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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c235bd2324778a816ca12d39dfa40ebcc028b780 | 73514ffb85a520aeded94f6d979c9712f41f32f4 | /BD/MEX.R | a429be94918fe2c51eed4173e8c18a791ac016dc | [] | no_license | NicoGaRo/BD | 71dbe6422f0c0fbd0b744b5a9b86509a98a60a1e | 8b45528c4a7a3a9b5dcf885c24b51d579a773d4d | refs/heads/master | 2021-01-21T11:23:22.034994 | 2017-05-18T21:57:36 | 2017-05-18T21:57:36 | 91,740,795 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,559 | r | MEX.R | .
#Tweets SelMexico
mex <- read.csv("m.csv", sep = ";", stringsAsFactors = FALSE)
mex <- mex[1:435,] #Se eliminan todas las observaciones vacias
mex$Sentiment <- factor(mex$Sentiment) #Conversion a factores de variable relevante
mex$Sentiment
str(mex)
library(tm)
#Limpieza datos
txt = mex$Tweet.Text
# remue... |
dedad97037f9e53c51ed38b1b96b3a0d2bad2ffd | f81fef0ba6045f35dd56a1e3f6a295afb44a8f4a | /R/harvest_subset.R | 53ed0ff8998700075e80267f3ddbbba231381913 | [] | no_license | zhoylman/MCO | 3ce8fa2077aad0c6265f690537c84ed860a27fa9 | ed8eb0aa79e60ea2cd4937745afc76fc5668df99 | refs/heads/master | 2022-02-23T00:12:42.594171 | 2022-01-27T18:15:11 | 2022-01-27T18:15:11 | 210,397,643 | 0 | 0 | null | 2020-06-18T19:27:35 | 2019-09-23T16:04:36 | HTML | UTF-8 | R | false | false | 4,113 | r | harvest_subset.R | library(tidyverse)
response = read_csv('/home/zhoylman/MCO/data/USFS/harvest_stats_rG.csv')
response$`system:index` = NULL
response_years = c('clim',paste0(c(0,seq(1:35)), '_NDVI'))
ndvi = response[,response_years] %>%
as.tibble() %>%
mutate(site = 1:200) %>%
gather("key", 'value', -clim, -site) %>%
mutate(xt... |
ba3273d387e6fb39106df344eceba272606e26a3 | 3c759a7f001e3b08f94f96648e0c8c27244e3dcb | /run_analysis.R | b00d57f7674a0c1846da6b6c21beb902daad3fc2 | [] | no_license | davejermy/GettingDataProject | a891393d789826cf6d1dd93badc7286cd4e19edc | e911cabfaeb865dda2967cca0f52333759f7b022 | refs/heads/master | 2016-09-06T11:47:23.060755 | 2014-07-27T15:35:52 | 2014-07-27T15:35:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,589 | r | run_analysis.R | ##---------------
## Reference Data
##---------------
## Load "activity labels" and "features" files
activity_labels<-read.table("./UCI HAR Dataset/activity_labels.txt")
features<-read.table("./UCI HAR Dataset/features.txt")
##---------
##Test Data
##---------
## Load the "y_test", "subject_test" and "x_test" fi... |
000df26ecc7d7cb13c367d51224352452993933d | f0b3c018aa244a6cf595a836c1cb94591a9777b8 | /R/style.R | 1805438f2e885e24ae8869ce7ffdcb1b923286df | [] | no_license | krisrs1128/treelapse | dc5e3fe4c6cbd90fdaa6146b00a117eaaaf8b59d | 55bea7e6256ca4e0fb88bbdfd54e04046fb9e0b2 | refs/heads/master | 2020-04-09T18:38:47.130805 | 2020-01-02T16:45:45 | 2020-01-02T16:45:45 | 68,158,499 | 7 | 0 | null | 2018-10-04T23:32:17 | 2016-09-14T00:22:51 | JavaScript | UTF-8 | R | false | false | 1,958 | r | style.R | #! /usr/bin/env Rscript
## File description -------------------------------------------------------------
## Functions for adapting display in treelapse views.
#' Merge in default display for timebox trees / treeboxes
#'
#' Completes a partially filled list of display options.
#'
#' @export
merge_timebox_display <- ... |
3accac89378dfffd022e2c778bb02c49cbede45b | beb988d8d6df6262d261451b72039d947cc4870b | /code.r | 7222c245629211e7f9075c18805b13ad76315202 | [] | no_license | adalardo/niche_neutral | f1ad91776325c698daf5c35edc8442857e3db093 | 731e27ef771a3a16788968d01293a6decd75bc59 | refs/heads/master | 2020-03-17T07:03:59.000470 | 2018-07-20T18:45:58 | 2018-07-20T18:45:58 | 133,381,660 | 1 | 1 | null | 2018-05-14T15:20:20 | 2018-05-14T15:20:19 | null | UTF-8 | R | false | false | 14,414 | r | code.r | ######################################################################################
# Partitioning niche and neutral dynamics on community assembly #####################
# Mortara et al ######################################################################
############################################################... |
bc9204801429cc231ccf08b73b78304fa08c314f | be9f6270df9ce29195f3181556228694475abbde | /Cryptography/ECdoubling.R | 8a555a6d141c97276c96ebea662e86d3a26624ee | [] | no_license | Arcsle09/Bitcoin-Economy-and-Blockchain | f97c3a2bc78e4e3445a41e2c40611cb8c2818480 | 08fb4cd98d5727df58a349611998c6a047abe795 | refs/heads/master | 2020-03-19T14:55:40.192936 | 2018-06-26T08:59:21 | 2018-06-26T08:59:21 | 136,646,179 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 244 | r | ECdoubling.R | # This is called point doubling, also invented for EC.
ECdouble <- function(a){
Lam <- ((3*a[1]*a[1]+Acurve) * modinv((2*a[2]),Pcurve)) %% Pcurve
x <- (Lam*Lam-2*a[1]) %% Pcurve
y <- (Lam*(a[1]-x)-a[2]) %% Pcurve
return (c(x,y))
} |
038d5fd951123b80c4d6e48569a04fb0b4fe530d | cee38f51779e94e49885cbc87f74b427184147f6 | /R/CalcYourTax.R | ff27a78f6cb52be7e3d9b78a0fefb02726f9459c | [] | no_license | atmdv/CalcYourTax | 49b25193b6eb4a30af5e2d9ac49223a9e5d6f228 | b166db00a484b9397b4aac2bce40a12975a03080 | refs/heads/master | 2020-04-14T05:28:25.638369 | 2019-01-04T14:21:03 | 2019-01-04T14:21:03 | 163,661,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,036 | r | CalcYourTax.R |
calcyourtax <- function(nordnet=NULL, degiro=NULL){
pacman::p_load(dplyr, tidyr, purrr, readr, stringr, lubridate, kableExtra)
if(!is.null(nordnet)){
# Import Nordnet transaction data
nordnet <- read.table(nordnet, sep=";", dec=",",
header=T, stringsAsFactors = F)
nordnet ... |
a8e3b99c58d44b560f973d184778138edefe1241 | 7147fbebcf896692544fc9dc3859aae0cf9270ad | /server.R | 1e10728f10c3cb53d73f16aa91c05ff2d601cf76 | [] | no_license | joecat6/SpatialTissueLocator | 2df087405032c59f5fec4287c9e09e43400ca3e4 | afa9c27b14cc0cf53e615167f9ca7eb65dee9d03 | refs/heads/master | 2022-04-20T22:04:20.031605 | 2020-04-20T16:10:27 | 2020-04-20T16:10:27 | 257,335,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,938 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(readr)
library(RColorBrewer)
library(EBImage)
pal = colorRampPalette(c("blue", ... |
b6398e874e53f165aec16e6cc281d290a88f27dc | ceadca5b319e44bcc73225b6ec29f05bc660c195 | /ROracle/src/test/R/oracle-test.R | 41b7266759b1702624fcc40885a9db007907c32c | [
"MIT"
] | permissive | perNyfelt/renjin-dbi | d1fbef965c627c425fffff12e4ac758354112588 | e2177521e6e794525d8ddcd94e4fc525f451e95b | refs/heads/master | 2023-07-20T04:11:40.372505 | 2023-07-10T09:04:03 | 2023-07-10T09:04:03 | 168,132,672 | 0 | 0 | MIT | 2023-07-10T09:04:05 | 2019-01-29T10:03:27 | R | UTF-8 | R | false | false | 182 | r | oracle-test.R | library(hamcrest)
library(ROracle)
test.driver <- function() renjinDBITest(dbConnect(ROracle(), url="jdbc:oracle:thin:@localhost/XE", username="renjintest", password="renjintest"))
|
921e273e5a94d4a644f704658687ad3281c27d18 | 6e7c335030b39054747d88b29844102e9989e245 | /R/model.matrix.hdlm.R | ca9fa369c0a3ed05755d31fe9dc56084c3ab126a | [] | no_license | cran/hdlm | 35011a8dd6576781bd21f4dd5166359e30ac8c75 | 8f6a01e2014b623cc9d2b1e78898358d104c0f1c | refs/heads/master | 2021-06-26T21:50:37.400120 | 2016-09-20T06:44:06 | 2016-09-20T06:44:06 | 17,696,612 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 297 | r | model.matrix.hdlm.R | model.matrix.hdlm <-
function(object, ...)
{
if(n_match <- match("x", names(object), 0L)) object[[n_match]]
else {
data <- model.frame(object, xlev = object$xlevels, ...)
NextMethod("model.matrix", data = data,
contrasts.arg = object$contrasts)
}
}
|
bdaa7ac01f2480f8fe2d4aa7555ecf5cb5124e39 | 14ee38b2616f18104d3d9921ff222017543e4a70 | /tests/testthat/test-rewind.R | e483f62821035928b765b60bc829c6a296ca9e71 | [
"MIT"
] | permissive | stevage/geojsonrewind | e88066b5c628381ea05cd3e0c0a72a5957afa89c | 71cd277d9a8e2f03032781b16fcbda6f591b459d | refs/heads/master | 2020-03-21T21:56:39.986587 | 2018-01-08T15:28:09 | 2018-01-08T15:28:09 | 139,094,647 | 0 | 0 | null | 2018-06-29T03:07:50 | 2018-06-29T03:07:50 | null | UTF-8 | R | false | false | 790 | r | test-rewind.R | context("rewind")
library("jsonlite")
test_that("rewind works with character input", {
x <- '{"type":"Polygon","coordinates":[[[100.0,0.0],[101.0,0.0],[101.0,1.0],[100.0,1.0],[100.0,0.0]]]}'
aa <- rewind(x)
bb <- rewind(x, outer = FALSE)
expect_is(aa, "json")
expect_is(unclass(aa), "character")
expect_ma... |
45328180a0cc281a7c9268d6b6262daeda0f120d | 9d3e3c3950c4101bc863a90e69606d7c7d03a4e9 | /chilling/04_make_figures/buggy/Do_Not_use_safe_box_average_over_models.R | 9f9329a28a08e7cadf386d1f8a48639daeda43a2 | [
"MIT"
] | permissive | HNoorazar/Ag | ca6eb5a72ac7ea74e4fe982e70e148d5ad6c6fee | 24fea71e9740de7eb01782fa102ad79491257b58 | refs/heads/main | 2023-09-03T18:14:12.241300 | 2023-08-23T00:03:40 | 2023-08-23T00:03:40 | 146,382,473 | 3 | 6 | null | 2019-09-23T16:45:37 | 2018-08-28T02:44:37 | R | UTF-8 | R | false | false | 9,504 | r | Do_Not_use_safe_box_average_over_models.R | rm(list=ls())
library(data.table)
library(dplyr)
library(ggmap)
library(ggplot2)
options(digit=9)
options(digits=9)
##########################################################################################
### ###
### ... |
9cf9af961ed2b480ba026fa92e948f54049f8e39 | 06b2d058411a9b1e69af1446e19bb422692c7ace | /R/LogLikelihood4Mixtures.R | 8dd71a08e8e21120f7ff8b2b084ed50a67eb204a | [] | no_license | Mthrun/AdaptGauss | 5a9a0460a04e759deb517a9100cbee6e53d5a346 | d0a5d20d90eb51faf41af16c01a507d6e41d9416 | refs/heads/master | 2023-07-21T01:15:55.704721 | 2023-07-14T09:28:26 | 2023-07-14T09:28:26 | 113,838,340 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,936 | r | LogLikelihood4Mixtures.R | LogLikelihood4Mixtures <- function(Data, Means, SDs, Weights, IsLogDistribution=Means*0){
# LogLikelihood <- LogLikelihood4Mixtures(Data,Means,SDs,Weights,IsLogDistribution)
# berechnung der Loglikelihood fuer ein Mixture model: LogLikelihood = sum(log(PDFmixture))
#
# INPUT
# Data[1:n] Daten, deren Verteilung ... |
7d7201b24078768bb7e11b0765562b1dc4f8fbab | daa2fa86e4146df7268cd96dc71298f785adfa72 | /man/cania.sub.ts.Rd | a20f9d485142f0328a002f429b7e73dba020c055 | [] | no_license | HYDauer/FlowScreen | e04db01eb256a175a976aa4d95b72f56579ad796 | ef208c7d529359ab73c18d87ba5bb54093fb9022 | refs/heads/master | 2020-04-16T08:04:14.493872 | 2019-01-12T16:31:42 | 2019-01-12T16:31:42 | 165,410,500 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,479 | rd | cania.sub.ts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cania.sub.ts.R
\docType{data}
\name{cania.sub.ts}
\alias{cania.sub.ts}
\title{Subset of the Caniapiscau River Daily Flows}
\format{Formatted as a data.frame with the following columns:
\itemize{
\item ID - Water Survey Canada Station ID... |
42d06384171dc51f94a9f736361cc9c56bb61895 | 67fef5dcc51a14471a34097c5074c27449e51856 | /Project4/code/part2.4-Q13.R | 0b9f4064fcdc320e6a7f6d184867116242491638 | [] | no_license | WendyCui1018/Data-Mining-and-Problem-Solving-on-Large-scale-Data-Sets | 6049020123286ecf1fd01d611c085e8a9bbc3cf2 | 68cea14d6924fe8a32075aafdf3a06d6731c43b8 | refs/heads/master | 2020-03-30T22:34:54.204832 | 2019-01-27T22:17:33 | 2019-01-27T22:17:33 | 151,671,797 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,644 | r | part2.4-Q13.R | filepath = "part1_data_original/movie_graph_edge_list.txt"
movie_network<-read.graph(filepath, format = "ncol",directed = FALSE)
fpath1 <- "part1_data_original/actorid_movieid_map.txt"
ori_actor_movie <- read.table(fpath1, header = FALSE, sep = '\t', col.names = c('actor_id','movie_id'))
valid_actor_movie <- subset(or... |
fb03333d33e1f6e9a5e23282802d3d83d261de71 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FinancialInstrument/examples/make_spread_id.Rd.R | 7466de8bde501ef019aa3ca1d926170fcfdf8306 | [] | 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 | 370 | r | make_spread_id.Rd.R | library(FinancialInstrument)
### Name: make_spread_id
### Title: Construct a primary_id for a 'spread' 'instrument' from the
### primary_ids of its members
### Aliases: make_spread_id
### ** Examples
ids <- c('VX_aug1','VX_U11')
make_spread_id(ids, format='CY')
make_spread_id(ids, format=FALSE)
make_spread_id(c("... |
61e52c8753c6171038cb6b0e3bca850f0770f119 | 6049000493ce2555976ae0c4d969762bd010e9cb | /R/utility_functions.R | 47497299dc3e20c09be458f3124a41278ea40646 | [
"MIT"
] | permissive | uit-hdl/nowaclite | 6b4ad6c57f4b2b1741b52f9e6b52618faa0bdcc7 | 9df4d60025634ab735fafc316c55a5134ce76c7f | refs/heads/master | 2021-08-07T20:56:23.509228 | 2020-05-15T08:21:07 | 2020-05-15T08:21:07 | 177,790,668 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,114 | r | utility_functions.R | #' Set up available designs for NOWAC
#'
#' @return vector with available designs
#'
#' @author Bjorn Fjukstad, \email{bjorn@cs.uit.no}
#'
#' @seealso \code{\link{selectDesign}}
#'
#' @keywords design
#'
#' @export
getDesigns <- function() {
return(c("case-control", "cross-sectional"))
}
#' Get hospital name
#'
#'... |
a2b3cb61eef9077dd17a1a0c3f4b2a8ff440ee97 | 0838e88f31f0d102fbb5503668a3c7f4124b4224 | /plot1.R | 1a988c2eb48ceffeb8fc183ce692080d12dbad01 | [] | no_license | LemonCanon/ExploratoryStatsFinal | 28c103eb2beb848275978ad142bdd5b103b8c563 | 21a41270105817a6c3e70a42c7327cc74145a28e | refs/heads/master | 2016-08-12T18:33:33.036680 | 2016-04-08T13:28:23 | 2016-04-08T13:28:23 | 55,526,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 849 | r | plot1.R | # Question 1 RP April 5, 2016
#Compair total PM2.5 between 1999, 2002, 2005, and 2008
#use Base plot system.
#set wd to dir of file to ensure files are found properly within dir
this.dir <- dirname(parent.frame(2)$ofile)
setwd(this.dir)
#test to see if pm25 exists ... |
ec2e04f9474d1d649286fcdb24cb2467cedd9aeb | da6483d1cf0b1c26f8ceb9dd6cf80f186feebff5 | /man/na_if.Rd | 45d98fdc52a3a7966a8a4c6c6d85a7c82febc941 | [] | no_license | mattdowle/expss | 6a60c8cecd651648359e187789175d76d5efd7e8 | 6ec5696737fd3edc39f5d427f9eb268bd33c1721 | refs/heads/master | 2020-04-22T09:20:12.982355 | 2018-12-11T20:13:07 | 2018-12-11T20:13:07 | 170,268,747 | 1 | 0 | null | 2019-02-12T06:59:03 | 2019-02-12T06:59:03 | null | UTF-8 | R | false | true | 1,994 | rd | na_if.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/na_if.R
\name{na_if}
\alias{na_if}
\alias{na_if<-}
\alias{\%na_if\%}
\alias{mis_val}
\alias{mis_val<-}
\alias{\%mis_val\%}
\title{Replace certain values with NA}
\usage{
na_if(x, value)
na_if(x) <- value
x \%na_if\% value
mis_val(x, value)... |
1b0ae540efaf999a21f0485a5c3053d3faafd5db | e008acb70ddefa14d4265a3582b36345c5542293 | /server.R | b2cf1ba9f8e0c25a7321088c88becb35d480af4f | [] | no_license | mingyaaa/STAI-App | 459ff58b59a96e7e81edc7e2b8523ce8cfb8e0db | a3640833fc67b6a9f41a5fb4dd32829eff4865a7 | refs/heads/master | 2020-12-24T19:05:11.790462 | 2016-05-06T00:53:09 | 2016-05-06T00:53:09 | 58,168,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,583 | r | server.R | library(shiny)
# Define server logic required
shinyServer(function(input, output){
# We create an output that will be a text with the results of the questionnaire.
output$resultAE <- renderText({
AE <<- as.numeric(input$r1)+as.numeric(input$r2)+
as.nume... |
510cd0cd0d3927067f4c00b066b027737aaa94d9 | 16e2994bdc209e7309c9876f3ea5c491b0046348 | /server.R | aad07cb4ca101b4a6c0a8c887130c68860750ffd | [] | no_license | jh668/Coursera-DS-Capstone | e56bfbe8deb40f300edd4ea45e8b342693141fc8 | 5dec6398f16da2d6f4823a86517435ceb86236e3 | refs/heads/master | 2022-11-06T22:27:27.003969 | 2020-07-14T04:33:11 | 2020-07-14T04:33:11 | 278,269,958 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 339 | r | server.R | library(shiny)
library(dplyr)
library(tidyr)
library(tm)
source("next_word_model.R")
shinyServer(function(input, output) {
output$out <- reactive({
validate(
need(input$box1, "Please type in your words in the above textbox")
)
next_word <- next_word(input$bo... |
429e17012c349a4e6b0ac10748365e4b4cb9181a | 175ec2deb5ec05b17d6fd1b97a8a00d160fcfa27 | /Eimeria_Lab_code/P3_112019_Eim_combine.R | 091ffd3bf0f3c4f9262df1489034b59ec786a7ae | [] | no_license | LubomirBednar/PhD | d26980dc3b7e755181bae3bdd3e8a236331017c6 | 3271be15b34d2f7e8c4171fae578c46a91955710 | refs/heads/master | 2021-11-26T10:01:21.070210 | 2021-10-29T09:04:19 | 2021-10-29T09:04:19 | 204,975,093 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,269 | r | P3_112019_Eim_combine.R | # P3 combining script for weight, oocysts, qPCR, RT-qPCR, ELISA and hopefully FACS
library(httr)
library(RCurl)
library(dplyr)
library(Rmisc)
# load in weight and oocysts
P3_oocyst1 <- read.csv("https://raw.githubusercontent.com/derele/Eimeria_Lab/master/data/Experiment_results/P3_112019_Eim_oocyst.csv")
P3_oocyst1$X... |
09b3c5dfabd5e78466fdb2fe8f7271d5db15457b | 6787f2313745bb168045e0c8c01347914776244d | /cachematrix.R | 94c3777a4bd46266ee0a1d0d541be2ce10744c8f | [] | no_license | neelb84/ProgrammingAssignment2 | 4e6cb827ed7b5f9b365ff24696dd53a5a2c1811e | 35fc6b34aed8dc0b2cfa24e25dc0d1c552be0a35 | refs/heads/master | 2021-01-18T03:06:40.857811 | 2015-03-22T20:01:19 | 2015-03-22T20:01:19 | 32,645,193 | 0 | 0 | null | 2015-03-21T18:46:50 | 2015-03-21T18:46:49 | null | UTF-8 | R | false | false | 2,287 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
##The couple of functions makeCacheMatrix and cacheSolve creates a matrix and cache its inverse, this is to avoid calculating the inverse of the same matrix multiple times
## instead cache the inverse matrix and call when required withou... |
5c781a6692515701711c7c044e1b4c3ae8a52424 | 36e231c2defb96012d7e75269654b43ac859d714 | /Analysis_after_BAM_Scripts/Fst_SauronPlots.R | 432424bc0fe37ffc24cc0f110ac17153a9b8c7c3 | [] | no_license | PaulKnoops/Experimental_Evolution_Sequence_Repo | 179b9b4124f19b707a604aa20d27a2b822953cc7 | 11f6af2ec5634181b11469f4a7f9cebf4e1ed5fe | refs/heads/master | 2020-03-12T16:01:25.734641 | 2018-05-04T18:51:18 | 2018-05-04T18:51:18 | 130,705,291 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,436 | r | Fst_SauronPlots.R | # Sauron Plots and quantiles:
require(tidyverse)
# Read in data:
# 115
# 1:2 = ConR1:ConR2, 1:3 = SelR1:ConR1, 3:4 = SelR1:SelR2, 2:4 = SelR2:ConR2
#CompR1 <- fread('../Data/Fst_combinedComparisons/combined_fst_1:3.csv')
#CompR2 <- fread('../Data/Fst_combinedComparisons/combined_fst_2:4.csv')
#Controls <- fread('../D... |
5b9297361230a0ce09404961a5bfa63aca6345f4 | 3282d51ed8f89ead3d9f16af1e843501f5fbe8cb | /man/fun.N_1.Rd | 8c7370f9af144f21375bff4071a5f122047231a7 | [] | no_license | cran/GMDHreg | 7d69b110f57df5e1220c007a88b6d3f0c695013b | 0104cbc52becf0515e3ea6007b77c66b625325ab | refs/heads/master | 2021-07-09T12:34:51.724176 | 2021-07-05T11:30:02 | 2021-07-05T11:30:02 | 174,552,055 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 253 | rd | fun.N_1.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fun.N_1.R
\name{fun.N_1}
\alias{fun.N_1}
\title{GMDH MIA auxiliar functions}
\usage{
fun.N_1(x, y)
}
\description{
Performs auxiliar tasks to predict.mia
}
\keyword{internal}
|
a62ac82d3ae8c278981fb45ca853bd71be75b69d | b11ff6361b702588dfd3ec40d95b6d2cb291f34d | /Desktop/Courserarprogrmming/UCI HAR Dataset/run_analysis.R | adbd13ad0b0174825ee307d3aa505d39c6d10574 | [] | no_license | kakelly49/Assignment---Tidy-UCIHAR-dataset | e988dd90214e24f60b8f3ed28cf498693d7f17cd | 88feba43b22d0533887433e6348c3efa6c490e87 | refs/heads/master | 2021-01-23T05:14:24.584330 | 2017-03-27T19:16:22 | 2017-03-27T19:16:22 | 86,287,887 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,136 | r | run_analysis.R | ## Run_analysis.R - creating a tidy version of a subset of the UCIHAR datasets
## Download and unzip files. Set directory in R to folder where files are saved
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", destfile="UCI_HAR_dataset")
unzip("UCI_HAR_da... |
ee17dd203f3d860c482c8b4fc2d5f70656b1e558 | 1aaa4bb83953b88fe67455aac703664c1ce7f7aa | /man/with_dataset.Rd | 687b3b94e66b1c9d95765afb740385e1d41071e7 | [] | no_license | rstudio/tfdatasets | 9773255c6ed697d68a42e17403426bfe96c30591 | 90c846b36b02c6ce44b87bf6d379f0dbcaece859 | refs/heads/main | 2022-07-07T02:56:17.570781 | 2022-06-30T00:09:11 | 2022-06-30T00:09:11 | 105,773,021 | 32 | 14 | null | 2022-06-30T00:07:12 | 2017-10-04T13:39:34 | R | UTF-8 | R | false | true | 985 | rd | with_dataset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dataset_iterators.R
\name{with_dataset}
\alias{with_dataset}
\title{Execute code that traverses a dataset}
\usage{
with_dataset(expr)
}
\arguments{
\item{expr}{Expression to execute}
}
\description{
Execute code that traverses a dataset
}
\de... |
4e7f41f0cfb0e0a56c4d7c6c3e79f82d81f44c7b | b3d2bedd9bbb288d7f4e8b9fdf6c2591a98beb72 | /Dashboard + feedback page (compleet).R | 69e0ae77b856a87b82ef04171ae67e8eb6dc8b79 | [] | no_license | Enzovs/Society_de_Waag | 77a9174e446d09dfea7d052155a8124dfde86509 | 21b241319765847526886bce3503e877c236058f | refs/heads/master | 2020-06-10T23:35:49.762571 | 2017-03-19T11:43:33 | 2017-03-19T11:43:33 | 75,844,964 | 2 | 0 | null | 2016-12-07T15:06:47 | 2016-12-07T14:42:40 | null | UTF-8 | R | false | false | 21,383 | r | Dashboard + feedback page (compleet).R | setwd("C:/Users/Win7/Desktop/HVA TWK/JAAR 3/Minor/Project de waag")
rm(list = ls())
load("C:/Users/Win7/Desktop/HVA TWK/JAAR 3/Minor/Project de waag/.RData")
library(lubridate)
library(shiny)
library(shinydashboard)
library(tidyr)
library(dplyr)
library(ggplot2)
library(ggmap)
library(dplyr)
library(leaflet)... |
fabafd99f4509bd361fa5d2396000bb52a0aa685 | 3dd54dec09f7d0c3cde2dae19178faf692a2b35e | /man/Hartnagel.Rd | add1eefadac73fa2794c87c86ef4300000d4d3d1 | [] | no_license | courtiol/LM2GLMM | 34c1f63d85996edbd16c3f6518750f805e6965ee | 6646de11a2309e63f7f3c2dd03483e4f0516b23a | refs/heads/master | 2022-07-22T01:39:49.159906 | 2022-06-29T05:28:23 | 2022-06-29T05:28:23 | 86,590,032 | 3 | 2 | null | 2022-06-28T17:58:13 | 2017-03-29T14:09:26 | R | UTF-8 | R | false | true | 1,754 | rd | Hartnagel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{Hartnagel}
\alias{Hartnagel}
\title{Canadian Crime-Rates Time Series (from package carData)}
\format{
This data frame contains the following columns:
\describe{
\item{year}{
1931--1968.
}
... |
b65dcf210e405ff147050a92f9519c4322542724 | f4f12d4e01d1e31a7797e11544e6f7bfb6e2e6a7 | /12th assignment/q1.R | 8b2da982b58b8b504cd54a0797b6b9f889bd335c | [] | no_license | inikhil/Monte-Carlo | 17dc44d5b0003a7c30c6fef34735e940d1850226 | e964f310d339e101d406dea3cb7e4f01c24efaeb | refs/heads/master | 2016-09-05T09:13:51.269013 | 2015-02-14T14:06:30 | 2015-02-14T14:06:30 | 30,796,687 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,112 | r | q1.R | a<-vector()
b<-vector()
x<-vector()
y<-vector()
z<-vector()
u<-vector()
convert<-function(n){
i=1
while(n!=0){
a[i]=n%%2
n=floor(n/2)
i=i+1
}
return(a)
}
radical<-function(b){
t=length(b)
sum1=0
for(i in 1:t){
sum1=sum1+b[i]*((1/2)^i)
}
return(sum1)
}
lcg<-function(a1,b1,m1,seed... |
89c7ec05a850d7972592ecc2bed6008858f169a9 | 4e9f6d4fed2a6efab4fd55d5e3ae69fcea01e435 | /plot4.R | 6c7399ab05e6c0034fc57532ea74979870303449 | [] | no_license | JosePenedes/ExData_Plotting1 | 81a56cd3a2b93842873908827ce16ddff216d129 | c8bd3ee721001bdc38c9843999028ec39487660a | refs/heads/master | 2020-12-11T07:32:11.010418 | 2014-11-06T18:19:16 | 2014-11-06T18:19:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,362 | r | plot4.R | ### read the data
library("data.table")
datos_elec <- read.table("household_power_consumption.txt", header=T, sep=";")
index_vec <- (datos_elec[,"Date"]=="1/2/2007")|(datos_elec[,"Date"]=="2/2/2007")
datos_elec <- datos_elec[index_vec,]
### plot multi-graph and save it in a .png file
png(filename = "plot4.png",width ... |
153c04d9eb2e06f854a0962781f15ecf38e170b0 | eb00ad46af18a572619229b4be25dd4b60fab314 | /R/population.R | 82a03569f2128e4300a669d202e769ab97a9de9e | [] | no_license | fransilvion/REvolution | 784894b9b35cf0a8129621f0ffc3629af609967b | 5b9a8bc2c93047be48a18ec32f6a390167e90751 | refs/heads/master | 2020-08-16T05:23:32.657524 | 2019-10-21T03:44:47 | 2019-10-21T03:44:47 | 215,460,214 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,480 | r | population.R | #' population - class definition
#'
#' For each geneartion (G_i) in the Game of Evolution
#' There is a collection of `organism` objects which make up a population
#' which are competing with one another to survive to the next generation.
#'
#' This is the class definition of a list of organisms and their associa... |
db34f6f502070efe088f0a9b01dc7c39421a4184 | cfe5552ea0ee7f47287aabd1d93ed36ebf7300ee | /plot2.R | 7d9246d3dba09d04123a22eb1aa26cea3cf98476 | [] | no_license | mohamedaref/Exploratory_Data_Analysis-course_project1 | 28c763283b8a03a979bfd1ce45c230d093e3959a | d11ffd4e72ce253d09ba4c83e6d3bbbefb852458 | refs/heads/master | 2016-09-05T11:09:02.346316 | 2015-01-10T20:59:57 | 2015-01-10T20:59:57 | 29,070,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,481 | r | plot2.R | electric_data <-
read.csv("D:/Data Science Track/Exploratory Data Analysis/Week1/exdata-data-household_power_consumption/household_power_consumption.txt",
sep=";" ,
stringsAsFactors=FALSE)
# subset the data to select the data of two dates only
target_data <- subset(ele... |
f61234ff4dddc028de2df83bafbb08eb5eb63b05 | 2eda38f279d21957a25ba8e9a7c92986d12dda27 | /NHSCOVIDResults.R | fa09879e7e8f7c44301fd9646487fb63bff07b97 | [] | no_license | VictimOfMaths/Publications | 41e5f8599d3ea0fd7d741338ef11b0450059da83 | ff842e2037cd1a1952485e0e53e2fc8d44ec6ce3 | refs/heads/master | 2023-06-23T08:14:04.880755 | 2023-06-14T09:18:48 | 2023-06-14T09:18:48 | 233,639,965 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,623 | r | NHSCOVIDResults.R | rm(list=ls())
library(tidyverse)
library(paletteer)
library(ragg)
library(extrafont)
library(scales)
library(ggtext)
library(ggrepel)
library(forcats)
library(readxl)
library(gt)
theme_custom <- function() {
theme_classic() %+replace%
theme(plot.title.position="plot", plot.caption.position="plot"... |
2841d760ab9e125a2fdaf2c472431a775a1564ca | 7e423e2e6a2fe1f9e89db4765ca44e127bcb4b9e | /R/kBET-utils.R | 5d872d929b95effd066cb4e17666dcd3cc2000e5 | [] | no_license | chitrita/kBET | a9a72f8a07e658f9488b4ea432b3c03691c1a67b | d872f8fe4e64ea850d829eb4b4836fb81e377a03 | refs/heads/master | 2020-04-10T22:52:38.050588 | 2018-09-18T10:16:21 | 2018-09-18T10:16:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,687 | r | kBET-utils.R | #' @importFrom stats pchisq pnorm
#a wrapper for kBET to fix a neighbourhood size
scan_nb <- function(x,df,batch, knn){
res <- kBET(df=df, batch=batch, k0=x, knn=knn, testSize=NULL,
heuristic=FALSE, n_repeat=10, alpha=0.05,
addTest = FALSE, plot=FALSE, verbose=FALSE, adapt=FALSE)
... |
7fc91615ab48afaaeae96521c926cf057e17a8da | f4f1cd0987ad6c7a71fc400f41ca3f22e3f6ed53 | /man/soImport2.Rd | 9ecbe2c5f74bdee64b2791e9cff7c109d419de5d | [] | no_license | jordandeherrera/prioritizationMatrix | 6ee0e89e12e281c111e2794d5565e7cccd3ab0ba | 16484b6f3a6a97e90dc3e899b57e9d77004e0f52 | refs/heads/master | 2022-01-21T03:50:31.217496 | 2019-06-25T05:55:01 | 2019-06-25T05:55:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 433 | rd | soImport2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\docType{data}
\name{soImport2}
\alias{soImport2}
\title{Sample Strategic Options - Differentiated Importance}
\format{An object of class \code{numeric} of length 6.}
\usage{
soImport2
}
\description{
An example of strategic optio... |
0f642a38ff3688adc2eb30d16aaf078a3438095a | 5b77d2ffc8d564f95927b099cc021ab9ab6d3477 | /man/addMaplibreGL.Rd | a4418dae0fbc9d30541cb2be4891581ac62495f4 | [
"MIT"
] | permissive | llongour/leaflet.maplibregl | d22ce32a02bf51086ade3db7e03a900f374bbe37 | 5bd8ccf5cd1e46aa2ed22e4fde9672ed73171bd7 | refs/heads/master | 2023-06-23T15:59:39.344317 | 2023-06-23T02:11:23 | 2023-06-23T02:11:23 | 563,372,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,377 | rd | addMaplibreGL.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maplibre.R
\name{maplibreOptions}
\alias{maplibreOptions}
\alias{addMaplibreGL}
\title{Adds a MapLibre GL layer to a Leaflet map}
\usage{
maplibreOptions(
attribution = "",
layers = NULL,
layerDefs = NULL,
opacity = 1,
position = "f... |
5c8ddf1855c6e7f12135d30e3e30add03349091e | e76322de5ff72e76fa605847242bfc12fe0e32db | /Whatsapp project/Data preparation and reading.R | a9189bdd90780eba572d05b4121c12c2ed242371 | [] | no_license | scozzarro/datascience_capstone | 0dc11aecd2e59f81c28f82be8e95fde88d47ba41 | 224927b09e876cf6c60d642a06626227e86e62ea | refs/heads/master | 2023-02-14T15:15:21.335951 | 2021-01-11T16:03:42 | 2021-01-11T16:03:42 | 265,833,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,828 | r | Data preparation and reading.R | #New Whatsapp project for text analysis
library(rwhatsapp)
library(lubridate)
library(tidyverse)
library(tidytext)
library(kableExtra)
library(knitr)
library(ggimage)
#1. Import chat ----
mychat<- rwa_read('chat_A_G.txt')
#2. Clean Data
summary(mychat)
str(mychat)
mychat<- mychat[-c(1:14),] #delete first raw with w... |
c22a5a4103a41f198f6cf98f52841701fc9fff0d | d51374463a818baaad2ddf0da01e1cca564c3d02 | /test/test_retrain.r | 563e20bc857887de486d5427cabe0f183295a1b2 | [
"MIT"
] | permissive | kumc-bmi/AKI_CDM | 99ad377c4a12f6e41bbbdc92ff14f7df65cf51a2 | a82ef4faa7ee491d745b77a991f62f5424d0fd1d | refs/heads/master | 2022-06-02T17:50:19.970746 | 2022-05-10T14:05:39 | 2022-05-10T14:05:39 | 146,350,129 | 8 | 10 | null | 2021-05-19T21:16:12 | 2018-08-27T20:26:51 | HTML | UTF-8 | R | false | false | 21,377 | r | test_retrain.r | #' ---
#' title: "Building and Validating Predictive Models for Acute Kidney Injury (AKI) using PCORnet CDM (Part II.1)"
#' author: "xing song"
#' date: "Feburary 09, 2019"
#' output: html_document
#' ---
#' ### Stage 2.2: Predictive Models Validation (Retrain)
#'
#' In this experiment, we will retrain the benchmark p... |
203232ebb69b2194b127798656f099111e7e3aec | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/rminizinc/man/StringSetDecl.Rd | 7380c1cfc34c0b828e14c0277db73fdd0ce3b587 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | true | 387 | rd | StringSetDecl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/DeclFunctions.R
\name{StringSetDecl}
\alias{StringSetDecl}
\title{set of string declaration}
\usage{
StringSetDecl(name, kind, value = NULL)
}
\arguments{
\item{name}{variable/parameter name}
\item{kind}{"var" or "par"}
\item{value}{value o... |
b4821938bb036274407890d889c029f0400c6914 | 0a8e1407476a41d1d9a27db0d8d8f22143c6d823 | /R/prep.score.files.R | b7a8bc67fa0e194d1b81481ec9195b8fee474997 | [] | no_license | cran/sumFREGAT | de8ab58682dc81871a6a0f280d3db22d738f8d25 | f0e227c90a1c8fbadf4834a564a949e5f7ac1d42 | refs/heads/master | 2022-06-29T17:49:16.481191 | 2022-06-07T07:10:13 | 2022-06-07T07:10:13 | 117,830,472 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,178 | r | prep.score.files.R | # sumFREGAT (2017-2022) Gulnara R. Svishcheva & Nadezhda M. Belonogova, ICG SB RAS
prep.score.files <- function(data, reference = 'ref1KG.MAC5.EUR_AF.RData', output.file.prefix) {
# 'CHROM', 'POS', 'ID', 'EA', 'P', 'BETA', 'EAF'
if (length(data) == 1) {
input.file <- data
if (requireNamespace("data.table", quietl... |
0e98d8651f97d71768d8657a6a0fb42d84db8162 | c750c1991c8d0ed18b174dc72f3014fd35e5bd8c | /pkgs/oce/tests/testthat/test_lisst.R | 1240f5a2de104b74985cfe2ba903b394026e6da3 | [] | no_license | vaguiar/EDAV_Project_2017 | 4b190e66fe7a6b4078cfe1b875bccd9b5a594b25 | 288ffaeec1cfdd873fe7439c0fa0c46a90a16a4f | refs/heads/base | 2021-01-23T02:39:36.272851 | 2017-05-01T23:21:03 | 2017-05-01T23:21:03 | 86,010,131 | 1 | 0 | null | 2017-05-01T23:43:04 | 2017-03-24T00:21:20 | HTML | UTF-8 | R | false | false | 1,421 | r | test_lisst.R | ## vim:textwidth=80:expandtab:shiftwidth=2:softtabstop=2
library(oce)
context("LISST")
test_that("as.lisst()", {
set.seed(1333334L)
t <- seq(0, 6, 1/15) * 3600 + as.POSIXct("2012-01-01 00:00:00", tz="UTC")
n <- length(t)
p <- 5 + sin(as.numeric(t - t[1]) / 12.4 / 3600 * 2 * pi)... |
24f50ed5f06755ea3a5f72400a68d68c1504644b | c5d2e15e3459939d040db7f64c876dfc29b0fa4b | /getdata_project/run_analysis.R | acb039156cf713a8af4a973f1520da2c81af826f | [] | no_license | heyroman/DSS_Assignments | 3307c49c1730b8b00d2a1facb829e910e203a2be | 3e1762b11e9d27b3d47406c645cd93a9a4e875a7 | refs/heads/master | 2021-07-14T03:43:37.551772 | 2016-01-07T14:15:10 | 2016-01-07T14:15:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 974 | r | run_analysis.R | library(plyr)
source('./create_subset.R')
# read train and test data in two datasets created by create_subset() function
trainset <- create_subset('./X_train.txt', './y_train.txt', './subject_train.txt')
testset <- create_subset('./X_test.txt', './y_test.txt', './subject_test.txt')
# create full dataset
fulldata <- r... |
669c4584618181bed4f1dc38b4a5d0c2e95a6c1e | 2a6b1b93b9388fb6c8f289efec52bb2f50963eb0 | /Sotkanet/tmp.R | 77af55de11c37fd29b37e4bc95eb524aa58ba2ff | [] | no_license | louhos/takomo | be80209cf3ee0f1773648d8b127219ad212ae487 | 7af1752f14821b879f80f052bebcc97ba5ff5804 | refs/heads/master | 2021-01-17T12:25:09.186161 | 2016-07-18T12:14:39 | 2016-07-18T12:14:39 | 3,610,040 | 8 | 4 | null | 2015-08-10T19:10:22 | 2012-03-03T10:49:30 | R | UTF-8 | R | false | false | 2,208 | r | tmp.R | sotkanet.indicators <- SotkanetIndicators(type = "table")
as.character(sotkanet.indicators[sotkanet.indicators$indicator == indicator, "indicator.title.fi"])
# Sort indicators by median time correlation
s <- names(sort(sapply(corlist, function (x) {median(na.omit(x))})))
#> unname(sapply(s[1:10], function (nam) {as.ch... |
5dd62cc62d1f34cd457c8d5c560e085ccf1f53de | 9f10dda88e199a456430f2141cc099db2807d645 | /tests/testthat/test_data.R | ff400561a6d1f5fb57eae145fd7f3a9bfb3d28bb | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | ldecicco-USGS/wateRuse_swuds | 056e5f6a5e0369af4a44237c92d99b4b00202a15 | feba0c21728c230e8793439bd7ee914b6ff1bac5 | refs/heads/master | 2020-05-26T00:27:27.303813 | 2019-12-16T20:27:58 | 2019-12-16T20:27:58 | 188,052,203 | 0 | 0 | null | 2019-05-22T14:10:17 | 2019-05-22T14:10:17 | null | UTF-8 | R | false | false | 785 | r | test_data.R | context("Sample Data")
test_that("Data", {
testthat::skip_on_cran()
expect_equal(1, 1)
expect_equal(ncol(swuds_sample), 154)
# Test loading:
path_to_sample <- system.file("extdata", package = "WUReview")
# Read in the water quantity table
dq <- read_swuds_quant(file.path(path_to_sample,
... |
e7b0124d39bb2a40056c1790ce410fa2d7a28803 | e23209ff19f5ad5bcc4f94b02dbd1c8c79659ed9 | /R/companies.R | e5a9e34feda1070c1e06f4e76bb9fa12bbedfb5f | [] | no_license | Pedromoisescamacho/intrinio.tcg | b18942fa7e58d8ea06203831a5dcc3b51eb32881 | ad0a3457556700a92cb64131068ad1fd958c323a | refs/heads/master | 2020-12-02T08:11:44.414611 | 2017-07-17T16:24:52 | 2017-07-17T16:24:52 | 96,784,263 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,687 | r | companies.R | #` companies i.e. Returns information for all companies covered by Intrinio. the class returned is a list composed of a table and a integer. The former is the information of all the companies and the latter is the api_credits consumed by the function. Usefull to know all the companies that we have information from.
#'... |
4edc3fa3129b04f80e29349c5c8cc08e14ad0b01 | f387c0a78caadf57b4cc783ad0ef5554fc352fb0 | /ch2vms/man/load_wc_logbook.Rd | 4706f22c73c78e3a653ecb5b887a0b6f3b324fd2 | [] | no_license | peterkuriyama/ch2vms | 9bb1285d4e6e863186e048b543bb701625254afd | 6a4b7ebabc9698472b02539ea86cef10abf630ab | refs/heads/master | 2020-07-03T14:25:13.218193 | 2017-07-05T00:09:49 | 2017-07-05T00:09:49 | 74,161,557 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 416 | rd | load_wc_logbook.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/load_wc_logbook.R
\name{load_wc_logbook}
\alias{load_wc_logbook}
\title{Load West Coast Logbook Data
Function to load West Coast Logbook data}
\usage{
load_wc_logbook()
}
\arguments{
\item{Parameter}{there aren't any}
}
\description{
Load Wes... |
e2903616b79ca4d0b5c62b5f56bb89610fa7e84d | b537f77912b851a89c5b691311ed42b8ef2d68f0 | /shinyapp/ui.R | 36425aec4fb9b55a16992ef8a258585ab80b199c | [] | no_license | lahdeaho/devdataprod-015 | 5b33ae0533bccabf50c8bbcc25e7fe696dd11228 | a90f18563cd3222a62ac5f64f6255dfef069a773 | refs/heads/master | 2020-05-31T07:49:17.187786 | 2015-06-16T21:53:35 | 2015-06-16T21:54:19 | 37,555,356 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | ui.R | library(shiny)
# Define UI for application that plots random distributions
shinyUI(pageWithSidebar(
# Application title
headerPanel("Shiny Love Calculator"),
# Sidebar with a slider input for number of observations
sidebarPanel(
p("Love calculator will calculate the love scor... |
b103dae479996be82ec17856b2d8b767a843417f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/subscore/examples/subscore.Wainer.Rd.R | aa9c5ae62472bfaa6bbb2b1e4e0334298ae001c2 | [] | 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 | 450 | r | subscore.Wainer.Rd.R | library(subscore)
### Name: subscore.Wainer
### Title: Estimating true subscores using Wainer's augmentation method
### Aliases: subscore.Wainer
### ** Examples
test.data<-data.prep(scored.data,c(3,15,15,20),
c("Algebra","Geometry","Measurement", "Math"))
subsco... |
fb91a48b6b55f4756d1f483ee7fee98e1e678640 | 447700157cab10a9ed89a364803b0ba9caced38f | /run_analysis.R | 2eabfba312fc45b1efb69a466ef2ac62fac89dfe | [] | no_license | hyhy20/Getting-and-Cleaning-data-final-project | 55b7fc23a8ac675b0328925f178c821b133de361 | a84dc65945ba8185ef4ef09f81861eaa6eacf261 | refs/heads/master | 2022-07-18T03:14:13.182139 | 2020-05-18T07:36:17 | 2020-05-18T07:36:17 | 264,830,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,201 | r | run_analysis.R | library(dplyr)
#Q1:Merges the training and the test sets to create one data set.
#Extract general files
file1 <- ("UCI HAR Dataset/features.txt")
features <- read.table(file1)
file2 <- ("UCI HAR Dataset/activity_labels.txt")
activity_label <- read.table(file2)
#Extract test files
test_file1 <- ("UCI HAR Dataset... |
d0ee0683e17942cd866224df4550d445e84479d7 | 9ee0ab61b9d870fa5237a2869b60e9e8877f8403 | /man/getRuntimePath.Rd | 746b94bf9f0898dc53ccf1f4b012544aab23fad5 | [] | no_license | AustralianAntarcticDivision/EPOC | 79390dc3456a9cbacfb64884f10cdcf5fedad393 | 778be35f146197c571bb1ebfb76eb2a629eaad70 | refs/heads/master | 2020-09-09T22:12:49.843987 | 2019-11-14T01:55:36 | 2019-11-14T01:55:36 | 221,583,790 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 778 | rd | getRuntimePath.Rd | \name{getRuntimePath}
\alias{getRuntimePath}
\title{
Universe methods
}
\description{
Return the path to the current scenarios runtime output directory with extPath appended if passed.
}
\usage{
getRuntimePath(.Object, extPath)
}
\arguments{
\item{.Object}{
Universe object
}
\item{extPath}{
optional path extension
... |
45ca2938f62aba931b7355ab784e1845c95d1eb7 | b34a4c11ca70403a24d7048c42ffdfa61d4b18c9 | /man/dalmatian.Rd | e3b657d692bdfb595aa58e95671ea3c917c1eb07 | [] | no_license | cran/dalmatian | df1365afbafff1b365828eefd3a77a52d7a59289 | fb89cfafe6c44c4b19c61fd58c11a7d633485705 | refs/heads/master | 2021-11-24T17:55:29.208495 | 2021-11-22T18:40:02 | 2021-11-22T18:40:02 | 119,381,934 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 6,067 | rd | dalmatian.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dalmatian.R, R/dalmatian_doc.R
\docType{package}
\name{dalmatian}
\alias{dalmatian}
\title{Run DGLM in \code{JAGS} via \code{rjags} or in \code{nimble}}
\usage{
dalmatian(
df,
family = "gaussian",
mean.model,
dispersion.model,
joint... |
9a87f78bcd60d88908a60c81cb81e60720d4f161 | 1c0aa99df5bfdf044f6da683dad7d3e8b354ce4c | /numerical_experiments_gwas/make_figures.R | 431b5656cf7e6e0aa86ea89d433520c61d40f254 | [] | no_license | lsn235711/transfer_knockoffs_code | 442bb4abed05247c23bda9a95af0bb4e864bea1f | 650631ae025c0ad053299bea009a4f2a359957ae | refs/heads/main | 2023-07-17T10:09:21.768844 | 2021-08-20T00:47:52 | 2021-08-20T00:47:52 | 396,407,145 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,166 | r | make_figures.R | library(tidyverse)
library(gridExtra)
snr <- 5
ifile <- "../results/summary.txt"
Summary <- read_delim(ifile, delim=" ")
methods.values <- c("Vanilla knockoffs", "Transfer knockoffs - Linear", "Transfer knockoffs - Adaptive", "Transfer knockoffs - Lasso")
methods.labels <- c("Vanilla", "Transfer - linearly re-ordered... |
160b946b409efd14422bd5443e116e1c65ce8b9a | 7b2830cfeda670fb70cb09daed4db370cc393e04 | /R/snp.bar.R | 50343d2cb6f51f68645c0d431b6f481d01979bd7 | [] | no_license | nanshanjin/script | 579d08e75cd5aa6e4bf4e558cefa581c2dc99f2e | c9fbfceca6c23ba21d908eaf91af5ec5fcdbecc3 | refs/heads/master | 2022-03-15T19:56:30.958094 | 2019-11-21T05:20:23 | 2019-11-21T05:20:23 | 53,825,691 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 515 | r | snp.bar.R | library(ggplot2)
a <- read.table("snp.type.new.txt",header=TRUE,sep="\t")
pdf("snp.bar.pdf",width=14, height = 12)
ggplot(a,aes(x=snp,y=number,fill=type),colour=c("red","yellow"))+geom_bar(stat="identity",position="dodge")+theme(axis.text.x=element_text(angle=30,hjust=1,vjust=1))+theme_bw()+theme(panel.background = ... |
978d7d1b7b249505af207516731d07b671dde267 | 666d6532dedaa93d516ae0a25a2a6680595bc33a | /R/test.600.VEME.MaC.incremental.R | 140b7ea99ec5ae346b2a1c156e84497ff0a85b64 | [] | no_license | wdelva/MiceABC | 39cdc18763af51c22669e52add212df72f122151 | 382bc3bf1100feae509fc191d34241f6b07ae44b | refs/heads/master | 2021-01-19T13:30:53.973769 | 2018-04-09T08:16:15 | 2018-04-09T08:16:15 | 88,095,477 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 13,326 | r | test.600.VEME.MaC.incremental.R | #setwd("/Users/wimdelva/Documents/MiceABC/R")
#source("simpact.wrapper.R")
#source("VEME.wrapper.R")
#source("dummy.wrapper.R")
#source("simpact.parallel.R")
#source("MaC.incremental.R")
#source("dummy.MaC.incremental.R")
#source("/user/data/gent/vsc400/vsc40070/phylo/scripts/VEME.wrapper.R")
#source("/user/data/gent/... |
1fb766b71233d12a0e55719c34d6801e7f149fed | 139d4617b8d76684364d0daeb2fc6076334bda99 | /206 project/project.R | c1e6f1c9692a4fc6afefe82dd8da4f074eec0aed | [] | no_license | robinbing/Some-analysis-work | b2ae5bc40b08bcd22d582e671b00f3a5fbe4241d | 961d6568a0020287810f9a21ba2544ba2c0e2e63 | refs/heads/master | 2021-01-10T05:12:51.823064 | 2015-12-07T00:11:03 | 2015-12-07T00:11:03 | 46,022,459 | 0 | 0 | null | null | null | null | GB18030 | R | false | false | 6,297 | r | project.R | setwd('e:\\STA206\\assignment\\project')
abalone = read.table("abalone.txt" , sep =',')
colnames(abalone) = c("sex","length","diameter","height","whole","sucked",
"viscera","shell","rings")
a = abalone
a$sex = factor(a$sex)
#
#split data
#
set.seed(12)
n = nrow(a)
index.s = sample(1:n , size = n*... |
05541487fa3372d866c05634202f2a6ab54412b9 | 77dc8c2ecfa4bb7ac0c2ad028a23257a9c8b9aba | /cachematrix.R | 79decaf4d00ebd5487c7a03f57941facb286366d | [] | no_license | 1nger/ProgrammingAssignment2 | 89962e54a5929ba22e4e27e4019bb6fdca581131 | f3d4908f3d697a2475eea46469e70359f6e652fb | refs/heads/master | 2021-01-18T05:14:55.510947 | 2014-08-15T23:01:49 | 2014-08-15T23:01:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,577 | r | cachematrix.R | # R-Programming Assignment 2:
# This set of two functions can store the values of a matrix and its inverse (if the matrix is invertible)
# in the global environment, and can then later retrieve the inverse from there, rather than calculating it again.
# 1. makeCacheMatrix: This function returns a special list, whic... |
bffe0e77e10ece71f62d25b08a86c49e21e620f4 | 1b73390902afc90781b72e7cf49f08878ddd4a08 | /R/convert.r | c2d5001a041e4996e900c044dea47d351843297c | [] | no_license | skranz/RTutor2 | cf018a114a724a666672399e73b8da38c58bd522 | cb19b69a6d11d2f957dad72df56a52680c2ff353 | refs/heads/master | 2021-01-17T13:24:57.440277 | 2017-10-07T01:51:22 | 2017-10-07T01:51:22 | 51,748,016 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,104 | r | convert.r | example.convert = function() {
setwd("D:/libraries/RTutor2/examples/auction")
source.file = "auction_old_sol.Rmd"
dest.file = "auction_new_sol.Rmd"
convert.sol.file(source.file, dest.file)
}
convert.sol.file= function(source.file, dest.file) {
txt = readLines(source.file)
new = convert.sol.rmd(txt)
wr... |
38128beb7dd31a56ba21167d7099c6c3f8ce1511 | cb2b7b7c748d197a127306920a48aa6551afe659 | /R/browse.R | a47aca604b7940a1dcf8475af7113f8aa507bcbc | [] | no_license | ColinFay/debugin | 2ee559a23c1714b898a812e77d388aadbd36a590 | 045e168f3f0b26dc7064a799193165fe7a90c87d | refs/heads/master | 2020-03-15T08:19:14.793987 | 2018-06-07T12:37:35 | 2018-06-07T12:37:35 | 132,048,062 | 8 | 0 | null | 2018-05-17T15:51:14 | 2018-05-03T20:50:52 | R | UTF-8 | R | false | false | 485 | r | browse.R | unbrowse <- function(){
a <- rstudioapi::getSourceEditorContext()
a$selection[[1]]$text <- gsub('[^#]{1}browser\\(\\)', '#browser()', a$selection[[1]]$text)
rstudioapi::insertText(location = a$selection[[1]]$range, a$selection[[1]]$text)
}
browse <- function(){
a <- rstudioapi::getSourceEditorContext()
a$sel... |
74fbb6714f77ac9c52b507e5efed0734a48bb77d | 7b92bf61c77a88c86c0c42a61d8effa32a155c53 | /man/cmhc_snapshot_params.Rd | 4cf1863204947d419fcf563de68fd5f061498221 | [
"MIT"
] | permissive | cgauvi/cmhc | 74a473a80682fc1431a24cae17a81602f6c8b8de | 0d68fe8647f3dfbade6bab0c21ad306a2e83affc | refs/heads/master | 2022-02-24T14:37:12.741330 | 2019-10-04T06:09:07 | 2019-10-04T06:09:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 581 | rd | cmhc_snapshot_params.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cmhc.R
\name{cmhc_snapshot_params}
\alias{cmhc_snapshot_params}
\title{Parameters for time series}
\usage{
cmhc_snapshot_params(table_id = "2.2.12", geography_id = 2410,
geography_type = 3, breakdown_geography_type = "CSD",
filter = list(... |
2fea7346d4b51b47f4015c401d4d7cd17e2c4b04 | 374145bc8c431c356e6543bb5965bee672b1c69c | /Internet privacy poll.R | 38c8ad51d00ea7c539db744aa788f0c3aa4592c8 | [] | no_license | pareekrachit/AnalyticsEdge | 379bb85803701f08a941b32f5d0bd766ffc20c0e | 3962efce757bf7dcfeb8583b7e4c77a1a17f05be | refs/heads/master | 2021-01-19T02:24:52.038887 | 2017-07-14T15:26:52 | 2017-07-14T15:26:52 | 87,275,096 | 0 | 0 | null | 2017-04-16T15:14:58 | 2017-04-05T06:38:51 | R | UTF-8 | R | false | false | 2,577 | r | Internet privacy poll.R | poll <- AnonymityPoll
#Number of people with smartphones
str(poll)
summary(poll)
sum(poll$Smartphone, na.rm = TRUE)
table(poll$Smartphone)
#States in the Midwest census region
table(poll$State, poll$Region == 'Midwest')
#State in the South census region with the largest number of interviewees
sort(table(po... |
14e19ac6f053444bf7a99deb411f903c01b715b1 | 37538b0e1aca52e57f5fb7abc489563a70ae6083 | /Code/E Commerce Dataset R.R | 739b5906cc0cbfb30a6c2945d21945916c26e98a | [] | no_license | pbagchi-DA/CIND_820-Big_Data_Analytics_Project | d959adc7bad57402e73273ffed441ef82f784dda | 35c6c2cb8d333586bb521e3c1793a54e8a9ed553 | refs/heads/main | 2023-06-27T23:40:07.673481 | 2021-06-08T04:01:43 | 2021-06-08T04:01:43 | 374,528,538 | 0 | 0 | null | 2021-07-06T00:10:14 | 2021-06-07T03:57:58 | R | UTF-8 | R | false | false | 3,758 | r | E Commerce Dataset R.R | library(readr)
library(tidyr)
library(tidyverse)
library(readxl)
library(reshape2)
Data <- read_xlsx("D:/Data Analytics, Big Data, and Predictive Analytics Certificate/CIND 820 DA0 - Big Data Analytics Project - P2021/Data/E Commerce Dataset.xlsx",
sheet = "E Comm",
col_nam... |
6bdda2f37e636f804fbf4338ee67451cf2e8a084 | a93540a5b40e337da3140dc8f241e3e0632ac641 | /man/tab2array.Rd | bc4ddb9596f4c36accecee18395c194c0dbdc683 | [] | no_license | Csun1992/algstat | 3819aecfe3abf775a858a5211e7e3bd4f5999656 | c4b42c6f1753ae8c936104de1898d2dbeff655f3 | refs/heads/master | 2020-04-22T13:43:49.461945 | 2019-02-12T20:38:56 | 2019-02-12T20:38:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 436 | rd | tab2array.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tab2array.R
\name{tab2array}
\alias{tab2array}
\title{Table to array conversion}
\usage{
tab2array(tab)
}
\arguments{
\item{tab}{a table}
}
\value{
an array
}
\description{
Convert a table into an array.
}
\examples{
data(handy)
handy
tab2ar... |
8b0487ccdcae3a0043231d070ae4002f786728e4 | 05df63a03336e8d7c5738c797f5969ff16f8f8c1 | /TP3/src/1_Classifieur_Euclidien.R | 85caaad427ffa280a39af08dd021d8b9328eae30 | [] | no_license | raphaelhamonnais/UTC_SY09_DataMining | a1fead6eb84aa2719a65688c599d0f6a407eee3e | 3816a16a32c7e5c9be9cc20e940bba65cb82cfa3 | refs/heads/master | 2021-03-22T04:20:04.637177 | 2017-06-21T15:46:47 | 2017-06-21T15:46:47 | 84,962,362 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,947 | r | 1_Classifieur_Euclidien.R | library(mclust)
library(xtable)
source("src/fonctions-tp3/distXY.R")
source("src/fonctions-tp3/front.ceuc.R")
source("src/fonctions-tp3/front.kppv.R")
source("src/fonctions-tp3/separ1.R")
source("src/fonctions-tp3/separ2.R")
source("src/Fonctions_Euclidien.R")
source("src/Fonctions_Utilities.R")
appData = read.csv("da... |
a3ac0ed2e40c19b469e66b7a06c6940f32f09e55 | a2b954f11b63717f7608fb900f31f83309ce4490 | /man/RSRC_estimators.Rd | 82d27bfc8cf1eb745fe568a1ddce56df9e514aa3 | [] | no_license | MeasurementErrorMethods/RRCME | 69afa65b4d48d50f70dd9c1d0f91de8ca901da8a | 20896d507f692a2fe4b0707ac2467864e3fa0749 | refs/heads/master | 2022-01-16T06:40:21.417064 | 2019-07-04T15:30:56 | 2019-07-04T15:30:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 907 | rd | RSRC_estimators.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RSRC_estimators.R
\name{FitRSRCModel}
\alias{FitRSRCModel}
\title{Calculates regression calibration estimates}
\usage{
FitRSRCModel(valid_dat, full_dat, sampling_type, beta_x_start,
beta_z_start)
}
\arguments{
\item{valid_dat}{Validation su... |
18ab9622ab2a33a5536233b1e4ff327500e64006 | d226838e64a1d55fdaf797893f7468651b725183 | /R/IlluminaFastqDataFile.R | 63e5a95f5da9a1756ded867300526b77a4f81f3e | [] | no_license | HenrikBengtsson/aroma.seq | 5fd673cc449d9c3b89daf1125e8cc95556d0641d | 6464f1e5e929c423978cf7dcb11ac7018d179a6d | refs/heads/master | 2021-06-21T13:53:21.618898 | 2021-02-10T02:57:15 | 2021-02-10T02:57:15 | 20,848,327 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 6,287 | r | IlluminaFastqDataFile.R | ###########################################################################/**
# @RdocClass IlluminaFastqDataFile
#
# @title "The abstract IlluminaFastqDataFile class"
#
# \description{
# @classhierarchy
#
# A IlluminaFastqDataFile object represents a FASTQ data file.
# }
#
# @synopsis
#
# \arguments{
# \item{...}{... |
cd6f1d49257e77363c0eaf9318f3985b7a8f4118 | 94b98e593d1f10c964e13fea8eb63fb7a49162aa | /010GDP2000_2018/moran.R | 08d0768cf09eb58abbcd2b208340b709d1088700 | [] | no_license | DWB1115/RDemo | 8dda2954baa2ba10047d8b6fc2dc39e6888a274c | ef5de115299b54d8061af93a5ad31542980dbb2d | refs/heads/master | 2023-02-03T21:34:50.834402 | 2020-12-18T07:04:35 | 2020-12-18T07:04:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,529 | r | moran.R | library(spdep)
library(rgdal)
w = read.gal("D:\\workspace\\DevWork\\github\\RDemo_gitee\\010GDP2000_2018\\data\\cngdp2000_2018_queen.gal",
override.id=TRUE)
w2 = read.gal("D:\\workspace\\DevWork\\github\\RDemo_gitee\\010GDP2000_2018\\data\\cngdp2000_2018_area7.gal",
override.id=TRUE)
cngdp <-... |
96310156e69780497423fa3a32102289912580de | 59c349f28498e89ab794f0c0f8e07dcb1e13042e | /bin/functions.R | 043936dc8bf09b14d9a07e50f87f25db6420f5ff | [] | no_license | ComunidadBioInfo/minicurso_abr_2021 | 87c4031110848a3b53d5f27492fcc727f1263fec | 4bfde7be836ce06dd862c84f0d11dd4f1c854943 | refs/heads/main | 2023-09-02T04:38:32.173607 | 2021-11-19T04:24:04 | 2021-11-19T04:24:04 | 328,829,455 | 3 | 8 | null | 2021-04-15T13:00:36 | 2021-01-12T00:39:39 | HTML | UTF-8 | R | false | false | 3,742 | r | functions.R | DEGResults <- function(qlf) {
##This function returns a dataframe with all DEG
##qlf: Object obatined from the generalized linear model
qlf <- topTags(qlf, n = Inf)
qlf <- as.data.frame(qlf)
return(qlf)
}
volcanoplotR <- function(dge.obj, logfc, p.adj, type) {
##This function adds a new column (T or F) ac... |
a8a0e6a5c670469c6034bce95ecd7d7bc79fb63d | e6e94a80958cd88d414e821df32b9258a279a7bd | /workout02-richard-jin/ui.R | 3a8b2062c5dbb49b81cade826826cff7df4b88fe | [] | no_license | richardjin0823/workout02-richard-jin | 0ee61bbf9c7b8d23982eaf2b2e373c612a55f8b4 | edc53f2507f3f49d080333dfa386b6677ae01f4d | refs/heads/master | 2020-05-14T12:14:51.308051 | 2019-04-17T19:38:38 | 2019-04-17T19:38:38 | 181,790,989 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,053 | r | ui.R | #
# This is the user-interface definition of a Shiny web application. You can
# run the application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
library(yarrr)
library(DT)
# Define UI for application tha... |
46e05d1a4036e7b15f68b066b0848ab84d20d65a | a8bd4a8d687b7a923bc82763a9d2e84a3235b186 | /man/glasso.complex.Rd | ef5bbe3c5fa0791b25569fd842768658f760f9f6 | [] | no_license | crbaek/lwglasso | 08e08c9d6091f5a7839ad50b7df36293c56c264f | 0be595bb514c498b3cd7028eeaee2a0b195b45b1 | refs/heads/master | 2022-12-06T03:51:29.511529 | 2022-12-01T03:46:49 | 2022-12-01T03:46:49 | 266,767,907 | 0 | 2 | null | null | null | null | UTF-8 | R | false | true | 1,543 | rd | glasso.complex.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mLRDglasso.R
\name{glasso.complex}
\alias{glasso.complex}
\title{Sparse estimation of inverse G using graphical lasso.}
\usage{
glasso.complex(
Ghat,
Tt,
lambda = "ebic",
type = "soft",
gridTF = TRUE,
gg = 1,
bound = c(0.05, 1),... |
71e05c8b56904539fa7ec4c5f36bd401a12d5d4e | a4ef53e8d087ce99848c7c4960af09927e0a56cf | /Metodos remuestreo/k-fold cross validation with caret.R | 0dfad35e2487e648f9f83e26e5756c480dcfd3f6 | [] | no_license | fhernanb/modelos-predictivos | 0ba16c309a1f726e9e8ca36d37fe7add3a7d0cb5 | 909d65c40ba666fd473b98cb6610b698eff87aad | refs/heads/master | 2023-02-19T05:57:33.799881 | 2021-01-22T12:46:22 | 2021-01-22T12:46:22 | 296,086,556 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,285 | r | k-fold cross validation with caret.R | # -------------------------------------------------------------------------
# En este ejemplo vamos a utilizar a aplicar k-fold cross validation
# usando la base de datos Auto de ISLR.
# Lo vamos a realizar de forma manual y automatica
# y vamos a crear un lm para explicar mpg en funcion de
# horsepower y de horsepow... |
26538817af048748908a118ceded2b76d55a51cb | 1d9fb87476c16af7ff610ae8c753c23e0fcf6c6c | /man/readEdges.Rd | 710de5ab60c44af0e84a3192b50f531374405be4 | [
"MIT"
] | permissive | hartmado/diveR | 8b3691f261427d86d0e33816dc9e73a1165f489d | 1100d0c54914c995d51a0fb3d7ed85240bb1e8f4 | refs/heads/master | 2021-01-17T08:33:31.227873 | 2015-09-16T18:44:40 | 2015-09-16T18:44:40 | 42,570,044 | 0 | 0 | null | 2015-09-16T06:49:17 | 2015-09-16T06:49:17 | null | UTF-8 | R | false | false | 755 | rd | readEdges.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/diversity.R
\name{readEdges}
\alias{readEdges}
\title{A procedure to read a list of edges from a file}
\usage{
readEdges(path, sepr, we = TRUE)
}
\arguments{
\item{path}{A string representing the path to a file. Rows and columns are d... |
a95c40322687838ff420654482f6f68f3afe067c | 7e1820d2fee01745b84deadeddcdb1593b035323 | /R-CapstoneExtra.R | 584bf6b1b680ebaa8e5048f7d7da17f32f836eda | [] | no_license | sagarnil1989/Template | 68ec90b5cc182faec2ec1e5ff787d02c56daab39 | 546ea809b5b6e7ff3401827b020b5c2b592deab6 | refs/heads/master | 2020-05-26T05:28:58.549356 | 2019-06-10T09:10:45 | 2019-06-10T09:10:45 | 188,121,587 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,719 | r | R-CapstoneExtra.R | #row-wise-check
row_na_val=(rowSums(is.na(dataset))
na_finder_columnwise(sagar)
#removing uniquie identifiers
dataset=select(dataset,-Customer_ID)
na_value_perecent=colSums(is.na(dataset))/prod(dim(dataset)[1])
col_na_val... |
9605119aace4b4c789b6e63e717d66a0236c74db | 217302b0f1e46c37a3d8506c0b4a61e4eae16d1e | /tidy_data.r | 107a2c1e5cbf0efb963643b4cab576d79da3e06d | [] | no_license | mattymo18/NFL_DRAFT | c472a8dca5fb801b62bee810321b2c81fed723c8 | f8ade147d9d485ca5aa9adab8b665d53bc8716f0 | refs/heads/master | 2023-03-14T08:40:56.839759 | 2021-02-23T17:57:16 | 2021-02-23T17:57:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,808 | r | tidy_data.r | library(tidyverse)
Draft <- read.csv("source_data/draft.csv")
Combine <- read.csv("source_data/combine.csv")
#I'm thinking i'm going to use only data from 2000 on. I think these observations will be more complete. Also since the game has changed so much, this sort of analysis is only really relevant in the modern era... |
0097fefd73baa0d91791baf89ec453fc12f99a31 | 43f6d2a89e611f49d1bff870e6381fa182184ce2 | /man/sfunc.Rd | a40af396a6301dc0c0ffd4363db625c8ed8e701d | [] | no_license | pedroreys/BioGeoBEARS | 23bab5299c44b4cfa2ab0e9dbe0de4ecf2196f69 | 9aef25ebf57b854e6f02d5a3a2ca420e31833123 | refs/heads/master | 2021-01-17T17:12:08.479890 | 2013-08-04T02:35:28 | 2013-08-04T02:35:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,414 | rd | sfunc.Rd | \name{sfunc}
\alias{sfunc}
\title{Extract the appropriate probability for a subset speciation event, given text code for rangesize of smaller descendant, and ancestor}
\usage{
sfunc(charcell, relprob_subsets_matrix)
}
\arguments{
\item{charcell}{The text in the cell, indicating the type
of speciation/cladogenesis... |
ea23e1dcc344d953a8df00ed446673960fb9b923 | 29585dff702209dd446c0ab52ceea046c58e384e | /Sim.DiffProc/R/OU.R | 980377ad225004e4230fc0cd5a56128839a2d584 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,922 | r | OU.R | ## Fri Mar 07 18:39:01 2014
## Original file Copyright © 2016 A.C. Guidoum, K. Boukhetala
## This file is part of the R package Sim.DiffProc
## Department of Probabilities & Statistics
## Faculty of Mathematics
## University of Science and Technology Houari Boumediene
## BP 32 El-Alia, U.S.T.H.B, Algiers
## Alge... |
0ed001d3330cab23ee196d08b28794477b36ec27 | 787726956d728f95d4c2c78e6c502641fab4ccb9 | /Problem_1.R | b2d818af55fee07e5c27022e5d8165d223889d6c | [] | no_license | boyko/econometrics2018-de-G2 | b9a4067d86b80aa2fba17177af0628b37c22bb6a | 601dfd19778173099e9a5b895a19bbe263da30f4 | refs/heads/master | 2021-04-06T01:23:44.363762 | 2018-03-19T08:41:31 | 2018-03-19T08:41:31 | 124,431,051 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,547 | r | Problem_1.R | # Installieren von R Packeten, die in diesem Programm benutzt werden.
# install.packages(c('tidyr', 'dplyr', 'ggplot2'))
library(tidyr)
library(dplyr)
library(ggplot2)
# Ergebnisse einlesen (muessen vorher heruntergeladen werden)
# Falls Fehler eintreten, checken Sie ihren aktuellen Arbeitsordner von R,
# indem Sie d... |
989c356edcdab83e2132307016416b69ec63cc29 | 67035cb9274eb660ac6836e5f13c9167e105255c | /man/pil_fb.Rd | 014a5f8cbd7fc79530376b28bce7f8262b924ec7 | [
"MIT"
] | permissive | SvenGastauer/KRMr | 39b941f9e3d3e40d28edadf98c14c168ab266b6a | f29ee41a7b55035cd43d0c4e632808e73e348091 | refs/heads/main | 2023-08-23T13:30:32.863360 | 2023-07-25T10:07:42 | 2023-07-25T10:07:42 | 602,591,946 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 578 | rd | pil_fb.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pil_fb-data.R
\docType{data}
\name{pil_fb}
\alias{pil_fb}
\title{Sardine Fish body Shape}
\format{
data.frame
}
\source{
https://www.fisheries.noaa.gov/data-tools/krm-model
}
\usage{
data(pil_fb)
}
\description{
Example Shapes of the fish bod... |
1ec14dc79fdd78df74e714afbb7d326213635590 | 01e6f98609708ebdfd6d1db5fda9cb443f9f7856 | /man/date_month_factor.Rd | 7dc8d36aae782d67002e66884ac6738f59b3efd3 | [
"MIT"
] | permissive | isabella232/clock-2 | 3258459fe4fc5697ce4fb8b54d773c5d17cd4a71 | 1770a69af374bd654438a1d2fa8bdad3b6a479e4 | refs/heads/master | 2023-07-18T16:09:11.571297 | 2021-07-22T19:18:14 | 2021-07-22T19:18:14 | 404,323,315 | 0 | 0 | NOASSERTION | 2021-09-08T13:28:17 | 2021-09-08T11:34:49 | null | UTF-8 | R | false | true | 1,364 | rd | date_month_factor.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/date.R
\name{date_month_factor}
\alias{date_month_factor}
\title{Convert a date or date-time to an ordered factor of month names}
\usage{
date_month_factor(x, ..., labels = "en", abbreviate = FALSE)
}
\arguments{
\item{x}{\verb{[Date / POSIXc... |
cfe3aa1ad763925059ad3c599f71e7a44994fb43 | 747b971f1a7e4dc3232fe9671255536b6c03c2a8 | /differential_analysis_example_scripts/process_E-MTAB-5697_microarray_5-time-points_3-replicates_1-dose.R | b072524e0a24ee34be0d5012b37c3ed30ab7c56f | [
"MIT"
] | permissive | wsj-7416/MechSpy | 1b58f9edbd2c7223b461b65d265006a2c2aa7fdb | 0450321d053ece3e3cbfbd49087799fc9f5a4d00 | refs/heads/master | 2022-11-09T23:10:27.777537 | 2020-06-23T17:09:04 | 2020-06-23T17:09:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,730 | r | process_E-MTAB-5697_microarray_5-time-points_3-replicates_1-dose.R | # To automate this:
# - Pass targetsdir as variable
# - Read the chip type directly from the Attributes file (and install the library as needed)
# - Come up with the control/treatment variable names from the Attributes file
library(limma)
library(affy)
library(annotate)
library(hgu133plus2.db)
library(stringr)
signif... |
f9a22fc72b84959074d7fe0271db631613132c95 | e124baf55e52643da84ab6d969b0349ed591248a | /chaetog_functions.R | fa7e84a2b10e58b6481307cf0d96a2610f7064b4 | [
"MIT"
] | permissive | RetelC/PDra_Phylogeography | 9c30b08fb8c251752b7ddccee90c9cdba1931122 | 7393f95035b2ad94d7894f3657d89b6266011413 | refs/heads/master | 2016-09-08T01:25:19.364787 | 2015-09-23T09:05:38 | 2015-09-23T09:05:38 | 41,354,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,755 | r | chaetog_functions.R | ################################################################
### Functions created during phylogeographic analysis of ###
### Pterosagitta draco along a basin-scale Atlantic transect ###
### Cas Retel MSc ###
### casretel@gmail.com ... |
22f179d1043b289ec85a77e702db64d6a1520b03 | 4f7cc757e1fb115e34b3e7b42f7573f9c4e99cfa | /scripts/get_discussions.R | 7744b3e953578ea660777abbb96492b78728dd1f | [] | no_license | mcc-apsis/UKParliamentaryCoalDiscourse | ff7ee96f0c16164729d8e83fa0688471b4819f9d | 715e9083be259494e564864666b3271d310e0172 | refs/heads/master | 2020-03-10T06:19:49.656066 | 2018-04-12T12:09:17 | 2018-04-12T12:09:17 | 129,237,341 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,906 | r | get_discussions.R | # Get path to file containing the discussions
v_discussions_file <- ifelse(u_discussions_file == "", paste0("../data/data_discussions_", u_searchTerm, ".RData"), u_discussions_file)
if (file.exists(v_discussions_file)) {
cat("Loading existing discussion file...")
load(v_discussions_file)
} else {
cat("Scraping H... |
25b8831e09b7ecfcb366fb0101e981fe2c94e63d | 180e7c14af8ef1e7809cfe95bfe0667ad72b9f02 | /plot1.R | 4f5890a5f0d982cdc022652d321b9112457c562f | [] | no_license | annrcannon/EDA-course | 7169903478fda7fdfb184b8990e0829b203b1b66 | 309d96b2daed511e02049b955a9d9453f15fe0df | refs/heads/main | 2023-02-23T23:26:56.825945 | 2021-01-28T02:59:48 | 2021-01-28T02:59:48 | 333,580,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 486 | r | plot1.R | #Read in the data
electric <- read.table("household_power_consumption.txt", sep=";", header=TRUE)
#Create the subset of interest
elec.sub <- electric[electric$Date == "1/2/2007"| electric$Date == "2/2/2007",]
#clean data
elec.sub$Global_active_power <- as.numeric(elec.sub$Global_active_power)
#Make graph
png(file... |
5e32daeb197f0c23d95423e917059a51c3a5932d | 4b5cecbbf56fa2704f2d24c9255adf3dd4df6ff6 | /AppliedDataMining/AppliedDataMining/HW2/2.1/sb2.R | ed89d6abc3d93b59ea3e094af5940c0bf18273ec | [] | no_license | keithhickman08/IUH | fa2a8c50eb4ab86f3ea10081a18620e27dc8f021 | 20bc22bdef9523310e1e1b9b6225e6a3eb039d20 | refs/heads/master | 2020-04-02T07:40:22.310044 | 2019-01-04T16:16:45 | 2019-01-04T16:16:45 | 154,208,374 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36 | r | sb2.R | summary(mydata)
sum(is.na(mydata))
|
294ae4c856be19214cd1c71f858afd5cf0da6278 | 14e19c36dba435df0a75158e14f7d00a0fa1096d | /man/composite-track.Rd | 8433dd652491dc56154b950f42bac959153834a5 | [] | no_license | Marlin-Na/TnT | 2fc227de7cae7266886104d6009b0b36add6f2b2 | 386cf9fc3c53ab861c954b73b47b3c83be35ea89 | refs/heads/master | 2021-01-11T20:36:04.356306 | 2020-01-30T07:08:50 | 2020-01-30T07:08:50 | 79,152,363 | 16 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,183 | rd | composite-track.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tntTracks-compositeTrack.R
\name{composite-track}
\alias{composite-track}
\alias{merge-track}
\alias{merge,TnTTrack,TnTTrack-method}
\alias{merge,TnTTrack,missing-method}
\title{Composite Track}
\usage{
\S4method{merge}{TnTTrack,TnTTrack}(x, ... |
2e86d71809765675ce12b2b0d9af422eb9e7045d | d62421e292d672e6cc430db51211eaa778e5bc07 | /Forecasting assignment Rcode-2.R | 00c582db15d5cfed96e2cc59d27856cb8fbfd32d | [] | no_license | sainath7276331303/sainath | e25aa0d172067bb2aebaff84e7d5652168adac59 | 518ca085bd4f738901d511b1dc4644a583d83059 | refs/heads/master | 2021-03-10T20:06:12.529471 | 2020-08-30T08:40:16 | 2020-08-30T08:40:16 | 246,482,240 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,828 | r | Forecasting assignment Rcode-2.R | library(readxl)
cocacola <- read_excel("F:/Excelr/Datasets/CocaCola_Sales_Rawdata.xlsx")
View(cocacola)
plot(cocacola$Sales,type = "o")
plot(log(cocacola$Sales),type= "o")
summary(cocacola)
# creating 12 dummy variables for months
x<-data.frame(outer(rep(month.abb,length = 42), month.abb,"==") + 0 )
View(x)
co... |
d3a3f91b80cb6dd8b05a6a2175a4f14a06007f1b | 465543cd0db7a47e3a05df3b2062fb34a025d230 | /climbr/tests/testthat/test-logbooks.R | 9ca44ce23385bd491a5c849def2e18a1730fa2d3 | [
"Apache-2.0"
] | permissive | yustiks/climbing_ratings | 22a4d0febb62de11b6a159f881ee075bf30e430b | 5768c542cac6b3e1559c78bded751a8c3213c2fb | refs/heads/main | 2023-06-13T08:30:42.122873 | 2021-07-03T02:42:55 | 2021-07-03T02:42:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,184 | r | test-logbooks.R | # Unit tests for logbooks.R
# Copyright Contributors to the Climbing Ratings project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... |
a2e9f939bbf60dbc9d931891e183cc8f85a82407 | 76f3689edf32fc688963c27477f91cd8641b312e | /man/getPars.Rd | f1295fe561e4c92d9a5fda9e1d277a74a46be81d | [] | no_license | ppernot/msAnaLib | c062dcf8056bab529081d26af531ef0eb65f75dd | 289a35c10e8570132c489a68e77b0a01c6002dcc | refs/heads/main | 2023-04-17T03:48:06.755794 | 2023-02-16T14:30:46 | 2023-02-16T14:30:46 | 355,917,779 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 907 | rd | getPars.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getPars.R
\name{getPars}
\alias{getPars}
\title{Get peak fit parameters}
\usage{
getPars(res, dimfit = c(0, 1, 2))
}
\arguments{
\item{res}{(nls-object) a nls fit result}
\item{dimfit}{(integer) dimension of the fit}
}
\value{
A list of best... |
74f1eeff375a6121ae62ccc86e0c1cc8773bbb89 | 7b0a3fd64e5e063cab834bc7f8edd91676955705 | /cachematrix.R | e0d0750c934882422f3dc9fade5db23c3f17e1f0 | [] | no_license | starknguyen/ProgrammingAssignment2 | 717854deb5aa728bbcf885a68a3e387f40585da3 | cb2fa1071fc3a5acb0be131974c10e3dbc5af525 | refs/heads/master | 2023-08-30T14:34:06.477220 | 2021-11-08T18:39:32 | 2021-11-08T18:59:05 | 425,949,325 | 0 | 0 | null | 2021-11-08T18:25:23 | 2021-11-08T18:25:21 | null | UTF-8 | R | false | false | 1,036 | r | cachematrix.R | ## Put comments here that give an overall description of what your
## functions do
## Write a short comment describing this function
# Create a matrix that cached its inverse
makeCacheMatrix <- function(x = matrix()) {
inv_matrix <- NULL
set <- function(y) {
x <<- y
inv_matrix <<- NULL
}
get <- funct... |
4661f191444284e9a1428a4132102a3a5283c064 | ba68b797f283557f3da60016c27382fb3c14b0b4 | /R/SWORD.R | af3d4c02d214fb4461b0cd62a75cc58c0a395eeb | [] | no_license | thehyve/dataverse-client-r | e6db5a6ee621ac7dd5a0e1dc52c2288f2e669ed3 | b354b2457be8ea5174d5817b1661eab63e445f55 | refs/heads/master | 2021-01-16T22:45:58.038293 | 2016-02-18T14:27:54 | 2016-02-18T14:54:39 | 52,015,956 | 0 | 0 | null | 2016-02-18T15:11:38 | 2016-02-18T15:11:38 | null | UTF-8 | R | false | false | 10,013 | r | SWORD.R |
# print.dataverse_dataset_atom <- function(x, ...) {}
parse_atom <- function(xml){
xmllist <- XML::xmlToList(xml)
links <- lapply(xmllist[names(xmllist) == "link"], function(x) as.vector(x[1]))
links <- setNames(links, sapply(xmllist[names(xmllist) == "link"], `[`, 2))
xmlout <- list(id = xmllist$id,
... |
18abb253677aa2a5dbc8bfa62bc939914a1bef75 | 7520b70d92bf85143fa8b50cbb6bab39a100ada4 | /util/scripts/Utils.R | 6e65577b755bb331ca2cb16a491419cadfe1889d | [] | no_license | mscrawford/IBC-grass | a57138df15a53438de57b4f0a4cd9f72788684b9 | f57e88b23c551f4c6cf68285bb0662f45edbcc5f | refs/heads/master | 2021-01-17T02:57:20.688048 | 2019-06-18T18:06:42 | 2019-06-18T18:06:42 | 27,534,454 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,317 | r | Utils.R | library(data.table)
read_data <- function(data_dir, file_type, verbose = FALSE) {
main_dir = getwd()
setwd(data_dir)
files <- list.files(full.names = T)
files <- files[which(grepl(file_type, files))]
d <- rbindlist(lapply(files,
FUN = function(files) {
... |
7e5caeb90e3369a2038bcd43bea958e763af782c | fad36ba43fc642cc4f05a37ff41a2bfdeb13ff6b | /R/in_silico_pcr.R | 1738390b3a86c79c2f0ec4e096e4e9b370164ca1 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | ctb/metacoder | fd7f4830189515bacd5101a35621e4087018f576 | f1203b17a0f90cb928cf176d040344fdfbbd0971 | refs/heads/master | 2022-06-04T09:11:45.810079 | 2017-08-22T20:14:49 | 2017-08-22T20:14:49 | 103,031,498 | 1 | 0 | null | 2017-09-10T13:29:41 | 2017-09-10T13:29:41 | null | UTF-8 | R | false | false | 12,124 | r | in_silico_pcr.R | #===================================================================================================
#' Execute EMBOSS Primerseach
#'
#' @param seq_path A character vector of length 1. The path to the fasta file containing reference
#' sequences to search for primer matches in.
#' @param primer_path A character vect... |
65b5b3b48f0389bcc674ba9f012910baf48a3885 | 237bcbdc6b09c57b251191471359eeefb8014410 | /image_analysis/MASTER R CODES/old versions/AUTO analysis Gquad HIST RUN new S6.r | a68073facf0ea4257420d47127a6cddebf4bd9ca | [] | no_license | achalneupane/rcodes | d2055b03ca70fcd687440e6262037507407ec7a5 | 98cbc1b65d85bbb6913eeffad62ad15ab9d2451a | refs/heads/master | 2022-10-02T20:35:18.444003 | 2022-09-09T20:53:03 | 2022-09-09T20:53:03 | 106,714,514 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 64,805 | r | AUTO analysis Gquad HIST RUN new S6.r | ## siCa2 parameters
## do.trace<-FALSE # tarce for non-linear fit
## robust.shaver<-2 # 2 / 1 is used to shave very close/close Ca screens when standard method fails, 0 to use standard method
## max.cells.per.field<-9000 # ususally 5000
## min.green.cells<-50
## min.red.cells<-10
## min.green.threshold<-50
## max.g1.p... |
29384693a1bf3990b9e3d4408a23e79d4a4a4d6e | 7781f8cd182574893f0421929eaabf51a1db53dd | /man/get_posteriors.Rd | bd042c7a7c1a7fc055b1ee563b254cdf0ead1baf | [
"MIT"
] | permissive | SWOT-Confluence/geobamdata | d154c15c461172de016d9eef3a1f5b3bf5f2fae3 | 51aa72a2f7376504724f6c580077c882d760825e | refs/heads/main | 2023-03-31T22:07:28.111915 | 2021-04-13T12:10:45 | 2021-04-13T12:10:45 | 318,612,305 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 444 | rd | get_posteriors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geobam_data.R
\name{get_posteriors}
\alias{get_posteriors}
\title{Generate and extract geobam posteriors from reach data.}
\usage{
get_posteriors(reach_data)
}
\arguments{
\item{reach_data}{list of reach data (input observations)}
}
\value{
l... |
3f0d4758bebcb589eea2014260a06d8547e119da | 014a09363ab8169a989848d12e1c84992bbfe38f | /tests/testthat/test-aov_ss.R | e527781f262971994d6a470b65ec10d2d0edf65a | [] | no_license | bassam-abulnoor/TestDimorph | 45b412e5ab0747b51fa6cb68ad3c9a5c19b4958a | f5d733b3bf2ee8d684d21fe5888afd5ceb2e60f4 | refs/heads/master | 2021-07-20T05:36:15.983925 | 2021-01-24T18:36:44 | 2021-01-24T18:36:44 | 234,365,880 | 1 | 0 | null | 2021-01-02T19:29:37 | 2020-01-16T16:46:23 | R | UTF-8 | R | false | false | 990 | r | test-aov_ss.R | test_that("aov_ss", {
library(TestDimorph)
testthat::expect_true(round(
aov_ss(
baboon.parms_df[1:3, ],
Pop = 2,
digits = 3,
letters = TRUE,
pairwise = TRUE,
)[[1]]$p.value[1],
3
) == 0.325)
testthat::expect_true(aov_ss(
baboon.parms_df[1:3, ],
Pop = 2,
digi... |
5f73f687021bf3b9d9b4a0e8e87917039da833a1 | bade93cbfc1f25160dfbe9493bfa83f853326475 | /doc/mwc/doc/mwc/alphabet/m.r | 877fead402b5355c643a3be39d058a94036eb2a8 | [
"BSD-3-Clause"
] | permissive | gspu/Coherent | c8a9b956b1126ffc34df3c874554ee2eb7194299 | 299bea1bb52a4dcc42a06eabd5b476fce77013ef | refs/heads/master | 2021-12-01T17:49:53.618512 | 2021-11-25T22:27:12 | 2021-11-25T22:27:12 | 214,182,273 | 26 | 6 | null | null | null | null | UTF-8 | R | false | false | 24 | r | m.r | .sp
.ce 1
\f(CHM\fR
.sp
|
039323543c7871997f965fbb5d78b744d91e136c | 213cddea3de10c4b51dce3629a8a481cd93f4872 | /man/filterIndustries.Rd | f437dd0a16b138acfff4f89ffad9b59ab7db2f5f | [] | no_license | ccwoolfolk/demandmap | 83186be02cad596a98483eba1452dd82af154017 | 9129aa8c237e6750e38dbf0c4add80aaca1d4568 | refs/heads/master | 2020-03-29T14:29:01.666719 | 2018-09-26T16:08:27 | 2018-09-26T16:08:27 | 150,020,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 431 | rd | filterIndustries.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{filterIndustries}
\alias{filterIndustries}
\title{Filter a tibble by industry}
\usage{
filterIndustries(dataToFilter, industries)
}
\arguments{
\item{dataToFilter}{tibble}
\item{industries}{character[n]. Industry codes to i... |
77e53477487c38f6cd4fc364b3af6b77fcaf8d81 | 8810a7bb371e125ec24c2978eb646fbf02e2c6ca | /man/LOOCV.Rd | a308eee1470dc221d3c6b4dcfec2c81414d3dde9 | [] | no_license | jayverhoef/KrigLinCaution | 758ad3a3c7c832e5df6a2d236ab560dc733d295e | cfee3a616a5256a817aa72046064cb5a6ee309ee | refs/heads/master | 2021-01-20T16:11:11.487098 | 2018-01-20T18:40:48 | 2018-01-20T18:40:48 | 90,825,285 | 10 | 0 | null | null | null | null | UTF-8 | R | false | true | 563 | rd | LOOCV.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/LOOCV.R
\name{LOOCV}
\alias{LOOCV}
\title{Leave-one-out crossvalidation (LOOCV) for a fitted spatial model}
\usage{
LOOCV(SigMat, obsVec)
}
\arguments{
\item{SigMat}{The fitted covariance matrix for the spatial data}
\item{obsvec}{a vector o... |
323ad24ad5f4aee4bd777934f5e6b28af997d0d9 | b30a7754d83c85a05a9e4d4043f4f86d4b8a146e | /R/filter.totcor.R | dd351c55bfd49a4b2a2c2941f75f08129f6dc573 | [] | no_license | cran/MetFns | ee916cbc684ecd2c8c8bd71241e87925ede01220 | b0bc3c7c4bd7724c0dc725c1560df6286f278b97 | refs/heads/master | 2021-01-17T10:05:52.743380 | 2018-10-13T21:50:12 | 2018-10-13T21:50:12 | 24,534,142 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 505 | r | filter.totcor.R | filter.totcor<-function(data,shw,r,C)
{
if(!is.numeric(r) || r<1.5 || r>4.5)
stop("Invalid input parameter specification: check value of r")
if(!is.numeric(C) || C<1)
stop("Invalid input parameter specification: check value of C")
if(!(all(c("F","Lmg")%in%names(data))))
stop("Err... |
726ab6cf8fcd9d101e34be90e04951b89ef83103 | b513e438cf48c3b2d1e603df9967aa06eba72263 | /demo/testing_pso.R | 1c9028d44f27774bd17265d3fece7d31f07599d9 | [] | no_license | cran/PortfolioAnalytics | da65651aacaff23b8b8e9e4d588ce9ed6fdcebf3 | 0a32da92a5caae29e26b2ecbc8107c66ad64dcf7 | refs/heads/master | 2023-05-25T06:40:44.201460 | 2018-05-17T21:48:29 | 2018-05-17T21:48:29 | 34,196,423 | 5 | 19 | null | 2023-05-25T18:30:23 | 2015-04-19T06:43:58 | R | UTF-8 | R | false | false | 2,024 | r | testing_pso.R | #' ---
#' title: "pso Demo"
#' date: "7/17/2014"
#' ---
#' This script demonstrates running optimizations using pso as the
#' optimization backend. Note that this script uses the v1 specification
#' previous to version 0.8.3.
#' Load packages
library(PortfolioAnalytics)
library(pso)
#' Load data and set general Par... |
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