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9ba7b21fd143273d044a5a1f9e3098cfe7339887 | 29585dff702209dd446c0ab52ceea046c58e384e | /eha/R/check.surv.R | 718376239394e040fcde36f60f8ce743c029a83b | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,263 | r | check.surv.R | check.surv <- function(enter, exit, event, id = NULL, eps = 1.e-8){
## The '.Fortran' version.
##########################
n <- length(enter)
if (length(exit) != n)stop("Length mismatch (enter/exit)")
if (length(event) != n)stop("Length mismatch (enter/event)")
if(!is.null(id)) if (length(id) != ... |
3a698f4fb0e1c1e12819c3e737ee1ebd05b087d0 | f27a99810f7f0ff5796c66d5704af358d8d6cf1d | /plot3.R | 712746da06c19b5655e5c748fc63053a98f38a3e | [] | no_license | allynlea/ExData_Plotting1 | 840c9d01cbced62bd984fc22bc64f9bafd34a4f7 | e2dab02617769badaa8727b5ade38a70e1b47cde | refs/heads/master | 2021-01-22T15:51:08.752769 | 2016-01-11T12:13:32 | 2016-01-11T12:13:32 | 49,390,087 | 0 | 0 | null | 2016-01-10T23:16:09 | 2016-01-10T23:16:08 | null | UTF-8 | R | false | false | 807 | r | plot3.R |
elec = read.csv("household_power_consumption.csv", sep=";", na.strings="?")
elec$Date <- as.Date(elec$Date, "%d/%m/%Y")
elecPower<- subset(elec, elec$Date == "2007-02-01" | elec$Date == "2007-02-02")
elecPower$DateTime<-paste(elecPower$Date,elecPower$Time)
elecPower$DateTime<-strptime(elecPower$DateTime, "%Y-%m-%... |
3d8798f8ab9941a6531595e2416a9400b95c55ee | eb3215ea01fc670cd0e705f8414ffad9dc51ff46 | /R/PerformanceTesters.R | e2996b22e8fb0fdfc623797fc6bf28ea0b09b018 | [] | no_license | ireyoner/tmParallel | e68204fe47c0760fa4b1f3a4e902bbbab7b5a1be | 24a2ebdbc110111e2f615fec11c6f53278e81104 | refs/heads/master | 2020-09-20T03:54:36.397638 | 2016-09-12T20:30:30 | 2016-09-12T20:30:30 | 66,380,880 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,540 | r | PerformanceTesters.R | #' tmParallel functions for performance testing
#'
#' Some functions usefull for performance testing - with diffrent implementations
#' for creating DocumentTermMatrix or TermDocumentMatrix
#'
#' For parametrs lists see \likn{ParallelDocumentTermMatrix}
#'
#' @rdname tmParallelPerformanceTesting
DocumentTermMatrixOrig... |
cfc44fe1bd8ecd6b0a131955b93cda5174ed6e7a | c58a1595115fea554db8cd6578279f574eabfa0e | /man/chk_omitted.Rd | b817a29f71325ca21f73d4b49079d788f832385f | [
"MIT"
] | permissive | bayesiandemography/demcheck | 129aca86fecda02be83bea73e639fb45d366c651 | c52c3e4201e54ead631e587ebf94f97f9c7a05a0 | refs/heads/master | 2021-12-28T15:40:54.771894 | 2021-12-17T03:10:50 | 2021-12-17T03:10:50 | 200,993,678 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,385 | rd | chk_omitted.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/chk-composite.R, R/err-composite.R
\name{chk_omitted}
\alias{chk_omitted}
\alias{chk_not_omitted}
\alias{err_omitted}
\alias{err_not_omitted}
\title{Check whether dimension(s) identified by an index are omitted,
or not omitted, according to '... |
5d34caf3de33534b43103ddbbd1bd87f1333959c | c0f1ad567a5f8ab8fb376242dc1a990d2ab6b3e8 | /Propensión/MatrizOD.R | 6a50538267cc041587c51ca029c140e34bee1263 | [] | no_license | RAS-WB-Uniandes-Urban-Cycling/proBikePolicies | edda6596b693f68b22c4ad50d6746833cef167e3 | 5c82094420a38421748bbb1f997550df4852fd17 | refs/heads/master | 2021-06-06T17:44:25.098109 | 2021-05-09T18:06:08 | 2021-05-09T18:06:08 | 135,209,976 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,560 | r | MatrizOD.R | ######Análisis Matriz OD Encuesta de movilidad 2015
#Se cargan librerias para transformación y visualizaciones
library(tidyverse)
library(readxl)
library(sf)
library(tmap)
library(tmaptools)
library(stplanr)
library(circlize)
######## Heatmaps demanda#######
# Carga de acap con ZATs, proyección en SRID 4326 longlat WG... |
7671fcb5f65406cfe2001f1b9225bfe49a047cdf | 1e098b440afdb0993b2805eec21f0d25afb0a0e8 | /man/tissue_scale.Rd | 581224128bff0be144b786b0e57d5c413caf13f8 | [] | no_license | HQData/CompTox-ExpoCast-httk | e9f13135042e804975cd538a4580b33b6ae22021 | 020314daa05af42979a205c395302377db2f9332 | refs/heads/master | 2020-04-20T04:27:43.094941 | 2018-01-24T15:45:39 | 2018-01-24T15:45:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 759 | rd | tissue_scale.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tissue_scale.R
\name{tissue_scale}
\alias{tissue_scale}
\title{Allometric scaling.}
\usage{
tissue_scale(height_ref, height_indiv, tissue_mean_ref)
}
\arguments{
\item{height_ref}{Reference height in cm.}
\item{height_indiv}{Individual heigh... |
13d4298ddcb001381abef9c1bf48b0f1ddd5ae85 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/fslr/examples/fslrange.Rd.R | 7cefdcf8ca7ca3e38505b957ca185776665b1a20 | [] | 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 | 230 | r | fslrange.Rd.R | library(fslr)
### Name: fslrange
### Title: Get range of an image
### Aliases: fslrange
### ** Examples
if (have.fsl()){
mnifile = file.path(fsldir(), "data", "standard",
"MNI152_T1_2mm.nii.gz")
fslrange(mnifile)
}
|
ca9a3197af9995c91ba0b6a059c3f6e249e680fc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EHRtemporalVariability/examples/plotIGTProjection-methods.Rd.R | 454c87f3e72bf368b6a4bf84c1425fa106b7a68d | [] | 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 | 841 | r | plotIGTProjection-methods.Rd.R | library(EHRtemporalVariability)
### Name: plotIGTProjection
### Title: Information Geometric Temporal plot
### Aliases: plotIGTProjection plotIGTProjection,IGTProjection-method
### ** Examples
load(system.file("extdata",
"variabilityDemoNHDSdiagcode1-phewascode.RData",
package="EH... |
123f65477ed2bd87b5eed4516fc90037bfc25125 | c192c8e32af24c49132befbe8bf613767b60ae66 | /R/my_t.test.R | 4970912c12d55db6110a64f80d608f2956a7ec15 | [] | no_license | Ali-Jahan/Project2 | f549f931a1fa628342b1040034e6997cf8d9f3c7 | b72b906b39e3b79830173c2f333e7103bdbaf1ca | refs/heads/master | 2023-01-30T14:29:19.986134 | 2020-12-17T16:17:23 | 2020-12-17T16:17:23 | 322,231,304 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,204 | r | my_t.test.R | #' T hypothesis test.
#'
#' This function implements one sided and two sided T hypothesis tests.
#'
#' @param x Numeric input of the data set.
#' @param alternative String input indicating whether the hypothesis is "less", "greater", or "two-sided".
#' @param mu Numeric indicating the mean (mu) that is being tested aga... |
bc85c87d114e50406b35776c3b62270b5fb1b2c6 | 2d88b1d2e158b33e1acdb1117d259b0fb110f378 | /R/mix_model_help.R | d73b937654b711be4fa8650840433df2a4c5c4dd | [] | no_license | shahcompbio/xseq | 3634b1a628844286b4f2c905e6b7eb74f2b14736 | 4290a6a1f0b533f1a514616b5a781a916c5f3993 | refs/heads/master | 2021-08-14T14:24:45.396870 | 2017-11-16T00:41:11 | 2017-11-16T00:41:11 | 103,323,499 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,116 | r | mix_model_help.R | # Some helper functions for mixture model analysis
#
# Date:
# Revised: February 15, 2015
#
# Author: Jiarui Ding <jiaruid@cs.ubc.ca>
# Department of Computer Science, UBC
# Department of Molecular Oncology, BC Cancer Agency
#
#==============================================================================
Mix... |
cc0e0d19f1758ae13eea87709825147e0697719c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SpatialExtremes/examples/condmap.Rd.R | c78d3af51686f726be467ce5a1c9a459d5580a2f | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,377 | r | condmap.Rd.R | library(SpatialExtremes)
### Name: condmap
### Title: Produces a conditional 2D map from a fitted max-stable process
### Aliases: condmap
### Keywords: hplot
### ** Examples
##Define the coordinate of each location
n.site <- 30
locations <- matrix(runif(2*n.site, 0, 10), ncol = 2)
colnames(locations) <- c("lon", "l... |
f0e64614d344de6bd5875ce035ef1c1dfb84d1a7 | 81ef20863b388397a6158289141ec90109109bc3 | /man/theme_myriad_new.Rd | 08c7e0c35f7efe29a3de99999ecf533b5fd7621b | [] | no_license | kathryntsai/myriad | ceb8ff67f22efafc90f40f24e8fb2d24813ae181 | 49d2abc0c12bbef2991ff2678c11fd3d845caff2 | refs/heads/master | 2022-12-22T03:49:17.901761 | 2020-09-26T19:49:25 | 2020-09-26T19:49:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,414 | rd | theme_myriad_new.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/myriad.r
\name{theme_myriad_new}
\alias{theme_myriad_new}
\title{theme_myriad_semi}
\usage{
theme_myriad_new(base_family = "Myriad Pro SemiCondensed", base_size = 11)
}
\arguments{
\item{base_family, base_size}{base font family and size}
}
\d... |
df1d0aab8754267d2fe014110fe811c3b0659410 | e03799f962db79cbf504f8fa368111f70aa00434 | /cachematrix.R | 1c744b57ff5e3b81f9d4049401b8e05e621b5c40 | [] | no_license | bogdanrus/ProgrammingAssignment2 | 19430c9a66a8ed66574a1c55289ef78b251326d7 | 1ffc51edda530aa5050bd8ae0fb5b759b3bef1da | refs/heads/master | 2020-12-29T01:54:49.657733 | 2014-11-23T19:24:14 | 2014-11-23T19:24:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,455 | r | cachematrix.R | ## The following functions will cache the inverse of a matrix(assuming
that the matrix provided is invertable)rather than computing it repeatedly.
This will save time for computing the inverse of the matrix, every time the
inverse is needed.
## The makeCacheMatrix function creates a "matrix" object that ... |
cb915818071df7ba25ff8f0be72bece94c05baa2 | 62e9ec62c910bac5eeb77c6cf9719a1fb30f566e | /R/MatricesTrasnformations/ImportandMatrixP2.R | 89a19f97cc7a3da333ebf80fed3c9bb36d07bb55 | [
"MIT"
] | permissive | bhupendpatil/Practice | 25dd22ccca706359aabe135a3cbfb813e2611cef | cae96a3636527100e4b98880e5d3e9ce9399335a | refs/heads/master | 2023-09-01T13:58:53.857928 | 2023-08-21T06:16:41 | 2023-08-21T06:16:41 | 112,185,402 | 5 | 1 | MIT | 2023-08-21T06:16:43 | 2017-11-27T11:02:33 | Jupyter Notebook | UTF-8 | R | false | false | 634 | r | ImportandMatrixP2.R | library(igraph)
library(Matrix)
# i) View data collection
View(onemode)
#plot
netgraph = graph.data.frame(onemode, directed = T)
plot(netgraph,edge.arrow.size=.7)
# ii) Basic Networks matrices transformations
# adjacency matrix
mode1 = matrix(netgraph[],6,6)
#get.adjacency(netgraph) #optional
t(mode1))
# Sum
# to... |
bdca832eb5707d789ca22e1ca30ce1977e5d7634 | 8caeff2957ae777eabbb17e92ac49a5f51f1937c | /Jeremie_Rehak_6:11:18.R | 573c0c351165b873d8ee7a71d88e9e81d4b4bacb | [] | no_license | ayusharora99/2018_Umpires | fbe382e7c3d1b6fbafeb2503cb9a9bffc26103db | ea344d33ad55e732a22c33ab155842834ded4422 | refs/heads/master | 2020-04-23T18:22:49.316852 | 2019-03-04T00:43:47 | 2019-03-04T00:43:47 | 171,364,791 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,711 | r | Jeremie_Rehak_6:11:18.R | # 6/11/18 : Giants vs __OPPONENT__ : Jeremie Rehak
# 148 pitches were called strikes/balls
# The robot-ump called 38 of those pitches as called strikes & 110 as balls
# Jeremie Rehak called 37 of those pitches as called strikes & 111 as balls
# Accuracy: 95%
Jeremie_Rehak <- read.csv("~/Desktop/Analyzing Baseball... |
274a168635bb495cf55b70fcfe745ae59e462b3c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/aire.zmvm/examples/download_meteorological.Rd.R | febb8c8aecc8ae8eccf8c0e62c7fb2e26c51d03c | [] | 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 | 231 | r | download_meteorological.Rd.R | library(aire.zmvm)
### Name: download_meteorological
### Title: Download Meteorological Data Archives
### Aliases: download_meteorological
### ** Examples
## Not run:
##D head(download_meteorological(2017))
## End(Not run)
|
f78d6d2c9ac202da1f5a6a6600bbdf9963622639 | 8869198b3f0a395a2d60c0d49e58dc02b9d161d3 | /R/stack_files.R | 09ba2e9a4082550078fabf97942c5fe8031da5c6 | [
"MIT"
] | permissive | ahasverus/argostools | a62c1c80e4e331f5468d0b3e4f691f99d00584a7 | beabb3bda0157ca4d0f5391cf39e616776c5ae13 | refs/heads/master | 2020-06-05T11:26:21.454534 | 2020-02-09T12:18:37 | 2020-02-09T12:18:37 | 33,409,994 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,027 | r | stack_files.R | #' @title Import and clean raw Argos locations data (multiple files)
#' @description Import and clean raw Argos locations data(multiple files).
#'
#' @param files Vector of filenames to read
#'
#' @return A data frame with 7 variables
#'
#' @export
#'
#' @examples
#' ## See vignette
#'
stack_files <- function(files) ... |
c94d40ca67cb09cdaaada99c8fbd684f53a5ea2d | 9123ac4623942513233434f8709f18803e13e9e2 | /Common/apk.R | 0ba4051ec454b515f2d72e2a1e82e43e1de589f5 | [] | no_license | ttvand/Facebook-V | fc41c0c2801feeeb25163d07237404882d5f722e | 0bb716574fcfca6c4fe3113a210649a9213b0c38 | refs/heads/master | 2020-12-25T06:02:36.159672 | 2019-06-07T16:14:05 | 2019-06-07T16:14:05 | 62,836,018 | 137 | 81 | null | null | null | null | UTF-8 | R | false | false | 1,664 | r | apk.R | # Adapted from: https://github.com/benhamner/Metrics/blob/master/R/R/metrics.r#L181
# Compute the mean average precision at 3
map3 <- function(actual, predicted, returnScores=FALSE)
{
scores <- rep(0, length(actual))
scores[predicted[,1]==actual] <- 1
scores[predicted[,2]==actual] <- 1/2
scores[predicted[,3]=... |
e49c36743999dfcb9afc4d2f8a2d76bf7186525f | 3703992a3018bb64ba40a7aa01c6f06ff7158266 | /plot3.R | fb3a8dbb574cbc2cc8ffb059a5651d0560cd507b | [] | no_license | abhilash-r/All-R-Programming | c55899e0389bf2f2d18df1101389f8af8326d706 | 574e8ed520b0dd08dea0c1ca9b908321b7e3d272 | refs/heads/master | 2016-09-10T12:20:32.609471 | 2015-07-12T22:17:36 | 2015-07-12T22:17:36 | 38,980,511 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 781 | r | plot3.R |
## Fetches all the dataset
data <- read.csv("household_power_consumption.txt", na.string="?", sep=";")
## Take data between "2007-02-01" & "2007-02-02"
data <- data[(data$Date=="1/2/2007" | data$Date=="2/2/2007"),]
# Combine Date and Time
data$DateTime <- strptime(paste(data$Date, data$Time, sep=" "),
... |
7fd3954ed7b1be6230996332c514bda2a89f9b7e | 1d9b89efbe08226b1598ce86e3e95d603d4552f7 | /man/renderSubNetSimple.Rd | a9a54d4f09e2f120670e37329aee4816f198d667 | [
"Apache-2.0"
] | permissive | laderast/surrogateMutation | 1e84f05c29369387c40c4a1bb86d49c06d7c4e34 | 50987f20ae56a72495996acfb8185b3b9c215c84 | refs/heads/master | 2021-01-22T05:06:47.613979 | 2018-01-06T05:32:31 | 2018-01-06T05:32:31 | 34,889,234 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,463 | rd | renderSubNetSimple.Rd | \name{renderSubNetSimple}
\alias{renderSubNetSimple}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
renderSubNetSimple
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
renderSubNetSimple(NodeName, sampleName, GeneName, intome = intome, gisticCopyCa... |
26a1456103f8bf493f5a9ad4976cd16ce3e42dbe | c0594b6c8ad34662469cb3369cda7bbbf959ae69 | /man/convert_to_numeric.Rd | 371401712ba8f16e4d3637445e2bb606023014b8 | [
"CC-BY-4.0"
] | permissive | bgctw/cosore | 289902beaf105f91faf2428c3869ac6bba64007f | 444f7c5ae50750ec7f91564d6ab573a8dc2ed692 | refs/heads/master | 2022-10-22T13:48:37.449033 | 2020-06-17T05:46:19 | 2020-06-17T05:46:19 | 269,271,512 | 0 | 0 | CC-BY-4.0 | 2020-06-17T05:46:20 | 2020-06-04T05:47:05 | null | UTF-8 | R | false | true | 498 | rd | convert_to_numeric.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{convert_to_numeric}
\alias{convert_to_numeric}
\title{Convert a vector to numeric, giving an informative warning.}
\usage{
convert_to_numeric(x, name, warn = TRUE)
}
\arguments{
\item{x}{Vector}
\item{name}{Name of object (for ... |
8dd983f166a226e70a1ac4e4088a9643750c655f | 9fbd7cafab56b8cb58ca7385a726a0070d9e050d | /man/sen_senator.Rd | c101bf43e503acd9d2a8c1213bf438bba2824dca | [] | no_license | duarteguilherme/congressbr | 6f343935a7734dfac70c6794a031db614dafd248 | e9f05428e877f56d31966b14ca00b4ec825fabf5 | refs/heads/master | 2022-11-22T05:40:27.177434 | 2020-07-14T23:20:22 | 2020-07-14T23:20:22 | 83,827,931 | 17 | 1 | null | null | null | null | UTF-8 | R | false | true | 1,397 | rd | sen_senator.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sen_senator.R
\name{sen_senator}
\alias{sen_senator}
\title{Downloads and tidies persnonal information on senators from the
Federal Senate}
\usage{
sen_senator(
id = NULL,
affiliations = TRUE,
mandates = TRUE,
absences = FALSE,
asci... |
96279dfc630496b49271a8a0396b0bb1bc6c78ed | 0767f577fc78e13f2bad7c0566f877898214c161 | /Source_docker_code/CIBERSORTxCode_fractions/R_modules/run_adjust_w_pseudo.R | 0e1fac74d94c937bfa8381cb372988fcaf3809cd | [] | no_license | toeric/Parallel_CIBERSORTx | 174f88a505a2af4507d38ac724b3f74a82585c1b | 7047ff68a6ca7cee8d7c3322ff1088e3193b7cf3 | refs/heads/main | 2023-05-27T07:22:48.454618 | 2021-06-15T11:09:38 | 2021-06-15T11:09:38 | 366,675,061 | 4 | 1 | null | 2021-06-15T10:50:45 | 2021-05-12T10:18:07 | R | UTF-8 | R | false | false | 4,193 | r | run_adjust_w_pseudo.R | suppressWarnings(suppressMessages(library(e1071)))
suppressWarnings(suppressMessages(library(parallel)))
suppressWarnings(suppressMessages(library(preprocessCore)))
suppressWarnings(suppressMessages(library(colorRamps)))
suppressWarnings(suppressMessages(library(bapred)))
suppressWarnings(suppressMessages(library(data.... |
7eb5f656391af7d3250220802c610370857b290e | c68a68122d541ae95052a21f9abbc8dc9f551a8d | /dataprep/dataprep-template.R | c72df1d17301676eee8675fa8414e76142ee99c2 | [] | no_license | iwensu0313/us-aquaculture | 11ed8c76e2df8f81166b610b83ad23199542772b | b92eaac9bf9d58961b978d609a91313b2bf603f4 | refs/heads/master | 2021-07-20T18:21:55.758307 | 2020-05-10T00:07:05 | 2020-05-10T00:07:05 | 154,531,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 345 | r | dataprep-template.R | ## DATA PREP TITLE
## Data Source
# Source Name
# Link:
# Downloaded:
# Timeseries:
# Format:
# Notes here
## Summary
library(tidyverse)
library(plotly)
library(viridis)
library(validate)
## Read in Data
## Tidy: Select subset, clean up data tables, fix strings, fix class
## Wrangle: Join, combine,
## S... |
6a1ecb00edf9b0a39e9dd022922d35c867c721b7 | 69e60c5f2db28cda637d98e39ce90c97d9aeb6d8 | /src/roc.R | 9c7e216ba50b1d335b4b233b0d4cb48b0b93a672 | [] | no_license | fivebillionmph/m223b-project1a | d7abdf3fa58e07aa24a6146dd650f647afc4633d | 34cd825910540a5e78b526205ab4600e7dc4cd6f | refs/heads/master | 2022-12-03T08:02:44.056176 | 2019-06-21T04:01:48 | 2019-06-21T04:01:48 | 193,025,123 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 272 | r | roc.R | args = commandArgs(trailingOnly=TRUE)
if(length(args) != 1) {
stop("need file name")
}
library(ROCR)
f = args[1]
d = read.delim(f)
pred = prediction(d[,2], d[,1])
perf = performance(pred, "tpr", "fpr")
perf.auc = performance(pred, "auc")
print(perf.auc)
plot(perf)
|
af8b3e71cbc5904b284139b2f3d5e4450dcd3d29 | 8eb4b0e89610dc7fdcc68463bdc57e5adbf10849 | /R/program_on_path.R | 4ab42c3730ca4ba030aaa2bb0fd10fd7f9937c56 | [] | no_license | cmcouto-silva/snpsel | f0b1fa1675fb10e15329cae874441f241f3a5e15 | e3898308f5b0b2ae071cefdc111f5334a0319cf7 | refs/heads/master | 2023-05-07T22:19:08.444440 | 2021-06-06T15:55:12 | 2021-06-06T15:55:12 | 127,948,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 168 | r | program_on_path.R | #' @export
program_on_path <- function(program) {
logi <- ifelse(Sys.which(program) == "", FALSE, TRUE)
if(!logi) stop(program, " not installed on system path!")
}
|
8e2412f7cb0e7f0aedfcb5a6ac2fdcc39a16a937 | 02617474275bde6a99bfee3d1e899056a806aba4 | /amylose_oct_2021.R | 04e45b9976121c64185df8e17a04736fa256a231 | [] | no_license | quadram-institute-bioscience/SSIIIa-Rscripts | 97f850c6e9f4d31b39ad32222e3015924f05c3d2 | 5bc3df58787a8476d39348aa401edd29dff0da74 | refs/heads/master | 2022-02-25T13:12:48.242669 | 2022-02-10T00:20:24 | 2022-02-10T00:20:24 | 254,044,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,371 | r | amylose_oct_2021.R |
library(readxl)
library(data.table)
library(ggplot2)
library(tidyr)
datAmylose <- read_excel("data/2021-09-23AmyloseContent.xlsx",
sheet="Gene Average",
range="A5:C161")
names(datAmylose) <- c("plant", "id", "amylose")
datAmylose <- separate(datAmylose, plant, c("G... |
55994903552cc0c9801a96d4c11ee6d96675030e | 67af11952ff7ef35d9cdbf490351abfb020b34da | /man/pm_gh_config.Rd | dc14f88746623009ca71e3c9950feac26c5637d0 | [] | no_license | quietsnooze/pmpackage | 1c68d1f1aa70c53a81fc1abc2e0182dec6ce30b0 | 45bf5a3694cfb2c162f65855a2b8a827649198c4 | refs/heads/master | 2021-04-15T09:26:15.434185 | 2021-02-28T17:57:34 | 2021-02-28T17:57:34 | 126,635,991 | 1 | 1 | null | 2018-12-29T13:57:30 | 2018-03-24T20:05:07 | R | UTF-8 | R | false | true | 449 | rd | pm_gh_config.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/githubFunctions.R
\name{pm_gh_config}
\alias{pm_gh_config}
\title{pm_gh_config - configure github with username and email}
\usage{
pm_gh_config(github_username, github_email)
}
\arguments{
\item{github_username}{Your username for github}
\it... |
73ccc477c5f7d69ada1e0154e2f40eef85ade999 | 4e1c85bc81fcc02a6b197fc44ab53376acba7084 | /scripts/Sector_Bar_Chart.R | 5fa507c33a30193b82ad1eb56ba6fe30d6063a51 | [] | no_license | darshils2001/info201-project | 24e38278c0ed08c1f7fe37b4a7448b5bb5e3d49b | 6d10cc82f09cc45d6f4f57c93eebb9ea783a46bf | refs/heads/main | 2023-01-28T19:21:31.070568 | 2020-12-10T21:21:57 | 2020-12-10T21:21:57 | 305,523,884 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,059 | r | Sector_Bar_Chart.R | # Load Packages
library("readxl")
library("ggplot2")
library("tidyverse")
# Load Dataset
sectors <- read_excel("datasets/National_Sector_Dataset.xls")
# Convert Sector Numbers to Names
sectors$NAICS_SECTOR <- gsub(11, "Agriculture", sectors$NAICS_SECTOR)
sectors$NAICS_SECTOR <- gsub(21, "Mining", sectors$NAICS_SECTOR... |
90211f301b0e9791a8142b6abf2fbe8ee54da16e | 688148891812cefeadebfd0340ead1a04b6e8a00 | /R/incdist.R | d25095e859162354a8b6eb225d05cef29e0fdf32 | [] | no_license | mjantti/incdist | 47a59545a59666e0f343f8cd4b5267a84bb5eba2 | 836fa9c6b74d0f475db686ee1a49e4e52cddaebe | refs/heads/master | 2023-08-16T13:40:23.974958 | 2023-08-09T07:21:59 | 2023-08-09T07:21:59 | 244,979,962 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,585 | r | incdist.R | ## test to see if both make it to NAMESPACE
# income -- R functions for income distribution analysis
# v 0.1 1998/01/07 mjantti@abo.fi
# Goals:
# This library will supply standard methods for income distribution
# research, including
# - (relative, absolute and generalized) inequality and poverty indices
# - (relative,... |
45b9e453f1be40149eb850115f4ef34ae811d505 | 00035781ff4e3bcc409b5b1178373e4f59a68d27 | /plot3.R | 390932932c7234e90f28afbeb50497cd975f8277 | [] | no_license | fmalvicino/ExData_Plotting1 | 467e71699819d193d4e5687ce37f7ffe9301dc52 | 97d71dfd6c2acdee2726d1daeaf15044474c4bff | refs/heads/master | 2021-01-17T21:28:07.895509 | 2015-08-08T01:39:09 | 2015-08-08T01:39:09 | 40,381,540 | 0 | 0 | null | 2015-08-07T21:26:23 | 2015-08-07T21:26:22 | null | UTF-8 | R | false | false | 747 | r | plot3.R | hpc<- read.csv("household_power_consumption.txt", sep= ";", na.strings="?")
library(dplyr)
library(lubridate)
hpc$DateTime <- dmy_hms (paste(hpc$Date, hpc$Time))
hpc2 <- filter(hpc, DateTime %within% new_interval(ymd("2007-02-01"), ymd_hm("2007-02-02 23:59")))
plot(hpc2$DateTime, hpc2$Sub_metering_1, xlab= "", yla... |
dfe2e6f87427624530c00914e6684338672d0d45 | 50066dae4216d17bd6f0dcb9a11d872e73246eb6 | /man/pk.calc.clast.obs.Rd | f4f173534b7b27cacb06c360472993afa33ac7a2 | [] | no_license | cran/PKNCA | 11de9db2cb98279c79d06022415b8772e7c1f5ea | 8f580da3e3c594e4e1be747cb2d8e35216784ed2 | refs/heads/master | 2023-05-10T16:54:19.131987 | 2023-04-29T18:30:02 | 2023-04-29T18:30:02 | 48,085,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 859 | rd | pk.calc.clast.obs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pk.calc.simple.R
\name{pk.calc.clast.obs}
\alias{pk.calc.clast.obs}
\title{Determine the last observed concentration above the limit of
quantification (LOQ).}
\usage{
pk.calc.clast.obs(conc, time, check = TRUE)
}
\arguments{
\item{c... |
fe03af73fe7b0d04a6efc966fdb1af570047fc8d | b2532b65845b352083b9c27e2a29c1237fd640e4 | /R/snp_asso_fig.R | 262bfa1a7aa5e61c61c1b56fa848b9425ada57d5 | [
"CC0-1.0"
] | permissive | kbroman/Talk_MAGIC2021 | dfadeb4956e7a1ea184b329c6c3b68510462931a | deedea16df3ac92e20480d8b02634e70aca98373 | refs/heads/master | 2023-05-05T11:19:29.981646 | 2021-05-22T01:53:40 | 2021-05-22T01:53:40 | 360,722,882 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,193 | r | snp_asso_fig.R | # SNP association scan
library(qtl2)
url <- "https://raw.githubusercontent.com/rqtl/qtl2data/master/ArabMAGIC/"
url <- "~/Code/Rqtl2/qtl2data/ArabMAGIC/"
arab <- read_cross2(paste0(url, "arabmagic_tair9.zip"))
arab <- drop_nullmarkers(arab)
file <- "_cache/snp_asso.rds"
if(file.exists(file)) {
out_snps <- readR... |
87231ec159ef74c90f23472f46a39ee183405112 | af493dbfe092b00363d7b03dd6f01399a83841f5 | /man/canonical_name_create.Rd | 90ade912b7ec183df69c415660f27d5441240931 | [] | no_license | antaldaniel/eurobarometer_old2 | 7665b252b20e950db427bd43956efc9d3e88f90b | 042e13e447f1a43c898f9e8887f7ed37aa3983c2 | refs/heads/master | 2021-10-09T15:33:20.307410 | 2018-12-30T19:06:55 | 2018-12-30T19:06:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 469 | rd | canonical_name_create.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/canonical_name_create.R
\name{canonical_name_create}
\alias{canonical_name_create}
\title{Create a canonical variable name for the R objects}
\usage{
canonical_name_create(x)
}
\arguments{
\item{x}{A vector of the GESIS variable names}
}
\des... |
2e228c704706e7fd4ddd47c435d9596f5b08a1ac | cd1e004315e9de253d4a02d928a72ff585de54c5 | /R/converters.R | 2db9c43c4058e9afac4b0dac4ce6266f508355be | [] | no_license | thefooj/cassandrasimple | 8e6898439bf7ad4fea587cf7bbd642437de60848 | e87092f52d8a7e2fe4b323f702d164cf05fa4e98 | refs/heads/master | 2021-05-23T04:09:46.075233 | 2020-10-06T15:32:14 | 2020-10-06T15:32:14 | 81,841,891 | 1 | 0 | null | 2018-06-19T20:48:19 | 2017-02-13T15:48:44 | R | UTF-8 | R | false | false | 618 | r | converters.R |
as_date_from_cql_date <- function(localdate) {
as.Date(rJava::.jcall(localdate, 'S', 'toString'), tz="UTC") # to YYYY-MM-DD
}
as_posixct_from_cql_get_timestamp <- function(javaDate) {
# getTime gives milliseconds since epoch 1970-01-01 as a Java long (64-bit signed).
# we convert to seconds and give the epoch
... |
a784ff68944d42cf9c7a787e5deacf743dcc89d6 | ebcaca53d888b5b6ecc2d281937eab35df236554 | /code/bias/editorial_rejections_by_journal.R | bf94dcaa8d004392ef636db45a76a9773e66ee7f | [
"MIT"
] | permissive | SchlossLab/Hagan_Gender_mBio_2020 | 4c4eb3dd8b4f030dbea2eca29ae4ebf1fa8da2b2 | 118e082fcb1d6a347e8e0f16def7a1ec1ca4907d | refs/heads/master | 2023-01-01T03:12:26.188371 | 2020-10-15T17:55:14 | 2020-10-15T17:55:14 | 155,252,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,275 | r | editorial_rejections_by_journal.R | #break decisions after review down by journal
j_ed_dec_data <- bias_data %>%
# filter(!(country %in% c("Japan", "Taiwan, Province of China",
# "China", "Singapore", "Hong Kong",
# "Korea, Republic of"))) %>%
filter(version.reviewed == 0) %>%
filter(grouped.ver... |
c351098e27312d33ade6f2f0d53b35609c266069 | 117bdbc2b2380aeacec87cf6c8b24b18ab8c5bee | /man/list.gene.snp.Rd | 0fd081ca10f05e8b46b5b25e8d5d99d276002f8d | [] | no_license | cran/PIGE | 1cc3f6aa9bfd47408be850188b1e3b7dfad90557 | 682c43bd19a050c6b5eb055f7184e5849e60cf94 | refs/heads/master | 2021-06-11T21:06:21.948691 | 2017-08-30T07:23:09 | 2017-08-30T07:23:09 | 17,681,352 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 444 | rd | list.gene.snp.Rd | \docType{data}
\name{list.gene.snp}
\alias{list.gene.snp}
\title{Fictive list for the case-control study example containing the names of the snp for each gene included in the studied pathways}
\format{A list containing the names of the SNPs belonging to each gene analysed.}
\description{
Fictive list for the case-con... |
c191a5d0ce76b43f2340b849f5f521cf2e213946 | 91969900434366f888c9e424e9078b95aa9cf9e4 | /pairwise_comparison_script.R | de63101b7bf139b5df1eecaa5d804b1da7d493d3 | [
"MIT"
] | permissive | evelienadri/herelleviridae | 138c98b5cb8c11c090c218cb4ad434f3cd25f290 | f7e9a3d99c046244ea99f5766e734fde926df7d1 | refs/heads/master | 2021-07-07T14:15:23.190995 | 2017-10-02T13:23:59 | 2017-10-02T13:23:59 | 105,536,381 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 628 | r | pairwise_comparison_script.R | # Import table
gene_presence_absence <- read.delim("~/path_to_file/filename", row.names=1)
m <- as.matrix(gene_presence_absence)
# Make a vector with the column sums
s <- as.vector(colSums(m))
# Pairwise sums between all columns into out matrix
n <- seq_len(ncol(m))
id <- expand.grid(n,n)
out <- matrix(colSums(m[ , ... |
7d4f57b4c87c19d5ac0804014349b7e12f1530a9 | 4c0394633c8ceb95fc525a3594211636b1c1981b | /tests/testthat/test-rename.R | ea757bb7892de989de19d8e90771e1788ee3e2fd | [
"MIT"
] | permissive | markfairbanks/tidytable | 8401b92a412fdd8b37ff7d4fa54ee6e9b0939cdc | 205c8432bcb3e14e7ac7daba1f4916d95a4aba78 | refs/heads/main | 2023-09-02T10:46:35.003118 | 2023-08-31T19:16:36 | 2023-08-31T19:16:36 | 221,988,616 | 357 | 33 | NOASSERTION | 2023-09-12T20:07:14 | 2019-11-15T19:20:49 | R | UTF-8 | R | false | false | 3,130 | r | test-rename.R | test_that("rename() works for one column", {
df <- data.table(x = c(1,1,1), y = c(2,2,2), z = c("a", "a", "b"))
df <- df %>%
rename(new_x = x)
expect_named(df, c("new_x", "y", "z"))
})
test_that("rename() doesn't modify-by-reference", {
df <- data.table(x = c(1,1,1), y = c(2,2,2), z = c("a", "a", "b"))
... |
452ade1e4e75907735b50deeb97a3f14621fce54 | 76660ce4a227e51225c521432b924dbb23082a4a | /eda_graphs_and_plotting_course_project_1/plot3.R | 5a83a1e22a82dc7b704ea8a0ee9be57a1f61ee5d | [] | no_license | esgarg/datasciencecoursera | 862ae29e5b883399e47b97797db39e27686b85bc | 5bfec82c1e9855fac87857aadfd061302dce0f77 | refs/heads/master | 2021-01-15T11:04:16.137661 | 2017-11-17T17:18:08 | 2017-11-17T17:18:08 | 99,611,844 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,240 | r | plot3.R | # data source, assuming it is downloaded
fileURL <- "household_power_consumption.txt"
# create the grep command
grepCMD <- paste("grep", "^[12]/2/2007", fileURL, "2>/dev/null")
# get the colNames by only reading one line
colNames <- colnames(read.table(fileURL, nrows=1, header=TRUE, sep=";", na.strings="?"))
# read ... |
db0f85b4d39162b2c6a46547f1a67fa373bcee0a | 622684fa19669ed8746dade3583c2207ad1b3d70 | /R/genoDist.R | 0ad29711f3aea0f3b87d1b2c8ac4757ff61fcf46 | [
"MIT"
] | permissive | CMWbio/geaR | b3ef3299467e07b026ece8cb755f4b4e0c0a7ab3 | fe837b8d19342deaa24fb733af563af35992f346 | refs/heads/master | 2023-04-29T11:17:33.254755 | 2023-04-21T07:23:12 | 2023-04-21T07:23:12 | 145,640,886 | 5 | 0 | null | 2020-08-10T10:07:52 | 2018-08-22T01:46:26 | R | UTF-8 | R | false | false | 2,747 | r | genoDist.R | #' Calculates the hamming distance between alleles
#'
#' @description Used in the calculation of diversity statistics
#'
#' @details Authours: Chris Ward
#' Calculates the hamming distance using matrix multiplication
#'
#'
#' @param genoMat A \code{matrix} \cr
#' Allele genotypes for each individual
#' @param pairwiseD... |
d912c46c9f0977756eec91a71db67219c4646a09 | 90e772dfeb9fc441424dcf5e5beaa545af606f1c | /inst/shiny/ui.R | 89f20d29ec2a7ec31e1bf0f5a34791a9574f689c | [
"GPL-3.0-only"
] | permissive | chenjy327/MesKit | 97d356c8c8ac73493ba6f60488d5a0c6aae23092 | c9eb589fca6471e30e45cb9e03030af5ade69f83 | refs/heads/master | 2021-08-17T07:48:53.618404 | 2021-06-24T06:19:08 | 2021-06-24T06:19:08 | 304,196,319 | 0 | 0 | MIT | 2020-10-15T03:10:38 | 2020-10-15T03:10:37 | null | UTF-8 | R | false | false | 148,596 | r | ui.R |
#required packages
suppressMessages(library(shiny))
suppressMessages(library(DT))
suppressMessages(library(shinydashboard))
suppressMessages(library(shinyWidgets))
suppressMessages(library(shinycssloaders))
suppressMessages(library(shinyBS))
suppressMessages(library(MesKit))
suppressMessages(library(BSgenome.Hsapiens.... |
4b65085c40b51dc27b0ed141d98b6de737dd095b | b49fc180baf1545f8a7188aeebd59304a5ea3bb2 | /man/ukFlex.Rd | d51f7104688ac83add3b0f004f8df713c1067d38 | [
"MIT"
] | permissive | dickoa/shinyuikit | fd7dc94571fe68036dca1e41896a2f7e81a820e7 | 10e1159a316d6b01c7063ce52dc867fd3e09b614 | refs/heads/master | 2020-03-12T11:58:29.988212 | 2018-06-15T19:58:06 | 2018-06-15T19:58:06 | 130,608,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 810 | rd | ukFlex.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/uikit-flex.R
\name{ukFlex}
\alias{ukFlex}
\title{UIkit Flexbox layout for shiny}
\usage{
ukFlex(..., inline = FALSE, wrap = NULL, horizontal_align = NULL,
vertical_align = NULL, direction = NULL, height = NULL, width = NULL,
muted_backgro... |
d4ec9c2251b7b2a4198d3e197e9192a894853e82 | a4cb5eb0e937b43293c0a5e6a778c5c3227517b5 | /03-Rscripts/shiny-cores/app-add_delete.R | 12e530808bdab5e54d45373e9f90d5a58c00a1ce | [] | no_license | Joacala/trini | 1e5c1c87ebc40155ff72b0057f960c00703dd572 | 0bf3fa8bf95590355a83e32c8acba08d3ef62c53 | refs/heads/main | 2023-04-19T05:28:08.299320 | 2022-10-13T08:27:29 | 2022-10-13T08:27:29 | 331,259,193 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,764 | r | app-add_delete.R | ### add and delete points from a scaterplot
library(shiny)
visual.cor <- function(res.s,imc, path){
ui <- fluidPage(
fluidRow(
div(id="container",
height = 500,
width = 500,
actionButton("reset", "Reset"),
actionButton("save", "Save"),
style="position:... |
ae59045071e5099f4f711f3b94fd79718fdf2e5e | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/openair/examples/timePlot.Rd.R | 4de1ba332e420801e50305f339f7a503476ce3d2 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,649 | r | timePlot.Rd.R | library(openair)
### Name: timePlot
### Title: Plot time series
### Aliases: timePlot
### Keywords: methods
### ** Examples
# basic use, single pollutant
timePlot(mydata, pollutant = "nox")
# two pollutants in separate panels
## Not run: timePlot(mydata, pollutant = c("nox", "no2"))
# two pollutants in the same... |
83957eba033e57967e223eae6447e240d4eba285 | db0e244c6d3c1aa0bef5d5906750d8f94c388387 | /analyses/informacio_interes_indepe/prepare_data.R | ae646a2bd1e3ff42028568ef1b95629ad9143ace | [
"MIT"
] | permissive | joebrew/vilaweb | ea9796aa7a5d4f0676608618ba975dac95346000 | f0b028c07484c750d75a101308c3937d81b40d80 | refs/heads/master | 2021-06-09T02:00:21.502677 | 2020-09-07T22:20:03 | 2020-09-07T22:20:03 | 159,472,849 | 23 | 9 | NOASSERTION | 2021-06-01T23:59:21 | 2018-11-28T09:01:42 | HTML | UTF-8 | R | false | false | 26,787 | r | prepare_data.R | # Libraries
library(vilaweb)
library(tidyverse)
library(databrew)
library(pageviews)
library(lubridate)
# Read ceo data
# Functions
mround <- function(x,base){
base*round(x/base)
}
round_percent <- function(x) {
x <- x/sum(x)*100 # Standardize result
res <- floor(x) # Find integer bits
rsum <- sum(res)... |
f82332dc123ba83fad5e7000d1ad61e9e7b6aa9a | 7c66f35138cf55d4848f399911c89c1fbe730c53 | /continuing-education-app/server.R | 8e01a31fd81ae7dbae87226af7d4fd649f6d2288 | [] | no_license | stecaron/cas-continuing-education | 6619e2cdd0b32d6766c0abee0029f851b64b1fd7 | 8e707b33ced34efc758c4fe1ab74e14f9c1aeb33 | refs/heads/master | 2021-11-09T22:05:22.254997 | 2021-11-08T21:35:02 | 2021-11-08T21:35:02 | 131,144,508 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,821 | r | server.R | # Define server function --------------------------------------------------
server <- function(input, output, session) {
input_new_log <- reactive(
data.table(
log_hours_type = input$new_log_type_hours,
log_number_hours = input$new_log_number_hours,
log_date = input$new_log_date,
log_... |
c0e34f8a8466f6803517e565efc608b04b6a2ebe | 0ec13d52c92625114567f2abb57de965c45ea0fc | /code/day-1-puzzle.R | 4b79f4af31e842e0f54e8bab99a8b134a256ca6a | [
"MIT"
] | permissive | klevan/adventofcode2019 | 9fda1b1fcf0b09c38c3329581e1185f03cfd2e35 | d18fe122a8dd8de5bb41ff3b08fa4fe87db0fcba | refs/heads/master | 2020-09-22T11:51:18.759211 | 2019-12-01T17:15:16 | 2019-12-01T17:15:16 | 225,181,816 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,677 | r | day-1-puzzle.R | #' ############################################################################
#' --- Day 1: The Tyranny of the Rocket Equation ---
#'
#' Santa has become stranded at the edge of the Solar System while delivering
#' presents to other planets! To accurately calculate his position in space,
#' safely align his warp d... |
62f731c3bcdbc074995561dbc2bd70d92a43ea08 | 337deca529928a9036c8939cb47a39b7435d0f1a | /man/taf.library.Rd | 53ff6736e18f879d4ea27b63fafa4e2cbb851242 | [] | no_license | alko989/icesTAF | 883b29e78ee69a5ef2dd5e5ca5a680cb220789d8 | a5beaaf64ed1cacc09ca7732e791e89373d1d044 | refs/heads/master | 2020-04-28T10:02:16.923481 | 2019-03-09T23:15:02 | 2019-03-09T23:15:02 | 175,188,781 | 0 | 0 | null | 2019-03-12T10:36:18 | 2019-03-12T10:36:18 | null | UTF-8 | R | false | true | 1,390 | rd | taf.library.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/taf.library.R
\name{taf.library}
\alias{taf.library}
\title{Enable TAF Library}
\usage{
taf.library(create = TRUE, quiet = FALSE)
}
\arguments{
\item{create}{whether to create the directory \file{bootstrap/library} if it
does not already exis... |
fe9d0951432f32fb211f2e67fe07887bc4163088 | d58b47e2da19df8c3c6b10238e0283655ea4c124 | /man/em38_pair.Rd | 038cfe1385d0800135414c8a6affbf0675f116aa | [
"MIT"
] | permissive | obrl-soil/em38 | 64d2c029f549553da582c2a473013e3cf53b0c9d | 1fe8ae01713f39a5179bff6eeef84152ad4dc509 | refs/heads/master | 2023-01-12T04:45:55.139406 | 2023-01-05T21:08:03 | 2023-01-05T21:08:03 | 132,758,232 | 4 | 4 | null | null | null | null | UTF-8 | R | false | true | 1,319 | rd | em38_pair.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/spatialise.R
\name{em38_pair}
\alias{em38_pair}
\title{Reconcile locations of paired data}
\usage{
em38_pair(decode = NULL, time_filter = NULL)
}
\arguments{
\item{decode}{spatial point dataframe for a survey line produced by
\code{\link{em38... |
70ae033c599ab62f4fffa65fc416e42d4cf2be78 | 1288617cb5321e9fe5549b957ec785cd677e9f0d | /ICU_FF_datacontrol/fileReader.R | f1f5b1fefa5b8b2ed5ff83aceb891c7af92516ac | [] | no_license | joytywu/ICU_frequent_flyer | a1ccaceb96a923a8f5a193aeb70b36c47146716b | 140a4875011b7066d4e9674047668b46f5895e8c | refs/heads/master | 2020-12-24T10:57:03.041748 | 2016-11-07T22:26:26 | 2016-11-07T22:26:26 | 73,117,382 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,402 | r | fileReader.R | #To run this file, save the file in the assets folder and type in R Console:
#setwd("~/Documents/ICU_Frequent_Flyers-addedfiles")
#source(paste(getwd(),"/assets/fileReader.R",sep=''))
#Should get two dataframes: summariesFinal and nsnFinal
op <- options(stringsAsFactors=F)
library(dplyr)
Resultsreader <- function(not... |
3cef5424bc8f16130b2d6c20db82cbab00fd6eee | d0d061329421401283a3db1f8e7aa016e61888d7 | /man/heatmapSpp.Rd | e93fc9d25ae81436f356b8bda0e354df822d4e8a | [
"MIT"
] | permissive | boopsboops/spider | 87885b53570a98aece6e7ca1ce600330d9b95d25 | e93c5b4bc7f50168b8a155a6dca7c87dfbdef134 | refs/heads/master | 2021-05-12T07:38:37.413486 | 2019-03-07T21:43:43 | 2019-03-07T21:43:43 | 117,250,046 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,185 | rd | heatmapSpp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/heatmapSpp.R
\name{heatmapSpp}
\alias{heatmapSpp}
\title{Visualise a distance matrix using a heatmap}
\usage{
heatmapSpp(distObj, sppVector, col = NULL, axisLabels = NULL,
triangle = "both", showData = FALSE, dataRound = 3, dataCEX = 1)
}
\... |
126d94726c7de102809f6eaa40404aef172ba7bf | 950030f19c1368f889700299bc36ecf7104f56b8 | /man/list_recent_uploads.Rd | b38de2536cb81d7884cbfbc222414d1cec6caf09 | [
"MIT"
] | permissive | ropensci/EDIutils | 0cadce6b8139417fcfa65194e7caf8c77ea087af | b1f59cccee3791a04d7702bcb37f76995ae2fcbe | refs/heads/main | 2023-05-22T09:49:03.633710 | 2022-09-09T16:12:30 | 2022-09-09T16:12:30 | 159,572,464 | 2 | 1 | NOASSERTION | 2022-11-21T16:22:08 | 2018-11-28T22:13:59 | R | UTF-8 | R | false | true | 1,254 | rd | list_recent_uploads.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/list_recent_uploads.R
\name{list_recent_uploads}
\alias{list_recent_uploads}
\title{List recent uploads}
\usage{
list_recent_uploads(type, limit = 5, as = "data.frame", env = "production")
}
\arguments{
\item{type}{(character) Upload type. Ca... |
5cbb0efc449d6161f8abd4972d504bf3d9ca812b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TOSTER/examples/powerTOSTone.Rd.R | 574560e2418ddd08d3ee15e175a4547db794134d | [] | 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 | 769 | r | powerTOSTone.Rd.R | library(TOSTER)
### Name: powerTOSTone
### Title: Power analysis for TOST for one-sample t-test (Cohen's d).
### Aliases: powerTOSTone
### ** Examples
## Sample size for alpha = 0.05, 90% power, equivalence bounds of
## Cohen's d = -0.3 and Cohen's d = 0.3, and assuming true effect = 0
powerTOSTone(alpha=0.05, stat... |
2ea8cf81076713fbb2ff72c4478c77d92b32ddf8 | 9b93f997e005af01c03bbe2a2f1e7cb9adeb5596 | /R_scripts/Advanced/Session#6.R | 4f3735ddddf4049d7e2b72678fcbc1e0373eeb97 | [] | no_license | brunobellisario/Rcourse | aca0e5b7b01333dc7849d78930c62d7c7d9bf269 | 9c2fd209e025cb7e26dafcd5dc21d2bb8a6500b5 | refs/heads/main | 2023-05-19T04:07:37.191537 | 2021-06-01T06:49:14 | 2021-06-01T06:49:14 | 359,836,395 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,873 | r | Session#6.R | #DATA ANALYSIS WITH R
#SESSION#6 - Multivariate analysis#1
#Dissimilarity/similarity matrix
#Broadly speaking, multivariate patterns amongst objects can either
#be quantified on the basis of the associations (correlation or covariance)
#between variables (species) on the basis of similarities between objects.
#The fo... |
53dec3abb5bda5a3465a3d87a206b43a486216a2 | 2ef11a80606df33e96aebea050eec8d740050192 | /makeslides.R | 5edf0df0ee8287589dcda0ee26988127cd54c546 | [] | no_license | jknowles/statsdc12-presentation | 8d2985a611d31d923f920d7be5671bdb34af4043 | 539ca87a91d2c988be4b281085f3c59e5078c5ec | refs/heads/master | 2020-05-19T14:16:12.948798 | 2012-07-11T16:32:46 | 2012-07-11T16:32:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 116 | r | makeslides.R | # Makeslides
system("pandoc -s -S -i -t slidy statsDCslides.md -o KnowlesStatsDCslides2012.html --self-contained")
|
3d39c8b3b052a0507b5a64e88de8d113105eb4ea | 0441a11b7d74d68d17c67a023268c97afcbd4627 | /BAMSandAllen/rCode/ColourDivisionsHeatMapReused.r | c816e1a8503fbe48a4ad65a3a934e09d8026e34b | [] | no_license | leonfrench/ABAMS | fbe847d8c4e21388ca9150ad55d2b4508883ebcf | a4d65e9c9a8a437db9326828ebe7bdaefd9a81ce | refs/heads/master | 2020-08-05T21:09:21.769589 | 2017-07-04T21:36:16 | 2017-07-04T21:36:16 | 7,511,456 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,280 | r | ColourDivisionsHeatMapReused.r | # run in data/correlation study
energyOptTable <- read.table("ConnectivityAndAllenExpressionMatrixPair.NewEnergies.out.topList.txt")
energy <- as.matrix(energyOptTable)
dim(energy)
length(energy[energy==0])
#region classification
regionClassification <- read.csv("RegionClassificationABAMSOut.csv", row.names=1)
#make ... |
de1c28d5e9c2d521ead346658f06918774e02f55 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/easyPubMed/examples/trim_address.Rd.R | ba1d26b67355a43f8a730b717b2122109b105f2a | [] | 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 | 328 | r | trim_address.Rd.R | library(easyPubMed)
### Name: trim_address
### Title: Trim and Format Address Information
### Aliases: trim_address
### ** Examples
addr_string <- " 2 Dept of Urology, Feinberg School of Medicine,"
addr_string <- paste(addr_string, "Chicago, US; Dept of Mol Bio as well...")
addr_string
trim_address(addr = addr_str... |
3cc7af191f401e18a81eb6fd9a12e33acfed2929 | 3af91945083aa604efc778ea52a17ad60766948b | /plot_bar_as_fig_condensed_genomicAve.r | 126333c091f69160589ada317a4469eea259d2e6 | [] | no_license | cjieming/R_codes | fa08dd1f25b22e4d3dec91f4fb4e598827d7492f | 5b2bcf78dc217bc606c22f341e1978b5a1246e0c | refs/heads/master | 2020-04-06T03:53:50.030658 | 2019-06-30T07:31:35 | 2019-06-30T07:31:35 | 56,031,249 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,356 | r | plot_bar_as_fig_condensed_genomicAve.r | # setwd("C:/Users/JM/thesis/mark_work/FIG/eQTLsnps")
setwd("C:/Users/JM/thesis/mark_work/FIG/allelicSNPs")
## data input
# filename1 = "fishersresults_enrichment-tfm-sf-yaoorder.txt"
# filename1 = "fishersresults_enrichment-tfp-sf-yaoorder.txt"
filename1 = "proportions_AS_rare_lt005_no0bins_noCDS.txt";
# filena... |
09172f38fadd00416fb3a516604215e10c7e46e6 | f2780a473c32c24d4c96076134839538647dabe8 | /fund-node/Rfile/get4433.R | a5fee79a49a3d40651d594498f09e3c5a70482ad | [] | no_license | ben00401/Robo_FrontEnd | e201364e467715d50ec3d82fdd47f1568bcf4d04 | 9ae39ed1fbb4c7b5f739fc2a294a5e9ff4e8ebae | refs/heads/master | 2021-06-08T06:50:10.464926 | 2016-11-01T07:44:53 | 2016-11-01T07:44:53 | 72,511,302 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,029 | r | get4433.R |
library(mongolite)
library(RJSONIO)
today <- as.Date("2015-01-01")
get_result4433 <- function(type){
type <- fromJSON(type)
if(type[[1]]=="4433_100"){
data_4433_100<-get_4433data(type[[1]],today);
toJSON(data_4433_100)
}else if(type[[1]]=="4433"){
data_4433<-get_4433data(type[[1]],today... |
981645cde8cb405501b9613895059f5ca2b57797 | 26c2b3f71d983d53ce984ea86fd9cf6c66194058 | /man/response.Rd | 8292b081058e7ff9d6ac7122a67f948518ae876b | [
"MIT"
] | permissive | r-lib/httr2 | 34088cf7d86cde8a5cf02cf7dd28d1a32f4f1008 | 9b85a8467d88d577b68899b52658fa5b6d4b1a1d | refs/heads/main | 2023-09-01T13:57:25.415243 | 2023-09-01T08:12:39 | 2023-09-01T08:12:39 | 158,719,104 | 182 | 44 | NOASSERTION | 2023-09-08T12:08:07 | 2018-11-22T15:32:29 | R | UTF-8 | R | false | true | 1,238 | rd | response.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resp.R
\name{response}
\alias{response}
\title{Create a new HTTP response}
\usage{
response(
status_code = 200,
url = "https://example.com",
method = "GET",
headers = list(),
body = raw()
)
}
\arguments{
\item{status_code}{HTTP stat... |
42b59456f69f1a57850dcee2864baa1abb30a53b | 657c68ca30dc1054f44d524a905cc7e8a9bc0abf | /R/max_mis.R | 4010345ff180f82f5d2f15e447fbeb27ee4476cb | [] | no_license | DataEdLinks/eeptools | cc97110ebdd1313e016f592ad6a9c17bdea9fcb9 | 29941d3cf35ed2fce5b8b51f2f2aa3adf8d17b0f | refs/heads/master | 2020-12-27T01:57:08.803401 | 2015-04-10T18:35:12 | 2015-04-10T18:35:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 245 | r | max_mis.R | max_mis <- function(x){
varclass <- class(x)
suppressWarnings(x <- max(x, na.rm=TRUE))
if(varclass == "integer"){
ifelse(!is.finite(x), NA_integer_, x)
} else if(varclass == "numeric") {
ifelse(!is.finite(x), NA_real_, x)
}
}
|
21c8d098f5643fdd1e884d87476b3350bf076385 | 0461931c3c89d572ab3233a193731edb4621186a | /logical_vector_operator.R | e743c705411421c266f1ea61c7c4f893d3ecac23 | [] | no_license | rsharma11/RCoding | acc80816a668fcd583326aa8d93de0ed505ab620 | 565487a7720db055b75d7fbe5bea22613463eb60 | refs/heads/master | 2020-03-21T00:59:46.394764 | 2019-07-31T14:54:44 | 2019-07-31T14:54:44 | 137,919,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,015 | r | logical_vector_operator.R | # Loading the data
data <- mtcars
# Output rows where column mpg is between 15 and 20 (excluding 15 and 20)
data[(data$mpg>15 & data$mpg<20),]
# Output rows where column cyl = 6 and column am = 0
data[(data$cyl == 6 & data$am == 0),]
# Output rows where column cyl = 6 and column am = 0
data[(data$cyl == 6 & da... |
04055f870f92ab42dd39dc45318907b32a56d8da | 86b7bf0357b9a7aac1075f67d418bc3c1f993ac0 | /tests/testthat/test_page.R | f89abe9c3c434be4747535ad193584e4e514ceed | [
"MIT"
] | permissive | kevinstadler/cultevo | 6c7acc2a325416660680490bd5861e91b38902d1 | 94af9e19e38e80b7081de6fec55b745d98034124 | refs/heads/master | 2021-01-17T16:55:39.043165 | 2018-04-24T13:22:03 | 2018-04-24T13:22:03 | 26,164,855 | 10 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | r | test_page.R | context("Page test")
test_that("Exact p-value calculation", {
expect_equal(1, page.compute.exact(6, 4, 224))
expect_equal(0.03932265, page.compute.exact(6, 4, 322))
expect_silent(page.test(rbind(1:10, 1:10), verbose=FALSE))
})
test_that("Approximate p-value calculation and border conditions", {
expect_error(pag... |
c9b0962535ce1e8a8ea0d08d929892a4903704d6 | 1cbdf941d1e17d772037746197a85fb4b313f4c8 | /R/visualize_ottawa_police_releases.R | 23e55fdf247cdcd736b9c0d58ea8c5c2ecae007f | [] | no_license | monkeycycle/policing | 55cdaa1af0a54ef9d9c692892318666c61ec5de1 | a60c02b969770f413ba3855bcd3b1063dd3b701e | refs/heads/main | 2023-08-17T19:19:10.841843 | 2021-09-24T15:09:13 | 2021-09-24T15:09:13 | 408,193,844 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,185 | r | visualize_ottawa_police_releases.R |
p_ottawa_releases_2018_2021_annual <- ggplot(ottawa_police_pressers_annual,aes(x=year_date,y=total_count)) +
geom_bar(stat="identity", fill=wfp_blue) +
geom_text(
aes(
x=year_date,
y=total_count,
label = paste(count, sep = "")
),
vjust=-1,
# fontface="bold",
size=4
) +
sca... |
48924e028fd2e2736b000a6ff7fe1af74caecff1 | d23dfbe4083d046b4104bf43b9a09e82f5b8bcdf | /评分卡模型.R | 72fb3be03451a5f8d5466d25d9d03913783b9d08 | [] | no_license | GuitarHero-Eric/IDL-script | 4b663f74415f3faef68876bcdbb485524c386396 | 1167e37571ce51d592c0c488289c175dfbc825b7 | refs/heads/master | 2020-04-06T17:41:34.062141 | 2018-11-15T07:33:20 | 2018-11-15T07:33:20 | 157,669,682 | 1 | 0 | null | null | null | null | GB18030 | R | false | false | 1,908 | r | 评分卡模型.R | # klaR: Classification and Visualization
library(klaR)
# InformationValue: Performance Analysis and Companion Functions for Binary Classification Models
library(InformationValue)
data(GermanCredit)
train_kfold<-sample(nrow(GermanCredit),800,replace = F)
train_kfolddata<-GermanCredit[train_kfold,] #提取样本数据集
test... |
96682ed5208f551099282b6422286b582cfb3b43 | d6a9c61fac38d849112e663a97f255b2203212b7 | /plot1.r | 5a52725584625b49fc5f27787d507d1b465a8557 | [] | no_license | ronteo/ExData_Plotting1 | 5f395c266c1d527b9ef267554377159080993926 | 59ee69cb85830de58ca25b4b43714e1d19425506 | refs/heads/master | 2021-01-10T04:28:14.217471 | 2016-03-18T21:48:57 | 2016-03-18T21:48:57 | 54,151,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 565 | r | plot1.r |
power <- read.csv("household_power_consumption.txt",sep=";",na.strings = "?", colClasses = "character")
power$Date <- as.Date(power$Date, "%d/%m/%Y")
power_sub <- subset(power, power$Date==as.Date("1/2/2007", "%d/%m/%Y") | power$Date==as.Date("2/2/2007", "%d/%m/%Y"))
power_sub$Time <- strptime(paste(power_sub$Date, po... |
2201c25dbb9b395ea5f9d10725070351ad226561 | 2eecc5fa6296798134a59ca708e32c774207fb72 | /Scripts/Bridge Conditions/1) Data Import.R | bef69c961f46d96c7081a6397d0bc434b1d0cbcf | [] | no_license | kevinanderson26/Tracking-Progess | 66a56810f6d4c35fb87fe80e53321cff4312c33e | 2633bb8f2d728f9e1ff1756761194c2c746b442f | refs/heads/master | 2020-07-05T05:21:56.003673 | 2019-08-16T00:22:26 | 2019-08-17T12:40:50 | 202,533,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,885 | r | 1) Data Import.R | #load packages
pack <- function(pkg){
newpkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(newpkg))
install.packages(newpkg, dependencies = TRUE)
sapply(pkg, require, character.only = TRUE)
}
packages <- c("tidyverse", "magrittr", "sf", "tidycensus",
"readxl", "units", "scal... |
582caa47ede881721832baae86f87c5327cfce9b | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613125612-test.R | 28f972f8014860df2e08bdba34489ad4c88732dc | [] | 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 | 450 | r | 1613125612-test.R | testlist <- list(A = structure(c(-7.29112201956225e-304, 2.02678106771417e+301, 2.04216934546089e+301, 2.04216934546089e+301, 2.04216934553044e+301, 3.85341333796043e-255, 5.84340601505517e-310, 7.29112432744838e-304, 2.26926929822129e-309, 1.39128504575632e-309, 991208021054980096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,... |
90e2d83e7398ba689566c2259c64f297678c3083 | e23b89d06af510b289d28797f7480e47014da6a8 | /Code/classification_final.R | e67f4be864aa7a5b39202577908e84ced4b6cbf6 | [] | no_license | Saurabh23/Sentiment-Analysis-for-Predicting-Elections | 0da1581a66d85f74e18baf3cc4b4a31725515ee9 | 8842d18208a456f05e6a6ce9986c221f711b7676 | refs/heads/master | 2021-06-08T03:55:21.435750 | 2016-10-28T21:12:21 | 2016-10-28T21:12:21 | 72,241,411 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,883 | r | classification_final.R | rm(list = ls(all = TRUE))
#LOAD TRAINING SET
rawTrainingData <- read.csv("C:/Users/LENOVO/Desktop/TUE Lectures/Q1/WEB IR/WebIR-Full-master/Data/SemEvalProcessed.csv", sep = ",", quote = '\"')
rawTrainingData <- read.csv("~/TUE/Quartile1/IRandDM/SentimentAnalysis/WebIR-Full/Data/SemEvalWithoutNeutral.csv", sep = ",", qu... |
94a56217a8b6423c18b32a3719bf4ed66330ec34 | bc9f92707d1ee12a17c44e2a3db46334dbf1bfa7 | /spotProb.r | 7f2289d6a28efd2d9ead9c12bd4e5d95a69c3fae | [
"BSD-2-Clause"
] | permissive | lparsons/TigerFISH | 6f841db80ed4b199d8d07c7b29e8bd4bfea4df55 | 20bcb520e5f59af7ce35d607d1f556754d64252d | refs/heads/master | 2022-11-11T10:39:25.143767 | 2013-12-18T02:17:19 | 2013-12-18T02:17:19 | 275,873,025 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 624 | r | spotProb.r | #function( rna ) {
rna = c ( .9, .8, .7, .88, .65, .75 );
no_rna = 1 - rna;
#Initialize the vector of probabilities for different number of mRNAs
prob = rep( 0, length(rna)+1 );
allInds = 1:length(rna);
#Zero RNAs
prob[1] = prod( no_rna );
library( 'gregmisc' )
# 1 RNA
for (i in 2:length(rna)){
... |
5d92edf3535f46293455d5e0b38876044e7226a0 | 6b3ca6134352e6692069d0a7e57c3a7ba14e21ae | /man/GetGeoNames.GeoStrata.Rd | 621690b956a75bdb5e14b3227733eb60c4192d89 | [
"Apache-2.0"
] | permissive | ovative-group/GeoexperimentsResearch | fe5baeec316ecc19c99fc621cd3f56e960ad2849 | c50a1d6f4b21ea7624c27cec5374f4a3a3d76c0e | refs/heads/master | 2020-07-05T08:07:57.434144 | 2019-08-15T17:29:42 | 2019-08-15T17:29:42 | 202,583,766 | 0 | 0 | null | 2019-08-15T17:27:12 | 2019-08-15T17:27:11 | null | UTF-8 | R | false | false | 634 | rd | GetGeoNames.GeoStrata.Rd | % Copyright (C) 2017 Google, Inc.
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getgeonames.R
\name{GetGeoNames.GeoStrata}
\alias{GetGeoNames.GeoStrata}
\title{Extracts the unique names of the geos from a GeoStrata object.}
\usage{
\method{GetGeoNames}{GeoStrata}(obj, groups = NULL)
}
\... |
f23a61ebb24cc15c242f908799f85e8885210d98 | 90118609911bb5a97941830cbc1f7020d239405d | /Smartphone/original/data/cleanData.R | 06cafca145b209a9830d58205247b9633e78d2e6 | [] | no_license | shannonrush/Contests | 6e0eba560413723a11d1de1ddb0e3b1f1f440c56 | 4b930624a5c6ea1030f1e82b1829d4fa0a1261df | refs/heads/master | 2021-01-01T05:31:27.616173 | 2014-06-05T18:56:17 | 2014-06-05T18:56:17 | 2,697,116 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,630 | r | cleanData.R | load("../../data/processed/test.rda")
load("../../data/processed/train_clean.rda")
# read test x, subject and y from txt files
# add subject and y to x
test2 <- read.table("X_test.txt",row.names=NULL)
testy <- read.table("y_test.txt",row.names=NULL)
test2_subject <- read.table("subject_test.txt",row.names=NULL)
names... |
077f45133ccffafc33443d1b38478a6c22a0e780 | 73606887f1a1d520a7fb4f17758e15a1aeff67cc | /man/euler_7.Rd | c58601f9dffcdc948355ca49cc90563010c17d02 | [
"MIT"
] | permissive | nathaneastwood/euler | a3dd8937fb0d21608dee2626c4fc8c9b574ba679 | f5338c7e4fd38f0fb2e2692b807edec57e2cd8bd | refs/heads/master | 2021-04-25T07:00:24.646923 | 2019-06-17T18:13:50 | 2019-06-17T18:13:50 | 122,224,945 | 21 | 4 | MIT | 2018-03-11T16:05:32 | 2018-02-20T16:36:20 | R | UTF-8 | R | false | true | 353 | rd | euler_7.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/euler_7.R
\name{euler_7}
\alias{euler_7}
\title{Project Euler 7: 10001st prime}
\usage{
euler_7(n)
}
\arguments{
\item{n}{An integer.}
}
\description{
This is a solution to
\href{https://projecteuler.net/problem=7}{Euler problem 7}.
}
\exampl... |
dacbc2406db37da21c181d36603775055e51225d | 1d1f20cb63cd72a226992006f2077d65f03ffbdf | /source/Models/pdPCA.R | aa6636e21d468039983a2491adfd7e06609e5f0c | [] | no_license | vineet1992/Gene-Selection | 5bbaf66fce760e0716db8254d16331fc0f50293d | 7425c96918568ea10af3ba1211444cf609339cf6 | refs/heads/master | 2021-07-05T10:55:53.528116 | 2019-05-01T21:16:54 | 2019-05-01T21:16:54 | 150,757,224 | 2 | 1 | null | 2019-01-24T19:03:08 | 2018-09-28T15:04:12 | R | UTF-8 | R | false | false | 1,718 | r | pdPCA.R |
###PiPrefDiv with PCA Summarization
pdPCA_wrapper = function(x_tr,y_tr)
{
nc = ncol(x_tr)
###Subset by standard deviation
sds = apply(x_tr,2,sd)
x_tr = x_tr[,sds>sdCutoff]
###Load prior information and create temp directory to store
dir = "./priors/"
tempDir = "./tempPriors/"
dir.create(tempD... |
8bbc6576768058adfa2ac35777e101b3379ac4ba | 899101532272b283fd7a18d3a302482f36925ed1 | /raw_job_description_structure.R | 96450fcd171b68dfb0708c2dd5928cafa0f3f9c1 | [] | no_license | nstempn1/data_science_project | c1d4f84b74bcbb089f0a394db5d8aa4f7a01667e | a3f25b48309f07997422a69133e29e9bc9445eed | refs/heads/master | 2021-01-23T20:23:55.054004 | 2017-10-25T00:53:23 | 2017-10-25T00:53:23 | 102,856,852 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 9,018 | r | raw_job_description_structure.R |
##################################################################################################################
##################################################################################################################
# Load glass door dataset
###############################################################... |
be2994f3353f0bb3c0cd810d565bb6befec3351c | 3766dcabe5e3e63e2e0521fb01f644b4f8ce831e | /man/inf_M.Rd | 58b9e116ef82d6c2006025623991e29080282fde | [] | no_license | cran/pdR | c18c9733edb1d57006d78a83834fc84237812f61 | 595a61acf3a7196beca38d3f3f109f085bf1a1b9 | refs/heads/master | 2023-08-30T22:42:58.958803 | 2023-08-21T10:50:02 | 2023-08-21T11:31:04 | 23,536,178 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,371 | rd | inf_M.Rd | \name{inf_M}
\alias{inf_M}
\docType{data}
\title{
Monthly inflation time series of 20 countries
}
\description{Monthly inflation time series of 19 countries, 1971.1~2011.12
}
\usage{data(inf_M)
}
\format{
A data frame with 20 countries
\describe{
\item{\code{AUSTRALIA}}{inflation of Austrlia}
... |
a11be5a23498dfecae4db410fee8eb0da9cf99f8 | 1aeb06e04dbc9de4f7a2546184a7b9b7de47114b | /man/sbif_indicators.Rd | 5d174df2381ef095efe1edfb0b24cb4124cb798f | [] | no_license | gvegayon/sbifapi | 2e12fac9e4f1150d181a761e9517bdd0724c2c88 | 1ecf860cf68e2c29de71317928e8f455f3b695b1 | refs/heads/master | 2021-01-13T02:36:49.238813 | 2016-02-23T06:05:18 | 2016-02-23T06:05:18 | 42,278,706 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 242 | rd | sbif_indicators.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/api.R
\name{sbif_indicators}
\alias{sbif_indicators}
\title{Returns available indicators}
\usage{
sbif_indicators()
}
\description{
Returns available indicators
}
|
3d8d0b4b94e71cc23ff21cf432a4eb88d78f6440 | 31362fdab2193f92b64f9a82b0fe1ca732fcf6df | /NonNormalEvidenceSynthesisSimulations/server.R | 97d61af297f8a4b4db99c9f845eb3fade9822a17 | [] | no_license | OHDSI/ShinyDeploy | a5c8bbd5341c96001ebfbb1e42f3bc60eeceee7c | a9d6f598b10174ffa6a1073398565d108e4ccd3c | refs/heads/master | 2023-08-30T17:59:17.033360 | 2023-08-26T12:07:22 | 2023-08-26T12:07:22 | 98,995,622 | 31 | 49 | null | 2023-06-26T21:07:33 | 2017-08-01T11:50:59 | R | UTF-8 | R | false | false | 19,208 | r | server.R | library(shiny)
library(ggplot2)
singleRankVector <- function(row) {
return(data.frame(type = row$type,
rank = row$start:row$end,
weight = 1 / (1 + row$end - row$start),
row.names = NULL))
}
computeRankVectors <- function(subgroup, descending = TRUE) {
to... |
cbb6436024c4dbb147784dbf5ad3cffc747d3790 | 6f42a7294cad97e68e591857e822652af9c7c750 | /R/dual_scale_plot.R | a4a022c3c1ab9d00688869e458dab4deeb35a46a | [
"Apache-2.0"
] | permissive | cgranell/symptoms-scheduler | 0e199bf3bc5930c5df708f931a3a41b514129ac4 | df152a07e669b4bccabe99a42278a46333a69813 | refs/heads/master | 2023-03-08T19:43:36.393076 | 2021-02-24T16:15:35 | 2021-02-24T16:15:35 | 249,478,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,096 | r | dual_scale_plot.R |
dual_scale_plot <- function(selection, plot_title) {
time_start <- min(selection$plan_date)
time_end <- max(selection$plan_date)
time_elapsed <- interval(time_start, time_end)
duration <- ceiling(as.duration(time_elapsed) / ddays(1))
ylim_delay <- c(min(selection$delay), max(selection$delay))
ystep ... |
549ccea1db5acab2523c40085361f9ecf73eb091 | 6ad68090db6626c3e1c648047d57437337fb75ae | /src/an1/08.r | 6bf7ba487c908e86f9f5179d6c4162f818eed7b8 | [] | no_license | int28h/RTasks | 8764ba7fb8f06eb1b7e09d1dc4dd3a26458d12d6 | 88c39bb8e6b34c8743e16182e33ec5935ef3598f | refs/heads/master | 2022-06-17T18:06:26.464545 | 2022-06-03T22:40:50 | 2022-06-03T22:40:50 | 116,028,292 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,846 | r | 08.r | # Постройте столбчатую диаграмму распределения цвета глаз по цвету волос только у женщин из
# таблицы HairEyeColor. По оси X должен идти цвет волос, цвет столбиков должен отражать цвет глаз. По оси Y - количество наблюдений.
#
# Чтобы построить столбчатую диаграмму в ggplot, вам нужно подключить нужный пакет,
# затем... |
35ab0f99f4d321b7304fbf1aefb58018b9f4025d | 4067ff0db87ec536dd48b38dbb2de6e13536cd67 | /Unit7/src/tweet.R | 268c4d19ecb46996b2652aecbb8ecb88587117af | [] | no_license | strgeon/rClassFiles | 2b4267676e39878da7077716f508ae1aee0ea3c3 | adeb452b82e4fac642f8e65b608d4843fee15e66 | refs/heads/master | 2020-12-02T19:41:00.846565 | 2017-08-22T03:00:02 | 2017-08-22T03:00:02 | 96,375,498 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,222 | r | tweet.R | # TODO: Add comment
#
# Author: Scott
###############################################################################
setwd("C:/Users/Scott/SkyDrive/Documents/Courses/AnalyticsEdge/Unit7/data")
getwd()
# [1] "C:/Users/Scott/SkyDrive/Documents/Courses/AnalyticsEdge/Unit7/data"
tweets = read.csv("tweets.csv... |
10b098940a31a72034c9c037a0f9d24982aa51d7 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /SelvarMix/man/SortvarClust.Rd | 309e88117206376965a3ed5b8be53a5eb56c62bb | [] | 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 | false | 2,455 | rd | SortvarClust.Rd | \name{SortvarClust}
\alias{SortvarClust}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Variable ranking with LASSO in model-based clustering
}
\description{
This function implements variable ranking procedure in model-based clustering
using the penalized EM algorithm of Zhou et al (2009).
}... |
430085cdbe2b8a1b35420a75d9a406eca4df49e7 | 507ca86f326549c382bedc323ad61950956165fd | /algorithm.r | 721a2623d3a52785fbe09df6b7ebfd80879da283 | [] | no_license | domsob/knapsack-strategies | d757f4a0a2772a743702f9c61084ecaa00b9ec53 | e8b1e436a094986461b3e64bf7e763f1ff4d9c2e | refs/heads/master | 2021-01-20T05:53:09.702650 | 2017-04-29T23:07:56 | 2017-04-29T23:07:56 | 89,817,763 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,102 | r | algorithm.r | # Tutorial for GA library: https://cran.r-project.org/web/packages/GA/GA.pdf
library(GA)
set.seed(42);
limit <- 10
objects <- 100
values <- sample(1:1000, objects, TRUE)
weights <- sample(1:15, objects, TRUE)
cat('\n\n+-----------------------------------+\n')
cat('| Settings |\n')
cat('+---... |
02b529512b277a5db26755d50b35f489d1cddaeb | cb178cdf2f6701767ca839975d9e734f9ec41bc3 | /R/a17.R | 8a38fcd6daaccb6018964cbe97f0b4bca0a9827f | [] | no_license | Kata-na/aoc_2020 | a38b81d3f3386744eb1e71f7beee286e8b0305eb | 45b7ca90b455bcb7141c870a03c704fede33310f | refs/heads/main | 2023-02-25T00:52:44.480746 | 2021-01-31T23:04:28 | 2021-01-31T23:08:53 | 323,988,176 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,482 | r | a17.R | rm(list = ls())
t1 <- Sys.time()
library(data.table)
library(magrittr)
library(stringr)
cycles <- 6
inp <- fread('../Input/a17.txt', header = FALSE, col.names = 'input') %>%
.[, input := gsub('#', 1, gsub('\\.', 0, input))]
inp_matrix <-
matrix(as.integer(unlist(strsplit(inp$input, ''))), ncol = nrow(inp)) %>%... |
baed7efe82f9786bfddf272f2b032840511aee93 | 349648b07a7c0d490096b87c4cac0fa171155203 | /bootstrap_cdf.R | f2726815724d06055788185ef060c2b3ca6ade35 | [] | no_license | SebastianKuzara/fit_distibution | 1dd372c4c9eb3fc5b0d7cb696f025e3dcd92c4dd | f30edf73d5186e1f7ae7633fc553c4c02f87362e | refs/heads/master | 2021-01-22T04:09:30.086768 | 2017-05-25T20:29:10 | 2017-05-25T20:29:10 | 92,432,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 820 | r | bootstrap_cdf.R | ## Bootstrap - dopasowanie danych do dystrybuanty
x <- seq(0, 1, by = 0.01)
boot.beta.cdf <- sapply(1:1000, function(i) {
s <- sample(norm.rozwody, length(norm.rozwody), replace = TRUE)
mme <- fitdist(s, "beta", "mme")
p <- pbeta(x, shape1 = mme$estimate["shape1"], shape2 = mme$estimate["shape2"])
return(p)
}... |
370db4430381a164deba65a71bacec0d4472d2d6 | cc2e368fe5fdfe8ed499c60ad15152a991b8a7cc | /man/gp_is_wd_geoplumber.Rd | fc31b14c2bbfada27d32301db97637dcae3b8787 | [] | no_license | ATFutures/geoplumber | c4a294b080eedec4f9b60a74fb56f21938d7da5a | 9d80d53d55b1400c18318677f4a54e3dd1ff913a | refs/heads/master | 2023-06-25T08:35:20.512274 | 2023-06-09T08:50:20 | 2023-06-09T08:50:20 | 133,939,396 | 60 | 6 | null | 2021-10-14T10:00:40 | 2018-05-18T10:21:47 | R | UTF-8 | R | false | true | 587 | rd | gp_is_wd_geoplumber.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/is_wd_geoplumber.R
\name{gp_is_wd_geoplumber}
\alias{gp_is_wd_geoplumber}
\title{Helper function to determin if working dir is a geoplumber app.}
\usage{
gp_is_wd_geoplumber(path = ".")
}
\arguments{
\item{path}{check particular path}
}
\valu... |
425780796c640ede5c79cd6144532287fee4a3d7 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/282_0/rinput.R | 6f0de9f79f0af9ee3054424c99863e0fc66c8cd1 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 133 | r | rinput.R | library(ape)
testtree <- read.tree("282_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="282_0_unrooted.txt") |
7495876def94df636cc9827dc3ab0ce6927e0d18 | 6e32987e92e9074939fea0d76f103b6a29df7f1f | /googleaiplatformv1.auto/man/GoogleCloudAiplatformV1ModelEvaluationSliceSlice.Rd | 48b47bc8325c2f9f2973153c072cf84c7ee9125b | [] | no_license | justinjm/autoGoogleAPI | a8158acd9d5fa33eeafd9150079f66e7ae5f0668 | 6a26a543271916329606e5dbd42d11d8a1602aca | refs/heads/master | 2023-09-03T02:00:51.433755 | 2023-08-09T21:29:35 | 2023-08-09T21:29:35 | 183,957,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 647 | rd | GoogleCloudAiplatformV1ModelEvaluationSliceSlice.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/aiplatform_objects.R
\name{GoogleCloudAiplatformV1ModelEvaluationSliceSlice}
\alias{GoogleCloudAiplatformV1ModelEvaluationSliceSlice}
\title{GoogleCloudAiplatformV1ModelEvaluationSliceSlice Object}
\usage{
GoogleCloudAiplatformV1ModelEvaluati... |
f922a37d4c988a7518a1c8d3cc6439b47c3a19a9 | fd29d26a6dedcd7aa00ea4f913e43625aaf035c2 | /code/pascal_code.R | 37f804eec8c7b017a92cb86fb761cdcf2a792074 | [] | no_license | majimaken/econometrics-3-project | 91d54c4e628712cdea5ea49fabd3d9e2d669a553 | 78bb813ed71f4c46fa4a0511d986af096b40489a | refs/heads/main | 2023-04-24T16:35:30.655217 | 2021-05-17T11:23:16 | 2021-05-17T11:23:16 | 363,714,186 | 1 | 0 | null | 2021-05-17T11:23:17 | 2021-05-02T17:48:17 | R | UTF-8 | R | false | false | 5,955 | r | pascal_code.R | ## FFN (1, 4)
## RNN (7)
## LSTM (9, 8, 2)
## GRU (8, 9)
rm(list = ls())
source("add/libraries.R")
source("add/Functions_RNN.R")
load("data/ETH_2021-05-05.rda")
head(ETH)
tail(ETH)
# Define log returns based on closing prices
logret <- diff(log(ETH$`ETH-USD.Close`))
logret <- na.omit(logret)
colnames(logret) <- "... |
708c361499f0fcee28372d6910fc8cbea3296574 | be58f5f0012d70db570de0e3a745ceefc91f91e0 | /Week4/Code/StatsWithSparrows13.R | f38c796babbedd0a18a130c790093e9a509dd765 | [] | no_license | amysolman/CMEECourseWork | caa2ad6ef11c819d2eb295cfe03f1571b7ad9cae | fb2a21bc7b625fed643eaad03bf51c458645abc4 | refs/heads/master | 2021-07-25T01:10:00.653892 | 2020-08-27T13:06:55 | 2020-08-27T13:06:55 | 212,303,861 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,030 | r | StatsWithSparrows13.R | # Amy Solman amy.solman19@imperial.ac.uk
# 28th October 2019
# Lecture 13: ANOVA
##########LECTURE THIRTEEN##########
rm(list=ls())
d <- read.table("../Data/SparrowSize.txt", header = TRUE)
str(d)
d1 <- subset(d, d$Wing!="NA")
summary(d1$Wing)
model1 <- lm(Wing~Sex.1, data = d1)
summary(model1)
boxplot(d1$Wing~d1... |
0edc3a6441d59615efc6b06efbc456f86eebbc1a | 4b52e06ea0908f8c7ca6fe102a95a890f642cac9 | /plot1.R | 9e5a6be8d698b460e3c8a30fa4fa52873ed7da31 | [] | no_license | shijieli123456/assignment-1 | 7d1f3040455ac9c7be14267bc234d68446366500 | 79f97543ea28dedd4ce614d7236ce9b7b555df4d | refs/heads/master | 2020-03-23T23:43:10.935394 | 2018-07-25T05:24:55 | 2018-07-25T05:24:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 229 | r | plot1.R | df <- read.table("h.txt",sep = ";",skip = 66637, nrow = 2880, na.strings = "?")
png(filename = "plot1.png",width = 480,height = 480)
hist(df$V3,12,col="red",main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
|
e9c868bf4a471fbd25a1e00ef07b5f466ad2e4a0 | 8f2e2dd58fa00eb39e0a92ec5f84050ab5ac553b | /Background/compare_cluster.R | dbcbab9d0a4480678d93f4bbd7dd2afd2fdbe4b0 | [] | no_license | luisrei/explore-IIEEC | 06dc148906be328bd573a35262cfa6b143f23daa | b07f609727d4de3d898c183279bc383ca119f1a2 | refs/heads/master | 2021-09-07T18:40:15.877299 | 2018-02-27T11:58:39 | 2018-02-27T11:58:39 | 116,312,905 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 869 | r | compare_cluster.R | #######################################
#
# Compare different clustering algorithms
#
# Author: Luis B. Rei
# Created: 26/12/2017
#
#######################################
# Clear workspace
rm(list=ls())
graphics.off()
library(clValid)... |
dfbef73a30ffddd1c02076801fc541a10926f2b3 | 14a13ec082413638cd4012a5372039256d366c3d | /man/box_setwd.Rd | 48a2bc6120dedd4bf3d4f59fc97a2f6216b6d1f1 | [
"MIT"
] | permissive | fxcebx/boxr | 7ebbd6848b74af83af724fc694a4d365eb03b6c1 | 8bda9d00671df55ef1cbd39ff957dd0d47956c6e | refs/heads/master | 2020-12-24T12:33:00.295757 | 2015-03-19T19:30:31 | 2015-03-19T19:30:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 375 | rd | box_setwd.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/boxr_misc.R
\name{box_setwd}
\alias{box_getwd}
\alias{box_setwd}
\title{set directory}
\usage{
box_setwd(dir_id)
box_getwd()
}
\arguments{
\item{dir_id}{The box.com id for the folder that you'd like to query}
}
\value{
Nothing. Used ... |
53218d04f28867b79c7c99fd28239bbb8cc885ee | 8c38c1f61f53bf4162cbfe81e29402dbdc7ed5fe | /R/getpars.R | 6f26ff853d97d176f17709609bbb45695fa80cf9 | [] | no_license | cran/depmixS4 | 4d55759581c91cedd59cb842ec40c8f48f4170ab | e1a137a5e49b2322dad48b4118b36af6496e60e4 | refs/heads/master | 2021-07-08T09:59:10.761902 | 2021-05-12T11:12:20 | 2021-05-12T11:12:20 | 17,695,463 | 11 | 8 | null | null | null | null | UTF-8 | R | false | false | 691 | r | getpars.R | setMethod("getpars","mix",
function(object,which="pars",...) {
parameters <- getpars(object@prior,which=which)
for(i in 1:object@nstates) {
for(j in 1:object@nresp) {
parameters <- c(parameters,getpars(object@response[[i]][[j]],which=which))
}
}
return(parameters)
}
)
setMethod("getpars","depmix",
... |
bb8903aee65deae2f825f1edc9fe11aa7607025b | d0e56294b9557fa2283f457ec05e197a924e2bd9 | /ACLS/man/GD.Rd | a5fa6389e2860c23c5ee89bb5da2e24e1c4d86a3 | [] | no_license | rruimao/ACLS | 33d4bfe2678daee3d034fc713f54c5b722244008 | 35ccd9efa983416b9f7264d58d47028f56e0078a | refs/heads/main | 2023-06-10T21:52:01.383348 | 2021-07-09T06:53:41 | 2021-07-09T06:53:41 | 305,150,547 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,003 | rd | GD.Rd | \name{GD}
\alias{GD}
\title{Steepest Gradient Descent Approximation Method}
\usage{
GD<-function(beta_0,tau,X,Y)
}
\description{
The steepest gradient descent approximation method find the approximation of the exact step size to minimize the loss function, and use gradient descent method to obtain the estimate
}
\argum... |
d2a0bc49cbf11203802be0d5b47a2647a1b28932 | fb1d9037123f5557c05de959555756afa601305b | /R/helpers.R | 65c1ba0a27c956d92c69f3a88c29b32260fa1833 | [] | no_license | cran/manymodelr | 00b7d0b958c71d10182d9fa07989cfa9e277042f | c1512ef3ad8c304ba7e1faa848c50a952bfa5496 | refs/heads/master | 2021-11-24T09:21:00.420042 | 2021-11-15T08:20:09 | 2021-11-15T08:20:09 | 173,755,551 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,602 | r | helpers.R | # 'These are helper functions that should never have been exported
#' @title Convenience functions for use with get_exponent
#' @param n Power to which a value should be raised
#' @details This is a function factory that creates an exponent from a given value of n
#' @return Returns n to the power of n
#' @keywor... |
aeba3eb60da2067fc910d098d039c8cb75701e58 | 9e459436584ee6f94bc46d145f334de411f3debe | /R/readBootstrap.R | 3de1f764903a32b838a1b0d0c90b113a3c86d7e9 | [] | no_license | NicWayand/IceCastV2 | 8d5ad041f7a10510b8be1dd745b1fd1e4bf3da24 | 8f91a2b2f56b8a131099dadbf6a930c293a7b238 | refs/heads/master | 2020-03-21T06:26:26.426120 | 2018-06-21T20:15:38 | 2018-06-21T20:15:38 | 138,219,718 | 1 | 0 | null | 2018-06-21T20:43:49 | 2018-06-21T20:43:49 | null | UTF-8 | R | false | false | 3,772 | r | readBootstrap.R | #' Read in individual binary files of monthly observation data. The observations are from the monthly sea ice concentration
#' obtained from the National Aeronautics and Space Administration (NASA) satellites Nimbus-7
#' SMMR and DMSP SSM/I-SSMIS and processed by the bootstrap algorithm. The results
#' are distributed ... |
a1b0b1d15587341b3cb0a9d2bf73307267208714 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/Renext/examples/CV2.Rd.R | ad3776b61ff0c579ab6d68bb80abdce13f5a8843 | [] | 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 | 254 | r | CV2.Rd.R | library(Renext)
### Name: CV2
### Title: Squared Coefficient of Variation
### Aliases: CV2
### ** Examples
n <- 30; nSamp <- 500
X <- matrix(rexp(n * nSamp), nrow= nSamp, ncol = n)
W <- CV2(X)
plot(density(W), main = "CV2 of exponential samples")
|
a91bdbd78dc7d6adacaf56c397ffc5e896f2adf2 | 326b9a1c197ce6638ecdf9647e74efd4397e2fde | /app.R | fe96d19b69afa2a56466d0773dc8c75b22853d70 | [] | no_license | nobaldhruw/shinyModules | 845a77a43d2f292c30a8af19f3eaff70e16b2b4c | d5dfd9a29245f9ad29d3d605ed482bf7aee87a99 | refs/heads/master | 2023-05-31T09:07:08.909974 | 2021-07-01T18:02:38 | 2021-07-01T18:02:38 | 382,261,008 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,129 | r | app.R | library(shiny)
library(shinydashboard)
header <- dashboardHeader(title="Shiny modules")
sidebar <- dashboardSidebar(
sidebarMenu(
menuItem("Histogram", tabName = "tab_histogram")
)
)
body <- dashboardBody(
tabItems(
tabItem(
tabName = "tab_histogram",
fluidRow(
... |
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