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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b9b748ee437a7e1f0ac51bd0cc78fe0db5e98019 | f6f96b6095fdba1ab68adfcc4565849cc7982d8c | /R/mvBM.getRate.R | 4c769aa548141231f83cf79de7be0218635dd82e | [
"MIT"
] | permissive | aniwaniuk/evomap | d0b51a1fb208bef813647b40063e14b0aac71834 | dfa7dfdc560d1fd04414dffedab7b6be765d8175 | refs/heads/master | 2020-04-15T20:25:04.527658 | 2018-05-29T14:03:25 | 2018-05-29T14:03:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 957 | r | mvBM.getRate.R | #' Lineage-specific rate estimation using multiple variance Brownian motion
#'
#' Computes lineage-specific rates using mvBM
#' @param tree an object of class "phylo".
#' @param tree_mvBM an object of class "phylo". The rescaled tree from an mvBM procedure.
#' @param branches vector listing the branch numbers ('edge' ... |
3af61ae4692d1dff65690d963171d2662755ccc0 | daccbc095ccb9be61622399c2cfa3c3319aafbe0 | /R/refine.R | cf9bbd617eaa8344808ccfc2a90df4b0bc9d998f | [] | no_license | menghaomiao/aitr | c2199837ef5e125b73838233779fc997aa8e3cd3 | 6cfb60c0ae63ef7dd43b3f8c0f78293c1eeea5bb | refs/heads/master | 2022-11-05T21:13:50.866225 | 2020-06-18T23:14:03 | 2020-06-18T23:14:03 | 110,192,891 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 186 | r | refine.R | refine=function(inner, delta) {
rule=matrix(0, nrow(inner), ncol(inner))
rule[inner>=delta]=1
ind=rowSums(rule)==0
if (sum(ind)>0) rule[ind, ][inner[ind, ]>=-delta]=1
return(rule)
} |
edece57f55f61686348bfe86cb37f40af03bf02c | 9f972d4bde1195b867fde81e4726c1bbaf562bd4 | /man/rss_varbvsr_iter_naive_reference.Rd | 49ab93baa76150096511ed483b8f4a1e3b8fc523 | [] | no_license | MoisesExpositoAlonso/rssr | d2d10ff3ef417d7b979b0d5f1cc4c0c55a7a6305 | c9a076bc7a3d36835eaa73a0b34cee1cf7a13657 | refs/heads/master | 2021-01-19T00:19:34.344067 | 2017-03-31T21:38:35 | 2017-03-31T21:38:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 696 | rd | rss_varbvsr_iter_naive_reference.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{rss_varbvsr_iter_naive_reference}
\alias{rss_varbvsr_iter_naive_reference}
\title{Single update of RSS with variational method
This function is a very close translation of the original implementation of RSS. It is kept her... |
890b0ec01dea6f0284d55056e1b6eeac5564e3c5 | 1ff0f0217347e7ec30167a5524ffb8260e49e823 | /man/readCounts.Rd | 9c527f9f8328185eb95caddce53406d03ba29b13 | [] | no_license | vaofford/amplican | 0ee096b58585ceb24c6e451872af2a2fd87b2de6 | 7774dda136bdd3dd78c6c8c1f596195b847f77f3 | refs/heads/master | 2020-09-15T08:21:02.149838 | 2019-06-06T18:33:47 | 2019-06-06T18:33:47 | 223,392,406 | 0 | 0 | null | 2019-11-22T11:48:36 | 2019-11-22T11:48:35 | null | UTF-8 | R | false | true | 545 | rd | readCounts.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AlignmentsExperimentSet-class.R
\name{readCounts}
\alias{readCounts}
\title{Alignments for forward reads.}
\usage{
readCounts(x)
}
\arguments{
\item{x}{(AlignmentsExperimentSet)}
}
\value{
(listOrNULL)
}
\description{
Set alignments for forwa... |
f3e55e462172f1e55bf092cacef1a05a02ea8d8d | a63fbd84fbc4aafb8d602adb36773f42991d0007 | /data-raw/readDataWithLoc.R | fdb87b3628529e07a8ad1a0786b0a04bba8966e0 | [
"LicenseRef-scancode-public-domain",
"CC0-1.0"
] | permissive | DIDSR/mitoticFigureCounts | 768dc5b5c6ceaa9cf3841dc546b9ea5061b83f1f | ed9886cac4e3c928ee543d5d3eec0777bc883eb7 | refs/heads/master | 2022-10-31T10:08:57.717486 | 2022-10-23T20:50:46 | 2022-10-23T20:50:46 | 214,683,089 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,275 | r | readDataWithLoc.R | library(xlsx)
library(iMRMC)
# * Creating `data-raw`. ####
# * Adding `data-raw` to `.Rbuildignore`.
# Next:
# * Add data creation scripts in data-raw
# * Use usethis::use_data() to add data to package
# Create usethis::use_data_raw()
# Open and read source data file ####
# We know that the study has... |
90cf09b5823ad0c4ce6999ccfeccb1a02d065997 | 47c5a1669bfc7483e3a7ad49809ba75d5bfc382e | /R/test.R | 45d62b3cbd403bdd1bdaf4cb382a56d0d5d4f891 | [] | no_license | tdhock/inlinedocs | 3ea8d46ece49cc9153b4cdea3a39d05de9861d1f | 3519557c0f9ae79ff45a64835206845df7042072 | refs/heads/master | 2023-09-04T11:03:59.266286 | 2023-08-29T23:06:34 | 2023-08-29T23:06:34 | 20,446,785 | 2 | 2 | null | 2019-08-21T19:58:23 | 2014-06-03T14:50:10 | R | UTF-8 | R | false | false | 4,871 | r | test.R | test.file <- function
### Check an R code file with inlinedocs to see if the
### extract.docs.file parser accurately extracts all the code inside!
### The code file should contain a variable .result which is the
### documentation list that you should get when you apply
### extract.docs.file to the file. We check for id... |
d3afac544eb38a1b90431264df83b2bc54ea3c16 | d4bf2f6857dc7b227ad321658e5d3a3dc12371f3 | /Recommenders/Data_Exploration.R | 96c5ac9187c9c5a94e8031347c1a5481dac4fd5e | [] | no_license | DInoAtGit/ALS | 942595ea7d4429cabc1cca251600a02bc20a91c5 | 980a14584fbd47bb8cb057297b3d9c17f1d7034c | refs/heads/master | 2023-01-02T23:26:25.244365 | 2020-10-28T15:14:42 | 2020-10-28T15:14:42 | 282,937,029 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,026 | r | Data_Exploration.R |
#Load packages
pacman::p_load(tm,slam,topicmodels,SnowballC,wordcloud,RColorBrewer,tidyverse, caret, corrplot, broom, ggpubr, MASS,relaimpo, car, e1071,interplot,caTools,lubridate,date,stringi,ROCR,IRdisplay,knitr,data.table,dplyr,RColorBrewer)
pacman::p_load(recosystem,softImpute,reshape2)
pacman::p_load(BiocManager... |
90aee80f1040708a0c3a04e5f57ec7f46e8c142c | 23b032127f268ff548a409598f34cd325698d77a | /code/Pcount_simulation.R | 6d913b0a26d18efdda93f37a4ab43b66694446be | [] | no_license | dlizcano/SeaUrchin | 7cf54df1af6e51044b32fe122780d2747e188baf | ae68a4ea71b94be225eab06905e636169f929d4b | refs/heads/master | 2016-09-06T10:14:52.557997 | 2015-09-16T19:34:25 | 2015-09-16T19:34:25 | 42,591,003 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 960 | r | Pcount_simulation.R |
# Simulate data
set.seed(35)
nSites <- 16
nVisits <- 4
x <- rnorm(nSites) # a covariate
beta0 <- 0
beta1 <- 1
lambda <- exp(beta0 + beta1*x) # expected counts at each site
N <- rpois(nSites, lambda) # latent abundance
y <- matrix(NA, nSites, nVisits)
p <- c(0.3, 0.6, 0.8, 0.5) # dete... |
c99982259837cedf558cbd614758d808273e1b95 | 49e905566ba104f056f36aca58bc18c428d1bacd | /R/document.R | 3153580e08d9208fc67442d3ed5ad06a85d0fb00 | [] | no_license | jamiepg1/RGCCTUFFI | 28391c502ef9e35217e7d6e1f87854101fed52a4 | 17d7d81e7738bf4a5feedd12445f1dbe6ef8df3a | refs/heads/master | 2018-01-15T09:16:53.818651 | 2014-09-03T15:04:54 | 2014-09-03T15:04:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,519 | r | document.R | # Enumerations - Values and individual variables and coercion methods. Constructors ?
# Use an abstract base class "RAutoDocumentation" which does not have any representation
# then introduce the now RAutoDocumentation. The intent is to allow RTUDocumentation
# have a common base class with the RAutoDocumentation.... |
032881e6cc05e79a1dffcee473c17718028cfab1 | 089612a894bea798afe72245c718f56eb3e0bae5 | /plot1.R | 7ae3ac030f0f51d902341e1363cef7878446bd8d | [] | no_license | amb54/ExData_Plotting1 | e89af308e8d580694f7b495568926973c466b728 | 8df8b6833acb1f0ff1834cdd1f399a32f1086e0d | refs/heads/master | 2020-12-25T07:14:20.889931 | 2014-08-10T18:47:01 | 2014-08-10T18:47:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 800 | r | plot1.R | ##Read the data into R, and give the data frame column names.
data<-read.table("household_power_consumption.txt", sep=";",skip= 66637, nrow=2880)
cN<-read.table("household_power_consumption.txt", header=TRUE,sep=";", nrow=1)
colnames(data)<-colnames(cN)
##Add a new column to data with Date and Time combined as.POSIXlt... |
6ad031ac9db3eff88af2285e72907f14f2f431c4 | d394b264f5e4d20df3bf03fe39d55281a1faaf71 | /src/Initial_DESeq2_Analysis_v2.R | fe97bbd4f423a8b02bebab98c07150d858ee7525 | [] | no_license | ercanlab/RNAseq | 86017f5a33b3049dea7176cff398eb1ea8aaa809 | 8c662697bba6a242f66b646701e6829a8801475c | refs/heads/master | 2021-04-29T18:27:20.452257 | 2019-06-05T14:10:32 | 2019-06-05T14:10:32 | 121,694,005 | 1 | 0 | null | 2019-05-30T21:43:53 | 2018-02-15T23:07:16 | R | UTF-8 | R | false | false | 17,291 | r | Initial_DESeq2_Analysis_v2.R | ################################################################################
## Performs standard DEseq analysis. Also saves DEseq results ##
## ##
## usage: Rscript Initial_DESeq2_Analysis.R YAML_CONFIG ... |
f5546cf35f18d836f9a61b7bc2000681bf49b69d | 924b4fd06d01d1968fd09c26a27e0be9a4aaa289 | /shiny_tabset_image&video.R | 2a8a54e6d3e200949b7b2f65fcf26d1bf4277939 | [] | no_license | arunkumaarb/R | 24b1b4ec2937885df826a349d15d02a107bfbae5 | 4dc98492e2a41ce86ddc37a75280eb1aa7f181f4 | refs/heads/master | 2021-09-10T02:11:49.208268 | 2018-03-20T16:55:21 | 2018-03-20T16:55:21 | 125,955,972 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,408 | r | shiny_tabset_image&video.R | library(shiny)
library(shinydashboard)
shinyUI(fluidPage(
headerPanel(title="Shiny Tabset"),
sidebarLayout(
sidebarPanel(
selectInput("ngear","select number of gears",c("cylinders"="cyl","Transmission"="am","Gear"="gear"))
),
mainPanel(
tabsetPanel(type="tab",
#... |
5a89985c40330111cfde4ede07ca98b505b2f861 | 8441bfe4d9012405140a9cd61fe0915cf5749f16 | /HIVBackCalc/man/KCplwh.Rd | 93222cd391575a76197b9ea6de93cd52ca81ea35 | [] | no_license | hivbackcalc/package1.0 | 6fc12d3e5545fcf2dd6d53a5a84ee200e394940d | 404da82d5db67b6a5885693934ae014e2e3d3d57 | refs/heads/master | 2021-01-17T13:07:59.862352 | 2019-07-08T22:17:06 | 2019-07-08T22:17:06 | 30,552,375 | 4 | 1 | null | null | null | null | UTF-8 | R | false | true | 745 | rd | KCplwh.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{KCplwh}
\alias{KCplwh}
\title{MSM living with HIV in KC 2006-2012}
\format{A data frame with 7 rows and 5 columns:
\describe{
\item{Year}{Year of estimate}
\item{White}{MSM living with HIV, White race... |
9376b9e0743cc65bdde580badb00be8cbb2ba779 | 6aeef9dc9bfc68752cf482a1214de670232af593 | /inst/isoscape example/4-predict_isoscape.R | 9928242bca00bee07a9a6d2a3a3c79d23309e891 | [] | no_license | medusaGit/isoscatR | 14138aaee6ac5d5c2a0d80de924b9b4e198bc4e6 | c1080d66d2d10fda3858998290b2f98e2cefa71f | refs/heads/master | 2021-01-21T07:30:29.346776 | 2015-03-04T16:17:52 | 2015-03-04T16:17:52 | 48,884,502 | 1 | 0 | null | 2016-01-01T17:28:15 | 2016-01-01T17:28:15 | null | UTF-8 | R | false | false | 346 | r | 4-predict_isoscape.R | library(spBayes)
load(file = file.path(data_dir,"gnip_data.Rdata"))
load(file = file.path(data_dir,"splm_fit.Rdata"))
p = spPredict(l, pred.coords = pred_data[,c("long", "lat")], pred.covars=pred_data, start=10020, end=30000, thin=20)
r = brick( pred_rasts[[1]], nl = ncol(p$y.pred))
values(r) = p$y.pred
save(p,r... |
cd3cd07d25007674a5d6385e107094c92fb29f2a | 7959d755e90a965e9aae96c6dbd0488f9bbd0461 | /R/ArrangeData.R | 599660425e06a5cb048e7735ff79e2fd8bc9a82e | [] | no_license | shineshen007/shine | 0fa037b731eefc13d3a28edc5c9335876c867bb0 | 2e53e87a1099fefe16b995732197f9bb0f16738f | refs/heads/master | 2023-01-24T20:55:17.881562 | 2023-01-18T13:52:33 | 2023-01-18T13:52:33 | 124,387,894 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 486 | r | ArrangeData.R | #' @title ArrangeData
#' @description a function to arrange data for svr
#' @author Shine Shen
#' \email{qq951633542@@163.com}
#' @return All the results can be got form other functions and instruction.
#' @export
ArrangeData <- function(){
data <- data.table::fread("peak.table.csv")
data<- data.table::setDF(data)... |
cf72e3929c5a852534bad9309bb5893ba4609bb8 | c5824359870ca766c2684c7ff3abe956de472377 | /Column/Make_Pair_Mapping_from_FactorInfo/Paired_to_Metadata.r | 9dd7f4a89e3932fdfb1209570a32b2f18179b9d9 | [] | no_license | MedicineAndTheMicrobiome/AnalysisTools | ecb8d6fd4926b75744f515b84a070e31f953b375 | 8176ca29cb4c5cba9abfa0a0250378e1000b4630 | refs/heads/master | 2023-09-01T21:10:39.942961 | 2023-08-31T22:43:03 | 2023-08-31T22:43:03 | 64,432,395 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,730 | r | Paired_to_Metadata.r | #!/usr/bin/env Rscript
###############################################################################
library('getopt');
options(useFancyQuotes=F);
params=c(
"paired_map_file", "p", 1, "character",
"metadata_output", "o", 1, "character",
"category_name", "c", 2, "character",
"acolname", "a", 2, "character",
"... |
4e7a364b8e4a54b911d1902e76cf91c21d5b3bb5 | ce2435ac0d405cc80cfaddc02bb709ea7491a5d5 | /Big Data Zacatecas/sesion6/tarea6WaffleCharts.R | 4f68002d9f4343c081a755686a363aee3a8aca82 | [
"CC0-1.0"
] | permissive | pauEscarcia/BigData-Zacatecas | b9e4014ee1242522c04a46a8fd40badd809cfe7c | 6ed59608d4583f8d0bdb5caa55c80f41a1c3844a | refs/heads/master | 2021-01-10T05:25:26.429723 | 2016-03-14T03:18:03 | 2016-03-14T03:18:03 | 43,478,578 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 407 | r | tarea6WaffleCharts.R | #Waffle charts
all.scores <- read.csv("bafijaporcada100habitantes.csv")
all.scores
suscripciones <- all.scores[,"Suscripciones.100.h"]
sus <- suscripciones[5:10]
sus
#anio <- c("2004","2005","2006","2007","2008","2009")
#total <- cbind(sus[i],anio[i])
#total
waffle::waffle(total, rows=5, colors=rainbow(length(sus)),... |
4134da6c3e3538352a38345e4df6e745e331391d | 51fdef26e2b65585f0200d90c2b25fe64444dcc3 | /One_Proportion_Obama_Care_Fa16.R | 677b3f6ddd80ca7cff08019f9a71334a2fbeff00 | [] | no_license | chenqi0805/Bayesian-Statistical-Methods | f2bfbec0d83281340cb6bd89b14acbb8c3d8a019 | 4385f862577fefab8681a7f729f789a6d47645f2 | refs/heads/master | 2021-01-22T23:26:47.165703 | 2017-03-21T00:08:06 | 2017-03-21T00:08:06 | 85,639,598 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,180 | r | One_Proportion_Obama_Care_Fa16.R | # Bayesian Inferences for θ,
# a related parameter, and future data - A conjugate (Beta) prior analysis
# See pages 51-52 of the Text by CJBH for some of the R commands described below.
# Suppose observed data are summarized as
# 16 favored Obama Care, out of n=48 constituents polled
# NOTE: A sufficient statistic, T... |
b753d1eb7dfb02a9ae5862f4c1fab5132ef27d0a | 5c78cf64814e074824b1d9d676aaf88f887d509c | /man/se.Rd | d7ede50eb0820b16c6a2034f85d169584805976f | [] | no_license | osoramirez/resumeRdesc | 739969df42fb0d60ae1825cb92588449ed98ff80 | f13df59da87bce98a6a96ed76b4b6b42212270bb | refs/heads/master | 2020-04-24T00:25:59.367878 | 2019-02-19T23:23:17 | 2019-02-19T23:23:17 | 138,966,885 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 501 | rd | se.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/se.R
\name{se}
\alias{se}
\title{A standard error}
\usage{
se(x)
}
\arguments{
\item{x}{is a numeric value, could be a a vector or data.frame}
\item{se}{get a standard error}
}
\value{
a data a standard error
}
\description{
The standard er... |
c23c992ddad2e029823de245e5e8ab8b2a62b4ba | e705fdc30047cff721ddd288cf38a5a55fcba2f4 | /scripts/single dataset workflows/five months soupx.R | 52bb2c814c96be34a6119d53a8cdf884d636a085 | [] | no_license | MillayLab/single-myonucleus | d5ef96f985a5638d788af5c9ec59e097a3e854d8 | f7d977fd5e66be286e1a1bf85b1dda36ff6ae2fb | refs/heads/master | 2022-12-28T17:54:53.048831 | 2020-10-12T14:53:06 | 2020-10-12T14:53:06 | 274,753,777 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,939 | r | five months soupx.R | fivemonth_soupx <- CreateSeuratObject(counts = fivemonthcounts, project = "A", min.cells = 3, min.features = 200)
fivemonth_soupx[["percent.mt"]] <- PercentageFeatureSet(fivemonth_soupx, pattern = "^MT-")
VlnPlot(fivemonth_soupx, features = c("nFeature_RNA", "nCount_RNA"), ncol = 2)
featurescatterplot <- FeatureSc... |
8b60ff9dbcd6f594ce7a9b10c1817a4747d86767 | 97f1e3e6e908a83489e4243268ba539316196176 | /man/getANTsRData.Rd | 7a25d07b30106181be359abfc139ec310242d3af | [
"Apache-2.0"
] | permissive | ANTsX/ANTsRCore | 1c3d1da3bea84859da7d18f54c34ae13d2af8619 | 8e234fd1363c0d618f9dc21c9566f3d5464655a2 | refs/heads/master | 2023-05-24T23:53:30.886217 | 2023-05-22T02:52:39 | 2023-05-22T02:52:39 | 83,897,912 | 8 | 22 | null | 2023-05-22T02:52:40 | 2017-03-04T14:09:48 | C++ | UTF-8 | R | false | true | 896 | rd | getANTsRData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/getANTsRData.R
\name{getANTsRData}
\alias{getANTsRData}
\title{getANTsRData}
\usage{
getANTsRData(
fileid,
usefixedlocation = FALSE,
verbose = FALSE,
method = ifelse(Sys.info()["sysname"] == "Linux", "wget", "auto"),
quiet = FALSE
)... |
8180343695e72a936ce06b0160b674f91ef0af67 | 4d9be777791f09cdf5c1dfb255c69e8a0cce3e80 | /getVarCombs Function.R | 7874fb25c252218f66319bc9dbd9427e1eea91b9 | [
"MIT"
] | permissive | NFSturm/utility_funs | d1f425fecb865b4ba66f778b21453c05e26eab1e | 4fc69f3c181b39a5029fd160939818788b540f9b | refs/heads/master | 2020-12-14T04:03:47.414152 | 2020-01-24T17:02:29 | 2020-01-24T17:02:29 | 234,632,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,085 | r | getVarCombs Function.R | # Variable Combination Generator Function (getVarCombs)
# Inputs:
# df: A dataframe or similar structure (data.table does not work at the moment)
# y: Name of independent variable
getVarCombs <- function(df, y) {
if(typeof(df) != "list") stop("Input must be a dataframe or similar structure")
if(typeof(y) != "char... |
b1fef19b2bdb841cb2f39f1784389c212d1ee5d3 | 5f2469bb233cde73acef9c59371bf8f9db12c782 | /ui.r | 2907d2c7af6cfafe37e5065dbfce1c79b3624323 | [] | no_license | msolanoo/WineQuality | 19be0c6cac89c010e8a1a8fd6d5b1f9a9b9cb06c | 1f11553b63ea5d93d817fd58014de68994fdc217 | refs/heads/master | 2021-01-10T06:48:43.779985 | 2015-05-24T18:46:34 | 2015-05-24T18:46:34 | 36,185,865 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,283 | r | ui.r | library(shiny)
filenames<-list.files(pattern="\\.csv$")
shinyUI(
navbarPage("Wine Quality",
tabPanel("Understanding Wine Quality",
h2("Wine Quality"),
hr(),
h3("This dataset is public available for research. The details are described in ... |
05ecbba153ef9faf44d873c72b4833ee458d195a | 3b21d51af3869a589e1eb96eae4fd756b3db6062 | /funciones-tipo.R | 21f30ae81cacfc30294ed482a3b1c9725cdec6f8 | [] | no_license | cristobalortizvilches/r4ds-cov | a515a9d98a6d76493e9fc1721234a2bccdccc698 | b38115e71aeff96d7632156d4365dc6453a0d067 | refs/heads/master | 2023-08-15T00:20:46.342840 | 2021-09-14T20:04:06 | 2021-09-14T20:04:06 | 394,477,462 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 331 | r | funciones-tipo.R |
# Agregar nuevo vetor a df ------------------------------------------------
add.vect.df <- fuction(df, vect) { #indico los argumentos (inputs)
new.df <- cbind((df, vect)) #procesamiento de los inputs que me entrega un resultado
return(new.df) #indico a la función que devuelva el resultado (... |
2741973178f02b435731ca2d7bf2759697485dba | da3fef2b47b1a586192d8f291dfb11edb589db3b | /10X_V_Gene_Plotting/3a_make combined vh file_v1.2.R | 25d4e99862bc577ddc1464ab23e373128d6eb0bf | [] | no_license | RachelBonami/AHAB | d25f9d7df74ae9de9d7f530eb641f9032fb8d75d | b431041317d56c1e8a87936ccd6656ace9f2119a | refs/heads/main | 2023-03-02T07:07:48.995486 | 2021-02-08T20:39:26 | 2021-02-08T20:39:26 | 303,472,507 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 9,606 | r | 3a_make combined vh file_v1.2.R | #Run the 2_vh... Rscript first to generate the input files needed here.
#This script combines all files into single summary file
#that includes VH family proportion and counts per sample.
#It also adds columns to use as grouping variables
#in ggplot and removes groups by sample for which n<10 sequences
#to avoid inappr... |
fd4c6714bf1eb50194fbf577fa982348bd508a31 | ecbf6731fc0c9db0fab7c055e106db7a1a9efb2e | /man/neglogLik.Rd | 5f9bdd36a14112514e195c1efc645997be28bfce | [] | no_license | cran/PtProcess | 462a88b5e203417b58af57a28b36d04d6092e5ba | 9a28067100be5e04cf73a961881f82c624557142 | refs/heads/master | 2021-07-08T18:17:45.206871 | 2021-05-03T17:30:02 | 2021-05-03T17:30:02 | 17,681,633 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,791 | rd | neglogLik.Rd | \name{neglogLik}
\alias{neglogLik}
\title{Negative Log-Likelihood}
\description{
Calculates the log-likelihood multiplied by negative one. It is in a format that can be used with the functions \code{\link[stats]{nlm}} and \code{\link[stats]{optim}}.
}
\usage{
neglogLik(params, object, pmap = NULL, SNOWcluster=NULL)
}... |
9ff6521ea3e159948d3d49bc266e2d96f55a136f | 1ea4338bc1036eca930cecd6d9be4c97b48d6072 | /TP1/Pruebas-Matias/analisis_usuarios.R | b8afb78ecc16d783ad974fb6e5b29a643f3167c0 | [] | no_license | blukitas/Tp-Data-Mining-2020 | fd13ad48e1057b6e0336a8a7bca4c4f9d473eeb5 | 83f057ba25f9b39df3187f770f7b6049f5030a59 | refs/heads/master | 2022-11-17T08:23:22.890202 | 2020-07-16T23:16:22 | 2020-07-16T23:16:22 | 264,476,975 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 7,420 | r | analisis_usuarios.R | library(infotheo)
names(df_users)
# Distribucion y Escalado
#nota: a todas se les suma un numero e, para evitar Log10(0)
# Sin transformacion: LOG10
# postXyear
summary(df_users$postsXyear)
plot(sort(df_users$postsXyear))
hist(df_users$postsXyear, xlab = "posteosXaño", ylab ="Frecuencia", main="Distribución de la va... |
384a052f98a6d56c34a7400b6c6edd9a35c5324e | 026b4d56086e2e6709b08f0e922e363dd9776a2c | /R/MTA_pattern.R | 40d041fc601bb50195ce7d353c65c30331f84fcc | [] | no_license | chanw0/MTA | 96f56493375905005e86064c9a20716a5f50e0ea | 98de0cf6910c601363c9eb9fbe6f2db1e5f30cfd | refs/heads/master | 2021-07-15T05:25:32.863236 | 2021-07-06T21:32:39 | 2021-07-06T21:32:39 | 230,279,409 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,904 | r | MTA_pattern.R | ############Extracted the dynamic trends from a group of subjects
MTA_pattern=function(x,M,proportion.explained, k,Laplacian.matrix,timevec,lambda1.set,
lambda2.set,lambda3.set)
{
N=dim(x)[1]; P=dim(x)[2];T=dim(x)[3];
if(is.null(timevec)) timevec=timevec=1:T
BS = create.bspline.basis(... |
423d7dca86d28bb49f13c1a6f0a6f4a8becf6785 | b3315fa1dfe0dfefff0213db814284d7288cdbd4 | /2-otu_analysis/R-specaccum_cur.r | 3921eaee3e712302bd5a584e62f441098fdaa42c | [] | no_license | myshu2017-03-14/16S_analysis_pipline | dee8ab3fb9b8dba9af7882e0fa8eac68c58d173b | f8866d61e07f72a44bde744617c0f53ccb3d281a | refs/heads/master | 2020-03-25T01:11:41.354516 | 2019-09-27T00:49:49 | 2019-09-27T00:49:49 | 143,225,718 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,343 | r | R-specaccum_cur.r | #!/usr/bin/Rscript
library(getopt)
# get options, using the spec as defined by the enclosed list.
# we read the options from the default: commandArgs(TRUE).
# character logical integer double
spec = matrix(c(
'input_table_file_with_taxa', 'i', 1, "character",
'help' , 'h', 0, "logical",
'output_file' , 'o' , 1, ... |
8a46ae9c821f63846e67121dd714f82a26a424a2 | 1e36964d5de4f8e472be681bad39fa0475d91491 | /man/SDMXServiceProviders.Rd | 6685355757541fc3a0e63c647947918adee494f3 | [] | no_license | cran/rsdmx | ea299980a1e9e72c547b2cca9496b613dcf0d37f | d6ee966a0a94c5cfa242a58137676a512dce8762 | refs/heads/master | 2023-09-01T03:53:25.208357 | 2023-08-28T13:00:02 | 2023-08-28T13:30:55 | 23,386,192 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,084 | rd | SDMXServiceProviders.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class-SDMXServiceProviders.R,
% R/SDMXServiceProviders-methods.R
\docType{class}
\name{SDMXServiceProviders}
\alias{SDMXServiceProviders}
\alias{SDMXServiceProviders-class}
\alias{SDMXServiceProviders,SDMXServiceProviders-method}
\t... |
fca60b299f14caa4b428032c6e0804d77a7107ba | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tswge/examples/fig6.2nf.Rd.R | be116938c3715156aad2e9899a974d9f75a56c7c | [] | 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 | 167 | r | fig6.2nf.Rd.R | library(tswge)
### Name: fig6.2nf
### Title: Data in Figure 6.2 without the forecasts
### Aliases: fig6.2nf
### Keywords: datasets
### ** Examples
data(fig6.2nf)
|
78cddedb83a5ff61e3ede24937305ed47ff1209d | ebb2a6c304eff697a7a016cc64218ba507f2af27 | /implementation/jd_ift_quadrature.r | be67d9c9c6ccb28667d8b3dfe38a38dcc9aebac0 | [] | no_license | Blunde1/it-ift | 1a57c55d0f3b7106530e2b80e4cca4c82d1136eb | 45014dd16118fb90fe490ebbdf4ec8f198ae9ba6 | refs/heads/master | 2021-08-24T02:15:14.548045 | 2017-12-07T16:08:14 | 2017-12-07T16:08:14 | 103,902,408 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,424 | r | jd_ift_quadrature.r | setwd("C:/Users/Berent/Projects/it-ift/implementation")
setwd("~/Projects/UiS-Git/it-ift/implementation")
library(TMB)
compile("jd_ift_quadrature.cpp")
dyn.load(dynlib("jd_ift_quadrature"))
# real data
real_data <- TRUE
if(real_data){
library(Quandl)
start_date <- "1950-01-01"; end_training <- "2017-01-01";
... |
23390623b432d8fb80f743bfa7cafc96628f73df | 57a607818308047a9c729a27afd112267556e5ce | /R/interaction.R | e167b3a0cc425c184dc303aaa4e9d710296f827d | [] | no_license | oscarperpinan/pdcluster | bf16799943a4e75bd6c4f7811b268e4e02cb0cf5 | db2c47535a5807ef9dc12670368fe40216c8cdd9 | refs/heads/master | 2021-01-02T08:56:24.038707 | 2018-02-18T10:11:17 | 2018-02-18T10:11:17 | 11,253,765 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,674 | r | interaction.R | setGeneric('identifyPD', function(object, ...){standardGeneric('identifyPD')})
setMethod('identifyPD', signature=(object='PD'),
definition=function(object, label='energy', column=1, row=1, pch=13, cex=0.6, col='darkgreen',...){
trellis.focus('panel', column, row, ...)
trellisType <- a... |
db8a180dbbf7141661e0755d6bc169fc111ba441 | 493ffb86b0a2d34cde36418185a0dd8380179aa3 | /R/sample_beetles.R | 68425969e8f9fe97a6de420b3c59221b69edc491 | [
"MIT"
] | permissive | atyre2/tribolium | 6b326598f5bc6c47c171ccbf1acbc1b78bb79d3d | 60f7a83f5b9e0386b164f272e1501f9133a2ab51 | refs/heads/main | 2023-03-09T14:17:29.594069 | 2021-02-11T18:27:59 | 2021-02-25T15:35:20 | 338,089,053 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,301 | r | sample_beetles.R |
#' Title
#'
#' @param N vector of larvae, pupae, adult to draw sample from
#' @param V total volume of habitat in units of 20 g
#' @param n vector of number of samples to take from each stage
#' @param v volume of sample
#' @param replacement (logical) sample with or without replacement
#'
#' @return data frame with ... |
37043786741230357607096cdbedf85371ff291f | c7a6c5249ffd79d262dbdbe42c9efaa313119a03 | /Scripts/Figures/Figure S2.r | 16a1e897aac830ef7dd36a3d13c9566507378607 | [] | no_license | YaojieLu/ESS-paper | 30b9ea52e0a6bdb67bc21870b2416da7c53b909b | 0ea34306e8a06a74e5d4a5d9e426f00b98787d28 | refs/heads/master | 2022-03-19T05:15:23.292379 | 2019-12-08T00:35:55 | 2019-12-08T00:35:55 | 106,539,077 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,323 | r | Figure S2.r |
options(digits=22)
source("Scripts/Derived variables/SII-F.r")
data <- read.csv("Results/SII-DV.csv")
# Parameterization
LAI <- 3
Vcmax <- 50
cp <- 30
Km <- 703
Rd <- 1
a <- 1.6
nZ <- 0.5
p <- 43200
l <- 1.8e-5
VPD <- 0.02
pe <- -1.58*10^-3
b <- 4.38
kxmax <- 5
c <- 2.64
#d <- 3.54
h <- l*a*LAI/nZ*p
h2 <- l*LAI/nZ*p/... |
9d8b83444223450e8680eea920f96921586ede06 | 4db2fca3393454228150cff9810407b03ce7e390 | /runner.R | ea5b8c11c97390fda6eaaa66400703d03076f3e4 | [] | no_license | mozilla/glamvalid | 336b8730ccc70119a293ca7601661eb75932cba1 | 737d6591d836fa21dd2c0b8491b9d0ecf62fa9e4 | refs/heads/master | 2023-08-31T08:23:01.262946 | 2020-07-08T16:33:58 | 2020-07-08T16:33:58 | 277,903,555 | 0 | 2 | null | 2020-11-19T23:24:03 | 2020-07-07T19:22:26 | R | UTF-8 | R | false | false | 2,253 | r | runner.R | source("libs.R")
basicConfig()
os = Sys.getenv("OS")
channel = Sys.getenv("CHANNEL")
date_start = Sys.getenv("DATE_START")
date_end = Sys.getenv("DATE_END")
build_start = Sys.getenv("BUILD_START")
build_end = Sys.getenv("BUILD_END")
major_ver = Sys.getenv("MAJOR_VER")
histos = Sys.getenv("HISTOS")
histo_path = '/root/... |
4b140ab49bbad7e1863c93b87f4e2d1df4fa83c2 | d48518ce86622333073b2cf6bbf040b5a149e483 | /R/preprocess_macro.R | fd831b8e750c56b337dddad2844b56c6e6cfd118 | [] | no_license | gdario/sberbank | 66c50072e7acdbaebd82c732e831d476b8e87777 | c6c865f907efef3d5ec33a084d08cae34a92d47e | refs/heads/master | 2020-12-30T18:02:17.860507 | 2017-06-05T14:34:30 | 2017-06-05T14:34:30 | 90,940,949 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,638 | r | preprocess_macro.R | library(magrittr)
library(tidyverse)
source("R/clean_dataset.R")
match_timestamps <- function(ts1, ts2) {
df1 <- data_frame(ts = ts1, idx_dataset = seq_along(ts1))
df2 <- data_frame(ts = ts2, idx_macro = seq_along(ts2))
df <- inner_join(df1, df2)
df
}
macro <- readr::read_csv("data/macro.csv.zip")
timestamp_... |
d3cb61254abd381ab2b028200c79ab9ed4deb6d6 | 7b8b5630a5cef2a21428f97b2c5b26b0f63e3269 | /tests/testthat.R | c42c70c7f58da0ee167145e6de519135b3b6d332 | [
"BSD-3-Clause",
"BSD-2-Clause"
] | permissive | cells2numbers/migrationminer | eb257733c4999f9af57ce10f2faf051d1e0b82fa | c25c692615953c33b3d73430117129fea980bcdb | refs/heads/master | 2021-01-23T07:34:53.509775 | 2019-04-29T17:45:18 | 2019-04-29T17:45:18 | 102,511,560 | 7 | 0 | NOASSERTION | 2019-04-09T16:13:56 | 2017-09-05T17:37:14 | R | UTF-8 | R | false | false | 72 | r | testthat.R | library(testthat)
library(migrationminer)
test_check("migrationminer")
|
beb204bd3923dc7aee6362d099cffafe234b4672 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /diffrprojects/inst/testfiles/dist_mat_absolute/libFuzzer_dist_mat_absolute/dist_mat_absolute_valgrind_files/1609961094-test.R | f9b413992feee097cab12667b1470193a3d1c928 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 181 | r | 1609961094-test.R | testlist <- list(x = c(618011183L, -1L, -1L), y = c(1869359146L, 1660944384L, 0L, 1944398335L, 16777215L))
result <- do.call(diffrprojects:::dist_mat_absolute,testlist)
str(result) |
c91581b38b27f3030c493fa63f884bb895fedc88 | 42554442d39db2549f5b221adc3f4020ced752c7 | /A07_dim3.r | 00056aac8aeae109427f0572fbaabb36a3958985 | [] | no_license | aky3100/TestR2 | ea2330ac2f54335a9e3564806834d992b5cca2f8 | 87d50fc5ff0abcfbc2b5d116cf2a8e90e5cd030b | refs/heads/master | 2020-12-02T08:16:00.738077 | 2017-07-10T16:22:27 | 2017-07-10T16:22:27 | 96,657,166 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 333 | r | A07_dim3.r |
pdf(file="plot7.pdf")
library(lattice)
a <- 1:10
b <- 1:15
eg <- expand.grid(x=a,y=b)
eg$z <- eg$x^2 + eg$x*eg$y
wireframe(z~x+y, eg)
t<-seq(-2*pi, 2*pi, length.out=200)
cloud(z~x+y,data.frame(x=3*cos(t),y=3*sin(t), z=2*t))
t<-seq(-2*pi, 2*pi, length.out=200)
cloud(z~x+y,data.frame(x=3*cos(t),y=3*sin(... |
1986ab9a295127fa779bff264df08b0004a34b44 | 608adcf47ef5c776429dfe2e555c20c0ef54547a | /R/H.Earth.solar.R | 040788de4cc801c1dcd0592f7900bc3ff9415292 | [] | no_license | cran/widals | b722ad1e1e0938998461d8fe83e8b76437cbc031 | c431b52c0455ad4568072220838b571bacc3b6ba | refs/heads/master | 2021-05-15T01:43:27.321897 | 2019-12-07T21:20:02 | 2019-12-07T21:20:02 | 17,700,881 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 671 | r | H.Earth.solar.R | H.Earth.solar <-
function(x, y, dateDate) { ######################
Hst.ls <- list()
n <- length(y)
tau <- length(dateDate)
equinox <- strptime( "20110320", "%Y%m%d" )
for(i in 1:tau) {
this.date <- dateDate[i]
dfe <- as.integer( difftime(this.date, equinox, units="day")) ; dfe
... |
585c79a0621339b52bbf4d99fbbbadd6e698ee73 | f02aae99becc67d3ee700d4cdd205a1e55d5ade2 | /testAlgo.R | 279b8f2b9a1642ade9e88d875a61b567e3d77cb1 | [] | no_license | sohamsaha99/mdp | 56cfc5ae958ef9df934cf8fdd9acdeb86aa5b702 | df809031d8ca452fff74746749f9d9d041fc97f7 | refs/heads/master | 2023-04-16T05:16:39.424315 | 2021-04-30T08:01:01 | 2021-04-30T08:01:01 | 315,692,654 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,617 | r | testAlgo.R | # Create Reward matrix
F_levels = c("zero", "negative_low", "positive_low", "negative_high", "positive_high")
F_values = c(0, -5, 5, -10, 10)
n_actions = length(F_levels)
Reward_matrix = matrix(0, nrow=n_states, ncol=n_actions)
for(i in 1:nrow(Reward_matrix)) {
v = getState(i)
if((which(x_levels == v[, 1]) %in%... |
e8142310408d34f0813bf7e35e28a5c786cbb17f | 5b55d8d4a1e6275605e7e740cfb3cec5528b485b | /R/getXlist.R | 8f03be0ef01d5f06aab564850b0aed532574c62d | [] | no_license | cran/MasterBayes | 2103a6dfddb562c02b37f32c79ca51bce477a6e6 | a2bbdc296453f21114f7fd9e1a8d825ed6d86730 | refs/heads/master | 2022-07-23T18:11:50.598009 | 2022-06-22T12:00:10 | 2022-06-22T12:00:10 | 17,691,892 | 1 | 2 | null | 2017-09-27T20:22:15 | 2014-03-13T02:32:13 | C++ | UTF-8 | R | false | false | 35,065 | r | getXlist.R | getXlist<-function(PdP, GdP=NULL, A=NULL, E1=0.005, E2=0.005, mm.tol=999){
if(is.null(GdP$id)==FALSE & is.null(PdP$id)==FALSE){
if(FALSE%in%(GdP$id%in%PdP$id)){
stop("genotype data exists for individuals not in PdataPed object")
}
if(FALSE%in%(PdP$id%in%GdP$id)){
stop("some individuals in Pda... |
a2c1255911e0b52f8f63b500599d4cf6311cf4ef | 8a8236ff110fd8876c38bf151327b75690f02d94 | /empirical_estimation.R | a7ee7f93ca04ca5155eaa4689bb2d0d11b743b3a | [
"MIT"
] | permissive | sl-bergquist/cancer_classification | b3bd46ce051b755693267b24e3fbd03c13e9ad9f | 22623bd8b86cc3efa3955859898639f9a1ecffde | refs/heads/main | 2023-06-15T09:21:31.326232 | 2021-07-06T04:51:12 | 2021-07-06T04:51:12 | 381,496,706 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 31,019 | r | empirical_estimation.R | #########################################################
# CancerCLAS Multiclass Prediction
# Empirical Data
# Naive No Variation, Naive Bootstrap, Weighted Bootstrap
#########################################################
options(scipen = 999)
library(MASS)
library(tidyverse)
library(glmnet)
library(nnet)
library(... |
88070c915494a9246f1271258451304d57dd525c | 3c6be520713201909819dc62961af9c4aa2d92ab | /select.montage.R | 078f338c9e8efaf890239813ce94368eee8ee3f8 | [] | no_license | tanyaberde/NS.plots.tidy | 467672d40312845d56432d4d5ce476b0a4eb6f97 | a4b3f4225838455b03a88e0e72bf95af69dbf7a7 | refs/heads/master | 2020-07-14T23:42:53.085024 | 2019-08-30T17:51:26 | 2019-08-30T17:51:26 | 205,429,131 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | select.montage.R | ### What is your montage? Change switch
### COLLAPSE HEMISPHERE
## FINAL ROIs
roi1 <- c(25,21,22,18, 14,10,9,8) ; roi1.name <- "Frontal" ### mediofrontal (P2)
roi2 <- c(54,37,42,53, 79,87,93,86); roi2.name <- "Dorsal" ### centroparietal (P3, dorsal N2)
roi3 <- c(64,58,57,63, 96,100,95,99); roi3.name <- "Ventral" ### i... |
f4a5cb6f74a6a815528381304de72ba68e0dceb6 | 8f2d33ce811c0667ad82056f70a372ead18478f6 | /R/klientulentele.R | 69efb9cbcc1acbed991dcc0a2de74ba3bbf8d9d3 | [] | no_license | Tomas19840823/transportas_v2 | 6806fd39d1bc0eedf9f9a65bab355d08de574724 | 5fdbe20538cdde54750d21fba457749ad5349a9a | refs/heads/master | 2021-01-19T03:49:08.649313 | 2017-04-24T15:25:39 | 2017-04-24T15:25:39 | 87,336,063 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 487 | r | klientulentele.R | klientulentele <- function(){
mat <- matrix(nrow = 8, ncol = 4)
mat[,1] <- c("Baze", "Klientas1", "Klientas2","Klientas3","Klientas4","Klientas5","Klientas6","Klientas7")
mat[,2] <- c(54.6872, 54.7017, 54.6500, 54.6842, 54.6814, 54.8061, 54.7587, 54.4390)
mat[,3] <- c(25.2797, 25.2547, 25.2200, 25.2779, 25.... |
25df74826a4ae3184c1d591309061587e9e630ef | e54c3f3d3538c676eff3140f889b8b454ec30324 | /memorymigration/man/runMissedRuns_res.Rd | bd0e5d15c8bc2d24fbb513398aed0ea7830d2dea | [] | no_license | EliGurarie/memorymigration | acaf4094ba4f580db31bd2b9e25af7bbb8778ca3 | 192d44e030eb73729a7f7c3969cba520ca386177 | refs/heads/master | 2023-08-21T17:09:24.312679 | 2021-09-20T16:49:40 | 2021-09-20T16:49:40 | 327,377,347 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 367 | rd | runMissedRuns_res.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RunningModel.R
\name{runMissedRuns_res}
\alias{runMissedRuns_res}
\title{Run Missed Runs Resource}
\usage{
runMissedRuns_res(
world_param,
parameters.df,
resource_param,
world,
resource,
filename = NULL,
results.dir = NULL,
..... |
a0d509c0c12e656c1f54a80a1ba491b49756c1c7 | 192728e70bb5c6a8fb0ad8f486d7634acb6ee5a1 | /R/feedly-search-contents.R | 04e58c193dc2a08593a2754df413167cd12d6a6b | [] | no_license | hrbrmstr/seymour | 1672c6100d6b212d07162c36a3444cecdae675f4 | 83a41922c94d019e91c0f39e325ca7796c02538d | refs/heads/master | 2020-04-13T19:34:40.239426 | 2020-01-22T10:01:12 | 2020-01-22T10:01:12 | 163,406,888 | 18 | 3 | null | null | null | null | UTF-8 | R | false | false | 3,411 | r | feedly-search-contents.R | #' Search content of a stream
#'
#' @md
#' @param query a full or partial title string, URL, or `#topic`
#' @param stream_id the id of the stream; a feed id, category id, tag id or a
#' system collection/category ids can be used as
#' stream ids. If `NULL` (the default) the server will use the
#' “... |
d83e0ca792f484216aa595026c5877de521bc330 | b6a4b68ec502322a8ba8a9151e67e818cd112cb8 | /man/StudentRecord.Rd | bce5b53c9407ea88d1372376b8167944bb4c10b3 | [] | no_license | ralmond/EABN | ffd67e3ba2e112bf69e42ee5c60eb1e2ec1734c5 | ff55aa44c756cb6157d907f66b7d54f33766c01c | refs/heads/master | 2023-07-25T13:29:02.241959 | 2023-07-12T20:45:02 | 2023-07-12T20:45:02 | 240,610,408 | 1 | 1 | null | 2023-07-11T22:00:12 | 2020-02-14T22:36:52 | R | UTF-8 | R | false | false | 3,589 | rd | StudentRecord.Rd | \name{StudentRecord}
\alias{StudentRecord}
\title{Constructor for \code{StudentRecord} object}
\description{
This is the constructor for a \code{\linkS4class{StudentRecord}}
object. Basically, this is a wrapper around the studnet model for the
appropriate user, with meta-data about the evidence that has been
... |
6c43812c7fe429413f0b0895fe80e5ddead5e0cb | 3ee04b4129e86c9218a34f402349649727baa646 | /man/jtrace_install.Rd | efd58811ad1480d6593d21de7093e0041ca513a6 | [
"MIT"
] | permissive | gongcastro/jtracer | c34233cfcebba4dce8e7c5be72f09b626c3573ec | ed4126d5a6b92034182eb9e77d6c357453af34c5 | refs/heads/master | 2023-09-04T11:31:43.980588 | 2021-10-15T15:37:16 | 2021-10-15T15:37:16 | 365,167,721 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 644 | rd | jtrace_install.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/install.R
\name{jtrace_install}
\alias{jtrace_install}
\title{Download and install jTRACE}
\usage{
jtrace_install(overwrite = FALSE, quiet = FALSE, check_java = FALSE)
}
\arguments{
\item{overwrite}{Logical value indicating whether to replace... |
d63efdbd4e7f6ae9d2b11ea1e583eec0a7322cf9 | 6b629e8bc4bb0b1c93bb217cb218af5ae5e587c8 | /gender_differences/read_in_data_gsh.R | 15082f1a86f5770c2d033a00a1c16806124b47d3 | [] | no_license | DashaZhernakova/umcg_scripts | 91b9cbffea06b179c72683145236c39f5ab7f8c2 | 1846b5fc4ae613bec67b2a4dd914733094efdb23 | refs/heads/master | 2023-08-31T10:45:17.057703 | 2023-08-23T14:47:43 | 2023-08-23T14:47:43 | 237,212,133 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,982 | r | read_in_data_gsh.R | library(rprojroot)
library(tidyverse)
config_path <- "/groups/umcg-lifelines/tmp01/projects/ov20_0051/umcg-dzhernakova/gender_difs/v5/config.yml"
script_folder <- "/groups/umcg-lifelines/tmp01/projects/ov20_0051/umcg-dzhernakova/scripts/umcg_scripts/gender_differences/"
cat("script folder:", script_folder, "\n"... |
581119c39f2871f9164ae9eac5f81a5abe722e1b | 2448d4800d4336b53489bcce3c17a32e442a7716 | /tests/testthat/infrastructure/tests/testthat.R | b95508e03b2c528a3f7d5112926ec36710dfa649 | [] | no_license | vsbuffalo/devtools | 17d17fd1d2fb620fef8d9883dffed389f80e39fb | 782e6b071d058eea53aae596a3c120d61df2f0b4 | refs/heads/master | 2020-12-24T10:41:24.637105 | 2016-02-18T14:03:05 | 2016-02-18T14:03:05 | 52,121,375 | 2 | 0 | null | 2016-02-19T22:42:43 | 2016-02-19T22:42:43 | null | UTF-8 | R | false | false | 72 | r | testthat.R | library(testthat)
library(infrastructure)
test_check("infrastructure")
|
e91bb0f368f8c9517f5a4c1ead0da93b0f6ad9bb | b742c81dc1128901fbd352c7ecd9a1378c8357ac | /checks/mfa_num.R | eac930d793370b51bbb6f675f47710e223fc98e8 | [
"MIT",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | arosas5/prince | 69e9b69e39dab53884721fec848a4a190136f7d0 | b05034fffd177ea75d38ab785eacc80a813cbfb6 | refs/heads/master | 2023-08-23T15:20:38.998656 | 2021-11-03T23:01:38 | 2021-11-03T23:01:38 | 424,382,589 | 0 | 0 | MIT | 2021-11-03T22:57:48 | 2021-11-03T21:14:40 | Python | UTF-8 | R | false | false | 453 | r | mfa_num.R | library(FactoMineR)
data(wine)
X <- wine[,c(3:31)]
mfa <- MFA(X, group=c(5,3,10,9,2), type=rep("s",5), ncp=5, name.group=c("olf","vis","olfag","gust","ens"), graph=FALSE)
print(mfa$global.pca$eig[1:5,])
print("---")
print("U")
print(mfa$global.pca$svd$U[1:5,])
print("---")
print("V")
print(mfa$global.pca$svd$V[1:... |
b3780196bb7d9a2a35e710edda4ae9c48836ba98 | c555092c911699a657b961a007636208ddfa7b1b | /man/ggplotGrob.Rd | e96dde94fd9c3b4387322415a480e63c6ba62dae | [] | no_license | cran/ggplot2 | e724eda7c05dc8e0dc6bb1a8af7346a25908965c | e1b29e4025de863b86ae136594f51041b3b8ec0b | refs/heads/master | 2023-08-30T12:24:48.220095 | 2023-08-14T11:20:02 | 2023-08-14T12:45:10 | 17,696,391 | 3 | 3 | null | null | null | null | UTF-8 | R | false | true | 309 | rd | ggplotGrob.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot-build.R
\name{ggplotGrob}
\alias{ggplotGrob}
\title{Generate a ggplot2 plot grob.}
\usage{
ggplotGrob(x)
}
\arguments{
\item{x}{ggplot2 object}
}
\description{
Generate a ggplot2 plot grob.
}
\keyword{internal}
|
4329cc973c0878a4df02cff34970f0757908f438 | 744080600e2df9d50b27fde5790bc2ddddad61a4 | /server.R | 018ba98338d06249cf1b96aa5dc06fd04dbba202 | [] | no_license | JC-chen0/IEEE-fraud-detection-kaggle | 3b10b889e9f7f32d64a7ed6eda324d1095d70521 | fe2167125b0562f7a3f585ec2ef073fdae50a8a9 | refs/heads/master | 2023-02-18T21:28:11.817179 | 2021-01-11T18:25:26 | 2021-01-11T18:25:26 | 327,829,522 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,099 | r | server.R | library(shiny)
library(rsconnect)
transction <- read.csv('train-2.csv', stringsAsFactors = FALSE)
identity <- read.csv('identity.csv', stringsAsFactors = FALSE)
# Define server logic required to generate and plot a random distribution
server <- function(input, output) {
transction2 = transction[sample(nrow(trans... |
096d134133351fd7a8f0d41286a23936c09cdc61 | 0e41cfa523fc0f183d49557027656ceae25d33fb | /plot2.R | 52f01b76bbd738a1e48f1632fd08c709cb7645c8 | [] | no_license | MadApe/ExData_Plotting1 | a597d643ed0ec7cb7fb34fbd1f5e0e8f2c35f80e | f49c21291d4afb240553e644d40c40c29db8ab3c | refs/heads/master | 2021-01-24T00:43:51.575565 | 2018-02-25T05:22:12 | 2018-02-25T05:22:12 | 122,777,346 | 0 | 0 | null | 2018-02-24T20:32:11 | 2018-02-24T20:32:11 | null | UTF-8 | R | false | false | 2,231 | r | plot2.R | # load libraries
library(data.table)
# initialize source and destination variables of the data files
wd <- getwd()
data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
data_dir <- file.path(wd, "data/")
data_zip <- file.path(data_dir, "household_power_consumption.zip")
d... |
cb6e2a1c0de2afee3c4f1eca2f5dff26b6d78ad1 | fa32c05f7b8cdcefd719e23001c52ee6a3e59015 | /initial_EDA.R | c0e8bee16be1c3ecb2e584a7b6e38558c22c5f5b | [] | no_license | atthegates25/ML_Lab | a02ae652a6bed9656fc355af603ed5d65f2fa5b3 | 43ea36b5fe5132f57113aa8dc1720b0f5a4810bd | refs/heads/master | 2020-03-26T12:22:24.740750 | 2018-09-28T05:02:44 | 2018-09-28T05:02:44 | 144,889,046 | 0 | 3 | null | 2018-08-16T00:36:41 | 2018-08-15T18:27:42 | R | UTF-8 | R | false | false | 4,128 | r | initial_EDA.R | library(data.table)
orders = fread('../../data/Orders.csv', stringsAsFactors = T)
returns = fread('../../data/Returns.csv', stringsAsFactors = T)
names(orders) # check column names
names(returns) # check column names
names(returns)[names(returns)=='Order ID']='Order.ID' # rename "Order ID" to Order.ID
order... |
99b48a73d234aee7e0cf640de643d5a110362eb2 | 2cb5dbfc14e6e24eeed4e846a0aaec35506547e3 | /man/loadcsv_multi.Rd | 470a0d57ab2b4f4fdfe5436a68c610fedbf00443 | [] | no_license | cran/easycsv | 1e0cbb4fed5da0855b63e8abb21df167699bc351 | c1c711c67d397ba2f5bbf9b686d58e4811fade8a | refs/heads/master | 2021-01-15T12:37:08.933963 | 2018-05-21T18:03:30 | 2018-05-21T18:03:30 | 99,650,455 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,464 | rd | loadcsv_multi.Rd | \name{loadcsv_multi}
\alias{loadcsv_multi}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
read multiple csv files into named data frames
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
Reads multiple files in table forma... |
6c38895c3e4facb51dfee4674be4dd4b00a92fd9 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DGVM3D/examples/triClose.Rd.R | 7dfb525e82431cba7b00d18fa5a35ea6f577df90 | [] | 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 | 1,289 | r | triClose.Rd.R | library(DGVM3D)
### Name: triClose
### Title: fill a polygon (number of vertices) with triangles
### Aliases: triClose
### ** Examples
par(mfrow=c(2,2))
for (m in c("plan", "fix", "center", "")) {
faces <- sample(12:20, 1)
vertices <- sapply(seq(0, 2*pi*(faces-1)/faces, length.out=faces),
f... |
988bf261ec79c2426c248bd9e7791db0143c5911 | b4cbfd634adf53ffc75a51eeec93e41c5ba4f5ac | /classifier/naive_bayes.R | eab39b5804012bd84744c0279d1f560182edd3a0 | [] | no_license | riskimidiw/tripadvisor-sentimentr | d4be9b8b7e7f008b77890f3e0e59764efb8c8e7f | 6efe566da53d42265183e52f52d8fcd44cc088d1 | refs/heads/master | 2022-09-21T11:19:38.896724 | 2020-06-05T00:30:10 | 2020-06-05T00:30:10 | 268,198,441 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,910 | r | naive_bayes.R | #Import package
library(dplyr)
library(tidyverse)
library(tm)
library(e1071)
library(caret)
features_rds_path = "classifier/features.rds"
naive_bayes_rda_path = "classifier/naive_bayes.rda"
# Membersihkan data dan merubah data menjadi bentuk corpus
clean_data <- function(data) {
corpus <- VCorpus(VectorSource(data)... |
79e602a5cb9fed9f8251f09729f4ca1657be4771 | 4c72e92a6fd6a2830ac7513bb7de071bb6cd6eb5 | /GoogleChartDemo_global.R | 7b51c23258a6a0283d24d08c3f60f6e36d7441d9 | [] | no_license | ATLAS-CITLDataAnalyticsServices/ShinyDataVisualization | 79bc1648d13bae66c9dbb0a3197fbcd702bdac29 | 94121c3382db8a3c3842aa729027c71bbc7b93b9 | refs/heads/master | 2021-01-09T05:27:13.834978 | 2017-02-02T21:46:43 | 2017-02-02T21:46:43 | 80,771,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,310 | r | GoogleChartDemo_global.R | ##############################################
# CITL Analytics Winter Project 2016-2017 #
# Liqun Zeng #
# #
# Data Visualization: #
# Shiny Google Charts #
# ... |
e0175ce92e39b50a8d15447322da60e9df30c520 | 3d2dd369a1beb4ae1886ac0347eadcca6905020b | /tests/testthat.R | 2d8c5da5e2e1fe26017a50b65547c6b08593a5c2 | [
"MIT"
] | permissive | sstoeckl/pensionfinanceLi | 1f5c501644d26cb293f7e9ad7d18ea1f90d420e9 | ba9be9cee4381b766ac41e719257ec603d584c5c | refs/heads/master | 2021-07-05T08:26:44.942479 | 2020-11-30T14:39:57 | 2020-11-30T14:39:57 | 207,890,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 76 | r | testthat.R | library(testthat)
library(pensionfinanceLi)
test_check("pensionfinanceLi")
|
9f021203619fc776a14ad17b46a248b379ce1e69 | 8e503e16eba5103da436c67a684360b013e8f78d | /Final_Project_Files/sentiment_classification.R | e618fb8684d922b1a44567f91a82d8270ca9cec1 | [] | no_license | adamsjt13/Stock-Sentiment | 10178629a2b6f91fef2eac6314657219d53a2761 | 29592c973831cea6415432e412e8bdf5a830a4cf | refs/heads/master | 2020-04-14T18:32:24.414700 | 2019-01-03T21:23:11 | 2019-01-03T21:23:11 | 164,022,874 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,909 | r | sentiment_classification.R | rm(list = ls())
setwd("~/Documents/BZAN_583_Text_Mining/FinalProject/stocks/Final_Project_Files")
intel_news <- read.csv("articles_for_intel.csv", stringsAsFactors = FALSE)
intel_stock_data <- read.csv("INTL_stock_data.csv")
apple_news <- read.csv("articles_for_apple.csv", stringsAsFactors = FALSE)
apple_stock_data <-... |
5aaf425a7c682e5bfc85c18691216bdf6436186f | d434ec91242aad694c4e2d78580b60a9da3ce29a | /R/display_selected_code_comments.R | 1e2f90cb2e79faebef630ef4d0f475a21bcfe06b | [
"BSD-3-Clause",
"LGPL-3.0-only",
"GPL-1.0-or-later",
"GPL-3.0-only",
"GPL-2.0-only",
"LGPL-2.0-only",
"MIT"
] | permissive | rmsharp/rmsutilityr | 01abcdbc77cb82eb4f07f6f5d8a340809625a1c5 | d5a95e44663e2e51e6d8b0b62a984c269629f76c | refs/heads/master | 2021-11-20T08:45:23.483242 | 2021-09-07T17:28:22 | 2021-09-07T17:28:22 | 97,284,042 | 0 | 2 | MIT | 2021-09-07T17:28:22 | 2017-07-15T01:17:14 | R | UTF-8 | R | false | false | 3,310 | r | display_selected_code_comments.R | #' Displays selected comments
#'
#' @returns Dataframe of selected comments with the base file name, the
#' comment label, the comment start line, and the comment text.
#'
#' Internally uses the \code{list.files} function with the \code{path} and
#' \code{pattern} arguments as defined in the call. Other arguments t... |
1686b8ac344d8810b3f3fabee9f130b8b8905064 | c4010945565fedf0c3da444545ce94b85df8790e | /man/E4.4.Rd | 4e47568018b7b3eebe279e5f42f274d72b623845 | [] | no_license | cran/SenSrivastava | adc924ed2e4a3068a65b8347d7418f96008f9612 | e834ccc473ed498c093df2a27c5a7633da46442e | refs/heads/master | 2016-09-06T04:57:59.016453 | 2015-06-25T00:00:00 | 2015-06-25T00:00:00 | 17,693,664 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,347 | rd | E4.4.Rd | \name{E4.4}
\alias{E4.4}
\title{ Measures of Quality for Agencies Delivering Transportation for
the Elderly and the Handicapped }
\concept{Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped }
\usage{data(E4.4)}
\description{
The \code{E4.4} data frame has 40 rows an... |
db65b0229f0c2b0682418ab693a7f6e64e56d6e4 | bf6201100e252d2636b2668a1fc682e71adb74ea | /R/oilCard.R | 31713d5cfc6a906f6322ca7a6876db01426c20af | [] | no_license | takewiki/caaspkg | 0ed9721cc0d39b587ffa6df478ea403db80d7e72 | f3ffede1785e4e9f8b05553271e8073386c9a714 | refs/heads/master | 2023-01-15T10:34:08.561659 | 2020-11-24T05:05:47 | 2020-11-24T05:05:47 | 259,510,309 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,540 | r | oilCard.R | #' 查询油卡
#'
#' @param conn 连接
#' @param FKeyWord 关键词
#'
#' @return 返回值
#' @export
#'
#' @examples
#' oildCard_selectDB()
oildCard_selectDB <- function(conn=tsda::conn_rds('nsic'),FKeyWord='ljiang1469') {
sql <- paste0("SELECT FOrderSouce 订单来源渠道
,FTBId 淘宝ID
,FOrderId 订单号
,FLiYu 礼遇
,FDealerNam... |
19f52c79056294e92fdbeaefe5ef8a655769ae07 | 1a9ad356a301a467f99b3ae09bb958d28ae6d20b | /indicator_heterogeneity_I/exploratory/12_compile.R | 2f0915c12afa21cbdf28476df59018f9aed4972b | [] | no_license | kateharwood/covidcast-modeling | 03d98f7cbc2aa5ce2ef851c2ca7905e9d0cb9826 | 5e44da23e1f39ca74647b67a842dc057d7589198 | refs/heads/main | 2023-07-17T20:51:52.416970 | 2021-08-26T19:38:14 | 2021-08-26T19:38:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 330 | r | 12_compile.R | #!/usr/bin/Rscript
for (geo_value_ in c('county', 'state')) {
rmarkdown::render('12_heterogeneity_longer_time_window.Rmd',
params=list(geo_value=geo_value_),
output_file=sprintf('12_heterogeneity_longer_time_window_%s.html',
geo_v... |
fe72ac114cb161992b046c5712c19184da05ab6e | 9cf3b2ed512749a257001170e3adf509b748d75b | /ySequencing.r | ee9f7589432e537072cd1bdb42b0eee7a50b4c2c | [] | no_license | yh86/R_Library | 81eee992ba39771356d2224285234e311d775919 | f28503bd813e97250fecea0be4a4f0b98262251c | refs/heads/master | 2021-01-21T19:28:53.958332 | 2018-02-22T21:05:26 | 2018-02-22T21:05:26 | 26,069,045 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,105 | r | ySequencing.r |
getSamSigGene <- function(samr_siggene_table=NULL) {
#
# FUNCTION
# combine up and down regulated genes into one data frame (based on SAMR package)
#
# PARAMETER
# samr_siggene_table: sig gene table from samr::samr.compute.siggenes.table
#
# USAGE
#
obj = samr_siggene_table
... |
b9100c0b2698f555b93cf3b957f10b643a3f3ac5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/epiDisplay/examples/Planning.rd.R | 383c26b3477831049fac940acddb5723b5693f94 | [] | 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 | 566 | r | Planning.rd.R | library(epiDisplay)
### Name: Data for cleaning
### Title: Dataset for practicing cleaning, labelling and recoding
### Aliases: Planning
### Keywords: datasets
### ** Examples
data(Planning)
des(Planning)
# Change var. name to lowercase
names(Planning) <- tolower(names(Planning))
.data <- Planning
des(.data)
# Ch... |
1f62ab8ffc09c74323e1761312647a250dcf30d6 | f2532a5bad45afaef76d4ae4b36a699d7bd35f6d | /stock/get_plot_stock.R | a75076ed0fbe1e1ea2359d28abe23248314b101d | [] | no_license | haradakunihiko/investigation_of_r | 07a8df599a700c50306bdad8d33de63111ac16fc | 73b2e065f6e0669332f2417f8866997b0d5bc489 | refs/heads/master | 2016-09-13T20:03:06.946929 | 2016-05-05T01:04:23 | 2016-05-05T01:04:23 | 58,093,999 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,479 | r | get_plot_stock.R | library(quantmod)
# 準備編
start = '2015-01-01'
end = '2015-12-31'
ticker = 'GOOG'
GOOG = getSymbols(ticker, src = 'yahoo', from = start, to = end, auto.assign=F)
summary(GOOG)
head(GOOG)
str(GOOG)
GOOG['2014-01/2014-12']
GOOG['2014-01::']
GOOG['2014-01']
GOOG['2014-01-30']
apply.daily(GOOG[, 6], max)
apply.weekly(GOOG[... |
fd8ebd5780b9760fe8bcde4ec70fe51645e5876a | 745d526cb4a0a7537f13762ec84ab7c4f1ec1cca | /tag_validation/11_bap2_quantify.R | 88aebb9381371494d64777e9b9e1290613966483 | [] | no_license | ning-liang/dscATAC_analysis_code | c4a1598e5cc5f84fe4d46b2159d69ea0c36643fe | b08f76c7add6464c06f7bb4aab95c0bd0b205404 | refs/heads/master | 2020-11-25T03:22:49.094691 | 2019-09-30T16:08:42 | 2019-09-30T16:08:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,163 | r | 11_bap2_quantify.R | library(data.table)
library(precrec)
library(dplyr)
# Thresholds that I used in bap1 for the tag thresholds
thresholds_tag <- c(0.01, 0.01, 0.005, 0.005, 0.005)
names(thresholds_tag) <- c("Sample1", "Sample2", "Sample3", "Sample4", "Sample6")
# ZB: update this path
path_to_csvgz_files <- "../../may24_2019_from_ZB/"
... |
2c1bee38d53225733b7905ec0314193c9f9e5781 | 6af19fc6836016681e9fbe6bae4d680f4589d33b | /R/predictInt.R | 713337f1dd8d1056e7a7e0b35841b71d2bd1d862 | [] | no_license | cran/plaqr | 0ee34d0bc3b1e1abcb1b161aac3ae7b998ab2b79 | 5a81423644b657143bc935ceae43ce297995384b | refs/heads/master | 2020-04-21T00:42:04.328863 | 2017-08-08T17:35:59 | 2017-08-08T17:35:59 | 34,162,155 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,358 | r | predictInt.R |
predictInt <- function(fit, level=.95, newdata=NULL, ...)
{
x <- fit
taulwr <- (1-level)/2
tauupr <- .5+level/2
# If newdata is NULL, use current values for prediction
if(is.null(newdata)){
# Median
if(fit$tau==.5){
median <- fit$fitted.values
} else {
x$call$tau <- .5... |
d125098cc283725f3a5217a5c370b566c1abbd0a | 9982377266ac28216180a7577be356ffc1015fac | /tmap.R | 1014e39b49e4f4fd91e8d33a0380a2a29ca862e3 | [] | no_license | sharapov98/R | 8608755536d449aaa59faaea1836f1a4445f5371 | bd4f9a9447553b6c3671817509a265aa23afdf87 | refs/heads/master | 2020-05-19T21:54:05.250799 | 2019-05-06T23:44:38 | 2019-05-06T23:44:38 | 185,235,409 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,537 | r | tmap.R | #TMAP by PM1
#Done by PM2
library(tmap)
data(World)
which(World$gdp_cap_est == max(World$gdp_cap_est, na.rm = TRUE))
World$gdp_cap_est[7] <- NA
World$log10gdp_cap_est <- log10(World$gdp_cap_est)
map <- tm_shape(World) +
tm_polygons(c("HPI", "log10gdp_cap_est"),
title = c("Happy planet index", "Log ... |
41a45c9f9a5ae61047232bb0f06980ed6ae47315 | 4acde36c651d9ae6d19cc2fc94438ed115104b01 | /ACC2.R | 39ffbfd41a0dd3bfb879288ba2b7e3ca2dd6be82 | [] | no_license | LucianoAndrian/tesis | ae8aa39cd948f69ea5f58ffc763ad9f3052a68e8 | b87b43074aec7f37bacb79783af451a0343e9cf7 | refs/heads/master | 2022-07-24T18:00:33.088886 | 2022-07-22T18:03:20 | 2022-07-22T18:03:20 | 221,673,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,291 | r | ACC2.R | # ACC "espacial"?
#### Apertura base de datos ####
#-------------------------------------------------#
### Observaciones. O(j,m) j años, m estaciones. ###
#-------------------------------------------------#
# necesito "estaciones_p_a_t" de datos_obs.R (ahora se va a llamar prom_est)
# los años y latitudes se mantien... |
c107eeffe760aa843164251c400739eb42303656 | 79b935ef556d5b9748b69690275d929503a90cf6 | /man/plot.leverage.ppm.Rd | 64291bc56cf9264151504a40d3ed1df9bc3349d2 | [] | no_license | spatstat/spatstat.core | d0b94ed4f86a10fb0c9893b2d6d497183ece5708 | 6c80ceb9572d03f9046bc95c02d0ad53b6ff7f70 | refs/heads/master | 2022-06-26T21:58:46.194519 | 2022-05-24T05:37:16 | 2022-05-24T05:37:16 | 77,811,657 | 6 | 10 | null | 2022-03-09T02:53:21 | 2017-01-02T04:54:22 | R | UTF-8 | R | false | false | 4,378 | rd | plot.leverage.ppm.Rd | \name{plot.leverage.ppm}
\alias{plot.leverage.ppm}
\alias{contour.leverage.ppm}
\alias{persp.leverage.ppm}
\title{
Plot Leverage Function
}
\description{
Generate a pixel image plot, or a contour plot, or a perspective plot,
of a leverage function that has been computed by \code{\link{leverage.ppm}}.
}
\usage{
\... |
534eb7ce514af9e016d7b6b1dc9be0334cd1c416 | 55719b6df5677aaa6b459bdea645c53899421a9b | /projectB/projectB.R | c05e55940103cf1a0d60f99f2f52eb7f8ceedb67 | [] | no_license | manav003/ComprehensiveProject | bb62ca3f39eaef7ac600591446fe11ea18f64100 | 42074b57a59de8b170bdc430de134910f382773b | refs/heads/master | 2022-06-11T13:07:41.268869 | 2020-05-08T23:54:36 | 2020-05-08T23:54:36 | 261,581,722 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,384 | r | projectB.R | # R Studio API Code
library(rstudioapi)
setwd(dirname(getActiveDocumentContext()$path))
# Libraries
library(tidyverse)
library(rvest)
library(httr)
# Data Import and Cleaning
## READ ALL PAPERS IN, ONCE
#there are 240 results, 10 per page
allPapers <- list()
for (i in 1:24) {
j <- (i - 1)*10
link <- paste... |
4f57b0b2bb89272fb342da4b0d60ae50f49c6133 | ce7998c8db9a3a3dc47aaffee3351b5f86f8b596 | /man/find_filepath.Rd | df023ec7c501bcd1c5afd0ea72b0c8c0c5c8566d | [] | no_license | WerthPADOH/sasconfigger | c2f0c1a5b62fa1dfebe39bcb430d41aa0a7ad573 | f92cd183ffaeeeba38dc3c461bd655fb13d8cbac | refs/heads/master | 2021-01-11T04:19:46.687203 | 2016-10-17T20:45:26 | 2016-10-17T20:45:26 | 71,179,838 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 475 | rd | find_filepath.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/find_filepath.R
\name{find_filepath}
\alias{find_filepath}
\title{Find where file paths occur in text
Finds the start and end positions of all file paths occurring in a text.}
\usage{
find_filepath(x)
}
\arguments{
\item{x}{Character vector}
... |
dd9cdb9eea1fcd46614ebb3e713113407a296be2 | 0cc86ecac7e9cb23cb97512ba4d7f5b81d48687e | /RNAseq/normalize_epic_arrays.R | 6b77e990fc1b3ba2d406c5d67b99fd525fb78a06 | [] | no_license | thangnx1012/RNAseq_Annalysis | d677862bd31a9187bd6ecd706a579d6577785e6c | 085f44d1bc7bbca43d7e19a8dc37ca58a8ea9d0a | refs/heads/main | 2023-08-27T20:33:45.453219 | 2021-11-14T16:46:28 | 2021-11-14T16:46:28 | 414,225,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,957 | r | normalize_epic_arrays.R | #### This analysis is for analysis of DNA EPIC array analysis. NOTE!!! There is not an annotation available for hg38, so the genomic coordinates are hg19.
setwd("Z:/Wendy_Kellner/DNMT1/DNMT1_AML_Epic_methylation_NYU")
library(limma)
library(minfi)
library(IlluminaHumanMethylationEPICanno.ilm10b2.hg19)
library(Illumin... |
bd35396a2d6b9646684310d81d35f0566a8fef15 | 7fd1e5f78328c67f0644bf7dafe7c308613dcc29 | /R/group_project_R.R | 4efd07e3436c8a30ae81b53ada7c6a9631d66386 | [] | no_license | yuywang1227/Stat-506-Project | 9be3243270bcd11a797644a655f7cc1b296b714f | 4aea873c43d52017cc3438ca2c8a5ae998067ec6 | refs/heads/master | 2020-09-25T23:17:08.755093 | 2019-12-12T07:11:12 | 2019-12-12T07:11:12 | 226,110,436 | 0 | 1 | null | 2019-12-10T13:50:07 | 2019-12-05T13:37:52 | HTML | UTF-8 | R | false | false | 2,661 | r | group_project_R.R | ## Group project by group 6
## Stats 506, Fall 2019
## Group Member: Yehao Zhang, Yuying Wang
##
## In this project, the team is going to apply statistical methods to answer the following question:
##
## Do people with higher carbohydrate intake feel more sleepy during the day?
##
## Author: Yehao Zhang
## Up... |
02b8da72754f1ba18e708bbaa61709dc0918c232 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /ICcalib/man/CalcNpmleRSP.Rd | ea05a3718760b1a4bb704e597466d85aec10942a | [] | no_license | akhikolla/TestedPackages-NoIssues | be46c49c0836b3f0cf60e247087089868adf7a62 | eb8d498cc132def615c090941bc172e17fdce267 | refs/heads/master | 2023-03-01T09:10:17.227119 | 2021-01-25T19:44:44 | 2021-01-25T19:44:44 | 332,027,727 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,998 | rd | CalcNpmleRSP.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CalcNpmleRSP.R
\name{CalcNpmleRSP}
\alias{CalcNpmleRSP}
\title{Calculating the probabilities of positive binary exposure status at a given time point using a nonparametric risk-set calibration models}
\usage{
CalcNpmleRSP(w, w.res, poin... |
da840f36178fcdd9def7cbf666a3187e3b86c530 | 315af6191046d18fa8856566add85b1586b052f4 | /Code/Environmental Factors/environ_flux_data.R | 3363bebeec26b2ce1702a266fbde2f62543bd716 | [] | no_license | twilli2/n2oflux | 40e1fbf12919b33d366800eacec62049d0347f97 | 73f0e143bfab81f458f7e1c2d02d9df228ed4226 | refs/heads/master | 2021-01-03T14:43:57.948139 | 2020-02-12T21:01:23 | 2020-02-12T21:01:23 | 240,113,520 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,347 | r | environ_flux_data.R | library(tidyr)
flux_data$date <- as.Date(flux_data$date)
flux_env <- left_join(flux_data, joined_env_data, by = c("date","field"))
summary(flux_env)
cp_n2o <- filter(flux_env, compound == 'n2o', plot == 'C'|plot == 'P') %>%
select_all() %>%
group_by(date, field, plot) %>%
summarize(mean_flux = mean(flux, na.... |
9f1d644915e3adfc0850d0df548c7d7a9744596c | dab05df8a6ddf8947638c2bc2c3b5946d13771e2 | /R/production_possibility_frontier.R | 5e1f7e41877f2462a612b06d6402e9e8a0fbe6f4 | [
"MIT"
] | permissive | tpemartin/econR | 2011047b7ef100b27fffd99148a7698ce7f99930 | 5df4fd5bf61b417b9860b3efc7ff20339e694fe4 | refs/heads/master | 2023-09-05T03:34:20.354596 | 2021-11-23T12:22:42 | 2021-11-23T12:22:42 | 335,521,237 | 0 | 4 | null | 2021-03-17T07:18:16 | 2021-02-03T05:48:23 | HTML | UTF-8 | R | false | false | 1,605 | r | production_possibility_frontier.R | #' Construct PPF
#'
#' @param endowment_L A number.
#' @param produce_x A production function of L input
#' @param produce_y A production function of L input
#'
#' @return An environment with above 3 input arguments, a plot_PPF function and an update_endowmentL function
#' @export
#'
#' @examples
#' produce_x <- functi... |
73814a3efd6989366d7782c9131a65c593c4c91e | 543c541ff5cf3342f32480bd2958770dcaa3ad63 | /US-EU-Soft-Commodity/R Code/First Differenced VAR.R | e438a78caf4fef6262e705174e65257f08fb5e6c | [] | no_license | jzt5132/Time-Series-Stuff | c439deecddd8aea573d1f6d85ad7292956ce5935 | d4f6f0a69900fcdd0c94a3907705aa08c7503e39 | refs/heads/master | 2016-09-08T01:50:16.127520 | 2015-09-16T08:43:00 | 2015-09-16T08:43:00 | 41,987,618 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 497 | r | First Differenced VAR.R |
###First Differenced VAR###
#This program determines the bivariate first difference VAR of all time serieses
#and conduct Granger Causlity Test on it. The output is returned in csl matrix.
csl <- matrix(data = NA,nrow = 7,ncol = 7)
for (i in 1:6)
{ for (j in (i+1):7)
{
v <- VAR(cbind(diff(a[,i]),diff(a[,j])),p = 2)... |
2ec4c593b753403ebcc8a53b79aa5faaf2018822 | db4118bc4c3fa27bce4c2d5039facbb9072479c0 | /coevo/h5_n1/h5_n1.R | 6f668d0d9e5ab96f132872c6bf7df8288bbc101a | [] | no_license | yaotli/Packaging_Type | 166d4a4b6b8d20daab88612bc497e02d9e8fc038 | 4dba547aed7105c13f5bf4042c121f2289081ae1 | refs/heads/master | 2021-01-23T01:26:21.502910 | 2019-06-17T03:44:33 | 2019-06-17T03:44:33 | 85,908,055 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,377 | r | h5_n1.R | source( "./function.coevo.R" )
#source( "./ha_classi.R" )
source( "./f.aa_recon.R")
require(ggtree)
require(ape)
require(tidyverse)
require(stringr)
H5_treefile = "./gsgd/processed/tree/raxml_c2344_2657/raxml_pH5_2657.tre"
N1_trefile = "./raw_data/processed/tree/fasttree_pN1_4696_.tre"
H5_seq = "./gsgd/processed/pH... |
1caddf117202cff05e88e106fa28878e0935a68d | b39713726afbf52fd03c8b3470e37f9c52e2085f | /plot2.R | 42e35f3c05e73985fb2f738e1234fb6947d16de7 | [] | no_license | tesszty/ExData_Plotting1 | f76eb3a192fa358a3205985a57c108bba4cb720e | d57ccc3990f84eb3f11cf3a0a3593165ef7f11e5 | refs/heads/master | 2020-12-30T22:57:45.442517 | 2016-03-27T09:32:12 | 2016-03-27T09:32:12 | 54,650,810 | 0 | 0 | null | 2016-03-24T15:03:29 | 2016-03-24T15:03:29 | null | UTF-8 | R | false | false | 179 | r | plot2.R |
with(mydata,plot(Time,Global_active_power,ylab="Global Active Power (kilowatts)",xlab="",type="o",pch=".")
)
dev.copy(png,'plot2.png', width = 480, height = 480)
dev.off() |
bfa1d89bd678232ed98994e16cc1c41e1400abc8 | c238ecf25d51558f4e57533422810b05f4c9bb6b | /plot4.R | 94f7e1ab632187b784aa93f46d4d8b047a3532f8 | [] | no_license | franciscoalvaro/ExData_Plotting1 | 2c1052e6f93870e87e64cf1c8727fa1471f97872 | 0446558c7658c3bec99a64f6cc7daebabb0294f9 | refs/heads/master | 2021-01-15T22:33:53.546223 | 2015-02-08T21:06:24 | 2015-02-08T21:06:24 | 30,434,735 | 0 | 0 | null | 2015-02-06T21:55:27 | 2015-02-06T21:55:26 | null | UTF-8 | R | false | false | 2,504 | r | plot4.R | library(lubridate)
mydata <- read.table("household_power_consumption.txt", header=TRUE,sep=";")
par(mfrow = c(2, 2))
selection<-c("Global_active_power","Date","Time")
plot1<-mydata[selection]
plot2<-plot1[which((plot1$Date == "1/2/2007") | (plot1$Date == "2/2/2007")),]
plot2$DateTime <- strptime(paste(plot2$Date, pl... |
31fbe2551024809d6bc29746d2c1322d0e77bfc3 | 6c800fc94df87bac4cd11bbe910bf483b85f6871 | /helpers/VisualMarketsTheme.R | 078cbd37bb86a8b5b48f5111d8cc7c79f19c5e16 | [] | no_license | visualmarkets/visualmarkets | f77141fb9aad92960e0898b228473f044e0d7f93 | e586058813c6fa65f7aa53cc30737cf98004fc23 | refs/heads/master | 2020-04-02T03:42:17.618212 | 2019-01-25T00:21:14 | 2019-01-25T00:21:14 | 153,980,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,923 | r | VisualMarketsTheme.R | hc_theme_vm <-
function (...) {
theme <-
list(colors = c("#6794a7", "#014d64", "#76c0c1", "#01a2d9", "#7ad2f6", "#00887d", "#adadad", "#7bd3f6", "#7c260b", "#ee8f71", "#76c0c1", "#a18376"),
chart = list(backgroundColor = "#ffffff",
style = list(fontFamily = "Droid Sans",
... |
3606bcdc3f2bcb3b1324a160915584563dfc7384 | 9c90c51d76a54580b67c6a6d8292facc693322b0 | /results/TEplot.R | 55001968ddd4388e84c9ae82ba482ce266133d3f | [] | no_license | altingia/REpipe | 7dd8fde51ab06989507c3c886cf56fc00490c7a6 | a0dc876ece9f50a3585ef84658f4baad2a1837d1 | refs/heads/master | 2021-09-15T14:44:01.936941 | 2018-06-04T19:18:27 | 2018-06-04T19:18:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 431 | r | TEplot.R | ##PLOTTING RESULTS FROM MULTIPLE SPECIES (on JeanLuc)
setwd("~/Copy/TAXON/results/combine")
table <- read.csv(file="REpipeResults.csv")
#create total read column
rawdata <- transform(rawdata, totalreads = mappedreads + unmappedreads)
#create nuclear reads column (remove organellar)
rawdata <- transform(rawdata, nucr... |
c86d7db093b73a6906bcb869a7f028fb4a1858cd | 20bfcff74f158557d50f1293c8f70404ece0d5a5 | /glmPR/R/RcppExports.R | 60276ae70fc1d2aeaf0cd283824eac5d948690b6 | [] | no_license | Xia-Zhang/Poisson-Regression | 76d047ccae6300841906929f5cfc875b4ab9258b | 82ed7237db8cbade82b1dcf3cc36a40cbec0e2a0 | refs/heads/master | 2021-01-18T07:26:29.615945 | 2017-05-11T15:37:48 | 2017-05-11T15:37:48 | 84,288,908 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 251 | r | RcppExports.R | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
glmPR <- function(X, y, lambda = 0.5, threads = 4L) {
.Call('glmPR_glmPR', PACKAGE = 'glmPR', X, y, lambda, threads)
}
|
7e101a34e1ab22bc1658537642d2b7800b50ee1c | e835f60e7ad4be41d40293d58e157b5689ae9525 | /cachematrix.R | 600edfa7341b4801ca229d9893ca1b661104e067 | [] | no_license | GutsIkari/ProgrammingAssignment2 | 01e076c9526b993f2b78cb6d53db9d82e4a09cfc | 1a44769dc1ffe6a30398b57f4b91db697e8202fe | refs/heads/master | 2021-01-18T11:37:08.751676 | 2015-06-16T13:08:06 | 2015-06-16T13:08:06 | 37,523,162 | 0 | 0 | null | 2015-06-16T10:20:25 | 2015-06-16T10:20:25 | null | UTF-8 | R | false | false | 1,123 | r | cachematrix.R | ## The purpose of makeCacheMatrix() is to be able to
## produce a matrix which is able to cache it's own
## inverse and to define functions which will allow
## cacheSolve() to either reproduce the inverses, or to
## simply calculate them if the inverse is defined as NULL
## makeCacheMatrix() will create a matri... |
a3a7bac78b73f95113fd32c37c3c8ae4fce91b5b | e9e0be3a532b12ed9a36e4f0d9254deaa209b38e | /inst/manuscript/MALAT1/Code/malat1_DataPreprocessing.R | 89cda8c9f66cbe22220e6e4f8c4db45bd31150d4 | [] | no_license | Leonrunning/scTenifoldKnk | 57da17d9e1a97d83bef406b0dce3dbb323342f27 | 09f4ebd2c5dffbd57a878c51f279334b9d83ff85 | refs/heads/master | 2023-06-30T14:02:26.167430 | 2021-07-30T18:32:22 | 2021-07-30T18:32:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,443 | r | malat1_DataPreprocessing.R | library(Matrix)
library(Seurat)
library(scTenifoldKnk)
source('https://raw.githubusercontent.com/dosorio/utilities/master/singleCell/scQC.R')
MALAT1 <- Read10X_h5('WT.h5')
MALAT1 <- scQC(MALAT1, mtThreshold = 0.05)
MALAT1 <- CreateSeuratObject(MALAT1)
MALAT1 <- NormalizeData(MALAT1)
MALAT1 <- FindVariableFeatures(MALA... |
4c6e5b8c9affd13ce42660f1d618056ad5293ae4 | cbdede81db4e81dc0372920d781b3b7e3b05e3e3 | /Kiiru_1.R | bbec34650838a7cb1c31e558538603025c2fcc87 | [] | no_license | kiiru60/Regression-and-hypothesis-testing- | 8718be2c014a9e3173019a3d6b6a404362d8641c | 0b86f4eee7e7fbc467fa2bc54fc7b54628cf6d1d | refs/heads/master | 2020-07-29T14:31:01.658629 | 2019-09-20T17:11:00 | 2019-09-20T17:11:00 | 209,842,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,265 | r | Kiiru_1.R |
#________________________________________________________________________________________________#
#************************************************************************************************#
# --> The following setwd() command should be commented out until your code is ready to be
# submitted. At that ... |
546aeceee74598747a9286ca2126f1df08dbd393 | 0e290f17d1c7798abd4e3b4883827e0e83426956 | /static_code.R | 3e3db2bb717607a923e77d81b9cdf75770d43f87 | [] | no_license | siare1023/ST558-Project3 | 4dca4a531135a85757e0304983df895b377fdfa9 | 8c39a7ba63bf2bd2b4e81f60eb1edfb1a53e7383 | refs/heads/main | 2023-06-29T01:52:44.091783 | 2021-08-03T01:40:53 | 2021-08-03T01:40:53 | 389,371,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,524 | r | static_code.R | raw_data_original <- read_csv("California_Houses.csv")
raw_data_original$Median_House_Value %>% summary()
# training and test data
training.percentage <- 0.1
set.seed(7)
training <- sample(1:nrow(raw_data_original), size = nrow(raw_data_original)*training.percentage)
test <- dplyr::setdiff(1:nrow(raw_data_original), t... |
b2839474a2003ef3d9d68d9e92960a2bb76cf0c5 | 39c8af74e550cfd4d2d6c9432707e951800c1cd1 | /cachematrix.R | 3822c0ebad23eda6041b09c0c4b1f7f740a86527 | [] | no_license | Matanatr96/ProgrammingAssignment2 | e084856a95c77e38e11f6909a41208c1b9e215ac | c5bd035d3e5d21c9e03436fee6de93264b0955ba | refs/heads/master | 2021-06-06T06:45:33.756800 | 2016-11-03T23:08:54 | 2016-11-03T23:08:54 | 72,792,559 | 0 | 0 | null | 2016-11-03T22:29:01 | 2016-11-03T22:29:00 | null | UTF-8 | R | false | false | 1,002 | r | cachematrix.R | ## This set of functions calculates the inverse of a matrix and stores the value in cache
## This saves time by avoiding the need to calculate the partial inverse every time
## This function creates a matrix with the ability to get and set its value and get and set its inverse
makeCacheMatrix <- function(x = matrix())... |
5a29fcd2c9c0d5b87bdd06d3c21996cbbb8a3292 | b2fceb19567b364f6ba7b16f318f396075a0d874 | /cachematrix.R | 3ccd01be178cfbf38680b2dc09b4e61e8f4e98c1 | [] | no_license | juraseg/ProgrammingAssignment2 | 93dc0b5be95e50609cf868c2c677766f7004930d | 7381b744b4bae8357284827d0324f639719cc041 | refs/heads/master | 2021-01-17T11:24:59.051294 | 2014-05-25T11:15:04 | 2014-05-25T11:15:04 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,058 | r | cachematrix.R | ## These functions perform caching of results of matrix inverse operation
## The function creates special "matrix" object which caches it's inverse
makeCacheMatrix <- function(x = matrix()) {
# initialize inverse as NULL
inverse <- NULL
set <- function(y) {
x <<- y
# set inverse to NU... |
596303c344856a31fc54d490ad322dca29a6be28 | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.machine.learning/man/sagemaker_stop_notebook_instance.Rd | 20cc4e7534ba4ab54e78d5db71dea4a21e56a629 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 1,273 | rd | sagemaker_stop_notebook_instance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sagemaker_operations.R
\name{sagemaker_stop_notebook_instance}
\alias{sagemaker_stop_notebook_instance}
\title{Terminates the ML compute instance}
\usage{
sagemaker_stop_notebook_instance(NotebookInstanceName)
}
\arguments{
\item{NotebookInst... |
b217d738749bfb488a936ec1eedff97c532b4636 | bdb8c969fedf227b6bb4f2ea5f0aaf0c3b3a4fa0 | /03_genome_genes/10_codeml_output_processing.r | c23bf69b5e2e708dcd7619dfe2ec7073bd48cc28 | [
"MIT"
] | permissive | schnappi-wkl/certhia_genomes1 | 9416ed445cfb5e811f03ca654533cf565fdc95c7 | 95cce3cf7375203fe8b9970e2b0b19f70bb18559 | refs/heads/master | 2023-03-18T22:53:12.301976 | 2021-03-08T15:42:57 | 2021-03-08T15:42:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,105 | r | 10_codeml_output_processing.r |
output_name <- "codeml_results_pvals_uncorrected.txt"
output_name2 <- "codeml_results_pvals_corrected.txt"
write(c("gene_number", "certhia_un_p", "ficedula_un_p", "parus_un_p", "taeniopygia_un_p"), file=output_name, ncolumns=5, sep="\t")
x <- list.files(pattern="*fasta")
x2 <- list.files(pattern="*txt")
x_numbers <-... |
7989ddb361a20e053af4751ddb4310ba3d060cf5 | 12e0ddae06438b748d12a7f9c26e67cf682a8c16 | /models/loadData.R | 68790ed13b7a1ea83afdb54837aa04f139991084 | [
"MIT"
] | permissive | christianadriano/ML_SelfHealingUtility | b05b2462c95a9aed9ac86af9e5eeb65bb07713d0 | 398ef99a7073c6383862fade85b8816e65a2fb1e | refs/heads/master | 2021-10-07T20:45:51.281121 | 2018-12-05T09:16:05 | 2018-12-05T09:16:05 | 105,566,942 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,918 | r | loadData.R |
#---------------------------------------------------------------
#Load all data into a dataframe
loadData<- function(fileName){
setwd("C://Users//Chris//Documents//GitHub//ML_SelfHealingUtility//");
data_all <- read.csv(fileName,header = TRUE,sep=",");
dataf <- data.frame(data_all);
#Remove NA's
da... |
ecf5d5bd52e9cca76667660e77ace41aded01f27 | 208aa0cbd5c25dc27f769627a53e81f980a5e817 | /deep_learning/rstudio/install_packages.R | 3abd335ce3d2e5fd37c0d0d946d2fa1f4785e4f2 | [] | no_license | GeertvanGeest/scs-docker | 50d2380a8668be072df7d00942863db27722021b | f82bcad3e3c78bd33ebfe7811d06414330fb6036 | refs/heads/master | 2023-07-15T22:27:32.765202 | 2021-09-03T07:26:39 | 2021-09-03T07:26:39 | 399,025,052 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 407 | r | install_packages.R | install.packages(c( "tensorflow", "keras", "BiocManager", "Matrix", "Rtsne", "rsvd",
"RColorBrewer", "umap", "reshape2"))
# Bioconductor packages:
BiocManager::install(c( "SingleCellExperiment", "scater", "cowplot", "scran",
"batchelor", "ComplexHeatmap", "tximeta",
... |
8543b0d3e36e188a7122b8c26ad6ec71b3c83d6b | 712c71892a6edd61227e2c0c58bbc1e9b43893e4 | /R/git_info.R | 9a8dd3b3b92d2df64dac40294e5ebc36f7ec0bc6 | [] | no_license | gelfondjal/adapr | 130a6f665d85cdfae7730196ee57ba0a3aab9c22 | b85114afea2ba5b70201eef955e33ca9ac2f9258 | refs/heads/master | 2021-01-24T10:20:14.982698 | 2020-01-28T22:56:18 | 2020-01-28T22:56:18 | 50,005,270 | 33 | 3 | null | 2018-10-18T16:09:57 | 2016-01-20T04:48:49 | R | UTF-8 | R | false | false | 928 | r | git_info.R | #' Retrieves the information from git about a file
#' @param gitdir string with git directory
#' @param filename string of file to query
#' @param branch git branch
#' @param git_args string argument for git
#' @param git_binary location of git executable
#' @return git log for filename
#' @export
#' @examples
#'\dont... |
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