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a33f32474bfdbce06eaf8fb6e7abfc723191b062 | 116eaeafe38f8df9aaa628df6e444144339376ba | /MechaCarChallenge.R | fddb04af170ef176168ecb47d7b3425d7093a444 | [] | no_license | kaytar23/MechaCar_Statistical_Analysis | cfa6f4b02093084af3827ae80dc64c993e3c4c5d | 4c6ad3670b2b51e664be56faeeccb1c003bb4ff8 | refs/heads/main | 2023-08-18T01:19:23.926486 | 2021-09-23T03:52:22 | 2021-09-23T03:52:22 | 409,409,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 749 | r | MechaCarChallenge.R | library(dplyr)
MechaCar_mpg <- read_csv("MechaCar_mpg.csv")
summary(lm(mpg ~ vehicle_length + vehicle_weight + spoiler_angle + ground_clearance + AWD,data=MechaCar_mpg))
Suspension_Coil <- read_csv("Suspension_Coil.csv")
total_summary <- Suspension_Coil %>% summarise(Mean=mean(PSI),Median=median(PSI),Variance=v... |
10b3df9cf919aefd3c5b361650ff4e2310499f98 | 2d7b44297ddfcedbea062dd960caa90c72841173 | /vote-rennes.R | f6d97c30914cccaed7ee264f16867c4f812dafa7 | [] | no_license | DataBzh/territoire | c7c342a4ea64697142fb2aa6c5d0134bf780362b | fb6a07f1a76dc2861cf37fb07455159948427735 | refs/heads/master | 2021-01-19T03:31:44.667322 | 2019-05-27T06:54:32 | 2019-05-27T06:54:32 | 64,167,586 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,081 | r | vote-rennes.R | library(tidyverse)
library(rgdal)
source("/home/colin/Dropbox/R/misc/data-bzh-tools-master/main.R")
rennes <- read_csv2("https://data.rennesmetropole.fr/explore/dataset/centres-de-vote/download/?format=csv&timezone=Europe/Berlin&use_labels_for_header=true")
rennes <- rennes %>%
separate(`Geo Point`, into = c("long","... |
8da72434959bf5e76f562413a904cefde8fbcf7d | f21245e27e040e5c02a3f4a1936e8e8b96f069b1 | /R/gplates_reconstruct.R | e1aa168366faed0e04190ec02c53a382eabf315f | [] | no_license | LunaSare/gplatesr | d642b59ac07353f83220419a534cf962e3b052be | 24c182c77caed9eb6125380084f56ca6d5884960 | refs/heads/master | 2022-09-13T22:37:07.489673 | 2022-08-29T16:43:44 | 2022-08-29T16:43:44 | 164,476,513 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,953 | r | gplates_reconstruct.R | #' Launch the service via docker
#' @export
launch_docker <- function() {
#docker <- stevedore::docker_client()
#docker$container$run("alpine:3.1", c("echo", "hello world"))
#system("docker run --rm -it -p 8888:80 gplates/gws")
system("docker run --rm -p 8888:80 gplates/gws", wait=FALSE)
}
#' Reconst... |
84332a105f40e144ad66fade97051a2b3c3df58f | 127141eb7e2897126bc81caa19b6e6cb5ceb9572 | /global.R | 4680b9b7666f68b5053731462ed0df6548c1e17f | [] | no_license | yogesh1612/network_data_prep_app | c04ecadc2a506f4d3f7701e1a03bd666e4f8e9e2 | 6c4b2229b93bebe73158bdb27d90f5cfb07a7f27 | refs/heads/main | 2023-03-12T23:29:02.274201 | 2021-02-25T09:33:27 | 2021-02-25T09:33:27 | 341,204,060 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,697 | r | global.R | ## --- functionize dataframe to adjacency mat for network-an app input
df2adjacency <- function(input_df, cutoff_percentile=0.25,id_var){
rownames(input_df) <- make.names(input_df[,id_var], unique=TRUE)
#rownames(input_df) <- input_df[,id_var]
input_df[,id_var] <- NULL
# first, retain only metric ... |
2fc358f9d4ea151f1019742fe343a99e2ea21fa2 | 137736b1a6048880f4d525e15f46ce4092239548 | /code/03d simulate sampling - stratified by zone.R | 30f1d9aa8498aa0e37068decccc1f916a93d82db | [] | no_license | BritishTrustForOrnithology/eodip5_earth_obs_power_analysis | 21a5a68f668924d40b254ed7a01bdccdf22484e7 | c7b77137d567080c7b600f6158ad8c251b8d7364 | refs/heads/master | 2022-11-06T17:59:13.021168 | 2020-06-23T07:37:52 | 2020-06-23T07:37:52 | 274,339,857 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,789 | r | 03d simulate sampling - stratified by zone.R | #simulate sampling and error - simple random sampling within classification zones
#Simon Gillings
#February 2016
library(plyr)
#set working directory
setwd('X:\\Shared Projects\\Defra80 - EODIP5\\gis analysis\\')
# LOAD DATA ------------------------------------------------------------------------------------------
#l... |
f3b9e5e9269fdedae937182c508ba7217788de3c | 33b7262af06cab5cd28c4821ead49b3a0c24bb9d | /RegressionTests/compare.R | 8e950023a4f331ee254ae289a95455cbab7c89bf | [] | no_license | topepo/caret | d54ea1125ad41396fd86808c609aee58cbcf287d | 5f4bd2069bf486ae92240979f9d65b5c138ca8d4 | refs/heads/master | 2023-06-01T09:12:56.022839 | 2023-03-21T18:00:51 | 2023-03-21T18:00:51 | 19,862,061 | 1,642 | 858 | null | 2023-03-30T20:55:19 | 2014-05-16T15:50:16 | R | UTF-8 | R | false | false | 3,820 | r | compare.R | setwd("~/tmp")
##############################################################
Old <- "2018_03_10_21__6.0-79"
New <- "2018_05_25_22__6.0-80"
oldResults <- list.files(file.path(getwd(), Old), pattern = "RData")
newResults <- list.files(file.path(getwd(), New), pattern = "RData")
oldOrphan <- oldResults[!(oldResults %... |
4714a7ca4647fe3ac13414e98d8101e5bf21e34f | 4d0db5c2e04637437fc318cb50267b65341f53cc | /DataAnalysis/analysis.r | 6bdf646d3f6c6d8ac73a129b3a903602dfad9758 | [] | no_license | blukaniro/TrainingGrad190716 | a70b065cdae046d4f945cdac978ca1f8b444c23a | c727e21d2319849ac77e84b6d304298211edaf5d | refs/heads/master | 2020-06-20T01:25:52.263030 | 2019-07-19T03:42:20 | 2019-07-19T03:42:20 | 196,943,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,713 | r | analysis.r | # 190719田島亮介
# データの読み込みと整理
d<-read.table("dataset.txt", header=T)
d2<-d[c(-4,-11),] #出液とSFWが著しく大きい値のものを外れ値として除外
# 分散分析
summary(aov(sap~treatment,d2))
summary(aov(SFW~treatment,d2))
summary(aov(panicle~treatment,d2))
summary(aov(root~treatment,d2))
## 新鮮重: 10%水準で，慣行>有機，節根数：5%水準で慣行>有機
## これらを栽培方法との関係から考察．ある... |
29c3e7b33a150e0dc72d1d191265937949231c7b | 3c38d8cbe00ffb6d1150682ea1f3c79acfc33d96 | /R/project_to.R | 0a267ccfe4c09591db0c68e5609248cce865d8d9 | [] | no_license | HughParsonage/grattan | c0dddf3253fc91511d122870a65e65cc918db910 | cc3e37e1377ace729f73eb1c93df307a58c9f162 | refs/heads/master | 2023-08-28T00:12:35.729050 | 2023-08-25T08:02:25 | 2023-08-25T08:02:25 | 30,398,321 | 26 | 11 | null | 2022-06-26T15:44:27 | 2015-02-06T06:18:13 | R | UTF-8 | R | false | false | 1,770 | r | project_to.R | #' Simple projections of the annual 2\% samples of Australian Taxation Office tax returns.
#'
#' @param sample_file A \code{data.table} matching a 2\% sample file from the ATO.
#' See package \code{taxstats} for an example.
#' @param to_fy A string like "1066-67" representing the financial year for which forecasts of ... |
c3c49b4d20e99321bae60826f2ca7a5066b71ef7 | fe734fd0801d41d36a223b66851e0b1d2a6b1eed | /data_analytics.R | c9c4a628fe22338f2a51c65de553a92b4055644e | [] | no_license | anyelacamargo/babycorn | a6ef1053d4842c8f351608c58530d784a273f046 | fbf90d0ffe5514ab5c1b57d21af1378a1f9ca682 | refs/heads/master | 2020-09-24T02:18:09.139475 | 2020-03-04T10:30:16 | 2020-03-04T10:30:16 | 225,638,213 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,222 | r | data_analytics.R | library(openxlsx)
library(ggplot2)
library(reshape2)
#library(dplyr)
library(caret)
library(outliers)
library(glmnet)
library(e1071)
library(xgboost)
parm_search_xgboost <- function(dtrain){
searchGridSubCol <- expand.grid(subsample = c(0.5, 0.75, 1),
colsample_bytree = c... |
877768eef339e735c20e57793986e2549ccab1d5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/emmeans/examples/rbind.emmGrid.Rd.R | 22ec369431f535cfdc2fe53a2f21b625e53df904 | [] | 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 | 609 | r | rbind.emmGrid.Rd.R | library(emmeans)
### Name: rbind.emmGrid
### Title: Combine or subset 'emmGrid' objects
### Aliases: rbind.emmGrid +.emmGrid [.emmGrid
### ** Examples
warp.lm <- lm(breaks ~ wool * tension, data = warpbreaks)
warp.rg <- ref_grid(warp.lm)
# Show only 3 of the 6 cases
summary(warp.rg[c(2,4,5)])
# Do all pairwise co... |
4801fda0b755959f97bef3118952599618e86bcd | 97106176566468697903370a30a9b68661167af3 | /ps4ex2.R | 9970808d5f71f83a344672023803a4d128779b69 | [] | no_license | rossihabibi/Econometrics4Law16 | 91d120dcba6793b432b49432b21ae8a8d795977a | a251c9de65edcb6a87a2e1ed41c4dc5268958c5d | refs/heads/master | 2020-12-24T06:13:50.626704 | 2016-12-09T20:45:33 | 2016-12-09T20:45:33 | 73,163,681 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,318 | r | ps4ex2.R | rm(list = ls())
library(tidyr)
library(dplyr) # for easy data manipulation
library(AER) # ivreg
library(foreign) # to use stata data formats
options(digits = 2)
download.file('http://fmwww.bc.edu/ec-p/data/wooldridge/mroz.dta', 'data/mroz.dta', mode="wb")
mroz <- read.dta("data/mroz.dta")
head(mroz)
dim(mroz) #17 ... |
b4f35f2f74fe27cfe04404d434c8842349082b00 | 62cfdb440c9f81b63514c9e545add414dc4d5f63 | /man/qat_plot_noc_rule_1d.Rd | c36529c5b33c7e15e6e1a56cdf545f6755e1bbab | [] | no_license | cran/qat | 7155052a40947f6e45ba216e8fd64a9da2926be4 | 92975a7e642997eac7b514210423eba2e099680c | refs/heads/master | 2020-04-15T16:53:45.041112 | 2016-07-24T01:26:59 | 2016-07-24T01:26:59 | 17,698,828 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,942 | rd | qat_plot_noc_rule_1d.Rd | \name{qat_plot_noc_rule_1d}
\alias{qat_plot_noc_rule_1d}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Plot a NOC rule result}
\description{
A plot of the result of a NOC rule check will be produced.
}
\usage{
qat_plot_noc_rule_1d(flagvector, filename, measurement_vector = NULL,
max_return_elem... |
d3d1280a59b973d26e56a3da712bf413c13b65c0 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /ROI.plugin.qpoases/man/Example_01.Rd | 44e21bb85c5c9aaf38db09917d56f1d09c7d77f9 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 701 | rd | Example_01.Rd | \name{Example-1}
\title{Quadratic Problem 1}
\description{
\deqn{maximize \ \ x_1^2 + x_2^2 + x_3^2 - 5 x_2}
\deqn{subject \ to:}
\deqn{-4 x_1 - 3 x_2 + \geq -8}
\deqn{ 2 x_1 + x_2 + \geq 2}
\deqn{ - 2 x_2 + x_3 \geq 0}
\deqn{x_1, x_2, x_3 \geq 0}
}
\examples{
require("ROI")
A <- cbind(c(-4, -3, 0),... |
cc6b3fb041b323628fffd80903b8170301139514 | 946f724c55b573ef4c0d629e0914bb6bca96f9e9 | /man/Posterior_phi.Rd | f32ab6c4b6ed2f611ef26e7c35dd991e1ab276ba | [] | no_license | stla/brr | 84fb3083383e255a56812f2807be72b0ace54fd6 | a186e16f22b9828c287e3f22891be22b89144ca6 | refs/heads/master | 2021-01-18T22:59:46.721902 | 2016-05-30T09:42:14 | 2016-05-30T09:42:14 | 35,364,602 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,208 | rd | Posterior_phi.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/posteriors.R
\name{Posterior_phi}
\alias{Posterior_phi}
\alias{dpost_VE}
\alias{dpost_phi}
\alias{ppost_VE}
\alias{ppost_phi}
\alias{qpost_VE}
\alias{qpost_phi}
\alias{rpost_phi}
\alias{spost_phi}
\title{Posterior distribution on the ... |
8f54b2deb1719945d90cf5168aa98b4d3c659614 | 4e38799dd969f4c2fef6b34e9539f63f8b17d666 | /example/archived_modules/scope_02_TMT_int.R | d5000ce61b825bf94f6d2f552743c27950cc2139 | [
"MIT"
] | permissive | SlavovLab/DO-MS | f97eb2626dd272156e19a25fe50488a036c59f4d | e637a3bd5bfd32558f33e0a23b7302109e3088f7 | refs/heads/master | 2023-08-08T01:37:54.457583 | 2023-07-30T16:30:25 | 2023-07-30T16:30:25 | 141,730,083 | 21 | 8 | MIT | 2023-07-30T16:30:27 | 2018-07-20T15:42:00 | HTML | UTF-8 | R | false | false | 2,807 | r | scope_02_TMT_int.R | init <- function() {
type <- 'plot'
box_title <- 'Reporter ion intensity'
help_text <- 'Plotting the TMT reporter intensities for a single run.'
source_file <- 'evidence'
.validate <- function(data, input) {
validate(need(data()[['evidence']], paste0('Upload evidence.txt')))
# require repor... |
6971a84bfe83115c0abb88684860ac2a1f19e79a | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /perccal/R/RcppExports.R | d4257b5a7db74fea311e411436c1a8b4abd4c017 | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 479 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
sample_rcpp <- function(N, nsamp) {
.Call('_perccal_sample_rcpp', PACKAGE = 'perccal', N, nsamp)
}
Cquantile <- function(xx, p) {
.Call('_perccal_Cquantile', PACKAGE = 'perccal', xx, p... |
4ee650ac361fd960f8ee8d002735722d35833868 | 5cae7a96ee29b4561c5c9e8c50106b6e3f8c3151 | /cachematrix.R | ea3b68eadb23ca480229a5c9bc57da7974feca23 | [] | no_license | HadidVera/ProgrammingAssignment2 | 09916d8742d3cbffc03b209dd5269abc1da2fce9 | 04f9f01bc7c677afb67df0662284cfc4090d5a51 | refs/heads/master | 2021-04-28T10:32:31.701697 | 2018-02-19T16:38:40 | 2018-02-19T16:38:40 | 122,069,152 | 0 | 0 | null | 2018-02-19T13:51:18 | 2018-02-19T13:51:18 | null | UTF-8 | R | false | false | 1,465 | r | cachematrix.R | # Coursera - R programming - Programming assignment (week 3)
# Assignment: Caching the Inverse of a Matrix
# HVera
# February 2018
####
# Matrix inversion is usually a costly computation and there may be some benefit to caching
# the inverse of a matrix rather than compute it repeatedly.
# The following ... |
62cd1d3ec02946d05c28a410ea76be66a28d3de1 | a8c00b380003dd12b4957ba02501574f53ef46a6 | /run_analysis.R | 7ff10c4418907a74abc36b1d761d4835e5d2148a | [] | no_license | problemsny/Coursera-Week3-CourseProject | 802366d07dc86b0298b99ad50cfe413cb5d7be02 | 2798ceeaf57d7f5e417a6ad18c3d6ea403d7d192 | refs/heads/master | 2021-01-01T20:05:32.555052 | 2015-05-19T00:54:01 | 2015-05-19T00:54:01 | 35,848,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,749 | r | run_analysis.R | # This is for Coursera - Week 3 - Course Project
# Assignment to get and clean data collected on 30 subjects doing 6 different activities
# Of those 30, 70% were selected for training (the "train" files) and 30% as test (the "test" files)
# Refer to the README.txt for further explanation.
# First part - Reading all fi... |
8686d26f321b36d916fe0bc21547d9c7bba00d4b | ddc2b096e681398f576a95e40c7fd366b65f50a2 | /SDPSimulations/AssortHetHeatMapMK.R | 84949f446e2c2bdc592dd90e304765b977efee08 | [] | no_license | sbellan61/SDPSimulations | f334d96743c90d657045a673fbff309106e45fce | cfc80b116beafabe3e3aed99429fb03d58dc85db | refs/heads/master | 2021-03-27T20:48:25.857117 | 2017-09-19T20:13:37 | 2017-09-19T20:13:37 | 21,144,447 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,020 | r | AssortHetHeatMapMK.R | ####################################################################################################
## Makes control files for each analysis within which each line giving one R CMD BATCH command line
## to run on a cluster.
###############################################################################################... |
f6fd1826e043fd39b25e9ac007e176765710a905 | b12c8b99b619a1f084cc85ed316f287b2ef29274 | /boxer_data_cleaning.R | dc29d853443fb5011f7098d5ee6b15194a5a823f | [] | no_license | LocalSymmetry/ChampionBoxers | a8cc094b521f0e072a21d35bc01b54ac1aac9382 | 4eae55c17b39460b0a82fa089a9196389cc5bdcf | refs/heads/master | 2021-01-21T21:15:36.632241 | 2017-06-21T14:36:14 | 2017-06-21T14:36:14 | 94,803,710 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 10,215 | r | boxer_data_cleaning.R | # Data cleaning for "A Search for Champion Boxers".
# Data were scraped from BoxRec.com for title fights
# of all 5 major boxing organizations (WBO, WBA, WBC, IBO, IBF).
#
#
# Data source: BoxRec.com
# Raw variable information for each league
# division: weight class of title fight (17 factors)
# boxer1id: Box... |
2defde7738b77642d8bbc9261c0e2024a039c8c1 | bb397c245cbe62a30db267f0f8ea54f1fd164119 | /Rfunctions/GllimFitIID.R | b9c8eefd6974cd93b3ca0923bf43a9605e63c5c2 | [] | no_license | Trung-TinNGUYEN/GLLiM-ABC-v0 | 503b0734c6669d9aad40b041718bd19fe9b86bec | f30ed29982b7244fe3d505ae9fe17c81b17c91e8 | refs/heads/main | 2023-07-26T01:21:50.087487 | 2021-08-21T23:22:46 | 2021-08-21T23:22:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,390 | r | GllimFitIID.R | GllimFitIID<-function(thetadata, ydata,iR, K, constr=list(Sigma="")){
# %%%%%%%% Gaussian Locally Linear Mapping and pre-computed quantities %%%%%%%%%
# % Description: Fit a GLLiM for iR IID data model using the xLLiM package with constraints
# % cstr= constr on covariance matrices Sigmak, can be
# constr=list(... |
b6bbce5339dfdaa0e8a666675ce1065bbaa009a5 | a7cef5b06a271bbe30affa1b45235ae2e814b87b | /man/xcusum.sf.Rd | cc96192831f4b93500a3e7ba15e4a4ad21d0c2a3 | [] | no_license | cran/spc | 23075576d12adf3641607accce3e2d4149d55e5c | 296041a2d3a3b082415fa676bceaded3f0d39f08 | refs/heads/master | 2022-11-10T04:04:21.939750 | 2022-10-24T11:30:02 | 2022-10-24T11:30:02 | 17,699,981 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,741 | rd | xcusum.sf.Rd | \name{xcusum.sf}
\alias{xcusum.sf}
\title{Compute the survival function of CUSUM run length}
\description{Computation of the survival function of the Run Length (RL) for CUSUM control charts monitoring normal mean.}
\usage{xcusum.sf(k, h, mu, n, hs=0, sided="one", r=40)}
\arguments{
\item{k}{reference value of the CUSU... |
94df418cd249f58467c2f67b961b7db96182b0b7 | 585a9c9b373cc7e0e048201f3d78ebf26b303e3d | /bin/sorts.R | e9a0fc098301860498ddaad3a6620df2b1b87413 | [] | no_license | rlowe/stalk2graph | 80cf07c852c012cb282ab2d7f8e3fa17324b12b9 | 11cc70b0a85d32b46efdf792e6b27151e9bfdd37 | refs/heads/master | 2020-04-10T16:29:30.567014 | 2016-05-01T06:56:50 | 2016-05-01T06:56:50 | 8,306,354 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,714 | r | sorts.R | args<-commandArgs(trailingOnly = FALSE)
require(ggplot2)
sorts_data<-read.csv(args[length(args)-1])
smp<-diff(sorts_data$merge_passes)
srange<-diff(sorts_data$range)
srows<-diff(sorts_data$rows)
ss<-diff(sorts_data$scan)
sorts_data<-data.frame(Time="", Type="", Value=as.numeric(""))
sorts_data<-sorts_data[-1,]
for... |
a0b1e3ea0f3e244a96c0f57cf7342ac91eb79f00 | 8117e00c26fd2906e1542aec58a88c3967c2fb2d | /tests/testthat/test-makeSuggList.R | 0c84f5e17fc2dceb90026ba110025dc355d4785e | [] | no_license | RGLab/corpusFreq | fbf280fa91f5905d2a5c5af12f6dbef11458cd50 | f44551c40ab82934db3496d135faa00316f8219e | refs/heads/main | 2021-05-02T15:06:50.119428 | 2021-04-26T20:46:49 | 2021-04-26T20:46:49 | 120,689,873 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,431 | r | test-makeSuggList.R | # Helpers-------------------------------------------------------
tmp <- c("lymphocyte is a dose of reality in a harsh world.",
"Doses would be too much.",
"And is critical for this test.")
freqTbl <- makeFreqTbl(tmp)
getRes <- function(vec, freqTbl){
metaData <- list()
metaData$medLength <- s... |
b71c02919c11019befce3bd7e8a147bd8a6b14d4 | 1b388061103d48f7e9fab752ff794e16222a3116 | /R/measure_missing.R | 73149ab8882c2b502cdc43090ed8f7229960adea | [] | no_license | neyhartj/gws | b5fc9257075a162fadebd8ea6a3634638555e929 | cb52791be50d96b3cb4b733c3aa08476b7c05249 | refs/heads/master | 2021-01-19T06:46:14.146652 | 2020-03-12T21:25:46 | 2020-03-12T21:25:46 | 63,906,263 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 252 | r | measure_missing.R | #' Measure the missingness of a SNP or entry
#'
#'
measure.missing <- function(x, type = "numeric") {
if (type == "numeric") {
return( sum(is.na(x)) / length(x) )
}
if (type == "nucleotide") {
return( sum(x == "NN") / length(x) )
}
}
|
2ff2a2d7f46bb7bfa07e1ab0fce9258e8baa8bf2 | 5ba559ffe9bea4744986265aaf0abb900f318232 | /R/pruning.r | 3b7c1ac9cf73907e4ec6f5edbabe23916a88476a | [
"BSD-3-Clause"
] | permissive | BenJamesbabala/autoBagging | e021721b5467f5bff267279256e76692912ffd44 | af6bca0552917953555c778e3c4964d41698f65f | refs/heads/master | 2020-12-02T12:46:51.029484 | 2017-06-23T12:45:35 | 2017-06-23T12:45:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,694 | r | pruning.r | #' Boosting-based pruning of models
#'
#' @param form formula
#' @param preds predictions in training data
#' @param data training data
#' @param cutPoint ratio of the total number of models to keepFORA
#'
#' @export
bb <- function (form, preds, data, cutPoint) {
class <- get_target(form)
prunedN <- ceiling(ncol(pr... |
d7bd4f0fccf05846848dd4c20aa8a8e475a9566f | 6a2f6ab46c35441db0288fbde4be1a5188f2ec30 | /R/ti_celltrails.R | 4a780d652ba02f2e91aa5a239f4ef99cf5c3dc77 | [] | no_license | herrinca/dynmethods | f7595c8ce4f06cb2cb4b809c49ceebd705330940 | 0a5768cf4452b2b745ee675bbd013140d54029da | refs/heads/master | 2020-03-26T22:19:11.513964 | 2018-08-21T18:03:51 | 2018-08-21T18:03:51 | 145,448,352 | 0 | 0 | null | 2018-08-20T17:17:18 | 2018-08-20T17:17:18 | null | UTF-8 | R | false | false | 3,331 | r | ti_celltrails.R | ######################################### DO NOT EDIT! #########################################
#### This file is automatically generated from data-raw/2-generate_r_code_from_containers.R ####
################################################################################################
#' @title Inferring a trajec... |
7a529848fb404863a72c8e3114929f9b03c1542d | 2c4de2241e8567a64987ab99983c49447691cff8 | /Samsung Smartphone Data Project/Samsung Data Code.R | b3def8fd46f801f3af6a43cf2b453c5c9d48b5a9 | [] | no_license | BenPiggot/Projects | 53b47081f7912d22d9cdeee9981dfcbc4db0a95a | 5dbb812b8bdf4bbf079130d9bb96fbbc2136f5b4 | refs/heads/master | 2020-05-30T23:01:02.807114 | 2014-06-24T05:26:02 | 2014-06-24T05:26:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,938 | r | Samsung Data Code.R | # Set working directory
setwd("~/Desktop/UCI HAR Dataset/")
# Read in "Features.txt" file and clean the variable labels with the sub function.
# This file will be used as the column labels for my new data frame.
features_df <- read.table("~/Desktop/UCI HAR Dataset/features.txt")
features <- as.character(features_df[,2... |
6cd39a04cdc9b16a00e82e165798373156bbf0d7 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/E_Penman/libFuzzer_E_Penman/E_Penman_valgrind_files/1612738781-test.R | 114b208e3f6b787a79fef2c2d246c8d4d95379d2 | [] | 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 | 605 | r | 1612738781-test.R | testlist <- list(Rext = numeric(0), Rs = NaN, Z = c(NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.96763823300824e+280, 3.65588327285767e+233, 4.71235854849405e+257, 1.0639991435071e+248, NaN, 4.78479882533389e-304, 5.44361528587885e-320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0... |
8e79d4dd686c3f9243491bfebb348d8aba71484d | 6b4fe2baa84e74af637f319ea5d887cb2fd6f9a2 | /kevin/rimod-analysis/rnaseq/burgholderia_infection_analysis.R | 65a604f46174d5d2b6391d0af32dcd35f83f449c | [] | no_license | dznetubingen/analysis_scripts | 1e27ca43a89e7ad6f8c222507549f72b1c4efc20 | 4fcac8a3851414c390e88b4ef4ac461887e47096 | refs/heads/master | 2021-06-25T10:47:40.562438 | 2021-01-04T16:02:34 | 2021-01-04T16:02:34 | 187,789,014 | 1 | 0 | null | 2020-09-03T11:37:25 | 2019-05-21T07:55:17 | Jupyter Notebook | UTF-8 | R | false | false | 11,403 | r | burgholderia_infection_analysis.R | ###########################################################
# Analysis of Salmon quantified RiMod frontal RNA-seq data
# !!! Analysis of Pathogengroup with apparenty Burgholderia infection !!!
#
#
##########################################################
library(tximport)
library(DESeq2)
library(GenomicFeatures)
libr... |
ff2b6f6cb9cb88c4f0bd8aa47c03582fa139551b | f91d3993ecdceba7e19fcdd16ac31fc30121e114 | /man/model_exponential_gamma.Rd | b43a2a80d1ff06a2a1f8ef7069b5641f44252ef7 | [] | no_license | eliobartos/bayeselio | 5e5b0e5e0bc0dd8808a93969da41d58fc0843234 | 97bba1bca1d54a83d22300ac14794dece7e3eb04 | refs/heads/master | 2023-02-26T22:52:33.625846 | 2021-02-02T11:50:37 | 2021-02-02T11:50:37 | 270,571,898 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,648 | rd | model_exponential_gamma.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/model_exponential_gamma.R
\name{model_exponential_gamma}
\alias{model_exponential_gamma}
\title{Run bayesian exponential-gamma model for estimating non-zero positive variable}
\usage{
model_exponential_gamma(shape, rate, n_sample, sum_sample,... |
d3732fd612b5e968d8d6c59fa86bf6cfa8e99e13 | 3a80a99645855f421ecf7fd902ff92f18d1afec8 | /codes/Analysis3.R | ecef3f42588184955f28723406e7dc2728735ded | [] | no_license | hjkim88/Mohamed_Flu | 729c0391900518d3b851bbc14f82a025adc90c3f | 13d33b396d05b0dd840e1d9ee0b1352ebf075b75 | refs/heads/master | 2021-04-09T20:44:51.058756 | 2020-04-09T20:36:44 | 2020-04-09T20:36:44 | 248,878,517 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,092 | r | Analysis3.R | ###
# File name : Analysis3.R
# Author : Hyunjin Kim
# Date : Apr 9, 2020
# Email : hyunjin.kim@stjude.org
# Purpose : There may be a concern that age can affect the cytokine analysis.
# e.g., we see cytokine level difference between IV+ and IV- but
# it can happen du... |
a3be76283e4e6477def67d9d2007b462afc1aa60 | 82eeee2eaf170541c9336a00d14e6ac1f3d6f4fa | /ADSP - 로지스틱 회귀모형.R | 093b834d7b65ba8dc95610462e779b4169631c81 | [] | no_license | wpzero1/RStudy | 58d19493f001f3a2d06e4864eeeb6ac44389c0a2 | 521bd66fcc8982303040b501190c348d7eb6140d | refs/heads/master | 2020-06-27T06:14:08.227373 | 2019-08-16T12:54:30 | 2019-08-16T12:54:30 | 174,159,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,407 | r | ADSP - 로지스틱 회귀모형.R | #로지스틱 회귀모형
# 8장 정형 데이터 마이닝
# 01. 데이터 마이닝 개요 : 거대한 양의 데이터 속에서 쉽게 드러나지 않는 유용한 정보를 찾아내는 과정
# (1) 분류 : 의사결정나무, memory-based reasoning 등
# (2) 추정 : 연속된 변수값 추정. 신경망 모형
# (3) 예측 : 장바구니 분석, 의사결정나무, 신경망 등
# (4) 연관분석 : 장바구니분석
# (5) 군집 : 데이터마이닝이나 모델링의 준비 단계
# (6) 기술
# 데이터마이닝 5단계 : 목적 정의 - 데이터 준비 - 데이터 가공 - 데이터 마이닝 기... |
948f246ba577b1f77ecb5c3b12adc9fc1b8c0196 | 99f3d631d68638ea24f16e3008fd48d883606982 | /Car_crash.R | 0b7e3a9f897b0a7f1598b516ca1136722ad5253a | [] | no_license | BN-project/BN-project | d1d9552a7acb9af479298dede8b4123503416620 | 0c6582270a666b66317a75eee95297e249cb9100 | refs/heads/master | 2021-09-03T07:03:14.065512 | 2018-01-06T17:36:31 | 2018-01-06T17:36:31 | 109,718,110 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,740 | r | Car_crash.R |
#### Probabilistic Modelling and Bayesian Networks ####
#### The Project: Car Crashes ####
#############################################################
# Reading the document:
crashes <-read.csv("Car_crash_data_project.csv",header=T,sep=",")
#### Preparation and exploration of dat... |
76823b0f87c1804347ea8025cf8bc9fe9f60283d | 3d78414d840fb586325c73f0b116e908e3179163 | /R/parse_quiz_metadata.R | faf839f29362a3ceaff8087755ef3808044feda1 | [] | no_license | kamclean/cowboy | cf537c956912c1b7b431ac5fd794e1222bf017d6 | c04a19ce231db60a70ece3ee16d48db85d8aa56a | refs/heads/main | 2023-04-02T11:28:22.789164 | 2021-04-05T13:45:06 | 2021-04-05T13:45:06 | 354,847,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,623 | r | parse_quiz_metadata.R | # parse_quiz_metadata--------------------------------
# Documentation
#' Creates a moodle quiz data dictionary
#' @description Wrangles files directly extracted from moodle to create a formatted data dictionary for the moodle quiz
#' @param question_xml Output from xml2::read_xml() applied to Moodle Quiz XML file. From... |
bdf2d1a16d9247a3a11920610294a4337c8fae48 | 567e72b5e2be621384ef613a914ed6f41ee1cc3a | /R/make_groups.R | d5dabf24daaf14070ae961e0ea2e4a625660ab0f | [
"MIT"
] | permissive | tidymodels/rsample | 2dce83f71b341938b00ecd39bde74c654fcb78c1 | be593b9e5502998832a0a939197bfc3a4b46738b | refs/heads/main | 2023-08-30T23:11:24.971458 | 2023-08-23T15:12:18 | 2023-08-23T15:12:18 | 89,093,570 | 251 | 63 | NOASSERTION | 2023-08-23T08:13:42 | 2017-04-22T19:19:58 | R | UTF-8 | R | false | false | 11,454 | r | make_groups.R | #' Make groupings for grouped rsplits
#'
#' This function powers grouped resampling by splitting the data based upon
#' a grouping variable and returning the assessment set indices for each
#' split.
#'
#' @inheritParams vfold_cv
#' @param group A variable in `data` (single character or name) used for
#' grouping obse... |
c4ca28474db07a49a2db2544d1775f27e0800b4f | 9132689eb7595fccb1811f9fe08cf1981bedeed9 | /man/imputeYn.extra.Rd | fdea4886ab9b738d3a0a29acc64faa1a1a184d90 | [] | no_license | cran/imputeYn | e2423f180bb0d74519291bd7943edb555340f625 | 579a5de507c184f87bad9a87e5844217ee693003 | refs/heads/master | 2021-01-24T06:13:24.603196 | 2015-10-23T22:08:14 | 2015-10-23T22:08:14 | 17,696,763 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,329 | rd | imputeYn.extra.Rd | \name{imputeYn.extra}
\alias{imputeYn.extra}
\title{
Imputing the Last Largest tied Observations with a new method}
\description{
The method provides one step ahead imputed values for tied censored observations.
}
\usage{
imputeYn.extra(Y, delta, hc.Yn, method = "km.TPQ", trans.sprob=NULL,
stime2=NULL, sp... |
ba2efa60bb70dca95462e8c149e022b15150e495 | 0a447c5e9562832c3456a893c1d1bc66a123289a | /exercise08_question1.R | 7ab407dd5c6246d2c20a4bde39135979a97e3b57 | [] | no_license | mcwang25/ICB2019_Exercise08 | 58f04b1f541dd3235701baa98f696733a591fcdc | 81e8a5483520a7c8347be9f52a21957528a4076b | refs/heads/master | 2020-09-11T05:14:59.822703 | 2019-11-22T01:53:30 | 2019-11-22T01:53:30 | 221,950,456 | 0 | 0 | null | 2019-11-15T15:24:26 | 2019-11-15T15:24:25 | null | UTF-8 | R | false | false | 615 | r | exercise08_question1.R | # Makes plot of UW v MSU game score vs time from 1-22-13
# Note assignment says not to worry about how please the graph is visually, merely to focus on necessary control structures
bball <- read.table(file="UWvMSU_1-22-13.txt", header=TRUE)
UWscores <- bball[bball$team=="UW",]
MSUscores <- bball[bball$team=="MSU",]
MSU... |
49fae1509d576bc7dbd9361bdb905a6edfd77198 | 6dec0f6aec45dff66fcc2f480b03a5ffe3b9408c | /man/gx.eb.Rd | 4c21ba313a116e0c785efdd92695d13b886fe26b | [] | no_license | cran/rgr | 9645638952aa019902d3308e7e6cf04c1112bab7 | 87383fabc4cb3529c4c97493c596f7fd347cf302 | refs/heads/master | 2021-01-21T21:55:06.570408 | 2018-03-05T22:42:52 | 2018-03-05T22:42:52 | 17,699,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,860 | rd | gx.eb.Rd | \name{gx.eb}
\alias{gx.eb}
\title{ Computation of Empirical Balances }
\description{
Computes empirical balances (ratios) for the stated columns of a \code{n} by \code{p} matrix of compositional data.
}
\usage{
gx.eb(r, s, xx, ...)
}
\arguments{
\item{r}{ number of parts in the numerator. }
\item{s}{ num... |
b8490fbaf83ffd87940f557c645dfc2d99679ccd | 44a2d03cd3f012721b41223c4bb3f4dc5de483a9 | /man/fbind.Rd | 0c3dd4f96d45e27b0bd742ec89320528a282b586 | [
"MIT"
] | permissive | sumalibajaj/modelbuilding_try | fd26b46d846ab897d3dee7fcb15c4dc7ed1339b8 | a79b3151a6c5bc9676b46acda2d521a1aecd51bd | refs/heads/main | 2023-03-30T19:10:43.135402 | 2021-03-29T14:50:58 | 2021-03-29T14:50:58 | 352,672,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 207 | rd | fbind.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fbind.R
\name{fbind}
\alias{fbind}
\title{Title}
\usage{
fbind(a, b)
}
\arguments{
\item{b}{}
}
\value{
}
\description{
Title
}
|
9da97534c0a22240293def3f74e75c1900a77c3a | eb1f7d5c5009758ed188a4ef3d32e67741b4a552 | /R/Expectation.R | cb0a338a571335d3933971f6d636fd48a74be671 | [] | no_license | l-modolo/fdrDEP | d80874419e8173ebfe045c3200dc0d8c0f888465 | e03c54bd02f2d050a5f3b6d556d25582b325c117 | refs/heads/master | 2020-12-24T13:18:11.323501 | 2015-03-23T11:09:39 | 2015-03-23T11:09:39 | 8,160,842 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,892 | r | Expectation.R | Expectation = function(parameters, Mvar)
{
res = list()
f0x = c()
f1x = c()
gammA = matrix(rep(0, parameters[['NUM']]*2), parameters[['NUM']], 2, byrow=TRUE)
omega = c()
f0x = 2*dnorm(parameters[['zvalues']], Mvar$f0[1], Mvar$f0[2]) * (1 - parameters[['kappa']]/Mvar$ptheta[1])
f0x[parameters[['zvalues']]==0] = ... |
b8dbf79fa2e8d8e3cdcc34f2e49d97756e11d8d2 | 40cf04ee6cf3ffa246ecc069bcb629c0fffc4691 | /R/data.R | 7f5402cdc615bca8ae929953ea5fa0ff69108842 | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | akleinhesselink/sedgwickenv | a5c1abf183c54634df2658450d9da875525800b7 | 66ae557277ebacb25b3aacd5dbb7ff11a5cacf38 | refs/heads/master | 2020-03-29T20:36:36.473096 | 2019-12-19T22:23:36 | 2019-12-19T22:23:36 | 150,320,819 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,531 | r | data.R | #' Geolocation and environmental characteristics of 24 study sites at the UCSB Sedgwick Reserve.
#'
#' A dataset describing the position, soil characteristics, light interception,
#' and surface temperature at 24 vegetation study sites at the UCSB Sedgwick Reserve.
#' Sites originally established by Nathan Kraft and Ga... |
9261d5fe272014c6b2035a9a8c189ef3d4a3a4cf | fb0eda2c5c5364c907b3836b3c849ca2598a09df | /Script_data_table_03_2.R | fbc1dbe77a316f9350a617e2bf51536a0582f924 | [] | no_license | CursoRUCE/R-Intermedio | d3fbec6ec66ab66c134f566e9d2cb1e8e7a17f70 | 67320956724f01dcad1b487e3b3ebd61b63b04c0 | refs/heads/master | 2021-01-10T17:38:43.034711 | 2015-11-29T02:42:16 | 2015-11-29T02:42:16 | 45,076,997 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,604 | r | Script_data_table_03_2.R |
# Creación objetos data.table
# data frame
DF <- data.frame(x=c("b","b","a","a"),y=4:1, z=rnorm(4))
DF
# data.table
#install.packages("data.table", dependencies = TRUE)
library(data.table)
DT <- data.table(x=c("b","b","a","a"),y=4:1, z=rnorm(4))
DT
# data frame vs data.table
head(mtcars, n = 4)
mtcarsDT <- data.t... |
f2260b66026b66a7550d4fe05d7739a0d4686660 | 3d29b0de9404f87361979a216214614efce9419c | /man/OC2c.Rd | bdc39cb1a70969abb81edc94ca6d00c9a5b53ecc | [] | no_license | andreaskiermeier/AcceptanceSampling | 2717f283cf840c28fc51a2918e2c2016805227ec | 250d891adda26e152d3404ea1c8aff40dc7b883a | refs/heads/master | 2023-04-30T14:33:51.511179 | 2023-04-19T06:38:16 | 2023-04-19T06:38:16 | 46,367,877 | 2 | 3 | null | 2015-11-23T23:51:11 | 2015-11-17T18:47:01 | R | UTF-8 | R | false | false | 3,516 | rd | OC2c.Rd | \name{OC2c}
\alias{OC2c}
\alias{plot,OC2c-method}
\alias{plot,OCbinomial,missing-method}
\alias{plot,numeric,OCbinomial-method}
\alias{plot,OChypergeom,missing-method}
\alias{plot,numeric,OChypergeom-method}
\alias{plot,OCpoisson,missing-method}
\alias{plot,numeric,OCpoisson-method}
\alias{show,OC2c-method}
\alias{show... |
9b42226ed8d5f2caf7ba261e93bfbc5ea311fccb | 08ac50745353290c77f57652ff0001623a425f4d | /analysis/analyze_data.R | dd193e9ca83f29d243ebe3de5984975df726708b | [] | no_license | cwru-robotics/turtlebot-estimation | e2f43d4b9c8018c7416359a127425783feb91c69 | 0b9013ff69be2a4ef88cbfebfcc2d47401185b54 | refs/heads/master | 2020-07-09T04:42:43.213587 | 2016-09-18T23:51:54 | 2016-09-18T23:51:54 | 66,859,804 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,158 | r | analyze_data.R | #!/usr/bin/env Rscript
# R Script to analyze data from experiments
# Usage is analyze_data.R experiment1 experiment2...
# If experiment names to render are not specified then all experiments are rendered
# Must change the data_dir to your user directory!
# TODO make this use environment variable to detect home direct... |
11065455bceabd80f6cc593b4f4ed0862849be15 | 5833b6528abf04acc1c595942a37d2cd336d59dd | /inst/shinyapp/capionis/dashboard/plot.survival.R | f518d5fcc9165338fbfed7645320fb31fd39855f | [] | no_license | sakho3600/WAHEco | f108a705e9372b07c098dcba9599128abde2e5e6 | 8dc13ed6df08bbee56860725d13f59df80845414 | refs/heads/master | 2020-05-20T12:49:52.297351 | 2017-08-21T22:06:14 | 2017-08-21T22:06:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,382 | r | plot.survival.R | survival.ROUTER <- "/survival"
survival.NAMESPACE_ID <- "survival-trans"
survival.view <- function(ns){
tagList(
fluidRow(
box(title = "Courbe de survie", status = "primary", solidHeader = TRUE,
plotOutput(ns("plot2"))),
box(title = "Évolution de la population", status = "primary", solidHea... |
a855323e4218a9c49d7be93a1f75e6b679747181 | 4bb1aca720b47e74d86202b6e4417c63a64d91d8 | /Ventdata_EEZcheck.R | fa2dfad3b28a52fb9e9d40073f011bb471da1720 | [] | no_license | sbeaulieu/vents-Drupal | 4cd7b04be8d79ff288f5b3ca2d5f574053064191 | cc41a073696db9de90638285fe019914f1e2b628 | refs/heads/master | 2021-07-09T23:18:48.904636 | 2021-04-17T12:27:54 | 2021-04-17T12:27:54 | 36,871,545 | 1 | 1 | null | 2021-04-17T12:27:54 | 2015-06-04T13:22:20 | PHP | UTF-8 | R | false | false | 921 | r | Ventdata_EEZcheck.R | # R script point-in-polygon to check older version EEZ categories used in Vents Database against categories in VLIZ World EEZ Ver 11 from Nov 2019
# I couldn't figure out how to do reverse geocode using R package mregions, nor could I figure out how to login to LifeWatch web services
library(rgdal)
setwd("C:/Users/sbea... |
ed7778e5867321f43a0e29773025159b0868996f | 62b3f0aaf7a0532c8f752052bf5ab9cb7f8b024e | /man/ggplot.profr.Rd | 7c582107d77044d7892e63917f94bde581a46ca3 | [] | no_license | Barbleiss/profr | c6ebedb922ab7edf0dc9af924e2c60f2a3c2fc18 | ab520c615d80bafefbde15eef85263b533d717b2 | refs/heads/master | 2020-06-04T08:37:13.450908 | 2018-12-06T13:41:55 | 2018-12-06T13:41:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 813 | rd | ggplot.profr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/output.r
\name{ggplot.profr}
\alias{ggplot.profr}
\title{Visualise profiling data with ggplot2.
Visualise profiling data stored in a \code{profr} data.frame.}
\usage{
ggplot.profr(data, ..., minlabel = 0.1, angle = 0)
}
\arguments{
\item{data... |
bedc03abc1f8a193d7c63c3c939debeef767a74a | 0b1b36fe7d540a2f1aeb024b8fc1d0805bb54dc5 | /R code/180918.R | 0565198704e90f33fb970d76eea3e03724f7b29c | [] | no_license | Kim-Ayeong/R_Hadoop_class | b67c8548396fd56b8d53828a3b0b159fff71dfb5 | 421fb2b0960fa06d5d642a2b48307bdb62b5b258 | refs/heads/master | 2022-04-07T14:51:26.434800 | 2020-03-13T11:18:57 | 2020-03-13T11:18:57 | 235,706,419 | 0 | 0 | null | null | null | null | UHC | R | false | false | 3,005 | r | 180918.R | #9월18일
#lec3_updated2
#factor
x <- factor(c("a", "b", "b", "a"))
str(x) #1,2는 임의로 부여한 factor 수준 값
x[2] <- "a" #가능
x[2] <- "c" #불가능, 미싱값 입력
x <- rep( x = c(0,1), times = c(3,17) )
(Gender <- factor(x)) #convert the numeric vector as a factor
(Gender <- factor(x, levels=c(0, 1), labels=c("M", "F")))
par(mfrow = c(1,2))... |
725d7300f000c9ccc365bf089ddd1e92e0dedc82 | a2aee752d7fd804ded63cafb587a25d6911f0db8 | /tests/testthat/test-KISdata.R | 71cd4e9f6421f7c1115c6a9e4d427cc35c9f1ac5 | [] | no_license | jeroenbrons/knmiR | e8ca0d3cf5130d464f9239847059a93c7b46ce32 | d1d9c9f3cdad6053455916a8fd1312130a8d42d5 | refs/heads/master | 2021-07-13T06:30:18.545480 | 2017-07-04T13:36:18 | 2017-07-04T13:36:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,081 | r | test-KISdata.R | library(futile.logger)
flog.threshold(DEBUG)
flog.appender(appender.file("knmiR.log"))
context("KIS data extraction")
node <- Sys.info()["nodename"]
test_that("Obtain temperature", {
skip_on_travis()
skip_if_not(grepl("knmi.nl", node))
expect_match(WriteKISRecipe("TG", "260_H", "2016"), "KIStable.txt")
expec... |
c1f8abf41514495fc6f8fac61a3b16d092dec596 | d30ae83d6357263f704e9d4ff5e18b0b997a9e7d | /Finding eigenvalues.R | d688d262c51799bd6a6cf5e9069b16383d639cbb | [] | no_license | gng-ucdavis/Chapter1-code | 5ee11538c5ec11ef8d41db471b0f887564eead80 | 65ed350177b4d469889c3675c0c1b1e30b02ba4c | refs/heads/master | 2023-07-02T06:45:37.781810 | 2021-08-07T23:28:00 | 2021-08-07T23:28:00 | 209,189,130 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,849 | r | Finding eigenvalues.R | ####9/17/19 This script is to look for eigenvalues in your Jacobian matrix. Solving for this will let me know if my solutions are stable or not (doesn't say if it is the only stable one)
###This is the first part of the stability analysis. The script 'solving for hyseresis' comes later
##The following are the partial d... |
bf1e66a0741efb76fa452b0ac25ae41545cefda2 | ea3318080191c56d0d83c189325534919cc86794 | /plot1.R | 1f813999c50d4e388b7fd02df1e5b2724bba25d8 | [] | no_license | agcode/Exdata_NEI | 150a8c8d29f66708bd6cc156430560267e2ece6e | 078ec2698db04378f513c8f6f07854225e8cc63f | refs/heads/master | 2021-01-10T14:54:24.667032 | 2015-11-02T08:42:16 | 2015-11-02T08:42:16 | 44,660,446 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,215 | r | plot1.R | ## Exploratory Data Analysis Assignment 2
## Have total emissions from PM2.5 decreased in the United States from 1999 to 2008?
## Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.
NEI <- readRDS("summarySCC_PM25.rds")
SCC <... |
8975ac018930eed8129bf82b80c23459220aaa7e | 560bac32951f722ddb6636bac4ca3ba02ca418f2 | /dmpabook/c07.R | 1284ce004fed9d0dbe2943a04e3738643991cadb | [] | no_license | anhnguyendepocen/rBooks | 26ddf16647e4435263ba5dc67a8ccf3f76f0798e | 274bf5178e1b4eb14427f74f7f825d4b25ece75b | refs/heads/master | 2021-09-20T01:12:50.148676 | 2018-08-02T05:41:55 | 2018-08-02T05:41:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 38 | r | c07.R | # Chap- 7: Preparing to Model the Data |
6643e7ae39d9fac217734b65c675d41d8ff79ef0 | 2196661c2667ec48fe05185b79ff4787e7fd9bb0 | /man/QQPlot-comma-AnnualAggLossDevModelOutput-dash-method.Rd | a52e7228fca87d18a3ce55d4a98be48776f92495 | [] | no_license | cran/BALD | 36549ff72ae813d1dc2bb25fd77dbd7db663cbbb | c528ec69adcab7ad3d278f88905782e8d5ec0c12 | refs/heads/master | 2021-07-10T02:35:40.347434 | 2018-10-22T11:00:07 | 2018-10-22T11:00:07 | 154,136,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 648 | rd | QQPlot-comma-AnnualAggLossDevModelOutput-dash-method.Rd | \name{QQPlot,AnnualAggLossDevModelOutput-method}
\alias{QQPlot,AnnualAggLossDevModelOutput-method}
\title{A method to plot a Q-Q plot for models in the BALD package.}
\description{A method to plot a Q-Q plot for models in the \pkg{BALD} package.}
\details{This function plots sorted observed log incremental payments... |
b4e3bb0e21bd20339f92d8011bbe56103220b399 | ddc72e526751d804a51d76dc1a69d066aa037810 | /global.R | 674f1e99fcf9624e7e9cbe23da2907ec10e46eea | [] | no_license | soulj/SkeletalVis-Shiny | 6c5a2dba0ed4913e776b8a090336e14a6b5bcfbb | 0517f551448d011f690454514016c8f56efaf73a | refs/heads/master | 2020-03-18T06:41:44.499049 | 2018-09-20T13:14:28 | 2018-09-20T13:14:28 | 134,410,379 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 819 | r | global.R | library(feather)
library(readr)
library(enrichR)
foldChangeTable <- read_feather("foldChangeTable.feather")
pvalTable <- read_feather("pvalTable.feather")
#load the accessions
accessions <- read.delim("accessions.txt",stringsAsFactors = F)
accessions$combined <- paste0(accessions$accession,"_",accessions$comparison)
... |
6ea87d4bb1fdf0cf2cb0452883239724ef003466 | fdd8b4769615fbd1c7dd9cb3fa98a144384046be | /Problem 4/4_2.R | ab4f483435f4e89ce3cb4d7c3c5acf8cc34ba3c3 | [] | no_license | Shruti0490/R | 2752cf88e2413a96cffdf438e1bbdaf586f2c87e | 3e77814b120fc70ac95cedb4221fa7c96c9dea61 | refs/heads/master | 2022-12-30T11:41:19.854820 | 2020-10-22T14:27:09 | 2020-10-22T14:27:09 | 291,740,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,784 | r | 4_2.R | ## Q 4.2
library(MASS)
data(package='MASS')
attach(UScereal)
summary(UScereal)
df <- UScereal
# 1. The relationship between manufacturer and shelf
# both are CATEGORIES
barplot(table(df$shelf, df$mfr),
main="4.2 - Manufacturer & Shelf",
xlab="Manufacturer",
ylab="Shelf",... |
171eba846156a1583082422b5742ce3b8047b552 | 42d44c41040f17ae75b6ec035fb4958f8a9302cf | /SCW.R | 27af1c678b72f91d149477aefbc013d6d4ca8e36 | [] | no_license | WenjuanW/Online-WAPA | 7a7f3145183dd21a28e40eb1abcf8899e6ab08b9 | eb5fc7d113af63c1f66ec7c714c6365928f6537d | refs/heads/master | 2020-03-20T21:57:14.320408 | 2018-06-20T08:55:10 | 2018-06-20T08:55:10 | 137,770,450 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,953 | r | SCW.R |
SCWI <- function(streamData1,label1,c,eta){
streamData1 <- as.matrix(streamData1)
label1 <- as.matrix(label1)
k <- 1
# k <- 3
streamData <- apply(streamData1, 2, rep, k)
y <- apply(label1, 2, rep, k)
Ncols <- ncol(streamData)
Nrows <- nrow(streamData)
u <- matrix(0, nrow = Ncols, ncol = ... |
6ce8be541f74d14c098bff86dffc6fd2b22d311a | 57d258a66ce8a56af95db6f37b1472f88e639b4f | /treelet_prepare_cv.R | 2c9c84fb92eced24b62cad447b879c0f6697cf79 | [] | no_license | dravesb/TreeletSmoothers | 97fc99ae91a95788aeb240cb82b817d534c3d6a7 | 991f775b2f770a935e39eb0a02f6ed40669fe030 | refs/heads/master | 2021-01-01T04:27:45.605741 | 2017-08-13T20:23:39 | 2017-08-13T20:23:39 | 97,177,475 | 0 | 0 | null | 2017-08-13T20:17:27 | 2017-07-14T00:55:08 | R | UTF-8 | R | false | false | 2,737 | r | treelet_prepare_cv.R | treelet_prepare_cv = function(grm_name, num_test = 50, snp_set_size = NA){
#--------------------------------
#Check for errors in the grm file
#--------------------------------
if(is.na(grm_name)){
stop("please specify grm file name \n this should be in the current directory")
}
#------------------------... |
7925715014632680dcb0bedbb22fa3f78459f79d | 1920a6ec39111d11bca8a24089b701a55eb73896 | /man/SNP.Pattern.Rd | fcf53f4311bbc2dbd8bce8a4000890564e9d1922 | [] | no_license | benliemory/BinStrain | 1014195bd161cefaeb278858a297e3528c372ca5 | 76465748595bd394cb37c9cae811b977b2eafde8 | refs/heads/master | 2020-04-27T07:38:24.557622 | 2013-11-24T02:39:59 | 2013-11-24T02:39:59 | 14,651,642 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 350 | rd | SNP.Pattern.Rd | \name{SNP.Pattern}
\alias{SNP.Pattern}
\docType{data}
\title{
Data for SNP Pattern
}
\description{
The data describes the SNP Pattern.
}
\usage{data(SNP.Pattern)}
\format{
A data frame with 15387 observations. Numbers from 1 to 14 represents the order of the reference genomes used to generate the SNP pattern file
}
\... |
6808e6b664e7f75a893b4c26da59232f000341c8 | 99f74103b2d72babd81ef255d99d8a7873779119 | /Ch02/2_6_String.R | 30d36d5cb6397172d0e5a1b08b43fd9872e7d161 | [] | no_license | leesiri1004/R | 6c652df0244de91ef1185adf6aa44c45382fa42f | 9b197e3258439098695f37270217bfbc167ae116 | refs/heads/master | 2023-03-08T18:08:11.641332 | 2021-02-24T07:15:31 | 2021-02-24T07:15:31 | 330,568,766 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,004 | r | 2_6_String.R | #날짜 : 2021/01/19
#이름 : 이슬이
#내용 : Ch02. 데이터 유형과 구조 - 문자 처리 교재 p84
#R 패키지 설치
install.packages('stringr')
#R 패키지 로드
library(stringr)
#문자열 정규표현식
str <- 'hong25이순신31정약용27'
rs1 <- str_extract(str, '[1-9]{2}')
rs1
rs2 <- str_extract_all(str, '[1-9]{2}')
rs2
rs3 <- str_extract_all(str, '[a-z]{3}')
rs3
rs4 <- str_extract... |
97a8d1919b7fbc3f9ab5de676e34d8c30f842ea0 | b45944145af4b2d86ff4f02393b984370006eb1d | /Hackathon Refugees.R | d1f22a762ceddc8f78089745cbda72dbef507535 | [] | no_license | madeleinenic/PODS-Hackathon | 1f035bcc3058b149d0e4f0ac5b1e795b31118780 | 90d831cec3b1c5cd3a73eaad35ca7d665a31a1b3 | refs/heads/master | 2022-02-13T23:32:59.227478 | 2019-06-25T13:16:11 | 2019-06-25T13:16:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | Hackathon Refugees.R | service_data$`Blended Sponsorship Refugee` <- as.numeric(service_data$`Blended Sponsorship Refugee`)
ggplot(data= service_data, aes(x= Province , y= `Blended Sponsorship Refugee`,
fill= `Service Type`)) + geom_bar(stat="identity")
service_data$`Government-Assisted Refugee` <- as.numeri... |
c0948155a954a9214afe20b747401e09ce0eb5fa | 0b551347a29f4e01e9273615ce0c5242f9bdb63a | /pkg/R/get_power_deriv.R | b0502c402f1f5005608057d9665b77fa91f40971 | [] | no_license | timemod/dynmdl | 8088fecc6c2b84d50ecb7d7b762bddb2b1fcf629 | 8dc49923e2dcc60b15af2ae1611cb3a86f87b887 | refs/heads/master | 2023-04-07T21:30:53.271703 | 2023-03-03T13:02:30 | 2023-03-03T13:02:30 | 148,925,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 454 | r | get_power_deriv.R | get_power_deriv <- function(x, p, k) {
# The k-th derivative of x^p (Used in f_dynamic)
# INPUTS
# x: base
# p: power
# k: derivative order
#
# OUTPUTS
#
if ((abs(x) < 1e-12) && (p > 0) && (k > p) && (abs(p - round(p)) < 1e-12)) {
return (0)
} else {
dxp ... |
7e48bb17aa4f268f9608e60116d0f65df6225bf8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/StrainRanking/examples/powderymildew.Rd.R | 60444ba52ba56949999522815269b2bb794eb2a1 | [] | 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 | 470 | r | powderymildew.Rd.R | library(StrainRanking)
### Name: powderymildew
### Title: Demographic and genetic real data
### Aliases: powderymildew
### Keywords: datasets
### ** Examples
## load the powderymildew data set
data(powderymildew)
## names of items of powderymildew
names(powderymildew)
## print powderymildew
print(powderymildew)
... |
a2487ff34e34a16105078dea6f61cc432809b803 | 4834724ced99f854279c2745790f3eba11110346 | /man/add_dup_markers.Rd | 4be1572417edcd093dbb6f4aae3d0719d15a7eae | [] | no_license | mdavy86/polymapR | 1c6ac5016f65447ac40d9001388e1e9b4494cc55 | 6a308769f3ad97fc7cb54fb50e2b898c6921ddf9 | refs/heads/master | 2021-01-25T12:37:09.046608 | 2018-02-13T17:07:35 | 2018-02-13T17:07:35 | 123,487,219 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 520 | rd | add_dup_markers.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exported_functions.R
\name{add_dup_markers}
\alias{add_dup_markers}
\title{Add duplicate markers to a map}
\usage{
add_dup_markers(maplist, bin_list)
}
\arguments{
\item{maplist}{A list of maps. Output of MDSMap_from_list.}
\item{... |
5bbe26521ac1c94c54925dab8608b9b23c44baaa | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /flying/man/birds.Rd | c383e810c74b9c9cb46738a2833506ca5a2b6548 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 923 | rd | birds.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/birds_documentation.R
\docType{data}
\name{birds}
\alias{birds}
\title{Sample 28 birds}
\format{A data frame with 28 observations and 5 variables not counting the
name.
\describe{
\item{Scientific.name}{Name of bird species}
\i... |
ce5b4b7a4e71698c67fcf80302e55c7c5bb8ff3e | 32e00dbb5c0b06a8aadb16a753479ce269310488 | /COVIDModel/server.R | c8212a2d4feb98d6a86d8e36ebd9d42096934dd1 | [] | no_license | lnsongxf/CovidShinyModel | d69ec6f372bf5db09fac0b3b3f0d6e0630119acc | e92a6dab43fcd4415cc2cddb23ffc7417348b8c0 | refs/heads/master | 2021-05-20T04:05:42.985126 | 2020-03-30T19:33:28 | 2020-03-30T19:33:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 43,339 | r | server.R | source('helper.R')
library(shiny)
library(ggplot2)
library(shinyWidgets)
library(data.table)
library(DT)
library(dplyr)
library(shinyjs)
# start simulation from this number of infections
# TODO: should do a test that this works...if we start with a different start.inf
# are the results different?
start.... |
6c1a96aabe5417b2926e87929a842fee397a2749 | 80a2e57ee2b6e1465fcb88f677a7075140acd0c9 | /MovieLens_Final project.R | 688d5aae56b4454bc426ba31ece90d66d5ffb168 | [] | no_license | SUBRAMANIANKN/movielens | 5016e70eb89e6cf0b632a9e9bb48d10d53fbe96f | 3fc53988e391b6d2621ec5cf841f3411122366fb | refs/heads/master | 2020-04-22T22:09:24.719239 | 2019-10-07T11:56:49 | 2019-10-07T11:56:49 | 170,698,049 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 16,856 | r | MovieLens_Final project.R | ## Data Loading and Preparation
#############################################################
# Create edx set, validation set, and submission file
#############################################################
# Note: this process could take a couple of minutes
if(!require(tidyverse)) install.packages("tidy... |
75dbed5dcd0c32da98beae424376effb617cf13f | 9083fbc538fc22e5deef1c38238cbb98eec0eca9 | /GENESIS_R/GENESIS/nullModelTestPrep.R | 7db785fe44b993cc178b479b01f57de9c614b4e0 | [] | no_license | drjingma/REHE | 8e75c988d10171cd37a49bc7630f0e62d4cfcfeb | 13490695d3035e2af80011066272a672cb79e531 | refs/heads/master | 2023-03-17T09:33:15.836142 | 2020-12-15T05:57:11 | 2020-12-15T05:57:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,915 | r | nullModelTestPrep.R |
## takes a null model and prepare specific arguments to streamline the testing
nullModelTestPrep <- function(nullmod){
Y <- nullmod$workingY
X <- nullmod$model.matrix
C <- nullmod$cholSigmaInv
if (length(C) > 1) { ## n by n cholSigmaInv (may be Diagonal)
if (is(C, "Matrix")) X <- Matrix(X)
... |
bc6755b3a0ec8205d3aadf259caad1c8f7a0013e | 3cc46de0679d5d63bed8eacd7db453ba122aa4c3 | /R/t1_analysis.R | 2a8916edce906e88f8e7243449f83be7b3802d21 | [] | no_license | fontikar/egerniasl | af8aaeb0f5eca77e741edaa7d5e5fc0efabfc31c | 367ee5ebf668e5008e90843fc0fe111190e6f66f | refs/heads/master | 2020-12-12T17:06:43.931493 | 2016-12-09T03:30:08 | 2016-12-09T03:30:08 | 52,262,907 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,416 | r | t1_analysis.R | #Setting working directory
getwd()
#setwd("C:/Users/xufeng/Dropbox/social learning/output/data/")
setwd("~/Dropbox/Egernia striolata social learning/")
#load library you need
library(plyr)
library(MASS)
library(lme4)
library(plyr)
library(survival)
#Read data
instrumdat <- read.csv("output/data/task1_finaldat.csv", s... |
314e75dbebd8c66c4d9247beab5c3f1a885dccf5 | cdf7ceb2b0aeb8e5ab46e2ffdf8358682b725b08 | /0.3 Fit simple GP model to simulated data.R | d79eef8d834084b5d413721b3cd6d98b6ff3cf16 | [] | no_license | jburos/explore-GP-models | 69aa7cdfe342ef2284bae8a349ea2a83a945d293 | 590f8a9f7d485a15a3fd174955d7cc37e661bc6d | refs/heads/master | 2021-01-13T14:56:22.099918 | 2016-12-16T14:46:38 | 2016-12-16T14:46:38 | 76,661,277 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,030 | r | 0.3 Fit simple GP model to simulated data.R | library(rstan)
library(tidyverse)
library(ggplot2)
library(lazyeval)
source('sim_data.function.R')
gp2 <- "
data {
int<lower=1> N;
vector[N] x;
vector[N] y;
}
transformed data {
vector[N] mu;
for (i in 1:N) mu[i] = 0;
}
parameters {
real<lower=0> eta_sq;
real<lower=0> inv_rho_sq;
real<lower=0> sigma_sq... |
e5a6fc954d5bf467479582bcb879a04fe50140a9 | 1ff5948cc363d8a195697c5ea3ae3e8505c7898d | /R/createGif.R | b93e5d7d5c2691c5a9e9a5ec92ffdca11417a0d2 | [] | no_license | MazamaScience/TBCellGrowth | 35399ef9918343145144c4330915f403d135a0b8 | 92e258ea7414361ad65136f3493e9ab9fdf16495 | refs/heads/master | 2020-12-11T03:32:19.119655 | 2016-04-19T22:00:30 | 2016-04-19T22:00:30 | 38,266,687 | 0 | 0 | null | 2015-06-29T19:25:26 | 2015-06-29T19:25:26 | null | UTF-8 | R | false | false | 1,816 | r | createGif.R | #' @export
#' @title Creates a gif animation.
#' @param dir the directory of images to read
#' @param filename name of output gif.
#' @param ext the image file extension to read.
#' @param framerate the number of frames per second in the output gif.
#' @param rescale dimensions of gif frames as a percent of original im... |
0cde6d528fd0ff7c2e5ebbb6020a7e7322822e12 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GLDEX/examples/fun.RMFMKL.ml.m.Rd.R | 8bd5aac3cc6856f96689b617278962ff158c5587 | [] | 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 | 323 | r | fun.RMFMKL.ml.m.Rd.R | library(GLDEX)
### Name: fun.RMFMKL.ml.m
### Title: Fit RS generalised lambda distribution to data set using maximum
### likelihood estimation
### Aliases: fun.RMFMKL.ml.m
### Keywords: smooth
### ** Examples
## Fitting the normal distribution
# fun.RMFMKL.ml.m(data=rnorm(1000,2,3),fmkl.init=c(-0.25,1.5),leap=3)
... |
70a77289f1d652bed6472ff576572b48d4d93c5e | 8f4687e2fd3c7fed3af1d443903d604921d2f289 | /R/centroidAssigner.R | e87a34e33c5754a0e06780f33383331191a6413f | [] | no_license | LuisLauM/ruisu | c446ae644abacc8f00cf6b64fea76891e111c683 | 76d8476586d5259f7014dfb3a7cc29b6efe2e017 | refs/heads/master | 2023-07-09T19:19:53.394787 | 2023-07-07T15:20:31 | 2023-07-07T15:20:31 | 41,390,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,317 | r | centroidAssigner.R |
# Main fx -----------------------------------------------------------------
#' @name centroidAssigner
#' @aliases centroidAssigner
#' @title Returns centroid values from grid codes.
#'
#' @description This function takes a vector of grid codes and returns centroids (center of mass) in lon-lat values.
#'
#' @param cod... |
df485889e9a58d67c18684b6ca7cb62cdb8c9a37 | 05b2d51dc5c9b4e3845d9c92223ab65f15d21450 | /examMarks/R/generateAnswerSheets.R | f3ad0fd8228bc8cc433e6f6ad253fc977ee38b0c | [] | no_license | ddavis3739/examMarks-R-package | ba83d37970d2f7d9893c0267ba25f638d0388113 | 4cd4e6a4c372beacc98ba210a4d8d066d1a4dde1 | refs/heads/master | 2020-07-30T09:31:07.897957 | 2019-09-22T16:50:15 | 2019-09-22T16:50:15 | 210,173,690 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,761 | r | generateAnswerSheets.R | #' @title Student Answers for Specific Student
#'
#' @description Outputs a students random answers to a multiple choice exam in
#' the form of a dataframe given the number of questions asked and total number
#' of questions available. Answers can be from a to e, with NA's indicating the
#' question was not answered. I... |
fd63363d44cbdd24f3e06954977123dc65a836e5 | 85b32dcd701ddf5ea9e95454fceb10687c03b2bb | /R/plot.rpsftm.R | 4b241c6d2db8040ab17d9cbbed9454f7e48762eb | [] | no_license | arallison/rpsftm | 4cdb97a9fc58bd60f9d8ad03d718d9611956025e | 466ad85e26949b8990f0fa704d20cfa0e9c7cb87 | refs/heads/master | 2021-01-17T22:20:52.103628 | 2016-01-05T17:25:34 | 2016-01-05T17:25:34 | 47,696,890 | 0 | 0 | null | 2015-12-09T14:39:52 | 2015-12-09T14:39:51 | null | UTF-8 | R | false | false | 668 | r | plot.rpsftm.R | #'Function used to plot the KM curves of the treatment-free transformed times
#'
#'@export
#'@title Plot Method
#'@name plot.rpsftm
#' @param x an object returned from the \code{\link{rpsftm}} function
#' @return a ggplot plot of the fitted KM curves
#' @author Simon Bond
#'
plot.rpsftm=function(x){
fit=x$fit
... |
66e7febb083ab853bb62498e6921410eb4cde2b7 | 406863c152cebfe70acd9a1d23cf063aed08cfd4 | /4. guardian_text_analysis.R | 78b1f9cdf174c2b72f614ea4a44e2a6a4ec43443 | [] | no_license | lopesmf/R-Projects | cdb19faa2c25cecc9cdba5a5d68a18c921926456 | d7ae205169e1d4eb8a986d45577218ecbd1ffcfd | refs/heads/main | 2023-04-20T10:25:18.701726 | 2021-05-10T18:34:54 | 2021-05-10T18:34:54 | 366,140,270 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,268 | r | 4. guardian_text_analysis.R | # Topic Analysis in news about AfD in The Guardian Newspaper
# Maria Fernanda Lopes Ferreira Del Ducca
# au516257 201400917
# Clean list
rm(list = ls())
# Set Working Directory
setwd("~/Documents/Masters'/2.2018/Political Data Science/Final Exam/The Guardian")
# Install necessary packages
library(tm)
library(quan... |
11838687f092ddf05938cdf01ef6dc31c2a1309c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/satellite/examples/plot.Rd.R | 2c311a4ff0e6a77688ba2a2d9798ba0a29e8e96b | [] | 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 | 485 | r | plot.Rd.R | library(satellite)
### Name: plot
### Title: Plot a Satellite object
### Aliases: plot plot,Satellite,ANY-method plot,Satellite-method
### ** Examples
## Not run:
##D ## sample data
##D path <- system.file("extdata", package = "satellite")
##D files <- list.files(path, pattern = glob2rx("LC08*.TIF"), full.names = ... |
4682ce2936f667f6c11e916217eac01f0ac7a345 | f20c8919a5a46ec2503ec02807877c1e96a9cee0 | /CPR by Nationality.R | 9bc27ede4ae31c4822786faf15177eac59609d86 | [
"MIT",
"CC-BY-4.0"
] | permissive | Track20/JordanFertilityFP | 78aceac6560091530c80805198119c87dec02799 | 4f7d4cd2f7421386bef1d5e1cd299751d590efd9 | refs/heads/master | 2022-12-22T17:09:02.702079 | 2020-09-29T14:17:34 | 2020-09-29T14:17:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,233 | r | CPR by Nationality.R | # Jordan Feritlity and Family Planning
# Kristin Bietsch
# 09/18/20
# CPR by Nationality
library(survey)
library(tibble)
library(dplyr)
library(tidyr)
library(haven)
library(xlsx)
library(stringr)
library(questionr)
library(jtools)
library(huxtable)
library(broom)
library(ggplot2)
setwd("C:/... |
df97f1f4b577ffd3b7a41c516268d2c0168c0f3c | bc88ee08398aa52f9eab0c4f608f45156f9551f3 | /tidytuesday_2020_week16/tidytuesday_2020_week16.r | 7ba2b701a1062fafbd2f04e9f1a1d33408c68238 | [] | no_license | lmaxfield/TidyTuesday | df432698acfb1311f53cc5c081d6c7021ba14299 | 999ee128c925ff39a9596d3ee8f4fd8a934a04c5 | refs/heads/master | 2020-09-14T16:45:28.588273 | 2020-04-18T17:08:59 | 2020-04-18T17:08:59 | 223,189,027 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,101 | r | tidytuesday_2020_week16.r |
library(tidyverse)
library(extrafont)
#library(here)
polls <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-14/polls.csv')
rankings <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-14/rankings.csv')
my... |
f0b976b6be91b97476cb9dcc824448e5f699dff3 | c7bc2076579a554bca7ddb1c91c5c5197dd3e4d4 | /Scripts/Analysis/plotDemographics.R | d3e2f2d493a267fcafeb3f413ff31a7902c618f9 | [] | no_license | ntustison/CrossLong | c55bf03fe3e85ea0f9c7267e4ebb76243940aaff | e6cdd22a7c950b2a68c3075c893ed1b74489bc61 | refs/heads/master | 2021-12-15T15:24:49.733578 | 2021-12-13T22:16:37 | 2021-12-13T22:16:37 | 62,259,676 | 9 | 3 | null | 2021-12-13T17:37:50 | 2016-06-29T21:39:39 | R | UTF-8 | R | false | false | 1,748 | r | plotDemographics.R | library( ggplot2 )
# Questions of interest:
# * Age ranges, male and females (https://rpubs.com/walkerke/pyramids_ggplot2)
# * Number of CN, LMCI, and AD
# * How many time points? Missing time points?
# baseDirectory <- '/Users/ntustison/Data/Public/CrossLong/'
baseDirectory <- '/Users/ntustison/Documents/Aca... |
f6c1ca88e29480dae81335162b4a60b573d5bb98 | ddf87d7410f5f63747758b8beaf0a4fe7c297796 | /man/ed_input_dir.Rd | 16fcf7afea5238fbaa56076436ab4adfb5d1c9fe | [
"MIT"
] | permissive | ashiklom/fortebaseline | 3142ff38f305906489cf6e012a8f55fa3efaa51e | 513ea9353c133b47f67b3023a78e56abb6384847 | refs/heads/master | 2021-07-23T16:30:12.074804 | 2020-04-30T17:15:43 | 2020-04-30T17:15:43 | 157,924,482 | 3 | 3 | NOASSERTION | 2023-01-22T10:39:45 | 2018-11-16T21:42:24 | R | UTF-8 | R | false | true | 241 | rd | ed_input_dir.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/run-ed.R
\name{ed_input_dir}
\alias{ed_input_dir}
\title{Directory containing ED2 inputs}
\usage{
ed_input_dir()
}
\description{
Directory containing ED2 inputs
}
|
61eff8e13145a514d39167f0ed901605e333a51d | e1302be32d3fdd297dcf5182dd437188f43923f0 | /GOTApriori.R | 130df2bb63adffde1b89acf8b22d3d2d359fc9a2 | [] | no_license | kritishaw/imdb-database-mining | d5d4928a52ff3058b4faf0d562985374c4e1619d | d12590af7ae7249a289a547f73f5ed83825071f7 | refs/heads/master | 2021-01-01T04:45:13.823482 | 2016-05-19T20:02:50 | 2016-05-19T20:02:50 | 59,238,436 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 895 | r | GOTApriori.R | library(arules)
westeros <- read.csv("game_of_thrones.csv", header = TRUE)
westeros <- merge(westeros[, 2:3], as.data.frame(sapply(westeros[,4:10], as.logical)), by="row.names")
westeros <- as.data.frame(sapply(westeros, gsub,pattern="House ",replacement=""))
westeros <- westeros[, 2:10]
Grules <- apriori(westeros)
... |
58bf8d26f6e168e6944b7d0f89460c299a228e37 | 53aa1a158928b60ddf4195da23b9c72206be3005 | /man/getQtlMap.Rd | ee4b6664594b348f1791ed9d3e388ec7fbd6deb9 | [] | no_license | hickeyjohn/AlphaSimR | 55b202591506bbae31f7ddadecb333075a9d8c9e | 1cedc22e022b2a628da3e9e5bb1ca0394ea2078f | refs/heads/master | 2023-01-05T19:52:32.118965 | 2020-05-19T08:30:02 | 2020-05-19T08:30:02 | 299,920,977 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 858 | rd | getQtlMap.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pullGeno.R
\name{getQtlMap}
\alias{getQtlMap}
\title{Get QTL genetic map}
\usage{
getQtlMap(trait = 1, gender = "A", simParam = NULL)
}
\arguments{
\item{trait}{an integer for the}
\item{gender}{determines which gender specific map
is retur... |
c6a7e00056bf95f5df4bfdcc2a088c29e5f3ec41 | d22a3a98441edc4713e16deff79bd6fef3e550b1 | /R/CBI_mapCreation.R | 4c62edbaac6339473e0e484fcbc116cabcce271b | [] | no_license | mxblsdl/CBREC_Maps | c0f559be659e3a1a3119a15f9e6536aa3a0367f3 | 9b357954b689272d48f9c43a0c2beaac1c4f7092 | refs/heads/master | 2022-12-11T05:19:43.830142 | 2020-09-10T02:29:50 | 2020-09-10T02:29:50 | 286,899,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,712 | r | CBI_mapCreation.R |
# bind data into data table function; works with specific list structure of outputs
bindDataTable <- function(data) {
require(data.table)
tr <-
lapply(data, function(d) {
# simplify list structure
d <- unlist(d, recursive = F)
# bind together as data table
d <- rbindlist(d)
retur... |
826197c4f2ec9578ba6488b2d9ed5800c6e5182f | 53a5427633aaad81b865b71133b017d262a733be | /Kalman_test.R | 89eedd94743cd60ee72e7e581903ab81fbe3053c | [] | no_license | xindd/TestModels | 9529ab8a354e3f4bb6e0cdd8365ee184d903a61d | ff4e642eedfe64683ddd7eebd54f319a8ced273c | refs/heads/main | 2022-12-25T04:12:00.403632 | 2020-10-10T00:18:40 | 2020-10-10T00:18:40 | 302,779,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,571 | r | Kalman_test.R | kl <- function(P_data, T_data, p_dot, t_dot, PL_data, t_time, tL){
#t_time = c(0, 1/6, 1/3, 1/2, 1, 2, 4, 8, 16)
#tL = 1/6
num_data = length(t_time)
true_a = PL_data/tL
Z = matrix(c(p_dot-true_a, t_dot-true_a), nrow = num_data, byrow=FALSE)
GG <- diag(1,2)
FF <- matrix(c(1, 3, 3, 2)... |
3c33a0232101a0b0c90fb9aa10819e0b031973e6 | 18bdef50ddaf2647ddec6308bf5d883dc6ebd995 | /NolaR/R/nola.R | d125b98008af21e13b8886e9d17d0b1d032a91f3 | [] | no_license | jsomekh/Nola | 0cdf40dc2353c7f21d0b8fe84eae4689b558e7c5 | 15a643b2736ef6b3467d64883b1306ffe9e8ac0c | refs/heads/master | 2020-03-09T18:30:32.338451 | 2018-04-11T10:11:24 | 2018-04-11T10:11:24 | 128,934,814 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 171 | r | nola.R | #' A Nola Function
#'
#' This function allows you to print hi Nola.
#' @param no params.
#' @keywords nola
myNola<-function()
{
print("Hi Nola")
print("Hi Jinjit")
}
|
d508ac209b72aeb49ecda6bb2fbf8cb6a71e45b7 | 15b43be459adec4f54507aafe759f61f1eadd5d5 | /man/rtimes-package.Rd | 962983ac9351f84e965e44581fc2fa8fd9cd175b | [
"MIT"
] | permissive | jpiaskowski/rtimes | 7d5c5ffe3bc250f2ee1d5bee98a10dbd98c141b6 | 4eaef46ca60a35c879db68edba6a697418693850 | refs/heads/master | 2022-01-28T10:42:27.313696 | 2019-07-19T17:12:07 | 2019-07-19T17:12:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,668 | rd | rtimes-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rtimes-package.R
\docType{package}
\name{rtimes-package}
\alias{rtimes-package}
\alias{rtimes}
\title{rtimes}
\description{
Interface to the Congress and Campaign Finance APIs from Propublica, and
the Article Search and Geographic 'APIs' from... |
8a494f981fa90f9be2e80e21b86d6d8d785d28e5 | 1e35733da24e026b16f7f82a4c3cd703abc0391b | /Diamond price prediction/Vector operations.R | 547006d75216b22033416622fda5b63225e4be1f | [] | no_license | deboshas/R-Handson | ec7a1a9b92c0125929b1d108b1e58ae9d2fbebcd | 48782b1965a6f2162a9553c7c57d038861084d49 | refs/heads/master | 2020-06-30T08:09:11.631045 | 2019-09-04T04:49:31 | 2019-09-04T04:49:31 | 200,773,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 792 | r | Vector operations.R | x <- c(10,20,30,40,45)
y <-c(1,2,3,4) #repeat the vector until it matches the x vector
z <- x+y
s <- x-y
logicalvector <- x > y
multiplicationvector <- x * 10
#operations
randomvector<- rnorm(5)
#R specific iteration, i is a vector
for ( i in randomvector){
print(i)
}
#
# print(randomvector[1])
# print(randomve... |
3b5b21b795c6047437c13ac29d3c30227e06339d | 6e1ed64a0769a0776a01f2db6c42510477acc13f | /inst/doc/introduction.R | 7a53e389ecbcb5ecdfc8b76b26fcc54926a1de96 | [] | no_license | cran/creditmodel | f9e008df63f25cdaefebe79a82aaf9aad3598c8c | a4f07950173e6e0f6c6565a08fc564ec2075cb36 | refs/heads/master | 2022-01-19T11:26:07.485897 | 2022-01-07T10:32:41 | 2022-01-07T10:32:41 | 183,883,743 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,853 | r | introduction.R | ## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(echo = FALSE)
library(creditmodel)
## ----fig.width = 10-----------------------------------------------------------
B_model = training_model(dat = UCICreditCard,
model_name = "UCICred... |
db1f1688967fbfff29864620d95d8f9bce416152 | 1f71bcffe9b3b1cb424702b3bdda1e7f161da9de | /man/NPP.Rd | e40798acd6c1b1346b0965fc398c80bb2261510f | [] | no_license | Mavbegg/MIAMI | 55abab9d8048cacba9a2ed22ce5a6b6f0c70b525 | 8e4e24670cb32ea7aa16bd145ef5422f115fe0ef | refs/heads/master | 2021-01-21T17:38:32.567589 | 2017-05-21T17:13:05 | 2017-05-21T17:22:39 | 91,974,117 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 452 | rd | NPP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/NPP.R
\name{NPP}
\alias{NPP}
\title{Net primary productivity (NPP) estimation from temperature and precipitation data}
\usage{
NPP(temp, pre)
}
\arguments{
\item{temp}{temperture precipitation vector}
\item{pre}{precipitation vector}
}
\desc... |
0bbde597c97d6b6bc680bf6a1b785307185aca04 | 39378092783a6f25aad182f44beb4f6b3197daf8 | /templates/save.R | 7c8807548ba14d169e64169bdf94101d4f239580 | [] | no_license | mariafiruleva/automated_processing_scrnaseq | 0216522a692ad6a6fb7d869aa8a746f8d614a633 | 86376dcb84d70ed8269df0579e6562621e4ed13b | refs/heads/master | 2020-07-25T08:15:39.658441 | 2019-09-14T07:07:26 | 2019-09-14T07:07:26 | 208,227,463 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 591 | r | save.R | file_out <- paste0("{{ RunName }}", '.RData')
save(list = c('whole', 'whole.markers'), file = file_out)
write.table(top50_log_fc, "top50_log_fc.tsv", sep="\t", quote=F, row.names=F)
write.table(top100_log_fc, "top100_log_fc.tsv", sep="\t", quote=F, row.names=F)
write.table(top200_log_fc, "top200_log_fc.tsv", sep="\t", ... |
22a8551e5c40e3d6de4e743699dbf8427aa07421 | a5e538a9125dcdd90bae6bf44cb1d67ea87f33b9 | /man/getDataset.Rd | 1984744bd78ea68dd52def1d4a7f21f67c8dfcb5 | [
"Apache-2.0"
] | permissive | mrhelmus/data.world-r | 3d45003aa13d1d7b596f9456306cc24ac0e49b6f | 81a648878d7ee68cb91e7cee82aa8156c59770b9 | refs/heads/master | 2021-01-21T19:05:32.717425 | 2017-03-28T20:28:50 | 2017-03-28T20:28:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 653 | rd | getDataset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getDataset.R
\name{getDataset}
\alias{getDataset}
\title{Retrieve a dataset via data.world public api}
\usage{
getDataset(connection, dataset)
}
\arguments{
\item{connection}{the connection to data.world}
\item{dataset}{the "agentid/dataseti... |
2d27cdf7fc2a2da7dfa8b95aa80cd36eea6c63cc | 83d93f6ff2117031ba77d8ad3aaa78e099657ef6 | /man/gframe.Rd | 7cc21f87995298e6bef6eae62203142b1e9a5ec6 | [] | no_license | cran/gWidgets2 | 64733a0c4aced80a9722c82fcf7b5e2115940a63 | 831a9e6ac72496da26bbfd7da701b0ead544dcc1 | refs/heads/master | 2022-02-15T20:12:02.313167 | 2022-01-10T20:12:41 | 2022-01-10T20:12:41 | 17,696,220 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,457 | rd | gframe.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gframe.R
\name{gframe}
\alias{gframe}
\alias{.gframe}
\title{Constructor for framed box container with label}
\usage{
gframe(
text = "",
markup = FALSE,
pos = 0,
horizontal = TRUE,
spacing = 5,
container = NULL,
...,
toolkit =... |
778aeb7c2ce716b31b798cceb17e6b487856bcf2 | d2eda24acceb35dc11263d2fa47421c812c8f9f6 | /man/run.advection.Rd | fae747ec478f8b6703ef2aef123c935ae331e40e | [] | no_license | tbrycekelly/TheSource | 3ddfb6d5df7eef119a6333a6a02dcddad6fb51f0 | 461d97f6a259b18a29b62d9f7bce99eed5c175b5 | refs/heads/master | 2023-08-24T05:05:11.773442 | 2023-08-12T20:23:51 | 2023-08-12T20:23:51 | 209,631,718 | 5 | 1 | null | null | null | null | UTF-8 | R | false | true | 692 | rd | run.advection.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/source.lagrangian.r
\name{run.advection}
\alias{run.advection}
\title{Advect Lagrangian Particle}
\usage{
run.advection(particles, model, advection, zlim = c(-6000, 0), verbose = T)
}
\arguments{
\item{particles}{a particle release dataframe ... |
19ef7cec3e7eabdd23abfc64880e8feb319c6df4 | 27453005e827f25ee1f4299ed40bd5f16873cb54 | /man/soft_threshold.Rd | 93726105c44bcbb611fb874d52786693e7efb936 | [
"MIT"
] | permissive | seanigami/stpca | 9bb4bece7113f9729e2fd6cf7e5afec1511cec0d | a8eebfc6f1bff0a4457a23b975238afa3ae664c2 | refs/heads/master | 2022-01-13T19:04:21.702988 | 2018-08-16T15:01:01 | 2018-08-16T15:01:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 416 | rd | soft_threshold.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sparse-W.R
\name{soft_threshold}
\alias{soft_threshold}
\title{Soft-thresholding operator.}
\usage{
soft_threshold(x, threshold)
}
\arguments{
\item{x}{the vector/matrix of values to be soft thresholded}
\item{threshold}{the threshold lambda... |
c958ca7b4a1a18bb7b38da4314b16c71dac42064 | d6bcbdfbc175110d2dffcc8518e066601c39539f | /software/binda/download/dorothea-analysis.R | f0bbe97d12375b992e8096fab7798b2af038915f | [] | no_license | strimmerlab/strimmerlab.github.io | 1b322732a8d73e1509a067012e3720bfd2e58b1b | e8fcdd763eee6dbed58b955c0d16fe85742df351 | refs/heads/master | 2023-08-31T22:15:35.651056 | 2023-08-28T17:04:48 | 2023-08-28T17:04:48 | 23,162,223 | 0 | 2 | null | 2023-02-08T21:15:23 | 2014-08-20T19:59:27 | HTML | UTF-8 | R | false | false | 3,127 | r | dorothea-analysis.R | # /*
# This is an R script containing R markdown comments. It can be run as is in R.
# To generate a document containing the formatted R code, R output and markdown
# click the "Compile Notebook" button in R Studio, or run the command
# rmarkdown::render() - see http://rmarkdown.rstudio.com/r_notebook_format.html
# *... |
e76b882513df30b546540710d71db0acdf7ff39e | 0c266c36fb25113e35afebdf6a037ade43b0e3da | /man/Wishbone.Rd | 62eccb585302f956ddd16328887a95a1899f5945 | [] | no_license | dynverse/Wishbone | 1c684546f7a9f21bdb4abedb07503c34cc2d51dd | 7f9ff10806ec5b959f76ab4aa1e513891581e5ba | refs/heads/master | 2021-01-23T20:07:18.058856 | 2018-06-06T12:58:05 | 2018-06-06T12:58:05 | 102,852,835 | 0 | 0 | null | 2018-06-06T12:58:06 | 2017-09-08T11:08:37 | R | UTF-8 | R | false | true | 974 | rd | Wishbone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Wishbone.R
\name{Wishbone}
\alias{Wishbone}
\title{Execute Wishbone}
\usage{
Wishbone(counts, start_cell_id, knn = 10, n_diffusion_components = 2,
n_pca_components = 15, markers = "~", branch = TRUE, k = 15,
num_waypoints = 50, normalize ... |
59834a69ef133638e7782f9b26266dbc1d349a6f | 8b42ce326b7641a6b14c1a6d42e6e2a3ff0a5ca0 | /model_ann1.R | 58142b6a589948576f45e8b5190d237a5fe45410 | [] | no_license | coldfir3/boston_ma | 8ce5819a843214ae1f6b4b0781ac07939112d3a5 | 98e5e3250939ee3dabc0cb6db2ab56432cd6f6c4 | refs/heads/master | 2020-03-25T21:51:41.144726 | 2018-08-10T14:35:46 | 2018-08-10T14:35:46 | 144,193,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,929 | r | model_ann1.R | rm(list = ls())
library(keras)
library(tidyverse)
dataset <- dataset_boston_housing()
c(c(x_train, y_train), c(x_test, y_test)) %<-% dataset
rm(dataset)
save(x_train, y_train, x_test, y_test, file = 'dataset.data')
mean <- apply(x_train, 2, mean)
std <- apply(x_train, 2, sd)
save(mean, std, file = 'scale.data')
x_tr... |
f191bd881bbdcb878a82420c74d3abc2643fc8b9 | e915168439022365a3f84cd995bca3808c6acaa7 | /sim-stuff/HD-settings/test3.R | 007ddfb3660ea73587b4418e68e99df18f62c49d | [] | no_license | samperochkin/learning-block-exchangeability | 675e79b6b5f2ee17ef1524a05329ba3f208ba443 | a45142de8137f19620f95e372aa1284eafca1a57 | refs/heads/master | 2023-03-10T02:37:50.353800 | 2023-02-22T15:23:42 | 2023-02-22T15:23:42 | 104,235,865 | 2 | 0 | null | 2018-03-28T16:05:11 | 2017-09-20T15:48:00 | R | UTF-8 | R | false | false | 1,659 | r | test3.R | dim(X)
n <- 150
X <- rmvnorm(n,rep(0,d),Sigma)
Delta <- constructMatrix(rep(1,length(d.vec)),d.vec)
Th1 <- cor.fk(X[1:125,])
Th2 <- cor.fk(X[126:150,])
Tt2 <- (Delta %*% (Th2-diag(d)) %*% Delta)/(Delta %*% (1-diag(d)) %*% Delta)
sum((Tt2-Th1)^2)/sum((Th2-Th1)^2)
sum((Tt2-Tau)^2)/sum((Th2-Tau)^2)
n <- 200
X <-... |
1c5be82e3d96a085bfb858b957e132e984636213 | fe17217bf85ed660a1fa3173f6078133c5bc49e0 | /man/lm.WZ.Rd | bc56ca8a85f768283b96d85564b1eb15b89d7f77 | [] | no_license | rgcstats/ODS | 5a4ba2107328175174b4874e10d8e47733c33397 | 0290071546fdd8dff1c8e9e6d8bc5920d1c04491 | refs/heads/master | 2020-12-10T10:30:36.232624 | 2020-01-13T10:20:29 | 2020-01-13T10:20:29 | 77,803,517 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,320 | rd | lm.WZ.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lm_WZ.R
\name{lm.WZ}
\alias{lm.WZ}
\title{Linear model fitting for stratified survey data by maximising the estimated likelihood of yu and xs}
\usage{
lm.WZ(ys, yr, xs, cutoffs, pi.s)
}
\arguments{
\item{ys}{vector of sample values of the dep... |
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