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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
198f65fcc165b9f8f92326f648b0f0d1f929e9da | 564f5fcfa159f5f3fde03bea679e3f17a28857b0 | /R/post_debt_correction.R | 7ae805115611b18c32c88dd01a002414b3eee346 | [
"MIT"
] | permissive | signaux-faibles/rsignauxfaibles | 44aa11c5a5defa0dd1dd62969ce51a08c4caf6c9 | 9fc81fcc17f51ff436851d893f8c38b12ff9aa78 | refs/heads/master | 2021-06-30T04:59:41.785386 | 2020-11-30T16:50:05 | 2020-11-30T16:50:05 | 198,392,876 | 1 | 0 | MIT | 2020-11-30T16:50:06 | 2019-07-23T09:01:10 | R | UTF-8 | R | false | false | 1,166 | r | post_debt_correction.R | #' Calcule une correction pour les entreprises endettées
#'
#' La correction est dans l'espace des log-vraisemblance (donc après avoir
#' appliqué un logit aux prédictions en probabilité).
#'
#' @inheritParams generic_task
#'
#' @return `data.frame` avec colonnes "siret" et "correction_debt"
#' @export
compute_debt_cor... |
2dcc8d347cbf8f455ce659db58fd1ea50a7a2e75 | fbaef343b4882ed40f7a9e74dd7a1e5708d73a68 | /Simulation_study/Post_simulation_data_separation_check/Script/fitting_models_wo_xgb.R | 0346c08fc41f0646befab5d62f9108739e491005 | [
"CC-BY-4.0"
] | permissive | Goorbergh/resampling_techniquesCPM | 8c968b9c67df190ddcd19cefdd3624b21f2ba5dd | 02a9e4900feec4bf8f01c5144fcc9084a52baf38 | refs/heads/main | 2023-04-27T07:19:47.345461 | 2021-05-14T14:12:52 | 2021-05-14T14:12:52 | 365,748,765 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,974 | r | fitting_models_wo_xgb.R |
################################################
########## Model fitting functions #############
################################################
# In this script the functions to fit all models are defined. In the ML logistic
# regression function a test for data separation is integrated.
########################... |
f4d8b69a9559197c9e3ab627c7c6e317646ff789 | ed1920915c1f7070c7cec39de8ca82672be18cc5 | /source/util/get.prm.os.R | 0f4df63f381bd487220bfa8404639e82febffec4 | [] | no_license | sthallor/miscRscripts | d28f7a9cdbc53fc7c7994c6000d1753b3679236d | c3a5a206c35cdbbb15f07a4ea9250ff861b2e7f1 | refs/heads/master | 2022-11-06T03:39:03.951539 | 2020-06-21T23:21:47 | 2020-06-21T23:21:47 | 273,998,540 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,653 | r | get.prm.os.R | #######################################################################################
# get.prm.os.R - read and decode ASCII parameters file for mainOffset.R
# Ensign Energy Services Inc. retains all rights to this software
# FHS, Feb 15, 2017
##########################################################################... |
a3e3d1f457c401747f23eea5d97ea8e22bb2719b | 84415effc813af58e57141e03fde482c8853afd7 | /Scripts/RMD4/remove_low_copy_RNAs.R | 387e860d26773a6ffb5932029eebf024255c857b | [] | no_license | brianpenghe/bteefy_piwi_transposon | 9943d9f980165d33cc6c3ea8e3924764707dd217 | 1e6d0a354b8544d96ef59b67b0b653de7debe274 | refs/heads/master | 2023-03-19T11:54:49.067882 | 2020-07-07T23:02:50 | 2020-07-07T23:02:50 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,720 | r | remove_low_copy_RNAs.R | setwd("/group/julianolab/bteefy/piwi_revisions/small_RNA")
data.out <- "/group/julianolab/bteefy/piwi_revisions/small_RNA"
#Ecto
Ect_S <- read.table("Ecto_S_Reads.rep_pirna.txt", header = F)
#retain sequences with greater than 3 copies
Ect_S <- subset(Ect_S, V3 > 3)
#keep only sequences
Ect_S <- as.data.frame(Ect... |
4aefffb4725cc3670b5b005e86f0de434fdca92a | ce94e221e5fd686cfb1218b0a9625decb77ac0c7 | /man/mdra.Rd | fa0cd91627b588e1f565da0ff5aeff0575d9053f | [] | no_license | daniel-gerhard/medrc | bb95f91a63e150dd4a114fbfa409dcddc89195ea | 232b2f3887510add1851e6eae21f6ac529b6bf33 | refs/heads/master | 2020-12-24T08:24:02.948797 | 2017-12-27T03:39:07 | 2017-12-27T03:39:07 | 10,939,171 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,726 | rd | mdra.Rd | \name{mdra}
\alias{mdra}
\docType{data}
\title{3T3 mouse fibroblasts and NRU assay}
\description{
The toxicity of sodium valproate was tested, using the 3T3 mouse fibroblasts and neutral red uptake (NRU) assay. 22 different experiments were performed independently in six laboratories, using eight concentration levels, ... |
bfaf8abc4bace0bbba356155631b03dc1794debb | 1afa5017b24a0964f80ec49e772bc260daadfe7d | /man/get_o3.Rd | 29a2e1c06dd432012f38a7966d7cedd89c2de591 | [
"MIT"
] | permissive | healthinnovation/innovar | 76b313f6c295f4001ab60d10ccf94c1856fb61c7 | 45a2b3cd36bac06fedb93b2a9b7e1432c66021ba | refs/heads/master | 2023-09-04T04:56:15.083794 | 2023-08-28T15:44:34 | 2023-08-28T15:44:34 | 296,215,442 | 5 | 5 | NOASSERTION | 2022-11-07T21:54:17 | 2020-09-17T04:04:55 | R | UTF-8 | R | false | true | 2,373 | rd | get_o3.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_o3.R
\name{get_o3}
\alias{get_o3}
\title{Extract Ozone data of Sentinel5}
\usage{
get_o3(from, to, band, region, fun = "max", scale = 1000)
}
\arguments{
\item{to, from}{it's a string object,starting and final date.}
\item{band}{name of ... |
0afb9adb0b1cff8e3eecdaa53a43c232428f8bd0 | dbaeb60398f6cc9420d2dbd3ade57bce56aca2d1 | /man/plot_TL.plateau.Rd | 671a874e8c91a2bc86a140295b25275b2a1b3105 | [] | no_license | dstreble/TLdating | 6125a9323a8b7c814323454dad4c436b1b189797 | ff7cbf39a67db240808f9b59d4135325744a42c7 | refs/heads/master | 2020-05-22T04:39:03.900638 | 2017-09-06T14:37:28 | 2017-09-06T14:37:28 | 52,873,093 | 5 | 1 | null | 2016-03-17T10:58:42 | 2016-03-01T11:51:17 | R | UTF-8 | R | false | true | 1,955 | rd | plot_TL.plateau.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_TL.plateau.R
\name{plot_TL.plateau}
\alias{plot_TL.plateau}
\title{plot plateau test result}
\usage{
plot_TL.plateau(sample.name, temperatures, names, doses, Lx, Lx.a, Lx.plateau,
LxTx, LxTx.a, LxTx.plateau, plotting.parameters = list(... |
8e06b02276dd0988c51b2e5b70787362af3f8c1d | 15ba08494f3ce8731aff56873d16e58e062d05a5 | /Lab05.R | f5485d9b6176ea79adfd99cebde2f7b1b509e732 | [] | no_license | zhaoleist/advStats | 98aea043c4ea5aec7a25e464592f4f8d9d9529cb | 92b7fdd7061897d128bf76a84cec1132e2654017 | refs/heads/master | 2021-05-09T19:22:07.546232 | 2018-04-26T03:07:12 | 2018-04-26T03:07:12 | 118,638,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,667 | r | Lab05.R | # lab 5
rm(list=ls())
# (1)
myT <- read.table("nc101_scaff_dataCounts.txt", header=TRUE, row.names = 1)
# (2)
par(mfrow=c(3,2))
myT_log10 <- log10(myT+1)
plot(myT_log10[,1], myT_log10[,2], main="log scale counts for the two samples")
lines(c(0,4), c(0,4), col="red")
# ANSWER:
# The biological replicates d... |
7d4b0aa8e6c0973d568d2d7d300a7700b05e5a62 | d5e77a217b54f36d5d187b14ecd1e537dc5d958b | /R/LogisticRegression.R | 7e8bad5ab6641a5a93b8712bd88cef0b16980f93 | [] | no_license | zzxxyui/metadarclean | 6328c7a51d04834795d3c39e148b61e9921f8635 | e548d007ba386ca26f62ab5004547fbf558e925c | refs/heads/master | 2020-09-03T22:21:55.737593 | 2019-11-04T20:16:41 | 2019-11-04T20:16:41 | 219,588,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,318 | r | LogisticRegression.R | LogisticRegression <- setRefClass("LogisticRegression", contains="Classifier",
methods = list(
initialize = function(...) {
callSuper(...)
buildClassifier()
.self
},
buildClassifier = function() {
.self$model <- glm(y~., family=binomial,
data=data.frame("y"=factor(.self$y), t(.self... |
45d059bfb18e99ede9538b7821587127bde6e756 | e88cbf37f6e9536d2467974017e5abed1ac30987 | /R/03b_create_getplotsfunction.R | f6a49e30eb31581d1646def8735d53b4e7b56c6f | [] | no_license | hkalvin/INFO550Project | f88699410ef6ef03f2c76cbe30d6c1e2cf1952cd | 4fc3a0cd40cc1b01789f2c490fef9dfea402c3c7 | refs/heads/master | 2023-02-01T10:21:47.347870 | 2020-11-19T04:00:52 | 2020-11-19T04:00:52 | 299,963,017 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 493 | r | 03b_create_getplotsfunction.R | #! /usr/local/bin/Rscript
#create function for figure generation
getplots<-function(r,n,c){
dat<-subset(ds2_fig,region==r)
plottxt<-paste("Figure ",c,". Age adjusted breast cancer mortality rate by \nstate and race (",r,")",sep="")
f<-ggplot(dat, aes(x=Race, y=Age_Adjusted_Rate))+geom_col(aes(fill=Race))+ggtitle... |
2d5fede7eebbfc80babbcc6fd95b57e92427e06d | cfa9a6c3519a17bcded7cb5091be11c02739434d | /R/utilities.eval.R | e87ecdfe0bd72066f6d0d750379c236ad2d591dc | [] | no_license | cran/ggloop | 5f3529804c9c94c35788689d17dd733615b6672f | e3aaa56cbd19c4c9d6f1d2598a48eb3571d16a75 | refs/heads/master | 2021-01-11T03:49:08.312424 | 2016-10-20T01:58:31 | 2016-10-20T01:58:31 | 71,409,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,076 | r | utilities.eval.R | #' @include utilities.eval2.R
# ops ---------------------------------------------------------------------
#
#' Arithmetic operators to search for.
ops <- c("/", "\\+", "-", "\\*", "\\^")
# is.op() ---------------------------------------------------------------
#
#' @title
#' Determine if an input uses a... |
6802464f2bd54d97b26ddefa79b1d018a1a61a3c | 368c9704acf4feddc6de68336cb57d0406d58b68 | /Final.r | f191657b65d41a07b8ea76425152715477585b64 | [] | no_license | alex-selby/MKTG-562 | 0b7148fc96f04e8dcf701b639c18fbf8ce8af39f | 2ea3029fcdd7cd6a7360893a06886b653c8bbc49 | refs/heads/master | 2021-05-06T23:03:07.116478 | 2018-09-24T03:25:06 | 2018-09-24T03:25:06 | 112,897,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,513 | r | Final.r | # 1) CLEAR ALL Variables (and also clear the screen)
rm(list=ls())
cat("\014")
# 2) Tell your Code WHERE your Data is (i.e., SET the PATH)
library(rstudioapi)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
# 3) Read in the data.
WebData = read.csv("webdata.csv")
# 4) Explore the data
str(WebDat... |
87d302ec77c2802ce14defa7491f25d5d0807980 | 3c0359eb76bc599da2dfac34e6cd1831715e52b9 | /regionalism_qca20/Rcode.R | 8a52abb877860661fd188900b106a94333b27f47 | [
"MIT"
] | permissive | yello-data/publish | ccd10b9ff1eb258d9e1eb82623582f9d321d3435 | 3865169bfe4e61ae356e0c163092e93a51d79f0b | refs/heads/master | 2020-09-28T01:58:05.064164 | 2020-02-24T13:55:52 | 2020-02-24T13:55:52 | 226,662,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,169 | r | Rcode.R | #libraries
library(readxl)
library(dplyr)
library(tidyr)
library(countrycode)
#Download sheets (see files "the_database.xlsx" and "igo_dyads.xlsx")
pol <- read_xlsx("the_database.xlsx", sheet = 2)
eco <- read_xlsx("the_database.xlsx", sheet = 3)
pow <- read_xlsx("the_database.xlsx", sheet = 5)
base <- read_xlsx("igo_... |
d0311e449614a1c630e1d53c0b0e88792f2829c8 | 92194ff532e7ec52f7d6733b4610795508bfdce5 | /Models/DataExtrapolation/structure_test.R | 01f19368b56710a63e6a6414e4fe5050510952cb | [] | no_license | JusteRaimbault/RealEstate | 9e567e4d28e490b7954077a5304902f20e724f26 | 0fd6634386947f9b3878809215e486d3c4e57d7b | refs/heads/master | 2021-01-10T15:57:46.849552 | 2020-02-19T09:29:07 | 2020-02-19T09:29:07 | 47,781,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 867 | r | structure_test.R | setwd(paste0(Sys.getenv('CS_HOME'),'/RealEstate/Models/DataExtrapolation/'))
source('inverseKernels.R')
years = c('01','02','03','04','05')
csp = c("EMP","OUV","INT","ART","CAD")
idcol = 'COM'
extrapolated <- read.csv(file='res/extrapolate_allyears_communes.csv',sep = ';')
#for(year in years){
year='01'
income <-... |
098ee0cec7403955e4def8859b6550b0054c04de | d26a96ee3fbbe01e7eca74e1c0eea795677365ef | /R/bq_reader.R | 2f14d1bcc0ad7fea7106eb0a1a2056bdb7d244df | [] | no_license | mcdelaney/mmkit | 0a353007950e941a7aada347dba037edca12f4bc | 45205acd675e6fe600c75bc59b41f8d506689582 | refs/heads/master | 2023-06-17T09:35:31.891343 | 2021-07-16T17:10:26 | 2021-07-16T17:10:26 | 129,094,522 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 918 | r | bq_reader.R | #' BQReader
#' @docType class
#' @description Bigquery database interface.
#' @param py_version_path Path to the python installation
#' @import DBI
#' @importFrom R6 R6Class
#' @import reticulate
#' @import arrow
#' @name BQReader
#' @export
#' @return Object of \code{\link{R6Class}} with methods for query execution.
... |
a274a62bffe1fb2b11d97f30a308f4dc3c998ce5 | 21c5a7da2cfd702eaec886182d3a847230fc739c | /Script-COVID-Floripa.R | b9ffb691f0a055f4d6e3cae27f21db94d8ab1ffc | [] | no_license | danielgonlopes/COVID-Floripa | 1b23d4fd3eeeed181ad2555cc90a2b827f6d83df | 8ae8351965e8b6c6385e5b78adab97df206d6068 | refs/heads/main | 2023-04-01T10:14:49.669695 | 2021-03-28T19:47:13 | 2021-03-28T19:47:13 | 350,546,043 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,314 | r | Script-COVID-Floripa.R | rm(list=ls())
#### PACOTES ####
library(tidyverse)
library(ISOweek) # Para agrupar dias em semanas
library(DescTools) # Para calcular PseudoR2 do modelo Logit
library(caret) # Para rodar Árvore de Decisão
library(rattle) # Para plotar Árvore de Decisão
library(randomForest) # Para rodar Modelo Random Forest
#### LEITU... |
2e2adc6e4408f30a788e8bd238627ca61975d1f5 | 6c0bd9d42918ef3ff3804c4169ef573c5bf6458f | /_old_versions/_versions(RStudio)/build_and_send_Query_with_cellbaseR_17-10.R | 374c34adf6c09e64a49ff93f183eba45053a55ba | [] | no_license | IsaFG/vcf_pk | 277ffe6885cbd7123f8ce0aab05710c0421f5884 | 950cfe487160d87516d4efe78878dcc63f370ada | refs/heads/master | 2021-01-22T18:07:10.428420 | 2018-02-27T15:55:08 | 2018-02-27T15:55:08 | 100,740,025 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,246 | r | build_and_send_Query_with_cellbaseR_17-10.R | ############## [INFO] SCRIPT 3 General information ############
# NOTE : UNFINISHED WORK
# IMPORTANT : this script will use the library "cellbaseR"
# Bioconductor link dor library "cellbaseR":
# https://bioconductor.org/packages/release/bioc/html/cellbaseR.html
############## [INFO] Input and Output #################... |
fda93f38f1203b191732115e51c4f9c304ee2178 | 0c90b72ff5b57481b7edb74620333a7c3e3c603f | /R_Youtube/airim/op_gg_popular.R | dcf7310e1978f9c80618527b4f500e446a0095fc | [] | no_license | diligejy/R | 41abd1cc6ef3731366f3196d3d428fd5541215e5 | 1b54334094c54e041b81d45f87c5c07336d62ff9 | refs/heads/master | 2022-09-11T18:13:10.614301 | 2022-08-21T14:57:21 | 2022-08-21T14:57:21 | 210,329,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 664 | r | op_gg_popular.R |
# op.gg talk 인기글 크롤링 연습
library(rvest)
library(RSelenium)
library(httr)
remD <- remoteDriver(remoteServerAddr = 'localhost',
port = 4445L, # 포트번호 입력
browserName = "chrome")
remD$open() # 서버에 연결
remD$navigate("https://www.op.gg/") # 홈페이지로 이동하라!
html <- remD$ge... |
d80cf1a2a54c7fd60c7601d4988eb8ccc772f79d | f2a1cebe0c88195da10e3ad8f3184e6738bab6a8 | /Functional_Depth_ARIMA.R | eadbc36e94df04b73e2e7c03737b238bc508e1e0 | [] | no_license | als23/identifying_and_responding_to_outlier_demand_in_revenue_management | 229ac85e0e723d0c2b352d0c186eeda0b16fde4a | bba9b8197a432bee65ea8fcf5fbc9d78d9b36726 | refs/heads/main | 2023-08-15T08:14:40.903841 | 2021-09-24T22:45:46 | 2021-09-24T22:45:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 324 | r | Functional_Depth_ARIMA.R | arima_extrap <- function(df,k){
return(t(apply(df,1, function (x) c(x[1:k],ceiling(as.vector(forecast(auto.arima(as.numeric(x[1:k])),h=30-k)$mean))))))
}
func_depth_arima_outlier <- function(df, k, maxiter=50, B=1000){
d <- arima_extrap(df,k)
output <- func_depth_outlier(d, maxiter=maxiter, B=B)
return(output)... |
936b9523b6cd396dc294e1e017b4146af3c5bc27 | 6220c165c0acc1c21f4f48cffab834cf343e94b8 | /scripts/intergenicRegionAnalysis.R | d97f893a43a79502209d60b6bf9fce365583d8e8 | [] | no_license | lkov0/bladderwort-analysis | 29d8e6886b56e61d98302e496229d88ea2c7a433 | 259b90210f9c2d881e9359d58b8ef2d198d7a078 | refs/heads/master | 2023-08-12T04:07:47.332127 | 2021-10-11T01:10:19 | 2021-10-11T01:10:19 | 166,107,557 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,128 | r | intergenicRegionAnalysis.R | # intergenic region analysis
# R code used to futher interrogate expression data around intergenic regions of interest.
# first, check orientation of candidates
library(ggplot2)
geneHits <- read.table("~/Xfer/jwlab/Bladderwort_3pri/1_Assembly/scaffolds_lk_anno_coordinates_in_PacBio.bed", stringsAsFactors = F)
head(g... |
c1e23a5bacc3bb0090dcdaa3522e499113d5885b | e86adb54dbc48cfa175a3fabe5d1790b0a15553e | /Código fuente de Hidro.R | ccb2dbf0fbb6164faeb0f90e3857470e33d356be | [] | no_license | RG-andrey/Trabajo-de-Hidro | 5d2d4b9b95fafeeb8b108bd48880b4380234b279 | 82432c8a32292a304d4e285a070b46b37c4540d7 | refs/heads/master | 2023-04-30T01:27:30.737015 | 2021-05-23T20:06:42 | 2021-05-23T20:06:42 | 370,145,680 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,870 | r | Código fuente de Hidro.R | #Datos hidrologicos trabajo exporativo
inp <- read.csv("FDC.csv")
head(inp)
dim(inp)
inp[!complete.cases(inp),]
#newinp <- na.omit(inp)
#en este caso observamos el río Estrella (segunda columna)y el río Banano (tercera columna)y se nos muestra la visualización de las dos series de tiempo de caudal.
plot(
inp[,2],... |
32dd621b8fcb23a8126475832078f28c134260a9 | 3f51298820aed88e7d0cc0569f49ab49a9fd1723 | /run_analysis.R | 1e7016f0ce16d65e8744cdde4d13d5d281790f2d | [] | no_license | franciscoalvaro/gettingandcleaningdata | f2a70c2460b831a2e8cd0c9a86f001458a58f986 | 3c928b00d8f228ca48699d8a1269349ae4020d7f | refs/heads/master | 2021-01-10T21:11:47.680462 | 2015-01-25T23:44:17 | 2015-01-25T23:44:17 | 29,836,593 | 0 | 0 | null | null | null | null | ISO-8859-10 | R | false | false | 15,678 | r | run_analysis.R |
library(plyr)
dataX_train <- read.table("X_train.txt")
dataY_train <- read.table("Y_train.txt")
dataX_test <- read.table("X_test.txt")
dataY_test <- read.table("Y_test.txt")
dataactivity_labels <- read.table("activity_labels.txt")
datafeatures <- read.table("features.txt")
datasubject_train <- read.table("... |
1f9f2caba13638f39591cda656e9648e69010df3 | 1ff75d75ca6f6a1d9accb8cfb20d42095cb91792 | /R/naive_fill_NA.R | cf330bbfdf0bb0a642554a101638dda925280088 | [] | no_license | minghao2016/miceFast | c7f33c4a17332e5e67b3a4fd56f4ade38f7c3614 | 35d8901cc814a3e1e43cf54477ecb36c232b7447 | refs/heads/master | 2022-11-23T11:25:59.522759 | 2020-07-11T20:36:56 | 2020-07-11T20:36:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,344 | r | naive_fill_NA.R | #' \code{naive_fill_NA} function for the imputations purpose.
#'
#' @description
#' Regular imputations to fill the missing data.
#' Non missing independent variables are used to approximate a missing observations for a dependent variable.
#' For numeric columns with any missing data a simple bayesian mean will be used... |
4472030d895447b1527bec28fc3c42126ed624a6 | da44ce19ae0c6d6573d2b1efc3e018339d1df9a5 | /packages.R | 3236d1ae1711c799aced494ab781b994c99f0ac6 | [] | no_license | jlehtoma/CDDA | b4c1a43b5f64c8fc59846367d38afa335e28509c | be5e68d49d51ca4a9c0a8b5a301f451aeac29707 | refs/heads/master | 2022-07-19T16:55:26.963197 | 2020-05-20T18:33:00 | 2020-05-20T18:33:00 | 265,581,637 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 241 | r | packages.R | ## library() calls go here
suppressMessages({
library(conflicted)
library(curl)
library(dotenv)
library(dplyr)
library(drake)
library(fs)
library(httr)
library(R.utils)
library(readr)
})
conflict_prefer("filter", "dplyr")
|
07fed6251e38e677f14db323535e62fc6548c0da | cb0401f0731240bd067a68563fac263ddbb0ac8e | /man/hypervolume_overlap_confidence.Rd | 7b3b812f085cb19b87a689f45f2bcf38669a5810 | [] | no_license | bblonder/hypervolume | e4a92a430230b843b6e445233b079c93dbd7d248 | f27eb9cc923c9937bb33f86879bed1e7d6b75cc4 | refs/heads/master | 2023-09-01T14:55:36.608891 | 2023-08-24T21:57:55 | 2023-08-24T21:57:55 | 22,171,728 | 20 | 8 | null | 2023-02-18T01:05:33 | 2014-07-23T19:24:49 | R | UTF-8 | R | false | false | 2,583 | rd | hypervolume_overlap_confidence.Rd | \name{hypervolume_overlap_confidence}
\alias{hypervolume_overlap_confidence}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Confidence intervals for overlap statistics
}
\description{
Generates confidence intervals of four different overlap statistics. In order to find the confidence interval fo... |
5340f01d45854220628b1a2e5a35d4d9a5688412 | 846eb90003c329750ca6078a7d4941cd87e578cc | /Section 8/4981_08_01_Code.R | 7ad0a4f8f2a354629357e26108a3c3228615168d | [] | no_license | PacktPublishing/Learning-Data-Analysis-with-R-Video- | 62685d9a9f9116184afb0791e243f6f8443bbf82 | 151713640dcdc4887f8e867064f73745749c49fa | refs/heads/master | 2021-06-27T06:03:25.744205 | 2021-01-19T13:09:03 | 2021-01-19T13:09:03 | 187,592,737 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,583 | r | 4981_08_01_Code.R | #Volume 2
#Section 4
#Video 1
#Author: Dr. Fabio Veronesi
library(sp)
library(rgdal)
library(raster)
#Setting the working directory
setwd("E:/OneDrive/Packt - Data Analysis/Data")
#Load the country boundary polygons from Natural Earth
NatEarth <- shapefile("Shapefile/ne_110m_admin_0_countries.shp"... |
ece05aeeaebaf82728f27c5a4aba384f60883a71 | f67b15c1b265b30bd4fa8eb9a9ffd25e1ab5567a | /R/Euler_20_Sum_of_Factorials.R | 1c21e1f05ad01575bcb35469bc959addfec71ccc | [
"MIT"
] | permissive | DougieWougie/ProjectEuler | 1e51984c0a8e15d68466a2cb7e772ef792b96f4a | 9617b048d8748f4f0b0f6e5df128e5bb77ec1f44 | refs/heads/master | 2022-07-12T06:17:52.629002 | 2020-05-15T17:23:40 | 2020-05-15T17:23:40 | 257,003,466 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 165 | r | Euler_20_Sum_of_Factorials.R | library(gmp)
sum.digits <- function(number) {
return(sum(as.numeric(unlist(strsplit(as.character(number), split = "")))))
}
print((sum.digits(factorialZ(100))))
|
10959909c547c3bd56542729ef3118671f21273f | 26648108b95b0b50e5cc6170ef103c8bfc463078 | /man/plot_cpue_spaghetti.Rd | c27df91414d72e02e016ebc2d28facbe7ec223c5 | [] | no_license | pbs-assess/gfsynopsis | 773a49e69735432a451adaabd87f39927c7f60b2 | 0ac1a42e96791a77b0a7f77c8914c83b3e814451 | refs/heads/master | 2023-08-17T08:21:02.676898 | 2023-07-27T22:30:04 | 2023-07-27T22:30:04 | 122,661,487 | 11 | 2 | null | 2023-07-17T21:49:45 | 2018-02-23T19:04:12 | TeX | UTF-8 | R | false | true | 882 | rd | plot_cpue_spaghetti.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cpue-plots.R
\name{plot_cpue_spaghetti}
\alias{plot_cpue_spaghetti}
\title{Plot locality specific estimates from a CPUE standardization model}
\usage{
plot_cpue_spaghetti(model, fleet, index_data, era = c("modern", "historical"))
}
\arguments... |
445d9fca1a5eeee4b6b0217763c17bbd0b9bbf5f | 5ba24fb1d16e2056b1ee6fedf4023c878364b0ff | /run_analysis.R | e1bc38651776a23b2132f917b7f91480c0e0380f | [] | no_license | pikerg/Getting-and-Cleaning-Data | 7b868de20567e8ad274a53d1f2d2134e7e1b54a1 | 85d4b8ada63d31e376d1163a5058186d87ca35fe | refs/heads/master | 2021-01-23T22:15:26.709302 | 2015-07-24T18:19:32 | 2015-07-24T18:19:32 | 39,515,889 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,525 | r | run_analysis.R | ####################################################################################################
## Assignment Objectives:
## Create one R script called run_analysis.R that does the following:
## 1. Merges the training and the test sets to create one data set.
## 2. Extracts only the measurements on the mean and... |
24fbd78a11a6f5a3a9d7d76c215f8f4b2513eb54 | 61b4adde63a7b434e028488d2158ef23014c4cfc | /tests/table.R | d4f38f2cce68a3850a44fcb63896651479214e77 | [] | no_license | SVA-SE/mill | a165deeae9612c1448d287caea73f5de6e87d02a | b5faa7738d6b475759c7f2f980e30628d7f15f35 | refs/heads/master | 2021-05-10T09:19:22.504502 | 2020-06-15T12:05:40 | 2020-06-15T12:05:40 | 103,141,739 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,660 | r | table.R | library(mill)
stopifnot(identical(mill:::merge_font_styles("\\textit{A }\\textit{B}"),
"\\textit{A B}"))
stopifnot(identical(mill:::merge_font_styles("\\textit{A} \\textit{B}"),
"\\textit{A B}"))
stopifnot(identical(mill:::merge_font_styles("\\textit{A. }\\textit{woodi}"),... |
e225f5c018791bb951f399bc75c80078e7fdba97 | de89fd4bfd470b4df26bac1f22ac7b594238c585 | /man/update_senate_database.Rd | abc38e0651ff4a9f0bc06462255c10bac6e70cfd | [
"MIT"
] | permissive | KWB-R/kwb.flusshygiene.app | d25414c614d3d5ba39b6a5225fea0ceda85d2b65 | 6cb657f79c8cb7ea627806797965ba716778d312 | refs/heads/master | 2021-07-21T01:23:36.782339 | 2019-10-30T10:15:53 | 2019-10-30T10:15:53 | 186,563,434 | 0 | 0 | MIT | 2021-07-12T09:43:55 | 2019-05-14T06:58:09 | R | UTF-8 | R | false | true | 1,132 | rd | update_senate_database.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/update_senate_database.R
\name{update_senate_database}
\alias{update_senate_database}
\title{Download New Files and Update Local "Database" of Senate's Data}
\usage{
update_senate_database(root = get_root(), dbg = 1)
}
\arguments{
\item{root}... |
dde962321ddf0a59c2c0c129c9fd800e95a49a40 | 3792ceaa3060ef1c8b2aede1f621ecc8b1777f5f | /man/baixar_julgados_trf2.Rd | b9e5d54b51c8f74d4f537d3b5a85c7a0de9aa323 | [
"MIT"
] | permissive | jjesusfilho/trf2 | 8283a3587c9f4dcd05bfd4b30198d4211345b29c | af3631695421499000f2bc970c656ee998648791 | refs/heads/master | 2020-07-02T13:22:01.753908 | 2020-06-02T01:51:35 | 2020-06-02T01:51:35 | 201,534,788 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 668 | rd | baixar_julgados_trf2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/baixar_julgados_trf2.R
\name{baixar_julgados_trf2}
\alias{baixar_julgados_trf2}
\title{Baixar inteiro teor do acórdão}
\usage{
baixar_julgados_trf2(urls, diretorio = ".")
}
\arguments{
\item{urls}{As urls podem ser obtidas da tibble lida
por ... |
b97bfc82185757bdf895c94b276661d533f25b18 | 09f489b818406f56e28f544d566121e5a2c1be2c | /download_ahn_sheets.R | ff1b889748ff9bb045da82d9ae2e9b35c9d2146a | [] | no_license | Martien1973/rAHNextract | e477e998509912c217061902548a3c0e9aadf332 | 696d66ec58d5e15eb076623ccd2bac7bd5d46285 | refs/heads/master | 2021-01-04T10:21:28.198351 | 2020-02-14T08:22:17 | 2020-02-14T08:22:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,179 | r | download_ahn_sheets.R | ahn_sheets <- function(name, surroundingBuffer, ahn = "AHN3"){
###BladIndex method ###
#get AHN bladIndex
my_EPSG <- "EPSG:28992"
directory <- paste("data", AHN, sep="/")
if (!dir.exists(directory)){
dir.create(directory)
}
bladIndex_shape_filepath <- paste(directory , sep="/")
bladIndex_shape_... |
6cd355ca877dea27b0dc7ef34bd8a0bdd6bb203e | b151f3472c1de0756675d41bc8f62598cc60df93 | /learn_local_dbn.R | 7714ca4019c871af3e8bb58d1e6463eb6d6fbfbf | [] | no_license | sap01/TGS-2 | d8450cbcf85d7b2619910fa0f4d685e2a866fe1a | 9c84d3b8a478a0c27479f67c31e23563afa6a13d | refs/heads/master | 2020-05-15T13:10:37.901192 | 2020-04-23T14:49:58 | 2020-04-23T14:49:58 | 182,055,893 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,848 | r | learn_local_dbn.R | ## Goal: Learn local Dynamic Bayesian Network (DBN)
##
#######################################################################################
## Goal: Learn local DBN using R package 'bnstruct'.
## This function allows nodes with less than two discrete levels.
##########################################################... |
e325bff2d201b1cde595a897c9ee777de65f5844 | f86eae36eaa4487bc67718b81e293c85ff78a6fb | /man/mics-package.Rd | f37c7d9fa7659432fbf97494200e09757d518a8a | [] | no_license | epix-project/mics | 829b51e0cfadc467668f559e72a3836027e1aae9 | fd13ab08910016baace45488ded801eac5e673d5 | refs/heads/master | 2020-03-28T05:58:10.235696 | 2018-10-09T09:54:39 | 2018-10-09T09:54:39 | 147,806,678 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 778 | rd | mics-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mics.R
\docType{package}
\name{mics-package}
\alias{mics}
\alias{mics-package}
\title{mics: analysing multiple indicator cluster surveys}
\description{
mics provides a collection of functions to analyse multiple indicator cluster
surveys such... |
501e40a93339f167ce25a63070a24dce9dd40207 | 28e5bcbacce8558e4198e2eab55b9c638c74bc8a | /implementation_2/NYCTaxi/R/RcppExports.R | 814531796360e39fb86f72f9306f11788e111446 | [] | no_license | huragok/STA242HW5 | 57dbb6579a3a4fc0e2e47d6cd7b9c696de1d89cc | 53a1b7303678bf2160375174a3524471e6c9fee9 | refs/heads/master | 2021-01-10T10:11:23.226890 | 2015-05-31T23:33:42 | 2015-05-31T23:33:42 | 36,768,675 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 868 | r | RcppExports.R | # This file was generated by Rcpp::compileAttributes
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Function to update the sufficient statistics of linear regression based on a bulk of data
#'
#' The sufficient statistics of the linear regression is recorded as a p-by-(p+1) matrix which is the row concate... |
8bf697dd10069a12c68ddb5f81ae9373a9020062 | 4498288a6df6d1cd6beedb6a59229c1784e70d29 | /r_script/func_var_test.r | b0385e123d5dbd2a04d588946cc6e1acaa263c65 | [] | no_license | bioticinteractions/r_script | f92672a6d6fff5a3e8cff31fc10635b595e0495c | 9deed66eca3ae6002bafcc48a9e2000d5a2db0b4 | refs/heads/master | 2021-10-01T18:07:28.340418 | 2018-11-28T03:25:22 | 2018-11-28T03:25:22 | 106,977,913 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,055 | r | func_var_test.r | ################################################################################
#function for calculating test statistics for potential variables for model use
################################################################################
var_test <- function(data, seg_col, formula){
library(car) # for VIF
df <-... |
6e9eed3e608df895415cbbce8f55e1597ba8e3c7 | dc7169116a18420ba27791d1ae937519cd3b7028 | /man/rlogitnorm.Rd | 0eaa6ca12706bcfdb55cf3a2eabca5bff2ef3c99 | [] | no_license | bgctw/logitnorm | 1ee55f1f36a4700f276b8c0ed3bde4b199e2215b | 527a5cf52b8d8a17b48ad9b5a2cfb3042e6093f6 | refs/heads/master | 2022-01-01T21:00:59.761492 | 2022-01-01T11:57:05 | 2022-01-01T11:57:05 | 73,286,222 | 1 | 1 | null | 2018-07-30T12:01:11 | 2016-11-09T13:25:06 | R | UTF-8 | R | false | false | 428 | rd | rlogitnorm.Rd | \name{rlogitnorm}
\alias{rlogitnorm}
\title{rlogitnorm}
\description{Random number generation for logitnormal distribution}
\usage{rlogitnorm(n, mu = 0, sigma = 1, ...)}
\arguments{
\item{n}{number of observations}
\item{mu}{distribution parameter}
\item{sigma}{distribution parameter}
\item{\dots}{argu... |
2d2cdfe3b193c825623ae200855199a9c3294c29 | 72d0d60685ff5e1b8f4b122bfc5cfc67234175ef | /man/send_invite_2_fun.Rd | 8066ec41a7fd0e60b71afd58d176f3788e58a32f | [] | no_license | oliverpurschke/smols | 90d1476045e31ac03b5467f0380879d28e8f2b56 | 1dbf5f5bcfa080d836d55fbf65b91173af81843a | refs/heads/main | 2023-06-21T08:52:27.823888 | 2021-07-27T10:35:30 | 2021-07-27T10:35:30 | 348,012,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 603 | rd | send_invite_2_fun.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/send_invite_2_fun.R
\name{send_invite_2_fun}
\alias{send_invite_2_fun}
\title{Einladungsmails ab 2. Wellen versenden}
\usage{
send_invite_2_fun(dat, senden)
}
\arguments{
\item{dat}{Dataframe mit Adressinformationen der Teilnehmer die angesch... |
fe1bcc74dd74cd9d3b350cb71ae3d6ac0b66c844 | 364bb477d657913eef1ab7a5461ceb9e6aa95a32 | /Airquality.R | e517910c5a42963fb4485ecc641021435ac610be | [] | no_license | Eduardo0396/Programaci-n-Actuarial-III | 2f76c6a30f36b26b1d6895fd38307088e4380627 | f212d048a039778fd64b9c9c66cd10d0ba904054 | refs/heads/master | 2021-01-18T23:14:36.348796 | 2016-06-15T18:34:40 | 2016-06-15T18:34:40 | 50,951,116 | 1 | 1 | null | 2016-02-03T13:42:17 | 2016-02-02T20:53:21 | null | UTF-8 | R | false | false | 186 | r | Airquality.R | y <- airquality
dput ( y , " airquality.R " )
hijo <- (airquality)
datos <- airquality [complete.cases (airquality),]
datos
nrow(datos)
dim(datos)
sum(complete.cases ( airquality )) |
eae216fb4fd08ba3a2e5a4bc3ba4809b0ada48d1 | 8560ce389e1cc0f6351bbe0daa17aba177ddc776 | /HRR_tool_ensemble_OLDcountHRRwalk.R | 458062a4701372da35e8545039818e0b51c29efd | [] | no_license | alexandrekl/fema_r1 | 28d8577fccc51020817602e37b8e8de6931a7bbe | ada4a6d588ab9dbae949c9e4e1082c1df7aa8222 | refs/heads/master | 2023-04-14T22:41:56.759243 | 2021-04-27T19:15:55 | 2021-04-27T19:15:55 | 310,356,329 | 1 | 0 | null | 2021-04-27T19:15:56 | 2020-11-05T16:25:53 | HTML | UTF-8 | R | false | false | 9,204 | r | HRR_tool_ensemble_OLDcountHRRwalk.R | # Get CDC emsemble data for HRR Excel tool
library(dplyr)
library(openxlsx)
latest_forecast_date <- '2021-01-11'
NEstfips <- c('09','25','23','33','44','50', '36') # FIPS of states in New England + NY
# truth URL from the CDC ensemble
turl <- 'https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data... |
f92edabd9737b8f534a604431d1f0a8c1f577d0d | 5e4af78accb607c8bc66e674ec29e3e010baf2c9 | /R/zzz.R | fd2102cecf4274ec058d6e2cb90f89a9ee86fad0 | [] | no_license | onebacha/connectir | fc472b8c08c88063ac787c819c9fdfe10a262e7d | baff25329326e8c1784cbe63a7c2efdf4034be9a | refs/heads/master | 2023-01-13T03:58:45.523952 | 2020-11-15T04:39:04 | 2020-11-15T04:39:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 258 | r | zzz.R | #' @nord
.onLoad <- function(libname, pkgname) {
library.dynam("connectir", pkgname, libname);
}
#.noGenerics <- TRUE # This was a problem, not used.
#' @nord
.onUnload <- function(libpath) {
library.dynam.unload("connectir", libpath);
}
|
ceacce9d5581458fd5a778596e8768b0efb2a592 | c9840d47330f946ec12a87acff7cb1f30b90090d | /tests/testthat/test-package.R | d704a1ff3455d0e805346b3de6d8ff80754b6b7e | [
"MIT"
] | permissive | kforner/debugme | f205edc66574ea954ef431a1182513a7f09a5f4a | 400ac11971ce694f0a55309020ed6ce2acb28f9a | refs/heads/master | 2021-01-16T18:08:48.117894 | 2017-08-14T08:45:24 | 2017-08-14T08:45:24 | 100,038,860 | 0 | 0 | null | 2017-08-11T14:15:24 | 2017-08-11T14:15:24 | null | UTF-8 | R | false | false | 742 | r | test-package.R |
context("debugme")
test_that(".onLoad", {
val <- NULL
mockery::stub(.onLoad, "initialize_colors", function(pkgs) val <<- pkgs)
withr::with_envvar(
c("DEBUGME" = c("foo,bar")),
.onLoad()
)
expect_identical(val, c("foo", "bar"))
})
test_that("debugme", {
env <- new.env()
env$f1 <- function() {... |
65a02f242541223ee8224d159076aa586c30cbbf | 8f536537be5bf214525ea11bb84c568c9fb82fe7 | /R/mp_y0.R | af56e4bf8acad92a8a43bb87927820344181d582 | [
"MIT"
] | permissive | yuliasidi/bin2mi | 5fa742f72d21034c7def62bb30d078e63c18d2ff | 51ec9b77d0afb0498ca59fbb91fd71e80479dede | refs/heads/master | 2021-06-22T15:01:14.716873 | 2021-02-20T18:53:31 | 2021-02-20T18:53:31 | 197,215,389 | 0 | 0 | NOASSERTION | 2021-02-20T18:53:32 | 2019-07-16T15:00:09 | R | UTF-8 | R | false | false | 603 | r | mp_y0.R | #' @title conditional missing probability for missing not at random
#' @description calculates probability of missing conditional on y=0
#' @param do_tar numeric, target dro-out rate
#' @param mp_y1 numeric, missing probability conditional on y=1
#' @param p_y1 numeric, probability of y=1
#' @return numeric
#' @details... |
ba0433849cfc2798c726c1c0662a102a4240b9df | 65f6febb549fe2b9a2d41074ebab5317a4489d1c | /R/SQRL.R | e453209d0e8d47a596b2833677a74ee071a11c89 | [] | no_license | cran/SQRL | 4c0d86211617631c630c16939e8e863e1aa5fd64 | ae6f80e41d9e4f9c80721c487523221b46b2cea8 | refs/heads/master | 2022-10-14T07:59:22.216921 | 2022-09-20T20:40:02 | 2022-09-20T20:40:02 | 110,434,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 265,030 | r | SQRL.R | ####################################################################### SQRL ###
# Wrapper about RODBC. On load, SQRL automatically generates a like-named user-
# interface function to each DSN it finds on the system. These functions enable
# immediate interaction with each data source, since channels and communic... |
cff38d3e13446b77d8f30ebf51357c955811ca89 | 3f9db7481425c63a1fd9078c2583d096287df74f | /man/relabel_tree.Rd | 13509f2c0c7a6d67f1545008dcee6eb9a8ce5c8e | [
"MIT"
] | permissive | ethanmoyer/ICCE | 2f8442a1afc3b66c0bb9c0bb8958c2bf5f3d0f02 | 0f23dc13b51e35b1a387f42a2e2ddc984ee991f9 | refs/heads/master | 2022-12-06T14:44:16.564361 | 2020-08-20T01:23:11 | 2020-08-20T01:23:11 | 278,681,090 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 511 | rd | relabel_tree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/editTree.R
\name{relabel_tree}
\alias{relabel_tree}
\title{Relabel node on tree}
\usage{
relabel_tree(icceTree, n_old, n_new)
}
\arguments{
\item{icceTree}{icceTree data structure}
\item{n_old}{node number}
\item{n_new}{node number}
}
\valu... |
09a4f08ceb32df2e1ff04f139a4752568a1aa303 | 0c131a3bee0e8659589add196303654f154e266b | /conf/install-reed.R | e3a06a94cd8a6af40cd86aaeb3a4d638a77bd37c | [
"MIT"
] | permissive | mccahill/docker_rstudio_ibiem | 7052b205339b619d8fd38b8a2cd91c7b0e31c5a6 | e4d693fec6fea546ec36c975bcbd43ca45a031c8 | refs/heads/master | 2021-09-14T04:29:50.160430 | 2018-05-08T14:57:01 | 2018-05-08T14:57:01 | 106,874,026 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 258 | r | install-reed.R | r <- getOption("repos")
r["CRAN"] <- "http://cran.r-project.org"
options(repos=r)
devtools::install_github("ismayc/reedoilabs")
devtools::install_github("ismayc/reedtemplates")
utils::install.packages("tufte")
devtools::install_github("andrewpbray/oilabs")
|
eb4ae0833e2780c16b2dda24b4aae74ee9a467f7 | 91ff77a02ca88dd9bb173961f61aa34229472a13 | /prep/labels.R | a15f8758cde76f4dff9e11880cc05c3966899789 | [] | no_license | krishnan-viswanathan/summarizeNHTS | 996b6cd447fa5c3d3a7a7b98aaf4ba96082a550e | afaea3c3168ddd1d82fb808e7d2298690bedb419 | refs/heads/master | 2021-01-15T16:29:37.402304 | 2017-07-26T18:58:16 | 2017-07-26T18:58:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 405 | r | labels.R | # See prep/variables.R to get variable lists
file_labels_2001 <- file.choose()
file_labels_2009 <- file.choose()
labels_2001 <- fread(file_labels_2001)
labels_2009 <- fread(file_labels_2009)
nhts_2001 <- list(
labels = labels_2001,
variables = variables_2001
)
nhts_2009 <- list(
labels = labels_2009,
varia... |
4a3d440b500d754a6968e90955b08df937a9db29 | b5ba5c578810105c9148fecadc61f124ae68118c | /man/lg.Rd | 827df2fa8ad1faf49f87a84fde09be4a1f97ba1f | [] | no_license | dangulod/ECTools | cce57dfe0189ee324922d4d014cb7a72bd97817d | a927092249a92ced28c6c50fe7b26588049a07d0 | refs/heads/master | 2021-01-25T10:51:04.021720 | 2018-05-16T10:31:25 | 2018-05-16T10:31:25 | 93,886,888 | 1 | 1 | null | null | null | null | UTF-8 | R | false | true | 290 | rd | lg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/maxcorlag.R
\name{lg}
\alias{lg}
\title{Modified lag function}
\usage{
lg(x = x, lag = lag)
}
\arguments{
\item{x}{vector}
\item{lag}{if possitive retard, negative delay}
}
\description{
Modified lag function
}
|
a4d1a2a06fc7154e9d5955046a593113d25f1a44 | efa677f6569ccaefaa7dab5965758c9d5c14bc36 | /lpa.mi.src/man/extract_class_proportions.Rd | a75349562e5e557724b8f61078a3f6df30ca2d1d | [] | no_license | marcus-waldman/lpa-mi-src | 2704f45f894184a81c0f934bf1ea9992e7b23c50 | eed7d99fbc2f3a0ce46a0e4f624c2ad40ecc3502 | refs/heads/master | 2021-06-04T03:54:58.475339 | 2020-01-02T01:20:34 | 2020-01-02T01:20:34 | 140,605,108 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 699 | rd | extract_class_proportions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/extract_class_proportions.R
\name{extract_class_proportions}
\alias{extract_class_proportions}
\title{Extract class proportions from Bayesian fitted LC model in Mplus .out file.}
\usage{
extract_class_proportions(file, path = getwd())
}
\argu... |
69da7e4f39906b15ff5c99eb34c80142cdc8551b | 2a0d3a8812926e947c8b91ee8b49951b29dc5198 | /scripts/checkplots_for_parallel_amarel/raref_for_test_3.R | 7c3185c81834c70683ad4e5b0e5aa75a66020985 | [] | no_license | dushoff/diversity_metrics | 27b6b883c816ba2af384a0458c73f8f7bb04b1ba | 8f5f4ac07e56281511788be1d471aa5c7e8c93e1 | refs/heads/master | 2021-10-19T15:11:01.044247 | 2020-04-07T12:46:33 | 2020-04-07T12:46:33 | 45,498,849 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 871 | r | raref_for_test_3.R | # load libraries
library(data.table)
library(tidyverse)
library(iNEXT)
library(furrr)
library(tictoc)
# we have some kind of results to read in
tic()
csamples<-fread("data/new_samples_for_rarefaction_2.csv")
logit<-function(x){log(x/(1-x))}
invlogit<-function(x)(exp(x)/(1+exp(x)))
clev<-invlogit(seq(0.5, 5, 0.25))[
4
]... |
8aaf9ffa92c3aade76b9df2bf72aa5a8fc557510 | 73552179a08604504e307cede5e12eba217eb8ad | /Weekly.R | 4d2ebe49a6c6a16752b283bb95a52ac4d9a5b0d6 | [] | no_license | nikhilraj0025/Weekly-percentage-returns-for-the-S-P-500-stock-index-between-1990-and-2010. | 94707f21bd9124c6c7a55af26a5a437dd8424842 | 22d93377b8b8439e942c49401a3ada1755d2d95e | refs/heads/master | 2020-06-06T12:37:56.030453 | 2019-06-19T13:52:57 | 2019-06-19T13:52:57 | 192,742,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,550 | r | Weekly.R | library(ISLR)
data("Weekly")
View(Weekly)
summary(Weekly)
?Weekly
dim(Weekly)
Weekly1<-Weekly
plot(Weekly[,-9])#########correlation for x#######################################
attach(Weekly1)
plot(Volume)##########Volume is increasing wrt time#################################
library(dplyr)
Weekly1<-mutate(... |
e17da90a081505be75bdd4fe92f4b18aa21af476 | 1f439c7cc390d6b1238990b5794f2156edc34929 | /exemplo_dataframe.R | dc72b08c55e7686cb926c63117907c5237f2e6be | [] | no_license | flavioti/aula_r | 4be9b872b7ffb0d77b126877362e71104a94f165 | 9c5d365fbc2c55e64ad52a0ed5d840e8d7b02e80 | refs/heads/master | 2020-03-19T16:40:32.723949 | 2018-09-04T01:02:39 | 2018-09-04T01:02:39 | 136,724,646 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 360 | r | exemplo_dataframe.R | # Exemplo de uso de dataframe
nome = c("Edmar", "Pedro")
idade = c(30, 20)
salario = c(1000, 2000)
cadastro = data.frame(nome,idade,salario)
filhos = c(1, 2)
# Inclusão de coluna no dataframe
cadastro$filhos = filhos
aumento = c((salario * 0.06) + salario)
cadastro$aumento = aumento
cadastro
write.csv2(cadastro, f... |
a21f7156225b0354140cf19a6fcb4fe699f6c34e | ab5845d7d934a087ef2d708a1d2776129999015a | /R/my_geoDA.R | 477c532ee622193662d6f801eca566f65b2aeddc | [] | no_license | gastonstat/DiscriMiner | 6b9dc6bbc9b4a6a301cf4adbb87353fad716570f | 61cc95e58d801ae6adb8446dbc9cf79ad473277b | refs/heads/master | 2021-06-01T13:55:55.807215 | 2021-02-26T15:22:50 | 2021-02-26T15:22:50 | 5,786,053 | 3 | 4 | null | 2017-02-28T17:45:21 | 2012-09-12T21:15:43 | R | UTF-8 | R | false | false | 1,574 | r | my_geoDA.R | my_geoDA <-
function(X, y, learn, test)
{
# Perform a geometric predictive discriminant analysis
# X: matrix or data.frame with explanatory variables
# y: vector or factor with group membership
# learn: vector of learning observations
# test: vector of testing observations
# how many observations
n = ... |
441393e695828ed9c64bc7f5541355bf12822f90 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed_and_cleaned/10233_0/rinput.R | d6d1c14341f6982c10024a286a5dfc7ab8a93da0 | [] | 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 | 137 | r | rinput.R | library(ape)
testtree <- read.tree("10233_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="10233_0_unrooted.txt") |
9d5deef40d98c191f4a645b202c10d4b27938251 | e5ebddef173d10c4722c68f0ac090e5ecc626b8b | /IL2/bin/PLSR/plsr.R | 6fa07e29848ddfb4fb702d95eb4fbf5983b56884 | [] | no_license | pontikos/PhD_Projects | 1179d8f84c1d7a5e3c07943e61699eb3d91316ad | fe5cf169d4624cb18bdd09281efcf16ca2a0e397 | refs/heads/master | 2021-05-30T09:43:11.106394 | 2016-01-27T15:14:37 | 2016-01-27T15:14:37 | 31,047,996 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,263 | r | plsr.R | library(pls)
library(iterators)
library(flowCore)
source('~nikolas/bin/FCS/fcs.R')
source('~nikolas/Projects/IL2/bin/common.R')
# partial least squares regression
# this individual looks ok
individual <- 'CB00086S'
day <- '2012-09-18'
BASE.DIR <- '~/dunwich/Projects/IL2/CD25-CD3-CD4-CD45RA-CD56-CD8-FOXP3-PSTAT5'
indi... |
8e90611b76811ac7cd994fd767f5dd015838fe8a | 46f795095e1601f46e5c7aee941da98a8062f722 | /man/geo.getTopTracks.Rd | e59d3b0f7e422834d870431b5b4c3fb640bd13ff | [] | no_license | cran/RLastFM | e76ab8d9eee4cb337682835cbb7ce4f761934aca | 8d6737c25922eb92631f6e67c623de0bf8266845 | refs/heads/master | 2021-01-21T09:59:32.612105 | 2009-08-24T00:00:00 | 2009-08-24T00:00:00 | 17,717,948 | 1 | 3 | null | null | null | null | UTF-8 | R | false | false | 981 | rd | geo.getTopTracks.Rd | \name{geo.getTopTracks}
\alias{geo.getTopTracks}
\title{API call to geo.getTopTracks} \description{API call to geo.getTopTracks}
\usage{
geo.getTopTracks(country, key = lastkey, parse = TRUE)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{country}{Country name}
\item{key}{API key, de... |
4519612c12c4088794d856d9cf4ca85c76bbcad5 | 22d53837167bb6fe1a6a962f9db9a066dfeddece | /R/benchmarking.R | d5dca5db3c860a868918573cc45521cb1ef660bd | [] | no_license | kliegr/QCBA | afc2cdc9d4470e6b152cb6721428e2df7bc8db17 | 6cd46329a0f3f0c830c6e2ae9f01a916b983843e | refs/heads/master | 2023-08-17T13:26:56.435887 | 2023-08-11T15:16:55 | 2023-08-11T15:16:55 | 91,793,994 | 10 | 2 | null | null | null | null | UTF-8 | R | false | false | 7,565 | r | benchmarking.R | library(arulesCBA)
library(qCBA)
set.seed(1)
#' @title Auto learn and evaluate QCBA postprocessing on multiple rule learners
#'
#' @description Learn multiple rule models using other rule induction algorithms and apply
#' QCBA to postprocess them.
#' @export
#' @param train data frame with training data
#' @param te... |
0e6d993c64643794b0113d4e6c96b4637ad87c47 | 0ca8a44786ec4a0dc0a54dd6da20796d62478285 | /plot1.R | 63238c9e1e8b510ce280a925091917c40b407d1c | [] | no_license | gianmarino/ExData_Plotting1 | d2720e2563f3c643191b22c278900c7d83b56a18 | e3edf64b60a69eaffeb736530dd161928062a6c1 | refs/heads/master | 2021-01-18T02:20:24.518203 | 2015-05-07T21:52:40 | 2015-05-07T21:52:40 | 35,221,385 | 0 | 0 | null | 2015-05-07T13:24:05 | 2015-05-07T13:24:02 | null | UTF-8 | R | false | false | 752 | r | plot1.R | plot1<-function(){
## Plot1 -> plots a Global Active Power histogram
## Extraction and preparation of data. Takes from fullData and dumps it, clean, on partData
fullData<-read.table("household_power_consumption.txt",header=TRUE,sep=";",na.strings="?",colClasses=c(rep("character",2),rep("numeric",7)))
fullDat... |
13474884a19d5f27634115825923700c8c0858ac | 403f786c7c85fa551326d1e077bc895fea26e7c9 | /tests/testthat/resources/venv-activate.R | 1fd36dbce6955314812dfa1ddc1934bb59eebafc | [
"Apache-2.0"
] | permissive | rstudio/reticulate | 81528f898d3a8938433d2d6723cedc22bab06ecb | 083552cefe51fe61441679870349b6c757d6ab48 | refs/heads/main | 2023-08-22T01:41:52.850907 | 2023-08-21T16:19:42 | 2023-08-21T16:19:42 | 81,120,794 | 1,672 | 399 | Apache-2.0 | 2023-09-13T20:35:47 | 2017-02-06T18:59:46 | R | UTF-8 | R | false | false | 223 | r | venv-activate.R |
args <- commandArgs(TRUE)
venv <- args[[1]]
Sys.unsetenv("RETICULATE_PYTHON")
Sys.unsetenv("RETICULATE_PYTHON_ENV")
reticulate::use_virtualenv(venv, required = TRUE)
sys <- reticulate::import("sys")
writeLines(sys$path)
|
cb6c06cadd72dbe43aebff4f18d572fca97b8224 | 18e34fdc32f1856ea62c518e9094cabfaf1b464f | /R/normalize_data.R | 23989a15821e2f9c7091dc441e94bb06dc6cdee8 | [] | no_license | cran/dematel | 200c784f9a1c314a9b1dcaedc02ba138270adb10 | 3f2335fdc1be6cdcf7645a6426518ced0203ad5d | refs/heads/master | 2023-03-07T15:41:26.311469 | 2021-02-22T10:10:05 | 2021-02-22T10:10:05 | 341,248,652 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 675 | r | normalize_data.R | #' Normalize Data
#'
#' Normalizes matrix format data
#'
#' @param x a matrix containing the values of direct relationship decision matrix.
#' @param data_control is a pre-defined logical parameter that whether data should checked.
#'
#' @return This function returns a \code{list} of data, and normalized matrix.... |
c7f83abaa4713686173b1e457aa89eea6560ee9f | 750423288021c0d0bcd0d656d09351e4f86870de | /analysis/2016/shiny_apps/snake_draft/heavy_lifting.R | f570b3adda4d3f3db172e165321ef6dc4a525e05 | [] | no_license | johnckane/fantasy-football | b51ae061dc221ad9e17900d1915b95c231a454ad | 2ccfdb62f0011738172d774f9f4e2ba72936de2b | refs/heads/master | 2022-12-05T09:18:40.070217 | 2020-09-03T02:28:39 | 2020-09-03T02:28:39 | 106,360,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,535 | r | heavy_lifting.R | library(dplyr)
library(tidyr)
# This is PPR data
data <- read.csv("/home/john/stats_corner/2016/shiny_apps/snake_draft/FFA-CustomRankings.csv",
stringsAsFactors = FALSE,
header = TRUE)
head(data)
str(data)
# we don't need all these variables
data <- select(data, 1,2,3,4,5,8,16,19,2... |
0adad1f23e5ece3f6dcef1734625540bbf00c784 | 0a2ae3dc46bf6cc0af67fec2f716954bcc3beb5b | /man/get_custom_palette.Rd | 13961175b9a5518f0184a7c0cac9f408f0e33158 | [] | no_license | borstell/flagrant | c18800d4f39830d3d811b2d2376585c8f3492eac | acf37e1b7b69ddccd60809082dd945901c99b76c | refs/heads/master | 2023-07-03T21:30:46.976930 | 2023-06-12T15:15:19 | 2023-06-12T15:15:19 | 263,183,662 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 629 | rd | get_custom_palette.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_custom_palette.R
\name{get_custom_palette}
\alias{get_custom_palette}
\title{Get custom palette}
\usage{
get_custom_palette(country = "Sweden", n = 3)
}
\arguments{
\item{country}{The name of a country, either official English name (e.g. ... |
e7630a386e77c17ccd62a12068b9268af61f83e7 | cdd5955cec0498b5c287c5f06efd641f363fa159 | /1/2.R | 2de845e0ddeaad9a660fe616dd39b8ea23c57284 | [] | no_license | amirhossein-alizad/EPS-using-R | 0aee04fcb200a2bed82731779386674a9b448da7 | 3cc4add2ee9de49de49f0c123c09fe35b02688fd | refs/heads/main | 2023-03-01T13:05:22.606658 | 2021-02-08T23:04:06 | 2021-02-08T23:04:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 228 | r | 2.R | times = 7000
x<-rep(0,500)
for(i in 1:7000)
{
num = floor(runif(1, min=1, max=500))
x[num] = x[num] + 1
hist(x, main = i, plot = T,col = "green", xlab = "guest's money", ylab = "guests count")
Sys.sleep(0.05)
}
|
2000d13c2e38c78739ef62b0ffd96cfb7b9b3a5e | 92a0b69e95169c89ec0af530ed43a05af7134d45 | /man/Make.dependency.graph.obj.Rd | 97308bffd088c11d9e35f85fadcd725ad1a13c9c | [] | no_license | gelfondjal/IT2 | 55185017b1b34849ac1010ea26afb6987471e62b | ee05e227403913e11bf16651658319c70c509481 | refs/heads/master | 2021-01-10T18:46:17.062432 | 2016-01-20T17:51:29 | 2016-01-20T17:51:29 | 21,449,261 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 486 | rd | Make.dependency.graph.obj.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/Make_dependency_graph.R
\name{Make.dependency.graph.obj}
\alias{Make.dependency.graph.obj}
\title{Creates an graph object from a dependency object}
\usage{
Make.dependency.graph.obj(dependency.out)
}
\arguments{
\item{dependency.out}{... |
7f5b19a695ab523612f36af43740387b8deb11ce | 8c5f0222a10ce128bcf20a0f62b03b8795ee4c3d | /R/spautocor.r | 9bf42a311b0eab86fbf2c1ab0640d993803c5fcd | [] | no_license | green-striped-gecko/PopGenReport | 15a58e5184b877b65791a14b2271487c5d979b81 | d6b970e91d2b90476704ff95586b0b6e40892111 | refs/heads/master | 2023-07-10T17:01:23.072377 | 2023-06-26T23:55:04 | 2023-06-26T23:55:04 | 33,985,286 | 6 | 5 | null | 2023-06-26T23:55:06 | 2015-04-15T09:33:23 | R | UTF-8 | R | false | false | 4,391 | r | spautocor.r | #' Spatial autocorrelation following Smouse and Pekall 1999
#'
#' Global spatial autocorrelation is a multivariate approach combining all loci
#' into a single analysis. The autocorrelation coefficient r is calculated for
#' each pairwise genetic distance pairs for all specified distance classes. For
#' more informati... |
0fd1cf9d5142d999a3acdd5508b5f929c09af3b9 | a1e3f742d80a225e9a2a35e8e88b3054f5408037 | /R/test.maker.R | 852c18802f5ec80355be1a12d65cac1c9aab0034 | [] | no_license | cran/MXM | 7590471ea7ed05944f39bf542c41a07dc831d34f | 46a61706172ba81272b80abf25b862c38d580d76 | refs/heads/master | 2022-09-12T12:14:29.564720 | 2022-08-25T07:52:40 | 2022-08-25T07:52:40 | 19,706,881 | 0 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,407 | r | test.maker.R | test.maker <- function(test) {
if (test == "testIndReg") { ## It uMMPC the F test
test <- testIndReg;
} else if (test == "testIndFisher") { ## It uMMPC the F test
test <- testIndFisher;
} else if (test == "testIndSpearman") { ## It uMMPC the F test
test <- testIndSpearman;
... |
f49619db2168b43c5da264c8709589a3e7381c6b | 47e9b28e603f83d4b28cffd42a3c548168300058 | /20170530_bayes_gibbs_sampling_01/run.r | 6febd52029d607da35cbc3e824e938e93e2e63ce | [] | no_license | kazufusa/til | 4fa4c2b201c1c566dd148074ea94d1de96cc62c6 | 321e6ef62d8510f20a4d56834c33e3f4518ecbce | refs/heads/main | 2023-08-08T07:13:48.103002 | 2023-07-19T14:21:56 | 2023-07-19T14:21:56 | 60,586,645 | 5 | 0 | null | 2023-03-06T22:37:47 | 2016-06-07T06:06:11 | Jupyter Notebook | UTF-8 | R | false | false | 1,415 | r | run.r | options(width=200)
set.seed(1)
N <- 100
a_true <- 0.4
mean1 <- 0
mean2 <- 3
sd1 <- 1
sd2 <- 1
Y <- c(rnorm((1-a_true)*N, mean1, sd1), rnorm(a_true*N, mean2, sd2))
data <- list(N=N, Y=Y)
write.table(Y, file="points.csv", sep=",", row.names=F, col.names=F)
model2 <- "
data {
int<lower=1> N;
vec... |
1677479608192bf6020d09272170eca68111eebc | 42355df3e045bfa63450f7b0c5c2af16baf06b77 | /surviving_phases_dataverse/MSM_Sim.R | 03a34583a3e5454c902c9b13433b3fe2a50f77f9 | [] | no_license | judgelord/DOT | 0f9ca40f392d3436904bacd2949bb616f5cd50b8 | 3d6a2c74b540e75759da6a290c5b3650da4b4174 | refs/heads/master | 2020-04-01T05:43:19.174053 | 2019-05-28T18:28:14 | 2019-05-28T18:28:14 | 152,916,995 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,465 | r | MSM_Sim.R | # Replicates with R 3.3.1, given the following package versions.
rm(list=ls())
library(msm) #v1.6.1
library(survival) #v.2.39.5 (!! Important.)
library(mstate) #v0.2.9
setwd("C:/Users/Shawna/Desktop/PA replic")
set.seed(031415)
### Simulate 250 individuals with common observation times
sim.... |
530037eb5b4e3075934ee571df4cbe384ec7d201 | f227db976d38b05a34245eb1a1c550cc51048499 | /tests/testthat.R | 8b7230396e31050d6e7c43d9df989101f7ab9bc6 | [
"Apache-2.0"
] | permissive | leeevans/Achilles | 1d05f319adc6f9abd575881d1f140e3fd315fd7c | 212211afaa77200ac9bbb809d85f440b99fbe9d6 | refs/heads/master | 2021-01-15T17:36:27.491108 | 2016-02-24T16:44:30 | 2016-02-24T16:44:30 | 52,451,809 | 0 | 0 | null | 2016-02-24T15:17:40 | 2016-02-24T15:17:40 | null | UTF-8 | R | false | false | 88 | r | testthat.R | Sys.setenv("R_TESTS" = "")
library(testthat)
library(Achilles)
test_check("Achilles")
|
a6736cf04645cde196eb83fa8135c9b473dc50f0 | 2a20ba73f6804363f0e4bcf17980bc4bb9d75592 | /inst/unitTests/test_AnimalQTLDB.R | 48d7b6ff28bd4e70098b61600c0cc139dee0d693 | [] | no_license | liuyufong/AnimalQTLDB | b03647428ae53798ce7892ca5a0154dd9bb03140 | 4ddf62d1c41041b2355d187aeacacc802758a826 | refs/heads/master | 2021-01-01T08:19:17.719641 | 2017-08-17T13:24:36 | 2017-08-17T13:24:36 | 96,750,432 | 0 | 0 | null | 2017-07-18T03:25:53 | 2017-07-10T07:48:05 | null | UTF-8 | R | false | false | 169 | r | test_AnimalQTLDB.R | test_AnimalQTLDB <- function(){
checkEquals(NROW(AnimalQTLDB()), 7)
checkTrue(AnimalQTLDB()[1,1] == 'table')
checkEqualsNumeric(NCOL(AnimalQTLDB()), 5)
} |
39fa04e49fd413d8c4f4dd3bd6dc29f934e720c5 | 2b5895474a98cca1d0d41e7f44a21b28ac07aee6 | /ad_hoc_analysis/30_point_rule.R | 49ccabbd4eccfa07382fd2563fa7364f622f15be | [] | no_license | insightlane/score-progression | 7666c36ebdeaf1ac346e1da4982208ab92371a7a | ecf24455dcdb46ff149a3bbccaf33806bfb02755 | refs/heads/master | 2023-05-07T20:19:04.581541 | 2021-06-06T10:37:16 | 2021-06-06T10:37:16 | 288,646,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,569 | r | 30_point_rule.R | library(dplyr)
library(tidyr)
comebacksyear <- score_progression_worm %>%
ungroup() %>%
mutate(Season = as.numeric(format(as.Date(Date.x, "%d-%b-%Y"), "%Y"))) %>%
#filter(Event != "PS" & Event != "S" & Event != "F") %>%
group_by(Season) %>%
summarise(count = n_distinct(GameID),
... |
fe314a86788d35619dda06e650fb2a305faa75f3 | c66a649227a633cbce7c1cd2307a34332670a3d8 | /singler_annotation.R | 36c9e9ff18e421b6f6af4a61956db62b8191248f | [] | no_license | chansigit/scSnippet | 64036602d4913b105ac4107bcc8c42f894e97696 | 5be7fc3ee2619177ceff21edc84f0e02ada22dc6 | refs/heads/master | 2022-11-17T01:50:33.027809 | 2022-11-11T08:38:05 | 2022-11-11T08:38:05 | 192,212,166 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,967 | r | singler_annotation.R | tic()
load("/data/hca/SingleRReference/HumanPrimaryCellAtlasData.rda")
load("/data/hca/SingleRReference/BlueprintEncodeData.rda")
load("/data/hca/SingleRReference/DatabaseImmuneCellExpressionData.rda")
load("/data/hca/SingleRReference/NovershternHematopoieticData.rda")
load("/data/hca/SingleRReference/MonacoImmuneData.... |
354a2eb7afbdd0b933579d64db38dfdafcfa6d54 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/8712_2/rinput.R | 46035c45b6ee0378604fab28f3bd7f0de78ac759 | [] | 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 | 135 | r | rinput.R | library(ape)
testtree <- read.tree("8712_2.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="8712_2_unrooted.txt") |
d4ed67b8444100607c4dd2719fc152287a3dcfdf | 6a2d44c6012f0715f1b1a3ca7c83bf07cb380d64 | /cachematrix.R | 806f7be3c4813f21fd049834e35ab2416d93f7f3 | [] | no_license | jjsjamesj/ProgrammingAssignment2 | c4ef17755fb8a1fa1ed9f03d88fd428bbccc22ec | 21a0ca6fcf7b7e1fe7ade54d7ad7b38dea078125 | refs/heads/master | 2021-01-12T13:46:49.922621 | 2016-09-25T12:27:36 | 2016-09-25T12:27:36 | 69,127,779 | 0 | 0 | null | 2016-09-24T21:08:12 | 2016-09-24T21:08:11 | null | UTF-8 | R | false | false | 1,183 | r | cachematrix.R | ## makeCacheMatrix takes as argument a square invertible matrix
## and returns a list of getter and setter methods for the matrix
## and its inverse.
## code of the form 'X<<-Y' assigns to X in the parent environment
makeCacheMatrix <- function(x =matrix() ) {
xinverse <- NULL
set <- function(y) {
x <<- y ... |
a653d72abe7d7b6e6cde679e2e8b15a97875fbf2 | a07ee789e553ab8f6e01f77e3d92b13995976550 | /Untitled.R | f7dfd380760814be86eabb958aa43d94796e8bfa | [] | no_license | hancampbell/PracticeCPSC292 | b0f1c31d958faca87504cd54699f99a6ebcac26d | 5bfcbda1f3993521a9c6c2bb3ace3929d5cbc733 | refs/heads/main | 2023-08-28T13:16:00.117860 | 2021-10-29T19:23:09 | 2021-10-29T19:23:09 | 422,687,827 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 169 | r | Untitled.R | #Practice code
print"Hello world"
library(usethis)
library(gitcreds)
usethis::create_github_token()
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
gitcreds::gitcreds_set()
|
0e9ca382884e58b9158787531ffbc4ecfa6001b7 | ee785dcfc8f3d826dd995602ee9e312d7d95bbb0 | /inst/save.v3.obj.R | 3f08dcbd6e3dec996994adf24cded8673db703a4 | [
"MIT"
] | permissive | morris-lab/CellTagViz-package | b54905f5b2d69c48aade115a058cb271da535d37 | 196169aca3a03a541482c73611b641b9f81d7a58 | refs/heads/master | 2020-04-20T23:10:54.723257 | 2019-08-27T19:30:30 | 2019-08-27T19:30:30 | 169,161,856 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 479 | r | save.v3.obj.R |
load("~/../Desktop/sham.sbr.integrated.RData")
saveRDS(object = integrated.subset, file = "~/../Desktop/integrated.subset.RDS")
path <- "~/../Desktop/integrated.subset.RDS"
dat <- readRDS(file = path)
monPath <- "~/../Desktop/unsupervised timeline all data.RDS"
mon <- readRDS(file = monPath)
#monSCE <- exp... |
a3a50647b3e6fc5cc510a2dada21b8f875a82b9e | 1f25974833ab7f542da6ead2c1a6857c5bafeb21 | /computations/network_distances.R | f833e667b129725cb5b32fddfbe7a580fdcd667e | [] | no_license | SugiharaLab/SIO276L | e08387018321e13953f607c15e7c7d23fdad0b7a | 26b9c5b7a9be32e4f3b6f5a25843050ff7d6952c | refs/heads/master | 2020-05-04T13:23:35.764642 | 2019-05-21T10:49:48 | 2019-05-21T10:49:48 | 179,158,404 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,983 | r | network_distances.R | source ("import_data.R")
library(qgraph)
library(rEDM)
library(ggplot2)
num_pairs <- length(modes_df)*(length(modes_df)-1)
weight_pairs <- data.frame(to=character(num_pairs),
from=character(num_pairs),thickness=numeric(num_pairs),
stringsAsFactors=FALSE)
GEN_CCM_TEMPORAL_GRAPH <- FALSE
SLIDING_WINDOW_NETWORK <- F... |
9881a111347e788f12cb81ff782160668b3f1ef6 | 35ae1abde4828b315a805ca5ed207bcf4d13722c | /reference.R | 914416eacf483066264cad248d76cf9d183add68 | [] | no_license | DongboShi/chinese_author_disambiguation | a0f49b98123971d7ce7d32457ba83372f14e4ba4 | 0081699c386f73de598941ed1b7aa4a0ffbf90bb | refs/heads/master | 2021-07-07T18:58:38.857284 | 2020-12-16T16:28:08 | 2020-12-16T16:28:08 | 215,990,534 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,925 | r | reference.R | library(dplyr)
library(rjson)
library(rhdf5)
library(stringr)
library(tidyr)
library(rlist)
library(parallel)
files <- list.files(path='/Users/zijiangred/changjiang/dataset/inputdata',pattern='CJ_')
id <- sort(as.numeric(str_extract(files,'[0-9]+')))
Reference <- c()
for (i in id){
data <- fromJSON(file=pa... |
8fa4eec4d3e7e17bd278f19cc4d10b2702dce5b2 | e22fec1de80f57545bca4c379e2f3c5d56d83333 | /man/geo_melbourne.Rd | b37f8b040c62c6bf55e3300a35805242eade0673 | [
"MIT"
] | permissive | SymbolixAU/geojsonsf | 0ac38b9702921af23355a200ab4eca05f540d180 | d1d8d3fefea8f5ee14297588552ae37dc17e22a6 | refs/heads/master | 2023-07-07T11:28:22.964093 | 2023-06-24T01:10:12 | 2023-06-24T01:10:12 | 127,064,889 | 69 | 7 | NOASSERTION | 2022-03-03T21:28:04 | 2018-03-28T01:14:20 | R | UTF-8 | R | false | true | 366 | rd | geo_melbourne.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geojsonsf-package.R
\docType{data}
\name{geo_melbourne}
\alias{geo_melbourne}
\title{geo_melbourne}
\format{
An object of class \code{geojson} (inherits from \code{json}) of length 1.
}
\usage{
geo_melbourne
}
\description{
GeoJSON data of Me... |
08f073ae36ff01253b59d1b29eab1431ec6bc486 | 39bd9f74565d4e24fb60546e8f61fc9340356296 | /Chapter 1/NimTotals.R | 56d4d93fae14ac8678ecb914058a05037551f6d8 | [] | no_license | afettouhi/GraphingDatawithR-R40 | a7d13797bd45fafd9890722cd1c65b7740253288 | e60f8d9222949603aefa259350eb5d9dddbcb8a4 | refs/heads/master | 2022-11-15T17:31:01.241036 | 2020-07-11T07:17:24 | 2020-07-11T07:17:24 | 275,732,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 781 | r | NimTotals.R | installed.packages()
install.packages("gmodels")
install.packages("XLConnect")
# The following group of commands is a script
library(gmodels) # required to use the CrossTable command
library(XLConnect) # must have installed XLConnect
Nimrod2 <- readWorksheetFromFile("/home/af/Dokumenter/Programs/GraphingDatawithR-R... |
6f4890b9600863f12986494497d7a9f1c9020ce2 | 51007a8928a04dfc0ca28e6a38a4ccdbcc42b9f2 | /code/R/utilities.R | 7bd8dae260c3dad56db8c4c5d1bc51ea8de8076c | [] | no_license | Joker-Jerome/utmost_update | ac02216c4299bc357b2bf3eeac4b9aef4e47e793 | 97ce581531b0e5f739394752bc1f6b29668aa0e7 | refs/heads/master | 2021-06-23T11:20:03.869583 | 2021-04-26T15:00:03 | 2021-04-26T15:00:03 | 214,357,198 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 453 | r | utilities.R | library(data.table)
library(dplyr)
library(ggplot2)
ghist <- function(vec) {
#vec <- mean_res
df <- data.frame(
x = vec
)
mean_val = mean(vec[is.finite(vec)], na.rm = T)
p <- ggplot(df, aes(x = x)) +
geom_histogram(aes(y=..density..), colour="black", fill="white")+
geom... |
e8183d88b39214e6e6c12b35925b809953fcce3d | ac0063d0365a6c8599069f8a1d00a7e90b763163 | /R/h2m.R | f2142c6bd8afb88a6397d5b17725c55fb2977fa0 | [] | no_license | pwj6/tR | bf488073d4c7630df25536ec6310f0763dc536fe | c89d50f21396d2c2ca2298578e77313fb812539c | refs/heads/main | 2023-04-22T22:07:27.843723 | 2021-05-13T09:50:25 | 2021-05-13T09:50:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 812 | r | h2m.R | #' how to make herb to molecule or cid of molecule
#'
#' @param x an variable
#'
#' @return molecule and cid
#' @export
#'
#' @examples
#' .h2m(x='Ziziphi Spinosae Semen',type='latin')
#' .h2m(x='houpu',type='pinyin')
#' h2m(x=c('Ziziphi Spinosae Semen','Abri Herba'),type='latin')
h2m<-function(x,type="latin")
{
y<-l... |
408fb98e73c631ed577e9776399ed2c15cd334af | f8f7371c1357b975a3721f9316f2c7a4a91888d1 | /R/plot_gradient.R | 559af131df8a32e2c58782d9ce18ad6ff2694a45 | [] | no_license | oldiya/LandClimTools | 5849fd3418fc5bb5633b8fae3c3c8fe2e3ad9df2 | 979a2e56d62606f4ed91be1f27394572ba224911 | refs/heads/master | 2021-09-07T23:03:15.957301 | 2018-03-02T17:16:54 | 2018-03-02T17:16:54 | 283,109,268 | 1 | 0 | null | 2020-07-28T05:27:33 | 2020-07-28T05:27:32 | null | UTF-8 | R | false | false | 438 | r | plot_gradient.R | plot_gradient <- function(x, y, col=NULL, ...) {
if(is.null(dim(y))) {
y <- cbind(null = rep(0, length(x)), y)
} else {
y <- t(apply(y, 1, cumsum))
y <- cbind(null = rep(0, length(x)), y)
}
if(is.null(col)) col <- rainbow(ncol(y)-1)
x.poly <- c(x, rev(x))
plot(y[,ncol(y)] ~ x, type="n",... |
e0a42ecfd7186f32423c154735a09297c39cdcb5 | 4fd287a7d873aaf616e4d45f06f068e88f59881f | /www/outputs/feedback_choices.R | 381b99b2ad177dbfb543d87d6b79485119d0da39 | [] | no_license | slphyx/comoTH | da20a855d49361cb3cd977373d729ebc045153d0 | 1afa22d1a4dcf13dce904a90639ab86d3b7fbf17 | refs/heads/master | 2022-07-04T01:20:44.196428 | 2020-05-15T08:46:11 | 2020-05-15T08:46:11 | 258,414,945 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 369 | r | feedback_choices.R | output$feedback_choices <- renderText({
return(
paste0(
strong("Selected Inputs:"),
span("Cases/Deaths:", span(class = "importanttext", input$country_cases),
", demographics: ", span(class = "importanttext", input$country_demographic),
", social contacts: ", span(class = "importantt... |
ee9cf0dab326ae11ce7adcf65d21db076c7ce2ac | b5b18f45016c0fdb3b3913c65e925ef3aaecad52 | /R/phy.sim.R | 75627b7aa50ac9fc5c755284bdd0264d27b9c84b | [] | no_license | cran/pez | 03e7fbde49853f699579c7fc7c7d44359eab338e | d22ffe708aae2c801b609bfc2f89204e7ccf0f72 | refs/heads/master | 2022-09-09T18:35:36.722610 | 2022-08-31T17:00:02 | 2022-08-31T17:00:02 | 26,346,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,121 | r | phy.sim.R | #' Simulate phylogenies
#'
#' Simulate phylogenies under pure birth/death or as a function of
#' trait evolution
#'
#' \code{sim.bd.tree} simulates a pure birth/death speciation
#' model. There are two important things to note: (1) speciation is
#' randomised before extinction, and only one thing can happen to a
#' lin... |
98c4edd1f8832fa2755dcb86ccb070f0df0af257 | 687df85904e5472055ddbfd2ddd155f4a5b4082a | /scripts/PlotMCMC.R | 3832af6dec7425c21a2a6fc12f08226f8d0e37ff | [] | no_license | barbagrigia/ComposableStateSpaceModels | 2fe80a04b0b8ef43e99dbfe87bf8906034216328 | d6c3c677efdfcddd0c97e44dfdf06e1c025e3ce3 | refs/heads/master | 2021-01-22T18:23:06.168525 | 2017-03-14T16:18:47 | 2017-03-14T16:18:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,104 | r | PlotMCMC.R | plot_running_mean = function(chains, parameters, actual_params, pages = 1) {
chains %>%
mutate(chain = as.factor(chain)) %>%
gather(key = parameter, value, -iteration, -chain) %>%
inner_join(actual_params, by = "parameter") %>%
filter(parameter %in% parameters) %>%
arrange(parameter, iteration) %>... |
0bb9c3db6f9862c27d7026dc3f77baa1a3b328ed | 78063f82eceb719b9cedc6b2d0a64b7d11cf4e53 | /Recommendations/R_UBCF.R | 0b85771bfcf378323acf8ad1576b991e034224af | [] | no_license | fagan2888/Twitch | 173263dec1895c1e4ec25fe8d0bfcb228b4a532f | 0475f95667d365615618c519a1df0cae5aacdcc2 | refs/heads/master | 2021-09-16T13:13:12.427357 | 2018-06-21T04:45:07 | 2018-06-21T04:45:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 223 | r | R_UBCF.R | install.packages("recommenderlab")
library(recommenderlab)
matrix <- as(read.csv("Games.csv"),"realRatingMatrix")
model <-Recommender(matrix, method = "UBCF")
games <- predict(model, matrix["101",], n=5)
as(games, "list")
|
d899df1d794ea6a449a306afd057b296420c675b | 2a739305dc86f75385e93d9de81f510d55a89862 | /code/work/kivisto/fig7.R | 77f92b5950f38588ddf2deffd253c25ba0c593b9 | [] | no_license | mikkosk/project_course_addison_steele_spectator_in_estc | 541c3c75c7a30bc979d60f1ff760562c2479e221 | 61164b306397a0996cf9ff57fc5ee5ea3c511960 | refs/heads/main | 2023-04-18T21:42:46.081179 | 2021-04-25T10:24:30 | 2021-04-25T10:24:30 | 323,597,343 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 616 | r | fig7.R | #FIG. 7 - Steele most published works
groupWorkS <- steele %>% group_by(finalWorkField) %>% dplyr::summarise(n = n())
groupWorkS <- arrange(groupWorkS, n)
groupWorkS$finalWorkField <- factor(groupWorkS$finalWorkField, levels = groupWorkS$finalWorkField)
fig7 <- ggplot(data = groupWorkS, aes(x = finalWorkField, y = n... |
163be60f4b40331c845f89fbcb9e21f97f85539e | 98fadffa9fb4a4fe81874afe37a02569791ce808 | /scripts/assign_tax.R | a458c8586229f9b0566006369596f78cae413241 | [] | no_license | cErikson/GeneLab_DADA2_snakemake_Pipeline | e04c9c54e5355b7f1e92a155425de5541b486559 | a27fc4d18791dbb7a66e142913be68a0428dc6a4 | refs/heads/master | 2020-03-21T04:39:51.206779 | 2018-09-07T18:14:50 | 2018-09-07T18:14:50 | 138,121,254 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,188 | r | assign_tax.R | ## ++=======================================================++
## || DADA2 taxnonomy script for marker gene GLDS datasets ||
## ++=======================================================++
##
## Christian Erikson: Bio.Erkson@gmail.com christian.b.erikson@nasa.gov
##
library(dada2)
library(dplyr)
library(readr)
# Log ... |
99b0b27f47cfe5ce40e85fa3bb106f2314cfad27 | dfe01adc03e83d935c2207695164f027a9aed9fd | /man/azureDataLakeMkdirs.Rd | 8cf7c703f16b846cfba5840c5212377b1598a466 | [] | no_license | CharlesCara/AzureSMR | 155502a9e260e4f1a7631cdd248e0727e59fbcb3 | 199dda1e10a8e313b80d08092c6ffa0526a494d5 | refs/heads/master | 2020-03-19T06:16:39.558535 | 2018-07-12T10:27:54 | 2018-07-12T10:27:54 | 136,006,301 | 0 | 0 | null | 2018-06-04T10:01:19 | 2018-06-04T10:01:18 | null | UTF-8 | R | false | true | 2,490 | rd | azureDataLakeMkdirs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AzureDataLake.R
\name{azureDataLakeMkdirs}
\alias{azureDataLakeMkdirs}
\title{Create a directory with the provided permission.}
\usage{
azureDataLakeMkdirs(azureActiveContext, azureDataLakeAccount, relativePath,
permission, verbose = FALSE)... |
1ebf29eb8924ac7fc58b10547ce1fb83e29f3b0b | 38a88f465320a9682d8b3d8f045059469cf60814 | /scripts_control.R | a9755a0e4d56cb3d803b550f5149958336bcbf8e | [] | no_license | ostroskianais/transportation-lp | 4baac427937ee135b91cbaa2363012efec95984a | b1177de02d90963ec642b2c5142f27c02715d735 | refs/heads/main | 2023-02-19T10:02:31.872261 | 2021-01-24T21:49:14 | 2021-01-24T21:49:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 190 | r | scripts_control.R | # Run before python opt
source("get_sets.R")
source("get_distances.R")
# Run after python opt
source("get_opt_results.R")
source("network_ij.R")
source("network_jk.R")
source("network_viz") |
0f30a19f64ed5a4b536ae41eb6e6b58e1d4eb7ff | c4fda47143d29ddbb284c742a48480a0cf0aa98c | /R/balanceAreaHarvested.R | fee54bce6e0c5ab7f7f10c26ef6c47f1b08659db | [] | no_license | SWS-Methodology/faoswsProduction | bad2d75be7a5a845ad0de79cb40b691ff0a6d80e | 6e1c6ab8042e3c7cd398d65bc6733e0a4d038f3b | refs/heads/master | 2023-04-08T16:10:34.598286 | 2023-03-17T13:05:52 | 2023-03-17T13:05:52 | 33,170,385 | 0 | 2 | null | 2023-03-17T13:05:55 | 2015-03-31T07:08:06 | R | UTF-8 | R | false | false | 4,923 | r | balanceAreaHarvested.R | ##' Function to compute area harvested when new production and yield are given.
##'
##' @param data The data.table object containing the data.
##' @param processingParameters A list of the parameters for the production
##' processing algorithms. See \code{productionProcessingParameters} for a
##' starting point... |
b0a978b7a1492a7bc280e9c584a9e989c7a29c5e | 9e0000dc133163ec7c89af560df6ee11ee4ea9b0 | /src/01_preprocess/01_unzip_and_subset_WDPA.R | ea08adcfbbb05bbcaa9fc3f5cd2e13940fe033b5 | [
"MIT"
] | permissive | cbig/gpan-connectivity | 374c2ac758da5448cf0e04bf4aee4b2275a3f60b | 8790a42fad5558fcd7815ca37c6e7b3cc1a07afe | refs/heads/master | 2016-08-05T09:51:03.946891 | 2016-01-12T20:06:52 | 2016-01-12T20:06:52 | 33,982,666 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,514 | r | 01_unzip_and_subset_WDPA.R | #!/usr/bin/env r
# Description -------------------------------------------------------------
# Simple helper script that can be used to unzip downloaded WDPA data and
# to select PAs other than marine (using ogr2ogr in gdalUtils).
# NOTE: script assumes that it is run within a RStudio project, i.e. paths
# are not r... |
bfb3674a0c92fa069de999d17410315e9bd24fc1 | 887dc03efc71b10900e0fcab0d56e85a877098f8 | /R/mod_email_validation.R | 84c18f243c8af03a1b863f789dc0bf44cbec7e56 | [
"MIT"
] | permissive | ove-ut3/survey.admin | 36a445459c532bf307dfb5f0f4747b6288494d2c | 98225711492f50931d868a9277d7fba039bd1efc | refs/heads/master | 2021-01-07T22:48:29.207281 | 2020-05-20T12:57:42 | 2020-05-20T12:57:42 | 241,842,349 | 0 | 0 | NOASSERTION | 2020-05-20T12:57:43 | 2020-02-20T09:23:39 | R | UTF-8 | R | false | false | 11,379 | r | mod_email_validation.R | # Module UI
#' @title mod_email_validation_ui and mod_email_validation_server
#' @description A shiny Module.
#'
#' @param id shiny id
#' @param input internal
#' @param output internal
#' @param session internal
#'
#' @rdname mod_email_validation
#'
#' @keywords internal
#' @export
#' @importFrom shiny NS tagLi... |
d09646f07d92ac19309e6f942b8e3c9492734003 | 4e158e6ae5dbe5f073a895689a800900cdc1b4fc | /R/uv_linear.R | 946e1eda055734ee7cd659da931812751570bf07 | [] | no_license | cran/fastStat | 7943ac0faa13cef1d00f813dfed91b9162e24c42 | ba5479eab0ec601bef4aaa0348d722b1272e2a1d | refs/heads/master | 2020-12-21T23:17:37.441282 | 2020-09-17T14:00:13 | 2020-09-17T14:00:13 | 236,596,867 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,111 | r | uv_linear.R | #' Looping for Univariable Logistic Regression
#'
#' @param data data
#' @param y y
#' @param variable variable names for univariable logistic regression. If missing, it will be column names of data except y and adjust
#' @param adjust adjust variable names for univariable logistic regression
#' @param round digi... |
2c38513be1a85789bb548d397aa32161c892d155 | 62c14804025c9b0a56b3dc43937cd365ec1481b3 | /output/sorted/GM12874/GM12874.R | 21e6be2f3e232dd93c7c39bef2add5d286a2d2a7 | [
"MIT"
] | permissive | Bohdan-Khomtchouk/ENCODE_TF_geneXtendeR_analysis | 98ad9dd688d78af0a412d7c3defde223c6d1ff50 | 4d055110f2015aa8d65bcd31eea3b0da8e19298f | refs/heads/master | 2021-05-04T06:55:12.446062 | 2019-04-19T00:46:01 | 2019-04-19T00:46:01 | 70,523,421 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 96 | r | GM12874.R | peaksInput("CTCF.GM12874.bed")
png("CTCF.GM12874.png")
linePlot(human, 0, 10000, 500)
dev.off()
|
bc954a705fcad1e06eadfc877ac9873f86356063 | 38373485330e50b09d27ea265ee0535b368f0579 | /code/pca-skill-scores-ggbiplot.R | 20bdd49b095b29ca6aeff8c5a1e96fc9b7ec8f95 | [] | no_license | s81320/vis | 5300e346349acd568cd7ff4ad06751960aeb42b8 | b96755388ebdbd50c42d145e9e6fc26b2c1c45c4 | refs/heads/master | 2022-11-18T03:34:05.794807 | 2020-07-21T17:25:05 | 2020-07-21T17:25:05 | 270,222,860 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,240 | r | pca-skill-scores-ggbiplot.R | # require(devtools)
# Sys.setenv(R_REMOTES_NO_ERRORS_FROM_WARNINGS="true")
# install_github("vqv/ggbiplot")
setwd("D:/msc-ds/course-resource/data-visualization/project")
rm(list=ls())
library(RColorBrewer)
library(ggbiplot)
soccer.preprocessed <- read.csv(
"soccer-preprocessed.csv",
encoding = "UTF-8"
)
# Broa... |
a66bd5bf1030528ceb911fa3e8e09a7e9334417f | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2017/mortality_code/mortality_estimation/life_tables/mltgeneration/R/recalc_u10_nlx_mx_ax.R | ba9fa7a478542b6056b1aa1623f35183ebfe5200 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,908 | r | recalc_u10_nlx_mx_ax.R | #' Calculate under-10 nlx, mx, and ax values
#'
#' Calculate under-10-specific nlx, mx, and ax values based on Human Life-Table Database k1 parameters
#'
#' @param dt data.table with variables: ihme_loc_id, sex, age, sim, age_length, qx, lx, dx
#' @param id_vars character vector of id variables (last one must be age)
#... |
7a3e5999fb47eb1453851ed64e7ffa8762719b79 | 2b7607fa78bf83b2515b9de2f9b40d15c81c2ab2 | /Scripts/antsASLProcessing.R | 83668b72e916024cc3c849170097309976cd00ea | [
"Apache-2.0"
] | permissive | ANTsX/ANTs | 3176341b8de664939eafde3e1ebf8c449809a9dd | dfd9e6664f2fc5f0dbd05c6c23d5e4895e82abee | refs/heads/master | 2023-08-24T20:43:33.986495 | 2023-08-08T18:23:45 | 2023-08-08T18:23:45 | 7,777,650 | 899 | 286 | Apache-2.0 | 2023-09-10T18:38:59 | 2013-01-23T15:43:41 | C++ | UTF-8 | R | false | false | 11,646 | r | antsASLProcessing.R | #!/usr/bin/env Rscript
library(ANTsR)
library(tools)
if(!usePkg('optparse') | !usePkg('ANTsR')){
stop("optparse and ANTsR packages required.")
}
optlist <- list(
make_option(c('-s', '--pCASL'), default='', help=' raw pCASL image'),
make_option(c('-o', '--outputpre'), default='CBF_',
help='output pr... |
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