
if (!exists("SCC")) {
SCC <-readRDS("Source_Classification_Code.rds")
}
BaltimoreSet <-subset(NEI, fips ==24510)
BaltimoreAggYearType <- aggregate(Emissions ~ year + type, BaltimoreSet,sum)
png("plot3.png",width=640, height=480,un... |
809e9edce84e3670b8e31bcaa385f2453c8d8b36 | e04c83847155110809e1eaaec794952fcceb523d | /answer.r | cb26261a0c7a95dfda49559567ac18f59ab58781 | [] | no_license | jahnvitandon/dataanalytics_assignment_8.2 | 78445c8348ccc406f45c44d6d6d6d34ccce74f6e | 44482e5a581fc2c48503586a26992d3e3173089c | refs/heads/master | 2020-03-22T14:27:09.229910 | 2018-07-08T15:45:16 | 2018-07-08T15:45:16 | 140,180,955 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,200 | r | answer.r | #Problem 1
#library(RcmdrPlugin.IPSUR)
#data(RcmdrTestDrive)
#Perform the below operations:
# 1. Compute the measures of central tendency for salary and reduction which variable has highest center?
# 2. Which measure of center is more appropriate for before and after?
#Answer 1
#first find the measures of centra... |
a14dd4c8540bd68a5a2aa75f8fdde45725779f4a | ced1fefafdf15797bd34f6f5768b8bd066dfa236 | /final.R | 46a065528454eb6c15fd99265fab9106aab5bf20 | [] | no_license | HongfanChen/Stats506_final_project | 5ccd5bc1f4ace5cff620c285279b58609fd874c4 | a4534cb5b9f0d0d84065adabb8c3454e80bc1dfc | refs/heads/main | 2023-01-29T07:03:00.819322 | 2020-12-12T06:26:11 | 2020-12-12T06:26:11 | 316,160,169 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,589 | r | final.R | ## Stats 506, F20
## Final Project
##
## This R script is for Final Project
## Author: Hongfan Chen, chenhf@umich.edu
## Updated: November, 25, 2020
# 79: -------------------------------------------------------------------------
# libraries:
library(tidyverse)
# directories: ------------------------------... |
91da14f626b3cff5bc8eab0fff3c417ed750eead | e2a55a3b310e9f8fa7a25d255422aa5cc82baf10 | /plot1.R | d036d75b90e7cc93fd144e51812cde5becb862e7 | [] | no_license | drbooshkit/ExData_Plotting1 | 3b4de5b1f5a21f0c894ff4ac23f6b5df4f763dc9 | e8cf75b665db57792aaea7844a3bea8073237c99 | refs/heads/master | 2021-01-15T11:23:45.067921 | 2014-05-10T19:58:14 | 2014-05-10T19:58:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 741 | r | plot1.R | # create vector of column classes:
cols <- c("Date", "POSIXct", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric")
# read in data from TXT
data <- read.table(file="data/household_power_consumption.txt",sep=";", header=TRUE, stringsAsFactors=FALSE)
data$Date <- as.Date(data$Date, "%d/%m/%Y")
... |
71c62cc8ec258c495630e2f19382566ed75f26c2 | 8c3a12894f1b01e10899817634afc1403b34c3f0 | /man/padmm.Rd | 9d4e9d18cf35aa33e174a0101fd308b464e52635 | [
"MIT"
] | permissive | fboehm/openFHDQR | db44c9833d861f22d40e28171d1784efe4dc102c | d4dc737a4fa1391306a202051f31e714df380789 | refs/heads/main | 2023-06-29T02:24:40.786808 | 2021-08-07T22:36:14 | 2021-08-07T22:36:14 | 387,862,159 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,130 | rd | padmm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{padmm}
\alias{padmm}
\title{Perform proximal ADMM for weighted L1-penalized quantile regression}
\usage{
padmm(
beta0,
z0,
theta0,
sigma,
X,
eta,
y,
l1,
l2,
w,
nu,
tau,
gamma,
max_iter,
eps1,
... |
0f94eb27cb2c222c7923415e10fb7bf1540d7fea | 5b2a03e70b27b35f4ab1bb7159e3fd9ae1fec133 | /PrepareForClassification.R | 583b40ba7433fdb7c9796d0d31d2607b3f5b5047 | [
"MIT"
] | permissive | Jakob-Bach/CS-Select-ML-Server | d84393f69319e1c83a56185d4ed52f57ce554dc0 | 620dafa77db5da632c6374afaecc27837662139d | refs/heads/master | 2022-11-12T18:28:14.474070 | 2020-07-13T13:46:00 | 2020-07-13T13:46:00 | 279,306,906 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,075 | r | PrepareForClassification.R | library(data.table)
source("UtilityFunctions.R")
# To specify by user
DATASET_NAME <- "populationGender"
#### Load data ####
# Read data
cat("Reading data ...\n")
dataset <- readRDS(file = paste0("datasets/", DATASET_NAME, ".rds"))
featureNames <- colnames(dataset)[-ncol(dataset)]
targetColumn <- colnames(dataset)[... |
41e59bed7cbc52642ed4bc32c6ab7f3ea63e1b80 | fe9076041f3e3c4d176f7ead51c3a9d5263081a7 | /server.R | cd88990646fd00901ac12c9dcaf54be998dc6580 | [] | no_license | al610/AH7_INFO_201_Project | d635872a3942a6de44af39e3cc3eee38c2de41e2 | 3e3ddaa7457e031c4f7eab8d328b4ea046930d33 | refs/heads/master | 2020-08-30T13:23:27.865798 | 2019-12-05T05:52:38 | 2019-12-05T05:52:38 | 218,393,444 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,280 | r | server.R | # set ups
library(shiny)
library("dplyr")
library("lintr")
source("datasets.R")
library(tidyverse)
# The backend which takes the front end data and compute the results.
server <- function(input, output) {
# Define a histogram to render in the UI
output$histgram <- renderPlot({
# make the input varaible into nu... |
ab49cb592f05ba023235ddcf4a1f8ef03a9eee84 | b0207ec605dcc5e7e745382b82409b1082bf3ee8 | /workflow/scripts/run_reg_gini_rca.r | f59a54da8751545a8f15c3073bb11bcb19fad52f | [] | no_license | possible1402/national-science-exports | afbe5b4196bf3d53c4982d65b7d1f7e16c2f99d6 | 98f463fc3abc8db669f4a254e1e83fc3402c6521 | refs/heads/master | 2023-04-23T17:16:25.958063 | 2021-05-13T18:50:54 | 2021-05-13T18:50:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,987 | r | run_reg_gini_rca.r | rm(list = ls())
library("plm")
library("stargazer")
library("dplyr")
library("data.table")
args = commandArgs(T)
PANEL_DATA_PATH = args[1]
RESULTS_PATH = args[2]
RESULTS_PATH_BALANCED = args[3]
RESULTS_PATH_BOTH = args[4]
panel_data <- read.csv(PANEL_DATA_PATH)
run_write_models <- function(panel_data, outpath, eff... |
42028babbf72acb2a47c9b2438aecc09347e3da0 | 8c2253bd47fd3d76f28950d1ef24450b24c4a0d7 | /tests/testthat/test-annealing-HRscale.R | e2e3562c035c3741ce8c2357dad073f971bf93e4 | [] | no_license | cran/StrathE2E2 | bc63d4f0dffdde94da1c7ea41133c09033c0cd4e | 629dc5e7f2e323752349352bb2d651a56c6f4447 | refs/heads/master | 2023-02-25T13:18:59.217896 | 2021-01-22T21:40:05 | 2021-01-22T21:40:05 | 278,343,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,237 | r | test-annealing-HRscale.R | test_that("test simulated annealing code (HR scaling parameters) produces correct output structure", {
skip_on_cran()
#-----------------------------------------------------------------------------------------------------------------
#
# This test runs the 1970-1999 version of the simullated annealing (HR scalin... |
201b7becd9a23441a9631b8869f032cd9ed7815a | d7a929cc1fa9dd9f56d739a9036d08ac86e29f88 | /gradrates_2021_10_6.R | 95a5382f949a9d2df887459d75bb0a175343bc3e | [] | no_license | ozshy/gradrates | 02169f16bda39c37cfa1a493e7f7daaff25c7ebd | 72888e44119ea46dde3d3f98e2fbe7b6833cc8ac | refs/heads/main | 2023-08-23T23:52:50.892075 | 2021-10-31T10:42:49 | 2021-10-31T10:42:49 | 315,382,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,983 | r | gradrates_2021_10_6.R | # gradrates_202_5_27.R start revision for JOLR
# gradrates_2020_11_27.R first submission to JOLR
# gradrates_2020_11_26.R deleting previous simulations and adding Section 4 (back to the technology change explanation), line 289
# gradrates_2020_10_25.R simulating the model with differentiated brands, Section 4, Lin... |
cf359e8ba0b754032cdc6c14f775ae118f284b16 | 88931c8cf916f9e8bacd99c65c1442e21e34e903 | /scripts/presentation_six_tss_seqlogos.R | 5ab9e6176dff47b73db5451568a37de929e16158 | [] | no_license | james-chuang/dissertation | cdb91652f9842da5ae75d72f2600c11dceb78721 | b44d9a88cd934c1862b415b5a3961afc8ce78ec6 | refs/heads/master | 2020-06-14T07:03:10.846181 | 2019-07-29T21:18:32 | 2019-07-29T21:18:32 | 194,939,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,999 | r | presentation_six_tss_seqlogos.R |
main = function(theme_spec,
data_paths,
fig_width, fig_height,
pdf_out){
source(theme_spec)
library(ggseqlogo)
#we don't use ggseqlogo plotting because it doesn't allow prior to be taken into account
#we just use their dataframes with font information for... |
6f095369163fca27222d5d70e2df9bfdfe539ed6 | 4c50f18ba0a5eb8b13f7efcab5804d59bc361c15 | /EBIO 1010/Oh_Mar15HW.R | a9f2608f272578ad7d35f2069d53563624c0bb23 | [] | no_license | dmwo/EBIO-R-Code | a1075f3ae8e0aeb00dec36f4e3134052a3eaaf88 | d8f9bf56e03355e00eac56bb2716c9ea39b7eb48 | refs/heads/master | 2023-06-19T23:02:26.203989 | 2021-07-14T06:53:46 | 2021-07-14T06:53:46 | 292,739,745 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,342 | r | Oh_Mar15HW.R | #------------------------------------------------------------------------------#
# Assignment: March 15 R Homework #
# Author: Dylan Oh #
# Date Created: 14 Mar 2018 ... |
576b369adacc1d108955bcf1bf8e7fe4f5d976c6 | ee8733c46c91949478b44143e4977ca0ca857968 | /R/getComposingPrimes.R | 7b1c12ff0d483f31e7cfaacab3f9fb875ddfe57b | [] | no_license | holgerschw/logicFS | 0a7919ef1012814b83a114dbc485e8be3d21e7ae | ed8b0be37da919754b39e1a46e793e253b06ddaf | refs/heads/master | 2021-06-06T01:10:52.594711 | 2020-04-12T21:34:42 | 2020-04-12T21:34:42 | 148,649,928 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 443 | r | getComposingPrimes.R | getComposingPrimes <- function (primes, vec.primes)
{
if(is.null(primes))
return(NULL)
a <- strsplit(primes, split = " & ")
b <- strsplit(vec.primes, split = " & ")
d <- numeric(length(vec.primes))
for (i in 1:length(primes)){
d <- d + sapply(b, function (x, y) ifelse(
all(x %in% y) && (length(x... |
29bbc5239f4350aec9d71a32ea4301959b6c1ec2 | 4928475849053b4f8374fa3883e162a4687c97f8 | /binomal/R/main function.R | e16965e3a996d16d2786af9b601c2358afeac8e6 | [] | no_license | stat133-sp19/hw-stat133-snowman36 | 7d527a21725081bd73a368a9e63e803bf0b2499d | 99fc30abfe65efc3f141fed49154c0ddfd30bafe | refs/heads/master | 2020-04-28T13:42:33.558688 | 2019-05-01T02:21:26 | 2019-05-01T02:21:26 | 175,313,702 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,458 | r | main function.R | #' @importFrom graphics barplot lines plot
#' @title bin_choose
#' @description Caculate the combinations "n choose k"
#' @param n the total number of trials
#' @param k the total number of successes
#' @return the combinations "n choose k"
#' @export
#' @examples
#' bin_choose(n = 5, k = 2)
bin_choose <- ... |
1441582ed89129ff0784e5a5cd7c9c11b006881b | c409ff3ea8b7c62efd962d37c83793d4fc0dc1bc | /man/cqc_defaults.Rd | 0d574d291f4502619e0e1d99a8c7111e46352fa0 | [
"MIT"
] | permissive | markdly/conquestr | e225ecb1347957dc025c5c719d46624f56e01207 | 7994b3768e26acf1be4ac20821da66ba7f564deb | refs/heads/master | 2021-04-30T10:41:43.513747 | 2018-09-12T05:46:02 | 2018-09-12T05:46:02 | 121,339,581 | 1 | 0 | null | 2018-09-12T05:46:03 | 2018-02-13T04:43:27 | R | UTF-8 | R | false | true | 431 | rd | cqc_defaults.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cq_syntax.R
\name{cqc_defaults}
\alias{cqc_defaults}
\title{List of default arguments for use with ConQuest commands}
\usage{
cqc_defaults()
}
\value{
A named list. Each element contains a default placeholder value. Designed for use with `cqc... |
106d0510fd414301fb2415587e4bbde90c53da55 | 71ab7a051c715f44e841fbc77cee4db54547347c | /plot2.R | 92c4b715dfbc4fe57563be4544cb5de377dbf803 | [] | no_license | SpyderChica/ExData_Plotting1 | db07859042ff98ebafb6e966faf615f956e391c9 | f5863e2d58710c1731d73fa459d8d12af406c8a4 | refs/heads/master | 2020-12-25T02:49:41.786179 | 2015-08-08T22:45:46 | 2015-08-08T22:45:46 | 40,416,034 | 0 | 0 | null | 2015-08-08T20:47:19 | 2015-08-08T20:47:19 | null | UTF-8 | R | false | false | 614 | r | plot2.R | #Our overall goal here is simply to examine how household energy usage varies over a 2-day period in February, 2007. Your task is to reconstruct the following plots below, all of which were constructed using the base plotting system.
df = read.csv("household_power_consumption.txt", sep = ";", na.strings="?", stringsAsF... |
3013d7b03c56c9b5b36463f263551a9dfc411848 | 7a7375245bc738fae50df9e8a950ee28e0e6ec00 | /R/SA4__Year_Sexunemployment.R | 52acc6b0c5fed422cd86401c767f5c2eff5ff1b9 | [] | no_license | HughParsonage/Census2016.DataPack.TimeSeries | 63e6d35c15c20b881d5b337da2f756a86a0153b5 | 171d9911e405b914987a1ebe4ed5bd5e5422481f | refs/heads/master | 2021-09-02T11:42:27.015587 | 2018-01-02T09:01:39 | 2018-01-02T09:02:17 | 112,477,214 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 150 | r | SA4__Year_Sexunemployment.R | #' @title Sex by SA4, Year
#' @description UnemploymentSex by SA4, Year
#' @format 642 observations and 4 variables.
"SA4__Year_Sexunemployment"
|
ba441c6e42b9db0c6696ce5b2ea70ec936edbe34 | ce2deb9f1b4e02f16592ed78acad49bec5a674c8 | /projects/acn/data/ONC_Art_Cooc_2011/ArtONCnetworksMay2011.R | 3e6605c5f727fcac388105c385c39361c0f5962c | [] | no_license | RavenDaddy/Brent-Thomas-Tripp- | e748b9e48ebd96bfc9ca11124283fb2acde7b86a | fe81f7952e67ab093d9e455b9bf520178c724383 | refs/heads/master | 2017-12-15T11:41:30.341714 | 2017-01-02T01:02:36 | 2017-01-02T02:31:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,381 | r | ArtONCnetworksMay2011.R | #Reading Art's data files
#source('/Users/aeolus/Documents/R_Docs/EcoNet/EcoNet_03.R')
#source('/Users/Aeolus/Documents/Active_Projects/GeneticNetworks/Genetic_Networks/ind_net.R')
source('/Users/Aeolus/Documents/Active_Projects/CorNets/CorNets.R')
library(bipartite)
library(vegan)
library(sna)
library(gdata)
#Standa... |
ba7ac25034fbd42262b95711f5a6c28868579eed | d276646df2e7165caa4f77c4b1ebf3ab54d08a68 | /man/training_data.Rd | 0544a71d0b793e1e1c40fc0fcf28cb34ef0e13d8 | [] | no_license | zhaoxia413/PreMSIm | c0298a86b76d7c8bc2b8665483cb8e8109ef1323 | 0f67934545033eaabe1908806274afc1a3056960 | refs/heads/master | 2023-01-01T09:56:28.150818 | 2020-10-17T07:26:56 | 2020-10-17T07:26:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,423 | rd | training_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/training_data.R
\docType{data}
\name{training_data}
\alias{training_data}
\title{Training set}
\format{A dataframe with 1383 rows (tumor samples) and 16 columns (MSI status of tumor samples, and 15 gene features). The column names
are ... |
577f1c55edc5fe2bd6e77e3473970a02d880d212 | 000a615bc4e146c9c47e8b1fe158df2fe3ae0996 | /Week7/Day1/Lecture/Wine-PCA.R | ed697041857c5d3480db3a423db339b2fa7b6e64 | [] | no_license | SheikMBasha/MLLearning | 4f10da4a354dd897b61f82b1b556d29b00942737 | 525a28875745dd1090cd85da20df8f5bfe3951cc | refs/heads/master | 2021-09-28T17:42:19.309258 | 2018-02-25T18:43:16 | 2018-02-25T18:43:16 | 115,577,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,504 | r | Wine-PCA.R | setwd("D:/Batch37/CSE7302c/Day04")
# Chemical analysis of Wine
# These data are the results of a chemical analysis of wines grown
# in the same region in Italy but derived from three different cultivars.
# The analysis determined the quantities of 13 constituents found in each
# of the three types of wines.
WineD... |
68fbab354acdaf59b559dd8d5773569de9c5d3f1 | dfa05ab954b371ba16feefc32ce82b6bd0476545 | /3_r_scripts/create_variables.R | d3c3f9afe551a4f1d85aa7690c594f55cf886c89 | [] | no_license | fotisz/travel-speeds | bcd7cfcaf4734602b8f8c8cb8c5686e78b93188d | 5f8ea23547ad6fcd19fd8109ccf5f5b73dc37ead | refs/heads/master | 2020-04-28T12:41:05.216275 | 2017-12-01T13:26:00 | 2017-12-01T13:47:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,263 | r | create_variables.R | ### Setup ----
library(tidyverse)
library(magrittr)
library(data.table)
dublin_csv <- fread("0_raw_data/attributes.csv")
# Melt data columns labelled as u00_00 ... u23_45 into one variable "Time" with value "Speed"
melted_dt <- data.table::melt(dublin_csv, measure.vars = grep("u\\d", names(dublin_csv), value = TRUE),
... |
9400712a60d5725b513353c589c48ea13fe78689 | 5833b6528abf04acc1c595942a37d2cd336d59dd | /R/waheco.R | 1d866a5d16d693b3bc0cdae5b319785d09516372 | [] | no_license | sakho3600/WAHEco | f108a705e9372b07c098dcba9599128abde2e5e6 | 8dc13ed6df08bbee56860725d13f59df80845414 | refs/heads/master | 2020-05-20T12:49:52.297351 | 2017-08-21T22:06:14 | 2017-08-21T22:06:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 725 | r | waheco.R | #' WAHEco: Web Application for Health Economics Decision Making
#'
#' `WAHEco` is an R toolset for creating a general custom
#' web application. It has a demo web modules for medico-economics
#' decision making, and also, anyone can create new customized modules
#' or components for later they are added to the web ... |
18cbc8545075d781e485d6fc6a40b268d200bfe2 | 29c16e6ef5368f71d6b7a9990b6e5cf04cfe7525 | /cachematrix.R | cb79bbad36d12aa0a29def52cff3c5111e7f6d8b | [] | no_license | dario-fabietti/ProgrammingAssignment2 | 87f17fca437443b8f1816c892280f13db07e5b1a | cda5fa732f30b87258b8c1d4437f60373b2ef66a | refs/heads/master | 2020-03-29T00:14:54.424233 | 2018-09-19T18:56:25 | 2018-09-19T18:56:25 | 149,331,110 | 0 | 0 | null | 2018-09-18T18:01:11 | 2018-09-18T18:01:10 | null | UTF-8 | R | false | false | 1,361 | r | cachematrix.R | ## Creates a function with getters/setters for base matrix (get/set) and inverse (getinverse/setinverse)
makeCacheMatrix <- function(x = matrix()) {
i <- NULL
set <- function(y) {
x <<- y
i <<- NULL
}
get <- function() x
setinverse <- function(solve) i <<- solve
getinverse <- function() i
list(... |
23883dd3c69b66894f8b822f227668ea804a5357 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MCMCpack/examples/HMMpanelRE.Rd.R | 391bc3fc2ad3457f3fa6570051ab155ff83fb640 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,231 | r | HMMpanelRE.Rd.R | library(MCMCpack)
### Name: HMMpanelRE
### Title: Markov Chain Monte Carlo for the Hidden Markov Random-effects
### Model
### Aliases: HMMpanelRE
### Keywords: models
### ** Examples
## Not run:
##D ## data generating
##D set.seed(1977)
##D Q <- 3
##D true.beta1 <- c(1, 1, 1) ; true.beta2 <- c(-1,... |
4aacde7e292370172915bdcd6ec1fa201b896615 | 773204c6936ef4c5fe916494ab61e8acb320758c | /WGCNA_StepwiseScripts_CAGEseq/WGCNA_S2_FronSamps_AllCasesNConts_CAGEseq.R | ffc892adfbe1fe28c38ffabce325628ac1fb87b9 | [] | no_license | Tenzin-Nyima-1/RiMod-FTD | 2f0c96df599776a83a74d0ade9550acfe971551c | f8bbb879397834011910b877a77090e89317ba4b | refs/heads/master | 2020-03-06T16:05:26.283354 | 2018-05-04T07:42:46 | 2018-05-04T07:42:46 | 126,967,131 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,289 | r | WGCNA_S2_FronSamps_AllCasesNConts_CAGEseq.R | # Date: 26-27 March 2018;
# Project: RIMOD-FTD;
# Aim: Weighted Gene Coexpression Network Analysis(WGCNA) of frontal samples: all cases and control samples;
# NOTE: the annotation of the count-table was carried using RIKEN's CAT gtf file as the gene model;
# NOTE 2: the same script could be adapted for the count table ... |
9a9f5234d58dbc8683f9d7971f4e1017e0ae2bb4 | 0bc5b269a91a2c771728c9a81994ca442f2a9e8f | /Week 03/pre-class-03.R | e6c1a31b03a2082099a2906140cbbdc2d4c3902c | [] | no_license | PHP-2560/pre-class-work-2018-lcamillo | d9841a57c9581d48ac4d48bbbc0190b9bf572cdd | 84b18e5f946dcaf868e9aa92dada5026a081ea70 | refs/heads/master | 2020-03-28T22:11:00.131805 | 2018-12-02T14:23:17 | 2018-12-02T14:23:17 | 149,211,088 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,930 | r | pre-class-03.R | #PHP2560 preclass03
#Lucas Paulo de Lima Camillo
#B01478147
install.packages("gapminder")
library(dplyr)
library(gapminder)
gapminder = gapminder # dataset is called gapminder
#1)
# 52 in Africa, 25 in Americas, 33 in Asia, 30 in Europe, 2 in Oceania
gapminder %>%
group_by(continent) %>%
distinct(country) %>%
... |
84fc49b3afd60671fb11da4003299e0c2a1eb49e | eeb0928fceb45fbb03cda7b01bd8aa2120b8b7c2 | /my_functions/trading_calendar.R | 375fbece2ba8fbc91835a054ebabfa6f02e9e073 | [] | no_license | wadewuma/R | 37ee486f49f94702e1ad1446c23b96e05d1b8a6b | f5648f08f15b5c7c7d516484dc557dcd901dce66 | refs/heads/master | 2020-05-29T10:30:45.239956 | 2018-04-27T21:14:05 | 2018-04-27T21:14:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,208 | r | trading_calendar.R | #
# Get a vector of trading days. These are week days, with holidays removed. The trading days
# are relative to the New York Stock Exchange (NYSE).
#
# startDate and endDate are Date objects. The function returns a date ordered vector of Date
# objects which are the trading days.
#
# This function requires the timeDat... |
7f228edc25d100e679a3d90906e294250e0c96db | ff23cd7fb4222edef3e18400c8b73ff3d468fe44 | /tests/testthat.R | 43bf8cc023acf7a7eeb25bb05d1473fb0edec429 | [
"MIT"
] | permissive | DavisVaughan/vacation | 428a1c6d08ea3630c827bb359ac2b418307baea7 | 5fcf4fecf83cbb43896b74ceda93f3413584937d | refs/heads/master | 2022-05-23T07:54:28.778391 | 2020-04-28T21:22:03 | 2020-04-28T21:22:03 | 258,014,145 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(vacation)
test_check("vacation")
|
64adc7ca94a0df201274666935194cdcd0c1ff8f | cd2b951244f109c24554502f0c17a3c8bc59f6bc | /R/data_preparation_target_species.R | 7360687ef8778861f9b0af1d84ffbb3c27febf3f | [] | no_license | SunnyTseng/RADI_Taiwan_2020 | 976089bab0283b99dab07357685268271599d867 | 002f00772354efcfc18847ed74d077c5e7d62aa4 | refs/heads/main | 2023-05-06T23:30:42.199016 | 2021-06-01T07:37:22 | 2021-06-01T07:37:22 | 319,213,515 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,748 | r | data_preparation_target_species.R | #############################################################################
### Author: Sunny ###
### Project: RADI ###
### Purpose: Select a specific target species and output a analysis-ready ###
### ... |
31fe3b17b67ef3de4047b79f3a3ff7b5ff871660 | 6a9ff2d2cc2a27326b8dc426681ab3fc3b3d7b01 | /man/facts.Rd | b0fb034dd0ad7b414dafd6be85412b5f3d43b2b1 | [] | no_license | cran/usa | f8a742862085bb1ffed296b5b8ac25aed1ad685a | e3e3055a3fe8f401b39b09526e0904ae1151b9ea | refs/heads/master | 2021-01-13T21:42:28.130219 | 2020-02-23T08:30:03 | 2020-02-23T08:30:03 | 242,503,998 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,847 | rd | facts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{facts}
\alias{facts}
\title{US State Facts}
\format{A tibble with 52 rows and 9 variables:
\describe{
\item{name}{Full state name}
\item{population}{Population estimate (September 26, 2019)}
\item{admission}{The da... |
42f9b3b3cfe599029cc7c59e7f66180ece8ead12 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ev.trawl/examples/ComputeB3Exp.Rd.R | e17bea56e3ba3543d55fe2463a2df3e1c5c36e6b | [] | 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 | 265 | r | ComputeB3Exp.Rd.R | library(ev.trawl)
### Name: ComputeB3Exp
### Title: Wrapper to compute difference area between 't1' and 't2' under
### exponential trawl function.
### Aliases: ComputeB3Exp
### ** Examples
ComputeB3Exp(1, t1 = 3, t2 = 5)
ComputeB3Exp(0.2, t1 = 7, t2 = 3)
|
9fbe6a6738a568aedb3cf93c290a8add0e9aedf7 | 18632d8838856a9752ed3a6e513e6c2f24b19fc3 | /man/extract_unique.Rd | 7c0c431b1b7478148446a8aaa27f714a7a04580b | [] | no_license | gibonet/distrr | fa31e66d62984a106cbf95023be4c43ad4c6643b | 140d2b339a492a4d4bbb81a4925b563c0c2a24f6 | refs/heads/master | 2021-09-22T19:24:30.861074 | 2021-09-13T06:17:33 | 2021-09-13T06:17:33 | 101,790,664 | 6 | 2 | null | null | null | null | UTF-8 | R | false | true | 721 | rd | extract_unique.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gibutils.R
\name{extract_unique}
\alias{extract_unique}
\alias{extract_unique2}
\alias{extract_unique3}
\alias{extract_unique4}
\alias{extract_unique5}
\title{Functions to be used in conjunction with 'dcc' family}
\usage{
extract_unique(df)
... |
50df3af1783279e1c807a833c7adb13bc8307c9a | 77157987168fc6a0827df2ecdd55104813be77b1 | /palm/inst/testfiles/pbc_distances/libFuzzer_pbc_distances/pbc_distances_valgrind_files/1612988054-test.R | 66817cff9ed26cd1348bec82ac38deb665fd46e8 | [] | no_license | akhikolla/updatedatatype-list2 | e8758b374f9a18fd3ef07664f1150e14a2e4c3d8 | a3a519440e02d89640c75207c73c1456cf86487d | refs/heads/master | 2023-03-21T13:17:13.762823 | 2021-03-20T15:46:49 | 2021-03-20T15:46:49 | 349,766,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,301 | r | 1612988054-test.R | testlist <- list(lims = structure(c(-1.76555087703612e-19, 5.22851419827981e+54, 9.8165023169435e-310, 5.22851419824833e+54, 5.22851419824833e+54, 5.22851419824833e+54, 5.22851419824833e+54, 5.22851419824833e+54, 5.36359494858027e+54, 5.22829688768159e+54, 1.09586952513592e-193, 7.81911558904982e-148, 0, 5.43164610... |
707f1bd6841768c2082bd960c27570c0714695b1 | b401f67c511f11bce10b226c04dbae1d6135df3d | /tests/testthat/test.R | 9ae154c09e58f389a18de4e58c225fcef49b4699 | [] | no_license | cran/variosig | 5584a40bb9f5971b279a6e829089d4e1ecaa54e5 | 5d83aa4d8b48ee5a23aa4459bd47272a96cd21b9 | refs/heads/master | 2022-04-03T06:34:25.501151 | 2020-02-09T18:20:02 | 2020-02-09T18:20:02 | 110,669,080 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,129 | r | test.R | library(variosig)
library(gstat)
library(sp)
library(testthat)
data(meuse)
coordinates(meuse) = ~x+y
vario0 <- variogram(zinc~1, data = meuse)
perm <- envelope(vario0, data = meuse, formula = zinc~1, nsim = 9, cutoff = 1500)
expect_that(perm, is_a("list"))
expect_that(envsig(perm, method = "eb"), is_a("list"))
expect_... |
25e4b7088a3ceabfd418ddbce306db436996684f | 3125c61f87df5ed3a56f0bb08a017ff7ee4d6f3a | /ERE-analysis/analysis/R_scripts/annotate_overlaps_local.R | 39032b143ca0f87262b59e920dbb5910a05e85c9 | [] | no_license | meyer-lab-cshl/transcriptomic-diversity-human-mTECs | 7b1bbadcb9105ec388867ad309f52871a88586b3 | d33bd5196a3ded0f7b0419c357f77f3fe3017993 | refs/heads/main | 2023-04-11T02:28:09.671527 | 2023-01-03T18:37:11 | 2023-01-03T18:37:11 | 313,447,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,091 | r | annotate_overlaps_local.R | library(tidyverse)
library(GenomicRanges)
<<<<<<< HEAD
library(glue)
working_directory = '~/Desktop/thymus-epitope-mapping/ERE-analysis/analysis'
results_df_local_ERE = readRDS(file = glue('{working_directory}/R_variables/results_df_local_ERE'))
input = readRDS(file = glue('{working_directory}/R_variables/GRanges_ERE... |
4d9d85fb73a7eeaaa427abbfda4e1470d245af8d | e182802d73e309594c913322614f2ba625af416a | /inst/examples/example2.R | 977115347212d656a44c4eb7703a04b86d28004a | [
"MIT"
] | permissive | smbache/zeroclipr | 9c2a7f8978b17711d1b7e0e41844b7d9e6a81bb1 | 9a9a319fa72cc41901e717ee71a0b9c85279695e | refs/heads/master | 2021-01-10T04:12:32.499888 | 2015-11-02T08:46:39 | 2015-11-02T08:46:39 | 45,118,516 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,357 | r | example2.R | library(zeroclipr)
library(shiny)
# Simple example of a clipboard button for copying data as csv to clipboard
#
runApp(list(
# The UI
ui = bootstrapPage(
# Control the styling of the button. This is not necessary but will make the
# button feel more natural, with the flash object on top:
singleton(ta... |
b108f383469f3d2fe6d2cd5d362452f4e0259c55 | 5747d6d6462b3df323d69950ef1ba559522d3fad | /ascca_CV_gamma_command_line.R | f22f7e03edf8075902438324870cc15939b976bf | [] | no_license | rzliu2001/TF-finder | 2a389ed411cb0c982067af1b86433e39f345ba62 | 087275d3e277b000a13126f40312271b754d199a | refs/heads/master | 2023-03-27T02:32:31.631582 | 2016-08-03T02:10:16 | 2016-08-03T02:10:16 | 574,774,812 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,924 | r | ascca_CV_gamma_command_line.R | #options(echo = FALSE)
library(MASS)
library(stats)
source("func.R")
# Import x.data
# Import y.data
args <- commandArgs(trailingOnly = TRUE)
x.data = read.table(args[1])
x.map = x.data[,1]
y.data = read.table(args[2])
y.map = y.data[,1]
#rows are observations, columns are genes(variables)
x.data = t(x.data[,-1])
... |
69c9525e65cfe4479d01cf1b1119194e12409abe | 29666f9152da5d06e208a248add2393d2130180c | /man/source_all.Rd | 4c2f23a8e4bad035f853aa97f912d99c6ba15c95 | [] | no_license | Nicktz/fmxdat | 147433ce18ec493fdf3431e4fe40e05fc8e4a253 | ec52cdcc65d3431edce67a0ff65b5258a5184bd7 | refs/heads/master | 2023-08-18T14:03:45.452330 | 2023-08-08T07:27:38 | 2023-08-08T07:27:38 | 205,252,472 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 646 | rd | source_all.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/source_all.R
\name{source_all}
\alias{source_all}
\title{source_all}
\usage{
source_all(Loc)
}
\arguments{
\item{Loc}{Provided location to source all functions from
On a Windows PC, this could e.g. be: 'C:/Temp_Folder/code'
Or if called from ... |
93c214c95d721e25d2cdee237c8ff5ff17edf0cd | f993dd62e1367e79077a8e898c1f6dd606a59490 | /source_scripts/Normailize_Functions.R | cce9ae7d99a2910bcbf5b37b3506af6ad8bb41ed | [
"Apache-2.0"
] | permissive | SEL-Columbia/nmis_R_scripts | 5f0450f8642eba7f15413af8756046c97781fa15 | daba5df29fcd58294aec69bb326887ee0b0fc5d9 | refs/heads/master | 2021-01-22T05:16:11.308603 | 2014-08-06T19:08:02 | 2014-08-06T19:08:02 | 12,162,798 | 1 | 1 | null | 2014-06-12T16:40:58 | 2013-08-16T16:21:58 | R | UTF-8 | R | false | false | 5,317 | r | Normailize_Functions.R | require(plyr)
require(doBy)
require(digest)
require(stringr)
# search for slug_names that contain part of the input string
slugsearch <- function(nm, df=edu_661){
names(df)[grep(nm, names(df), ignore.case=T)]
}
# Basically the table function, but shows NA value
see <- function(nm, df=edu_113)
{
table(df[,nm]... |
3c1f43b56e5fc031c62f73bece72d7a1293a113a | 6be70ffdb95ed626d05b5ef598b842c5864bac4d | /old/dev/dv_iv_plot_senate_confint.R | 5cfafec875a5e73029ccd08dd1031a37f9bbaf3d | [] | no_license | Hershberger/partycalls | c4f7a539cacd3120bf6b0bfade327f269898105a | 8d9dc31dd3136eae384a8503ba71832c78139870 | refs/heads/master | 2021-09-22T17:54:29.106667 | 2018-09-12T21:16:56 | 2018-09-12T21:16:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,477 | r | dv_iv_plot_senate_confint.R | library(partycalls)
library(ggplot2)
theme_set(theme_bw())
load("test_data/senate_data_lm.RData")
senate_data <- senate_data[drop == 0, ]
senate_data[congress == 107 & caucus == "Republican", maj := 0]
senate_data[congress == 107 & caucus == "Democrat", maj := 1]
senate_data[, south := as.factor(south)]
senate_data... |
433d45f3c55472a8840cadb7ff2ae87512e3985b | 16f9d05fa1d0b6aadd313cc2896d7730d0e0d7c6 | /RGtkGen/man/coerceRValueCode.Rd | cf8168a2531866195115367904f54f366047f9ac | [] | no_license | statTarget/RGtk2 | 76c4b527972777c567fb115587418f9dea61bf29 | 42c2d5bc7c8a462274ddef2bec0eb0f51dde1b53 | refs/heads/master | 2023-08-24T05:13:35.291365 | 2021-10-24T18:27:46 | 2021-10-24T19:24:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,899 | rd | coerceRValueCode.Rd | \name{coerceRValueCode}
\alias{coerceRValueCode}
\alias{getPrimitiveTypeAs}
\title{Generate R code to coerce R object to type.}
\description{
This generates R code which checks and
potentially coerces an argument
to an S function to the appropriate type
as required by C code to which the argument
will be pass... |
8ed8212be6acf8144ae8d24461168746fd43119c | d1c45cc19ea6a37e57f57ef8d973114f58ca6c24 | /scripts/eggs.R | a08abab47e32980566c0cad525c740b8148cdc4a | [
"MIT"
] | permissive | genner-lab/reproduction-temporal-trends | 236063a4eba4d94691705cfe2f77a3c76dcb559f | 53dc62b0085459f66b8887aca82d2909cf579f95 | refs/heads/main | 2023-04-18T03:28:13.427305 | 2022-07-18T19:41:05 | 2022-07-18T19:41:05 | 362,065,020 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,023 | r | eggs.R | #!/usr/bin/env Rscript
# load libs and funs
source(here::here("scripts/load-data.R"))
# collapse eggs
trad.collapsed <- trad.master %>% collapse_taxonomy(rmfw=TRUE,lifestage="Eggs",collapse=TRUE)# EGGS
edna.collapsed <- edna.filt %>% collapse_taxonomy(rmfw=TRUE,lifestage="Eggs",collapse=TRUE)# EGGS
# get sample n
gl... |
80bfe77e7f1bcb9c2d830a0f26c54afea0b03575 | b8412af1de74d092cc0959886d55219bab555e68 | /SV/MetastasisPairs/StructuralVariation_Metastasis_Pairs.R | 9ee89fc9a6bdbc168dbd694a99be74d00fadcd08 | [] | no_license | DGendoo/PDACDiseaseModels | df49fea5b7c9bb0eaf46fc8a4e7931ac2666895a | 53e14a3faed8d854b843fe6f4ff3b90688ef62eb | refs/heads/master | 2021-03-27T12:02:12.169406 | 2020-12-17T09:54:38 | 2020-12-17T09:54:38 | 71,823,065 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,936 | r | StructuralVariation_Metastasis_Pairs.R | # Deena M.A. Gendoo
# November 1, 2016, and final update on June 23, 2017
# Parse Structural Variation (SV) data (in TSV format) for Paired Tumour-PDX samples
# FINAL_StructuralVariation_LIVER_PairsNews.R
##################################################################################################################... |
75590caa4f47b8029c7521de8b616d2e9e23e2b7 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /multivariance/inst/testfiles/doubleCenterBiasCorrectedUpperLower/libFuzzer_doubleCenterBiasCorrectedUpperLower/doubleCenterBiasCorrectedUpperLower_valgrind_files/1612796315-test.R | cd184c92ca48d7a099614645260b945eedbe62f3 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 319 | r | 1612796315-test.R | testlist <- list(n = 0L, x = structure(c(1.06559867695611e-255, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(6L, 8L)))
result <- do.call(multivariance:::doubleCenterBiasCorrectedUpperLower,testlist)
str(result) |
c573a9ca1f6dc69c8b5c6389ef9dd85839921775 | d1c4c959363620cb261dc1b6c2f484bf85a35fa3 | /RScripts/WebScraping.R | f53ea08f9c72e258590bc3857d6ed24c0bfe8feb | [] | no_license | fungyip/MTR | fa6e0747fa7961599e0bf8df65537a1c6a025390 | 1aa0250bfd77dc99ecff563d188644cb18b70feb | refs/heads/master | 2021-03-24T13:06:46.840018 | 2017-02-04T08:54:39 | 2017-02-04T08:54:39 | 80,493,229 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,820 | r | WebScraping.R | library(rvest)
library(tidyverse)
url = "https://en.wikipedia.org/wiki/List_of_MTR_stations"
# All <- read_html(url, encoding="UTF-8") %>% html_nodes(xpath="//tr/td") %>%
# html_text() %>%
# as.data.frame()
# Name <- read_html(url, encoding="UTF-8") %>% html_nodes("td:nth-child(2) a") %>%
# html_text() %>%
# ... |
f98f5af8526064181398e580462f7d00ac231e88 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hIRT/examples/summary.hIRT.Rd.R | 05524f054ce5cd3709c3ccddeb864d6c4256a99f | [] | 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 | 344 | r | summary.hIRT.Rd.R | library(hIRT)
### Name: summary.hIRT
### Title: Summarizing Hierarchical Item Response Theory Models
### Aliases: summary.hIRT print.summary_hIRT
### ** Examples
y <- nes_econ2008[, -(1:3)]
x <- model.matrix( ~ party * educ, nes_econ2008)
z <- model.matrix( ~ party, nes_econ2008)
nes_m1 <- hgrm(y, x, z)
summary(nes... |
2b2a9799bed57fa8b8384a39c56f767a4fc196b5 | e63dc395ac553ac461b77128b4bea171832ad7e6 | /Scripts/MaAsLin2.R | df9596332447f8c2add05fc675580b14d78bca9a | [
"MIT"
] | permissive | yassourlab/GMAP | 2c97318c5df37c02e24f7072edc4e6d8ecbbe576 | 14e94efab1d0c3a87a0e834bcd63814ce06f8f12 | refs/heads/main | 2023-06-19T11:09:44.761200 | 2021-07-09T13:30:44 | 2021-07-09T13:30:44 | 384,099,327 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,577 | r | MaAsLin2.R | ##########################################################
###################### MaAsLin2 Run ######################
##########################################################
# data
{
rm(list = ls())
try(setwd("~/GMAP/Docs/GMAP_Paper/"), silent = T)
try(setwd("/Volumes/ehudda/GMAP/Docs/GMAP_Paper/"), silent = ... |
107fae300061ffab0be28df653518e4e36f570a9 | b112ed17ecb6841790ece799460f0e138a1078dc | /R/handlers.R | e08ccff8e8d0b1e96e223a2470fb3cb75607a6bb | [] | no_license | iugrina/glycanr | 546d469dad77f8fcc7415e52c4228b658c8691c2 | 1055905c75a92398061d3c10d6c7c12f7f21fc91 | refs/heads/master | 2021-07-12T06:56:37.944197 | 2021-03-29T15:56:54 | 2021-03-29T15:56:54 | 22,675,446 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 214 | r | handlers.R |
.onAttach <- function(...) {
msg <- "From version 0.3 functions tanorm and glyco.outliers expect data frames in long format."
packageStartupMessage(paste(strwrap(msg), collapse = "\n"))
return(TRUE)
}
|
e4dd68a7877418f614fd299b4309d442397d72ba | eed0d83d4b1060efbaaf167fe1908490ef43ea8d | /plot3.R | 786f1ccc78830a51b2e9aa81ede8b9742cd671c9 | [] | no_license | MSamranB/ExData_Plotting1 | 13d286f4920feaf9bc1b0f524c0d4265ff705151 | af94bd8651f3f1493d63f89a907994ddb7171df9 | refs/heads/master | 2022-11-13T20:57:00.083490 | 2020-07-09T07:14:00 | 2020-07-09T07:14:00 | 277,103,378 | 0 | 0 | null | 2020-07-04T12:14:07 | 2020-07-04T12:14:06 | null | UTF-8 | R | false | false | 1,118 | r | plot3.R | my_data<-read.csv("household_power_consumption.txt",header=T,sep=';',na.strings = "?",quote='\"')
head(my_data)
maindate1<-"1/2/2007"
maindate2<-"2/2/2007"
x<-as.Date(maindate1)
y<-as.Date(maindate2)
my_data1<-subset(my_data, Date %in% c(maindate1,maindate2))
mydata1Date<-as.Date(my_data1$Date,format="%d/%m/... |
060da73a935fc010c81cc7fa9aced00cb752b17c | 0f97039d2b7be6f7c7c6f2b94914e72401ef12fc | /man/bntest.plot.Rd | ee587603dac4806370ebd88c99b85de2901fbe47 | [
"MIT"
] | permissive | jhk0530/Rstat | aaa6692c2f4c8a768707e1e51992f5f2dbd817ad | 6b79d8047b6bdbb56e8ac7a6c6eed6c31694b079 | refs/heads/master | 2023-07-31T22:28:09.874352 | 2021-09-12T06:07:15 | 2021-09-12T06:07:15 | 263,038,018 | 4 | 3 | null | null | null | null | UTF-8 | R | false | true | 720 | rd | bntest.plot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bntest.plot.R
\name{bntest.plot}
\alias{bntest.plot}
\title{Exact Binomial Test}
\usage{
bntest.plot(x, n, p0, alp = 0.05, side = "two", dig = 4, dcol)
}
\arguments{
\item{x}{Vector of number of successes}
\item{n}{Sample size}
\item{p0}{Po... |
2267cc678b1c0387c83d8314bcc0786dde60e8f1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pairwise/examples/gif.Rd.R | b6d4cfeb84a69e8b12347e3e99f806fb044e551b | [] | 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 | 408 | r | gif.Rd.R | library(pairwise)
### Name: gif
### Title: Graphical Item Fit Plots
### Aliases: gif
### ** Examples
########
data(bfiN)
pers_obj <- pers(pair(bfiN))
#### plot empirical category probabilities
gif(pers_obj = pers_obj, itemnumber = 1 )
gif(pers_obj = pers_obj, itemnumber = 1 , integ=8) # integration over 8 points
gi... |
74c7926da2c196f5a0587a2ccd914bfa18e2f494 | dfe1c6596f4195d1a8e521373262be9b5fd61bf4 | /test.R | 7e969d1d92702e03246a64ed22437f47a9cc2aa0 | [] | no_license | ashaia-rclco/hello | dce7a115a85c83cdcaac97eaa42d78e389729c7e | 9da4ecbf5b0e039b78afea36bd8cd53bc4c0f80a | refs/heads/main | 2023-03-25T14:38:53.614011 | 2021-03-26T17:40:59 | 2021-03-26T17:40:59 | 351,859,706 | 0 | 0 | null | 2021-03-26T17:41:00 | 2021-03-26T17:22:53 | null | UTF-8 | R | false | false | 44 | r | test.R | a = 1
b = 12
print (a+b)
print(Sys.time())
|
679afde3242567b0a40187e61ffd761eda664cf5 | 7669f7ac75623e7c4fa6d1ff3fd406f670de90a8 | /plot1.R | b321df0c76e4e39ed39ddd789dde1e12bcacf479 | [] | no_license | israelcazares/ExData_Plotting1 | 41a83b55589a3c8dbc4a0050e682cd439af2813e | d5bc19976fa7dc9441c4a1db81ca72d6b7a5e546 | refs/heads/master | 2021-01-17T21:02:30.264762 | 2014-10-12T19:36:41 | 2014-10-12T19:36:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | plot1.R | # Plot 1. household_power_consumption.txt set should be in the working directory with R files.
#---------------------------#
# code for reading the data #
#---------------------------#
# Reading the Data
hpc_data <- read.csv("household_power_consumption.txt", na.string="?", sep=";")
# Extracting the correct dataset
... |
f756953bb2cf9bdea605b46b5ee8a6d9bf1177d0 | 0f28a286662bb60e5734d493e0e34f00b83883f7 | /october2020_uploads/zip_supplemental/recover_tweets.R | fd75c5afad2c3cd938ad4738ba90f3064d55c2ce | [
"MIT"
] | permissive | fboehm/jse-2019 | b17edf417efe810a718e41e234933612e067570d | ba1c1c8d73ed9db87b543813d64930828caa7070 | refs/heads/master | 2021-07-04T20:38:10.267757 | 2020-11-29T16:57:43 | 2020-11-29T16:57:43 | 203,395,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,242 | r | recover_tweets.R | #' Recovers Twitter damaged stream data (JSON file) into parsed data frame.
#'
#' @param path Character, name of JSON file with data collected by
#' \code{\link{stream_tweets}}.
#' @param dir Character, name of a directory where intermediate files are
#' stored.
#' @param verbose Logical, should progress be display... |
09b69c2ff6b8510e5e0410829abcd8bca8269d80 | bb10ea2c03c9cd1a0d4458772ca2440f488b1008 | /R/adjustment.R | 869cccb70a7874102953655b50b1276f65ef980c | [
"GPL-3.0-only"
] | permissive | Henning-Schulz/forecastic | 1b375c95c77f3ceb88db049213b0762b5997c2c1 | 3f7517749b2701f7670e895568a2d85a84ff2a4f | refs/heads/master | 2021-08-08T03:07:22.922224 | 2020-07-16T15:27:15 | 2020-07-16T15:27:19 | 202,161,301 | 0 | 0 | Apache-2.0 | 2019-08-13T14:25:12 | 2019-08-13T14:25:12 | null | UTF-8 | R | false | false | 1,376 | r | adjustment.R | # adjustment.R
#' @author Henning Schulz
library(tidyverse)
adjustment_logger <- Logger$new("adjustment.R")
#' Adjusts the intensities using the specified adjustments
#' If required, it loads the behavior models from the elasticsearch and includes them in the adjustments
#'
#' @param intensities The (forecasted) in... |
873cb82af3baf354db30b933bf36a65b64e79219 | 48741a62f53570304779b3a4ae5729c8d4b436e4 | /Otolith.images/Morphometrics.R | 73992ca875ad4b7f9b28c570857b00d0bfe24320 | [] | no_license | Otoliths/Otolith-shape | d722385d991db55f6fc04f0c1fde474610a39fb7 | 5d859801459d6d167e07186bddb7c0b04d819f50 | refs/heads/master | 2022-04-11T22:27:38.749446 | 2020-03-13T04:08:55 | 2020-03-13T04:08:55 | 110,910,282 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,989 | r | Morphometrics.R | ###FIGURE 3 Reconstruction of the mean otolith shape in Schizothorax nukiangensis, based on Wavelet transformation, differentiated by sampling site
library("shapeR")
#read data and image
shape1 = shapeR("Otolith.images/img_bzlz","BZLZ.39.csv")
shape1 = detect.outline(shape1, threshold = 0.2, write.outline.w.org = TRU... |
4bcafd2512d27c9f45730feacaf94097b5009983 | da1997ab28c8dade0c3127c0a465ca990596ded9 | /tests/testthat/test-perfectcorr.R | cb1c726874133ec0bfacc94c9ef26fb2fab12dfc | [
"MIT"
] | permissive | kbroman/jeremyash | a5bb91ccae7e4586fe7f503547e8c241fb653904 | f7e3ecbe4b4218c6ed2d4ce990b9b705245af7ed | refs/heads/master | 2021-01-16T22:22:16.851195 | 2015-05-05T14:27:03 | 2015-05-05T14:27:03 | 34,909,755 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 249 | r | test-perfectcorr.R | context("perfect correlation")
test_that("vector_corr returns a perfect correlation (= 2)", {
perfect_corr <- 2
set.seed(1001)
x <- matrix(rnorm(200, 3, 1), ncol=2)
x_x <- vector_corr(x, x)
expect_equal(perfect_corr, unname(x_x[1]))
}) |
41cbcaa51feaf65ff07b12f3460cff2cb6861ac5 | 2a7e39ab70308111591d3f688cca438cc1b95045 | /exploration/SecondApproachWithCaret.R | cb6d27306f0691cbaf200b12845122132ab03f86 | [] | no_license | cokeSchlumpf/30200-miniprojects | 748fcb6a0db90c3406cacede78c35e892251f21c | 857be78c12168174d3102b72ea6c72844049cf87 | refs/heads/master | 2020-12-21T04:23:07.329898 | 2020-01-28T09:52:18 | 2020-01-28T09:52:18 | 236,305,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,171 | r | SecondApproachWithCaret.R | rm(list = ls())
#--------------------------------
# Load Libraries:
library(caret)
library(dplyr)
library(ggplot2)
library(tictoc)
library(MLmetrics)
#--------------------------------
#Load Datasets:
#-----------
#Load: Comp. 1 Data - Risse
setwd("~/Studium/DataScience/HS Alb. Sig/Machine Learning 3... |
e474ec6a4d019fdddb68d8399437e455e0e6cf2e | 4c8dd7c80f4f8e9618a70ad2bfab5432b95028c5 | /other_models/scripts/data_scripts/known_lat_all_chases_data_cleaning.R | a6603d003bc14ce4503aee3579da3f1b422bf472 | [] | no_license | MCMaurer/Fish_Group_Models | fc907c329fbba78fb3834b92e4cdb2a466015f3c | add641c4aaaff3d0c45e2158344ba1318817cf4d | refs/heads/master | 2021-04-28T08:06:01.787605 | 2019-11-14T03:50:55 | 2019-11-14T03:50:55 | 122,240,331 | 0 | 1 | null | 2019-08-09T19:35:30 | 2018-02-20T18:47:49 | HTML | UTF-8 | R | false | false | 1,122 | r | known_lat_all_chases_data_cleaning.R | library(tidyverse)
chase_data <- read_rds("chases/data/cleaned/full_chase_group_size.rds")
known_lat_data <- read_rds("latency/data/cleaned/latency_typical_food.rds") %>%
select(-date_time, -camera, -video)
all_chase_spread <- chase_data %>%
filter(measurement == "numchases") %>%
mutate(percap_numchases = va... |
3420898cbce6e822130624a685cbd651ad8dd60f | f69c24cf40bfddce66cb7fc74176d726037eb37e | /man/list_values.Rd | d7241e1f50f205decc2e8166b907507345f3dbfb | [] | no_license | cran/AntMAN | 899ff61b2b7bac112e30e9d7aa37c5aa133f2d7d | 5e4f4a093ea9b80a20d05d7261fe368481a8a0b8 | refs/heads/master | 2021-07-30T18:18:38.715761 | 2021-07-23T09:00:02 | 2021-07-23T09:00:02 | 236,548,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 362 | rd | list_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AM_mcmc.R
\name{list_values}
\alias{list_values}
\title{Internal function that produces a string from a list of values}
\usage{
list_values(x)
}
\arguments{
\item{x}{a list of values}
}
\description{
Internal function that produces a string f... |
8808e615b24657d482765b47b4cc9894c9826464 | e35fd2643c18e9a62b20b7f6dea0db140896d76c | /man/igstd.Rd | 4e7cdc4e60bb1fa36d4758d70ce47c736fb11588 | [] | no_license | PBG-Ec/igstrd | f6273e65e6ffde19f33105389985e7e21cbc471c | c583980d4acce62d506d881db4d27519966cd823 | refs/heads/master | 2020-04-23T03:48:28.062889 | 2019-02-15T16:06:25 | 2019-02-15T16:06:25 | 170,888,613 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,024 | rd | igstd.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/igstrd.R
\name{igstd}
\alias{igstd}
\title{Modified igrowup}
\usage{
igstd(mydf, sex, age, age.month = F, weight = rep(NA, dim(mydf)[1]),
lenhei = rep(NA, dim(mydf)[1]), measure = rep(NA, dim(mydf)[1]),
headc = rep(NA, dim(mydf)[1]), armc... |
0c9469760b323ca0bd21d6d79d809a75e22b0e02 | 126934bebe894dc5b4f376873a9e46ac2012588d | /R/rolling_apply_specialized.R | 822ceca2e92e2d968a40b911d7af6156821ba85d | [] | no_license | andreas50/utsOperators | b978e373d25e8b23f0eb9cc19f0d35ac3cd42b66 | b8d2fc43bd8ba8be80f5072352182480a753000c | refs/heads/master | 2021-03-30T16:57:32.053060 | 2018-08-13T14:58:41 | 2018-08-13T14:58:41 | 35,906,344 | 13 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,891 | r | rolling_apply_specialized.R | #############################################################################
# Specialized implementations of rolling_apply() for certain choices of FUN #
#############################################################################
#' Apply Rolling Function (Specialized Implementation)
#'
#' This function provides ... |
c137cb961efabbc233ceeb006ee436bee301ae8e | 1ca3bd0b72b17af6364e97607c8e88c45c040464 | /2015_Bombus_Survey/2015Survey.R | c6642b0b44d3c4755e69ef0d325d6c31285e3842 | [] | no_license | samanthaannalger/AlgerProjects | 9662bf7bdc373acbf76a1262222c3a08a90e4ace | 96f4a7812c4d3782433e5c4923c5700067ef2c18 | refs/heads/master | 2020-12-30T23:47:37.920687 | 2019-10-25T13:07:46 | 2019-10-25T13:07:46 | 80,625,540 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 39,321 | r | 2015Survey.R | ###########################################################################################
# Data Analysis for 2015 Bombus Virus Study
# Samantha Alger and P. Alexander Burnham
# July 10, 2017
# Edited by Alex on June 30, 2018
###########################################################################################
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.