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f290966e0f25bbcbe24ab5cdb7f3b90273a11732 | 7cf30a07f20590b80e8a46929410daf8c8f663df | /significance_testing/multiple_comparisons_coverage.R | ceb302c717b12217ba8e978a2c7ef91449c20fc0 | [] | no_license | CPJKU/recommendation_systems_fairness | e2cda569b7be5ab8dca01b9e1b1d9e7382f622b4 | 480829541e5a7038e07aca9ccd1e5888df934dd2 | refs/heads/main | 2023-06-14T02:13:21.721708 | 2021-07-13T07:40:13 | 2021-07-13T07:40:13 | 317,505,054 | 11 | 3 | null | null | null | null | UTF-8 | R | false | false | 91,157 | r | multiple_comparisons_coverage.R | library(haven)
library(here)
library(readr)
library(car)
library(psych)
library(ggpubr)
library(ggplot2)
library(onewaytests)
library(tseries)
library(poolr)
library(dplyr)
library(corrplot)
library(FSA)
library(lsr)
library(rstatix)
library(readr)
library(scran)
library(metafor)
library(rcompanion)
get_result <- fun... |
b76ae5bf6599de0bfe029cf1ce03f67993bc1549 | cc0b570a7153348a644c9a15514a89327b241855 | /testCacheMatrix.R | 714224d118a260bb58b6704eb95449f44d231095 | [] | no_license | amarallr/ProgrammingAssignment2 | 91c65953f1f650a8795f2513f1442adb31920128 | 893a3bb50f13e59e3c2f3390b62804f25c804d76 | refs/heads/master | 2021-01-15T17:07:20.543333 | 2014-09-14T12:55:43 | 2014-09-14T12:55:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 497 | r | testCacheMatrix.R | ## setwd("C:\\courseraworkspace\\rprog\\pa2\\ProgrammingAssignment2")
## source("testCacheMatrix.R")
source("cachematrix.R")
x <- stats::rnorm(16000000)
dim(x) <- c(4000,4000)
t0 <- date()
print(paste(t0, " Calling cacheSolve(x)..."))
xi <- cacheSolve(x)
print("=================================================")
t1 <- ... |
30e9ca6aa36bee8d3c6857a1684cf8c4fd849dae | 6292a37c62159e1ec96200c61fd5e5bd0ce03c2e | /dsia_demo_codes/ch1424.R | e7618bb31368c5d21773da31869747d5aea785dd | [] | no_license | jtlai0921/AEL018600_codes | 5270eb9bc11acc653f1ba24ff1f6eee340625eb5 | 1e4267ea82b75a2c9d57f92edfbc5e8f5a429dbf | refs/heads/master | 2020-12-18T18:07:52.593639 | 2020-01-22T01:55:00 | 2020-01-22T01:55:00 | 235,479,335 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,249 | r | ch1424.R | library(ggplot2)
library(gridExtra)
library(MASS)
get_train_validation <- function(labeled_df, validation_size=0.3) {
m <- nrow(labeled_df)
row_indice <- 1:m
shuffled_row_indice <- sample(row_indice)
labeled_df <- labeled_df[shuffled_row_indice, ]
validation_threshold <- as.integer(validation_size * m)
val... |
c8494d1bb164e1f021bbab189b838513d97440d9 | 153ab3d71ee4b2c95c1f3bee838619a7e0882951 | /titanic_decisiontree_20140608.R | 3723afb5472ec8007cbc87422c19c8e3b70b66c6 | [] | no_license | yujim78/kaggle_titanic | 7f49cc0a813ce69c49e905634461f96470de5661 | f222d43c830a186ca8d9f7f88d02b430b3f0ac7d | refs/heads/master | 2016-08-06T13:08:55.074716 | 2014-07-06T19:31:58 | 2014-07-06T19:31:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 651 | r | titanic_decisiontree_20140608.R | # set working directory and load data csv
setwd("~/Desktop/kaggle/titanic/kaggle_titanic")
train <- read.csv("~/Desktop/kaggle/titanic/kaggle_titanic/train.csv")
test <- read.csv("~/Desktop/kaggle/titanic/kaggle_titanic/test.csv")
library(rpart)
fit <- rpart(Survived ~ Pclass + Sex + Age + SibSp + Parch + Fare + Emb... |
d7a51226226298caf564cc2f18f87e9dd656b6c9 | db12b990924703cd74748d8585cd9c11fafa6746 | /h2o-r/H2O_Load.R | 96058f24465e3689a904b6690662e3383e6051eb | [
"Apache-2.0"
] | permissive | h2oai/h2o-3 | 919019a8f297eec676011a9cfd2cc2d97891ce14 | d817ab90c8c47f6787604a0b9639b66234158228 | refs/heads/master | 2023-08-17T18:50:17.732191 | 2023-08-17T16:44:42 | 2023-08-17T16:44:42 | 17,371,412 | 6,872 | 2,345 | Apache-2.0 | 2023-09-14T18:05:40 | 2014-03-03T16:08:07 | Jupyter Notebook | UTF-8 | R | false | false | 1,569 | r | H2O_Load.R | # Change this global variable to match your own system's path
SPENCER.ROOT.PATH <- "/Users/spencer/0xdata/"
LUDI.ROOT.PATH <- "/Users/ludirehak/"
ARNO.ROOT.PATH <- "/home/arno/"
AMY.ROOT.PATH <- "/Users/amy/"
MAGNUS.ROOT.PATH <- "/Users/magnus/Git/"
USER.PATHS <- c(SPENCER.ROOT.PATH, LUDI.ROOT.PATH, ARNO.ROOT.PATH, AMY... |
36b95b9ed27f63db681d08620fed4a398e71c9a0 | 72cacfe29831236bfee1a2569ff531c79d4f3971 | /TestingData.R | 5812d0688daae763ed4fa11c10277d0f4656d970 | [] | no_license | iarlaith/Week7 | 8683fdc05241084e5edfc280dbfc83b39be9274b | c2f891a362b0d9d52e56d94aee9628620cc227dc | refs/heads/master | 2020-05-18T17:22:18.312211 | 2019-05-15T13:46:55 | 2019-05-15T13:46:55 | 184,553,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,230 | r | TestingData.R | # Example using mobile phone usage while driving
# Comparing 2 samples (with and without phone)
# and comparing mean reaction times f both groups
# so we'll use a paired t-test
install.packages("pwr")
library(pwr)
power_information <- pwr.t.test(d=0.8, sig.level = 0.05, power = 0.90,
t... |
dcc2af7ff336c12f1600dcbcb800608058ca958f | c9d66d6a0b9ed0a49eb13e252770e30f7739f6fa | /Chapter 10/CreateDataFrameJsonParquet.R | 04fb49fc54759dab08961160ea23cdd3df5d04e5 | [
"MIT"
] | permissive | PacktPublishing/big-data-analytics | ac5ea8e14b8c552b7850ef08ae3d41f82f2ef0ae | 937d12ec23a94be33d68e3bbd6c3bbe597bbda04 | refs/heads/master | 2023-01-24T09:12:07.409511 | 2023-01-18T10:21:11 | 2023-01-18T10:21:11 | 70,221,427 | 33 | 30 | null | null | null | null | UTF-8 | R | false | false | 505 | r | CreateDataFrameJsonParquet.R | # Get into SparkR shell and run these commands.
people_json <- read.df("file:///home/cloudera/spark-2.0.0-bin-hadoop2.7/examples/src/main/resources/people.json", "json")
people_json
DataFrame[age:bigint, name:string]
head(people_json)
# To write the people DataFrame as Parquet file, use below command. This... |
dd60bcd6f54484e195a78d627c2fb778abf801b3 | f677ad4de11474ee34e3e8c1fef92f8cc7ae8d82 | /R/prepare_BioGeoBEARS_point_input_cluster_allrealms_mammals.R | 549dc1f11b0878b6ea638b64ff748f18181c3652 | [] | no_license | javierigea/hotspots_mambird_paper | 1eb711be9c867996eddd32e4ce03dbb3b6367749 | 275b4fc731ce027b6027a584b7bb0e83c071f852 | refs/heads/master | 2020-03-22T20:18:25.031169 | 2019-02-17T16:22:34 | 2019-02-17T16:22:34 | 140,588,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,832 | r | prepare_BioGeoBEARS_point_input_cluster_allrealms_mammals.R | #run model in hydrogen cluster
.libPaths('/home/ecosys/ji247/R/x86_64-pc-linux-gnu-library/3.3')
setwd('/home/ecosys/ji247/hotspots_vertebrates/')
library(ape)
library(plyr)
library(phangorn)
library(geiger)
library(parallel)
library(diversitree)
###############################################
########################... |
bb0ddcbb6503d5a3aa3fe887b268a4f18e1a645b | 79d755cbdc90fd322e6d9fe8dcf3af14e52cfc2a | /man/DoEstRare.Rd | a67024d92207c3f07c9fed13fcb6de151ef0b198 | [] | no_license | cran/DoEstRare | 1eb822b268aa3e6bf64c6e3c796a5723ba6b39e1 | 6f018fe785a9fe39fff1a855ecc81372a29c8c97 | refs/heads/master | 2021-01-02T08:24:32.222930 | 2017-08-01T12:24:30 | 2017-08-01T12:24:30 | 99,005,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,706 | rd | DoEstRare.Rd | \name{DoEstRare}
\alias{DoEstRare}
\title{DoEstRare}
\description{
Rare variant association test comparing position density functions and mutation counts between cases and controls.
}
\usage{
DoEstRare(pheno, geno, position, genome.size,
perm=NULL, alpha=NULL, c=NULL,
autosomal=TRUE, gend... |
3f6ec5017932ab3b3ddf1b28cd92df34666e277d | 6d5a7d0a5f55520fceb0a2868bc6b7fb7903075a | /R/class-node.R | 7c2e1411bab9e47720ff00251229239b121da243 | [] | no_license | meta-QSAR/simple-tree | 7dbb617aff4e637d1fcce202890f322b99364494 | 28ff7bf591d3330498a3c8a85d8ae5a3d27b37d5 | refs/heads/master | 2016-09-14T07:08:33.743705 | 2015-08-04T09:31:24 | 2015-08-04T09:31:24 | 58,349,277 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 127 | r | class-node.R | .node<-setClass(
Class = "node",
slots=c(
id = "character",
name = "character",
parent.id = "character"
)
)
|
9ea98dda19440f402ef378724672c9ac1a5f32bb | d3fe5e108e4dbf3ba50aba0c184ddbe9884bc635 | /scripts/eda.R | 2953c410eca8c132236bbec6577a8c9d7ffe60b5 | [] | no_license | JasonDude16/Data-Science-Capstone-NLP | 0c3b41c1e82ed7496f5d64ae0d3a6bec3d4b9250 | 70c5f2e439b5f4787f52f7af81b41ca4ee66f623 | refs/heads/master | 2023-02-18T06:23:11.749069 | 2021-01-14T00:01:58 | 2021-01-14T00:01:58 | 284,143,973 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,894 | r | eda.R | # EXPLORATORY ANALYSIS ---------------------------------------------------
dat_dfm2_df <- tidy(dat_dfm2)
colnames(dat_dfm2_df)[2] <- "word"
# Word cloud, top words
dat_dfm2_df %>%
count(word) %>%
with(wordcloud(word, n, max.words = 100))
# Bar chart, top words
data.frame(word = attributes(topfeatures(dat_dfm2,... |
3b22e9914278aaa7e7d17be58b88b6e4e41fa741 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /dvmisc/man/bmi3.Rd | c12d19fa6d3b9b2395830dbc9a9f25564d7895e8 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 722 | rd | bmi3.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bmi3.R
\name{bmi3}
\alias{bmi3}
\title{Convert Continuous BMI Values into 3-Level Factor}
\usage{
bmi3(x, labels = TRUE)
}
\arguments{
\item{x}{Numeric vector of BMI values.}
\item{labels}{If \code{TRUE}, factor levels are labeled... |
5066485c78ee9908a23106e9852dea07366fed7b | 87be7889592534040bcc835fc5ee76ec2934c98c | /Russell_DBST667_Week2_Exercise_R_Script.R | 78ac9eccf88ab5608c5559d1cbf5b60ef92151d1 | [] | no_license | rbrandon87/DBST667_Week2_Exercise | 83831331dabaf44c13e5823b7b64d700e428839e | 0da9c27a566c5f57039e8c6e0429a87d57cf2f0d | refs/heads/master | 2020-03-18T19:41:57.760146 | 2018-06-02T16:06:23 | 2018-06-02T16:06:23 | 135,171,249 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,850 | r | Russell_DBST667_Week2_Exercise_R_Script.R | #Part 2aii
#Load data and read first 10 rows
credit <- utils::read.csv(file="CreditApproval.csv", head=TRUE, sep=",")
head(credit, n=10)
#Part 2aiii
#Run str command on credit dataset
str(credit)
#Part 2bi
#Run summary command on credit dataset
summary(credit)
#Part2bii
#Run summary command on specific attributes
summa... |
2352444e46e4e9a4f889734eb66d60577ddb9674 | 2b2345b0b67a866ecbe686f7162ac1bc9922c50c | /cachematrix.R | 9c09d298bfb50f2e40171ce113ee2dcce3e3924a | [] | no_license | abulmohaimin/ProgrammingAssignment2 | 67628254c197dec61f9e1612e2fbdab1cab49f3a | 3e4f949be66062ab27ddb5c5bbb8ebb442462fdb | refs/heads/master | 2020-12-24T18:23:20.872757 | 2016-01-23T03:39:22 | 2016-01-23T03:39:22 | 50,221,267 | 0 | 0 | null | 2016-01-23T03:17:42 | 2016-01-23T03:17:42 | null | UTF-8 | R | false | false | 664 | r | cachematrix.R | ## Calculate inverse of inputted matrix
## Cache the matrix
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(x) {
x <<- x
m <<- NULL
}
get <- function()x
set_inv_mat <- function(inv_mat) m <<- inv_mat
get_inv_mat <- function() m
list(set = set, get = get,
set_inv_ma... |
76c0474043e3708cfc755af5907e7ba11b0d656f | f7cd9023746b8bdd29b90731ec389ad9ccf626d1 | /haplotyping/convert_VCF_into_PED.R | e975a2b113f0413cdfdc686d3f37e62762e09fcb | [] | no_license | kibanez/analysing_STRs | 1a498f121763d7cb411e3b66b25ec22eea608244 | b922ab7d2b9668dc8add9e73f1d4e44a0aac1fd4 | refs/heads/master | 2022-02-25T05:28:43.480585 | 2022-02-04T10:29:30 | 2022-02-04T10:29:30 | 218,277,677 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,198 | r | convert_VCF_into_PED.R | # Function or R script, that given as input files PED and phenotypic files, it completes the PED file
date ()
Sys.info ()[c("nodename", "user")]
commandArgs ()
rm (list = ls ())
R.version.string ## "R version 3.6.1 (2019-07-05)"
# libraries
library(dplyr); packageDescription ("dplyr", fields = "Version") #"0.8.3"
libr... |
d7935cf06bfa3eacc57c03f53561ed25f264d8ab | ffe18353716e2a910d687f0892ab524e74c752a3 | /appt/server.r | fd1fd49cd1e53991607af652d3c3c69bd8d4c1a9 | [] | no_license | joanrojas/crime_data_shinyapps | c20b69c7b109853a5c2f199677ce8cd9ab97256a | ef8f58e81bd4e5ec6127d2ec443879931bea3872 | refs/heads/master | 2021-01-19T08:42:16.122836 | 2016-12-26T10:46:06 | 2016-12-26T10:46:06 | 29,781,843 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,527 | r | server.r | library(shiny)
library(gridExtra)
require(gridExtra)
require(ggplot2)
# Define server logic required to draw a histogram
shinyServer(function(input, output) {
#district function returns a data frame with data only from the distric input in the selectInput from UI
data_try <- reactive({
crime_data <- read.cs... |
e640bdaa3b799c259cb80dd84f858c3f9840b20a | bd8a7c215d851e6b3c44165baec15e3f13efb665 | /man/es_renderPlot.Rd | 2b9a8948c3a35c4abc8ad3a923ea7bb771f8f1fc | [] | no_license | mYstar/easyshiny | dfe36d11f97d390cb3e7e5548f64d6939b9de36a | 9987d571a65ecdb6004cfa112ad80f027694b0fd | refs/heads/master | 2020-04-12T15:02:55.283045 | 2019-06-19T08:19:46 | 2019-06-19T08:19:46 | 162,569,346 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,661 | rd | es_renderPlot.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/es_add_output.R
\name{es_renderPlot}
\alias{es_renderPlot}
\alias{es_renderDataTable}
\alias{es_renderImage}
\alias{es_renderPrint}
\alias{es_renderTable}
\alias{es_renderText}
\alias{es_renderUI}
\title{Add Output Object}
\usage{
es_renderPl... |
f171b6a85d572854f426b766c05c4a4099049fa7 | d6793e6154e4373b20fbeab75bd227800a2cb7fa | /Revised_1_app copy 4_v2.R | c000095765725f24343b2bc96ef074a5241593e0 | [] | no_license | Laava5892/R-recent-scripts | c94985fc52a7608c20cb78e41e0e84cd38d84eff | db5c7c5ae8c30bff99b069042472152a8f0d2e6d | refs/heads/master | 2021-06-16T12:50:37.434202 | 2021-05-15T18:26:28 | 2021-05-15T18:26:28 | 200,422,233 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 240,883 | r | Revised_1_app copy 4_v2.R | ##### PACKAGES #####
# install.packages('rsconnect')
# library('rsconnect')
#
# rsconnect::setAccountInfo(name='paml-adobe', token='F2029EBA9515AEA913E45F3CA4B43BF0', secret='GGmXl7C77AlltpYQq6GjoA8vJPqg9Ug1BCYxZINH')
#
# rsconnect::deployApp('path/to/your/app')
#
# install.packages("rvest")
# library("rvest")
# g... |
6ef31472f49d73b5c23e0473030b37a7eaab6b83 | aa4f45999038440a4aafd0c4c00e894c2c668aa8 | /plot4.r | 9a2c3fc61affeb218873a15c7448107b58d6fa8e | [] | no_license | charlieZha/ExData_Plotting1 | 50cd4384187c01eaccd5f42957de1e4ece623b92 | 53ad5aa1eb6b1d2de786fe17e0b39a1ae57ff1d2 | refs/heads/master | 2020-04-05T23:12:37.516038 | 2014-07-12T14:46:23 | 2014-07-12T14:46:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 872 | r | plot4.r | temp<-read.csv("household_power_consumption.txt", sep=";",header=T)
data<-temp[temp$Date %in% c("1/2/2007","2/2/2007"),]
data$datetime<-strptime(paste(data$Date,data$Time),"%d/%m/%Y %H:%M:%S")
png("plot4.png", height=480,width=480)
par(mfrow=c(2,2))
plot(data$datetime,data$Global_active_power, xlab="",ylab="Global... |
45ce18b1b78cd56a266122b44f2756b189584f1d | 5c2ac9974888c4c7d65440c59eea61f55a2c760b | /BrewHome/R/USbreweryhomevalues.R | 4f35a436cdb7e95939bef5dbf9481d60f61d044a | [] | no_license | ecwalters112/BrewHome-Package | 78043596c73237449d390ecbea6f9bf602090215 | 2d88f0efec5382badc775364e55e3e7520981126 | refs/heads/master | 2020-12-26T14:09:55.061604 | 2020-02-04T16:34:27 | 2020-02-04T16:34:27 | 237,533,456 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,687 | r | USbreweryhomevalues.R | #' Brewery & Home Info
#'
#' This function makes it easy to search home sales and rental
#' valuations by breweries in different states.
#'
#' The function asks the user to provide a U.S state that they would
#' like to search for information on home sale and rental valuations
#' around breweries.
#'
#'
#' @param state... |
bfdae2d9b3dd0a26c53a8029350351667f889fba | bdf28f7b04438ab58e2ccabe178dd91f019accf2 | /plot_training_20200701/0202-折线图.R | 18d227df1e4fffecbb2bf4a7fc5c789174eb2f5e | [] | no_license | yxr123321/Drawing_scientific_research | bc6e7d9075c17501c6e447249d10ae28bec8fb1b | 4060eabcd41447a3efb0cb550ba4a8650f119da5 | refs/heads/main | 2023-03-06T17:26:34.525381 | 2021-02-21T15:06:29 | 2021-02-21T15:06:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 594 | r | 0202-折线图.R | ## ----------------------------------------------------
library(tidyverse)
library(cowplot)
library(gapminder)
data("gapminder")
## ----------------------------------------------------
gapminder_filter <-
filter(gapminder, country %in% c('China', 'India', 'Japan'))
gapminder_filter
## ---------------------------... |
f8eeb6b96573c6b0b0e5c8f38f4182c55cc1b40d | 03c99906a94c70e9a13e7714aad996f461f339c1 | /R/dbMANOVAspecies.R | 11cfb60fe1e1caef0f37e24467cb2c4e639095ba | [] | no_license | cran/adiv | 6a111f6a1ef39fe302a2f882b9a9d04e7d652c04 | d65d6e0301e4611a94a91933299bff1fdc06d96b | refs/heads/master | 2022-10-28T08:07:33.352817 | 2022-10-06T12:40:04 | 2022-10-06T12:40:04 | 97,764,074 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,434 | r | dbMANOVAspecies.R | dbMANOVAspecies <- function(comm, groups, nrep = 999, method = c("Euclidean", "Manhattan", "Canberra", "BrayCurtis"), global = TRUE, species = TRUE, padjust = "none", tol = 1e-8){
global <- global[1]
species <- species[1]
if(!inherits(global, "logical")) stop("Incorrect definition of argument global")
... |
542673228e90002e4509f8cd188db64c213a5f60 | ef5b13299d011f1362445e6c48c5dd9a9f7c25e5 | /inst/doc/q23.R | 468f946bb2ce397f58af5aeb8b414d7bd4b177aa | [] | no_license | mclements/biostat3 | faa45c9578ae36e7b972a9895ab2ba1164fa99e4 | 187e21adc3a2f9694f1c25fe181853f9d55868bf | refs/heads/master | 2021-07-05T15:25:48.004841 | 2021-05-10T14:42:02 | 2021-05-10T14:42:02 | 99,109,204 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,527 | r | q23.R | ## Purpose: Solution for Biostat III exercises in R
## Author: Mark Clements, 2017-11-03
###############################################################################
## Install needed packages only need to be done once
## install.packages("foreign")
## install.packages("survival")
## install.packages("dplyr")
###... |
b6986bf169654e147d83bf440a071327888f5e3c | de47db6a8a358c999904444361c8c10ce7a2aa45 | /modules/rtm/R/defparam.R | 72bbb6aeba06061c1e21ddba9e50241ae77f01dd | [
"NCSA",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | davidjpmoore/pecan | 0d42d0983203ad0791ef4668862726a4fc47ec97 | 73ac58ad36764fbf8c44290bb8f2cd4b741e73b8 | refs/heads/master | 2021-01-15T17:56:19.229136 | 2015-09-01T15:11:40 | 2015-09-01T15:11:40 | 41,746,837 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 617 | r | defparam.R | #' @name defparam
#' @title Get default parameters
#' @details Extract default parameter values from `model.list`
#' @param modname Model name. Must match `modname` in `model.list`
#' @return Named vector of default parameter values
defparam <- function(modname){
data(model.list)
setkey(model.list, modname)
... |
3b552106672f9b94db570aa47563a33be0925840 | 136d7cc6af0c39ad06c41b559b6b09f5d2ca270e | /R/data.R | 555f0bb9ccc4ad5110d6b13752fcb255c836d98d | [
"MIT"
] | permissive | sempwn/cr0eso | 40383e9ecd76f804050a06c7183d15e7839d5136 | 9e0316526fbf74654534aca40b6c2b49ccffb823 | refs/heads/master | 2023-04-18T05:07:51.960035 | 2022-08-17T21:44:56 | 2022-08-17T21:44:56 | 325,662,500 | 0 | 2 | MIT | 2021-09-17T19:34:34 | 2020-12-30T22:42:04 | R | UTF-8 | R | false | false | 4,472 | r | data.R | #' Long Term Health Care facility COVID-19 outbreaks in British Columbia, with 100 imputations of missing data
#'
#' A dataset containing 100 replicates of LTHC covid-19 outbreaks in BC, Canada, each
#' with a random imputation of missing symptom onset times.
#'
#'
#' @format A list with 100 elements, corresponding to ... |
02651a0208e5ea53ce55f11747a6c480ad05ea69 | c7db766c230fe59d8186d6fd1038e2cfe2464eaa | /man/ExoLVsCor.Rd | 605fcfca787761e685a36915e545f5bbec5d3ec2 | [] | no_license | cran/dgmb | 3c751847db4eba84104a6161565df0723bc3998e | 886b56b03deb6697706329b0a3edc7f7c13386a2 | refs/heads/master | 2016-09-05T11:55:28.454485 | 2015-10-22T15:23:16 | 2015-10-22T15:23:16 | 17,695,489 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,716 | rd | ExoLVsCor.Rd | \name{ExoLVsCor}
\alias{ExoLVsCor}
\title{Calculating correlations between exogenous constructs}
\description{A function to calculate correlations between exogenous constructs.}
\usage{ExoLVsCor(N, n, bex, rie, y.ex, a.nle, a.ie)}
\arguments{
\item{N}{The number of data sets.}
\item{n}{The sample size ... |
e658bdb77db77782794406d364adef3fee5b09d8 | 46b30340db3a4f368e9ff948b540e9de4ffe3b92 | /hbook2/hbook2.rd | cff6398228e448ba32ee762f9eb242970a3521ee | [
"LicenseRef-scancode-philippe-de-muyter"
] | permissive | kaitanie/ruby-cernlib | 499654a2975c5122e17a8db4aa7deaaddab857e9 | 2c2775fde146f97fa191d4cb80715d5c2556233b | refs/heads/master | 2016-09-15T20:18:05.745659 | 2009-06-30T15:31:15 | 2009-06-30T15:31:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,353 | rd | hbook2.rd | =begin RD
= HBook2 extension library
HBook2 is a histogram library using the CERNLIB HBOOK package.
Some histogram classes, N-tuple classes, a directory stream class and so on
are defined under CERN::HBook2 module.
= HBook2
Histogram module using the CERNLIB HBOOK package.
== module function:
--- HBook2.exist?(i... |
1d793517bb777a6ea81fe5b4f83f9a0587621fd2 | 3b234546e5d374714f25c755608fdfddaeade31e | /Similarity.r | 12cdd5314b9484a670dd212009b2fa25f1f039ec | [] | no_license | Tbhangale/Similarity-News-Articles | 13b18ece12df230e442f15c31e9f3eaa0f94974f | a138c70cb71f2a9a3c4959f41919c3a2d2196a26 | refs/heads/master | 2020-04-03T03:35:22.240081 | 2018-10-27T17:34:15 | 2018-10-27T17:34:15 | 154,990,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,997 | r | Similarity.r | #==============================================
#load these libraries
install.packages('tm')
require('tm')
require('SnowballC')
#data pre-processing
all <- Corpus(DirSource("D:/Sem 2/Temporal and spatial data/20news-18828", +
encoding = "UTF-8", recursive=TRUE), +
readerControl=list(reader=readPla... |
8700706baf84aa8ad006c3ea41fefb81c925e059 | 88dde69ede665f2bc9f606781d3de00562e8b3ed | /man/create.poset.Rd | 5357aab370e6790cc401cfabd9fde6982664aaf7 | [] | no_license | vishalbelsare/pim | 64ebfb621d618e7e85457c9d41c83f7b6251d6d1 | 26545a0cbcc668c0015f6289e634837548d442b2 | refs/heads/master | 2023-06-09T12:34:55.536755 | 2020-02-03T17:17:01 | 2020-02-03T17:17:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 840 | rd | create.poset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/create.poset.R
\name{create.poset}
\alias{create.poset}
\title{Create a poset}
\usage{
create.poset(compare = c("unique", "all"), n)
}
\arguments{
\item{compare}{a character value, either 'unique' or 'all'}
\item{n}{an single integer value i... |
1b8e52378d287fd153cba770fcb5b269e15d0d59 | f6f3e0e473e8c12fa977f36e9bca348b66adc51f | /plot2.R | ec9205d64bd495b1fc4acb24025ceffa5a90fd8f | [] | no_license | shuoyenl/ExData_Plotting1 | f16809fa5f7373fbc54d37dcb7e5ca89a6850fc8 | 1cc7ed90b530ec2f6f790132576e1e0b3788a372 | refs/heads/master | 2020-05-29T11:45:32.510419 | 2015-04-11T22:32:23 | 2015-04-11T22:32:23 | 33,718,840 | 0 | 0 | null | 2015-04-10T08:58:37 | 2015-04-10T08:58:37 | null | UTF-8 | R | false | false | 705 | r | plot2.R | if (!file.exists("household_power_consumption.txt"))
{
unzip("exdata-data-household_power_consumption.zip")
}
partialData <- read.table("household_power_consumption.txt", sep=";", header=TRUE, na.strings=c("?"), nrows = 100)
classes <- sapply(partialData, class)
allData <- read.table("household_power_consumption.tx... |
4f799d272e73317431041ad24fa5ad6045dfd70c | b99c39e52d62173235c1351682735deddba6ace5 | /MAPR_integrated_population_model.r | f361d3738d9bda1368ef203455520e47a81872c3 | [] | no_license | steffenoppel/MAPR | 6c80d333a4ff9e4a0ae78a73218683b8c6b05be4 | c6bd16a53021b85dfae416e17d4b08011db969f5 | refs/heads/master | 2023-08-11T09:11:15.463363 | 2021-09-30T18:46:04 | 2021-09-30T18:46:04 | 305,317,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,472 | r | MAPR_integrated_population_model.r | ##########################################################################
#
# MACGILLIVRAY PRION POPULATION MODEL FOR GOUGH ISLAND
#
##########################################################################
# population model adapted from Jiguet, F., Robert, A., Micol, T. and Barbraud, C. (2007), Quantifying stochas... |
aea5380a35113d8a166323fdbf2fab5f4538e9d7 | 17785aec04c48e0c3c5461eb0940d6876936c317 | /feature_combinations_detail.r | b860d636107690b7456db4b58319fa01f25c5740 | [] | no_license | sweb/kaggle_otto | ee113295ed10263566054304e0cf255a5e6dc9b8 | 858f267c7e2d37025709be9e1a83e4a500ee05a6 | refs/heads/master | 2021-01-15T17:46:54.004347 | 2015-05-10T19:13:29 | 2015-05-10T19:13:29 | 33,920,798 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,153 | r | feature_combinations_detail.r | setwd("C:/dev/repositories/R/kaggle_otto")
source("init_ws.r")
class2_accus <- read.csv("observations/feature_combinations.csv") %>% select(-X)
require(tidyr)
vis_class2_accus <- class2_accus %>%
filter (validation > 0.845) %>%
mutate(feat = paste("feat", feat, sep="_")) %>%
gather("type", "accuracy", 2:3)
g... |
cf61304e4f965ab474311db25093104d2160b5e4 | dd3cac999fbb8e88a6c9a02cbf899fc83de415d9 | /func/Task_1.5_Tokenization_func.R | 048d9cf9ab9517e8329266b4bd7d5d2d9366c9ad | [
"MIT"
] | permissive | rjcc/SwiftKey-Natural-language | 4852f920ce8c9a441577e0bc6314d259c77e34d7 | 08e38c0426708936fa70db0ab1ba5a5f1a85ba61 | refs/heads/master | 2020-12-11T12:30:11.804690 | 2019-06-30T12:43:18 | 2019-06-30T12:43:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,685 | r | Task_1.5_Tokenization_func.R | tokenization <- function (docs, trans = c(T,T,T,T,T,T,T,T), ChartoSpace = c('/','@','\\|'),
stopWords = 'english', ownStopWords = c(), profanity = data.frame()) {
cat(paste('\nPlease wait for initializing and summrising the input files......'))
require(tm); require(SnowballC)
cat(paste('\... |
e76add8659e0b2d024ba832d250009ffe870106a | 8c353819bc833ce88ff5c1f2d27f31d40ac2b162 | /R/SOcrs.R | db8edc9a8a14b93df32e55e9b46772ea83088fc1 | [] | no_license | AustralianAntarcticDivision/SOmap | 6e1e91ec59a59be6471ce1b940c9363154f949b7 | 0297ee8aea87015e32a2d3e4f9b009d00a549d29 | refs/heads/master | 2023-03-12T07:42:44.477742 | 2023-03-07T02:44:57 | 2023-03-07T02:44:57 | 155,124,496 | 24 | 5 | null | 2023-02-02T22:18:42 | 2018-10-28T23:06:11 | R | UTF-8 | R | false | false | 745 | r | SOcrs.R | #' SOmap coordinate system
#'
#' Set or return the coordinate system currently in use.
#'
#' If argument `crs` is NULL, the function returns the current value (which may be `NULL`).
#' @param crs provide PROJ string to set the value
#' @export
#' @examples
#' \dontrun{
#' SOmap()
#' SOcrs()
#' }
SOcrs <- function(crs =... |
b895fb5b0482591dde76de36c34389b384980a7e | c28a77e18cb220184dd84963e8c3809969606273 | /001-uniform-swing.R | 81ab3bc361f2e26cff6af2bd36a909b77eaf17c7 | [] | no_license | RomanSalzwedel/BTW17_constituency_forecast | 469bec6f0aeac832616ac14332f7d83b4803a321 | 87f35fb952445afa5d4d684a85b5c12ed665af88 | refs/heads/master | 2021-01-02T09:24:39.860481 | 2017-08-03T08:37:08 | 2017-08-03T08:37:08 | 99,210,323 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,488 | r | 001-uniform-swing.R | ####################################
### Election Forecasting Project ###
### HU Seminar SoSe 2017 ###
####################################
### Requierements ------------------
source("packages.r")
source("functions.r")
### Data Manipulation --------------
# Import
df <- read.csv2("data/kan... |
995c9ed9c4326acbf99caad6423c7cecf1496ae6 | 35098889de63b37c470a409d5f06d6fec10d0c70 | /R-main/02-move-figures.R | c4d04fd531bb9c696ae9a380e48f03f28bcd639c | [] | no_license | eriqande/gaiah-wiwa | 3cd206d059fba73c0a965036877a5130c77e9953 | 8d3056a10256fb0422d5e8e2718a774e8567b682 | refs/heads/master | 2023-05-15T10:05:23.103418 | 2023-04-27T15:19:17 | 2023-04-27T15:19:17 | 79,846,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 785 | r | 02-move-figures.R |
# just simple code to move figures created in 01-wiwa-analysis.R
# to where they need to be for LaTeXing the document
#### DEFINE PATHS: You must do this for your own installation ####
figoutpath <- "../../git-overleaf-repos/gaiah-overleaf/figures/"
figinpath <- "outputs/figures"
#### NAME FIGS TO MOVE ####
paper_f... |
bf05ad09ceeb5bf8be47f8399777821d9f6ce6cc | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/11984_0/rinput.R | 0032ddcaf61da7cea0b3e25748be5b458f861797 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 137 | r | rinput.R | library(ape)
testtree <- read.tree("11984_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="11984_0_unrooted.txt") |
fb28e887113db761bfea299c51ee669ee51b7b6a | ddd5dd6898d18fa111a54cfa7a130b7bc1b8718a | /R/scale_x_tickr.R | cd2112021d01c128b4f967b72552b883c4c7a18b | [
"MIT"
] | permissive | ben-williams/funcr | ed30c0863aabdc9624ae6506100bc9ceb3eae65b | c828d4f464d2d6921644352cc3e4fd6891e9b9fb | refs/heads/master | 2021-07-09T05:09:22.053009 | 2020-11-18T15:13:10 | 2020-11-18T15:13:10 | 214,511,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 595 | r | scale_x_tickr.R | #' Adjust x-axis tick marks and labels
#'
#' @param data = input dataframe
#' @param var = variable of interest e.g., year
#' @param to = step increase desired e.g., every 5 years
#' @param start = adjust the start value
#' @param end = adjust the end vlaue
#' @param min = lowest value to label
#'
#' @return scale_x_ti... |
48937f9a5271a02dddbe91dcc269b8aa2dcc272d | e1b73386edd9b767153192f4b359f0d1e98b33b2 | /man/IdentifyStructure.Rd | dfee740158995e9de0cb00627158d523679811bf | [
"MIT"
] | permissive | RodolfoPelinson/AtlanticForestMetacommunity | 28200f5e7964c922cd02f04efcc4e579271ce5d5 | be954ca5273ebe3673ec8eb58987b0c0a57e9450 | refs/heads/master | 2023-08-08T15:50:58.715554 | 2023-07-27T18:40:40 | 2023-07-27T18:40:40 | 355,654,618 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,016 | rd | IdentifyStructure.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IdentifyStructure_function.R
\name{IdentifyStructure}
\alias{IdentifyStructure}
\title{Run the EMS analysis and identify the idealized structure}
\usage{
IdentifyStructure(
comm,
names = NULL,
scores = 1,
CoherenceMethod = "curveball"... |
b92b3b737e3e855f8bd899187e5a23fb95672bfd | 93ea1e4f53ec560d4f7d9f799de2508485e6afa3 | /Ominer_miRNA_workflow/generate_venn.R | 2093a4e71d5ec2f0eb305fcd5fdbe806953e63aa | [] | no_license | JMarzec/Ominer | da8bfe1fcbac4fbcc09da8f2cefb69d76fc263ad | 35c9b0e7869c96e2e892c441432c03a05d570393 | refs/heads/master | 2021-04-26T22:19:05.060397 | 2017-08-08T12:24:22 | 2017-08-08T12:24:22 | 124,069,488 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,582 | r | generate_venn.R | ##########################################################################################
#
#File name: generate_venn.R
#Authors: Ajanthah Sangaralingam (a.sangaralingam@qmul.ac.uk)
#
#
#
#Barts Cancer Institute
#Queen Mary, University of London
#Charterhouse Square, London EC1M 6BQ
###################################... |
956dda457c143c214276d256f8756d195d87434a | 57e2c05ea9069edb9c8becc57997adfaa70646e7 | /leoniGeneComparisons.R | d1457d777aab5b8441ec98c62e1c59fd0d197034 | [
"MIT"
] | permissive | uhkniazi/Scratch | b399c1aff87a712d5fcc80f3ec99fae613463fdd | b3157e5f54179753ed6d77a47ff9ebebce1d4cd6 | refs/heads/master | 2022-05-20T09:53:07.761740 | 2022-05-04T12:50:23 | 2022-05-04T12:50:23 | 92,270,968 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | leoniGeneComparisons.R | # leoniGeneComparisons.R
table(grepl(pattern = genesIn[1], week.3$gene, ignore.case = T))
s = sapply(seq_along(genesIn), function(x) grep(pattern = paste0("^", genesIn[x], "$"), week.3$gene, ignore.case = T))
week.3.sub = week.3[unlist(s),]
s = sapply(seq_along(genesIn), function(x) grep(pattern = paste0("^", genesI... |
acbb8aaca29c31f99fb3da6f8f281845922d940b | b8c4095d11d3512c21e7ff9d1c77b09400b3f6c6 | /Módulo 6 - Bancos de Dadps (SQLite)/SQLite_no_R.R | 6b940f350be583c02d502163d53b396c64b265d8 | [
"MIT"
] | permissive | andreljunior/R-projects | b8987fb5cd5de9b18cf5a3a304a7054c4fe4a342 | 3dbe341caa851bc88bbc8349f27d753da1d9ef94 | refs/heads/master | 2022-11-30T01:23:28.188925 | 2020-08-10T14:05:32 | 2020-08-10T14:05:32 | 284,563,186 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 393 | r | SQLite_no_R.R | library(RSQLite)
#conectar ao BD
conn = dbConnect(dbDriver("SQLite"),"carteira.sqlite")
conn
df = dbGetQuery(conn, "SELECT * FROM Estoque WHERE data = '2018-06-15'")
df
str(df)
rows = dbExecute(conn,"INSERT INTO Estoque (preco,quantidade,vencimento,papel,data)VALUES (3150.208525,540,'2040-08-15','NTN-C','2018-06-15'... |
b99389efa55d5e344ca79d2fbda52ca5f6b5c189 | 604655aa047641f92f72454b7524333f0661409e | /man/getDateOffset.Rd | 79de15fe0d31c379466611198b6da34d1a3c77f6 | [] | no_license | troyhill/TTFF | dcbb03563cf8b99a23c525dd56bfdfce0ac4db91 | b4fb6f1f0e2094b4b4bb846798c7567a8090b0a4 | refs/heads/master | 2021-11-27T17:57:06.130433 | 2021-08-04T19:27:16 | 2021-08-04T19:27:16 | 175,022,979 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,038 | rd | getDateOffset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getDateOffset.R
\name{getDateOffset}
\alias{getDateOffset}
\title{getDateOffser}
\usage{
getDateOffset(dataset, dateColumn = "date", day = "Friday")
}
\arguments{
\item{dataset}{input dataframe}
\item{dateColumn}{name of column with datestam... |
e81bc1b862927ea386e9b7a95f930b7bb67ab3a1 | 1e93457070996e0cc20cbcfe261047dfc5957e0b | /demo/boosting.R | 94db35f9be914d38e3f04145b40aab8aab79c2dd | [] | no_license | caojilin/stat154 | b8aeb74758122bfc293acff3a49e50adce969b74 | e405f16f13aa30421646e7b8df3856d551d42e47 | refs/heads/master | 2020-05-14T14:23:51.399836 | 2019-06-11T16:22:23 | 2019-06-11T16:22:23 | 181,831,569 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,291 | r | boosting.R | library(gbm)
library(MASS)
library(glmnet)
library(spatstat)
#boosting
set.seed(1)
train = sample (1: nrow(Boston ), nrow(Boston )/2)
boston.test=Boston[-train ,"medv"]
boost.boston = gbm(medv ~ ., data=Boston[train,], distribution="gaussian", n.trees = 5000,
interaction.depth = 4)
summary (boost.bos... |
e8816a979f8d4088147393023838304952d1f874 | 4d7bdf931cafae81679072816c0f1b03efaf63a8 | /tests/testthat/test-timeconversion.R | b3ff6a8fc4b719e57603f3d74550c66ffdc882dd | [] | no_license | CC-HIC/ccanonym | b8826f815758c32ac169629cf646f40571d7ba79 | 09a2e03607932e08db5bcd371e1eefb9baee2923 | refs/heads/master | 2021-03-22T07:41:27.297340 | 2018-01-22T10:52:18 | 2018-01-22T10:52:18 | 66,528,436 | 1 | 1 | null | 2018-01-22T10:52:19 | 2016-08-25T05:43:02 | R | UTF-8 | R | false | false | 1,333 | r | test-timeconversion.R | context("Date/time to numeric conversion")
test_that("convert to numeric", {
dtype <- sapply(cleanEHR:::ITEM_REF, function(x) x$Datatype)
tdate <- dtype[dtype == "date"]
ttime <- dtype[dtype == "time"]
tdatetime <- dtype[dtype == "date/time"]
create.test.data <- function(hic_code, val) {
... |
424b3a5606f4067e627f32f759d291bf60d1ce83 | 796aa6d8e9f42024946275b2cc52006e7f487a1d | /man/MultiLociPOE.Rd | 7d2a00dcb64953ce8247c7676459cec18e11377f | [] | no_license | proman666/CCMO | 9108d9954e46b4423c200bd9202378e172506dd3 | 1ab0f40555bd56e2ac9f5fab69a51d3b3a0c3cea | refs/heads/master | 2023-03-12T11:39:37.737973 | 2021-03-03T01:12:24 | 2021-03-03T01:12:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,401 | rd | MultiLociPOE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CCMO.R
\name{MultiLociPOE}
\alias{MultiLociPOE}
\title{POEs analysis using multi-locus genotype data from case-control mother-offspring pairs}
\usage{
MultiLociPOE(Y, gmm, gcc, X, loci, hap, f, ppi)
}
\arguments{
\item{Y}{a \code{n}-vector of... |
bab5d142d410f85bc7040a342f2cb6dfa71a3d21 | ede2f028adc9ca3f7b170d2779b836124918c1dd | /man/cluscomp.Rd | e144572f24bbde56a8cc3df06f922b849763e3a1 | [] | no_license | biomedicalinformaticsgroup/clusterCons | 7167a224ba1b926c87f5ce99fd6a5211be09e940 | 114dc338d8919825e2c52e4db8a84c1da2cef2d3 | refs/heads/main | 2023-04-11T22:57:27.135255 | 2022-02-22T21:17:55 | 2022-02-22T21:17:55 | 461,762,921 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,078 | rd | cluscomp.Rd | \name{cluscomp}
\alias{cluscomp}
\title{Perform consensus clustering with the option of using multiple algorithms and parameters and merging}
\description{
Calculates an NxN consensus matrix for each clustering experiment performed where each entry has a value between 0 (never observed) and 1 (always observed)\cr
When ... |
953cbd7a4e3b7030b70bc1aea2e4da6eae67026f | e3ad0a98623d6a1cc96cff58aa92c494657b1bc4 | /Section4/Sim-RI.R | a17ccd88d8ceca29cb8a64e62f3a42484a1d96b3 | [] | no_license | sshonosuke/EHE | 91a8780d8627202fb3a399e4e0059c06218b86db | 6277d5e7138395183ccfde8c883c15f2d9d83ed5 | refs/heads/master | 2023-08-22T13:11:28.032202 | 2021-10-07T13:55:44 | 2021-10-07T13:55:44 | 261,907,836 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,549 | r | Sim-RI.R | library(MASS)
library(MCMCpack)
library(bayesm)
source("Function-RI.R")
set.seed(123)
R <- 500 # number of Monte Carlo replications
om <- 0.05 # contamination ratio
aa <- 15 # outlier location: 5 or 10 or 15 or 20
p <- 10 # the number of predictor variables: 10 or 20
m <- 50 # the number of subjects
nn... |
0705b8dac6eb058f5724e6d0e998bf0a6b1af6b2 | b54789971135132705b66808f7d62b391c80255f | /inst/doc/shapper_classification.R | e0f40058353e2218992dd8d442509b33a6124e6a | [] | no_license | cran/shapper | 983faa88eb35042452334a1b21e8587becb79002 | 9cac7a9a22a775eaed98c88a8d29398f1de51da0 | refs/heads/master | 2021-07-06T05:21:26.493422 | 2020-08-28T08:00:03 | 2020-08-28T08:00:03 | 173,755,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,989 | r | shapper_classification.R | ## ----setup, include=FALSE, eval=FALSE-----------------------------------------
# knitr::opts_chunk$set(echo = TRUE,
# message = FALSE,
# warning = FALSE)
## ---- eval = FALSE------------------------------------------------------------
# shapper::install_shap()
... |
dbd3bac62c72cb4e903c3f7dd4dc7cb0a6451e9f | 53191a96a722c594ca5e87c964fdd267474e8ce1 | /irr.R | a7b2531a90646a346c27ce96f519e021fa79a026 | [] | no_license | FPupillo/hippocampal_ranging | 8189169041ba7c26b21382de0b32bfe44ad4bbbf | 4964a9b14d5389fe7b04e57d369a9a61183cef5c | refs/heads/main | 2023-05-30T17:38:18.501835 | 2021-06-21T09:07:25 | 2021-06-21T09:07:25 | 369,465,447 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,401 | r | irr.R | #------------------------------------------------------------------------------------------------#
# ----------------------- Calculate interrater reliability---------------------------------------#
# ------------------------ hippocampal ranging --------------------------------------------------#
#----------------------... |
b22ebf9713f67cb5f77dcbbb55c67321dcde99d1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/IMIFA/examples/get_IMIFA_results.Rd.R | 6c48b31e867d3eff4c33bdfda6e3bf8499ab26b2 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,343 | r | get_IMIFA_results.Rd.R | library(IMIFA)
### Name: get_IMIFA_results
### Title: Extract results, conduct posterior inference and compute
### performance metrics for MCMC samples of models from the IMIFA family
### Aliases: get_IMIFA_results print.Results_IMIFA summary.Results_IMIFA
### Keywords: IMIFA main
### ** Examples
# data(coffee)
#... |
df043ca34d4152e3d9513e2cfcc9c742727014ab | 0d681f86c8b93184d0f1068d785099ba00b5750a | /R/RcppExports.R | 29c733a3833240c667f27e5be7a08a675f78f0eb | [
"Apache-2.0"
] | permissive | liyangyang12/GNET2 | a6340fd1ee1ee07a6ee94b0eeadfbcffb1be083b | f450971edb064aa1bba750585b1a7e0ac70b3356 | refs/heads/master | 2022-12-27T11:12:12.924442 | 2020-10-17T02:26:22 | 2020-10-17T02:26:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,405 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Fit a regression tree.
#'
#' Fit a regression tree based on Gaussian Likelihood score. Provided in case the best split
#' is not applicable for R dnorm() function.
#' @param X A n by... |
98b1397cc4cc2ffc2975096944e1f3dd5b109336 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rsurfer/examples/eliminateabnormalities.rows.Rd.R | ed5bb475acd03a3efcc8b26ef1c436ff64ac4553 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 223 | r | eliminateabnormalities.rows.Rd.R | library(rsurfer)
### Name: eliminateabnormalities.rows
### Title: Eliminate Abnormal Rows
### Aliases: eliminateabnormalities.rows
### ** Examples
data <- generaterandomsubjects(10)
eliminateabnormalities.rows(data)
|
5b2afb2dd9ae23d3c74e375255aa515d1c305f13 | df149ed84544c17b0218fae9f4c9fbb1852a31ff | /R/set-line-distance.R | 435f656275947732c0dc51303933a790eb577aba | [
"MIT"
] | permissive | data-science-made-easy/james | c15ca1467a418dba71d23b183bd0d1799d78abe7 | 38c40f3a1de8c69fa0ea8b92f44e67359100128d | refs/heads/master | 2021-06-17T07:48:57.087651 | 2021-01-08T15:04:13 | 2021-01-08T15:04:13 | 129,382,477 | 0 | 0 | null | 2018-04-16T09:27:17 | 2018-04-13T09:47:29 | R | UTF-8 | R | false | false | 123 | r | set-line-distance.R | set_line_distance <- function(p) {
print_debug_info(p)
# Set line distance
par(lheight = p$line_distance)
p
} |
a3e569dba5ff76c8c427082d3cee201d4c354e23 | 76f966ef048df6e295658adc1e179386f4db9ff2 | /OpenIIT_15_Main.R | f26ffe336019771fcdf5e12e2b68a8692af1f6bd | [] | no_license | evijit/OpenIIT_DataAnalytics_2015 | 49410df7e0815406ab414f710114be11fc4a287c | 30bf43e121a57705633232e5ef6fe1baa1d8f5b8 | refs/heads/master | 2021-06-01T01:33:03.147170 | 2016-04-22T09:32:26 | 2016-04-22T09:32:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 685 | r | OpenIIT_15_Main.R | require("plyr")
require("ggplot2")
options(max.print=1000000)
maindata <- read.csv('MainData.csv')
require('scales')
maindata <- maindata[order(maindata$Dept),]
maindata<-plyr::arrange(maindata, Dept, Product, City)
X <- split(maindata, maindata$Product)
prod20<-X[[1]]
prod20_hist <- prod20[!is.na(as.numeric(as.char... |
ec1f940035f317fef124e33dec4c911baca85998 | f6cea49ec83440cc5dd37602fb94d07132953339 | /code/hist_feature_selected.R | d86aaa09e49b2f08187db01dca329adc804e61fe | [] | no_license | hainguyenct/geneFS | 39c4d6e7e2ad9580ecb45016c59a7aacc7f95ce8 | 49597614d987ca9d4474ebcef115855c915250a5 | refs/heads/master | 2023-04-18T18:05:52.610486 | 2021-05-07T02:04:46 | 2021-05-07T02:04:46 | 242,274,581 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 948 | r | hist_feature_selected.R | #step 1: read data####
path_machine = "/Users/dcongtinh/gene-abundance/experiment/results/fc_model_with_feature_selected/qtf_pc576_10_fillseqf_nb10_auy_gray/"
path_r =path_machine
setwd(path_r)
dataset_name <- "Wt2dgene"
file_name <- paste0('feature_selected_', tolower(dataset_name), '.csv')
table = read.csv(file_name,... |
da2e748bfeb5e5b2cee97bb2910f526b7ff734cc | 7e5e5139f817c4f4729c019b9270eb95978feb39 | /Introduction to Tidyverse/Chapter 2-Data visualization/6.R | bc60eefefda8fd13351f952e8b163d99015f82f8 | [] | no_license | Pranav-Polavarapu/Datacamp-Data-Scientist-with-R-Track- | a45594a8a9078076fe90076f675ec509ae694761 | a50740cb3545c3d03f19fc79930cb895b33af7c4 | refs/heads/main | 2023-05-08T19:45:46.830676 | 2021-05-31T03:30:08 | 2021-05-31T03:30:08 | 366,929,815 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 694 | r | 6.R | # Adding color to a scatter plot
# In this lesson you learned how to use the color aesthetic, which can be used to show which continent each point in a scatter plot represents.
#
# Instructions
# 100 XP
# Create a scatter plot with population (pop) on the x-axis, life expectancy (lifeExp) on the y-axis, and with conti... |
c466d1bd1c01467536443a0fcc90b8efa0ae500b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sommer/examples/DT_technow.Rd.R | 333aed30296ecaa7181067f1d8b5ab70c5a69dc3 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,612 | r | DT_technow.Rd.R | library(sommer)
### Name: DT_technow
### Title: Genotypic and Phenotypic data from single cross hybrids (Technow
### et al. (2014))
### Aliases: DT_technow
### Keywords: datasets
### ** Examples
####=========================================####
#### For CRAN time limitations most lines in the
#### examples are s... |
ebd652f29b0d33bc04cbfbe90a3485a444b50bef | 352159789935579fe40d10389862fa29f610c1fa | /Kode R/kode R weight matrix dan moran test.R | 5d39cb162c59e47d5e07d3f497f5e6ea41323e74 | [] | no_license | novalsaputra/Data | a3318907fa8f1d02e6f6a99e0ce0bbc156bd741b | 6c2cf6834b7ff283fd72a7dde23d2e3462d921ba | refs/heads/master | 2020-11-24T07:54:34.803155 | 2020-06-14T09:49:27 | 2020-06-14T09:49:27 | 228,038,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,975 | r | kode R weight matrix dan moran test.R | ### OLS data ASLI
mydatap <- read.table("D:/tugas s2/TESIS..... FIGHT!!!/Kode R/data asli.txt",header=T)
head(mydatap)
names(mydatap)
attach(mydatap)
### Scatterplot
myvar=mydatap[c("Y","KP")]
plot(myvar)
myvar2=mydatap[c("Y","H","K","P","TG","S")]
plot(myvar2)
####### creating neighbour map ###########
library... |
ff82c6a91e87b3519ab014892fd4fb06d60f7ed7 | a10d3e11fb352cf896326c34bd0290cb44f2c0fe | /demo/CMHF_Dashboard.R | 1a1193e0f773985fb8f040e29ce0a8c0963fd0f7 | [
"CC0-1.0"
] | permissive | Romi111/intro_rmd | 9ec3c39ae7c653695abfda5ba43c5a7e6c7431bf | 45e7bcd432a517f17b83f64d74625926a50ddf66 | refs/heads/main | 2023-09-03T18:49:10.756609 | 2021-11-02T21:23:01 | 2021-11-02T21:23:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,525 | r | CMHF_Dashboard.R | library(tidyverse)
library(pander)
my_plot <- function(data) {
if (data == 'mtcars') {
mtcars %>% ggplot() +
geom_point(aes(x = wt, y = mpg)) +
geom_smooth(aes(x = wt, y = mpg))
}
else {
iris %>% ggplot() +
geom_point(aes(x = Sepal.Length, y = Sepal.Width)) +
geom_smooth(aes(x = Sepal.L... |
a2a88c7fdd130a3509ff9af3d30360cb4d224f52 | a698a346bfb430de48a5961716f2066bdc25b86e | /plot4.R | 5567a60fde69ccaed3e1c7770cab2ddf0b0a7b73 | [] | no_license | Nyanez615/ExData_Plotting1 | c7c6bc135e9488e715071a59df65c40b601b48ef | 24388d6b698df671bff3bbf36b87574ca8d9ca7e | refs/heads/master | 2020-06-17T10:58:57.664986 | 2019-07-09T20:02:18 | 2019-07-09T20:02:18 | 195,904,287 | 0 | 0 | null | 2019-07-09T00:33:07 | 2019-07-09T00:33:07 | null | UTF-8 | R | false | false | 2,638 | r | plot4.R | # This script recreates Plot 4 of the Course Project 1 of the Exploratory Data
# Analysis Coursera course, based on the Individual household electric power
# consumption Data Set, found in the UC Irvine Machine Learning Repository.
# Loading libraries
library(tidyverse)
library(lubridate)
# Setting file names and ... |
abb12c2d005ced17cbc40acabd2e9a2d49081cbb | 8ee94e3fedadbb772afdc11f634caaccae490967 | /Rcode/TrigGerber.R | d0f867cc14d1519b08b32d27836368b449d8c81b | [] | no_license | ShinyMCS/TAS-Bartko-BB | b7008cfdf35cfee2b4a5b741393139015effdd7a | 17a2aff3d7a5f473676fe2c6a248505627b9988b | refs/heads/master | 2020-06-06T18:52:08.781737 | 2014-03-01T23:15:21 | 2014-03-01T23:15:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 998 | r | TrigGerber.R | #Trig Gerber
Trig = c(0.96, 1.16, 0.97, 1.01, 1.25, 1.22, 1.46, 1.66, 1.75, 1.72,
1.67, 1.67, 1.93, 1.99, 2.01, 2.28, 2.15, 2.29, 2.45, 2.40, 2.79,
2.77, 2.64, 2.73, 2.67, 2.61, 3.01, 2.93, 3.18, 3.18, 3.19, 3.12,
3.33, 3.51, 3.66, 3.95, 4.20, 4.05, 4.30, 4.74, 4.71, 4.71, 4.74,
5.23, 6.21)
... |
63c5f3ee0166d411cee3a8c6e07d74a6df1cb3ba | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hoa/examples/anova.rsm.Rd.R | a31a9b6d2e95437081b04a4a04ea2d726ea5c939 | [] | 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 | 671 | r | anova.rsm.Rd.R | library(hoa)
### Name: anova.rsm
### Title: ANOVA Table for a RSM Object
### Aliases: anova.rsm
### Keywords: internal methods models regression
### ** Examples
## Sea Level Data
data(venice)
attach(venice)
Year <- 1:51/51
c11 <- cos(2*pi*1:51/11) ; s11 <- sin(2*pi*1:51/11)
c19 <- cos(2*pi*1:51/18.62) ; s19 <- sin(... |
1a0c6a5df9cff58b03d0b6d44d77f41c663664b9 | bffd95e4ee6d169caa6687e18b22611550c8df93 | /man/tiDaily.Rd | 85a880c6a6ced0f8c5decdd1d4b9634933cab937 | [] | no_license | cran/tis | 286eb68a7aeb0be636a07babcb16a362d62aa5f2 | f254c391711e5cbdc4deba8ea68c2a234317d4bd | refs/heads/master | 2021-10-13T14:19:03.631652 | 2021-09-28T18:50:02 | 2021-09-28T18:50:02 | 17,700,509 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,027 | rd | tiDaily.Rd | \name{tiDaily}
\alias{tiDaily}
\alias{tiBusiness}
\title{ Daily and Business Day Time Indexes }
\description{Return a daily or business day \code{ti} corresponding to a
specified position within a time index.}
\usage{
tiDaily(xTi, offset = 1)
tiBusiness(xTi, offset = 1)
}
\arguments{
\item{xTi}{
a \code{ti} object... |
2a5a4dca465885608c9e043250260d4cc66ee5c4 | 73744a740941b13641c0175c8e583b20cfd023a1 | /analysis/books/16_audience_models_SI.R | 8fc294716555f48374556d764e85ddc1ca28cf53 | [] | no_license | mllewis/WCBC_GENDER | 8afe092a60852283fd2aa7aea52b613f7b909203 | ed2d96361f7ad09ba70b564281a733da187573ca | refs/heads/master | 2021-12-25T22:41:21.914309 | 2021-12-22T19:08:36 | 2021-12-22T19:08:36 | 248,584,454 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,618 | r | 16_audience_models_SI.R | # predict reviews with other measures using glmer; make plots for SI
library(tidyverse)
library(lme4)
library(here)
library(broom.mixed)
REVIEWS_DATA_PATH <- here("data/processed/other/amazon_gender_scores.csv")
IBDB_TIDY_PATH <- here("data/processed/other/ibdb_tidy.csv")
BOOK_MEANS_PATH <- here("data/processed/books/... |
01b7aa93bd4d7e149372256151658511407a433e | fda4611281af0bc21fd28b376e26266a101bdd3b | /IHDP/hill-2011/code/example1.code.R | e5d4a8c86b83a03c22dde7eb470fb930dd1b6bb9 | [
"MIT"
] | permissive | imkemayer/causal-inference-missing | bc75749682273ef2a5536540ff4af84cd7276ee8 | e440ecc7084bc80205f2291c1a9e1dff55723325 | refs/heads/master | 2021-09-27T00:13:39.469262 | 2021-09-22T13:32:50 | 2021-09-22T13:32:50 | 185,151,389 | 8 | 2 | null | 2020-10-13T14:08:06 | 2019-05-06T08:05:23 | HTML | UTF-8 | R | false | false | 10,026 | r | example1.code.R | ### try fitting various matching methods to the data and compare to bart
### save treatment effect estimates and s.e.'s (approx for matching) for all
### save balance summaries for unmatched and all matched
set.seed(3847293)
library(MatchIt)
############# first load the data
load("example.data")
source("functions.R")... |
735dad15c69e71672b1cfd2022bc168e3c81c4a2 | eb14e64b646bb33b8f89da5b75bd4b7531aecdad | /man/bmProxy-class.Rd | 69fcd7ac3d83857046b009060aba3865ebdd2bd2 | [] | no_license | 5vxssbdahves/RBMproxy | 1bc26f6362d0ac22353123b44760a22245fbd8a5 | 430ab921fb8c2225f80c1f3a8039fe576ebe9d77 | refs/heads/master | 2020-12-25T10:38:18.018974 | 2014-02-10T09:24:24 | 2014-02-10T09:24:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | rd | bmProxy-class.Rd | \docType{class}
\name{bmProxy-class}
\alias{bmProxy-class}
\title{CLASS bmProxy}
\description{
bmProxy Class uses the RESTAPI to communicate with the
BrowserMob Proxy.
}
\details{
bmProxy is a generator object. To define a new bmProxy
class method `new` is called. The slots (default value)
that are user defined are:
}
... |
2ec9b049deb869417d44ce81616ce5a6c1525ec0 | 9dd1972f821e4b8c07836e4a44566c1fb332a68d | /pig/allele_validation.R | 1ef99d8690c094fa4f7f4f51de7483593de9cff4 | [] | no_license | russ-dvm/scripts | d2ec5c7618ae180a72f238bcc5b1f5de291ffaa2 | 63b177f96a7b23add9b69fc60cca563978448aff | refs/heads/master | 2020-12-14T04:51:46.704108 | 2018-05-31T19:19:10 | 2018-05-31T19:19:10 | 51,455,634 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,095 | r | allele_validation.R | library(ggplot2)
alleles <- read.table("~/sandbox/allele_freq_comparison/results_for_R.txt", h=T)
##CALCULATE TOTALS
alleles$total_aff <- alleles$AFF_alt + alleles$AFF_ref
alleles$total_unaff <- alleles$UNAFF_alt + alleles$UNAFF_ref
##CALCULATE ALLELE FREQUENCIES
alleles$ill_MAF_diseased <- alleles$ill_alt_diseased/... |
d7d7bc64c4c50c601b3b97b4571f7bfdbd22c2e9 | 4ea3ee297cc89dd3b807efcca79c14b29c9e6eb5 | /man/DeLorean.Rd | 6f5869c93fe0d9190d6d6ae8a47abdca4e0fbb89 | [] | no_license | bgoodri/DeLorean | 125a14af08373207f714a306ea6cc1a84d67058b | 0e44768658d004aa4e86dfa656771020b799e5fa | refs/heads/master | 2020-04-01T15:38:59.955595 | 2018-10-16T10:11:00 | 2018-10-16T10:11:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 208 | rd | DeLorean.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/package.r
\docType{package}
\name{DeLorean}
\alias{DeLorean}
\alias{DeLorean-package}
\title{DeLorean.}
\description{
DeLorean.
}
|
07e247809ea912a653386985a78354cd74745a10 | 184180d341d2928ab7c5a626d94f2a9863726c65 | /valgrind_test_dir/arcDistMat-test.R | ee86b06d88691662a822b6ce7519cfd368209506 | [] | no_license | akhikolla/RcppDeepStateTest | f102ddf03a22b0fc05e02239d53405c8977cbc2b | 97e73fe4f8cb0f8e5415f52a2474c8bc322bbbe5 | refs/heads/master | 2023-03-03T12:19:31.725234 | 2021-02-12T21:50:12 | 2021-02-12T21:50:12 | 254,214,504 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 183 | r | arcDistMat-test.R | function (X, r)
{
e <- get("data.env", .GlobalEnv)
e[["arcDistMat"]][[length(e[["arcDistMat"]]) + 1]] <- list(X = X,
r = r)
.Call("_signnet_arcDistMat", X, r)
}
|
ba20dccc139401eead91573de9bcb0d90a643388 | 65b3a69f095f85495e00c96e0b8263606b93eff8 | /R files/sace6.R | 9a1167d0cb6f8011bee2e78c0e56cb40ed3d791d | [] | no_license | nbbrd/jd3-rtests | 57786d88e967a96f211bbc50ad80c20ea2aba7c8 | 3ee3582f4e230a0e5ff4d248ae9ec159ea35722e | refs/heads/master | 2021-05-07T20:57:33.385080 | 2019-10-08T08:48:58 | 2019-10-08T08:48:58 | 108,976,840 | 8 | 2 | null | 2019-10-08T08:49:00 | 2017-10-31T09:58:01 | HTML | UTF-8 | R | false | false | 2,079 | r | sace6.R | source("./R files/jd3_init.R")
source("./R files/jd3_fractionalairline.R")
source("./R files/jd3_x11.R")
source("./R files/jd3_stl.R")
source("./R files/jd3_holidays.R")
usclaims<-read.table("./Data/usclaims.txt")
w<-jd3_periodicAirline(usclaims[,2], periods=365.25/7, outliers=c("ao", "ls", "wo"), criticalValue = 6)
... |
5e5331d7434d5e7216de21c3263a3f371f610a4d | 8cb3dd50c76e465ea06101dc53ed7c5a12517a30 | /scripts/visualization.R | ba354ca6280d5bfbf8465ef557eb5032599c01f6 | [] | no_license | The-Institute-for-Perception/Course_2021 | 0d743d3d28fb9de4a52b04c1d25c803ddc92241f | 3915d8177cac96ef35fd03c2fc1aac9dc77bcfea | refs/heads/main | 2023-02-18T17:26:33.744627 | 2021-01-19T15:25:19 | 2021-01-19T15:25:19 | 331,021,079 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,050 | r | visualization.R | ### Plot descriptive data and output to PowerPoint
###
### Author: Will Russ
###
### This script demonstrates how to plot descriptive data and then export the plots to PowerPoint
###
# load libraries
library(tidyverse)
# load local functions
source("functions/make_descriptive_plot.R")
source("functions/add_graphical_... |
d8706e4d9748f60d755fda6f3d070f23ee03a8a5 | de1d0064b49664f711f217028046cc73007d5d9f | /man/twtr_auth.Rd | 1f6ec95c1f4c15da4b909b4fc8b260bdce6be272 | [] | no_license | yutannihilation/twihttr | aeeab32c31372dc345e5a008127ca02e6bf3a4f9 | 5bf5b7a7792c7e82e5865310e668e6b96a89bcc4 | refs/heads/master | 2021-01-20T21:06:10.949602 | 2016-11-30T03:16:14 | 2016-11-30T03:16:14 | 65,869,824 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 233 | rd | twtr_auth.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{twtr_auth}
\alias{twtr_auth}
\title{Get Authentication}
\usage{
twtr_auth(key = NULL, secret = NULL)
}
\description{
Generate OAuth1 token
}
|
87c2b25b971c22ee4e7f0c5f1f5ae90854c6cc5b | 26c0d024a84f6bcf461eb5f4ae97e7ca1fd9eaba | /man-roxygen/common.R | 7dc241782c639be36d0674006003fe43f323ae06 | [] | no_license | rBatt/trawlData | 11deca8341155dbd09afbdb0fcab046e4ff06c3f | 266c5cda94b78790474ed8a5b3e8a66b6bde04d8 | refs/heads/master | 2021-01-21T08:57:44.568307 | 2018-06-24T20:55:19 | 2018-06-24T20:55:19 | 44,205,244 | 8 | 3 | null | 2017-06-28T20:43:56 | 2015-10-13T21:14:11 | R | UTF-8 | R | false | false | 76 | r | common.R | #' \itemize{\item <%=common.name%> the common name of the organism sampled}
|
43aa55e3a851a6efc74f7846af48b2bc07dd8ca6 | a031335810ac3183e9ba7d2cdc668299a71ae983 | /Code_OtherML.r | 420945b82d7da8ac93101e8689e191a47283d7db | [] | no_license | Joonhyun0982/2017-Travelers-Case-Competition | e700c5a8e7f550ff42fd418ff8df0b30f64de0f5 | 55afdbff7fd01e308ebfbe84fbdbf52bab67bd9b | refs/heads/master | 2020-03-15T22:08:45.334599 | 2018-06-12T02:24:09 | 2018-06-12T02:24:09 | 132,367,828 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,365 | r | Code_OtherML.r | #=============================================================================
# 2017 Travelers Case Competition
# Method: Other Machine Learning Methods
#=============================================================================
# ============== #
# 1. Preparation #
# ============== #
# install.pac... |
c19f4c7d74d0e313931517c3ad9ce6739a9b3ab9 | 3aea7bdf5abf8298bf0c2508cdd13ee87aeebf48 | /plot1.R | 74d972744a2a7a23753d8cc15f55b24895b13624 | [] | no_license | Tomasyzy/ExploratoryDataAnalysis | e7af8026c8bbcbaba67ad070d0bf7f11e1cdb56a | 7d8f258705eea6e568b89073c51f6bf5b3a0a1d8 | refs/heads/master | 2020-05-18T03:56:53.568485 | 2014-09-07T10:05:50 | 2014-09-07T10:05:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 504 | r | plot1.R | data3<-read.table("household_power_consumption.txt", header=TRUE, sep=";")
data4<-data3[1]
data5<-data4=="1/2/2007"
data6<-data3[data5,]
data6
head(data6)
data7<-data4=="2/2/2007"
data8<-data3[data7,]
data9<-rbind(data6,data8)
data9$Global_active_power<-as.numeric(levels(data9$Global_active_power))[data9$Global_activ... |
ab8789e21062d6464c70478342e1756e39c2f62e | 69a919e839126b7a1a44ccfc2388990a1fabaa8f | /Project_data/Plotting.R | a01b77dc05ad8436aea50072bf396098ce7dd00a | [] | no_license | dkopp3/Insect_Traits | 71455ca4686e35c514edcda8f8797285c9ff422b | 9c085e8bbf27431174b0ef091d79faa05d6f1003 | refs/heads/main | 2023-02-28T18:47:16.218857 | 2021-02-16T16:17:17 | 2021-02-16T16:17:17 | 332,040,595 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 18,720 | r | Plotting.R | ##################
#Create plots
#################
library(ggplot2)
library(rethinking)
library(randomForest)
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
legend
}
project... |
7cdf2ed9dc45db3a00af9c5018bd3b6ce7982163 | e87b48bbd92d3a8626b86ee0049807ed02f2323e | /R/bind_tweets.R | 9b9e4481153996d4329c3a82b1b8317808b96c0b | [
"MIT"
] | permissive | justinchuntingho/CrowdtangleR | 64f4c267f01621991e33c63794d0867b05666ab0 | a5fc58427126b299478995b70659e25a8d712109 | refs/heads/master | 2023-06-02T12:05:52.111160 | 2021-06-23T22:33:23 | 2021-06-23T22:33:23 | 379,681,974 | 13 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,029 | r | bind_tweets.R | #' Bind information stored as JSON files
#'
#' This function binds information stored as JSON files. By default, it binds into a data frame containing tweets (from data_*id*.json files). If users is TRUE, it binds into a data frame containing user information (from users_*id*.json).
#'
#' @param data_path string, file... |
aea4597fc14d3a37c1bb23889c05e44f970f93a9 | d05acb1d4eed8d421d4bd4ab4c9caa63f53446df | /man/deployTFModel.Rd | bf070d2adbb758252f269128204d4fc99f328eaa | [] | no_license | btongson/rsconnect | 80ccdffeeaee56de8b33a533996bc36a46fe18c9 | f5854bb71464f6e3017da9855f058fe3d5b32efd | refs/heads/main | 2023-07-07T11:48:31.574955 | 2021-08-10T17:08:46 | 2021-08-10T17:08:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,664 | rd | deployTFModel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deployTFModel.R
\name{deployTFModel}
\alias{deployTFModel}
\title{Deploy a TensorFlow saved model}
\usage{
deployTFModel(modelDir, ...)
}
\arguments{
\item{modelDir}{Path to the saved model directory. MUST contain
\emph{saved_model.pb} or \em... |
5cc0c78c218d27cf79118c2c69be9803c1fe0c11 | 0f104ea64886750d6c5f7051810b4ee39fa91ba9 | /playgrounds/decimal-comma.R | 935f504097d4e58805a7c19db81a8e357ed6384f | [
"MIT"
] | permissive | OuhscBbmc/REDCapR | 3ca0c106e93b14d55e2c3e678f7178f0e925a83a | 34f2154852fb52fb99bccd8e8295df8171eb1c18 | refs/heads/main | 2023-07-24T02:44:12.211484 | 2023-07-15T23:03:31 | 2023-07-15T23:03:31 | 14,738,204 | 108 | 43 | NOASSERTION | 2023-09-04T23:07:30 | 2013-11-27T05:27:58 | R | UTF-8 | R | false | false | 4,377 | r | decimal-comma.R | d_expected <-
tibble::tribble(
~record_id,~last_name,~height,~weight,~bmi,~demographics_complete,
1, "Valdez" , 1.54, 52.3, 22.1, 2L,
2, "Rich" , 1.84, 92.3, 27.3, 2L,
3, "Furtado" , 1.95, 123.4, 32.5, 2L,
4, "Akbar" , 1.61, 45.9, 17.7, 2L,
)
# ---- both commas & dots ------------------... |
fa45c26d417edaa0019dfaa0dbf8f23820e39fdc | 0e576756a3abeeabf00250c01530b1048d19e444 | /Tessa/real_data.R | 151d8c1264b9e57d49610f872b5e729e2a7accd5 | [] | no_license | renzy93/TESSA | 4cc24c42afb971837601be62325118d2d63c0b16 | 9887b20cc54a06d8b1eb8d5fed5c4e1c52229d4d | refs/heads/master | 2023-05-02T04:55:47.072122 | 2021-01-14T16:12:55 | 2021-01-14T16:12:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,130 | r | real_data.R | args=commandArgs(trailingOnly = TRUE)
exp_file=args[2]
contigs_file=args[3]
cdr3_file=args[4]
save=args[5]
is_sampleCluster=as.logical(args[6])
fixed_b=args[7]
xi=1e+25
g=0.001
initialize_cluster_factor=6
source(paste(args[1],'update.R',sep='/'))
source(paste(args[1],'initialization.R',sep='/'))
source(paste(args[1],'M... |
e9421398be3c9e12cacfb7b5511456ade6a6a462 | 518e98ca0e674e5b61f248ecc689ff26fe3de95d | /plot1.r | 6e42d062b1fec6655736b10c8f0358f93880671f | [] | no_license | tnt16dj/Exploratory-Data-Project-2 | 92a6412a0e5d98ae0d5a50f3b625ff62195a1c93 | d41c104461285653228a1571aebacd33f9f421f8 | refs/heads/master | 2021-01-16T19:20:38.612924 | 2015-01-25T17:18:22 | 2015-01-25T17:18:22 | 29,821,892 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,187 | r | plot1.r | ## check if data is downloaded, if not download
if (!file.exists("./EPAData.zip")) {
## download the EPA Data file.
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL,destfile="./EPAData.zip",method="curl")
## unzip the file
... |
4d8408b1615fae58c4e041c25b837f889df90b6a | 818bcedc9cc432fbdcaa0f9474727094a28d3004 | /R/test.R | e95e696c94bd58192e796ef486f348933deeb5dc | [] | no_license | naveen519/data_science | e366b75adf2ebceb9873d7ffefcec9f1f55857d7 | bd0bc2be087b3e5d8e9d7291a1f77114face12b9 | refs/heads/master | 2020-05-19T05:26:51.201782 | 2019-05-04T07:56:43 | 2019-05-04T07:56:43 | 184,849,000 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,747 | r | test.R | a=10
a
b=20
b
a+b
id = c(1,2,3,4,5)
age = c(22,31,34,40,45)
gen = c("M","F","M","F","M")
df1 = data.frame(id,age,gen)
id = c(1,2,3,6,7)
sal = c(100,200,300,400,500)
desg = c("A","B","A","B","A")
df2 = data.frame(id,sal,desg)
df1
df2
### inner merge
df3 = merge(x=df1,y=df2, by ="id") ## inne... |
0faeca027b2cc1618f67cf31eb17a40c809243de | a546edb72260612a371847728a903f704cd15918 | /man/NoNA_df.Rd | e7c77f0c86b0679436bacea2de35ef5ec7ef1a18 | [
"MIT"
] | permissive | wizbionet/wizbionet | adcf0366d002892a67209357a6802cd6a179348c | b5fe22074d770df36b3afc47805cf899c69a7bfa | refs/heads/master | 2022-12-08T07:18:00.668772 | 2020-09-02T21:20:01 | 2020-09-02T21:20:01 | 292,099,931 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 773 | rd | NoNA_df.Rd | \name{NoNA.df}
\alias{NoNA.df}
\alias{NoNA_df}
\alias{wizbionet:NoNA.df}
\title{
missing values into NAs
}
\description{
This functions changes all kinds of missing values into NAs ("", NULL", "NaN" etc)
}
\usage{
NoNA.df(x)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{x}{
data frame
... |
929d19f69e4563c0d57eaffc049d553134081685 | c85471f60e9d5c462de6c60c880d05898ec81411 | /cache/thebioengineer|TidyTuesday|2019-09-10__Amusing_Injuries.R | 8c36fe40393f5e1c97b826a46efb47dae9d7ff96 | [
"CC-BY-4.0",
"MIT"
] | permissive | a-rosenberg/github-content-scraper | 2416d644ea58403beacba33349ee127e4eb42afe | ed3340610a20bb3bd569f5e19db56008365e7ffa | refs/heads/master | 2020-09-06T08:34:58.186945 | 2019-11-15T05:14:37 | 2019-11-15T05:14:37 | 220,376,154 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,043 | r | thebioengineer|TidyTuesday|2019-09-10__Amusing_Injuries.R | ## ----load_libraries------------------------------------------------------
library(tidyverse)
library(tidytuesdayR)
library(plotly)
library(geofacet)
tt<-tt_load("2019-09-10")
tt
## ----transform-----------------------------------------------------------
injuries_by_state<-tt$saferparks %>%
select(state = ... |
835632b6eaf72ee2ec58fdf0469a8d9fc0d159e3 | a2fff0cf44ba7b3c00bb77e337282e43976b54c4 | /FinalFunction.R | 8ecf6abfde905cfd833d37c7d029bb27f4cb93bc | [] | no_license | krrishsgk/Scraping | e5aabc95c47136a945a8d2f3b50e4fe90911f125 | 41f34b1f6934766921b6c27891f54f1af5fac4a3 | refs/heads/master | 2021-06-04T11:21:47.614052 | 2019-02-18T00:12:59 | 2019-02-18T00:12:59 | 32,724,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 785 | r | FinalFunction.R | #All the required functions have now been coded. Creating this code to call all the functions
#in this giant big function
#Run all the other functions before running this one as this calls all the other functions
#Submit the link that has the results of all the games to this function
getItTogether <- function(eplurl... |
57b0ea58fa1414b32290af7f1f39e0932df37910 | 57733a660c18345d4c5c05b940974064d203b3bb | /r/fusion-model.r | 10fdab61e52caadf38bfb77a200b412fcd52345a | [] | no_license | patkilleen/twitter_sentiment_analysis | 1e4bda48224402d66601b7fae0cd18adcb620bfc | 4d10ed967ad023a42b3563aecc87b5ef33433add | refs/heads/master | 2022-04-23T03:27:12.817121 | 2020-04-23T21:21:10 | 2020-04-23T21:21:10 | 257,695,222 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,782 | r | fusion-model.r | library("kappalab")#
args <- commandArgs(TRUE)
fusionTrainingDatasetPath <- args[1]
fusionTestDataDatasetPath <- args[2]
resultFilePath <- args[3]
alphaParam <- args[4]
#read the fuision training dataset
#fusion dataset format: pi(j),pi(j+1),...,pi(j+n),realtag
#where pij is the prediction of the ith sample made by mo... |
de58ec9d8e28e18c096c78ac7fb8c2cdb1599694 | 9591f5820092cf51ce5fb1a42dfe30eb5ab441b9 | /Nowicka2017/02_heatmaps_codes_pvs.R | 83fcc2fd75e170a358ad4502a69e2d562e137db8 | [] | no_license | yhoang/drfz | 443d58837141ffd96a3b0037b07a8d6f67d56350 | 38925398d30737051a3df4b9903bdc8774c18081 | refs/heads/master | 2021-06-30T23:12:36.186438 | 2021-02-09T11:10:32 | 2021-02-09T11:10:32 | 218,074,297 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 33,130 | r | 02_heatmaps_codes_pvs.R |
Sys.time()
# Load packages
library(FlowSOM)
library(ConsensusClusterPlus)
library(gdata)
library(ComplexHeatmap)
library(RColorBrewer)
##############################################################################
# Test arguments
##############################################################################
pre... |
ffad43abed444960624fa236f573beb27ae0de53 | c1225525697e27a0c2fe46455c465930f25db6df | /man/belplau.Rd | 4dcfaca45bdd090bc7e013d3fc806229e577faee | [] | no_license | yyuzhong/dst | 2754ac80f5bb656731959e1cf5c2dec2d9cfd552 | c7a1151071c8299a23e3ee666d4ddebbf92f7a50 | refs/heads/master | 2021-01-15T12:41:46.821871 | 2014-12-30T00:00:00 | 2014-12-30T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,369 | rd | belplau.Rd | \name{belplau}
\alias{belplau}
\title{Calculation of the measures of belief and plausibility for each focal element of a matrix}
\usage{
belplau(x, remove = FALSE)
}
\arguments{
\item{x}{A matrix of propositions, i.e. 1 col of masses plus boolean matrix describing focal elements.}
\item{remove}{= TRUE removes foc... |
cdd51eea6cfffa8520b95590a88fab4157c17535 | c1f40916fafdd40be5aade0117e17c3caa3d5737 | /script.R | 0fb4dcf065928bf290bd862ff58f98e856c48d5e | [] | no_license | darokun/ME-GIS | 8f44b6c588d936c267ab699b74202700dacb0f85 | a3bd7ea3f85fd817cc83c95acdfe92cca8593ecd | refs/heads/master | 2021-01-12T08:25:36.475625 | 2016-12-15T17:02:37 | 2016-12-15T17:02:37 | 76,575,808 | 0 | 0 | null | 2016-12-15T16:19:53 | 2016-12-15T16:19:52 | null | UTF-8 | R | false | false | 2,300 | r | script.R | # Middle Earth Maps
# https://www.r-bloggers.com/interactive-and-styled-middle-earth-map/
rm(list = ls())
library(dplyr)
library(maptools)
library(highcharter)
library(geojsonio)
library(rmapshaper)
fldr <- "~/Documents/GitHub/ME-GIS"
shp_to_geoj_smpl <- function(file = "Coastline2.shp", k = 0.5) {
d <- readShap... |
1845c184734d4b3df237865b0bdbdf678e8d69b9 | 243b1751d4870b8b20ab3fab297eecfb4102872f | /Aula1.R | f4624a6df19bfe668c62c89138c71bf1d8a4f55a | [] | no_license | Lucasc27/POO-e-Funcional | af1ef245194db64afc7bbda1aabd480f0dea5ab3 | 27f7a537eaec2d4710eb6064ec2caec2c46a7e3a | refs/heads/master | 2020-04-24T06:12:31.651365 | 2019-04-10T23:40:47 | 2019-04-10T23:40:47 | 171,757,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,937 | r | Aula1.R | # Programação Funcional e Orientada a Objetos
# Aula 1
# Prof. Neylson Crepalde
# Lucas Cesar Fernandes Ferreira
# ------------------------------- #
5 + 3 # Soma
5 - 4 # Subtração
15 / 5 # Divisão
5 * 3 # Multiplicação
5 ^ 2 # Potência
16 %/% 5 # Divisão inteira
16 %% 5 # O resto da d... |
6d98f02ec076a5eb154b88b13c899cbf84a98ce1 | c5f494b0295e63ff26288db7dfce713313b82371 | /man/prune.Rd | d5e0fad8fd87323b64bcf164850018a79a8e33e0 | [] | no_license | briandconnelly/mutationtree | 42476df911cfeda760df877422b996ca424551ca | 03b95889bee0d30f7a9e0ef696500517eb9a5a71 | refs/heads/master | 2020-05-20T06:12:24.185997 | 2016-01-19T18:32:15 | 2016-01-19T18:32:15 | 31,677,053 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 615 | rd | prune.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{prune}
\alias{prune}
\title{Remove extinct leaf nodes from the population graph}
\usage{
prune(population)
}
\arguments{
\item{population}{A mutationtree population}
}
\value{
A pruned mutationtree population
}
\description{
To help minimize the size of a graph... |
c6f7bfa9b7c98b1afbf11a81fc7b43c43daf47a6 | ee816637db73015d86002e55c496c2fdeae52b99 | /0009.special_pathagorean_triplet.R | dfd581c4614432b3688528050ba224d352030048 | [] | no_license | OTStats/euler-project | a5c54e4ef989549c2ebba81ad9a61ed91debdbd5 | 3688b53382068e437cd84d19b0570b91b857a6a8 | refs/heads/master | 2021-11-16T02:31:42.894898 | 2021-09-27T13:30:51 | 2021-09-27T13:30:51 | 204,961,979 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 604 | r | 0009.special_pathagorean_triplet.R | # A Pythagorean triplet is a set of three natural numbers, a < b < c, for which,
#
# a^2 + b^2 = c^2
# For example, 3^2 + 4^2 = 9 + 16 = 25 = 5^2.
#
# There exists exactly one Pythagorean triplet for which a + b + c = 1000.
# Find the product abc.
# -- Load libraries
library(tidyverse)
crossing(a = 1:999,
b... |
5db013d167b9d98d558a2482a97d2a211b01cc1d | 66cf4adc85683e73290da2139c071365b40b6209 | /man/plot_GenePosition.Rd | 486d8b67885db17f32e75fc67395fd18a3a834b6 | [
"MIT"
] | permissive | dpelegri/EASIER | db51d623eae85300b4b63f30f97ac93c9676a004 | ce9101b990874c13f6d8564867fdb3cbdc5a7841 | refs/heads/main | 2023-08-21T21:33:43.968027 | 2021-10-22T15:28:07 | 2021-10-22T15:28:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 539 | rd | plot_GenePosition.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_GenePosition.R
\name{plot_GenePosition}
\alias{plot_GenePosition}
\title{Gene Pisition Plots}
\usage{
plot_GenePosition(
x,
outputdir = ".",
outputfile = NULL,
main = "",
xlab = "",
...
)
}
\arguments{
\item{x}{Dataframe with... |
af6d3357a6fec210dc4537aae5cb20f0aef5a266 | 83c43061a32c1c899aefb564ba6c63c32f48b210 | /General Code/CMcCode/man/vTable.Rd | 3038be392a361b2b48506f1e9c33717db8f32323 | [] | no_license | chacemcneil/Personal | c694b71439d4884884a22a599f7dfd6e3549f49c | 021c2f270f644654730c601b33458fad23185c26 | refs/heads/master | 2021-12-10T22:49:48.057640 | 2021-11-09T01:36:40 | 2021-11-09T01:36:40 | 45,845,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 679 | rd | vTable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/UsefulFunctions.R
\name{vTable}
\alias{vTable}
\title{Venn Table Function}
\usage{
vTable(..., prop = FALSE, sums = FALSE)
}
\arguments{
\item{...}{Vectors to compare}
\item{prop}{Whether to show proportions instead of counts. Default is FAL... |
5482e29f3669adfd20d4f28d77a2502ca29db158 | d28c7b35872ef4a5fe6baf91e6b4b8721e58a174 | /backup/topo_elements.R | c74c15309053fed255ed5fed85d510d5349a7313 | [] | no_license | emiliolr/abm | b00c63d945d28e4258f371ea3447b08156dc1377 | c9de884fda6ff1b3fede3600e26a662f27787c08 | refs/heads/master | 2023-02-20T22:12:02.361291 | 2021-01-24T19:54:42 | 2021-01-24T19:54:42 | 288,858,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,467 | r | topo_elements.R | library(tidyverse)
library(sf)
library(raster)
library(rayrender)
library(rayshader)
library(png)
#setup stuff...
setwd("~/Desktop/DATA\ 440/Deliverables")
path_to_add_data <- "~/Desktop/DATA\ 440/ABM_Summer_Stuff/section_2.1/data"
#bringing in all the data
load("adm2_urban_areas.RData")
load("for_roads_and_healthcar... |
e8b442094b54071554cbb34b58685f3fc9ebc9ad | cf9e466f05004b2c4c02ebe7c24d67846229a9bd | /phylodynamic/subset_clades.R | fdfc38de47deafce15d2b616359a78be424e4f48 | [] | no_license | JuliaPalacios/Covid19_Analyses | afadd0bc2b481c1a96ed8201c468fab44da72566 | 55d54686df7e44405b60471b50400247441d24ec | refs/heads/master | 2021-04-18T07:42:19.198979 | 2020-08-11T02:53:58 | 2020-08-11T02:53:58 | 249,518,712 | 3 | 1 | null | 2020-04-05T19:03:25 | 2020-03-23T18:59:20 | HTML | UTF-8 | R | false | false | 3,178 | r | subset_clades.R | rm(list=ls())
library(phylodyn)
library(ape)
library(lubridate)
library(phangorn)
library(phytools)
library(ggplot2)
library(ggtree)
library(gridExtra)
setwd("~/Documents/Covid_Analysis/phylodynamic/")
#base.dir <- '~/Desktop/Coronavirus/R/tree_processing/'
#setwd(base.dir)
source("function_serial.R")
#Read fasta file
... |
74c87607effb340146206e7b9d1a5cb50b215854 | 122b73ddcab44dfcc4f2da4ac32253247bf21ba1 | /JagsScript.R | 92b42bd0db272741a2f38fe745abe9cc948021af | [] | no_license | jBernardADFG/ChenaSalchaStateSpace | c5ebd4bffdbb31817242f34bddea037930d4342d | d614f384bad81e4d27abf3aa881280b0a83a1d06 | refs/heads/master | 2022-10-22T02:45:56.767096 | 2020-06-17T16:27:20 | 2020-06-17T16:27:20 | 273,022,905 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,339 | r | JagsScript.R | # PROJECT OUT A FEW YEARS #
# SOME THOUGHTS -- WE CAN PROJECT THE NUMBER OF SPAWNERS/RECRUITS OUT A FEW YEARS #
# THINK ABOUT TOSSING IN A TERM FOR SEA-SURFACE TEMPERATURE #
# ADDITIVE VS LINEAR EFFECTS #
# HIERARCHICAL TERMS FOR ALPHA AND BETA ?
# READ IN THE AND ORGANIZE DATA FOR USE IN JAGS MODEL #
{
rm(list=ob... |
2cd1154c204f1264d9c778e89aaf4410ccda3bdf | 28c0bb9cf47bc8a8f629b389ba62c1808fd34691 | /R/lactation.calf.model.r | 4fb99a333cf02ff8aafb85d0cb9a161f3c96a252 | [] | no_license | gcostaneto/ZeBook | 836e3dc8ab80de9ecce782e809606f4d647f30c0 | b892a7e80a233b1c468526307eb5f7b49d95514d | refs/heads/master | 2020-05-14T08:41:25.584061 | 2018-11-09T16:40:03 | 2018-11-09T16:40:03 | 181,727,649 | 1 | 0 | null | 2019-04-16T16:33:07 | 2019-04-16T16:33:07 | null | UTF-8 | R | false | false | 7,165 | r | lactation.calf.model.r | ################################ FUNCTIONS #####################################
# Contribution of Juliette Adrian, Master2 internship, january-jully 2013
#' @title The Lactation model
#' @description \strong{Model description.}
#' This model is a model of lactating mammary glands of cattle described by Heather et ... |
fb36ef0e493f86235fb9262dae6cfd07f712a71b | 8474e5591c6e2564895bde0522424f7cb60c90d1 | /R/diff_methylsig.R | aa817f06d2a5786eaea3da0d8e9b068930139066 | [] | no_license | ajpatel2007/methylSig | 398504ffe01d51c806098ee9da2751e09d260f65 | cb469678e2e4b5c3569d0927675d698dbe0f8f01 | refs/heads/master | 2022-04-14T04:20:20.587995 | 2020-03-25T18:38:33 | 2020-03-25T18:38:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,925 | r | diff_methylsig.R | .weight_function <- function(u) (1-u^2)^3
.derivative_phi <- function(phi, local_c, local_t, mu, weight) {
derivative = 0
indicator_c = local_c > 0
indicator_t = local_t > 0
indicator_ct = local_c + local_t > 0
if(nrow(local_c) == 1) {
derivative =
sum( indicator_c * ( mu * (di... |
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