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9c9a226156c0eec121b004aca4f926df15ce5244 | 570d4141186786df5179cc4346dd3808c1c41f26 | /wrangle/AMP-AD/rosmap.R | e080d90751dcddc0259106dfcfb20e7002e3fccc | [
"MIT"
] | permissive | ArtemSokolov/amp-ad | 552fee92c0ec30539386745210f5ed2292931144 | dd5038f2497698b56a09471c89bb710329d3ef42 | refs/heads/master | 2021-06-21T21:04:44.368314 | 2019-09-10T17:40:48 | 2019-09-10T17:40:48 | 114,150,614 | 0 | 4 | MIT | 2019-09-10T17:40:49 | 2017-12-13T17:39:02 | HTML | UTF-8 | R | false | false | 3,643 | r | rosmap.R | ## Wrangling of ROS/MAP RNAseq data and matching clinical annotations
##
## by Artem Sokolov
suppressMessages(library( tidyverse ))
suppressMessages(library( synapseClient ))
library( stringr )
## Composes a mapping between ENSEMBL IDs and HUGO names
ens2hugo <- function()
{
edb <- EnsDb.Hsapiens.v86::EnsDb.Hsapi... |
278e4541a7e5eb551538ea5585c6b2385847cbb5 | 0a19c5a8ee204ddf9d6c8ac95692e551c20913b7 | /knn_tuning_FP.R | e6b44237a789ab8505948e62abd92ff71a9923a9 | [] | no_license | noahholubow/airbnb_nyc | 53f20e4177f4014d6b17ad9306e7217493e640c2 | be69e0e0b3d2547118071a9cc529d98561f45bfc | refs/heads/main | 2023-03-17T21:17:16.840135 | 2021-03-15T15:40:28 | 2021-03-15T15:40:28 | 348,025,324 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 750 | r | knn_tuning_FP.R | # Boosted Tree tuning
# load packages
library(tidyverse)
library(tidymodels)
# set seed
set.seed(2021)
# load necessary items
load("airbnb_setup_log.rda")
# define model
knn_model <- nearest_neighbor(
mode = "regression",
neighbors = tune()) %>%
set_engine("kknn")
# setup tuning grid
knn_params <- parameter... |
c24be13e10e6346ce45850eb3d4ef2b4c48e56e3 | e79df1c2164b29c127c2102bc4495b2384f3895e | /R/preview_images.R | 7186cdb782826ea9cf990b938b3ed8bf01fc2093 | [
"CC-BY-4.0",
"MIT"
] | permissive | feddelegrand7/ralger | 1b263a3c15c3b208a96a633f30ec2433efb4aabc | 57ebc6b07511675c23d91007e701a9722aeb86d4 | refs/heads/master | 2023-03-13T08:54:24.066033 | 2023-03-05T20:41:03 | 2023-03-05T20:41:03 | 241,394,878 | 162 | 18 | NOASSERTION | 2022-06-18T19:16:22 | 2020-02-18T15:21:00 | R | UTF-8 | R | false | false | 2,027 | r | preview_images.R | #' Scrape Images URLs
#'
#' @param link the link of the web page
#' @param askRobot logical. Should the function ask the robots.txt if we're allowed or not to scrape the web page ? Default is FALSE.
#'
#' @return Images URLs
#'
#' @examples \donttest{
#'
#' images_preview(link = "https://rstudio.com/")
#'
#' }
#'
#' @e... |
38dcba31dec840cb36c1067e27068bf07fe92540 | 93286036db17d0a5d6f49031ff109e48ee4688d0 | /isogram/isogram.R | ef96e4e4530e58447d3324528f4257933d209290 | [
"MIT"
] | permissive | stephenfeagin/exercism-r | 378dbf2f1c89e1d0703a48cbb2ab52df37f40056 | 0ac8a5b4a0e726fa97dce2b7e9ae9ffaa77e08f0 | refs/heads/master | 2020-04-11T19:02:55.171832 | 2018-12-17T01:23:32 | 2018-12-17T01:23:32 | 162,020,200 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 386 | r | isogram.R | is_isogram <- function(word) {
# Extract just the alpha characters
chars <- gsub("[^[:alpha:]]", "", tolower(word))
# Split into a vector
chars_vec <- strsplit(chars, "")[[1]]
# If the length of the original vector is equal to the length of the vector
# with duplicates removed, then the word i... |
0ca4901e5e7637444c077a5206a61709955309a0 | 187e9cc9d5ef51c4024ff20418416cdf8883cece | /R/probAffectedRelative.R | 2a0a8f0ac9c1951d21469ed598c8f7f19f52fd20 | [] | no_license | DudbridgeLab/familialdisease | adc67735415b6c5ba2d3d5da78fc88e4c2353524 | 7a4dadcb2d8112c2af01ce89607eca33e0a6e65e | refs/heads/master | 2021-01-02T08:59:29.244137 | 2018-07-17T19:42:47 | 2018-07-17T19:42:47 | 99,115,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,801 | r | probAffectedRelative.R | #' Probability of a relative being affected with familial disease
#'
#' Given a series of pedigrees of different sizes, each with at least r affected relatives of the proband,
#' probAffectedRelative estimates the probability of a relative being affected with familial disease,
#' where this probability is an averag... |
8b0ae95dab69baa360ba2140c8f3dce0dea6ab44 | 556fbe5b5bfec4a57f03d70656132ad36e4703b7 | /raw_scripts/DimExploreRel.R | e3d9a9a6f87974b72fa6aaf10414e3577fa0a638 | [] | no_license | mastoffel/scent | 1cf03da1b41f4a161421d5e98a884877da2ee667 | cbc1beca6a455f3f2d23ba7c51dbe9e4aa706e61 | refs/heads/master | 2021-01-20T05:33:37.163758 | 2015-05-18T16:21:25 | 2015-05-18T16:21:25 | 24,264,638 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,938 | r | DimExploreRel.R | DimExploreRel <- function(y, x, method = "fa", distance = "bray", subset = "all") {
## function to explore different dimension reduction / ordination methods for similarity matrix models
##
## libraries
library(psych)
library("ggplot2")
library("grid")
library("v... |
404589c0377a92bbfb22b924e0663412667b2aef | 9474632c610ac788f0c6338ada158ef54a58ddb8 | /R_history_05_03_pm.R | 981f8ad151d434caadf7e9d4d936f836116ce192 | [] | no_license | davekk/summary_r_scripts | 62aa6aed3820733fb50c686ed91f88d3e807797b | c00a8092024b1c30a4084f01af0090581f428c85 | refs/heads/master | 2020-03-22T20:12:21.538896 | 2019-07-10T14:24:05 | 2019-07-10T14:24:05 | 140,581,071 | 0 | 4 | null | null | null | null | UTF-8 | R | false | false | 17,511 | r | R_history_05_03_pm.R | ## IMPORTING DATA FROM FILES
# main function is read.table()
getwd() # identical to Bash's pwd
getwd
getwd()
getwd("~")
getwd("/home/jbde/")
setwd("~")
getwd()
setwd("Trainings/Biostats_and_R_bixcop_github/module2_R_biostats/")
setwd("~/Trainings/Biostats_and_R_bixcop_github/module2_R_biostats")
# LOADING THE DATA
read... |
f0722d2d7c7238f1eb45fdf6447977478a9b1bfe | f549e55767e873c144811ec2c7fd9e7db93717fa | /run_analysis.R | 06b34d50523353eddd68ba82d975196bf82dca65 | [] | no_license | bammoss/tidydata | 1c5127115f09d110a1ee04e3c2abd19f1b8c454f | a66c45e8ea0672ff0637b1b4fa12da026abfb213 | refs/heads/master | 2021-01-10T20:59:40.900493 | 2015-06-21T21:44:56 | 2015-06-21T21:44:56 | 37,824,369 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,867 | r | run_analysis.R | run_analysis<-function(){
#This is a major note, I changed the title in file so that there were no spaces
#The next 7 lines are for extracting the relevant information from the file.
subject_test<-read.table("UCIHARDataset/test/subject_test.txt")
x_test<-read.table("UCIHARDataset/test/X_test.txt")
y_test<-read.table("U... |
0ed632786641f855ff560b27b28edf364fc3918c | 4467cfb9f142f3b709ef8f1245f740fc2924280b | /filter_contig_by_length.R | e00d477419e89bf9a5281683a5923e4f970a86de | [
"MIT"
] | permissive | laninsky/genome-scripts | 550c0883296b46fe7267a3a8412a2b2107dbc241 | a5942d4406fe0ab9a5599f10955cd6b471b4ca2d | refs/heads/master | 2020-05-21T19:22:43.811028 | 2019-08-12T04:17:29 | 2019-08-12T04:17:29 | 65,322,730 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,259 | r | filter_contig_by_length.R | filter_contig_by_length <- function(file_to_filter,contig_length) {
if(missing(file_to_filter) | missing(contig_length)) {
print(paste("This script needs you to define the location and name of your fasta file and the minimum contig length you want to retain in the outfile: length_filtered_genome.fasta"))
print(pas... |
05f0e1a310800c1f7bf79421681354108ac92846 | 7dafb67a629570e6e3ba72c5ae24d441fca5e208 | /library_prep/01_simulate_library_prep.R | bb53a375c94a3f5daabb9de90d05ffafe80af987 | [] | no_license | ksamuk/dpse_gbs | 09f97fb1e10c163cc9c46c924a2e82fe6b9f3e0d | c96b7d86fb0faae84ee8352a49b2320e81b72ffe | refs/heads/master | 2020-12-25T15:07:57.184459 | 2017-05-24T14:08:13 | 2017-05-24T14:08:13 | 67,240,974 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,339 | r | 01_simulate_library_prep.R | # simulate a GBS library prep using the Drosophila pseudoobscura reference genome
# examine resultant coverage/fragment distribution for different restriction enzymes
# kms sept 2016
############################################################
# libraries
############################################################
#... |
8af9e29072139467a3671f389d481903bae552fb | 7dc24ce2d943197c2d8d20e9cb25d32f7e4399be | /man/Stool_subset.Rd | 4d7d7d74585432022d1d23d45cccd8f15017e0a8 | [] | no_license | biobakery/SparseDOSSA2 | 26f9ceb91a2965b119d783b07b3cd02ee75d6027 | e013d9e3c0fd79e1c343340775f33f14f22b8c5e | refs/heads/master | 2023-01-24T09:26:23.553053 | 2023-01-19T16:45:46 | 2023-01-19T16:45:46 | 219,829,612 | 9 | 2 | null | 2022-10-21T17:36:22 | 2019-11-05T19:05:37 | R | UTF-8 | R | false | true | 538 | rd | Stool_subset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{Stool_subset}
\alias{Stool_subset}
\title{A subset of the HMP1-II stool samples}
\format{
A matrix with 5 rows (species) and 5 columns (samples)
}
\source{
\url{https://www.hmpdacc.org/hmp/}
}
\usage{
Stool_subset
... |
423985adec48f0ee7eb6517548180def62f08a9d | 9d5c31dae452a3a06ba98911327dd719561acaa0 | /PlotLowerBoundCases.R | 3d63089f26d685b32b7f73baf17620d055ffcdf3 | [] | no_license | nickwkoning/Thesis | 3f37b58dfa8f8e4723c0c775fa03b032f3b33f3f | 1af11be7ea1b4e8ffb539b3d78e04acbf62e2eb2 | refs/heads/master | 2022-11-04T11:27:23.868684 | 2020-06-16T13:30:12 | 2020-06-16T13:30:12 | 263,144,109 | 0 | 0 | null | 2020-05-11T19:54:45 | 2020-05-11T19:54:45 | null | UTF-8 | R | false | false | 2,647 | r | PlotLowerBoundCases.R |
source("Packages.R")
source("Thesisggtheme.R")
baseplot = ggplot() +
geom_rect(aes(xmin = 0, xmax = 2, ymin = 0, ymax = 2),
alpha = 0, color = "black") +
scale_x_continuous(breaks = c(0, 1, 2),
labels = c(0, TeX('$C_{\\alpha} - \\delta_{p,n}^2$'), TeX('$\\delta_{p,n}^2$')),
... |
88878ed31c85bc63ecb9952d4960eee3389c6e88 | bde44ebcaa25a97b55d8f553b85ae00e3980375b | /inst/essais/ellipsoid.R | 0b22d03389a735b1ff61f6209f027f367d65acca | [] | no_license | stla/uniformly | 34a1165db694709c90294a4e19499102c5bb5bf9 | 9eea0f9ecf6606529173f169586ef791d9e84a6a | refs/heads/master | 2023-07-20T14:58:32.464075 | 2023-07-18T07:11:43 | 2023-07-18T07:11:43 | 142,665,262 | 10 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,301 | r | ellipsoid.R | library(PlaneGeometry)
library(uniformly)
A <- rbind(c(2, 1), c(1, 1))
r <- 1
sims <- runif_on_ellipsoid(10000, A, r)
alpha1 <- 10 * pi/180
alpha2 <- 60 * pi/180
ell <- EllipticalArc$new(ell, alpha1=0, alpha2=360, degrees = TRUE)
perimeter <- ell$length()
mean(atan2(sims[,2], sims[,1]) > alpha1 & atan2(sims[,2], si... |
217555a84d0d26df5a1a432f8a29447bd218330f | 969d4316ad794a0eef0213b01a7b06ddfdf8d90d | /13_expressions/07_walking_ast/exercise6.r | 34dbcab2e2dcf138abbcc4a5442ae9901308877e | [] | no_license | Bohdan-Khomtchouk/adv-r-book-solutions | adaa5b5f178999d130aff1359a23e978e39e86ae | e1b3a63c0539de871728b522604110c0aa18c7d1 | refs/heads/master | 2021-01-22T00:36:17.450660 | 2015-12-06T02:54:02 | 2015-12-06T02:54:02 | 47,481,353 | 1 | 1 | null | 2015-12-06T02:49:46 | 2015-12-06T02:49:46 | null | UTF-8 | R | false | false | 800 | r | exercise6.r | ### Compare bquote2() to bquote(). There is a subtle bug in bquote():
### it won’t replace calls to functions with no arguments. Why?
bquote(.(x)(), list(x = quote(f)))
# .(x)()
bquote2(.(x)(), list(x = quote(f)))
# f()
bquote(.(x)(1), list(x = quote(f)))
# f(1)
# Here's the source for `bquote` (from `base`):
bquote ... |
6d4eb4a06d4c323c645a25031cc9ed9d5b14d2e8 | c9e9fb81b2680f5adaa8600426f713a43853b80a | /AdventofCode2015_1.R | 7daaa8695a7e5117ce7a7a1944b29b6074003457 | [] | no_license | codesformochi/AdventofCode_2015 | 55c55552e2ce9d535904e7220d96cea8250ac430 | c8d1f4747da56d27069a7481bb3c14e5d557285d | refs/heads/main | 2023-06-10T07:22:28.963444 | 2021-06-22T15:59:11 | 2021-06-22T15:59:11 | 336,143,592 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 848 | r | AdventofCode2015_1.R | #libraries
library(stringr)
#read in rtf
floors <- striprtf::read_rtf("adventofcode2015_1.rtf")
#PART1
#Count all occurences of '(' and all occurrences of ')'
start_parentheses <- str_count(floors, fixed("("))
end_parentheses <- str_count(floors, fixed(")"))
#Find the final floor
total_floors <- start_parentheses -... |
a620b22b580754cad06b2a04538711b1cd59af75 | 76cf5e3c9d1ca3a7b1c8c395de7ab89dade4f3b6 | /man/WAD.Rd | d1fec52ab100676cf0e16c38feabc42586099b03 | [] | no_license | swsoyee/TCC | 4a12ac14f87bce91c0a3123167e6e33f324f4bc0 | e0d41326d1e725cb12a23c87102c3454746dddf8 | refs/heads/master | 2023-04-29T05:14:06.435553 | 2021-03-11T10:56:29 | 2021-03-11T10:56:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,910 | rd | WAD.Rd | \name{WAD}
\alias{WAD}
\title{Calculate WAD statistic for individual genes}
\description{
This function performs WAD method to identify differentially expressed genes
(DEGs) from two-group gene expression data. A high absolute value for the WAD
statistic is evident of a high degree of differential expression.
}
\usage{... |
a249da6a1753369772a0d82a9f2c367282c01c03 | 7cf590876873d130474a448f55bd696d0aa08689 | /pack7/R/visualize_airport_delays.R | 4d371d8ef500a88aef8ca1308aaf9d1db7d40b78 | [] | no_license | jvf96/pack7 | d8bb67e71863334286cd768850d0a1fed9d9dae0 | ee5a999f0dc1255153d7c6dc7693124f526374d0 | refs/heads/master | 2021-08-16T12:59:27.780246 | 2017-11-19T23:15:01 | 2017-11-19T23:15:01 | 111,150,110 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 947 | r | visualize_airport_delays.R | #' Visualize airport delays
#'
#' @return a plot with latitude and longitude
#' @importFrom stats na.omit
#' @export visualize_airport_delays
#'
visualize_airport_delays <- function(){
flights <- nycflights13::flights
airports <- nycflights13::airports
data_flights <- na.omit(flights)
data_f... |
e3c3ed3e23f3066e5a68cb631cfb4a35dbe456f4 | dd726f4f83fdb6ef8c4a2b7486795da27b1b4fc2 | /r/2_19제출/CCTV/map_practice/map_practice/Script.R | 1dbfd9ccb9e85cebd7b6823b3598d28183e638df | [] | no_license | mgh3326/big_data_web | 84890dc72cd0aa1dd49be736ab1c6963611ee4a5 | f5cae3c710414697a1190ad57469f26dd9c87d8a | refs/heads/master | 2023-02-20T07:28:32.024292 | 2019-09-04T15:49:13 | 2019-09-04T15:49:13 | 119,160,730 | 0 | 1 | null | 2023-02-15T21:30:18 | 2018-01-27T12:02:38 | HTML | WINDOWS-1252 | R | false | false | 334 | r | Script.R | setwd("C:\\easy_r")
install.packages("ggmap")
library(ggmap)
gangbuk <- read.csv("project_gangbuk_data.csv", header = T)
#°ºÏ±¸_Àüü CCTV
g_m <- get_map("gangbukgu", zoom = 13, maptype = "roadmap")
gang.map <- ggmap(g_m) + geom_point(data = gangbuk, aes(x = LON, y = LAT), size = 2, alpha = 0.7, color = "#980000")
gang... |
02ad58a99cd086a7bbbdf8f1f83a398f4a3613d6 | 880aff9580efa6b508db8aab0c16e36fd305acba | /NSF_Awards/nsf_awards/ui.R | 3a10f609990fb2d7f98f57408dde30cf7ddb6fd0 | [] | no_license | cmwright12/shinyapps | 5b8e225e10c468fed6376470cd997b6fb22b3eb3 | 69fc60a49f08a9c611c36d730164ae6ad001833b | refs/heads/master | 2021-01-22T01:06:10.450687 | 2017-09-05T16:09:37 | 2017-09-05T16:13:43 | 102,199,480 | 0 | 0 | null | 2017-09-05T16:13:44 | 2017-09-02T13:46:09 | R | UTF-8 | R | false | false | 1,147 | 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(dplyr)
library(tidyr)
filename <- "Awards.csv"
data <- read.csv(fil... |
685fddaeca08b40438851e94c3575c3bad7e30f8 | bc113c18c979f88158d1e2557efb81bf01f44e35 | /man-roxygen/roxlate-ml-feature-estimator-transformer.R | 2529450a0192af62d78aa8cfd9792dd98ed0e903 | [
"Apache-2.0"
] | permissive | awblocker/sparklyr | 6524ce9ac1d9c24392cd9e179ca8836851d3c93f | 18d9df6a6755f8bd10f81721e71c4f818a115084 | refs/heads/master | 2020-04-08T09:27:47.362403 | 2018-11-26T20:02:49 | 2018-11-26T20:02:49 | 159,225,148 | 0 | 0 | Apache-2.0 | 2018-11-26T20:03:28 | 2018-11-26T19:59:20 | R | UTF-8 | R | false | false | 910 | r | roxlate-ml-feature-estimator-transformer.R | #' @param dataset (Optional) A \code{tbl_spark}. If provided, eagerly fit the (estimator)
#' feature "transformer" against \code{dataset}. See details.
#'
#' @details When \code{dataset} is provided for an estimator transformer, the function
#' internally calls \code{ml_fit()} against \code{dataset}. Hence, the met... |
2d8efd6f45e57e4e752198df159e36939f2395fe | 4974323011c90a93fdaeb44cd0b50d3b8ba605a9 | /cachematrix.R | 846df7c96d253ebf8efa20f2e88acb0fd0bbd338 | [] | no_license | ricardocarvalhods/ProgrammingAssignment2 | ed01a5414ad46a44291a7679bbc4c51550ff8bf4 | ad837edaca837bb33bc1a972fa03ccf3c3e3c49e | refs/heads/master | 2021-05-28T11:09:05.136713 | 2014-04-28T01:32:23 | 2014-04-28T01:32:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,688 | r | cachematrix.R | ## This file contains functions that manipulates matrix and its
## inverse and enables caching the inverse to avoid recomputation
## This function take a matrix as input and creates a list with
## function variables that enables getting/setting the matrix and
## its inverse
makeCacheMatrix <- function(x = matrix()) {... |
fa1950994524d1ae6d43165b8267224b3504c29d | 901331f01a6cd4ebdba4e25f30fd4a36da924191 | /man/objfc.multipic.Rd | 90f50d678e79aa0e2c8496e3345f13a0838475ea | [] | no_license | priscillafialho/mopt | f49a26add6ef11096fc97bf6ea89d1cb2d7cc29d | 6b7fc8124a56a0239225296114ff6128cf9b4a40 | refs/heads/master | 2021-01-22T16:37:34.838400 | 2014-10-22T23:11:01 | 2014-10-22T23:11:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 389 | rd | objfc.multipic.Rd | \name{objfc.multipic}
\alias{objfc.multipic}
\title{create an objective function n dimensions and
k local maximums, returns maximum and function}
\usage{
objfc.multipic(n, k, x.max)
}
\description{
create an objective function n dimensions and k local
maximums, returns maximum and function
}
\examples{
x.max = ru... |
c6e85b32309660e6d0f15e5868715e71f295f37e | dbec26938945a66e2ea26308f04cde7fec7ce198 | /Visa Project/global.R | fe0ba5ee5360242733d6c127b3dc7018340d2f9d | [] | no_license | nmaloof/ShinyVisas | b283f0543c7f87c58261d07c008fcc8098ee0437 | 38631bebe562ea4bb6e458a3ea18ce099bc62139 | refs/heads/master | 2021-07-10T21:11:56.673678 | 2017-10-14T03:08:28 | 2017-10-14T03:08:28 | 106,484,724 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 76 | r | global.R | library(shiny)
library(shinydashboard)
library(plotly)
library(googleVis) |
8ab1a4e92edab70be46399f7ecf599a682631abd | d7d0182f961c37c9cf268e240d06b35f5fd36a5c | /cachematrix.R | 5cd9f636b873365fdc356056069a7613b59604f3 | [] | no_license | djarman/ProgrammingAssignment2 | 4c398058148edfb258737ed1297006c4c861d757 | ced2a059d119077e565c5f7facc9aa820e9a1d2e | refs/heads/master | 2021-01-14T08:54:33.227622 | 2014-10-20T18:13:50 | 2014-10-20T18:13:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,600 | r | cachematrix.R | ## Devon Jarman
## Coursera: R Programming
## Week 3
## Solution to Programming Assignment 2
## 1. `makeCacheMatrix`: This function creates a special 'cacheMatrix' object
## that can cache its inverse.
## 2. `cacheSolve`: This function computes the inverse of the special
## 'cacheMatrix' returned by `makeCacheMatr... |
f62c5b3bd463fca42a9ea6511881ed2d4d424107 | 1256464f6234f9a9ff380a4b4739142201655f36 | /man/get_GM_network.Rd | 1cd9ddee2e51e1a3ba462fbd01cc2f25befe43ca | [] | no_license | hclimente/martini | 6dd8a6c13454e739171d82d51b82a133a22b3ee0 | 544b8dd6762f5ede704a9471b940dba258ede8ed | refs/heads/master | 2023-02-01T00:05:37.682562 | 2023-01-11T08:20:52 | 2023-01-11T08:20:52 | 87,456,798 | 4 | 2 | null | 2023-01-11T08:20:54 | 2017-04-06T17:31:13 | R | UTF-8 | R | false | true | 1,608 | rd | get_GM_network.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networks.R
\name{get_GM_network}
\alias{get_GM_network}
\title{Get gene membership network.}
\usage{
get_GM_network(
gwas,
organism = 9606,
snpMapping = snp2ensembl(gwas, organism),
col_genes = c("snp", "gene")
)
}
\arguments{
\item{g... |
b416b0d59aac6789e183bbd5920e30887bbdd950 | 6e22438c19b27043c4b3a188d663b37df935d6cd | /src/analysis/bee/RF_analysis_plots_bees.R | ecdd5d299cefa6cc2ed78b100d212721199393ef | [] | no_license | vitorpavinato/Tracking-selection | fc42a930dee0e251123f539473091310dc9a1521 | d05f49f81932ef0d45e288f1be1906145d84a3fc | refs/heads/master | 2022-11-01T18:01:36.226996 | 2022-10-24T14:14:04 | 2022-10-24T14:14:04 | 113,848,735 | 0 | 2 | null | 2022-05-18T13:35:41 | 2017-12-11T11:04:57 | R | UTF-8 | R | false | false | 13,634 | r | RF_analysis_plots_bees.R | ########################################
## Manuscript plots ##
## Application ##
########################################
library(gtools)
## DENSITY PLOTS
##---------------------------------------
## JOINT INFERENCE OF DEMOGRAPY AND SELECTION
##------------------------------... |
5ad31e9e02b73d7f45a8a569ba1f3ff9b0d08200 | 24851be32893bfb1027b2a33164ef515fc4fb76b | /code/plotting/OLD/plotics.r | bfbb054f085578f356a2fad7836441a713df07b1 | [] | no_license | qdread/forestlight | acce22a6add7ab4b84957d3e17d739158e79e9ab | 540b7f0a93e2b7f5cd21d79b8c8874935d3adff0 | refs/heads/master | 2022-12-14T03:27:57.914726 | 2022-12-01T23:43:10 | 2022-12-01T23:43:10 | 73,484,133 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,315 | r | plotics.r | # Visualize the information criteria
# Edit 15 Aug: use new data and include new file paths
ics <- read.csv('~/google_drive/ForestLight/data/data_forplotting_aug2018/ics_by_fg.csv', stringsAsFactors = FALSE)
# Midsize
#ics <- read.csv('C:/Users/Q/Dropbox/projects/forestlight/ics_by_fg_midsizetrees.csv', stringsAsFact... |
5725f280552beb36a3c0d6dbccbcbaaa9cb51e0c | 772e23d19ad10ddd4a6bb3ed98354ce0fe7f06fa | /plot4.R | 7e6cd4b7f85494a71bb8078560415c5ab8b8d19e | [] | no_license | MarcelaGuevara/ExData_Plotting1 | 40fd17d2d04c73674f69e97a318850a22bc34ea2 | 566fba1a9a8471a5e19fc90b294f2b9e7396f24b | refs/heads/master | 2022-12-11T08:43:04.688862 | 2020-09-10T04:25:40 | 2020-09-10T04:25:40 | 294,159,442 | 0 | 0 | null | 2020-09-09T15:50:39 | 2020-09-09T15:50:38 | null | UTF-8 | R | false | false | 1,701 | r | plot4.R | library(dplyr)
#Read the data
unzip("./exdata_data_household_power_consumption.zip")
data=read.csv("./household_power_consumption.txt",sep = ";",nrows = 100000)
#Filter the dates we are going to use for the analysis
data=filter(data,Date=="1/2/2007" | Date=="2/2/2007")
#Transform into one column with date ... |
b5ef50e6ac3a4624a876bd306c47d0201422278f | 66d0e5ad41a55ef019de7c3e370b8de64dd1da44 | /inst/doc/graph_comparisons_4.R | 3adcc5ca7a705dab2d324caf295c3ba30779e791 | [] | no_license | paigemaroni/graph4lg | 29bce7f42756267c791df74280fe1614b30e7e7b | 1687c04b05c2125c0217d57dfc29618d4229a0a3 | refs/heads/master | 2023-06-02T20:47:22.424437 | 2021-06-22T00:05:48 | 2021-06-22T00:05:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,417 | r | graph_comparisons_4.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(graph4lg)
library(igraph)
## ---- echo = FALSE, eval = TRUE-----------------------------------------------
data("data_tuto")
mat_dps <- data_tuto[[1]]
mat_p... |
d6caacc32c3e75919a9f27c2793ffb74b4eb0c31 | 761b4aaa1cd79d422ae960028578f180637d7a09 | /R/sv.R | 25f83ed4ac2c6e14ecc3c267b82c48a333594547 | [] | no_license | cran/dhglm | 279ee39c3be14c587aabb9a7b7bc3859d94f445e | a83ad49e7074126dded7395a6fb613cc0c441a02 | refs/heads/master | 2020-06-04T04:34:41.668052 | 2018-10-25T07:30:03 | 2018-10-25T07:30:03 | 17,695,491 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 685 | r | sv.R | sv <-
function(RespDist="gaussian",BinomialDen=NULL, DataMain, MeanModel,DispersionModel,
PhiFix=NULL,LamFix=NULL,mord=0,dord=1,REML=TRUE,Maxiter=200,convergence=1e-02,Iter_mean=3) {
n<-nrow(DataMain)
phi<-matrix(1,n,1)
lambda<-matrix(1,n,1)
tau<-matrix(1,n,1)
date<-matrix(c(1:n),n,1)
Da... |
0c95769c6747b987e33b1151010427e1a65988ec | 15b2666efdeade833221c1b2ae1f2c97db7ed010 | /MegaCorr_GenerateImputedValues_ADRC_kNN.R | 5efacaaf036a5bf9c07289bdc83a8f35ec015e3e | [] | no_license | jwisch/BiomarkerClustering | a819b5ff8bbe3844ed56a9fcdcfeb9303ae47430 | 4b56efd9bcea4f595d36040f6dd19fe585457325 | refs/heads/master | 2020-12-21T21:23:40.870520 | 2020-01-27T18:54:57 | 2020-01-27T18:54:57 | 236,566,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,735 | r | MegaCorr_GenerateImputedValues_ADRC_kNN.R | library(corrplot)
library(mice)
library(UpSetR)
library(tableone)
FILEPATH_DATA<-"C:/Users/julie.wisch/Documents/MegaCorr/"
df<-read.csv(paste(FILEPATH_DATA, "ADRC_cleaned.csv", sep = ""))
df$NFL<-log(df$NFL)
#####################################################################
#Visualizing - checking for... |
f9ab1e520b4ab8b33e692418db8aec6229047a10 | eadbf21c897318c61d7d8a0f74ba5a6c90a49ce7 | /man/padova.Rd | 1f580b5d470ce593b60849f24357aa7b67084087 | [] | no_license | ycroissant/descstat | 9b6c89f12fe5d6993ea1c36b5e763370582feea1 | 99d2bf54c959fe8d7b11a1971eec2072f3681a61 | refs/heads/master | 2023-03-21T03:29:40.542496 | 2021-03-17T05:28:05 | 2021-03-17T05:28:05 | 316,519,197 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,130 | rd | padova.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{padova}
\alias{padova}
\title{Housing prices in Padova}
\format{
a tibble containing
\itemize{
\item zone : one of the 12 zones of Padova,
\item condition : \code{new} for new housings, \code{ordinary} or \code... |
df92115bd4bcf52ec2fc124af20a56cb13ddb766 | a31b1b02270fee9b3291327ac6672efb209c12b5 | /R/regression_formula.R | 6349aec2e5400cad7da7cf323134b5250158460a | [] | no_license | sukhyun23/tpa | e1acabfafee45b701f88da10a3b204baf10259fe | db767a8358bb9bc43f4a29ba9fe9a380e545afd6 | refs/heads/master | 2021-07-03T18:28:39.391557 | 2020-08-15T05:32:25 | 2020-08-15T05:32:25 | 142,148,911 | 0 | 1 | null | 2018-08-18T12:13:47 | 2018-07-24T11:27:44 | R | UTF-8 | R | false | false | 1,494 | r | regression_formula.R | linear_formula <- function(x) {
xf <- paste(x, collapse = ' + ')
return(xf)
}
poly_formula <- function(x, poly = 2) {
xp <- sapply(2:poly, function(p) paste('I(', x, '^', p, ')', sep = ''))
xp <- paste(xp, collapse = ' + ')
return(xp)
}
inter_formula <- function(x) {
df <- expand.grid(x1 = x, x2 = x, stri... |
dd6c9a5a5131c2b97af1869d76f4953770ce90b0 | f57bcbec1f356c30a279d9930d4fce8752b79274 | /hw4/9.R | 5eabb0534d2b6e571cb5c71d515f6ddb3f55f33a | [] | no_license | Roger7410/R_Data_Mining | 6867a5da25b266e77c44163476e9f9a9623f46f7 | 7145780a777d53fb256543a5e49117ed0db301a3 | refs/heads/master | 2020-09-20T21:59:59.552058 | 2016-08-22T11:53:30 | 2016-08-22T11:53:30 | 66,268,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 428 | r | 9.R | Auto=read.csv("Auto.csv",header=T,na.strings ="?")
fix(Auto)
dim(Auto)
Auto=na.omit(Auto)
attach(Auto)
range(Auto$mpg)
sapply(Auto[, 1:8], range)
sapply(Auto[, 1:8], mean)
sapply(Auto[, 1:8], sd)
Auto2<-Auto[-(10:85),]
Auto2
head(Auto2,10)
dim(Auto2)
sapply(Auto2[, 1:8], range)
sapply(Auto2[, 1:8], mean)
sapply(Auto... |
d57ddc6038df843c81903518dd052b59161afa25 | 9549a7b626118518dab96d3c295fd755ccecfb25 | /R/mechBiogeo.R | e7c76d7509746f0038ce6a50fd001493c798485d | [] | no_license | KevCaz/figTalks | 9848e64f1a808060f6502e7f3ed349e6b96635e3 | b7802263c727fbb3e4429a7077193498acc797ca | refs/heads/master | 2021-08-29T16:04:31.926426 | 2017-12-14T08:15:26 | 2017-12-14T08:15:26 | 113,661,195 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,154 | r | mechBiogeo.R | ##---
myspec <- function(nb, cx, cy, ry, seed = 1987, ...){
set.seed(seed)
theta <- runif(nb)*2*pi
rayon <- runif(nb)*ry
points(cx + rayon*cos(theta), cy + rayon*sin(theta), ...)
}
##---
"#3fb3b2";
"#ffdd55";
"#c7254e";
"#1b95e0";
"#8555b4";
"#8ddd75";
"#787878";
"#CCCCCC";
##---
figAll <- function(filename... |
7a78623f156e505e4b6141aed1a0e4c5bc6ace32 | 6cbafc6d5ba417d0a16f56f309889ab7e3725dda | /simple_linear_regression.R | a5888fd19861c989dfbfe0d7349573db4f8663a0 | [] | no_license | johnnylazoq/Artificial-Intelligence | bac7c95bbe2c46909234da280194d1e237be6c5b | 73d69f56d220bf32e573d0c52b4f5ee6510afa86 | refs/heads/master | 2021-09-06T06:34:37.796140 | 2018-02-03T08:18:45 | 2018-02-03T08:18:45 | 120,072,518 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,822 | r | simple_linear_regression.R | ##
#Machine Learning algorithm - Simple Linear regression
##------------------------------------------------------------------------------------
# The dataset contains a linear dependency in years of experience vs salary.
# This ML model predicts salaries for comming years.
#install.packages('caTools')
librar... |
bd85507c110fe4d1eb35256e28f8dcded43a9d69 | b8024e65e4f0fd08085697b6de672a3e75da26f2 | /Stockreturns.R | 8a6f6f47168d7913287e93ab8912b2874ce38807 | [] | no_license | Rajesh16702/GLIM-Advance-Stat-Assignment | 0fd4e8bee8cb78173958ae79b5d5cb61a8fba41c | fd2d5f0a6a1b4560e3d4a20b54672fe7afc9b6a1 | refs/heads/master | 2021-04-06T18:14:42.026542 | 2018-03-14T16:34:34 | 2018-03-14T16:34:34 | 125,240,871 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,976 | r | Stockreturns.R | ###Step1: Include the following library packages:
library(lawstat)
library(psych)
library(car)
library(MASS)
library(Rcmdr)
library(ggplot2)
library(graphics)
#Step 2: Load the data file into R
setwd("F:/Sridhar/Data Sets/t/Two-way ANOVA")
df<-read.table("sensexr.txt",header=TRUE)
#Step 3: Clear... |
feeea1095ac5f6b382af9462e3b57994c0d57901 | 76487c270ad5ec7b1b6a1e7ec6f49a6d293a04d1 | /man/bmeasures-package.Rd | 3fcfdd59f808c16173e03c6347cddbc20a170aa6 | [] | no_license | shwijaya/bmeasures | 20d57754ec8f08420eed4da1840c351be80013aa | b7e8aaf47a7078f4517c16095bfb9e2ed0d75946 | refs/heads/master | 2021-01-18T14:22:34.612179 | 2016-12-07T05:46:24 | 2016-12-07T05:46:24 | 26,023,167 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,809 | rd | bmeasures-package.Rd | \name{bmeasures-package}
\alias{bmeasures-package}
\docType{package}
\title{
Binary similarity/dissimilarity measures
}
\description{
This package generates the quantities of the OTUs table, calculates the binary similarity/dissimilarity measures between two vectors, and finding the most suitable binary similarity/dis... |
fe270de0154c9087194586096cd263b8fe890eaa | 2818ff9dee771dce4d21e8ae1a8addff66165b98 | /airquality arturo.R | 5b40864bdc27b78d91ad55dc1b17ea2854f6b0e5 | [] | no_license | LordRickard/Programacion_Actuarial_III_OT16 | 92e008a905e4f954b78ea5792c63290901c3f5f2 | e04a91bff9af72eae5035331cf01de6176e2d676 | refs/heads/master | 2020-12-01T18:42:38.580125 | 2016-10-19T03:46:27 | 2016-10-19T03:46:27 | 66,847,291 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38 | r | airquality arturo.R | dput(airquality, file="airquality.R")
|
51bfad96866d69b42968c7b7d1efb2ccebd3722e | ebc487eed0ccae8a2db104261627a802988e2406 | /man/make_filename.Rd | 660d2ad1d9f48dd23099e07d530d2634023a617e | [] | no_license | RussellPolitzky/fars | 2292c40ccae32c4274f80de033514c3272be1787 | 84e55f4725028c974888fedd1a3b9e71936c58f3 | refs/heads/master | 2021-01-20T05:43:59.340767 | 2017-05-07T12:23:36 | 2017-05-07T12:23:36 | 89,803,584 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 802 | rd | make_filename.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/make_filename.R
\name{make_filename}
\alias{make_filename}
\title{Build an accident data file name}
\usage{
make_filename(year)
}
\arguments{
\item{year}{Accident data year expressed as a four digit number e.g. 2012.
This parameter may also b... |
d9b7f41b52950caf67c34f0d75970f8d466268ec | 242737293b846c619d2aef6762d88c42bf6c8553 | /R/simulateCC.R | 7e853743c316c5f0a1396706f9cf6bb17f899519 | [] | no_license | dtharvey/eChem | d95006456b06d8ce142b1e1fc683a9935b7f0a34 | 2811d48c1d47d591214c55ec1f1cb05aa81ac409 | refs/heads/master | 2020-03-21T09:30:26.450840 | 2019-07-06T12:23:44 | 2019-07-06T12:23:44 | 138,403,219 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,422 | r | simulateCC.R | #' Simulate a Chronocoulometry Experiment
#'
#' Simulates either a single pulse or a double pulse
#' chroncoulometry experiment as either an E, EC, or CE
#' mechanism, where E is a redox reaction and where C is a
#' chemical reaction that either precedes or follows the redox
#' reaction. The function operates on an obj... |
d8bd9706690a70b356ff9cc994e9f23f59df5e26 | cb37e962ef00a6ecef5fb1757a8d949e6003a70e | /R/convert_to_data.R | e68b652010e578ba8fe9fa7596b03f5b73690e8b | [] | no_license | robertgambrel/tabler | 374a9b35c84a9f87c04c259bfde66458e5e1f866 | 5974540f00372dfc62b6df37f553eb34197b625a | refs/heads/master | 2020-12-28T21:39:53.021086 | 2016-10-05T16:03:55 | 2016-10-05T16:03:55 | 66,608,933 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,952 | r | convert_to_data.R | #' Convert a model output to a dataframe
#'
#' This takes each model used in the final table and converts it to a tidy
#' dataset, formatted and ready to be merged with others in a nice table.
#'
#' @inheritParams tablify
#' @param model A single model result
#'
#' @importFrom magrittr %>%
#'
convert_to_data <- functi... |
8ef2d1ef379fec04983e8ea4fbe1b4683094d5d6 | 96476973d90e1936a29563c0b716a8bf0f170fbe | /Lesson_2_10-11-19.R | 8b521d99e61bd78b10ff4df783832e75db102b03 | [] | no_license | Manish-Dahivadkar/Machine-Learning-in-R | 8c5c66505ba957ca4332184682b75d94c4038438 | 6c8e21dd6a2a42486b6a1a81422de71b802411f8 | refs/heads/master | 2022-08-06T14:32:13.654584 | 2020-05-18T09:19:31 | 2020-05-18T09:19:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,937 | r | Lesson_2_10-11-19.R | How to run Array function
vector1=c(2,6,7,11,12,13,20,21,2,3,4,5)
arr1=array(vector1, dim=c(3,2,2))
arr1
****************************************************
List1=list(mtcars,iris) # this is creating list
List1
#how to print value of variable
a<-50
a
print(a)
print(paste("The value of a is ...",a))
#h... |
35d561b4c79c34f88749792242d698c5c9f6e919 | d2c5ba4826787014da2658b2de5af9287d33f5b6 | /landuse.R | 1a8b1df4e48669e7eca7f0fee97a6a26f77add64 | [] | no_license | kjbark3r/Migration | 2536ca3ef2c9886d4cdecaf5508afa67ff3353a2 | 126311095f6d5145453a21538d807b65561b292b | refs/heads/master | 2021-01-23T08:44:09.949472 | 2018-11-06T18:55:54 | 2018-11-06T18:55:54 | 67,642,868 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,905 | r | landuse.R | ### ### ### ### ### ### ### ### ### ### ### ### ### ### ###
# CREATING HOME RANGES FOR HERDS AND INDIVIDUALS #
# TO ASSESS FACTORS INFLUENCING MIGRATORY BEHAVIOR #
# KRISTIN BARKER #
# OCTOBER 2017 #
### ### ### ### ### ... |
1a4285eb205b38c06fe184ee957071013c372dfc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/VGAM/examples/acat.Rd.R | 630efc10cff5373d00da9fe2b6a3c52fb2d83b2a | [] | 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 | 355 | r | acat.Rd.R | library(VGAM)
### Name: acat
### Title: Ordinal Regression with Adjacent Categories Probabilities
### Aliases: acat
### Keywords: models regression
### ** Examples
pneumo <- transform(pneumo, let = log(exposure.time))
(fit <- vglm(cbind(normal, mild, severe) ~ let, acat, data = pneumo))
coef(fit, matrix = TRUE)
con... |
254c4363136ae1b1380993c379488ba79701be7e | 4343cc3a96791b9fd5acc767f1705bb4a8c5e95e | /reader/Models/08 Beer Game/01 Beer Game.R | 4ebfb7725d274f03fe0be8f858169c751f441b97 | [
"GPL-1.0-or-later",
"MIT"
] | permissive | mohammad-miftakhus-sholikin/SDMR | 5d96e6748cc465f8b2d040d752212479f620b1b8 | 06b1bd78c1bdb5815834ea4f04ee4d91a3a7ead9 | refs/heads/master | 2020-05-17T13:32:45.542242 | 2019-04-22T14:33:07 | 2019-04-22T14:33:07 | 183,738,775 | 1 | 0 | MIT | 2019-04-27T06:47:42 | 2019-04-27T06:47:42 | null | UTF-8 | R | false | false | 11,243 | r | 01 Beer Game.R | ###########################################
# Translation of Vensim file.
# Date created: 2017-11-10 16:57:13
###########################################
library(deSolve)
library(ggplot2)
library(tidyr)
#Displaying the simulation run parameters
START_TIME <- 0.000000
FINISH_TIME <- 40.000000
TIME_STEP <- 0.125000
#Sett... |
b1db1af2427e4849b56606205dff34ae70cd449f | a3020c890e3782b684f68a935472368e39a043e7 | /man/PCP_plot.Rd | 1144417711264018522091b12da98591fd897c06 | [] | no_license | trilnick/sharpshootR | 2193ac1625a8b9aa314c05256ebe4dd773878f10 | 92be9a67c9ee542d33cd46be8bbf4f43582813df | refs/heads/master | 2023-02-22T05:33:22.679766 | 2021-01-21T06:33:04 | 2021-01-21T06:33:04 | 327,691,598 | 0 | 0 | null | 2021-01-07T18:18:26 | 2021-01-07T18:18:25 | null | UTF-8 | R | false | false | 1,722 | rd | PCP_plot.Rd | \name{PCP_plot}
\alias{PCP_plot}
\title{Percentiles of Cumulative Precipitation}
\description{Generate a plot representing percentiles of cumulative precipitation, given a historic record, and criteria for selecting a year of data for comparison.}
\usage{
PCP_plot(x, this.year, this.day = NULL, method = "exemplar",
... |
78d096d5bbdeb16bd7bec914ac8a7f21d6e72e2b | 3c72d7144acd35317e329ef994fbea33d08526ad | /mapStats/man/jiggleClass.Rd | 2f3aae77e7e906588fcd0e78be0ec0aaa4009b7d | [] | no_license | sam-data-guy/mapStats | b1d75506c5f4ed8be95388738d43b942853b737d | 403064184f5d98823bcc89e494f42b7a758a987a | refs/heads/master | 2021-05-21T16:25:42.468919 | 2020-04-03T11:47:42 | 2020-04-03T11:47:42 | 252,716,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,367 | rd | jiggleClass.Rd | \name{jiggleClass}
\alias{jiggleClass}
\title{
Adjust class boundaries to protect from rounding errors
}
\description{
When using \code{\link[classInt]{classIntervals}} to compute classes, occasionally there are rounding errors so that
when the data is plotted and the class breaks are used for colors, for instan... |
c066f7971103bbbc72d3f7fed9ca75b0e6870a02 | c727a5dcac33f7bafc53b9ff4732aae824bf0058 | /benchmark.R | 3744dc94b16209e75d76e79f3b9c07d3e70a6a8c | [] | no_license | QuayAu/openml100bm | e3af81fa79be1cc908acb66a25cb7405b81f744d | a81ee6c70ee95e281f732748310c6fa2b0666059 | refs/heads/master | 2021-01-21T10:34:47.019106 | 2017-09-26T14:21:45 | 2017-09-26T14:21:45 | 83,457,692 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,987 | r | benchmark.R | # getting the data
# openml 100 data sets: study_14
library(OpenML)
setOMLConfig(server = "https://www.openml.org/api/v1")
#setOMLConfig(apikey = "1536489644f7a7872e7d0d5c89cb6297")# batchtools experiment
library(mlr)
library(BBmisc)
library(parallelMap)
library(batchtools)
source("defs.R")
datasets = listOMLTasks(tag... |
c54651a76d989dc8ebaa3356719ab4d951e78a0d | 2eac833e9d0cec33683cac42558143a160834bc4 | /R/stat-fivenumber.R | 026fa2b81e63189f3a2cc32bce82926ec9e88bc2 | [
"MIT"
] | permissive | jrnold/ggtufte | c1c457c046e350e6afb7807fe41088e2899b8521 | 7ba3e4f0e185b1b22e7120bde51bc5502cd77e3a | refs/heads/master | 2020-03-19T06:52:58.469107 | 2018-06-09T03:58:37 | 2018-06-09T03:58:37 | 136,063,123 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,617 | r | stat-fivenumber.R | #' Calculate components of a five-number summary
#'
#' The five number summary of a sample is the minimum, first quartile,
#' median, third quartile, and maximum.
#'
#' @param na.rm If \code{FALSE} (the default), removes missing values with
#' a warning. If \code{TRUE} silently removes missing values.
#' @param qs ... |
966864dd948cb992da45d3b7c5c1adc2c940c0cb | 35e707c55cff22002c6c66f967837736e3e0a0d8 | /R/sensitivity.R | da398e45ecad78a9122ebbcea9c53ddbe6635e4e | [] | no_license | spesenti/SWIM | 9f4416b990e9bc82109b87b849ffd208f8bfe77f | 4e9cd0b2b4e4ad36e0798b1d67fdcd03c2d7114a | refs/heads/master | 2022-05-04T10:16:25.964880 | 2022-01-10T12:41:16 | 2022-01-10T12:41:16 | 185,445,679 | 5 | 2 | null | 2022-01-09T23:04:06 | 2019-05-07T17:08:21 | HTML | UTF-8 | R | false | false | 11,399 | r | sensitivity.R | #' Sensitivities of a Stressed Model
#'
#' Provides different sensitivity measures that compare the stressed
#' and the baseline model.
#'
#' @inheritParams summary.SWIM
#' @inheritParams stress_moment
#' @param f A function, or list of functions, that, applied to
#' \code{x}, constitut... |
d6f71f10e1cc847ff92c30e03b4b7ee76049025e | 4c2835dcc76cdff0f3c7f2fcae6fc59896a6f632 | /man/beamht_std.Rd | 298a5f5813c910410e960c1e21c293e5ff52171c | [] | no_license | birderboone/Radar | fe747a1d3991a4e1ab99616d4b5efe765d786f46 | b1715b1006faf676152cef697c05f49e230f017b | refs/heads/master | 2021-05-06T23:16:38.482865 | 2017-12-03T20:31:02 | 2017-12-03T20:31:02 | 112,961,156 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 835 | rd | beamht_std.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/beamht_std.R
\name{beamht_std}
\alias{beamht_std}
\title{Calculates the height of the radar beam using standard refraction}
\usage{
beamht_std(range, groundht = 0, elev = 0.5, radar = radar,
nexrad_n = "V:/Documents/nexrad_site_list_with_ut... |
cbf3caf7f1247ba244dda740a0d39bd1741d5cb5 | 59ad950a6706b65979c064a231d0e80860e1f9b9 | /ui.R | f93c1de524efa8546da447f23cec03a0e5f46511 | [] | no_license | carlosrochap/ddp-project | 3d6854667bd5d8d44f3eb27a346e85e35b734701 | 0793d418c523b3f09bd11f05df0ea9f97f76b101 | refs/heads/master | 2020-03-29T08:32:22.537494 | 2018-09-21T05:48:29 | 2018-09-21T05:48:29 | 149,715,778 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,331 | 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)
# Define UI for application that draws a histogram
shinyUI(flui... |
75585a3a87012ef7b72413ac4be5d29b2c5ae3ee | 13cef36ee0392f108b0f97228efc7c855e7dba3d | /man/h5fileLocking.Rd | 3010ae5831360811d18f0664dcd5525e1739b320 | [] | no_license | MatthieuRouland/rhdf5 | a6068de28a21a0e0715fc6c930f819009dc23dbc | bf49c81e1e255b7d4cae6d1048dc835d8921081a | refs/heads/master | 2022-11-07T22:05:51.323176 | 2020-07-06T10:33:29 | 2020-07-06T10:33:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,628 | rd | h5fileLocking.Rd | \name{h5testFileLocking}
\alias{h5testFileLocking}
\alias{h5enableFileLocking}
\alias{h5disableFileLocking}
\title{Test and set file locking for HDF5}
\description{HDF5 1.10 uses file locking by default. On some file systems
this is not available, and the HDF5 library will throw an error if the user
attempts to ... |
8c3de6a1ca8581d0ca42100749096cc9b1e8a05d | c5c8edf59f5ac6458e75439fae252381c57fcba8 | /RestaurantRevenue/caret - using random forest.R | 21a0b5d3e3334aee5bb1c3417de3635baaad67f0 | [] | no_license | jiunsiew/UFLDL | ceb8db3f3b2a9ee6b1650d977cd6d332a993ee5a | 8be24944de7cc0b24daa45cdd3fa0e03d2db8bbe | refs/heads/master | 2020-12-24T17:25:53.657052 | 2016-04-29T07:14:13 | 2016-04-29T07:14:13 | 39,235,669 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,796 | r | caret - using random forest.R |
# get data ----------------------------------------------------------------
library(RODBC)
conn <- odbcConnect('EVORA')
trainData <- sqlFetch(conn, 'restaurant_revenue_prediction.training_data')
trainPercent = 0.8
# look at relatioship with P ----------------------------------------------
library(tidyr)
plotDf <- ... |
011064342999c8052dead6a35195958e1e311979 | b2e75a3733198ee98618041005fec8a60f8ba7fb | /man/rawpathfinder.Rd | 3b6d8c67a3ea6c3a47ba6262863370d746c296c4 | [] | no_license | MatteoLacki/rawpathfinder_R | 4d09fc105e711d8973575c7f4048a5ef38b202d2 | 7d5690dd628a87044d119a352fdb64549cc496ce | refs/heads/main | 2023-08-01T04:25:33.018014 | 2021-09-10T15:15:26 | 2021-09-10T15:15:26 | 404,970,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 814 | rd | rawpathfinder.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rawpathfinder.R
\name{rawpathfinder}
\alias{rawpathfinder}
\title{Query rawpathfinder for files/folders raw unix paths.}
\usage{
rawpathfinder(query, protocol = "http", ip = "192.168.1.209", port = 8958)
}
\arguments{
\item{query}{A character... |
b9d186ef4cad534ca4d597482b17008f463d1f6e | 858fac00773dc2e419d9002850d3145822d79691 | /week6_stat/stat.R | 6196a205dedc19dd9fa7c3b455457f4d448241f9 | [] | no_license | uh-sheesh/AIT602_Spring2021 | 538f98e999dba4ef6125e106535fa77580435ad4 | f6b64ba9d9fca7640c335d44a548f0da293ec5a3 | refs/heads/main | 2023-06-09T04:52:23.198008 | 2021-04-15T18:27:10 | 2021-04-15T18:27:10 | 340,192,883 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,739 | r | stat.R | library(ggplot2)
library(stringr)
library(readr)
library(dplyr)
library(reshape2)
library(PerformanceAnalytics)
library(rtweet)
library(sm)
library(car)
setwd("~/git/AIT602_Spring2021//week6_stat/")
#############
# 1. Load the data.
data <- read_delim("data/corona_tweets_03042021.csv", delim = ",",col_names = TRUE)... |
e6f31d8bb44e26d4bf6b769d7e1887ce0094bce0 | 0171ecb9bfbbcc1181855a6790861dacad824b63 | /src/differential_expression/Differential_expression_TR_BR_Subsample_3.R | ce50c399781b8aca4abaf591190d37fb93270b1e | [] | no_license | aerugo/BB2490-RNASeq-Project | 7f063bc86d0b77aed1cebb3e2beb3c1c196e2937 | 9ec36446be1efae262b0a69674e92d1b921fef89 | refs/heads/master | 2021-08-18T13:42:33.498687 | 2021-01-13T15:35:14 | 2021-01-13T15:35:14 | 51,921,805 | 1 | 2 | null | 2016-02-17T16:25:26 | 2016-02-17T12:43:24 | Shell | UTF-8 | R | false | false | 18,638 | r | Differential_expression_TR_BR_Subsample_3.R | ## Differential Expression analysis for Subsample 3
###### Awk codes for combining files together for technical replicates (Possible for cooler solutions)
## join -j 1 SN11_UNST_TTAGGC_L003_count.txt SN11_UNST_TTAGGC_L002_count.txt| join -j 1 SN11_UNST_TTAGGC_L004_count.txt - | join -j 1 SN11_UNST_TTAGGC_L005_count... |
8433eea4174b927c28c9ca2452a2a1f6950813b0 | 88cb244ee0be3e5407995dbc15bf928325e9e44f | /tests/testthat/test-simframe.R | 0bd5251039ff5f01cfb1e027d552806aee2f6cf0 | [
"MIT"
] | permissive | kant/individual | 8d0af92928e4aa2b52d6fb72610069de4220d283 | b58c7d7aa28c97190f0a9124ad63d56f721cf66b | refs/heads/master | 2022-08-25T05:39:50.852454 | 2020-04-02T08:44:31 | 2020-04-02T08:44:31 | 264,268,090 | 0 | 0 | null | 2020-05-15T18:22:18 | 2020-05-15T18:22:17 | null | UTF-8 | R | false | false | 1,626 | r | test-simframe.R | test_that("getting the state works", {
S <- State$new('S', 10)
human <- Individual$new('test', list(S))
simulation <- Simulation$new(list(human), 1)
frame <- simulation$get_current_frame()
expect_length(frame$get_state(human, S), 10)
I <- State$new('I', 100)
human <- Individual$new('test', list(S, I))
... |
daee10859ed2d8a109f9e3ca0b42179d770f2532 | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.analytics/man/glue_create_partition_index.Rd | d70467df4eec4a607fb28e35461bbb92efc99634 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 949 | rd | glue_create_partition_index.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/glue_operations.R
\name{glue_create_partition_index}
\alias{glue_create_partition_index}
\title{Creates a specified partition index in an existing table}
\usage{
glue_create_partition_index(
CatalogId = NULL,
DatabaseName,
TableName,
... |
d7dd532963aaf98bdbb817f5eb9421434b410b48 | d2034a80affde15fb3e69f2466bde58cca096013 | /응용통계학/10장.R | 43336f45f7989a1ca20ff7cf459ba9a4f329a490 | [] | no_license | SANGDONKIM/MASTER | dacb5f1b5cf032965c2fcf5f2076f56c510057f9 | 7720d6bdb2465d0b0908dbae70a8f6da570962d6 | refs/heads/master | 2021-04-13T14:03:06.281917 | 2021-03-24T12:18:09 | 2021-03-24T12:18:09 | 249,167,267 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,730 | r | 10장.R | # permutation test
# https://www.jwilber.me/permutationtest/
# ex 10.1
attach(chickwts)
head(chickwts)
x <- sort(weight[feed == 'soybean']) # feed = 'soybean' 인 weight 값
y <- sort(weight[feed == 'linseed']) # feed = 'linseed'인 weight 값
detach(chickwts)
R <- 999
z <- c(x, y)
K <- 1:26
reps <- numeric(R)
t0 <- t.... |
c124e68cc251e83f770bd50c766d68964e1a011b | 809984e753f947edadeefcda433baa3fccbfa280 | /plot2.R | 6edbc5f1cc2fc1fce7baf8cee1a03a57b1aa84ee | [] | no_license | evanfish/ExData_Plotting1 | 5733628c470e3edc036d05c437266a028071c009 | f911a4f3925c5a0a066d553486f5a479785e889e | refs/heads/master | 2021-04-06T01:10:16.418245 | 2018-03-19T02:54:14 | 2018-03-19T02:54:14 | 124,826,924 | 0 | 0 | null | 2018-03-12T03:09:20 | 2018-03-12T03:09:20 | null | UTF-8 | R | false | false | 1,204 | r | plot2.R | #Exploratory Data Analysis - Johns Hopkins University
#Plot 2
data_loc <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp <- tempfile()
download.file(data_loc, temp)
pwr_data <- read.table(unz(temp, filename = "household_power_consumption.txt"), header=TRUE, sep=";")
... |
13746f06436230a77be7bb706a64886d710c1d87 | f6a1a598a76168efe4a32a4490ea4e88ecdad607 | /r_review.R | 57009798a672d580cc848bd7bddfd387bf8d2ead | [] | no_license | siddisis/altmetrics | 5bf07f628de16aa44f1c313b2668f0d76aeb6195 | 13372dbf720537edef27bfc2f63052f6422683e1 | refs/heads/master | 2020-05-30T16:13:12.119602 | 2015-09-16T16:45:09 | 2015-09-16T16:45:09 | 42,600,740 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 951 | r | r_review.R | count.raw<-read.delim("/Users/siddisis/Desktop/SWC/altmetrics/data/counts-raw.txt.gz")
count.norm<-read.delim("/Users/siddisis/Desktop/SWC/altmetrics/data/counts-norm.txt.gz")
count.raw[1:3,10:12]
count.raw$pmid[1:3]
head(count.raw$daysSincePublished)/c(7,1)
str(count.raw$journal)
levels(count.raw$journal)
anyNA(count.... |
15bb26a6f2bae7a6d12ab183cdb8d81a56ddf92f | 865cecdd703505d582b91242f9c37c0bed740095 | /R/plot_sets.R | 8daf3e3e9e8c6148037267de3830774cff5f0771 | [] | no_license | csiu/CEMTscripts | 4fb78442e540e2ff6e95b734b8f96043e3509e74 | 74baea3a0a59f4e21405c96a91d65d7a36764048 | refs/heads/master | 2021-08-16T16:44:42.236037 | 2017-11-20T05:19:39 | 2017-11-20T05:19:39 | 71,932,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,459 | r | plot_sets.R | #' Total unique elements in embedded list
#' @param x a list whereby each element is a character vector/set and
#' the name of the element is the name of the set
#' @param getunique boolean; if TRUE, will instead return the
#' list of unique elements
set_total <- function(x, getunique=FALSE) {... |
e207eeba0eeb60b793abdff72b356e0b3186f074 | 094e952da4fa8698b04fb88b69fbf67668218d24 | /code/ch.8/run-model8-7.R | d48509056eed10154e107177b7aaf5cd25898b12 | [
"MIT"
] | permissive | rhyeu/study_rstan | 42a773beef840f56f64fcd20c5b1b24f88d45e1b | a5b998772358ba64996bc7ca775566f0706fa8f3 | refs/heads/master | 2021-07-08T15:51:37.488890 | 2020-10-04T07:10:03 | 2020-10-04T07:10:03 | 195,388,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | r | run-model8-7.R | library(rstan)
d <- read.csv('ch.8/input/data-conc-2.txt')
N <- nrow(d)
Time <- c(1, 2, 4, 8, 12, 24)
T_new <- 60
Time_new <- seq(from=0, to=24, length=T_new)
data <- list(N=N, T=length(Time), Time=Time, Y=d[,-1],
T_new=T_new, Time_new=Time_new)
fit <- stan(file='ch.8/model/model8-7.stan', data=data, seed... |
a462bc638a31f2f4f8889d87a0cec8a38e0d8b80 | e622cedeca09b762cdce8d93218ad215b648de2a | /week 6 assignment.r | cf5ee9c79bb6dfbb7eeacb2c3d050f52ae8cc65a | [] | no_license | ct3080a/IS360-Data-Acquisiton-and-Management | 2f5a126d333abccbfbf03e0c38c41292718894e8 | 240fc00ce90d3097406dec9542c81d02f7da1b36 | refs/heads/master | 2021-03-12T23:44:34.198145 | 2014-12-20T12:44:12 | 2014-12-20T12:44:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 164 | r | week 6 assignment.r | #choose an interesting dataset
women
#scatterplot
plot(women$height, women$weight)
#create histogram
hist(women$height, breaks = 5)
#boxplot
boxplot(women$height)
|
9ed5cbe63e91963b8b9c7ce1a07a57a57a9ab269 | 34865809e852fdb92071a580e1da1d3732f48ec3 | /plinker_dev.R | 38ed7669ce85dfd9dd2f79049bd8f99861c1acce | [] | no_license | quartzbio/plinker_pkg | 5c4d436b29ee06fb9d7068b3f0940bf5dfda7e5a | 0e137d20c03dcf8a0f64197be023b8e8c28eb0fe | refs/heads/master | 2021-09-21T21:07:34.813166 | 2018-08-31T13:41:58 | 2018-08-31T13:41:58 | 110,011,530 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 888 | r | plinker_dev.R | library(devtools)
check_man('plinker')
test('plinker')
test('plinker', 'annotations')
test('plinker', 'bed$')
test('plinker', 'bed_plink$')
test('plinker', 'bed_plink_lm')
test('plinker', 'bedmatrix')
test('plinker', 'bim')
test('plinker', 'convert')
test('plinker', 'covars')
test('plinker', 'dist')
test('plinker', '... |
0ed9ef68000ca009ccd2fa67ecb51da050742cfa | 3b0830c657f493cc609727cd92da3a3c9fa12d80 | /R/MCMethod1.R | 508dcb2da7371661e2cd768e40a213e44b68f6f0 | [] | no_license | HaiyangYu1999/CodeBackup | a1d8018fbbe2f7e05455042a1438d635f6f2a17f | a023f93e85b2c8172f98d833e3f7d2c1de67a51e | refs/heads/master | 2021-04-20T23:43:12.813114 | 2021-01-03T13:30:48 | 2021-01-03T13:30:48 | 249,726,445 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 568 | r | MCMethod1.R | set.seed(1000)
N=10000
P=matrix(NA,2,2)
X=rep(NA,N)
Y=rep(NA,N)
Px=0.8
Py=0.7
X[1]=rbinom(1,1,Px)
Y[1]=rbinom(1,1,Pygx[X[1]+1])
Pxgy=c(2/3,6/7)
Pygx=c(1/2,3/4)
for(i in 2:N)
{
X[i]=rbinom(1,1,Pxgy[Y[i-1]+1])
Y[i]=rbinom(1,1,Pygx[X[i]+1])
}
x00=0
x01=0
x10=0
x11=0
for(i in 1:N)
{
if(X[i]==0&&Y[i]==0)
x00=x00+... |
ae3ee8b21f60a20a6c49a53b3cf8c3b180d943ce | a7325500905b466ac3f55f63280249e710a9c37e | /global.R | 73c32297655b412958ed42202e627f15433ab536 | [] | no_license | DanTruong/NYS-Influenza-Map | 406ef4a8133f6e504ee2efb997c72ce910689376 | b6bf7422ab31f14ee58a31e0b3204a1760dd4e77 | refs/heads/master | 2023-04-07T17:47:49.712211 | 2023-04-02T02:28:40 | 2023-04-02T02:28:40 | 218,870,438 | 0 | 0 | null | 2019-11-05T19:48:57 | 2019-10-31T22:16:38 | R | UTF-8 | R | false | false | 1,364 | r | global.R | # Load requisite libraries
library(dplyr)
library(plyr)
library(tidyr)
# Load in dataset file
rawData <- read.csv("data/fluData.csv")
# Select specific columns to work with
fluData <- data.frame(
County = rawData$County,
Date = rawData$Week.Ending.Date,
Disease = rawData$Disease,
Incidents = as.integer(rawD... |
250063e5421c05f116302073ec081f3c15b77e64 | cb93cf0799e3eedca6f9e720e09bb60e0f77ff10 | /tests/readDataFrame.R | b19200ae1598c602886f9966af1f621388836ed7 | [] | no_license | HenrikBengtsson/R.filesets | 254c37b4546e8280b9972d06840b918e12e0b4e9 | 17181ae1c84dbf7bad1214d37e6f133ed2deeba4 | refs/heads/master | 2023-01-08T23:58:09.708417 | 2022-07-21T09:52:18 | 2022-07-21T09:52:18 | 20,844,863 | 3 | 1 | null | 2018-04-03T22:12:45 | 2014-06-15T00:25:31 | R | UTF-8 | R | false | false | 1,221 | r | readDataFrame.R | source("incl/start.R")
message("*** readDataFrame()")
path <- system.file("exData", "dataSetA,original", package="R.filesets")
pathnames <- list.files(path=path, pattern="[.]txt$", full.names=TRUE)
pathname <- pathnames[1]
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Basic reading
# - - - ... |
2111a4900309c8a2580e7e8d8abacb5d82a9e2f1 | 0014e7337864cc16efbdea8215389c2267073e8f | /Digital Channel Atribution/markov_vs_lastclick.R | 45ef550fd6203e3284abc9d1ec5b5f1edacaa354 | [] | no_license | azzikl/R | efadf75df6dad5501b5f7708f8a9c44627e673dc | cb3a517ede522c3be3b49c94e720315a32942165 | refs/heads/master | 2021-01-20T01:10:35.753966 | 2017-07-14T12:35:35 | 2017-07-14T12:35:35 | 89,227,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,502 | r | markov_vs_lastclick.R | library(dplyr)
library(reshape2)
library(ggplot2)
library(ChannelAttribution)
library(RGA)
#generate ga token
authorize()
#set property ID, start and end dates
#ids = propertyid
start.date = as.Date("2017/01/18")
end.date = as.Date("2017/01/18")
#getting real data via google analytics API
paths_data = get_mcf(
ids... |
6284ceca392ef28c1e92d5375868e21938cef041 | 25923c6f6895f0f1b4c32786787e66d43cb01e3d | /man/maturity_assignment.Rd | 7465cb73f3d037148e4a994e80e7e69e7d9d1299 | [] | no_license | pbs-assess/gfplot | 4bb1fc9546eed8e9d4cda5cd2f42f1adb50cc8ca | 7cebc7376ecf0d5576a486a7e260130973d21cb5 | refs/heads/master | 2023-07-19T21:36:10.716669 | 2023-07-12T00:30:06 | 2023-07-12T00:30:06 | 103,994,126 | 2 | 3 | null | 2023-05-31T19:11:41 | 2017-09-18T21:31:13 | R | UTF-8 | R | false | true | 362 | rd | maturity_assignment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{maturity_assignment}
\alias{maturity_assignment}
\title{A data frame with maturity categories and assignments.}
\format{
A data frame
}
\usage{
maturity_assignment
}
\description{
A data frame with maturity categor... |
d083b8aeaa8ec57e35f5f38e72e344f9585c7474 | b23aa367ece060a8a3c6697d6cb59cb84c1bca0d | /app.R | 89a1a814440f55659ba6c53ed5e13c9d16539fe6 | [] | no_license | DavidBarke/distributions | 2d53d3e4891308920ce620a20242dc10926de919 | 6cc6fd558d204570aecc6351d44b669f0fe13687 | refs/heads/main | 2023-04-12T09:23:08.155376 | 2021-05-04T16:42:37 | 2021-05-04T16:42:37 | 350,862,494 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,523 | r | app.R | library(shiny)
library(sortable)
library(magrittr)
source("init/source_directory.R")
source_directory("modules")
sass::sass(
sass::sass_file("www/scss/styles.scss"),
output = "www/css/styles.css",
options = sass::sass_options(
output_style = "compressed"
),
cache = FALSE
)
distribution_he... |
bc0880f35ff1db49520c2a5987b8a7eb309eecc5 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/pseudorank/tests/testthat.R | 9de052badeab02d686fbe49630621e51ce9100b1 | [] | 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 | false | 66 | r | testthat.R | library(testthat)
library(pseudorank)
test_check("pseudorank") |
b6f285525bc24c40a056f3aef9fcd9dd1c34fa54 | 34a1872c598758ad8b6c0a82132f52b2f124489e | /ExtensionScripts/merging with HCAD functions/looking_for_vacant_buildings.R | 34f22eb7a6c50fd9f86b07d2a44e54c6acf34ce4 | [] | no_license | DataAnalyticsinStudentHands/SyntheticDataSet | 2f73599723d53c5ca0e04535de55bf05c44aaaac | 82877e75367dbd9ff68976c61b9e8f237224cf2d | refs/heads/master | 2023-08-22T20:00:35.665190 | 2023-08-16T17:14:39 | 2023-08-16T17:14:39 | 77,935,514 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,107 | r | looking_for_vacant_buildings.R | #I ran this stuff before but am putting it here so you can see it
#library(sf)
#parcels <- st_read("../hcadparcelstuff/Parcels/Parcels.shp")
#parcels$valid=st_is_valid(parcels, reason = TRUE)
#validparcels=subset(parcels,parcels$valid=="Valid Geometry")
#Read in Texas Census Tract Files
#TXCensusTracts <- st_read("..... |
6d78be103a00fd75e0088734baa95921aa024ad0 | 7a343e1b7a3c2ef50d684a5ce991ebef800f32be | /shells/variant_calling/new/11-tests/xp-ehh/chr12.R | bf2d930e6c22b88c9e57f61c5ba3648c0e1f1187 | [] | no_license | melisakman/Helianthus | 04d2dc8315f8d14d2f38faa8bce7282e2cc2b439 | 5500207a2bbfe2e63c639f3194f732a41d527cb7 | refs/heads/master | 2021-11-09T05:12:31.331797 | 2021-11-03T05:30:32 | 2021-11-03T05:30:32 | 60,215,326 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,417 | r | chr12.R |
library(rehh)
setwd("/global/scratch/makman/GATK/final/")
lr <- data2haplohh("chr12_SNP_lr_heteroFiltered.vcf", min_perc_geno.mrk = 30, polarize_vcf = FALSE)
wd <- data2haplohh("chr12_SNP_wd_heteroFiltered.vcf", min_perc_geno.mrk = 30, polarize_vcf = FALSE)
lr_scan = scan_hh(lr, limhaplo = 2, limehh = 0.05, limehhs ... |
644d48e36326b0aee94e2528127c075428bb201a | 422403bc85c06475bda8e8f8d5c8c149b6c92dc9 | /StanModelStage2.R | e8f4cc5e7b3e74e075ddf7474e315196e06fca8b | [] | no_license | Shusei-E/B.A.Thesis | 3c4ae24a2de7c725a5e7bb3825cd3486955527d4 | 8f3dc06c549e979ffd245ba91bbbb199e7dab1f4 | refs/heads/master | 2016-08-12T12:44:27.487522 | 2016-01-12T13:31:34 | 2016-01-12T13:31:34 | 49,500,331 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,541 | r | StanModelStage2.R | stancode = "
data {
int<lower=0> n; #データの数
int<lower=0> time;
int<lower=1> L; #グループの数 (ここではDem OR Non-Dem)
int<lower=1,upper=L> ll[n]; #データの1点がどのグループに属しているかのindex
vector[n] p4_polity2;
vector[n] polity_lag;
vector[n] Giniall;
vector[n] Aid_All;
vector[n] GDP;
vector[n] Corruption... |
35454ab7193c996a0855870e1233bc4dae0ce960 | dff4be894fcfabd2b7a201a0372170bd865a9e3e | /R/simpson.R | a7333130236531b68cc8593a8d9a7dc797a3cee2 | [] | no_license | zhaozhg81/AIXZ | ba106545a1aae22984dcbb4453318e63b7262f1a | 49f835bf9d2ca9c9642ea3a40eb36ec925172f57 | refs/heads/master | 2023-05-04T20:19:10.771017 | 2023-04-17T14:51:10 | 2023-04-17T14:51:10 | 213,441,232 | 7 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,393 | r | simpson.R | ## Simulation
## Two age group, 5-10, 50-55
n1 <- 10
n2 <- 10
x1 <- sample( c(55:75), n1, replace=TRUE)
x1 <- x1-55
y1 <- 10 + 0.8* x1 + rnorm(n1)
x2 <- sample(c(70:85), n2, replace=TRUE)
x2 <- x2-55
y2 <- -10 + 0.8* x2+rnorm(n2)
plot( x1, y1, col='black', xlim=c( min(x1,x2)-1, max(x1,x2)+1), ylim=c( min(y1,y2)-0.... |
643957a9c6db1c4b7c50db44f2c03c671d59d71c | 803770e54b1ef0af4806041dc364d34145d2304f | /cachematrix.R | e574e58ceca4b371a892d7138690b0958c944f52 | [] | no_license | mfrechtling/ProgrammingAssignment2 | aff30a365470463780b7bf56946a05c73fdfa264 | 96f9ffee10871b399244d753597d55099a6482ac | refs/heads/master | 2021-01-15T09:36:54.544900 | 2014-12-19T08:12:32 | 2014-12-19T08:12:32 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,166 | r | cachematrix.R | ## The following functions will calculate and cached the inverse of an input
## matrix, allowing the inverse to be retreived from the cache at a later point
## rather than being recalculated
## Get and set functions for the input matrix and it's inverse
makeCacheMatrix <- function(x = matrix()) {
xinv ... |
b8991bdb6fb6763df4a700b51b38551da1dd6de8 | 2314161607114335089ecd0bb61116ea6bcbf62b | /simplesymmetric/code_from_sugitani.R | b2b59de5d953f5216e6df49bcc11d83f5265c204 | [] | no_license | floatofmath/cml8r | 5a969a26e58c454e7142ab708f41932bc0db6ebc | 5b19400186adc3ba85b603874896be852a9d185d | refs/heads/master | 2016-09-06T07:56:04.189540 | 2013-12-12T14:47:50 | 2013-12-12T14:47:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,480 | r | code_from_sugitani.R | library(mvtnorm)
library(gMCP)
#- Setting parameters -#
weight1 <- 0.5; weight2 <- 0.5;
alpha <- 0.025; seed <- 4989;
e <- 0.0000000000001
c <- alpha/2
#- Start of function "sim" -#
sim <- function(n,nsim,mean,gamma1,gamma2,p,q,rho){
#--- Step1: Data generation ---#
sigma <- diag(4)
sigma[1,2]<-sigma[2,1]<-sigma[3,4... |
ac9be3cc1236249bd688c565be31cbe7bc09ad54 | 78fb338b3a288c75f2f00e0e67ceaecdc5aeecdd | /man/getPlayerStatTypes.Rd | 4b5e96d6f3834b11dbf05031670e56a760c66333 | [
"MIT"
] | permissive | pbulsink/nhlRapi | f5b9c8f80b5030b9fc5ce92c811937ccb7c7f2f6 | 88c65b93dbf0e8b787ffbf02ef915f84330b50f9 | refs/heads/master | 2020-04-22T06:01:11.780541 | 2019-06-26T13:27:45 | 2019-06-26T13:27:45 | 170,176,071 | 0 | 1 | NOASSERTION | 2019-06-26T13:27:46 | 2019-02-11T18:03:09 | R | UTF-8 | R | false | true | 461 | rd | getPlayerStatTypes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/players.R
\name{getPlayerStatTypes}
\alias{getPlayerStatTypes}
\title{Get Player Stat Types}
\usage{
getPlayerStatTypes()
}
\value{
a list of player stat types to call with \code{\link{getPlayerStats}()}
}
\description{
Only certain stat type... |
fe8b4c40aaefaf0864dd125030bbf4f0e348c341 | 06cdfccf8d44f11742fec1162afdfe2421c22302 | /man/compute_ABC_cpp.Rd | f97b319950dfe989a6e2b1a265a1d7bcdc00dbbc | [
"MIT"
] | permissive | lgaborini/rdirdirgamma | 06feabefb12a42d0496818ecc9a0f70f7ccc1c5c | f3087f0a81c9e4b08ff56efcc260873eaa16232d | refs/heads/master | 2023-04-18T00:18:29.380512 | 2021-03-05T18:06:26 | 2021-03-05T18:06:26 | 290,997,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,982 | rd | compute_ABC_cpp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{compute_ABC_cpp}
\alias{compute_ABC_cpp}
\title{Perform ABC sampling using the stick breaking procedure, returning the acceptance ratio.}
\usage{
compute_ABC_cpp(
n_sample,
m_sample,
alpha_0,
beta_0,
nu_0,
mtx_... |
1d7a7f775ff01962cb9fe5160406587b91d3fb29 | 0d6d355a50a3e8dc4fcba663fa5e29d84e3d958e | /Spatial Data Analysis - Spring 2017/Final/Part_B.r | 975741637cb0a14c7a8e507e1e9b855b913a01b7 | [] | no_license | JunchaoMei/Data-Science | d651415add2a0f4fde7d62dbedfb30d962a4200e | 5ee831ce0ec8a75f629939f217cc26d5dfb412d7 | refs/heads/master | 2020-04-12T13:35:46.777260 | 2018-12-20T07:46:51 | 2018-12-20T07:46:51 | 162,526,357 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,099 | r | Part_B.r | # Part_B
#import libraries
library(rgdal)
library(ggplot2)
library(spatstat)
library(maptools)
library(ape)
library(spdep)
library(spgwr)
library(sp)
library(gstat)
#read shape files
setwd("G:/GIS/DataFiles_FinalExam")
Community <- readOGR(dsn="2_Community", layer="2_Community")
nrow(Community)
summary(Community)
Com... |
2a15bf85c48f68452cc8975284aace5f7d216ece | e3ce3ad557ebd51429ed7acfea936723149a8d4c | /R/sof.engvall.R | c314ccce57e61c80596e15d14ad2d470764eb4ba | [] | permissive | jakobbossek/smoof | 87512da9d488acfe3a7cc62aa3539a99e82d52ba | d65247258fab57d08a5a76df858329a25c0bb1b8 | refs/heads/master | 2023-03-20T02:05:12.632661 | 2023-03-08T13:59:27 | 2023-03-08T13:59:27 | 22,465,741 | 32 | 27 | BSD-2-Clause | 2022-01-21T10:02:19 | 2014-07-31T10:39:43 | R | UTF-8 | R | false | false | 1,235 | r | sof.engvall.R | #' @title Complex function.
#'
#' @description Two-dimensional test function based on the formula
#' \deqn{f(\mathbf{x}) = (x_1^4 + x_2^4 + 2 x_1^2 x_2^2 - 4 x_1 + 3}
#' with \eqn{\mathbf{x}_1, \mathbf{x}_2 \in [-2000, 2000]}.
#'
#' @references See \url{https://al-roomi.org/benchmarks/unconstrained/2-dimensions/116-eng... |
e2d41de26b0c1468701858c5495cb681e3d0b09e | b8f66037ac2f9008dcad89624a4f0bb5f4389784 | /Calculate_RealizedNe.R | 98050df24d9fb22853ee25f6e8a1ee231acb2320 | [] | no_license | kjgilbert/SlimSimCode | bca5fa3e665043bbf8895f2f67f610ec12eba8e2 | 1e91181c2cb721c008ca7ad2f8e0aea65262e672 | refs/heads/master | 2020-04-10T20:10:57.159157 | 2017-04-27T14:20:21 | 2017-04-27T14:20:21 | 68,229,248 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,435 | r | Calculate_RealizedNe.R | setwd("/cap1/kgilbert/WestgridOutputs")
# calculate pi:
source('/cap1/kgilbert/WestgridOutputs/CalculatePi.R', chdir = TRUE)
# gives back: pi overall, pi_n, pi_s
## sample.output.files <- system("ls SampleOutput_Nov19_N10000_25mbp_*", intern=TRUE)
## full.output.files <- system("ls FullOutput_Nov19_N10000_25mbp_*",... |
e29ee7cf123b22b803162805dfb0811f95db3812 | 9bdf92270f7476021959c1b39e6b92388fafb61b | /tests/testthat/test-bfactor-interpret.R | e134a8c51348d26dd4f3458aae1192233cbff9d3 | [
"MIT"
] | permissive | ptfonseca/pcal | 7ed6df79bdd5c3917f4316145657e577676846e7 | 198659789d55cd3c669775f807e603e5c03faac0 | refs/heads/master | 2023-01-13T12:14:05.389014 | 2020-11-17T23:53:39 | 2020-11-17T23:53:39 | 264,692,683 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,377 | r | test-bfactor-interpret.R |
context("bfactor_interpret function - jeffreys")
test_that("bfactor_interpret test 1", {
expect_equal(
bfactor_interpret(
10 ^ c(
-3.10,
-1.78,
1.06,
-1.40,
1.21,
0.89,
-2.37,
1.23,
-8.88,
3.81,
-8.38,
0.62)
... |
ff1ed1e799f38398163ad0282e6be22494ec6d33 | 5ab78268f2f47fc850c99ec48b1c650d7f4c7959 | /inst/tests/test-macros.R | bf39b1c118391fdfe49acdc97f032d791d6e7cd1 | [] | no_license | rcodo/vadr | a08799c3218e4ea8e4e0fb5c4822cffeaf3227ec | ec837ec27e9bcd365eb40327fc04efc8c82ac6cd | refs/heads/master | 2021-08-31T12:49:26.768373 | 2015-09-02T03:13:35 | 2015-09-02T03:13:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | test-macros.R | context("macros")
`%is%` <- expect_equal
test_that("quoting.env", {
en <- quoting.env(c('+', '(', 'a', 'b', '*'), environment())
expect_equal(evalq(a+b, en), quote(a+b))
expect_equal(evalq(a+(b*a), en), quote(a+(b*a)))
z <- 100
expect_equal(evalq(a+(b*a)*z, en), quote(a+(b*a)*100))
})
test_that("quoting.e... |
1295b27427dc710f739bda0812090e0a74645fab | d6f588905b2113dfca208a60d851a3c65f0a8d70 | /wheat-ML-project.R | c2d12ea0e67f6ed05730398fe3cce43e9accab61 | [] | no_license | thilinik/Projects | 38b1fd54d6e42d1eddba7bc679b655bf0666572a | a22adc9a602c8f71d8eaff5bbf557eb7f0aeac4c | refs/heads/master | 2021-07-11T08:51:30.447492 | 2021-03-08T19:54:42 | 2021-03-08T19:54:42 | 56,555,293 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,610 | r | wheat-ML-project.R | library(MASS)
library(nnet)
##load the source file
source("C:\\Users\\Toshiba\\Google Drive\\jobs\\ML\\unsupervised\\wheat-ML-functions.R")
##load the data set
Wheat.data <- read.csv("C:\\Users\\Toshiba\\Google Drive\\SFU\\stat\\fall15\\Stat852\\datasets\\wheat.csv",header=TRUE,sep=",")
head(Wheat.data)
##summariz... |
89e77b480949ef146c4d240fd985f9ac1d378f3f | fe063f55b246e7c4ffb870b5ba507c2316f05700 | /data-raw/municipal_status_data.R | 907abd8afef711fafb6869b2102a4c2af7c9dc5c | [] | no_license | DJSmallSquare/suistats | fdcbfc9d29390476d22473a9468f8d4c90827a29 | b5b913bbbf2588f5d1fcd4c017e08c8e57362365 | refs/heads/master | 2020-06-12T01:40:58.364833 | 2019-06-27T20:19:41 | 2019-06-27T20:19:41 | 194,154,145 | 0 | 0 | null | 2019-06-27T19:46:31 | 2019-06-27T19:46:30 | null | UTF-8 | R | false | false | 3,130 | r | municipal_status_data.R | # ====================================================================================================================
### Preparing municipal merger data
# ====================================================================================================================
### Packages
check_packages <- function(pkg){
... |
5dd740f4ac4d59c7a4692231dd54480817e3593b | 11b1e40e532a57ea775c1b711b308185b36ead6f | /scripts/integrate_portions.R | 840e724777ee0beeffb9664cef471b988b8fb610 | [
"MIT"
] | permissive | connorcl/swiftkey-nlp | 0ea41f676d53ab5dc02e94743a1ef5970f89a5ab | 87f4c8f432f6ecac46ffb142d8515caa57012d4c | refs/heads/master | 2020-03-27T06:12:34.004642 | 2019-08-14T21:00:39 | 2019-08-14T21:00:39 | 146,088,296 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | integrate_portions.R |
library(dplyr)
path <- "~/Projects/swiftkey-nlp/data/"
setwd(path)
generate_ngram_re <- function(n) {
word_re <- "[a-z]+'{0,1}[a-z]*"
re <- paste0("^", word_re)
if (n > 1) {
for (i in 1:(n-1)) {
re <- paste0(re, "_", word_re)
}
}
re <- paste0(re, "$")
return(re)
}
for (n ... |
598c35fd37b51f661d252bc5840ec0619efcf431 | 75f3fa8a6040edcd8c34668b4a19f2b07dde7ab1 | /열추가 하는 벡터 생성 .R | 0cc759820368abca385311e5deab0600e30a7821 | [] | no_license | Yu-Hayung/w3resource_R-Language | 9452ef505f81fc448b6f01f09427496ac128e728 | 672151ba2ce154005491beb3d09d992a659f6595 | refs/heads/main | 2023-04-03T17:56:31.543520 | 2021-04-18T13:18:23 | 2021-04-18T13:18:23 | 350,978,553 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 323 | r | 열추가 하는 벡터 생성 .R | # 3 개의 정수로 3 개의 벡터 a, b, c를 생성하는 R 프로그램을 작성하십시오.
# 세 벡터를 결합하여 각 열이 벡터를 나타내는 3x3 행렬이됩니다. 행렬의 내용을 인쇄합니다.
a<-c(1,2,3)
b<-c(4,5,6)
c<-c(7,8,9)
m<-cbind(a,b,c)
print("Content of the said matrix:")
print(m) |
00b3632fa344914776b0288590c70ccd5835d0c2 | bcb3643083a56b1aad8879354bae95f926c308e9 | /code_rmd.R | cd223b023680eebf5a6134095d9b09b53832ccea | [] | no_license | anarinsk/cossim | 3bdb65549ce2f268bfa0bc93da4d14883cdfca8f | a2870a0841eb49062eb2a65799e0fc1f5ff0bd10 | refs/heads/master | 2020-06-23T23:24:37.584240 | 2019-11-19T05:51:01 | 2019-11-19T05:51:01 | 198,783,609 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,931 | r | code_rmd.R | #devtools::install_github("yihui/xfun")
#install.packages("Rcpp", dependencies=TRUE, INSTALL_opts = c('--no-lock'))
#install.packages("testthat", dependencies=TRUE, INSTALL_opts = c('--no-lock'))
#devtools::install_github("tidyverse/tidyverse", dependencies=TRUE, INSTALL_opts = c('--no-lock'))
#devtools::install_g... |
43ea40b2f763d84357fdb2233781e6d52639df9e | 2d47450c41c23f6d008bfca5bf08d3161bb13491 | /tests/sweetpotatobase/test_sp_phenotypes_search_post.R | a8ae2469a1ff24f1462ee8290e83f8a457684e50 | [] | no_license | khaled-alshamaa/brapi | 2c14727d65fc82a77d243bdc40c10b67955a04d5 | 5f2a5caa48d72e2412ead128b9143cc1882a060c | refs/heads/master | 2022-03-21T20:19:07.470329 | 2019-10-16T15:51:00 | 2019-10-16T15:51:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 500 | r | test_sp_phenotypes_search_post.R | context("sp phenotypes_search_post")
con <- ba_db()$sweetpotatobase
test_that(" are present", {
#skip("Very slow implementation")
res <- ba_phenotypes_search_post(con = con, pageSize = 1, studyDbIds = "136")
expect_true(nrow(res) > 1)
})
test_that(" out formats work", {
#skip("Very slow implementation")... |
60a0078d1ce2712a057afd3a9b1858f62a6f9a84 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/HDclust/man/HMM-class.Rd | 7d4fc22a4230f9984255d64906eb0812229f7e38 | [] | 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 | false | 1,791 | rd | HMM-class.Rd | \docType{class}
\name{HMM-class}
\alias{HMM-class}
\alias{HMM}
\alias{show,HMM-method}
\alias{getPrenumst,HMM-method}
\alias{getHmmParam,HMM-method}
\title{Class "HMM" to represent parameters associated with a variable block in the HMM-VB}
\description{
An S4 class to represent the model parameters associated with one ... |
b36f338f02313061803656f230bb5c5bceb30b79 | 31b5ffe76f689c8ad96877a81b40fecb698f2de2 | /R/skellam.reg.R | d2ab937969301fd5b0609da27982538449cd7d80 | [] | no_license | cran/skellam | 424ededa24b7ee726221f6e5540c2a445782ce36 | b1d3322e64490ac2d7c7e0586f291686951dcc8f | refs/heads/master | 2020-06-05T18:47:43.203205 | 2016-12-15T14:57:06 | 2016-12-15T14:57:06 | 17,719,412 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,219 | r | skellam.reg.R | #' @export
skellam.reg <- function(y, x) {
n <- length(y)
x <- stats::model.matrix( ~., data.frame(x) )
p <- dim(x)[2]
skelreg <- function(pa) {
b1 <- pa[1:p] ; b2 <- pa[ -c(1:p) ]
a1 <- x %*% b1 ; a2 <- x %*% b2
lam1 <- exp(a1) ; lam2 <- exp(a2)
a <- 2 * sqr... |
d923ade64fd8845b00b7fddfdac243cb3a37487c | 8cee9f0ff3e587075e3a588477cdf0a785ed3e66 | /install.R | 9ba2e46d6338001e9c08289af077fd9fb02a1228 | [
"Apache-2.0"
] | permissive | n8mauer/VisionEval | 2ae15902cebbf63573c232cf32dbc1174e842e92 | 35b7398a10d6f2793a199141bb4f09b5e71dff3b | refs/heads/master | 2020-03-19T07:31:16.838580 | 2018-04-16T17:24:01 | 2018-04-16T17:24:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,952 | r | install.R |
#Download and Install VisionEval Resources
#library(httr)
#If working within a proxy server, run the following commands to enable install from GitHub
#set_config(use_proxy(url="proxynew.odot.state.or.us", port=8080))
#set_config( config( ssl_verifypeer = 0L ) )
# Download and install the required libraries and their... |
b5ed4e19cd8f41eb554c38873458c098f7776d5d | 348d080bff29e2bd962a0e588d1d2e59024e928a | /man/scAlignOptions.Rd | c252102dfb971185d44b41e639e1ce904fd39df7 | [
"Apache-2.0"
] | permissive | zorrodong/scAlign | 5975cfb3f0aeb75d18ef6040c24635bf87886cee | 4100daa165e79b74cd4cd0be646950b38b08a769 | refs/heads/master | 2020-07-05T05:38:24.571835 | 2019-08-09T18:43:59 | 2019-08-09T18:43:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,542 | rd | scAlignOptions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scAlignClass.R
\name{scAlignOptions}
\alias{scAlignOptions}
\title{Set training options}
\usage{
scAlignOptions(steps = 15000, batch.size = 150,
learning.rate = 1e-04, log.every = 5000, architecture = "large",
batch.norm.layer = TRUE, dro... |
a353347c68267c2f2f367afcd96d73cdc6f67afe | 4f163a508f9d79967f058a1b12d12312f68b330f | /run_analysis.R | 038eee02c74ce877202b343b7c7c3d00f2a6fd26 | [] | no_license | eluciv/Getting-and-Cleaning-Data-Course-Project | a5594c2cfadadda41acf39d96e37c5deceb42977 | cc381899b1095119e9c1f8b574db8d2f42b15c1a | refs/heads/master | 2021-01-12T12:13:24.306200 | 2016-10-31T15:46:06 | 2016-10-31T15:46:06 | 72,370,550 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,608 | r | run_analysis.R | library(dplyr)
run_analysis <- function(data_dir = ".") {
data_directory <- paste(data_dir, "/UCI HAR Dataset", sep = "")
read_and_prepare_data <- function(data_set_file_name, activity_id_file_name, subject_id_file_name, activity_labels, feature_names) {
#Read train data set
data_set <-
read.... |
53512e6d7f049e217d4e6eae217c54791716befc | d7f4e9860534b2799c89d6bf29ac949e9131d19d | /func.R | be347d06af2ab3bf0f2d1209132fab1626782f53 | [] | no_license | cperez58/chem160module10 | d2ba942c6c0608cfa28dda815eaa6d973c041250 | 7299f09ebfdc90aa29daff3d47414bfc213e1dde | refs/heads/master | 2020-08-22T13:43:15.087107 | 2019-10-20T18:26:20 | 2019-10-20T18:26:20 | 216,407,637 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | func.R | func=function(x) {
return(-x^2*exp(-3*x))
}
opt=optimize(func,c(0.,5.))
cat(opt$minimum,opt$objective)
plot(func,0,5)
abline(h=opt$objective,col=4)
abline(v=opt$minimum,col=4) |
38e6474f244670fbc3942b052e66d0abb4302872 | bff946953e51145a5f7c85dbb0d9358d021e9c8d | /R/geom_signif.R | 96ddca6d6e0bdf80cd94a888e9b782c6edfcee82 | [] | no_license | ZhonghuiGai/ggroup | cd4a66689707acc1743d89eb83a7b1700980c3d0 | f561baa88f0db7a79d7b37cdb899ad506ddb2f84 | refs/heads/main | 2023-08-11T05:24:53.026446 | 2021-09-25T08:30:55 | 2021-09-25T08:30:55 | 380,489,489 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 19,687 | r | geom_signif.R | #' Create significance layer
#'
#' @param comparisons A list of length-2 vectors. The entries in the vector are
#' either the names of 2 values on the x-axis or the 2 integers that
#' correspond to the index of the columns of interest.
#' @param test the name of the statistical test that is applied to the values of... |
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