content large_stringlengths 0 6.46M | path large_stringlengths 3 331 | license_type large_stringclasses 2
values | repo_name large_stringlengths 5 125 | language large_stringclasses 1
value | is_vendor bool 2
classes | is_generated bool 2
classes | length_bytes int64 4 6.46M | extension large_stringclasses 75
values | text stringlengths 0 6.46M |
|---|---|---|---|---|---|---|---|---|---|
\name{landmass}
\alias{landmass}
\docType{data}
\title{
Global Coastlines
}
\description{
A \code{SpatialPolygonsDataFrame} with global coastlines.
}
\usage{data("landmass")}
\note{
Most of the times it might be desirable to only flag records far away from the coast as problematic rather than those close to the coastl... | /man/landmass.Rd | no_license | azizka/speciesgeocodeR | R | false | false | 741 | rd | \name{landmass}
\alias{landmass}
\docType{data}
\title{
Global Coastlines
}
\description{
A \code{SpatialPolygonsDataFrame} with global coastlines.
}
\usage{data("landmass")}
\note{
Most of the times it might be desirable to only flag records far away from the coast as problematic rather than those close to the coastl... |
#' .. content for \description{} (no empty lines) ..
#'
#' .. content for \details{} ..
#'
#' @title
#' @param Phenotypes
#' @param dest
clean_Pop33_phenos <- function(Phenotypes = PhenoFile, dest = here("data",
"Pop33_Geno_csvs.csv")) {
# Read in the phenotype file
Pop33Pheno <- rea... | /R/clean_Pop33_phenos.R | no_license | jhgille2/OH_33_34_Manuscript | R | false | false | 896 | r | #' .. content for \description{} (no empty lines) ..
#'
#' .. content for \details{} ..
#'
#' @title
#' @param Phenotypes
#' @param dest
clean_Pop33_phenos <- function(Phenotypes = PhenoFile, dest = here("data",
"Pop33_Geno_csvs.csv")) {
# Read in the phenotype file
Pop33Pheno <- rea... |
testlist <- list(doy = 2.84870483508747e-306, latitude = c(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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ), temp = c(8.5728629954997e-312, 1.56898424065867e+82,... | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/AFL_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1615830703-test.R | no_license | akhikolla/updatedatatype-list3 | R | false | false | 832 | r | testlist <- list(doy = 2.84870483508747e-306, latitude = c(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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ), temp = c(8.5728629954997e-312, 1.56898424065867e+82,... |
#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
t... | /final_project3/app.R | no_license | benjaminvillaw/The-Sacred-and-The-Profane- | R | false | false | 1,601 | r | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define UI for application that draws a histogram
ui <- fluidPage(
# Application title
t... |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/dataset_doc.R
\docType{data}
\name{bisland}
\alias{bisland}
\title{Icelandic coastline, hi-resolution}
\format{A data frame with 19841 observations on the following 2 variables.
\describe{ \item{lat}{a numeric vector} \item{lon}{a num... | /man/bisland.Rd | no_license | cran/geo | R | false | false | 448 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/dataset_doc.R
\docType{data}
\name{bisland}
\alias{bisland}
\title{Icelandic coastline, hi-resolution}
\format{A data frame with 19841 observations on the following 2 variables.
\describe{ \item{lat}{a numeric vector} \item{lon}{a num... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_regres_line.R
\name{add_regres_line}
\alias{add_regres_line}
\title{Add a regression line and confidence band to a plot}
\usage{
add_regres_line(fit, from = NULL, to = NULL, band = TRUE,
ci.col = "#BEBEBEB3", ...)
}
\arguments{
\item{fi... | /man/add_regres_line.Rd | no_license | RemkoDuursma/nlshelper | R | false | true | 1,527 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/add_regres_line.R
\name{add_regres_line}
\alias{add_regres_line}
\title{Add a regression line and confidence band to a plot}
\usage{
add_regres_line(fit, from = NULL, to = NULL, band = TRUE,
ci.col = "#BEBEBEB3", ...)
}
\arguments{
\item{fi... |
################# Part 3 - Main Simulation - Non-Destructive Search - Levy Model
Thresh<-Thresh # Threshold of visual range
Lags<-Lags # Lags at which there are effects of encounters ... | /Simulation-NonDestructive-LevySearch.R | no_license | ctross/adaptivesearch | R | false | false | 10,405 | r | ################# Part 3 - Main Simulation - Non-Destructive Search - Levy Model
Thresh<-Thresh # Threshold of visual range
Lags<-Lags # Lags at which there are effects of encounters ... |
# -----------------------------------------------------------------------------
# Ordenar dados de Net
# coletas 024/17 até 260/2017 estava no arquivo de fluxos médios 30 min,
# TOA5_XF_ddddyy.data
# a partir de 300/2017 tem arquivo próprio com aquisiçao de 10 min
# TOA5_XF_ddddyy_10.data
# a partir de 115/2019... | /ext_codes/Rad/net_organize.R | no_license | ebrasilio/lcbtools | R | false | false | 7,379 | r | # -----------------------------------------------------------------------------
# Ordenar dados de Net
# coletas 024/17 até 260/2017 estava no arquivo de fluxos médios 30 min,
# TOA5_XF_ddddyy.data
# a partir de 300/2017 tem arquivo próprio com aquisiçao de 10 min
# TOA5_XF_ddddyy_10.data
# a partir de 115/2019... |
# Analysis of the results
# Code to:
# - perform all the analyses of the article
# - draw Figure 3 (Individual pressures: regional comparisons)
# - draw Figure 4 (Density distribution of pressure percentiles and frequency of occurrence of top pressures in refugia vs non-refugia)
# - draw Figure S1 (Correlation among p... | /analysis/Analysis.R | permissive | sparkgeo/local-reef-pressures | R | false | false | 11,084 | r | # Analysis of the results
# Code to:
# - perform all the analyses of the article
# - draw Figure 3 (Individual pressures: regional comparisons)
# - draw Figure 4 (Density distribution of pressure percentiles and frequency of occurrence of top pressures in refugia vs non-refugia)
# - draw Figure S1 (Correlation among p... |
initialize.icm <- function(param, init, control) {
## Master List for Data ##
dat <- list()
dat$param <- param
dat$init <- init
dat$control <- control
# Set attributes
dat$attr <- list()
numeric.init <- init[which(sapply(init, class) == "numeric")]
n <- do.call("sum", numeric.init)
dat$attr$activ... | /R/ext_exp/_icm.mod.init.seiqhrf.R | no_license | franzbischoff/covid-19-pt-north | R | false | false | 5,842 | r | initialize.icm <- function(param, init, control) {
## Master List for Data ##
dat <- list()
dat$param <- param
dat$init <- init
dat$control <- control
# Set attributes
dat$attr <- list()
numeric.init <- init[which(sapply(init, class) == "numeric")]
n <- do.call("sum", numeric.init)
dat$attr$activ... |
VecPermutFun <- function(n.ages, n.classes, reps, alpha, gamma, intro.cost, sex.ratio,
samples.to.draw, tot.chains, joint.posterior.coda, posterior.names)
{
# intro.out.elast <- fade.out.elast <- rep(NA, reps)
# 19 age classes; 2 environmental states.
fade.out.elast <- intro.elast <- r... | /R/VecPermutFun.R | no_license | kmanlove/BighornIPM | R | false | false | 3,462 | r | VecPermutFun <- function(n.ages, n.classes, reps, alpha, gamma, intro.cost, sex.ratio,
samples.to.draw, tot.chains, joint.posterior.coda, posterior.names)
{
# intro.out.elast <- fade.out.elast <- rep(NA, reps)
# 19 age classes; 2 environmental states.
fade.out.elast <- intro.elast <- r... |
#~ ,''''''''''''''.
#~~ / USEPA FISH \
#~ >~',*> < TOX TRANSLATOR )
#~~ \ v1.0 "Doloris" /
#~ `..............'
#~~
#~ N. Pollesch - pollesch.nathan@epa.gov
#' Kernel Component - Reproduction Kernel
#'
#' Integral projection models (Ellner et al., 2016) have the gen... | /R/ReproductionKernel.R | no_license | npollesch/FishToxTranslator | R | false | false | 1,707 | r | #~ ,''''''''''''''.
#~~ / USEPA FISH \
#~ >~',*> < TOX TRANSLATOR )
#~~ \ v1.0 "Doloris" /
#~ `..............'
#~~
#~ N. Pollesch - pollesch.nathan@epa.gov
#' Kernel Component - Reproduction Kernel
#'
#' Integral projection models (Ellner et al., 2016) have the gen... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parameters.R
\name{parameters}
\alias{parameters}
\title{Parameter names of an JointAI object}
\usage{
parameters(object, expand_ranef = FALSE, mess = TRUE, warn = TRUE, ...)
}
\arguments{
\item{object}{object inheriting from class '... | /man/parameters.Rd | no_license | cran/JointAI | R | false | true | 1,036 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parameters.R
\name{parameters}
\alias{parameters}
\title{Parameter names of an JointAI object}
\usage{
parameters(object, expand_ranef = FALSE, mess = TRUE, warn = TRUE, ...)
}
\arguments{
\item{object}{object inheriting from class '... |
#' Correction factor for Sun-Earth distance variation thoughout the year
#'
#'@param month is the month (1 to 12)
#'@param day is the day of the month (1-31)
#'
#'@return Returns a factor that multiply F0 to account for Sun-Earth distance variation at any given date.
#' This factor can change the irradiance up to 3.3\... | /R/orbex.R | no_license | belasi01/Cops | R | false | false | 558 | r | #' Correction factor for Sun-Earth distance variation thoughout the year
#'
#'@param month is the month (1 to 12)
#'@param day is the day of the month (1-31)
#'
#'@return Returns a factor that multiply F0 to account for Sun-Earth distance variation at any given date.
#' This factor can change the irradiance up to 3.3\... |
library(emayili)
library(rmarkdown)
library(dplyr)
# This script will render a Rmd to HTML and attach it to an email.
#
# NOTE: Images in the HTML will not appear in the attachment.
render(
input = "junk-report.Rmd" ,
output_format = "html_document",
output_file = "junk-report.html"
)
SMTP_USERNAME = Sys.geten... | /examples/rmarkdown-render-attachment/junk-report.R | no_license | adam-gruer/emayili | R | false | false | 690 | r | library(emayili)
library(rmarkdown)
library(dplyr)
# This script will render a Rmd to HTML and attach it to an email.
#
# NOTE: Images in the HTML will not appear in the attachment.
render(
input = "junk-report.Rmd" ,
output_format = "html_document",
output_file = "junk-report.html"
)
SMTP_USERNAME = Sys.geten... |
getIndicators = function(frequency = "daily", save = FALSE,
type = c("all", "policyTradeWeight",
"TradeWeight", "count", "countryIndicators"), aggregateRussia = F){
library(data.table)
library(zoo)
library(dygraphs)
library(xts)
library(countrycode)
source(... | /R/getIndicators.R | no_license | BDalheimer/FoodPriceVolatility | R | false | false | 7,811 | r |
getIndicators = function(frequency = "daily", save = FALSE,
type = c("all", "policyTradeWeight",
"TradeWeight", "count", "countryIndicators"), aggregateRussia = F){
library(data.table)
library(zoo)
library(dygraphs)
library(xts)
library(countrycode)
source(... |
#' Transform data to be vector of rank or value difference across defined time difference
#'
#' @param group1.1 is meant to be a dataframe with 2 columns. The columns should be the toxicity in a location and the count of the population of interest in
#' that location. Column names are not important, but columns must b... | /R/getTimeChangeData.R | no_license | amd112/rseiAnalysis | R | false | false | 2,199 | r | #' Transform data to be vector of rank or value difference across defined time difference
#'
#' @param group1.1 is meant to be a dataframe with 2 columns. The columns should be the toxicity in a location and the count of the population of interest in
#' that location. Column names are not important, but columns must b... |
#' ADEPT Similarity Matrix Computation
#'
#' Compute ADEPT similarity matrix between a time-series \code{x} and a collection
#' of scaled pattern templates.
#'
#' @param x A numeric vector. A time-series \code{x}.
#' @param template.scaled A list of lists of numeric vectors, as returned by
#' \code{scaleTemplate}. ... | /R/similarityMatrix.R | no_license | oslerinhealth-releases/adept | R | false | false | 5,729 | r |
#' ADEPT Similarity Matrix Computation
#'
#' Compute ADEPT similarity matrix between a time-series \code{x} and a collection
#' of scaled pattern templates.
#'
#' @param x A numeric vector. A time-series \code{x}.
#' @param template.scaled A list of lists of numeric vectors, as returned by
#' \code{scaleTemplate}. ... |
BD_CalulateSenSpecNPVPPV<-function(ProbCalibStruct , prob_thresh){
DecsionVector <- ProbCalibStruct[ , 1] > prob_thresh
N <- sum(ProbCalibStruct[ , 2] == 0)
P <- sum(ProbCalibStruct[ , 2] == 1)
TP <- sum((DecsionVector == 1)*(ProbCalibStruct[ , 2] == 1))
TN <- sum((DecsionVector == 0)*(ProbCalibStruct[ , 2... | /WaveformCode/BayesianDiscrepancySourceFunctions.R | no_license | BenLopez/UHSM_BHF | R | false | false | 680 | r | BD_CalulateSenSpecNPVPPV<-function(ProbCalibStruct , prob_thresh){
DecsionVector <- ProbCalibStruct[ , 1] > prob_thresh
N <- sum(ProbCalibStruct[ , 2] == 0)
P <- sum(ProbCalibStruct[ , 2] == 1)
TP <- sum((DecsionVector == 1)*(ProbCalibStruct[ , 2] == 1))
TN <- sum((DecsionVector == 0)*(ProbCalibStruct[ , 2... |
setwd('D:\\git_R\\papers\\Discovering_Play_Patterns')
options(scipen = 999, digits=21)
########################################################################################################
library(parallelDist) # https://www.rdocumentation.org/packages/parallelDist/versions/0.1.1/topics/parDist
source("corFuncPt... | /Discovering_Play_Patterns/time_series_cluster.R | no_license | eat-toast/papers | R | false | false | 5,333 | r | setwd('D:\\git_R\\papers\\Discovering_Play_Patterns')
options(scipen = 999, digits=21)
########################################################################################################
library(parallelDist) # https://www.rdocumentation.org/packages/parallelDist/versions/0.1.1/topics/parDist
source("corFuncPt... |
setwd("~/Documents/UCD/BA Prac/ReligionStudy")
require(gutenbergr) # For downloads of The King James Version Bible (#10) and The Tao Te Ching (#216)
bible <- gutenberg_download(10)
names(bible) <- c("doc","text")
bible$doc <- "The King James Bible"
tao <- gutenberg_download(216)
names(tao) <- c("doc","text")
tao$do... | /Holy_books_analysis.R | no_license | Nashavi/ReligionStudy | R | false | false | 17,392 | r |
setwd("~/Documents/UCD/BA Prac/ReligionStudy")
require(gutenbergr) # For downloads of The King James Version Bible (#10) and The Tao Te Ching (#216)
bible <- gutenberg_download(10)
names(bible) <- c("doc","text")
bible$doc <- "The King James Bible"
tao <- gutenberg_download(216)
names(tao) <- c("doc","text")
tao$do... |
library(tidyverse)
library(ggpubr)
library(ggprism)
old_cluster <- readRDS("clusters_old.rds") %>% dplyr::rename(AssignedClusterOld = AssignedCluster)
tt_rank <- readRDS("data/tt_kinaseRank_updated_clust.rds") %>% mutate(AccMod = paste(Accession, MasterMod, sep = ":")) %>%
left_join(old_cluster %... | /cymotif_anaysis.R | no_license | NotValdemaras/WEE1-phosphoproteomics | R | false | false | 17,593 | r | library(tidyverse)
library(ggpubr)
library(ggprism)
old_cluster <- readRDS("clusters_old.rds") %>% dplyr::rename(AssignedClusterOld = AssignedCluster)
tt_rank <- readRDS("data/tt_kinaseRank_updated_clust.rds") %>% mutate(AccMod = paste(Accession, MasterMod, sep = ":")) %>%
left_join(old_cluster %... |
### quality checking:
# check format of columns (id, variable, value)
# all ids have the same variables
# within a id, the same length is required for each variable,
# it can be forced to the interesection, but default it is a stop
# no missing days inbetween the date range
# no NAs
#sanity check, all end dates a... | /R/tidy_global_ar.R | no_license | pmontman/covid19forec | R | false | false | 16,071 | r |
### quality checking:
# check format of columns (id, variable, value)
# all ids have the same variables
# within a id, the same length is required for each variable,
# it can be forced to the interesection, but default it is a stop
# no missing days inbetween the date range
# no NAs
#sanity check, all end dates a... |
library(jsonlite)
library(dplyr)
library(ggplot2)
# read in business data
filename <- "data/yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_business.json"
business_json <- lapply(readLines(filename), fromJSON)
city_state <- factor(paste(sapply(business_json, '[[', 'city'), ", ",sapply(business_json, '[... | /prep.R | no_license | vpnagraj/yelp-academic | R | false | false | 1,221 | r | library(jsonlite)
library(dplyr)
library(ggplot2)
# read in business data
filename <- "data/yelp_dataset_challenge_academic_dataset/yelp_academic_dataset_business.json"
business_json <- lapply(readLines(filename), fromJSON)
city_state <- factor(paste(sapply(business_json, '[[', 'city'), ", ",sapply(business_json, '[... |
/man/SK.nest.Rd | no_license | klainfo/ScottKnott | R | false | false | 6,261 | rd | ||
# Source file for prior and likelihood functions. Not needed separately.
logprior <- function(theta, samp_mean=132){
p1s = c(theta[c(8,9)], 1-sum(theta[c(8,9,12)]), theta[12])
p2s = c(theta[10], 1-sum(theta[c(10,13)]), theta[13])
p3s = c(theta[11], 1-sum(theta[c(11,14)]), theta[14])
sig2eps = dgamma(theta[1], ... | /extras/my_model.R | no_license | dajmcdon/dpf | R | false | false | 3,901 | r | # Source file for prior and likelihood functions. Not needed separately.
logprior <- function(theta, samp_mean=132){
p1s = c(theta[c(8,9)], 1-sum(theta[c(8,9,12)]), theta[12])
p2s = c(theta[10], 1-sum(theta[c(10,13)]), theta[13])
p3s = c(theta[11], 1-sum(theta[c(11,14)]), theta[14])
sig2eps = dgamma(theta[1], ... |
#'
#'Funcion para calcular el indicador mdesv, que compara la desviacion estandar del periodo simulado y los datos reales
#'@param real son los datos reales
#'@param simu son los datos simulados
#'@return rmdesv indicador mdesv
mdesv <- function(real,simu) {
mreal<-sd(real)
msimu <- sd(simu)
rmdesv<-msimu/mreal
... | /storage/app/templates/1/rscript/main/funciones/mdesv.R | permissive | davidpachonc/maep-backend | R | false | false | 337 | r | #'
#'Funcion para calcular el indicador mdesv, que compara la desviacion estandar del periodo simulado y los datos reales
#'@param real son los datos reales
#'@param simu son los datos simulados
#'@return rmdesv indicador mdesv
mdesv <- function(real,simu) {
mreal<-sd(real)
msimu <- sd(simu)
rmdesv<-msimu/mreal
... |
rm(list=ls())
setwd("Analysis")
source("Rcode/SQL/DatabaseHandler.R")
source("Rcode/SQL/MLmethodsQueries.R")
source("Rcode/SQL/BaseQueries.R")
dataJoiner = function(match, lastMatches) {
homeData = sqlData(db, match$HomeTeamID, match$Date, lastMatches)
awayData = sqlData(db, match$AwayTeamID, match$Date, lastM... | /Rcode/Other/datasetBuilder.R | no_license | kaidolepik/NBA | R | false | false | 2,573 | r | rm(list=ls())
setwd("Analysis")
source("Rcode/SQL/DatabaseHandler.R")
source("Rcode/SQL/MLmethodsQueries.R")
source("Rcode/SQL/BaseQueries.R")
dataJoiner = function(match, lastMatches) {
homeData = sqlData(db, match$HomeTeamID, match$Date, lastMatches)
awayData = sqlData(db, match$AwayTeamID, match$Date, lastM... |
\name{quantregTable}
\alias{quantregTable}
\title{
Quantile regression table.}
\description{
Produces a quantile regression table.
}
\usage{
quantregTable(x, digits = 2, significance="none")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
summary for quantile regressi... | /man/quantregTable.Rd | no_license | cran/psytabs | R | false | false | 1,063 | rd | \name{quantregTable}
\alias{quantregTable}
\title{
Quantile regression table.}
\description{
Produces a quantile regression table.
}
\usage{
quantregTable(x, digits = 2, significance="none")
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
summary for quantile regressi... |
% Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Main.R
\name{sensitivity}
\alias{sensitivity}
\title{sensitivity}
\usage{
sensitivity(actuals, predictedScores, threshold = 0.5)
}
\arguments{
\item{actuals}{The actual binary flags for the response variable. It can take values of eit... | /man/sensitivity.Rd | no_license | selva86/car2 | R | false | false | 1,564 | rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Main.R
\name{sensitivity}
\alias{sensitivity}
\title{sensitivity}
\usage{
sensitivity(actuals, predictedScores, threshold = 0.5)
}
\arguments{
\item{actuals}{The actual binary flags for the response variable. It can take values of eit... |
#Camila Kosma 24/05/2020
library(leaps)
library(ggplot2)
library(reshape2)
library(MASS)
library(ggcorrplot)
library(plotmo)
library(rpart)
library(rpart.plot)
library(tidyverse)
library(corrplot)
library(gridExtra)
library(GGally)
library(knitr)
#Get the dataset from URL
red_data <- read.csv("https://query.data.worl... | /Wine Analysis R.R | no_license | CKosma/pbd-ck | R | false | false | 2,718 | r | #Camila Kosma 24/05/2020
library(leaps)
library(ggplot2)
library(reshape2)
library(MASS)
library(ggcorrplot)
library(plotmo)
library(rpart)
library(rpart.plot)
library(tidyverse)
library(corrplot)
library(gridExtra)
library(GGally)
library(knitr)
#Get the dataset from URL
red_data <- read.csv("https://query.data.worl... |
#####
#Simulated Annealing
#####
library(GenSA)
# Try Rastrgin function (The objective function value for global minimum
# is 0 with all components of par are 0.)
Rastrigin <- function(x) {
sum(x^2 - 10 * cos(2 * pi * x)) + 10 * length(x)
}
# Perform the search on a 30 dimensions rastrigin function. Rastrigin
# func... | /Optimization/GenSATools.R | no_license | anhnguyendepocen/RCodeResearch | R | false | false | 1,404 | r | #####
#Simulated Annealing
#####
library(GenSA)
# Try Rastrgin function (The objective function value for global minimum
# is 0 with all components of par are 0.)
Rastrigin <- function(x) {
sum(x^2 - 10 * cos(2 * pi * x)) + 10 * length(x)
}
# Perform the search on a 30 dimensions rastrigin function. Rastrigin
# func... |
## This file contains two functions, makeCacheMatrix and cacheSolve.
## The first, makeCacheMatrix, takes as an argument a matrix (x) and creates a list
## of four function objects. The second, cacheSolve, takes the list resulting from
## makeCacheMatrix and computes or returns the inverse of x.
## makeCacheMatrix: ar... | /cachematrix.R | no_license | nrfrank/ProgrammingAssignment2 | R | false | false | 1,526 | r | ## This file contains two functions, makeCacheMatrix and cacheSolve.
## The first, makeCacheMatrix, takes as an argument a matrix (x) and creates a list
## of four function objects. The second, cacheSolve, takes the list resulting from
## makeCacheMatrix and computes or returns the inverse of x.
## makeCacheMatrix: ar... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/homogenize.R
\name{homogenize}
\alias{homogenize}
\alias{dehomogenize}
\alias{is.homogeneous}
\alias{homogeneous_components}
\title{Homogenize a polynomial}
\usage{
homogenize(x, indeterminate = "t")
dehomogenize(x, indeterminate = "t")
is.... | /man/homogenize.Rd | no_license | dkahle/mpoly | R | false | true | 1,223 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/homogenize.R
\name{homogenize}
\alias{homogenize}
\alias{dehomogenize}
\alias{is.homogeneous}
\alias{homogeneous_components}
\title{Homogenize a polynomial}
\usage{
homogenize(x, indeterminate = "t")
dehomogenize(x, indeterminate = "t")
is.... |
#' Header function for optimization routines
#'
#' Create some output to the screen and a text file that summarizes the problem you are tying to solve.
#'
#' @inheritParams RS_opt
#' @inheritParams evaluate.fim
#' @inheritParams blockexp
#' @inheritParams Doptim
#' @inheritParams create.poped.database
#' @inheritPara... | /PopED/R/blockheader.R | no_license | ingted/R-Examples | R | false | false | 6,517 | r | #' Header function for optimization routines
#'
#' Create some output to the screen and a text file that summarizes the problem you are tying to solve.
#'
#' @inheritParams RS_opt
#' @inheritParams evaluate.fim
#' @inheritParams blockexp
#' @inheritParams Doptim
#' @inheritParams create.poped.database
#' @inheritPara... |
#' Print Brief Details of a bootstrap correction for a high-risk zone
#'
#' Prints a very brief description of the bootstrap correction for a high-risk zone.
#'
#' A very brief description of the bootstrap correction x for a high-risk zone is printed.
#' This is a method for the generic function \code{\link[base]{print... | /highriskzone/R/genericbootcorr.R | no_license | ingted/R-Examples | R | false | false | 2,700 | r | #' Print Brief Details of a bootstrap correction for a high-risk zone
#'
#' Prints a very brief description of the bootstrap correction for a high-risk zone.
#'
#' A very brief description of the bootstrap correction x for a high-risk zone is printed.
#' This is a method for the generic function \code{\link[base]{print... |
library(QGglmm)
### Name: QGvcov
### Title: Compute the phenotypic variance-covariance matrix on the
### observed / expected scale
### Aliases: QGvcov
### ** Examples
## Example using a bivariate model (Binary trait/Gaussian trait)
# Parameters
mu <- c(0, 1)
P <- diag(c(1, 4))
# Note: no phenotypic, nor genetic ... | /data/genthat_extracted_code/QGglmm/examples/QGvcov.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 1,705 | r | library(QGglmm)
### Name: QGvcov
### Title: Compute the phenotypic variance-covariance matrix on the
### observed / expected scale
### Aliases: QGvcov
### ** Examples
## Example using a bivariate model (Binary trait/Gaussian trait)
# Parameters
mu <- c(0, 1)
P <- diag(c(1, 4))
# Note: no phenotypic, nor genetic ... |
## Read the data
fileName <- "household_power_consumption.txt"
rawdata <- read.table(fileName, header = TRUE, sep = ";", stringsAsFactors=FALSE, dec=".")
## Construct Data and Time
timeData <- paste(rawdata[, 1], rawdata[, 2])
timeData <- strptime(timeData, "%d/%m/%Y %H:%M:%S")
## Select only the data with in data ra... | /plot3.R | no_license | gongshan0521/ExploratoryGraphProject | R | false | false | 1,161 | r | ## Read the data
fileName <- "household_power_consumption.txt"
rawdata <- read.table(fileName, header = TRUE, sep = ";", stringsAsFactors=FALSE, dec=".")
## Construct Data and Time
timeData <- paste(rawdata[, 1], rawdata[, 2])
timeData <- strptime(timeData, "%d/%m/%Y %H:%M:%S")
## Select only the data with in data ra... |
library(LSAfun)
# NOTE: You will need to change this filepath to wherever
# your TASA space is
load("/Users/Alice/Desktop/Code/r_scripts/dyad_lsa/TASA.rda")
setwd("/Users/Alice/Desktop/Code/r_scripts/dyad_data/dyads/")
# Set up initial variables
# NOTE: All files in the current working directory
# must be dyad file... | /dyad_lsa.r | no_license | ichthala/dyad_lsa | R | false | false | 2,158 | r | library(LSAfun)
# NOTE: You will need to change this filepath to wherever
# your TASA space is
load("/Users/Alice/Desktop/Code/r_scripts/dyad_lsa/TASA.rda")
setwd("/Users/Alice/Desktop/Code/r_scripts/dyad_data/dyads/")
# Set up initial variables
# NOTE: All files in the current working directory
# must be dyad file... |
#' \code{chart_eia_steo}
#' @description Supply Demand Balance from EIA Short Term Energy Outlook.
#' @param key Your private EIA API token.
#' @param from Date as character "2020-07-01". Default to all dates available.
#' @param market "globalOil" only currently implemented.
#' @param fig.title Defaults to "EIA STEO G... | /R/chart_eia_steo.R | no_license | fmair/RTL | R | false | false | 3,169 | r | #' \code{chart_eia_steo}
#' @description Supply Demand Balance from EIA Short Term Energy Outlook.
#' @param key Your private EIA API token.
#' @param from Date as character "2020-07-01". Default to all dates available.
#' @param market "globalOil" only currently implemented.
#' @param fig.title Defaults to "EIA STEO G... |
# Intrinio API
#
# Welcome to the Intrinio API! Through our Financial Data Marketplace, we offer a wide selection of financial data feed APIs sourced by our own proprietary processes as well as from many data vendors. For a complete API request / response reference please view the [Intrinio API documentation](https://i... | /R/BulkDownloadLinks.r | no_license | Aggarch/r-sdk | R | false | false | 3,228 | r | # Intrinio API
#
# Welcome to the Intrinio API! Through our Financial Data Marketplace, we offer a wide selection of financial data feed APIs sourced by our own proprietary processes as well as from many data vendors. For a complete API request / response reference please view the [Intrinio API documentation](https://i... |
library(tidyverse)
library(NMF)
reg_stats_compact <- read_csv("data/DataFiles/RegularSeasonCompactResults.csv")
tmp <- bind_rows(
reg_stats_compact %>%
select(Season,
TeamID = WTeamID, OTeamID = LTeamID,
Score = WScore,
OScore = LScore),
reg_stats_compact %>%
select(Seas... | /src/processed/nmf.R | no_license | kur0cky/NCAA2019 | R | false | false | 1,760 | r | library(tidyverse)
library(NMF)
reg_stats_compact <- read_csv("data/DataFiles/RegularSeasonCompactResults.csv")
tmp <- bind_rows(
reg_stats_compact %>%
select(Season,
TeamID = WTeamID, OTeamID = LTeamID,
Score = WScore,
OScore = LScore),
reg_stats_compact %>%
select(Seas... |
# ASReml
#R
library(asreml)
asreml.license.activate()
#enter this code CDEA-HECC-CDAH-FIED
setwd("/local/workdir/mh865/ASreml")
source("is.symmetric.matrix.R")
source("is.square.matrix.R")
source("is.positive.definite.R")
FileDir<-"/local/workdir/mh865/GCA_SCA/"
load(paste0(FileDir,"OneTime1920/data/","dataNHpi_wit... | /OneTimePrediction_tst/code/ASreml_Genetic_Cor.R | no_license | MaoHuang2020/GCA_SCA | R | false | false | 5,422 | r | # ASReml
#R
library(asreml)
asreml.license.activate()
#enter this code CDEA-HECC-CDAH-FIED
setwd("/local/workdir/mh865/ASreml")
source("is.symmetric.matrix.R")
source("is.square.matrix.R")
source("is.positive.definite.R")
FileDir<-"/local/workdir/mh865/GCA_SCA/"
load(paste0(FileDir,"OneTime1920/data/","dataNHpi_wit... |
\name{scale_colour_gradient}
\alias{scale_color_continuous}
\alias{scale_color_gradient}
\alias{scale_colour_continuous}
\alias{scale_colour_gradient}
\alias{scale_fill_continuous}
\alias{scale_fill_gradient}
\title{Smooth gradient between two colours}
\usage{
scale_colour_gradient(..., low = "#132B43",
high = "#... | /man/scale_gradient.Rd | no_license | djmurphy420/ggplot2 | R | false | false | 4,055 | rd | \name{scale_colour_gradient}
\alias{scale_color_continuous}
\alias{scale_color_gradient}
\alias{scale_colour_continuous}
\alias{scale_colour_gradient}
\alias{scale_fill_continuous}
\alias{scale_fill_gradient}
\title{Smooth gradient between two colours}
\usage{
scale_colour_gradient(..., low = "#132B43",
high = "#... |
#' @title Tidy Catdesc
#' @description Uses FactoMineR::catdesc function to describe the categories of one factor by categorical variables and/or by quantitative variables
#' @param df data frame to analyse
#' @param cluster cluster column name
#'
#' @examples
#'
#' library(FactoMineR)
#' data(iris)
#' # Principal C... | /R/tidy_catdesc.R | permissive | HanjoStudy/quotidieR | R | false | false | 1,997 | r | #' @title Tidy Catdesc
#' @description Uses FactoMineR::catdesc function to describe the categories of one factor by categorical variables and/or by quantitative variables
#' @param df data frame to analyse
#' @param cluster cluster column name
#'
#' @examples
#'
#' library(FactoMineR)
#' data(iris)
#' # Principal C... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LossFunctions.R
\name{loss_MAE_hess}
\alias{loss_MAE_hess}
\title{Mean Absolute Error (hessian function)}
\usage{
loss_MAE_hess(y_pred, y_true)
}
\arguments{
\item{y_pred}{The \code{predictions}.}
\item{y_true}{The \code{labels}.}
}
\value{
... | /man/loss_MAE_hess.Rd | no_license | BruceZhaoR/Laurae | R | false | true | 639 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LossFunctions.R
\name{loss_MAE_hess}
\alias{loss_MAE_hess}
\title{Mean Absolute Error (hessian function)}
\usage{
loss_MAE_hess(y_pred, y_true)
}
\arguments{
\item{y_pred}{The \code{predictions}.}
\item{y_true}{The \code{labels}.}
}
\value{
... |
beads <- rep(c("red", "blue"), times = c(2,3)) # create an urn with 2 red, 3 blue
beads # view beads object
sample(beads, 1) # sample 1 bead at random
mean(beads)
B <- 10000 # number of times to draw 1 bead
events <- replicate(B, sample(beads, 1)) # draw 1 bead, B times
tab <- table(events) # make a ... | /probability.R | no_license | mafis103/RFiles | R | false | false | 569 | r | beads <- rep(c("red", "blue"), times = c(2,3)) # create an urn with 2 red, 3 blue
beads # view beads object
sample(beads, 1) # sample 1 bead at random
mean(beads)
B <- 10000 # number of times to draw 1 bead
events <- replicate(B, sample(beads, 1)) # draw 1 bead, B times
tab <- table(events) # make a ... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{calc_zipstat_over_duration}
\alias{calc_zipstat_over_duration}
\title{Calculate the ZIP window statistic over all durations, for a given zone.}
\usage{
calc_zipstat_over_duration(duration, p, mu, y, maxdur, tol = 0.01)
}
\... | /man/calc_zipstat_over_duration.Rd | no_license | rfsaldanha/scanstatistics | R | false | true | 1,351 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{calc_zipstat_over_duration}
\alias{calc_zipstat_over_duration}
\title{Calculate the ZIP window statistic over all durations, for a given zone.}
\usage{
calc_zipstat_over_duration(duration, p, mu, y, maxdur, tol = 0.01)
}
\... |
#' Calculate the thermal conductivity of air, W/(m K).
#'
#' Calculate the thermal conductivity of air, W/(m K).
#'
#' @param Tk: value of air temperature in Kelvin.
#'
#' @return Thermal conductivity of air, W/(m K).
#'
#' @author Ana Casanueva (05.01.2017).
#' @details Reference: BSL, page 257.
################... | /R/thermal_cond.R | no_license | jonasbhend/HeatStress | R | false | false | 673 | r | #' Calculate the thermal conductivity of air, W/(m K).
#'
#' Calculate the thermal conductivity of air, W/(m K).
#'
#' @param Tk: value of air temperature in Kelvin.
#'
#' @return Thermal conductivity of air, W/(m K).
#'
#' @author Ana Casanueva (05.01.2017).
#' @details Reference: BSL, page 257.
################... |
#' @title A checkFunction
#'
#' @description A \code{\link{checkFunction}} to be called from
#' \code{\link{check}} for identifying numeric variables that have
#' been misclassified as categorical.
#'
#' @param v A character, factor, or labelled variable to check.
#'
#' @param nVals An integer determining how many uni... | /R/identifyNums.R | no_license | epijim/dataMaid | R | false | false | 2,866 | r | #' @title A checkFunction
#'
#' @description A \code{\link{checkFunction}} to be called from
#' \code{\link{check}} for identifying numeric variables that have
#' been misclassified as categorical.
#'
#' @param v A character, factor, or labelled variable to check.
#'
#' @param nVals An integer determining how many uni... |
library(shiny)
library(tidyverse)
library(scales)
library(bslib)
library(rsconnect)
library(shinythemes)
library(plotly)
library(shinyWidgets)
raw_data <- read_csv("data.csv") %>%
select(
-QKEY, -INTERVIEW_START_W56, -INTERVIEW_END_W56, -DEVICE_TYPE_W56, -SAMPLE_W56, -FORM_W56, -WHYDATE10YRHARDOE_M1_W56, -WHYDAT... | /final_project_app/app.R | no_license | mflesaker/SDS235-FP | R | false | false | 86,397 | r | library(shiny)
library(tidyverse)
library(scales)
library(bslib)
library(rsconnect)
library(shinythemes)
library(plotly)
library(shinyWidgets)
raw_data <- read_csv("data.csv") %>%
select(
-QKEY, -INTERVIEW_START_W56, -INTERVIEW_END_W56, -DEVICE_TYPE_W56, -SAMPLE_W56, -FORM_W56, -WHYDATE10YRHARDOE_M1_W56, -WHYDAT... |
library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
library(ggpubr)
library(ggplotify)
library(viridis)
data <- read.csv("/Users/mar/BIO/PROJECTS/APOBEC/NONBDNA/Denek3/united_RT_X0.txt",sep='\t',header = TRUE)
data <- data.table(data)
setnames(data,c("cancer","structure","isAPOBEC","sample","trgIn","... | /fig3b.R | no_license | mkazanov/nonbdna | R | false | false | 5,185 | r | library(data.table)
library(ggplot2)
library(grid)
library(gridExtra)
library(ggpubr)
library(ggplotify)
library(viridis)
data <- read.csv("/Users/mar/BIO/PROJECTS/APOBEC/NONBDNA/Denek3/united_RT_X0.txt",sep='\t',header = TRUE)
data <- data.table(data)
setnames(data,c("cancer","structure","isAPOBEC","sample","trgIn","... |
context("Test google_form_decode()")
correct_responses <- data.frame(
user = rep("sean", 6),
course_name = rep("Google Forms Course", 6),
lesson_name = rep("Lesson 1", 6),
question_number = rep(2:3, 3),
correct = rep(TRUE, 6),
attempt = rep(1, 6),
skipped = rep(FALSE, 6),
datetime = c(1465226419.39813,... | /tests/testthat/test_google_form_decode.R | no_license | MeganLBecker/swirlify | R | false | false | 2,525 | r | context("Test google_form_decode()")
correct_responses <- data.frame(
user = rep("sean", 6),
course_name = rep("Google Forms Course", 6),
lesson_name = rep("Lesson 1", 6),
question_number = rep(2:3, 3),
correct = rep(TRUE, 6),
attempt = rep(1, 6),
skipped = rep(FALSE, 6),
datetime = c(1465226419.39813,... |
# Use Siham's Goat data and load it into GenABEL for QTL scanning, an example for Uwe
#
# copyright (c) 2016-2020 - Brockmann group - HU Berlin, Danny Arends
# last modified Jul, 2016
# first written Jul, 2016
# Uncomment the following line if you do not have genable installed in R
#install.packages("GenABEL")
library... | /Uwe/example_genable.R | no_license | DannyArends/HU-Berlin | R | false | false | 2,773 | r | # Use Siham's Goat data and load it into GenABEL for QTL scanning, an example for Uwe
#
# copyright (c) 2016-2020 - Brockmann group - HU Berlin, Danny Arends
# last modified Jul, 2016
# first written Jul, 2016
# Uncomment the following line if you do not have genable installed in R
#install.packages("GenABEL")
library... |
\name{DOPE}
\alias{DOPE}
\title{Generate a distribution of possible effects.}
\description{Wrapper for parallel \link[DOPE]{simfun}. Takes a linear regression model fit by \link[stats]{lm} and returns the results information on the distribution of possible effects. Currently implimented in both R and C++. The C++ ve... | /man/DOPE.Rd | no_license | christophercschwarz/DOPE | R | false | false | 1,520 | rd | \name{DOPE}
\alias{DOPE}
\title{Generate a distribution of possible effects.}
\description{Wrapper for parallel \link[DOPE]{simfun}. Takes a linear regression model fit by \link[stats]{lm} and returns the results information on the distribution of possible effects. Currently implimented in both R and C++. The C++ ve... |
library(shiny)
shinyUI(
pageWithSidebar(
# Application title
headerPanel("BMI Calculator"),
sidebarPanel(
h5('Enter your weight and height in lb/ft'),
numericInput('weight', 'Weight lb',50, min = 50, max = 200, step = 5),
numericInput('height', 'Height ft',100, min = 20, max = 300, step ... | /UI.R | no_license | racheljiling/bmi-code | R | false | false | 1,041 | r | library(shiny)
shinyUI(
pageWithSidebar(
# Application title
headerPanel("BMI Calculator"),
sidebarPanel(
h5('Enter your weight and height in lb/ft'),
numericInput('weight', 'Weight lb',50, min = 50, max = 200, step = 5),
numericInput('height', 'Height ft',100, min = 20, max = 300, step ... |
## The following R code is to create two functions, which are:
## 1. makeCacheMatrix : This function takes a matrix as an input. It will cache the inverse of a matrix returned from the function below.
## 2. cacheSolve : This function will take the output from makeCacheMatrix. It shall first find if an inverse of the ma... | /cachematrix.R | no_license | CosterwellKhyriem/ProgrammingAssignment2 | R | false | false | 1,412 | r | ## The following R code is to create two functions, which are:
## 1. makeCacheMatrix : This function takes a matrix as an input. It will cache the inverse of a matrix returned from the function below.
## 2. cacheSolve : This function will take the output from makeCacheMatrix. It shall first find if an inverse of the ma... |
library(testthat)
library(gt)
test_check("gt")
| /tests/testthat.R | permissive | rstudio/gt | R | false | false | 48 | r | library(testthat)
library(gt)
test_check("gt")
|
source('/home/yiliao/OLDISK/software/TopDom/TopDom_v0.0.2.R')
setwd("/home/yiliao/OLDISK/genome_assembly/hic_explorer/13_bnbc/100k/TopDom_bnbc")
TopDom(matrix.file="matrixHL2.chr01.csv.TopDom.matrix",window.size=5,outFile="matrixHL2.chr01.csv.TopDom.matrix.output")
TopDom(matrix.file="matrixHL2.Chr02.csv.TopDom.matrix"... | /Bin/Rscript/LJHL2.topdom.r | no_license | yiliao1022/Pepper3Dgenome | R | false | false | 1,597 | r | source('/home/yiliao/OLDISK/software/TopDom/TopDom_v0.0.2.R')
setwd("/home/yiliao/OLDISK/genome_assembly/hic_explorer/13_bnbc/100k/TopDom_bnbc")
TopDom(matrix.file="matrixHL2.chr01.csv.TopDom.matrix",window.size=5,outFile="matrixHL2.chr01.csv.TopDom.matrix.output")
TopDom(matrix.file="matrixHL2.Chr02.csv.TopDom.matrix"... |
##' Bayes factors or posterior samples for binomial, geometric, or neg. binomial data.
##'
##' Given count data modeled as a binomial, geometric, or negative binomial random variable,
##' the Bayes factor provided by \code{proportionBF} tests the null hypothesis that
##' the probability of a success is \eqn{p_0}{p_... | /R/proportionBF.R | no_license | cran/BayesFactor | R | false | false | 4,648 | r | ##' Bayes factors or posterior samples for binomial, geometric, or neg. binomial data.
##'
##' Given count data modeled as a binomial, geometric, or negative binomial random variable,
##' the Bayes factor provided by \code{proportionBF} tests the null hypothesis that
##' the probability of a success is \eqn{p_0}{p_... |
makeCacheMatrix <- function(x = numeric()) {
#initializing inv as empty
inv <- NULL
#sets x equal to y in the parent environment
#clears inv from any previous calculations
set <- function(y) {
x <<- y
inv <<- NULL
}
#grabs a created x from the parent environme... | /cachematrix.R | no_license | KJRenaud/ProgrammingAssignment2-1 | R | false | false | 1,531 | r | makeCacheMatrix <- function(x = numeric()) {
#initializing inv as empty
inv <- NULL
#sets x equal to y in the parent environment
#clears inv from any previous calculations
set <- function(y) {
x <<- y
inv <<- NULL
}
#grabs a created x from the parent environme... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_esri_features_by_ids.R
\name{get_esri_features_by_ids}
\alias{get_esri_features_by_ids}
\title{get features}
\usage{
get_esri_features_by_ids(
ids,
url = paste0("http://portal1.snirh.gov.br/ana/",
"rest/services/Esta\%C3\%A7\%C3\%... | /man/get_esri_features_by_ids.Rd | no_license | ibarraespinosa/ana | R | false | true | 1,086 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_esri_features_by_ids.R
\name{get_esri_features_by_ids}
\alias{get_esri_features_by_ids}
\title{get features}
\usage{
get_esri_features_by_ids(
ids,
url = paste0("http://portal1.snirh.gov.br/ana/",
"rest/services/Esta\%C3\%A7\%C3\%... |
# ---------------------------------------------------------------------------
# *************************---------ooo---------*****************************
#
# Start of Requirement 1
# Merges the training and the test sets to create one data set.
#
# *************************--------... | /W4_GCD_Project/run_analysis.R | no_license | wikusjvr3/W4GCD | R | false | false | 29,731 | r | # ---------------------------------------------------------------------------
# *************************---------ooo---------*****************************
#
# Start of Requirement 1
# Merges the training and the test sets to create one data set.
#
# *************************--------... |
library(WGCNA)
library(dplyr)
library(rstatix)
library(ggpubr)
library(ComplexHeatmap)
data <- read.delim("Datasets/GSE4843.txt", row.names = 1)
data <- as.data.frame(t(data))
#sampling genes
gsg = goodSamplesGenes(data, verbose = 3);
data = data[gsg$goodSamples, gsg$goodGenes]
#soft threshold - ident... | /Figures/Fig. 3/Code/Fig3B_S3AB_WGCNA.R | no_license | csbBSSE/Melanoma | R | false | false | 5,777 | r | library(WGCNA)
library(dplyr)
library(rstatix)
library(ggpubr)
library(ComplexHeatmap)
data <- read.delim("Datasets/GSE4843.txt", row.names = 1)
data <- as.data.frame(t(data))
#sampling genes
gsg = goodSamplesGenes(data, verbose = 3);
data = data[gsg$goodSamples, gsg$goodGenes]
#soft threshold - ident... |
library(BaPreStoPro)
### Name: jumpDiffusion-class
### Title: S4 class of model informations for the jump diffusion process
### Aliases: jumpDiffusion-class
### ** Examples
parameter <- list(phi = 0.01, theta = 0.1, gamma2 = 0.01, xi = c(2, 0.2))
b.fun <- function(phi, t, y) phi * y
s.fun <- function(gamma2, t, y) ... | /data/genthat_extracted_code/BaPreStoPro/examples/jumpDiffusion-class.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 807 | r | library(BaPreStoPro)
### Name: jumpDiffusion-class
### Title: S4 class of model informations for the jump diffusion process
### Aliases: jumpDiffusion-class
### ** Examples
parameter <- list(phi = 0.01, theta = 0.1, gamma2 = 0.01, xi = c(2, 0.2))
b.fun <- function(phi, t, y) phi * y
s.fun <- function(gamma2, t, y) ... |
X<-c(141, 162, 150, 111, 92, 74, 85, 95, 76, 68, 63, 74, 103, 81, 94, 68, 95, 81, 102, 73)
total = sum(X);
num = 20;
Xbar = mean(X);
lcl = Xbar - 3*sqrt(Xbar);
ucl = Xbar + 3*sqrt(Xbar);
cat("UCL is",ucl)
cat("LCL is",lcl)
for (i in 1:20){
if(X[i]> ucl){
total = total - X[i]
num= num -1
}
}
X... | /Introduction_To_Probability_And_Statistics_For_Engineers_And_Scientists_by_Sheldon_M._Ross/CH13/EX13.5.a/Ex13_5a.R | permissive | FOSSEE/R_TBC_Uploads | R | false | false | 973 | r | X<-c(141, 162, 150, 111, 92, 74, 85, 95, 76, 68, 63, 74, 103, 81, 94, 68, 95, 81, 102, 73)
total = sum(X);
num = 20;
Xbar = mean(X);
lcl = Xbar - 3*sqrt(Xbar);
ucl = Xbar + 3*sqrt(Xbar);
cat("UCL is",ucl)
cat("LCL is",lcl)
for (i in 1:20){
if(X[i]> ucl){
total = total - X[i]
num= num -1
}
}
X... |
list.files("data_geo", pattern = "*.RData", full.names = T) %>% lapply(load, .GlobalEnv)
research_area <- sf::st_read("data_manually_prepared/research_area.shp")
load("data_analysis/regions.RData")
load("data_analysis/bronze1.RData")
bronze1_sf <- bronze1 %>% sf::st_as_sf(
coords = c("lon", "lat"),
crs = 4326
)
l... | /R/real_world_analysis/general_maps/general_map.R | no_license | nevrome/neomod_analysis | R | false | false | 2,274 | r | list.files("data_geo", pattern = "*.RData", full.names = T) %>% lapply(load, .GlobalEnv)
research_area <- sf::st_read("data_manually_prepared/research_area.shp")
load("data_analysis/regions.RData")
load("data_analysis/bronze1.RData")
bronze1_sf <- bronze1 %>% sf::st_as_sf(
coords = c("lon", "lat"),
crs = 4326
)
l... |
writeModuleGenes <- function(results.dir, mart){
module.dir <- get.module.dir(results.dir)
for(i in 1:length(module.dir)){
results.file <- paste0(module.dir[i], "/Module.Gene.Info.csv")
decomp.mat.file <- list.files(module.dir[i], pattern = "decomp", full.names = TRUE)
if(length(decomp.mat.file) == 0){
... | /code/raven/writeModuleGenes.R | no_license | MahoneyLab/HhsFunctionalRankings | R | false | false | 853 | r | writeModuleGenes <- function(results.dir, mart){
module.dir <- get.module.dir(results.dir)
for(i in 1:length(module.dir)){
results.file <- paste0(module.dir[i], "/Module.Gene.Info.csv")
decomp.mat.file <- list.files(module.dir[i], pattern = "decomp", full.names = TRUE)
if(length(decomp.mat.file) == 0){
... |
%% File Name: mice.impute.catpmm.Rd
%% File Version: 0.07
\name{mice.impute.catpmm}
\alias{mice.impute.catpmm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Imputation of a Categorical Variable Using Multivariate Predictive
Mean Matching
}
\description{
Imputes a categorical variabl... | /man/mice.impute.catpmm.Rd | no_license | cran/miceadds | R | false | false | 2,794 | rd | %% File Name: mice.impute.catpmm.Rd
%% File Version: 0.07
\name{mice.impute.catpmm}
\alias{mice.impute.catpmm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Imputation of a Categorical Variable Using Multivariate Predictive
Mean Matching
}
\description{
Imputes a categorical variabl... |
#Julian Ramirez-Villegas
#UoL / CCAFS / CIAT
#December 2011
#Modified by Carlos Navarro
# February 2016
stop("error")
src.dir <- "X:/ALPACAS/Plan_Regional_de_Cambio_Climatico_Orinoquia/01-datos_clima/_scripts"
source(paste(src.dir,"/01a_GHCND-GSOD-functions.R",sep=""))
#base dir
bDir <- "S:/observed/weather_station/... | /plan_regional_cc_orinoquia/00_wth_stations/01_GSOD-read.R | no_license | CIAT-DAPA/dapa-climate-change | R | false | false | 3,684 | r | #Julian Ramirez-Villegas
#UoL / CCAFS / CIAT
#December 2011
#Modified by Carlos Navarro
# February 2016
stop("error")
src.dir <- "X:/ALPACAS/Plan_Regional_de_Cambio_Climatico_Orinoquia/01-datos_clima/_scripts"
source(paste(src.dir,"/01a_GHCND-GSOD-functions.R",sep=""))
#base dir
bDir <- "S:/observed/weather_station/... |
# Load required libraries
library(plyr) # For spliting, applying and combining data
library(tidyr) # For cleaning and structuring data
library(lubridate) # For data manipulation
library(ggplot2) # For plotting
library(dplyr) # For data manipulation
# Load script to get data
if (!exists("flights")) {
source("src/getD... | /src/analyzeYear.R | no_license | dkalisch/flight_delays | R | false | false | 7,268 | r | # Load required libraries
library(plyr) # For spliting, applying and combining data
library(tidyr) # For cleaning and structuring data
library(lubridate) # For data manipulation
library(ggplot2) # For plotting
library(dplyr) # For data manipulation
# Load script to get data
if (!exists("flights")) {
source("src/getD... |
#' The RowTable class
#'
#' The RowTable is a virtual class where each row in the \linkS4class{SummarizedExperiment} is represented by no more than one row in a \code{\link{datatable}} widget.
#' In panels of this class, single and multiple selections can only be transmitted on the features.
#'
#' @section Slot overvi... | /R/family_RowTable.R | permissive | BadSeby/iSEE | R | false | false | 4,702 | r | #' The RowTable class
#'
#' The RowTable is a virtual class where each row in the \linkS4class{SummarizedExperiment} is represented by no more than one row in a \code{\link{datatable}} widget.
#' In panels of this class, single and multiple selections can only be transmitted on the features.
#'
#' @section Slot overvi... |
#' Quasi Minimal Residual Method
#'
#' Quasia-Minimal Resudial(QMR) method is another remedy of the BiCG which shows
#' rather irregular convergence behavior. It adapts to solve the reduced tridiagonal system
#' in a least squares sense and its convergence is known to be quite smoother than BiCG.
#'
#' @param A an \eqn... | /R/lsolve_QMR.R | no_license | harryprince/Rlinsolve | R | false | false | 6,493 | r | #' Quasi Minimal Residual Method
#'
#' Quasia-Minimal Resudial(QMR) method is another remedy of the BiCG which shows
#' rather irregular convergence behavior. It adapts to solve the reduced tridiagonal system
#' in a least squares sense and its convergence is known to be quite smoother than BiCG.
#'
#' @param A an \eqn... |
#--------------------------------------------------------------------------------------------------------------------------------------------
#In this script vegetation is prepared to estimate the spatial model
#------------------------------------------------------------------------------------------------------------... | /vegetation/R/comparison_umw_neus.R | no_license | mtrachs/stepps_prediction_MT | R | false | false | 4,926 | r | #--------------------------------------------------------------------------------------------------------------------------------------------
#In this script vegetation is prepared to estimate the spatial model
#------------------------------------------------------------------------------------------------------------... |
source("info_theory_sims/sim3source.R")
####
## Identity case
####
allresults <- list()
## parallelization
mc.reps <- 1e4
mc.abe <- 1e2
abe.each <- 1e2
mcc <- 39
data.reps <- 75
## problem params
ss <- sqrt(seq(0, 200, by = 5))
ress <- array(0, dim = c(data.reps, 10, length(ss)))
p <- 10
mi_trues <- sapply(ss, fun... | /info_theory_sims/sim3i_fig4b.R | no_license | snarles/fmri | R | false | false | 2,638 | r | source("info_theory_sims/sim3source.R")
####
## Identity case
####
allresults <- list()
## parallelization
mc.reps <- 1e4
mc.abe <- 1e2
abe.each <- 1e2
mcc <- 39
data.reps <- 75
## problem params
ss <- sqrt(seq(0, 200, by = 5))
ress <- array(0, dim = c(data.reps, 10, length(ss)))
p <- 10
mi_trues <- sapply(ss, fun... |
\name{sample_gamma_esem}
\alias{sample_gamma_esem}
\docType{data}
\title{
Replications of the estimated eta on x regression coefficient matrix
}
\description{
A list containing 200 replications of the estimated eta on x regression coefficient matrix provided by replication numbers 1 through 100 and 4701 through 4800 in... | /man/sample_gamma_esem.Rd | no_license | cran/REREFACT | R | false | false | 674 | rd | \name{sample_gamma_esem}
\alias{sample_gamma_esem}
\docType{data}
\title{
Replications of the estimated eta on x regression coefficient matrix
}
\description{
A list containing 200 replications of the estimated eta on x regression coefficient matrix provided by replication numbers 1 through 100 and 4701 through 4800 in... |
#' Create a data frame of mean expression of genes per cluster
#'
#' This function takes an object of class iCellR and creates an average gene expression for every cluster.
#' @param x An object of class iCellR.
#' @return An object of class iCellR.
#' @examples
#' demo.obj <- clust.avg.exp(demo.obj)
#'
#' head(demo.ob... | /R/F021.clust.avg.exp.R | no_license | yandgong307/iCellR | R | false | false | 2,033 | r | #' Create a data frame of mean expression of genes per cluster
#'
#' This function takes an object of class iCellR and creates an average gene expression for every cluster.
#' @param x An object of class iCellR.
#' @return An object of class iCellR.
#' @examples
#' demo.obj <- clust.avg.exp(demo.obj)
#'
#' head(demo.ob... |
# ----------------------------------------------------------------------------
# PROJECT
# Name: *
# Professor: *
# Author: Heather Low
# ----------------------------------------------------------------------------
# CODE
# Name: 1-
# Date: *
# Purpose: Wrangle checks and points.
# Input: "all_employeesInChecks.RData",... | /1-clean.R | no_license | hl-py/restaurantData | R | false | false | 13,073 | r | # ----------------------------------------------------------------------------
# PROJECT
# Name: *
# Professor: *
# Author: Heather Low
# ----------------------------------------------------------------------------
# CODE
# Name: 1-
# Date: *
# Purpose: Wrangle checks and points.
# Input: "all_employeesInChecks.RData",... |
boston = read.csv("boston.csv")
#video2
str(boston)
plot(boston$LON, boston$LAT)
points(boston$LON[boston$CHAS==1], boston$LAT[boston$CHAS==1], col="blue", pch=19)
points(boston$LON[boston$TRACT==3531], boston$LAT[boston$TRACT==3531], col="red", pch=20)
summary(boston$NOX)
points(boston$LON[boston$NOX>=0.55], boston$LA... | /unit4/Unit4-Recitation-Boston.R | no_license | hmartineziii/15.071x | R | false | false | 2,740 | r | boston = read.csv("boston.csv")
#video2
str(boston)
plot(boston$LON, boston$LAT)
points(boston$LON[boston$CHAS==1], boston$LAT[boston$CHAS==1], col="blue", pch=19)
points(boston$LON[boston$TRACT==3531], boston$LAT[boston$TRACT==3531], col="red", pch=20)
summary(boston$NOX)
points(boston$LON[boston$NOX>=0.55], boston$LA... |
# Exercise 4: Creating and operating on vectors
# Create a vector `names` that contains your name and the names of 2 people next to you.
names <- c("Soobin", "Tejveer", "Emily")
# Use the colon operator : to create a vector `n` of numbers from 10:49
n <- 10:49
# Use `length()` to get the number of elements in `n`
le... | /exercise-4/exercise.R | permissive | soobkwon/module7-vectors | R | false | false | 1,039 | r | # Exercise 4: Creating and operating on vectors
# Create a vector `names` that contains your name and the names of 2 people next to you.
names <- c("Soobin", "Tejveer", "Emily")
# Use the colon operator : to create a vector `n` of numbers from 10:49
n <- 10:49
# Use `length()` to get the number of elements in `n`
le... |
TreeStat <-
function(myinput,mystat,method="complete",metric="euclidean",metric.args=list()){
#index table
if(data.class(myinput)=="dist")hc<-hclust(myinput,method=method)
if(data.class(myinput)=="matrix"){
if(metric=="define.metric"){
#define.metric<-match.fun(define.metric)
define.metr... | /R/TreeStat.R | no_license | cran/TBEST | R | false | false | 4,655 | r | TreeStat <-
function(myinput,mystat,method="complete",metric="euclidean",metric.args=list()){
#index table
if(data.class(myinput)=="dist")hc<-hclust(myinput,method=method)
if(data.class(myinput)=="matrix"){
if(metric=="define.metric"){
#define.metric<-match.fun(define.metric)
define.metr... |
#install.packages("alphavantager")
library(alphavantager)
library(shiny)
library(readr)
ui <- fluidPage(
textInput("Stock","US Stock Ticker ","MSFT"),
actionButton("go","Go"),
dataTableOutput("df"),
h3("Stock"),
# textOutput("text")
plotOutput("p1")
)
server <- function(input, output, session) {
... | /2020/Assignment-2020/Individual/FE8828-Ge Weitong/Assignment 2/Assignment 2_3.R | no_license | leafyoung/fe8828 | R | false | false | 960 | r | #install.packages("alphavantager")
library(alphavantager)
library(shiny)
library(readr)
ui <- fluidPage(
textInput("Stock","US Stock Ticker ","MSFT"),
actionButton("go","Go"),
dataTableOutput("df"),
h3("Stock"),
# textOutput("text")
plotOutput("p1")
)
server <- function(input, output, session) {
... |
context("sp genomemaps")
con <- ba_db()$sweetpotatobase
test_that("Genomemaps are present", {
res <- ba_genomemaps(con = con)
expect_that(nrow(res) == 1, is_true())
})
test_that("Vector output is transformed", {
res <- ba_genomemaps(con = con, rclass = "vector")
expect_that("tbl_df" %in% class(res), is_t... | /tests/sweetpotatobase/test_sp_genomemaps.R | no_license | ClayBirkett/brapi | R | false | false | 331 | r | context("sp genomemaps")
con <- ba_db()$sweetpotatobase
test_that("Genomemaps are present", {
res <- ba_genomemaps(con = con)
expect_that(nrow(res) == 1, is_true())
})
test_that("Vector output is transformed", {
res <- ba_genomemaps(con = con, rclass = "vector")
expect_that("tbl_df" %in% class(res), is_t... |
###########################################################################################
# #
# #
# Produce Plot of Gene F... | /silverstandard/analysis/Figure6_Genes_Stability_R_3.5.0.R | no_license | SaskiaFreytag/cluster_benchmarking_code | R | false | false | 3,809 | r | ###########################################################################################
# #
# #
# Produce Plot of Gene F... |
# testing the new fs function example
setwd("x:/vervoort/research/ecohydrology/2dmodelling")
rdir <- "x:/vervoort/research/rcode/ecohydrology/2dmodelling"
source(paste(rdir,"20120724_FluxfunctionsforElise.R",sep="/"))
source(paste(rdir,"soilfunction.R",sep="/"))
source(paste(rdir,"vegfunction.R",sep="/"))
soilpar <... | /OlderScripts/20130621_fs_function_example.R | permissive | WillemVervoort/Ecohydr2D | R | false | false | 1,321 | r | # testing the new fs function example
setwd("x:/vervoort/research/ecohydrology/2dmodelling")
rdir <- "x:/vervoort/research/rcode/ecohydrology/2dmodelling"
source(paste(rdir,"20120724_FluxfunctionsforElise.R",sep="/"))
source(paste(rdir,"soilfunction.R",sep="/"))
source(paste(rdir,"vegfunction.R",sep="/"))
soilpar <... |
## Skyline Rearrange and Compound Name Check
# Define custom file name for export
csvFileName <- paste("data_intermediate/", software.pattern, "_combined_", file.pattern, "_", currentDate, ".csv", sep = "")
# Function to remove syntactically incorrect values usually produced by Skyline
replace_nonvalues <- function(x... | /src/Skyline_Rearrange.R | no_license | R-Lionheart/THAA_Test | R | false | false | 2,223 | r | ## Skyline Rearrange and Compound Name Check
# Define custom file name for export
csvFileName <- paste("data_intermediate/", software.pattern, "_combined_", file.pattern, "_", currentDate, ".csv", sep = "")
# Function to remove syntactically incorrect values usually produced by Skyline
replace_nonvalues <- function(x... |
#' Drive concepts for motorcars.
#'
#' This many-valued context is adapted from an example of conceptual scaling.
#'
#' @format A many-valued context, stored as a data frame.
#' \describe{
#' \item{De}{drive efficiency empty}
#' \item{Dl}{drive efficiency loaded}
#' \item{R}{road holding/handling properties}
#'... | /R/data.r | no_license | corybrunson/context | R | false | false | 648 | r | #' Drive concepts for motorcars.
#'
#' This many-valued context is adapted from an example of conceptual scaling.
#'
#' @format A many-valued context, stored as a data frame.
#' \describe{
#' \item{De}{drive efficiency empty}
#' \item{Dl}{drive efficiency loaded}
#' \item{R}{road holding/handling properties}
#'... |
library(testthat)
test_check("jaatha")
| /jaatha/tests/testthat.R | no_license | ingted/R-Examples | R | false | false | 39 | r | library(testthat)
test_check("jaatha")
|
# the German Credit Data
# read comma separated file into memory
data<-read.csv("C:/Documents and Settings/MyDocuments/GermanCredit.csv")
#code to convert variable to factor
data$property <-as.factor(data$ property)
#code to convert to numeric
data$age <-as.numeric(data$age)
#code to convert to decimal
data$amoun... | /script/german credit data.r | no_license | goal1234/score | R | false | false | 10,461 | r |
# the German Credit Data
# read comma separated file into memory
data<-read.csv("C:/Documents and Settings/MyDocuments/GermanCredit.csv")
#code to convert variable to factor
data$property <-as.factor(data$ property)
#code to convert to numeric
data$age <-as.numeric(data$age)
#code to convert to decimal
data$amoun... |
context("Tidying data with tidy_pol")
test_that("tidy_pol tidies a data set with separator", {
data <- tibble::tibble(
'Ctrl-Ctrl' = 1:3,
'M1-Ctrl'= 1:3,
'Ctrl-LPS' = 4:6,
'M1-LPS' = 11:13
)
data2 <- tibble::tibble(
'Ctrl.Ctrl' = 1:3,
'M1.Ctrl'= 1:3,
'Ctrl.LPS' = 4:6,
'M1.LPS' = 1... | /tests/testthat/test_tidy_pol.R | no_license | ksedivyhaley/katehelpr | R | false | false | 1,147 | r | context("Tidying data with tidy_pol")
test_that("tidy_pol tidies a data set with separator", {
data <- tibble::tibble(
'Ctrl-Ctrl' = 1:3,
'M1-Ctrl'= 1:3,
'Ctrl-LPS' = 4:6,
'M1-LPS' = 11:13
)
data2 <- tibble::tibble(
'Ctrl.Ctrl' = 1:3,
'M1.Ctrl'= 1:3,
'Ctrl.LPS' = 4:6,
'M1.LPS' = 1... |
#' summary
#' @description Generate a summary of the results.
#' @return The posterior mean and 95 percent credible intervals, n_eff, Rhat and WAIC.
#' @param object An object from \link{fit}.
#' @param digits An optional positive value to control the number of digits to print when printing numeric values.
#' @param ..... | /R/summary.R | no_license | VNyaga/NMADAS | R | false | false | 2,409 | r | #' summary
#' @description Generate a summary of the results.
#' @return The posterior mean and 95 percent credible intervals, n_eff, Rhat and WAIC.
#' @param object An object from \link{fit}.
#' @param digits An optional positive value to control the number of digits to print when printing numeric values.
#' @param ..... |
"Refer to the previous question. Brain volume for adult women is about 1,100 cc for women with a standard deviation of 75 cc.
Consider the sample mean of 100 random adult women from this population.
What is the 95th percentile of the distribution of that sample mean?"
p = 0.95
mu = 1100
sd = 75
n = 100
sd_err = sd/s... | /qnorm_02.R | no_license | vcwild/statinference | R | false | false | 367 | r | "Refer to the previous question. Brain volume for adult women is about 1,100 cc for women with a standard deviation of 75 cc.
Consider the sample mean of 100 random adult women from this population.
What is the 95th percentile of the distribution of that sample mean?"
p = 0.95
mu = 1100
sd = 75
n = 100
sd_err = sd/s... |
# R Statistical System
# $ Rscript hello.R
# Parts adapted from tutorialspoint.com
# help:
# ?lm
# ??lm # fuzzy verbose
# help(lm)
# Created: Mon 18 Apr 2016 10:46:02 (Bob Heckel)
s <- 'hello world'; print(s)
s <- 'hello world'
s
# Colon operator creates sequence
s2 <- 0:9
s2
s3 <- seq(0, 9, by=... | /misc/hello.R | permissive | bheckel/code | R | false | false | 3,622 | r | # R Statistical System
# $ Rscript hello.R
# Parts adapted from tutorialspoint.com
# help:
# ?lm
# ??lm # fuzzy verbose
# help(lm)
# Created: Mon 18 Apr 2016 10:46:02 (Bob Heckel)
s <- 'hello world'; print(s)
s <- 'hello world'
s
# Colon operator creates sequence
s2 <- 0:9
s2
s3 <- seq(0, 9, by=... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maximumSpendingForMinimumRuinTime.R
\name{maximumSpendingForMinimumRuinTime}
\alias{maximumSpendingForMinimumRuinTime}
\title{Calculates scenarios of future value of annuity payments (fv) with stochastic returns}
\usage{
maximumSpendingForMin... | /man/maximumSpendingForMinimumRuinTime.Rd | no_license | eaoestergaard/UNPIE | R | false | true | 1,382 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maximumSpendingForMinimumRuinTime.R
\name{maximumSpendingForMinimumRuinTime}
\alias{maximumSpendingForMinimumRuinTime}
\title{Calculates scenarios of future value of annuity payments (fv) with stochastic returns}
\usage{
maximumSpendingForMin... |
library(tidyverse)
library(magrittr)
library(ggpubr)
library(igraph)
library(viridis)
EXPERIMENT_NAME <- "main_transmission_probability"
source(file.path("..", "helpers.R"))
# read and format config ---
arrow::read_feather(file.path("..", "..", "..", "experiments", EXPERIMENT_NAME, "configs.feather")) %>%
mutate(
... | /analyses/scripts/main_transmission_probability/data_processing.R | permissive | JohannesNakayama/EpidemicModel.jl | R | false | false | 8,922 | r | library(tidyverse)
library(magrittr)
library(ggpubr)
library(igraph)
library(viridis)
EXPERIMENT_NAME <- "main_transmission_probability"
source(file.path("..", "helpers.R"))
# read and format config ---
arrow::read_feather(file.path("..", "..", "..", "experiments", EXPERIMENT_NAME, "configs.feather")) %>%
mutate(
... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ResourceFiles.R
\name{getCohortsToDeriveTarget}
\alias{getCohortsToDeriveTarget}
\title{Get the cohorts to derive from the resource file}
\usage{
getCohortsToDeriveTarget()
}
\description{
Reads the settings in /inst/settings/CohortsToDeriveT... | /man/getCohortsToDeriveTarget.Rd | permissive | harryreyesnieva/HERACharacterization | R | false | true | 332 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ResourceFiles.R
\name{getCohortsToDeriveTarget}
\alias{getCohortsToDeriveTarget}
\title{Get the cohorts to derive from the resource file}
\usage{
getCohortsToDeriveTarget()
}
\description{
Reads the settings in /inst/settings/CohortsToDeriveT... |
library(influenceR)
### Name: bridging
### Title: Valente's Bridging vertex measure.
### Aliases: bridging
### ** Examples
ig.ex <- igraph::erdos.renyi.game(100, p.or.m=0.3) # generate an undirected 'igraph' object
bridging(ig.ex) # bridging scores for each node in the graph
| /data/genthat_extracted_code/influenceR/examples/bridging.Rd.R | no_license | surayaaramli/typeRrh | R | false | false | 283 | r | library(influenceR)
### Name: bridging
### Title: Valente's Bridging vertex measure.
### Aliases: bridging
### ** Examples
ig.ex <- igraph::erdos.renyi.game(100, p.or.m=0.3) # generate an undirected 'igraph' object
bridging(ig.ex) # bridging scores for each node in the graph
|
% Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/HomogeneousEnsemble.R
\name{getHomogeneousEnsembleModels}
\alias{getHomogeneousEnsembleModels}
\title{Returns the list of fitted models.}
\usage{
getHomogeneousEnsembleModels(model, learner.models = FALSE)
}
\arguments{
\item{model}{[... | /man/getHomogeneousEnsembleModels.Rd | no_license | dickoa/mlr | R | false | false | 652 | rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/HomogeneousEnsemble.R
\name{getHomogeneousEnsembleModels}
\alias{getHomogeneousEnsembleModels}
\title{Returns the list of fitted models.}
\usage{
getHomogeneousEnsembleModels(model, learner.models = FALSE)
}
\arguments{
\item{model}{[... |
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DTRFunctions.R
\name{diurnal_temp_variation_sine}
\alias{diurnal_temp_variation_sine}
\title{Hourly Temperature Variation assuming a Sine Interpolation}
\usage{
diurnal_temp_variation_sine(T_max, T_min, t)
}
\arguments{
\item{T_max, T_min}{\c... | /man/diurnal_temp_variation_sine.Rd | permissive | trenchproject/TrenchR | R | false | true | 1,525 | rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DTRFunctions.R
\name{diurnal_temp_variation_sine}
\alias{diurnal_temp_variation_sine}
\title{Hourly Temperature Variation assuming a Sine Interpolation}
\usage{
diurnal_temp_variation_sine(T_max, T_min, t)
}
\arguments{
\item{T_max, T_min}{\c... |
/Endre løsmasser fra vektor til raster.r | no_license | NINAnor/stisykling | R | false | false | 1,163 | r |
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