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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
cd3c38a11b0803f6f2a3fe18b205faf59d449530 | 9ecec80b4649a01517c11061b1fdb2a48dbc20a2 | /Data_Masterfile.R | 9955a75ec7506b824a515e24e923b1714de45676 | [] | no_license | evanczopek/Madness-Analytics | b7262f71b509b6f531d3683869a64f8477aa5c0e | f81e5fc2fec7c59b20171a597165ba30e0790974 | refs/heads/master | 2021-01-23T04:45:08.665857 | 2017-03-15T23:08:40 | 2017-03-15T23:08:40 | 80,380,332 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,431 | r | Data_Masterfile.R | # Evan Czopek - 1/29/2017 6:00pm
# Input for desired analyzed categories and weights.
cat_choose = c('wlp', 'sos', 'points_against', 'trb', 'tov')
cat_weight = c(3, 5, 2, 1, 1)
# Query of Sports Reference to get relevant table on all stats for NCAA teams.
library(XML)
url_one <- "http://www.sports-reference.com/cbb/... |
22cce926998410b35f71b06069e2f3a35f765766 | ac771259d6e3469b75e0fdac251839ab1d070767 | /R/VTLSession.R | 4a156ab7e7f5d1802b921362da062e092d23a7a3 | [] | no_license | amattioc/RVTL | 7a4e0259e21d52e8df1efe9a663ca20a7d130b15 | 630a41f27d0f5530d7c3df7266ecfaf25fe4803a | refs/heads/main | 2023-04-27T17:52:39.093386 | 2021-05-14T09:22:24 | 2021-05-14T09:22:24 | 304,639,834 | 0 | 1 | null | 2020-10-19T19:19:41 | 2020-10-16T13:46:02 | JavaScript | UTF-8 | R | false | false | 9,789 | r | VTLSession.R | #
# Copyright © 2020 Banca D'Italia
#
# Licensed under the EUPL, Version 1.2 (the "License");
# You may not use this work except in compliance with the
# License.
# You may obtain a copy of the License at:
#
# https://joinup.ec.europa.eu/sites/default/files/custom-page/attachment/2020-03/EUPL-1.2%20EN.txt
#
# Unless re... |
b75b17c0066e5612ce3408f0946324b4a8691113 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ggstatsplot/examples/subtitle_anova_parametric.Rd.R | 9b314eb2a9a9cb841703595a40831d9d9219895e | [] | 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 | 487 | r | subtitle_anova_parametric.Rd.R | library(ggstatsplot)
### Name: subtitle_anova_parametric
### Title: Making text subtitle for the between-subject anova designs.
### Aliases: subtitle_anova_parametric
### ** Examples
# with defaults
subtitle_anova_parametric(
data = ggplot2::msleep,
x = vore,
y = sleep_rem,
k = 3
)
# modifying the defaults... |
552b22890485a4aaad0d3772e53ac17fccdeb108 | 04cbd24eb8b30234998386e79198875da960c541 | /man/scale_max.Rd | 8c55f9aedfaea5bb45676142a6bbd85c04ef942a | [] | no_license | neuroimaginador/dl4ni | 3ce88244575d1496a0575b01558affb6ce89c2ce | d0646921cdb6fb23f6b3b11e13fe345ebe12baba | refs/heads/master | 2020-04-04T09:59:47.966496 | 2018-11-02T15:15:59 | 2018-11-02T15:15:59 | 155,838,762 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 303 | rd | scale_max.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scales.R
\name{scale_max}
\alias{scale_max}
\title{FUNCTION_TITLE}
\usage{
scale_max(V)
}
\arguments{
\item{V}{(name) PARAM_DESCRIPTION}
}
\value{
OUTPUT_DESCRIPTION
}
\description{
FUNCTION_DESCRIPTION
}
\details{
DETAILS
}
|
86247e5d351fab952c3f79c75eb7ac5ecbc19351 | 29585dff702209dd446c0ab52ceea046c58e384e | /DAKS/R/state2imp.r | 22ab7840ef6559e91a090f585f124592a7623522 | [] | 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 | 921 | r | state2imp.r | ##################
# tranformations #
##################
####################################################
# #
# This function transforms a set of knowledge #
# states to the corresponding set of implications. #
# ... |
d67017cf35b1658da1bb0c1d73d420d5dde28f78 | 7a236c31e12686fb79fda5e4e5d1c1c4688e8068 | /man/tumors.Rd | ba79c46a79276e72ef12b97e9ce988cf98c00612 | [] | no_license | cran/denoiseR | abbe62dc2b658d3d57216fd1447d237de63a6284 | 496a05fc8db5b7628d2432add4c60028bcb6a733 | refs/heads/master | 2021-01-09T20:38:00.611851 | 2020-02-26T06:10:09 | 2020-02-26T06:10:09 | 64,213,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 919 | rd | tumors.Rd | \name{tumors}
\alias{tumors}
\docType{data}
\title{
Brain tumors data.
}
\description{
43 brain tumors and 356 continuous variables corresponding to the
expression data and 1 categorical variable corresponding to the type of tumors (4 types).
}
\usage{data(tumors)}
\format{
A data frame with 43 rows and 357 columns.... |
f63868d88928ca2be34f6c67ddd607eb3880fa5d | f6a0dd0987b0286c22411eb4a57173838f714141 | /metadata/bialystokMetadata.R | 825a1c61d17dc47ea056651c8641d41deab94712 | [] | no_license | kontrabanda/mgr-project | 735442f0360856dbd61e7d464d207532af2e98d4 | f0bdeebcc7ab3f8493142b147627fbdfe54d670b | refs/heads/master | 2021-09-03T02:26:36.676601 | 2018-01-04T22:31:16 | 2018-01-04T22:31:16 | 106,453,365 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,236 | r | bialystokMetadata.R | library(raster)
library(sp)
library(latticeExtra)
library(RColorBrewer)
library(ggplot2)
bialystok <- shapefile("../../data/bialystok/bialystok.shp")
bialystok <- spTransform(bialystok, CRS("+init=epsg:4326"))
crime <- read.csv("../../data/Polska/zdarzenia_rsow.csv", sep = "|")
crime <- crime[!is.na(crime$LAT)&!is.na... |
ae84ea1411e3b11042d7ed5f76b8ba0449ede652 | d4a2668077fe1c2561e4fac54a1f3b36523fec3d | /R/IdentifiedORFswithSAPsAltINDELsIsoform.R | 8802ad6bfc5e797bbc74e975eee94f4c8582c08b | [] | no_license | saha-shyamasree/Proteomics | 23c58cc00b812140e85638911f603b1737599151 | 3c07a069c87dcc1c09f2665da0ac29e055e40da2 | refs/heads/master | 2020-12-06T20:36:11.158420 | 2016-05-27T12:53:56 | 2016-05-27T12:53:56 | 24,229,701 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,508 | r | IdentifiedORFswithSAPsAltINDELsIsoform.R | ##This code takes list of known proteins, known proteins with SAPs and possible isoforms produced by 'IdentifyProteinIsoformSAP.py' and
##identify how many of these are found from peptide and protein identification.
source("D:/Code/Proteomics/R/RLib.R")
readList<-function(filepath)
{
as.matrix(read.csv(file=filepa... |
008cb6a21dd93477fcd63e47cf9910131ee90581 | 3b0bc265b1c2ebed261cb7d93a3bf778e7e286f7 | /wine_ratings.R | 8d22256d9fd7b0379881cc79ef7af4ceb7afec7c | [] | no_license | tknoch8/ml_projects | 942285f015410059468a3a407cbc8e22c45d618e | d7a0a2551c560668cc0d024f6dcbff71041d169e | refs/heads/master | 2023-01-24T19:53:51.498566 | 2020-12-04T22:17:44 | 2020-12-04T22:17:44 | 276,287,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | wine_ratings.R | require(tidyverse)
wine_ratings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/winemag-data-130k-v2.csv")
|
12bd4ce0fbb25242595f86f1460fe28732e90d55 | 450cf51141602b88597d17dc8daa0170f3f1dba2 | /data-raw/HCP_overview.R | e03d866fe3da93439b615761143c57d04707fbf8 | [] | no_license | jacob-ogre/ecosscraper | 6b16b133738076cb75e1336c28dfc932e1823496 | 63bafcc0213c52a2d2620cc1d17ef290d150d13b | refs/heads/master | 2021-04-30T23:24:42.375356 | 2018-01-23T16:07:03 | 2018-01-23T16:07:03 | 61,709,830 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,656 | r | HCP_overview.R | library(ecosscraper)
library(ggplot2)
library(ggthemes)
library(stringr)
library(viridis)
data(HCP_SHA)
data(CCAA)
data(CCA)
data(state)
dim(HCP_SHA)
names(HCP_SHA)
names(HCP_SHA)[12] <- "FR_Date"
names(HCP_SHA)[13] <- "Date_Agmt_Permit"
names(HCP_SHA)[15] <- "Date_Agmt_Permit_Expired"
names(HCP_SHA)[21] <- "Non_Lis... |
50a3d56a0fc80f818ab4ff37d4099a9b8a17cf8f | c71bb83278a21d2be8ce99ee7560783b762d1d8f | /man/taxaaccum.Rd | e1ac6dedb68093702f1a041f4eff1f75c62c67ed | [] | no_license | hhsieh/biotaxa_Rpackage | bc6b6768263984855c372357abe1337e2f99d0e6 | fbfdae55a1e92a754dfe9120e4132a80728407f3 | refs/heads/master | 2018-09-18T21:39:55.095438 | 2018-06-06T06:59:38 | 2018-06-06T06:59:38 | 103,230,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 494 | rd | taxaaccum.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/taxaaccum.R
\name{taxaaccum}
\alias{taxaaccum}
\title{Report the accumulative numbers of a rank of a given taxa overtime}
\usage{
taxaaccum(taxa, rank)
}
\arguments{
\item{taxa}{A string.}
\item{rank}{A string.}
}
\value{
accumulation curve ... |
5f9f074161831af1bd46671556addd40ee0b1437 | 2c59b1e35becfa9a24751ca29eb120ef38e8a6a2 | /man/grid_one_raster.Rd | 28c321f143115f877aa7e7c8e28c22a56ad3b999 | [] | no_license | land-4-bees/beecoSp | c81af0c1b64eff9923e32e5fdc5a5d72da72deb2 | 91fdd28b56fa23b0d1fc17df7adc52aed7e5b720 | refs/heads/master | 2023-04-15T21:36:23.236685 | 2023-02-23T21:07:08 | 2023-02-23T21:07:08 | 120,662,758 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,339 | rd | grid_one_raster.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/grid_one_raster.R
\name{grid_one_raster}
\alias{grid_one_raster}
\title{Split regional or national raster into gridded tiles}
\usage{
grid_one_raster(
rasterpath,
rasterID,
regionalextent = NA,
div,
buffercells = c(0, 0),
NAvalue,... |
46193012fa1ef4a2d5223386f5cbc7aa03ffc08d | 58f3548b037a642c3f771d0e19838339a47b5d77 | /quora/R/caret.R | 0000de5ff91b4c88d9923ba64a9f9bb8c67634de | [] | no_license | mkearney/competitions_kaggle | c99e8333d4193e3fa1edb10b5b3286294a4f2b7c | bc968ed349e5d60ebd92927c1709b27cfeb5d180 | refs/heads/master | 2021-06-16T06:58:07.959313 | 2017-05-18T01:28:07 | 2017-05-18T01:28:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 457 | r | caret.R | library(caret)
traindata <- na.omit(e)[, -1]
trainclass <- na.omit(e)[, 1]
trainclass$is_duplicate <- factor(trainclass$is_duplicate)
mod_caret <- train(is_duplicate ~ .,
data = data.frame(na.omit(e)),
method = "binda",
lambda.freqs = .01,
preP... |
5723d0b3a48cb8d3dccc145d3fb1ea84e54f329d | 6c76ee43bf182f66205a03a24d15e628d6a9c277 | /StatQuest Lasso Ridge ElastN.R | c5e7fddca47a336c31e9be3849fbd5c492086fe0 | [] | no_license | Ismail17stats/Machine-Learning- | 74159901ea93233f503a4a6aafd1e2246e6d1802 | 5a7f7c8f0c66ab7ab63255c1610dd930f74f7f1a | refs/heads/main | 2023-06-27T16:58:06.548379 | 2021-08-02T13:13:33 | 2021-08-02T13:13:33 | 380,471,272 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,108 | r | StatQuest Lasso Ridge ElastN.R | #StatQuest !!!!
library(glmnet)
set.seed(12345)
### Setting up our data
n <- 1000 # made up data set will have 1000 samples
p <- 5000 # parameters to estimate
real_p <- 15 # only 15 parameters will help us predict the outcome
x <- matrix(rnorm(n*p), nrow = n,ncol = p)
# Now we will create a vector y that we wil... |
4b9a9b857a9ad9caaa2fc9d88f49176ba538e627 | 7670edeaa1ae7fec20d66b174d5b6d7c8fe50318 | /create_mask.R | 7af48bad2cf1f68253f3ec0290e5e605e7814b04 | [
"LicenseRef-scancode-public-domain"
] | permissive | PalEON-Project/composition | ed4174da595df34d4c75e73b2179111fe8436a09 | 0deef92068a1cee59f965bb29bbebad8bab5a2fe | refs/heads/master | 2021-01-19T22:33:29.118627 | 2016-02-05T03:12:50 | 2016-02-05T03:12:50 | 17,234,057 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,795 | r | create_mask.R | #!/usr/bin/Rscript
# code to create netCDF file with Paleon mask for Paleon Albers grid
source('config')
require(raster)
require(ncdf4)
region = t(as.matrix(raster(file.path(dataDir, 'paleonDomain.tif'))))
water = t(as.matrix(raster(file.path(dataDir, 'water.tif'))))
region[is.na(region)] <- 0
domain <- region > ... |
b12bb6e43928e647defee3c8de8536e192ebd72d | dd2da0383712af02a76300c92656124160848aee | /R/assesmentstringprocessing.R | c018c73da8c651f2f6c9b6378c194098ee07c66a | [] | no_license | mvillamea/DataScience-Exercises | 1f429cf839f09db2a98442f1ed0c383dd1f71aad | 38d73468cdd6a49333782fd15973aff0a15fa82d | refs/heads/master | 2023-02-21T15:15:24.630373 | 2021-01-21T20:21:40 | 2021-01-21T20:21:40 | 279,113,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,246 | r | assesmentstringprocessing.R | s <- c("5'10", "6'1\"", "5'8inches", "5'7.5")
tab <- data.frame(x = s)
#allow you to put the decimals in a third column called “decimal”. In this code, you extract three groups: one digit for “feet”, one or two digits for “inches”, and an optional decimal point followed by at least one digit for “decimal”.
extract(d... |
e6dfe711c0af41fd92113e4a7c90d6e8d491376b | f839b94d8de824a4a2ff7d84f4daecb1540abd17 | /scripts/main-data-cleaning.R | c5d833c46aea917c45d638c9bb2ba034507eaf3b | [] | no_license | dxre-v3/college-scorecard | de6117f23ec47378f4ebf640882ffb06f2446e42 | e9dcd88a3b3221f177998e9cfbe0ed9c4c8847dc | refs/heads/master | 2022-10-09T18:51:06.469948 | 2020-06-12T16:44:51 | 2020-06-12T16:44:51 | 262,709,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,627 | r | main-data-cleaning.R | library(tidyverse)
library(janitor)
# Read in main data set
initial_read <- read_csv('data/unprocessed/Most-Recent-Cohorts-All-Data-Elements.csv',
na = c("", "NA", "NULL"))
privsupress <- problems(initial_read)
terms <- read_rds('data/processed/institutions.rds')
privsupress <- privsupress ... |
1746e476d6bdb3da4fa40c0fd85fe9a19904d83b | 25c86104b6123005f91ec250a6958c13112e8885 | /R/dilog.R | bc8cb422caa76492556f0abb302f13ffa31de76d | [] | no_license | cran/HMMcopula | 32770d797f93f4362399bed5c42194f757897ec7 | fd96e87761cb8dff3ffc1b291f676647c4c70209 | refs/heads/master | 2023-04-09T16:21:37.078422 | 2020-04-21T06:50:02 | 2020-04-21T06:50:02 | 155,122,276 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 334 | r | dilog.R | #'@title Dilogarithm function
#'
#'@description This function computes the dilogarithm of a number.
#'
#'@param x a real number
#'
#'@return \item{out}{dilogarithm}
#'
#'@export
dilog <- function(x){
if(x==1) { return(NA)}
out = stats::integrate(function(y) log(y)/(1-y), lower = 1, upper = x)$value
re... |
a6fdce34c958d049d734a28d9aa7f6b13ac2c3d2 | 6a21f44be0787a48dcc07239bb306be76150fa72 | /demo.R | 41f144b46b85e99b9b12a0942dd476f2557404de | [] | no_license | lozsandino/MarkovMusic | ff7ada79ad31d0b22f66083d382720f4324b43e1 | e42bf50f996f3cd7f6a25b5a0e547d8019b95036 | refs/heads/master | 2020-03-14T08:30:26.391904 | 2018-04-29T20:09:36 | 2018-04-29T20:09:36 | 131,526,283 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,876 | r | demo.R | library(MarkovMusic)
# Lectura de los datos de entrenamiento.
Cm <- readMusicXML(paste0(system.file(package = "MarkovMusic"),
"/TrainingBlues.xml"))
Cm <- Cm$song[, "note"] # Se preserva solo la información del tono.
Cm <- c(Cm, rev(Cm)) # Melodía en escala menor de blues en C.
Fm <- Cm + 5 ... |
fe4720f5607a61cff32905302cdf8af44d8df2a8 | b3921db7e6ac213db389b4f2f5c4cb19e32a3411 | /ECP/boxp_k_ecp_fast.R | bc76193a70be13419c3e8533aec4afdace6af8e1 | [] | no_license | 12ramsake/MVT-WBS-RankCUSUM | cebb8c84aeec47c57d816b3281baca5cfd326a2b | e227f96fbf8ac752d78c6f755b71d79647297199 | refs/heads/master | 2023-08-09T08:13:50.768287 | 2023-07-19T20:02:00 | 2023-07-19T20:02:00 | 195,120,113 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,006 | r | boxp_k_ecp_fast.R |
library(MASS)
library(stringi)
library(xtable)
dirr<-""
setwd(dirr)
Ns<-c(1000,2500,5000)
sim.size=100
thetas<-list(c(.333,.666),
c(.25,.5,.75),
c(1/6,2/6,3/6,4/6,5/6))
ds=c(2,3,5,10)
distributions1=1:3
names(distributions1)<-c("Normal", "Cauchy", "Skew Normal")
##Create ... |
9a346a2ccf882890afcb8ad2cc7c4e07b37a75bc | ddd5dd6898d18fa111a54cfa7a130b7bc1b8718a | /man/mode.Rd | 7215c3c004f5aa2b5f2c7f19a99488ca2ada5048 | [
"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 | true | 366 | rd | mode.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mode.R
\name{mode}
\alias{mode}
\title{Get the mode of a variable}
\usage{
mode(x)
}
\arguments{
\item{x}{= input variable}
}
\value{
mode
}
\description{
Get the mode of a variable
}
\examples{
df <- data.frame(year = 1970:2019,
... |
707d5676ad6b44249547929d18030b66dd6f2d76 | aa26052173994c5ce2363f11340f771d83d380a4 | /R/printFFTrees_function.R | 43564fb9e28ebde1a7fece969826607a5fb9a1ec | [] | no_license | ronypik/FFTrees | ff92103e0c7d3105d9da96580da66d311e5a71ff | 21421d9e7a48db3508bc721cd5b2ed9e60b0b19b | refs/heads/master | 2021-01-11T16:31:20.673528 | 2017-01-26T08:04:48 | 2017-01-26T08:04:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,050 | r | printFFTrees_function.R | #' Prints summary information from an FFTrees object
#'
#' @description Printing function for an FFTrees object
#' @param x FFTrees. A FFTrees object created from FFTrees()
#' @param ... additional arguments passed to print.
#' @export
print.FFTrees <- function(
x = NULL,
...
) {
goal <- x$params$goal
n.trees <-... |
d080663360fe89fc8a6618225f97a594ab023fc0 | 184a5b70c5bf8642501c82610e8ea5562445029b | /R/norm.R | 2816d2d9feb4d4e5f208bc82ce1238236469b1cd | [] | no_license | cran/QuantumOps | 4910e53dda44803981801cbe1545e4ab63154538 | 35e2a8be5a6bbefbdc53a732eb6145a04dcd9e8e | refs/heads/master | 2020-04-07T09:57:13.065211 | 2020-02-03T08:20:18 | 2020-02-03T08:20:18 | 158,270,510 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 148 | r | norm.R | #Compute the norm of wavefunction
#' @export
norm <- function(v){
sqrt(inner(v,v))[1] #which is the square root of the inner product with itself
}
|
385b028a27bfa9a77e029578ca8f06da83e147c3 | e4cb1e8aadcd175c4fd23d38b2689f0740c4f21d | /basketball_win_prediction/basketball_scrape.R | fe694b7dadb1e28c0101179f8e6299e31ad55dab | [] | no_license | thpossidente/COGS-298-Project | 52add812856e4a3713f65045b92f03173a1ad9de | 5dc626f8111cbb7c7d679d680d7284f3b5a564af | refs/heads/master | 2021-01-24T09:50:28.592240 | 2018-11-10T17:44:38 | 2018-11-10T17:44:38 | 123,028,382 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,514 | r | basketball_scrape.R | library("rvest")
library(dplyr)
main.table <- data.frame(Minutes=integer(), FG=integer(), FGA=integer(), FGP=double(), ThreePoint=integer(),
ThreePointA=integer(), ThreePointP=double(), FT=integer(), FTA=integer(),
FTP=double(), ORB = integer(), DRB=integer(), TRB=int... |
68bbd7da5864bd391aa368c96e3efae225871cfd | 4c29b2e8cd6d5d12889958a408ecde2498a25eba | /man/bandit-package.Rd | e195297103859ac3d5f12d02860d5f7f3d213a14 | [] | no_license | ssimeonov/bandit-1 | f5fc38cc2c4e97dca8039b65fba89dddc8a76422 | 485deccebf06b77d68d00df45b877dc28da45e33 | refs/heads/master | 2021-01-18T00:22:34.032009 | 2012-08-23T22:07:22 | 2012-08-23T22:07:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 651 | rd | bandit-package.Rd | \name{bandit-package}
\alias{bandit-package}
\alias{bandit}
\docType{package}
\title{
Functions for simple A/B split test and multi-armed bandit analysis
}
\description{
A set of functions (intended to be used by the ruby gem metricizer) for doing basic analysis of A/B split test data and recommending distribution of n... |
eba85aa37fd61ae2c3dd3a5cda48f32b14ab5141 | ada3800c6de8acfc2d0b7bbe7426ce5bcbc1901f | /micro-services/api/R/example/test_micsvc.R | a7181e28e776ed78fd23923d4239f526b2b25e1d | [] | no_license | chaitanyawolfe/docs | 9fb6728166fd30226c5930456bf29957060edee4 | c4de365c1972450577192fdc2f40378b4092ab24 | refs/heads/master | 2020-04-28T15:22:10.572483 | 2019-03-06T11:05:04 | 2019-03-06T11:05:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,181 | r | test_micsvc.R | ## Author: Kartik Arora
## Date : 13-Feb-2018
##
## File shows simple example of using Risk Model/Optimization API
## Source the API file
source('micsvc.R')
# Setup a connection object
conn1 <- qes.microsvc.Conn$new(username = '<username here>', password = '<password>')
# Get instance of risk model builder
risk_mo... |
995a5e03b9a45e4efb892ee7c9d2142675a15ea0 | d666f106be302a4b676c10736461dc94390fc0bf | /R/functions.R | 9d60d4583077e2aeb1c7d0969fd6dbf21ba2b7f1 | [] | no_license | travitzki/censoescolaR | d7bf48542ca75c34825507f2eb19facff5755ea8 | c2f4babdde1cdcebf42c71050cf867011226d931 | refs/heads/master | 2023-02-02T12:35:29.750304 | 2020-12-16T17:40:47 | 2020-12-16T17:40:47 | 311,745,530 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,804 | r | functions.R |
# 0- funcao para download do censo 2019 ------------------------------------
#' Download school census microdata
#'
#' @param year numeric, at the moment works with 2018 and 2019 data
#' @param method character, default is "wget". Other options: "internal", "libcurl", "curl"
#'
#' @return .zip file with all microdat... |
65133a827169fcda98cd47c9a0ac784716e09a96 | 4e3580312132efb5da4a385878c9394e16a9d70a | /man/modify_max_lk.Rd | 0cdc32a2add251138a149b02672df650752a02ba | [] | no_license | KerstinSpitzer/ModifyMaxLkAndPairwise | ed1c0bb9ba2e95789936a44f641659a70a447a1d | 5599edf27bd14a2254b82af65391f5b0325efe47 | refs/heads/master | 2022-11-23T08:59:50.856527 | 2020-07-18T17:00:05 | 2020-07-18T17:00:05 | 257,632,907 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,832 | rd | modify_max_lk.Rd | \name{modify_max_lk}
\alias{modify_max_lk}
\alias{ModifyMaxLkAndPairwise}
\title{
Rescaling of the max_lk output in LDhelmet to reduce the mean squared error
}
\description{
The output of the max_lk function in LDhelmet is improved in terms of the mean squared error by rescaling it with a certain constant, see Gaertner... |
22a16aaef0d26694ee94f8a2c7e7cde96ba17898 | 31362fdab2193f92b64f9a82b0fe1ca732fcf6df | /Covid19VaccineAesiIncidenceCharacterization/server.R | 3affb08d375244dab9077f77f647842d5d7c8969 | [] | no_license | OHDSI/ShinyDeploy | a5c8bbd5341c96001ebfbb1e42f3bc60eeceee7c | a9d6f598b10174ffa6a1073398565d108e4ccd3c | refs/heads/master | 2023-08-30T17:59:17.033360 | 2023-08-26T12:07:22 | 2023-08-26T12:07:22 | 98,995,622 | 31 | 49 | null | 2023-06-26T21:07:33 | 2017-08-01T11:50:59 | R | UTF-8 | R | false | false | 12,349 | r | server.R | shiny::shinyServer(function(input, output, session) {
filteredSexGroups <- reactiveVal(NULL)
shiny::observeEvent(eventExpr = {
list(input$sexFilter_open,
input$tabs)
}, handlerExpr = {
if (isFALSE(input$sexFilter_open) || !is.null(input$tabs)) {
result <- input$sexFilter
filteredSe... |
eac3ba8076821d50feff67e8f1f1b0e892a66d88 | e0ce905fc0d5cdde31fcb70219da6ae5585afdf2 | /R/package.R | 1487e2746b99ec791d5c67127abb157e35a42ba4 | [] | no_license | cran/easypackages | 57febd9e044bdb4a4421d8eef72caeed751cbe37 | 1d5734cc996eb0cf46970e84322f086580541adb | refs/heads/master | 2016-08-11T15:10:41.647828 | 2016-01-28T10:09:32 | 2016-01-28T10:09:32 | 50,570,866 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,871 | r | package.R | ## ============================================================================
##
## package - an S3 class that contains attributes about an R package that can
## be used to load or install that package
##
## ============================================================================
get_repo_info <- funct... |
1d21e612959b564d1dda538e594709b756357f73 | 29585dff702209dd446c0ab52ceea046c58e384e | /sqlutils/R/sqlexec.R | 8f9df6d25020a7f8ffcfb13ee0da64784f95c98e | [] | 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 | 1,316 | r | sqlexec.R | #' Generic function for executing a query.
#'
#' @param connection the database connection.
#' @param sql the query to execute.
#' @param ... other parameters passed to the appropriate \code{sqlexec} function.
#' @return a data frame.
#' @export sqlexec
sqlexec <- function(connection, sql, ...) { UseMethod("sqlexec") ... |
b12ed7aec81664b7ce138327666a6a6245c4b2da | 29585dff702209dd446c0ab52ceea046c58e384e | /darch/R/rbm.Getter.R | 10b2479678fff514be25c46f25618163ec76c6bd | [] | 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 | 10,256 | r | rbm.Getter.R | # Copyright (C) 2013-2015 Martin Drees
#
# This file is part of darch.
#
# darch is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# da... |
f96934190855834fcd8d4405a0799ded875e8970 | f7105536a44be844d652f28e5c5b5bab0db66aa8 | /R/CMF/ml/hw3/hw2.R | 29f0532c7a60d333e496ab3e9009ace1e181264b | [] | no_license | DmitryZheglov/code | e6f143c21287a250c01c639659b672fdef089bbe | fdec951c8dcf3145109076bc78f0646217b1b822 | refs/heads/master | 2022-12-13T11:43:00.962046 | 2019-08-12T18:58:55 | 2019-08-12T18:58:55 | 93,278,292 | 1 | 0 | null | 2022-12-07T23:49:07 | 2017-06-03T23:00:52 | Jupyter Notebook | UTF-8 | R | false | false | 3,221 | r | hw2.R | #Viboru
library(kernlab)
source("C:/Users/Dmitriy/Desktop/proga/R/cmf/learn/hw2/SVM_func.R")
# файл с пользовательскими функциями
datX= read.csv("C:/Users/Dmitriy/Desktop/proga/R/cmf/learn/hw2/mtrain.csv",header=TRUE,stringsAsFactors = FALSE,sep=",")
datY= read.csv("C:/Users/Dmitriy/Desktop/proga/R/cmf/learn/hw2/mtes... |
9aee8f0e240ff4a957a0698d3ac95667b78459a9 | 25a0c00d980650b8549e88002f30d542a7ebb42b | /WordCloud_Laxman.R | 48d00892d2c3b322a7817937e06d4759fec9d564 | [] | no_license | bhavish2207/Airline-Customer-Churn-Analysis | 6f499f07c2cc364375dc7b2dc6c198ea506eac70 | b124c72b03e32cf745b241eb7c226de60af36adc | refs/heads/master | 2022-03-23T09:53:19.422564 | 2019-12-15T21:43:05 | 2019-12-15T21:43:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,164 | r | WordCloud_Laxman.R | install.packages("wordcloud", dependencies = TRUE)
install.packages("SnowballC") # for text stemming
install.packages("wordcloud") # word-cloud generator
install.packages("RColorBrewer") # color palettes
library(wordcloud)
library(tm)
library(NLP)
library(SnowballC)
library(RColorBrewer)
library(tidyverse... |
66918125e486586e164f49a70f5266bde2a5cdce | b64995fe2715647319869f7b4d411dc6f846e559 | /partitionComparison/tests/testthat/test-helper.R | 9ab8a1543f2c72774f91a1cf753ac6eb6891793e | [
"MIT"
] | permissive | KIT-IISM-EM/partitionComparison | 6040346e48a9a3d497724bc6d33abfbec8afc5ca | 741b25768d0669e4ed7feff513bdac3d2ef7b3ec | refs/heads/master | 2021-10-22T00:39:28.757424 | 2019-03-07T10:58:38 | 2019-03-07T10:58:38 | 114,737,331 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,501 | r | test-helper.R | library(testthat)
library(partitionComparison)
context("Test helper functions")
test_that("setOverlap produces the correct results", {
expect_equal(setOverlap(c(0, 0, 0, 0), c(1, 1, 1, 1), 0, 1), 4)
expect_equal(setOverlap(c(0, 0, 0, 0), c(1, 1, 1, 1), 0, 0), 0)
expect_equal(setOverlap(c(0, 0, 0, 0), c(0, 0, 1, ... |
26c4d36475bb1f10b3b0c20a540d92c8c7c8129a | 72ad4953ea2c100a03a9ddd364857988a9d1b2de | /man/eeg_ica_summary_tbl.Rd | 222d90baf86ea3ba5fb6fd24effb97b363009e73 | [
"MIT"
] | permissive | bnicenboim/eeguana | 28b46a8088f9ca0a370d955987b688542690547a | 3b475ac0472e6bedf2659a3f102abb9983e70b93 | refs/heads/master | 2023-05-23T09:35:20.767216 | 2022-10-10T11:53:21 | 2022-10-10T11:53:21 | 153,299,577 | 22 | 9 | NOASSERTION | 2022-11-06T13:40:13 | 2018-10-16T14:26:07 | R | UTF-8 | R | false | true | 1,292 | rd | eeg_ica_summary_tbl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/out.R
\name{eeg_ica_summary_tbl}
\alias{eeg_ica_summary_tbl}
\title{Show a table with a summary of the results of the ICA.}
\usage{
eeg_ica_summary_tbl(.data, ...)
}
\arguments{
\item{.data}{An \code{eeg_ica_lst} object}
\item{...}{If left e... |
c094b6ff36f8ab7fefb9abfce6f4db61729f3055 | 6e5cb9a8d877de59c7290263929d2045962128d5 | /v1/FanCodeV1/R/my_calc_cor.R | 775e9fb4213ef06e8d5c70a94fff89ee0e2291b0 | [
"Apache-2.0"
] | permissive | FanJingithub/MyCode_Project | 92a3c6732cdb4cafbaac537a85c3c28e213e9f45 | eb356d243f2c0b6548326d5cd1baffed96dfde63 | refs/heads/master | 2022-04-26T07:02:07.134102 | 2020-04-27T18:17:51 | 2020-04-27T18:17:51 | 259,377,390 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,648 | r | my_calc_cor.R |
#' Calculate the correlation for two kinds of score
#'
#' @param data_1 tibble/matrix
#' @param data_2 tibble/matrix
#' @param score string, "each_row" - score by row, "each_col" - score by col
#'
#' @return matrix
#' @export
#'
#' @examples
my_calc_cor<-function(data_1, data_2, score="each_row"){
if (length(inters... |
e3e472974e7d75bc9699a927ee5ec8d38cfd5979 | 0b776535f2b134ec26c5dfbb0095e7a4dba49beb | /bin/ppca/pca_ppca.R | dd90e71beffa2db7e9613dcf40542bed157965a6 | [] | no_license | RonakSumbaly/Ancestry-Mapping | 3fc65aab87aea672470b2bfe8083d3293ff145b4 | 5ac774020b1ebda09d85d4cb7ca698529e110ab1 | refs/heads/master | 2021-01-09T20:38:12.717319 | 2017-10-09T03:14:47 | 2017-10-09T03:14:47 | 60,070,665 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,516 | r | pca_ppca.R | ############################################################################
# Project Title: Ancestry Mapping
# Done By: Ronak Sumbaly
# Description: perform pca and ppca
############################################################################
library(pcaMethods)
library(flashpcaR)
# pca mapping
pca.baseline = f... |
f2941308f4a22a1509aa17e62011c9f71c7e0ec3 | 7c5bd5fd8072e57dd8742b2cbcad27d256b2573d | /cdashQC-master/R/summary_stats.R | d48473f7034c2bbb89e3ce43e448bc9355577bf4 | [] | no_license | statswork/cdashQC | 101a03c276ca0d4156f4316ac7c14c59f8bee938 | d69ca24ccca12f73c625d48dcef5a356617d1b08 | refs/heads/master | 2021-01-24T10:53:17.631165 | 2017-04-24T19:20:19 | 2017-04-24T19:20:19 | 69,898,757 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,759 | r | summary_stats.R |
#' Summary statistics for vital signs or eg
#'
#' @title summary statistics
#' @param data_clean the clean data set returned by \code{\link{replicate_clean}}.
#' @param inter_digit if rounding happens for the intermediate averages, what digits should be kept.
#' @param final_digit what is the digit for final summary... |
bfa608a2141ecfb98374e54ea55a4fa97aa8127c | fd3e853bdee84f2aa5641b8d58c29f9e9290e608 | /zip_1km_population.R | 458594639394ea92e1459f18916ed7a287bfb26a | [] | no_license | jsimkins2/urban_climate | 3f3aff201c3055403bc8886f6fab5b04ebece96f | f8bcccbd7f6ac6e55a517aa3deb6cb67539ee7d3 | refs/heads/master | 2023-01-03T12:23:50.042359 | 2020-10-26T18:00:32 | 2020-10-26T18:00:32 | 177,999,563 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,373 | r | zip_1km_population.R |
years1 = seq(2010, 2050, 10)
years2 = seq(2060, 2100, 10)
ssp = paste0('SSP', seq(1,5), '_', '1km_NetCDF')
typelist = c("rural", "total", "urban")
ssp2 = paste0('ssp', seq(1,5))
for (s in ssp){
for (ty in typelist){
setwd(paste0("/Users/james/Documents/Delaware/urban_climate/datasets/1km_urban_population_projec... |
e4f28a45d1dbac90ae93723147a99f1b7f3a456a | 1c5de214a549fde2a2ac0baeac1982fccb126f40 | /inst/doc/billboarder.R | 24564ed390ecc7bf32e9187cf74a493114940b3c | [] | no_license | cran/billboarder | 59fefd44336f1a6da8e80f57fa8abbf3cfd6454f | 07fb86e80f6effe899e9c080c40e671a62b081ae | refs/heads/master | 2023-01-22T22:21:51.452308 | 2023-01-08T18:00:06 | 2023-01-08T18:00:06 | 101,809,914 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,072 | r | billboarder.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
screenshot.force = FALSE
)
library(billboarder)
## -----------------------------------------------------------------------------
set_theme("insight")
set_color_palette(scales::... |
2e9c0db30632e9abbedc34574c09f6fa0230dbe0 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sloop/examples/otype.Rd.R | 4847d2fa269350a110c485f5ed839cfc54434066 | [] | 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 | 142 | r | otype.Rd.R | library(sloop)
### Name: otype
### Title: Determine the type of an object
### Aliases: otype
### ** Examples
otype(1:10)
otype(mtcars)
|
8f71ebb4a01b56e2220cd698e31e47837558c11d | f67c710d5ff00df3dbafe5ca07e0447d7acdfe7e | /cachematrix.R | 15fdd93808e0df5741679fd43e1344ef8eba64b3 | [] | no_license | RezaJalayer/datasciencecoursera | 48968f789b6f4711a95b3df868140b044464445e | fa2a0941b12e8a978c9e9ae44c4aadf4a4f83e06 | refs/heads/master | 2020-03-17T12:19:10.451485 | 2018-05-26T18:02:16 | 2018-05-26T18:02:16 | 133,583,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 965 | r | cachematrix.R | ## This is to compute the inverse of a matrix efficiently.
## The efficiency comes from calculating the inverse only once until the matrix is changed
## This function gets a matrix and returns a list of 4 functions that will be used in the next function for inverse calculation
makeCacheMatrix <- function(x = matrix... |
5fd1e14b95eb9dec8ae292a2d3b7dd0cfdc4db4d | 2011b48a27d5efd3b3267e565026d82019fd13cf | /Rscripts/01_ExtracciónValores.rsx | 3670008b714e347c191f6c7bf30b86ebfd94f1c4 | [
"MIT"
] | permissive | klauswiese/QGIS-R | 9203f367d6525a7b7c34e547ac6b4ea6d1cd0838 | d4da3aa782427c082c82299b7374dfc79843f019 | refs/heads/main | 2023-09-06T07:13:07.192458 | 2021-10-28T13:51:11 | 2021-10-28T13:51:11 | 395,768,565 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 951 | rsx | 01_ExtracciónValores.rsx | ##CTE534_ClasificacionSupervisada=group
##Imagen=raster
##Vector=vector
#Directorio=string
##Codigo=field Vector
##Usos=field Vector
#TablaUsos=output table
#CodigoUsos=output table
#EntrenamientoRaster=output raster
#Covariables=output raster
#Trainingbrick=output raster
##ValueTable=output table
#showplots
#showplot... |
0d26233157f7eaf7660fd83dd2adebd4a844d955 | 9262b19dac497b74eb35c29baf7daa2c41da69d3 | /src/OptimzationLab.R | b7c26f65c6899127579168715e02f36e1d2a96de | [] | no_license | uwesterr/CoronaPredict | dfde5c84b6041555684413b5ca45035a253dc4bd | 8d2c1a6d3026a12a8f1bc99e6e00f50d6a202962 | refs/heads/master | 2021-04-17T15:41:41.005467 | 2020-05-18T19:40:29 | 2020-05-18T19:40:29 | 249,456,068 | 1 | 2 | null | 2020-05-15T07:58:45 | 2020-03-23T14:38:28 | HTML | UTF-8 | R | false | false | 3,127 | r | OptimzationLab.R | library(GA)
library(staTools)
library(shinyWidgets)
library(shinyalert)
library(writexl)
library(rlang)
library(DT)
library(modelr)
library(tidyr)
library(jsonlite)
library(shiny)
library(tidyverse)
library(lubridate)
library(zoo)
library(plotly)
library(readxl)
library(scales)
library(optimx)
source(file = "src/Re... |
8a7c03564985463c8b79a5d95f866f75b4f8c166 | 692e4a75bd72a4deba02328a10bade2b67a17d70 | /ShinyApp/server.R | efd74726fdc0957a917b38af89a7d1db821e6d0d | [] | no_license | reemabdullah/Visu_Superheroes | 96d367f8c28843fb872045efcba5eff299249a5a | e4e484e1e9b9c98c4f83ac8061175423509ce76f | refs/heads/master | 2021-03-29T14:29:10.719568 | 2020-03-23T15:59:51 | 2020-03-23T15:59:51 | 247,957,804 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 803 | 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(ggplot2)
# Define server logic required to draw a histogram
shinyServer(function(... |
580d8f3ab76fa65e8ad8e1901bc194344675b611 | 38988692dd3e89dcc6fde5a0c48cfc98a3d16f51 | /tests/testthat/test-ct_search.R | 5fbd1b514c259959da4278c91014fa06d87d43b1 | [] | no_license | rtaph/comtradr | 7c8ae748b872b0110f556443876fbbee8526b223 | 8acf1e0a76173940ffff3ecd815da80d48371450 | refs/heads/master | 2021-01-19T15:55:32.179919 | 2017-08-09T03:56:31 | 2017-08-09T03:56:31 | 100,980,391 | 1 | 0 | null | 2017-08-21T18:21:42 | 2017-08-21T18:21:41 | null | UTF-8 | R | false | false | 5,862 | r | test-ct_search.R | context("ct_search")
# All tests on the expected return data.
test_that("search return values are correct, and fail when expected", {
#skip_on_cran()
#skip_on_travis()
countrydf <- ct_countries_table()
# Get monhtly data on all German imports into Canada,
# 2011-01-01 thru 2011-05-01.
ex1 <- ct_search(re... |
79b989879b09ccd56cef092a398cf55200cdbdfb | 5874ae0a22213c3a692763f285a64433bd512f94 | /R/d3_R_eg1.R | e818fa36b305a2292618690e89dd3900d63b98a2 | [] | no_license | d8aninja/code | 8356486291a2db9f419549edaa525d9bbe39abfc | 80a836db49a31ecd6d0e62aaf8b6e4417c49df68 | refs/heads/master | 2021-09-06T11:35:13.956195 | 2018-02-06T04:11:33 | 2018-02-06T04:11:33 | 80,898,202 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,134 | r | d3_R_eg1.R | library(xml2)
library(htmltools)
library(rvest)
library(dplyr)
library(sunburstR)
url <- "http://www.rolltide.com/services/responsive-roster-bio.ashx?type=stats&rp_id=3153&path=football&year=2016&player_id=0"
ridley <- read_html(url)
#
games <- ridley %>%
html_node('table') %>%
html_nodes('tbody tr t... |
d7d7f3bd685d70af9c1dbd356bcd6b9ab7090b7d | 56878bcfe97163e0320567ae1c762087fefecfc0 | /Untitled.R | 7360cb135b38d1d05ac8721b98bf14b647cc294e | [] | no_license | beehoover/EcoDataSci | 78e2d0eb91fcd039416eda1716cff255fc8c0cd4 | 4cbd17f43d91f4849c37fa5c6608ede46b839a89 | refs/heads/master | 2020-04-28T12:21:49.673074 | 2019-03-12T19:51:19 | 2019-03-12T19:51:19 | 175,273,886 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 199 | r | Untitled.R | #merging my datasets
old_data<-read.csv(here("raw_data", "iris.csv"))
new_data<-read.csv(here("raw_data", "iris_mythica.csv"))
data<-rbind(old_data, new_data)
write.csv(data, file="iris_four.csv")
|
0a068f6a0707b66edc88f8a3a1597405cecbf155 | fe612f81a3118bf3ebef644bae3281bd1c156442 | /man/h2o.ifelse.Rd | ab562d397dfe215a5e70900c6c85e3cccf271a45 | [] | no_license | cran/h2o | da1ba0dff5708b7490b4e97552614815f8d0d95e | c54f9b40693ae75577357075bb88f6f1f45c59be | refs/heads/master | 2023-08-18T18:28:26.236789 | 2023-08-09T05:00:02 | 2023-08-09T06:32:17 | 20,941,952 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 1,079 | rd | h2o.ifelse.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/frame.R
\name{h2o.ifelse}
\alias{h2o.ifelse}
\alias{ifelse}
\title{H2O Apply Conditional Statement}
\usage{
h2o.ifelse(test, yes, no)
ifelse(test, yes, no)
}
\arguments{
\item{test}{A logical description of the condition to be met (>, <, =, ... |
adfcd7daae3c6158fb5151e006bfdb3db37b0663 | d46e60e1246b3899bf6f91ddb62fe01f9fc568b4 | /Project2/plot3.R | 937454f9591f4ef2d8fdf9005da886c059ef70a0 | [] | no_license | BLKhoo/Exploratory-Data-Analysis | deaf72172f1fd6c17f20c26851e4f7c06eb1220e | b478cdcd805d4745da2f32fce7f852111f9f5438 | refs/heads/master | 2016-08-04T18:57:20.484337 | 2015-07-26T19:42:32 | 2015-07-26T19:42:32 | 38,964,175 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 498 | r | plot3.R |
# Read the R object data
NEI <- readRDS("data//summarySCC_PM25.rds")
SCC <- readRDS("data//Source_Classification_Code.rds")
BC <- subset(NEI, fips =="24510") # select only Baltimore City, Maryland records
ggplot(BC, aes(x=factor(year),y=Emissions)) + geom_bar(stat="identity") + xlab("Year") +
ylab(expression(... |
8d9d9227ae219917a1251b1199458fe737c310fd | df2efa599c39310e4c6c52be6d17163828b04aa4 | /R Programming/rprog-data-specdata/corr.R | 7e0f16ca973cff7e88ccb35b9a8bd8441970c95b | [] | no_license | pappjr/datasciencecoursera | ba6f7dd7294d68d05879a8252fdd92f794200947 | bb77b89e6ab8962651f73611d9ddc4b3b1a8382b | refs/heads/master | 2021-01-13T02:26:30.963874 | 2015-08-21T23:42:07 | 2015-08-21T23:42:07 | 38,457,637 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 761 | r | corr.R | corr <- function(directory, threshold = 0) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'threshold' is a numeric vector of length 1 indicating the
## number of completely observed observations (on all
## variables) required to compute the correlation bet... |
131ddddd40931759a3a68a6ecb0776596f9d0093 | a361f14c000fc1c153eaeb5bf9f4419951c7e3aa | /tests/testthat/test-covars_make.R | b8059ff197fef4a1ac5cee37ad6621f0372eaf28 | [] | no_license | kbenoit/sophistication | 0813f3d08c56c1ce420c7d056c10c0d8db4c420e | 7ab1c2a59b41bd9d916383342b6ace23df2b1906 | refs/heads/master | 2021-06-04T23:19:22.944076 | 2021-05-08T13:40:26 | 2021-05-08T13:40:26 | 101,664,780 | 43 | 7 | null | 2020-08-25T11:19:22 | 2017-08-28T16:40:21 | R | UTF-8 | R | false | false | 5,265 | r | test-covars_make.R | context("test covariate creation")
test_that("covars_make works as expected", {
# 6 4 words
# 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 syllables
# 3 + 5 + 6 + 3 + 3 + 11 + 3 ... |
d6929cc572eb7b95dbbc4f29871ed89f0c16d3f8 | d475ef97805a1b25f00b1f1aea15ae2abd44768b | /load_data.r | 089bc0379f6c283201ebd10b6f43d6b05c740f6a | [] | no_license | MaximeJumelle/Prediction-of-air-quality-in-Paris-subway | f48e32c495f4ac793a944b123a00aa36afffe84e | 8fc68905ccdbecece3a58f34d75523727049435e | refs/heads/master | 2021-05-06T19:02:30.929248 | 2017-12-22T20:59:29 | 2017-12-22T20:59:29 | 112,007,751 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 976 | r | load_data.r | # Maxime JUMELLE
# Youva MANSOUR
# Wassil BENTARKA
# You must install this package to read the dataset file
#install.packages("rjson")
library(purrr)
library(ggplot2)
dataset<-read.csv("/home/maxime/Documents/R/TSA/data/dataset.csv", sep=";", header= TRUE)
dataset<-dataset[order(as.Date(dataset$DATE, format="%d/%m/%... |
84280eda30b932a93e65859cf9d688d2184a90cc | 6ac1931eeba6b0e0e4e073d67a3167b9186bfed4 | /R/lrEMplus.R | a24ab795a42786e5ca81325b0f5e7309f58bd15b | [] | no_license | Japal/zCompositions | cdebd661ee5c2a5bf24e384d03a9df2b1abf0924 | 773f2afe00311c676cc88bcd8ff47b6bb43db9e5 | refs/heads/master | 2023-09-01T05:55:34.994363 | 2023-08-23T17:47:23 | 2023-08-23T17:47:23 | 23,953,744 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,100 | r | lrEMplus.R | lrEMplus <- function(X, dl = NULL, rob = FALSE, ini.cov = c("complete.obs", "multRepl"), frac = 0.65,
tolerance = 0.0001, max.iter = 50,
rlm.maxit=150, suppress.print = FALSE, closure=NULL, z.warning=0.8, delta=NULL){
if (any(X<0, na.rm=T)) stop("X contains negative values... |
a6a01b11f4b62d98873faf8bc13d05fe5a1fcabd | f91369d3ff4584d909ff5f0f4be382e54594d95c | /man/make_double_pipe.Rd | fa186271d28eb3fc94e0a4bf79413184c6076814 | [
"Apache-2.0"
] | permissive | Novartis/tidymodules | e4449133f5d299ec7b669b02432b537de871278d | daa948f31910686171476865051dcee9e6f5b10f | refs/heads/master | 2023-03-06T01:18:55.990139 | 2023-02-23T15:01:28 | 2023-02-23T15:01:28 | 203,401,748 | 147 | 13 | NOASSERTION | 2020-04-02T16:09:32 | 2019-08-20T15:16:40 | R | UTF-8 | R | false | true | 620 | rd | make_double_pipe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pipes.R
\name{make_double_pipe}
\alias{make_double_pipe}
\title{make_double_pipe: Pipe function generator}
\usage{
make_double_pipe(l, r, f = FALSE, rev = FALSE)
}
\arguments{
\item{l}{Left module.}
\item{r}{Right module.}
\item{f}{fast boo... |
e04bb0a921a448b5a5bf16c6b181cc7c32025582 | 924108a0fc572ad2cbad4230c582f19e2164fd00 | /server.R | d294c6f179662af868b1ba24439c8b8f6e23578d | [] | no_license | Athiette/diceThresholdometer | 9180778736e9cc13d52f76b696bc5f73efcb829f | 5d69c0f987ebd0c34799e9596e59f16599f47f79 | refs/heads/master | 2021-05-28T01:42:14.892817 | 2014-07-26T23:46:06 | 2014-07-26T23:46:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 722 | r | server.R | library(shiny)
chanceSuccess <- function(nD6,nSuccess,Type){
chance <- 0
if (Type == "4s") {
for (n in nSuccess:nD6) {
chance <- chance + (choose(nD6,n)*(3/6)^(n)*(3/6)^(nD6 - n))
}
}
if (Type == "5s") {
for (n in nSuccess:nD6) {
chance <- chance + (choose(nD6,n)*(2/6)^(n)*(4/6)^(nD6 -... |
92ed85222b45948358c2e3b2d49a47b16ee98bb4 | 206aab419371d80bab70ab01ef9a7f78e3d8232f | /man/get_data.Rd | f8919e72030f0ea3e291b72e93853c24dadef76c | [] | no_license | anubhav-dikshit/rLab5 | c09b5e4913887b38849b5e6deddefc983191c969 | c6ab9f3dc2381206960b2b6221fdbd3670652b80 | refs/heads/master | 2020-03-29T23:54:20.340409 | 2018-10-01T10:01:19 | 2018-10-01T10:01:19 | 150,486,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 522 | rd | get_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_data.R
\name{get_data}
\alias{get_data}
\title{Title Function to Get Weather Data for a given city}
\usage{
get_data(input_city, forecast_days)
}
\arguments{
\item{input_city}{The name of the city that weather needs to be fetched}
\item{... |
ac222baffb43091920fbca14dca95e0b3d6f99d9 | 39da6ebcf2d578230dbaf05713864757ac85da0c | /Plot2.R | d434deeda59fe898d5f81a6e1d5f10b89659b214 | [] | no_license | HuichunChien/ExData_Plotting1 | 6b106fa75d6f4ce4528e5ab564260b5a418941cb | b7d78d1c5c1d0323fdbc2e32122a4514b87056e6 | refs/heads/master | 2021-01-17T15:49:20.792191 | 2014-10-12T22:57:34 | 2014-10-12T22:57:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 857 | r | Plot2.R | # Plot2.R
data=read.table("household_power_consumption.txt", na.strings = "?", sep=";", header=TRUE)
#get subdata with data are 1/2/2007 and 2/2/2007
subdata<-subset(data,data$Date=='1/2/2007'|data$Date=='2/2/2007', select=c(Date,Time,Global_active_power,Global_reactive_power,Voltage,Global_intensity ,Sub_metering_1,S... |
bad974b57e9e3a78377db4f7dcab29b8dff3dd33 | 69d96f973b5e3a782d1e6ea05ad32e15db00f762 | /2_covs/4_read_campus.R | 791432327a89e61281f8e694cfe4022b5757be2a | [] | no_license | meghapsimatrix/retention | 5d639e8149baa7cf2c8d6fb7152663866a3b28b0 | fbed5e5715be27d060d423c8b7890ce6f3fd3b0d | refs/heads/master | 2022-12-06T07:59:00.324608 | 2020-09-02T19:39:23 | 2020-09-02T19:39:23 | 278,442,784 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 959 | r | 4_read_campus.R | library(tidyverse)
# Read data ---------------------------------------------------------------
# get to the TEA folder and extract all files with class in it
files <- list.files("NewFilesReleased/TEA", pattern = "p_campus", full.names = TRUE)
files <- files[!str_detect(files, "f")][10:19]
# go through each files an... |
bd6079d2720bc103b6976ae39f04b22c49cf468c | 66b97061d0e512b898cdc5736da6ac1d98edd644 | /R/setMBOControlInfill.R | 8b96962d4f87936ec4f0e72e590d395500d872a6 | [] | no_license | jakobbossek/mlrMBO | 3e28a1de56dda76bac7f8974a3c73b9903c82d14 | d383ea239591c41717c2a861424d642fb78dfdbe | refs/heads/master | 2021-01-18T08:21:44.437766 | 2016-02-09T22:02:56 | 2016-02-09T22:02:56 | 51,073,599 | 1 | 0 | null | 2016-02-04T12:07:42 | 2016-02-04T12:07:40 | R | UTF-8 | R | false | false | 13,573 | r | setMBOControlInfill.R | #' @title Extends mbo control object with infill criteria and infill optimizer options.
#'
#' @template arg_control
#' @param crit [\code{character(1)}]\cr
#' How should infill points be rated. Possible parameter values are:
#' \dQuote{mean}: Mean response.
#' \dQuote{ei}: Expected improvement.
#' \dQuote{aei}:... |
577872b9e213876303b3a56520245de08dd1136e | cbdeb7b5f3e8df763c5a36c487a1ac81eab130a6 | /InitialDataCombineAndExplore.R | 511322f3615ce7d971ad8b0d0137855869d70848 | [] | no_license | raywoo32/MSCI718 | 80b8faa0bc3639a689cc50c5ab3ef54de410ac23 | 73834982be1c2e1af7786a5ca53de851988aa4a5 | refs/heads/main | 2023-04-03T21:17:30.263383 | 2021-04-23T03:32:23 | 2021-04-23T03:32:23 | 360,751,698 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,073 | r | InitialDataCombineAndExplore.R | # Intial Data Visualization
# Imports
library("tidyverse")
library(dplyr)
library(ggplot2)
#rm(list = ls())
# Read in
active <- read.csv("./Data/activeltcoutbreak.csv", stringsAsFactors = FALSE)
colnames(active) <- c("Date", "PHU_Num", "PHU", "LTC_Home", "LTCH_Num", "City", "Beds", "Total_LTC_Resident_Case... |
dcb33c926950cf631acc06e44c74e3c3cc0b2db0 | 14abd6e0490877b881bce35711865ec310e43621 | /milestone1/lab2britsearch.R | a710c850b5cabfcd4d332191028ef38a17f95a2c | [] | no_license | planetbridging/R | 3f4f4aa111841cec3e033f0870e1a92c01de33bd | f83516448f4d8f1f041bbc02c9cccab4e03c73d4 | refs/heads/master | 2022-11-20T10:32:23.511716 | 2020-07-16T01:34:16 | 2020-07-16T01:34:16 | 280,020,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,714 | r | lab2britsearch.R | # Import and attach libraries/packages
pkgs <- c("devtools","vosonSML","magrittr","tm","igraph","stringr")
lapply(pkgs, library, character.only = TRUE)
#library(writexl)
#library(openxlsx)
# Set up authentication variables
appname <- "declair"
my_api_key <- "33iRrzrqiUk28ChE1kaZN7bIv"
my_api_secret <- "VPtlcriRrID3... |
5047f9fc3929cc937267cf3e0e38428741326540 | fa55322eb3674cb43ca87445c20dc53abfa70082 | /man/summarised_lm.Rd | 091299c997874e62158313b63e22fac1b207eedf | [] | no_license | alberto-mateos-mo/data.analyseR | 4b942b08d764132ced392a6b962236d630437335 | 75283eed47f9130504a03650bd8f2f444c2e48c2 | refs/heads/master | 2021-07-14T11:46:56.302131 | 2021-03-23T02:17:50 | 2021-03-23T02:17:50 | 237,685,570 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 433 | rd | summarised_lm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/funcs_reg_models.R
\name{summarised_lm}
\alias{summarised_lm}
\title{Runs a summarised linear regression model}
\usage{
summarised_lm(formula, data, ...)
}
\arguments{
\item{formula}{An object of class formula}
\item{data}{A data frame with ... |
970b358e12c72156748a6ba7f0caca230f704293 | 8d7d4e1cfba177ed658c2fee39f67012e4b61a95 | /code/pre_process.R | 06d3c77660011e2ee047223328f82c59adfa020c | [] | no_license | leixxli/NYC-uberpickup-prediction | ce6ab5d36a4a084d5fdf9fadf6ffb48fb968e212 | 75172966426b9f4587e30c4a89254e8c3486eedf | refs/heads/master | 2022-11-07T20:56:22.489892 | 2020-06-16T22:20:09 | 2020-06-16T22:20:09 | 265,139,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,655 | r | pre_process.R |
library(lubridate)
library(ggplot2)
library(cowplot)
library(MASS)
library(ggpubr)
library(PerformanceAnalytics)
library(mctest)
library(fastDummies)
# --- import data
#setwd("/Users/spyker/Desktop")
data = read.csv(file = "uber_nyc_enriched.csv", header=TRUE, sep=",")
# ---data cleaning and mining
data$Month = ... |
5ba0f78a5ad810b5077b236ea5adfb03b4da443c | 3d329fe1bc2c17007859e1fee013516c769a75d0 | /Lab_06.R | 85127a70fb12e470c5051b927380b17cc1e0feab | [] | no_license | KarenZhuqianZhou/HUDM5123_Lab06_TwoWayANOVA | ca94e75e46ea4b646e2c1961906822a186493867 | 6e7bf4793a50599a7809cd0fb6f394934d060ab3 | refs/heads/master | 2022-06-14T03:34:30.753131 | 2020-05-05T19:25:26 | 2020-05-05T19:25:26 | 260,261,164 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,418 | r | Lab_06.R | # Load dat1 and dat2 by loading file "Lab_06_Prestige.Rdata"
load(file = "Lab_06_Prestige.Rdata")
# Start with dat1
head(dat1)
# Reorder the levels of the type factor
levels(dat1$type)
dat1$type <- factor(x = dat1$type,
levels = c("bc", "wc", "prof"))
dat2$type <- factor(x = dat2$type,
... |
d10168a3b398307224ff7318aba281d3cd5b5dbc | c53e367a5a155cfb1ee3a41e8b0351aeaa8d331d | /rgl/demo/bivar.r | 2589e036952135e60207ad0819412d19c4ad27e4 | [
"MIT"
] | permissive | solgenomics/R_libs | bcf34e00bf2edef54894f6295c4f38f1e480b3fc | e8cdf30fd5f32babf39c76a01df5f5544062224e | refs/heads/master | 2023-07-08T10:06:04.304775 | 2022-05-09T15:41:26 | 2022-05-09T15:41:26 | 186,859,606 | 0 | 2 | MIT | 2023-03-07T08:59:16 | 2019-05-15T15:57:13 | C++ | UTF-8 | R | false | false | 1,041 | r | bivar.r | # rgl demo: rgl-bivar.r
# author: Daniel Adler
rgl.demo.bivar <- function() {
if (!requireNamespace("MASS", quietly = TRUE))
stop("This demo requires MASS")
# parameters:
n<-50; ngrid<-40
# generate samples:
set.seed(31415)
x<-rnorm(n); y<-rnorm(n)
# estimate non-parameteric density surface ... |
bd6e68531e27cc2cc5e50a19aac4d019df32c0a1 | 38bf1be78abe8aa0337c2df690d8856b0dab278b | /data-wrangling-automation/main.R | e0565cfb933e06f4b2affcb31e01ac6894f4427f | [] | no_license | GrejSegura/my-projects | 8d3954b6f3afc0e8eb9dca1061edeab33a5dfc9e | d4d50fb2078e8cb79b2244b7acfff1c14b9b4d75 | refs/heads/master | 2022-01-08T14:29:18.623861 | 2019-04-28T19:31:20 | 2019-04-28T19:31:20 | 111,678,070 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 493 | r | main.R | gc()
rm(list = ls())
options(java.parameters = "-Xmx4g") ## memory control in RJava
library(tidyverse)
library(lubridate)
library(data.table)
library(xlsx)
library(XLConnect)
fileDirectory <- winDialogString("Please enter the file path below.", "")
setwd(fileDirectory)
source("./src/save_one_sheet.R")
data <- read... |
6b0892e0070d6fd65a52c7e16ce6012123722dd0 | 956f493986a2e4836cd7d5565fb23be636645a24 | /man/Variable.Rd | 9488af65cd6baa9c43119193c3983fd53ce85faf | [
"MIT"
] | permissive | Bhaskers-Blu-Org2/CNTK-R | d2fcb0eab33a5f6a9134fa20937622b308590b4a | 5e2b8f5320a48dc492fa7dd1654532dd9e3e856a | refs/heads/master | 2021-08-20T10:36:09.764093 | 2017-11-28T22:55:53 | 2017-11-28T22:55:53 | 276,122,540 | 0 | 1 | MIT | 2020-06-30T14:28:11 | 2020-06-30T14:28:10 | null | UTF-8 | R | false | true | 552 | rd | Variable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/variables.R
\name{Variable}
\alias{Variable}
\title{Variable}
\usage{
Variable(shape = NULL, dtype = "auto", needs_gradient = FALSE,
is_sparse = FALSE, dynamic_axes = rep(c(get_default_batch_axis()), 2),
name = "")
}
\arguments{
\item{sha... |
670694cc1117c638b441bbc7534cd128547f5c11 | 0de866f206f7aef72ac01061e69bd85c58ff8956 | /ncaaPredictions/plumber.R | 58eb9cafdcfd86ea3f9dd97c3d2ed18a75c84f8d | [] | no_license | rahuljungbahadur/NCAA538 | 55f90b6076ee62f609d6b96ec14b88bc641be7be | e13f9e1217a8ca8e7372966530e07794041734ef | refs/heads/master | 2023-01-07T03:22:34.649524 | 2020-11-08T22:04:33 | 2020-11-08T22:04:33 | 311,163,011 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 882 | r | plumber.R | #
# This is a Plumber API. You can run the API by clicking
# the 'Run API' button above.
#
# Find out more about building APIs with Plumber here:
#
# https://www.rplumber.io/
#
library(plumber)
library(tidymodels)
#* @apiTitle Plumber Example API
#* Echo back the input
#* @param msg The message to echo
#* @get /e... |
8a83ebfe45c2f7a59d53e1034882e329bf2efca6 | 9b419f8d44b87b4913b26c267a02bc3bdc5e6b44 | /Class_18/mangaManipulate.R | 59ef0b4b1502cd7a6abfa9e2b3707d45bdaf19b2 | [] | no_license | djwrisley/IMUH1511 | 62502fddae3323f5d6116244d04e997536130ee9 | a625b4256260964bfc98e8ddf2f5acb81b70bc01 | refs/heads/master | 2020-04-21T11:37:33.735425 | 2019-04-10T06:48:01 | 2019-04-10T06:48:01 | 169,532,342 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 748 | r | mangaManipulate.R | # exercise adapted from ch 8 of Humanities Data in R (Arnold/Tilton)
# loading the jpeg library for reading jpeg images
library(jpeg)
# reading the image into an array
manga <- readJPEG("a114.jpeg")
# explore the array; can you tell how the image has been turned into numbers?
class(manga)
dim(manga)
range(manga)
... |
55cff3294be2e4760c30ed7329042bd2a25082d1 | 68e768c4761f93c9745a033c3fbcf97ebee417b3 | /R/coef.est_multi_poly_within.R | ac73af32649e84d800a08463471111dabd8f2f8f | [] | no_license | cran/MLCIRTwithin | 2b15f70102e15514c8fed30dc635f449ee79734c | 8fcce562ed6e258c152c265a09c8e618eff1de55 | refs/heads/master | 2021-01-21T04:19:31.549149 | 2019-09-30T14:20:06 | 2019-09-30T14:20:06 | 36,813,236 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 836 | r | coef.est_multi_poly_within.R | coef.est_multi_poly_within <-function(object, ...){
# preliminaries
out = object
# print output
cat("\nEstimated abilities for the 1st latent variable:\n")
print(round(out$Th1,4))
cat("\nEstimated abilities for the 2nd latent variable:\n")
print(round(out$Th2,4))
cat("\nEstimated item parame... |
b93c2eeecc8943dada671a2b69348acc03646b3b | bfcebe2f5231d8a6e446b086c82dc1a65879d985 | /pollutantmean.R | 7fc91b0531eb5c0d526c4a627b28e5ef6a39736c | [] | no_license | dadhichmohit/program | cbe039d940b90d9c18651950eb145f718d72d759 | 9b962582b78050e299db7a86cc56fef454987783 | refs/heads/master | 2020-05-16T14:54:58.219775 | 2015-01-09T20:36:54 | 2015-01-09T20:36:54 | 29,032,376 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 691 | r | pollutantmean.R | pollutantmean<- function(directory, pollutant, id = 1:332)
{
if(id[1]>=1 && id[length(id)]<=332)
{
pollutant<-tolower(pollutant)
if( pollutant=="sulfate" || pollutant=="nitrate")
{
id<-sprintf('%03d',id)
data<-sapply(paste("D:/",directory,"/",id,".csv",sep=""),read.csv)
Mean<-sapply(data[poll... |
b0fcc1012eef60ff92697a73b127769ebc9e9a17 | 58332751f752592649b7c1b2491b8e800fe6532d | /man/R.t.Rd | 8a7606d81e84909e887c7821ce8c5da4c94b9c76 | [] | no_license | JPNotts/Package | 57c1e38651ddbd557fd4c0ad1640e938878613dd | b6fb969cac267bc69b6bb246819e98cf970c8718 | refs/heads/main | 2023-08-14T08:02:27.972089 | 2021-10-04T12:05:10 | 2021-10-04T12:05:10 | 316,275,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 357 | rd | R.t.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/QQ_KS.R
\name{R.t}
\alias{R.t}
\title{A function that returns R(t), the integrated intensity function.}
\usage{
R.t(q, time.step)
}
\arguments{
\item{q}{q}
\item{time.step}{time.step}
}
\value{
}
\description{
A function that returns R(t), ... |
57a2060144a945bd1c4d1a75a9761073361a5c21 | fe612f81a3118bf3ebef644bae3281bd1c156442 | /man/h2o.get_best_model_predictors.Rd | ffdc43b45b8003c9ad44702bafebe3a04c43097a | [] | no_license | cran/h2o | da1ba0dff5708b7490b4e97552614815f8d0d95e | c54f9b40693ae75577357075bb88f6f1f45c59be | refs/heads/master | 2023-08-18T18:28:26.236789 | 2023-08-09T05:00:02 | 2023-08-09T06:32:17 | 20,941,952 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 521 | rd | h2o.get_best_model_predictors.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modelselection.R
\name{h2o.get_best_model_predictors}
\alias{h2o.get_best_model_predictors}
\title{Extracts the subset of predictor names that yield the best R2 value for each predictor subset size.}
\usage{
h2o.get_best_model_predictors(mode... |
80be2be7eaf07c404ae3bde8688ecd4788f3d963 | 09d57d28dc46e6a1029ac7370ae9fffdd84113d6 | /iris_machineLearning_knn.R | c55f31a97c0837b890701c7f056147f187f85664 | [] | no_license | QSChou/Rlearning | 2a7780a67c077e337d74a058f776b1c582bfd137 | afafb67390d85c9f57dd7628076341afb1d881b3 | refs/heads/master | 2020-05-21T22:59:20.905405 | 2017-09-12T15:31:28 | 2017-09-12T15:31:28 | 48,810,298 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,469 | r | iris_machineLearning_knn.R | # Below example use the iris data and knn to do classification
# Author: QS Chou Version 0.1 Date: 2016/01/12
# Although normaliztion is not required in the iris data set, below code still includes the normalization step for learning purpose
attach(iris)
normalize <- function(x) {
num <- x-min(x)
denum <- max(x) - ... |
0a977128b244615722c7746eab6a076effc4407c | 619c4321ffc122fa0333a7e35e6fba16d1f9368b | /man/FarmerI_yld_henrys_2018.Rd | 092b1e0e91fd47d9150b35c8d7f7b8442caead97 | [
"MIT"
] | permissive | paulhegedus/OFPEDATA | 4a6c3add78208f0250139c55d061f31775d56789 | ae70c5d8ad4fe154fd4b746a7ec43f3031a6e0a9 | refs/heads/master | 2022-12-22T21:51:29.566593 | 2020-09-14T00:14:19 | 2020-09-14T00:14:19 | 278,233,610 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,564 | rd | FarmerI_yld_henrys_2018.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/FarmerI_yld_henrys_2018.R
\docType{data}
\name{FarmerI_yld_henrys_2018}
\alias{FarmerI_yld_henrys_2018}
\title{FarmerI_yld_henrys_2018}
\format{
A data frame with 31276 rows and 39 variables:
\describe{
\item{\code{gid}}{integer Observation i... |
dcb60e62940a72ccf212b571c8453a4d9050a127 | bbf40006c142498db5b8e6d62f65a2b1023757ec | /test.r | f13130c7ccfd667c73ef5e7808edadc2ccba7019 | [] | no_license | amymscott/test | 7e2f50d75b4fd88000da39608126d8b35359112f | 756dd5b330350f8b69caf1804a777ea1647899de | refs/heads/master | 2021-06-26T07:31:03.736033 | 2017-09-15T15:36:38 | 2017-09-15T15:36:38 | 103,672,551 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18 | r | test.r | f<-4+5
a<-"apple"
|
6a21f0083d04d680fed63622f5a1dd22bc743dab | e431281e3de2743f23355c20fb378fc4cc534c34 | /R/Intermediate Practice.R | 822c81928dec3f4b4905c3ad53de1b531979b349 | [] | no_license | sue-wallace/datacamp_datascience | 4f02a207a4e78c9c440a77b3f6145d3b7637c564 | 80216a8dbf0b93e02b719dae3d6613ea16d02065 | refs/heads/master | 2020-03-11T08:04:02.707467 | 2018-04-23T13:13:28 | 2018-04-23T13:13:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,620 | r | Intermediate Practice.R | #Sue Wallace
#30/12/2017
#Intermediate Practice
##dim(x) - to see how many variables and rows there are
####LAPPY----
#useful for applying a function to lots of variables within the data
lapply(x, my_fun)
#where x is the data and my_fun is the function (i.e. sum)
d <- mtcars
sum(d$carb)
lapply(d, class)
lapply... |
c89c138b85e967e94a67aed12f2570ec4fb17b33 | c2f3e71eb4842cb5b02f367dad4680634017b92c | /.init.R | 78fe2c2167a200d83c43bfb2c7ddaee4f8ec12db | [] | no_license | MElhalawany/R_Intro-Environment | 9b7c40f1e4944be2302fc8a0bdbc2de3f890b20d | 0671311f61f5f1dd86a4e9fca07a90d4e06fc1f9 | refs/heads/master | 2022-02-01T07:48:18.677752 | 2016-09-13T21:37:52 | 2016-09-13T21:37:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 189 | r | .init.R | # .init.R
# Functions to initialize this tutorial session
# Boris Steipe
# ====================================================================
file.edit("R_Intro-Environment.R")
# [End]
|
3e58e754e1eb5c376a4557c12d51231630edf4ae | 3fdb12a1fe34aca6b96aa9047df4593404a5fc52 | /rhocnps.R | b369abab801131e01184ee2308727b1c32f93afe | [] | no_license | carnegie-dpb/bartonlab-modeling | 06c90e10df8fc37973a02db41f2c882bc8ceedfd | 7d875f16f675bf94fc04a360ae8f6855d4642619 | refs/heads/master | 2021-01-22T03:48:47.674881 | 2018-04-18T22:29:04 | 2018-04-18T22:29:04 | 81,460,423 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,135 | r | rhocnps.R | ##
## numerically solve linear transcription model for a primary and secondary target using numerical integration using input time array
##
require("deSolve")
rhocnps = function(turnOff=0, rhoc0, rhon0, nu, gammae, gamman, rhop0, etap, gammap, rhos0, etas, gammas, t) {
## derivatives for rhoc, rhon and rhop
deri... |
47f83cfb3d8c0c4ece6981eabc7ea00eddd8cfcd | 5ffffcfbf8c869c9ff1ff3620f61fe31bb7ece36 | /plot4.R | 6600853eb3ffb7fa69d989900f2707303830294c | [] | no_license | litannalex/ExData_Plotting1 | 1aa7af08fbda70f34c2c22ef7d7d893ecbd2567e | 70e67c458f4b55afba5945c22ab184b53ddeb67c | refs/heads/master | 2021-01-01T18:57:19.021576 | 2017-07-27T01:22:49 | 2017-07-27T01:22:49 | 98,469,133 | 0 | 0 | null | 2017-07-26T21:58:46 | 2017-07-26T21:58:46 | null | UTF-8 | R | false | false | 1,729 | r | plot4.R | # cleaning the workspace
remove(list = ls())
# downloading and unzipping files
URL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
f <- file.path(getwd(), "household_power_consumption.zip")
download.file(URL, f)
unzip("household_power_consumption.zip", exdir = getwd())
# rea... |
bbc1900676e4a1bad89fc385dc1ce9cc5adfb0f0 | 6f81b6ed71739feaf017ab7e8fbc011d3bd2920b | /ui.R | 221fef2a7085a96cdaa5782bfe87349415071ead | [] | no_license | Invictus666/Portfolio | 24f7d0ee37d083a7d415e9c67a6bb4fbbdcbed67 | 7d3a772524cb09d709a10e40453d6ad95df23dc5 | refs/heads/master | 2020-06-05T16:47:22.102820 | 2014-06-12T08:49:20 | 2014-06-12T08:49:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | r | ui.R | library(shiny)
shinyUI(pageWithSidebar(
headerPanel("Stock Portfolio Simulator"),
sidebarPanel(
width=2,
radioButtons("radio", label = h3("Select your asset:"), choices = list("STI ETF" = "ES3.SI", "Asian Bond ETF" = "A35.SI","Commodities ETF" = "A0W.SI","Permanent Portfolio" = "PP"),selected = "PP"),
n... |
447cc49c5b2d8f16befddea7c3bd18c94318fcc5 | 13fd537c59bf51ebc44b384d2b5a5d4d8b4e41da | /R/tests/testdir_autoGen/runit_simpleFilterTest_tnc3_49.R | e3ee57c737940da54c35984bdc7730ad1ba5e677 | [
"Apache-2.0"
] | permissive | hardikk/h2o | 8bd76994a77a27a84eb222a29fd2c1d1c3f37735 | 10810480518d43dd720690e729d2f3b9a0f8eba7 | refs/heads/master | 2020-12-25T23:56:29.463807 | 2013-11-28T19:14:17 | 2013-11-28T19:14:17 | 14,797,021 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 18,067 | r | runit_simpleFilterTest_tnc3_49.R | ##
# Author: Autogenerated on 2013-11-27 18:13:58
# gitHash: c4ad841105ba82f4a3979e4cf1ae7e20a5905e59
# SEED: 4663640625336856642
##
source('./findNSourceUtils.R')
Log.info("======================== Begin Test ==========================... |
90746ba141aeb33fda43a9e17fea2f8a5ad85f2f | b1f52acdcf0dc387702357cd18d26a54c0defbc1 | /R/get_log_density_ph.R | 0ae8c6bd7de1168012c5d16ea3e3e338bd2529bb | [] | no_license | oliviayu/robust-HHJMs | b0af6060a38af9d56a8f5c1f008c422b17ccf188 | 87179785c6a0b394871db3366967059346484a92 | refs/heads/master | 2023-06-05T23:05:20.965988 | 2021-06-28T23:33:30 | 2021-06-28T23:33:30 | 216,898,246 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 984 | r | get_log_density_ph.R | #' Get log density function of propotional hazard model
#'
get_log_density_ph <- function(model_object){
if(is.null(model_object$distribution)){
# Cox PH model
log_density <- paste(model_object$event, "*( log(hazard0) +",
model_object$reg_equation, ") - exp(",
... |
ecb1c0521d076b665328c857a47fc4dc22e2372a | 9e8936a8cc7beae524251c8660fa755609de9ce5 | /inst/add-in/parsnip_model_db.R | 37cb8c1cc37f312f432300873dad8e6992b3c8cc | [
"MIT"
] | permissive | tidymodels/parsnip | bfca10e2b58485e5b21db64517dadd4d3c924648 | 907d2164a093f10cbbc1921e4b73264ca4053f6b | refs/heads/main | 2023-09-05T18:33:59.301116 | 2023-08-17T23:45:42 | 2023-08-17T23:45:42 | 113,789,613 | 451 | 93 | NOASSERTION | 2023-08-17T23:43:21 | 2017-12-10T22:48:42 | R | UTF-8 | R | false | false | 2,798 | r | parsnip_model_db.R | # ------------------------------------------------------------------------------
# code to make the parsnip model database used by the RStudio addin
# ------------------------------------------------------------------------------
library(tidymodels)
library(usethis)
# also requires installation of:
packages <- c("pa... |
60c42e17dd03232dd909f81fb441441484c4a849 | 706bab2a3d9a6b6372820609f7f2154d0df38fae | /R/localize.R | a8a8ca8638eb49e9bd752f416901e9c64bc0ea85 | [] | no_license | vjcitn/AnVIL | 3c2ea79e6dcd72d7e2fc37dcc2ec94a5d11d6171 | 1e2790a22b5128209297b5a5ff8dbe032f279f48 | refs/heads/master | 2021-07-02T17:27:27.074280 | 2021-06-29T10:40:16 | 2021-06-29T10:40:16 | 172,901,323 | 0 | 0 | null | 2019-02-27T11:16:39 | 2019-02-27T11:16:39 | null | UTF-8 | R | false | false | 6,947 | r | localize.R | #' @rdname localize
#'
#' @title Copy packages, folders, or files to or from google buckets.
#'
#' @description `localize()`: recursively synchronizes files from a
#' Google storage bucket (`source`) to the local file system
#' (`destination`). This command acts recursively on the `source`
#' directory, and... |
217f76bd24f5b222c4617c794ac646692ea924ac | 421ae58289f144dfe3d8b6797ab5a3d76c491ae1 | /script.R | 301cb29d365d419caa3cab141cbc7a6ea0bc25c6 | [] | no_license | Jkang-alien/Predictive_Model | 4970d25cf7a5d9b234e886bc5ebdf9b5340d2e58 | 22457ddc2049d4e1b34c7598a13a848da6eea1ec | refs/heads/master | 2020-04-07T10:52:56.953500 | 2018-12-18T03:52:04 | 2018-12-18T03:52:04 | 158,303,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,518 | r | script.R | library(AmesHousing)
data("ames_raw")
library(purrr)
map(ames_raw, names)
names(ames_raw) <- gsub(' ', '_', names(ames_raw))
map(ames_raw, class)
library(dplyr)
ames_raw <- ames_raw %>%
mutate(log_sale_price = log10(SalePrice))
summary(lm(log_sale_price ~ Lot_Area, ames_raw))
library(tidyverse)
library(rsample)... |
4524da6c48242b585eba93cb7ad25b47e82cbf7b | 24140a55b535e2207ebc717590a961f737a31674 | /FantasyFootball/Tests.R | e95efbbf9b8eea3f1af7f380a0d59d675544e156 | [] | no_license | im281/R | 066410decf6a783947873049bd5f65df60312bb9 | 26ed130bdfd9abc5f3a84a9cc2fca1e5863aaa9a | refs/heads/master | 2020-05-25T15:43:53.482411 | 2017-12-01T23:06:40 | 2017-12-01T23:06:40 | 69,046,643 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,255 | r | Tests.R | library(data.table)
source('FFfunctions.R')
source('FootballOptimizer.R')
#fanduel data http://rotoguru1.com/cgi-bin/fyday.pl?week=1&game=fd&scsv=1
#2015 stats
#Training#
#read the player FFP tables######################################################################################
w1 <- read.csv('C:/Users/Owner/Sou... |
3e653249a461ac054325bd30215e31252b7b9634 | 13e8faf5de82d75d115dcbe2863719798523f160 | /workout02-jacqueline-wood/workout02-jacqueline-wood.R | 6698153eac61dbd49c0fe15390cfb5c13a709089 | [] | no_license | stat133-sp19/hw-stat133-jacquiwood1 | 5c8cee72388b35b557459e41c35d844ba91e7066 | 2243a55762227c4cd2470c5d7b73a9976d6d2d67 | refs/heads/master | 2020-04-28T19:10:58.065360 | 2019-05-03T05:01:58 | 2019-05-03T05:01:58 | 175,503,117 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,351 | r | workout02-jacqueline-wood.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
library(reshape2)
# Define UI for application that draws a histogram
ui <- fluidP... |
32e4641fa93ac1a8d68f2343eebd13b0dad6520c | e573bc7fd968068a52a5144a3854d184bbe4cda8 | /Recommended/survival/R/print.survreg.R | 7b87054e799c5676eec77cff376373c325bfb6eb | [] | no_license | lukaszdaniel/ivory | ef2a0f5fe2bc87952bf4471aa79f1bca193d56f9 | 0a50f94ce645c17cb1caa6aa1ecdd493e9195ca0 | refs/heads/master | 2021-11-18T17:15:11.773836 | 2021-10-13T21:07:24 | 2021-10-13T21:07:24 | 32,650,353 | 5 | 1 | null | 2018-03-26T14:59:37 | 2015-03-21T21:18:11 | R | UTF-8 | R | false | false | 1,878 | r | print.survreg.R | print.survreg <- function(x, ...)
{
if(!is.null(cl <- x$call)) {
cat(gettext("Call:", domain = "R-survival"), "\n", sep = "")
dput(cl)
}
if (!is.null(x$fail)) {
cat(gettext(" Survreg failed. ", domain = "R-survival"), x$fail, "\n", sep = "")
return(invisible(x))
}
coef <- x$coef
... |
e2148b50ee44b81d572d40681aef58fae127426e | 3de36a93bafc5f58aaaeb316d2d7bf7c774e2464 | /R/ordiArrowTextXY.R | 08c43935719d01d1f69ca7ee085de657d9a38f5f | [] | no_license | vanderleidebastiani/vegan | fc94bdc355c0520c383942bdbfb8fd34bd7b4438 | dd2c622d0d8c7c6533cfd60c1207a819d688fd1f | refs/heads/master | 2021-01-14T08:27:15.372938 | 2013-12-17T18:19:10 | 2013-12-17T18:19:10 | 15,258,339 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 968 | r | ordiArrowTextXY.R | ### Location of the text at the point of the arrow. 'vect' are the
### coordinates of the arrow heads, and 'labels' are the text used to
### label these heads, '...' passes arguments (such as 'cex') to
### strwidth() and strheight().
`ordiArrowTextXY` <-
function (vect, labels, ...)
{
w <- strwidth(labels, ...... |
5a824045b7e65f7b153ee3e0c66c248a8b90e663 | 93d1fcc7758e5e99927be0529fb9d681db71e70c | /man/ma_r_ad.int_rbOrig.Rd | c143ba7d798ba6f580316c01b57def7f9f043ae5 | [] | no_license | psychmeta/psychmeta | ef4319169102b43fd87caacd9881014762939e33 | b790fac3f2a4da43ee743d06de51b7005214e279 | refs/heads/master | 2023-08-17T20:42:48.778862 | 2023-08-14T01:22:19 | 2023-08-14T01:22:19 | 100,509,679 | 37 | 15 | null | 2023-08-14T01:06:53 | 2017-08-16T16:23:28 | R | UTF-8 | R | false | true | 1,217 | rd | ma_r_ad.int_rbOrig.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ma_r_ad_rb_orig.R
\name{ma_r_ad.int_rbOrig}
\alias{ma_r_ad.int_rbOrig}
\title{Interactive artifact-distribution meta-analysis correcting for Case II direct range restriction and measurement error}
\usage{
ma_r_ad.int_rbOrig(x)
}
\arguments{
\... |
c88b3ed765546e87cdab643ea64290dfcfa369c7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BSDA/examples/Homework.Rd.R | 5c79144cb4409c5a83ad83b0e03406fc33efe2e4 | [] | 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 | 340 | r | Homework.Rd.R | library(BSDA)
### Name: Homework
### Title: Number of hours per week spent on homework for private and
### public high school students
### Aliases: Homework
### Keywords: datasets
### ** Examples
boxplot(time ~ school, data = Homework,
ylab = "Hours per week spent on homework")
#
t.test(time ~ school, d... |
0919d16c74e701c2bd396127430a6eae2f037672 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/AFL_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1615831425-test.R | 4317fd6f931940bcb7ba5393fd78fb4c283ed32a | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 985 | r | 1615831425-test.R | testlist <- list(doy = numeric(0), latitude = numeric(0), temp = c(1.11755167473006e+282, 4.71368177582468e+139, 5.65292823326516e-40, 1.53140141627925e+224, 1.50419290762429e+129, -4.04978652716019e-308, 2.86826423903782e+281, -9.75369706896322e-89, -4.15938612166902e-209, 2.49946050374498e+237, 1.24978552383655e-... |
7eb9b0672710516cdfa3ef92c531841a764ea520 | d4d2d370f8cb50e002a3489d2e2b9186651ef92f | /man/kolmogorov_smirnov_test.Rd | d905c2bc6d4811cca9e674ede0b4239cdd8d62b0 | [] | no_license | momeara/RosettaFeatures | 2c45012b042a76b0176a0924f1cc60fe3ba06e8b | 2700b0735071971bbd2af91a6b1e7454ceeaa2a6 | refs/heads/master | 2021-01-19T03:54:05.386349 | 2017-03-24T14:07:21 | 2017-03-24T14:07:21 | 47,008,643 | 1 | 3 | null | 2016-06-16T23:00:32 | 2015-11-28T03:28:34 | R | UTF-8 | R | false | true | 486 | rd | kolmogorov_smirnov_test.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/support-comparison_statistics.R
\name{kolmogorov_smirnov_test}
\alias{kolmogorov_smirnov_test}
\title{Prefer to use the anderson_darling_2_sample comparison
cf https://asaip.psu.edu/Articles/beware-the-kolmogorov-smirnov-test}
\usage{
kolmogo... |
b38a42d38bffbe46e43b04fe304914eaa83d591a | bced4e9f5c173c81572eb12c131b7e8fb3b13816 | /R/summary.BsProb.R | 54f06b438929ac52833ceca5a00eea7c46334b0c | [] | no_license | cran/BsMD | 6708ccdab9632b351066d06088a2be2bb612d8a8 | 19040212ddc6a08f8629fb1395084a0efea10424 | refs/heads/master | 2023-07-19T11:15:59.320221 | 2023-07-07T18:10:11 | 2023-07-07T18:10:11 | 17,678,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,356 | r | summary.BsProb.R | summary.BsProb <-
function (object, nMod = 10, digits = 3, ...)
{
nFac <- ncol(object$X) - object$blk
cat("\n Calculations:\n")
if (object$INDGAM == 0) {
if (object$INDG2 == 0) {
calc <- c(object$N, object$COLS, object$BLKS, object$MXFAC,
object$MXINT, object$P,... |
b92896e400f15384fd93b72b2ba32140e659f7e4 | be93098682095c32c706f55c1e424669d5ea4971 | /man/scREhurdle-package.Rd | dcef8c41d1328f261947f539492be8a4d24c1087 | [] | no_license | mnsekula/scREhurdle | 2aa7f77ce9ce5ab64311a05a7f8a5254615bfa21 | 1a99d834673f1585011f588e3001b409925c5071 | refs/heads/master | 2020-04-17T22:40:51.653391 | 2019-02-24T21:27:36 | 2019-02-24T21:27:36 | 167,004,331 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 525 | rd | scREhurdle-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scREhurdle-package.R
\docType{package}
\name{scREhurdle-package}
\alias{scREhurdle-package}
\title{The 'scREhurdle' package.}
\description{
R package implementing the methods from "Detection of differentially expressed genes in discrete singl... |
dddff4306fcfdf80a9263c1e6fc6e6cb3f37ac02 | 631f70c82fb93562f0836ea4f17e0f18cf0b2b12 | /man/schoenersD.Rd | 0e13d9ef9182a0a67f8d028474cb800b4212918d | [] | no_license | cjcampbell/isocat | 3ec1b78d64b081e50479f020fbb6cb5466cecaa3 | 3c1a50cdfa250a657244d19b570845b61652d920 | refs/heads/master | 2022-03-08T18:08:56.885218 | 2022-03-02T21:05:54 | 2022-03-02T21:05:54 | 130,004,512 | 1 | 2 | null | 2020-07-02T16:19:21 | 2018-04-18T04:38:47 | R | UTF-8 | R | false | true | 1,219 | rd | schoenersD.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/schoenersD.R
\name{schoenersD}
\alias{schoenersD}
\title{Calculates Schoener's D-value between two RasterLayers.}
\usage{
schoenersD(rast1, rast2)
}
\arguments{
\item{rast1}{Input RasterLayer}
\item{rast2}{Input RasterLayer 2}
}
\description... |
22d0659aec2e8af9dc6733ec76553f28c7a1badc | 7527b44b1ad97f7b7481aa7d2677f308925fdda1 | /R/submit.r | 71ccbc3e1421ef36ba1e6914660ec7f91d30e1ac | [] | no_license | turbaevsky/indicators | 7c8c6d6497cab872068f90f9c0205aad55c99456 | 7e1fbca4e071384739bd9199d168f2bf0da3e9e1 | refs/heads/master | 2020-09-14T10:02:20.815776 | 2019-01-22T16:41:42 | 2019-01-22T16:41:42 | 67,811,823 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,505 | r | submit.r | #####################################################################
# To show the units numbers did not send (submit) data in time
#####################################################################
#place <- readRDS('DBCopy/PI_Place.rds')
#placeAttributes <- readRDS('DBCopy/PI_PlaceAttribute.rds')
#submit <- readR... |
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