id stringlengths 40 40 | repo_name stringlengths 5 110 | path stringlengths 2 233 | content stringlengths 0 1.03M ⌀ | size int32 0 60M ⌀ | license stringclasses 15
values |
|---|---|---|---|---|---|
9bc97bfbf4abbe37558b0939ef3401178a42f46f | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/isatomic/tc_isatomic_11.R | expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(0, -0.0555555555555556, 0.02, 0.0625, 0.0625, 0.04, 0, 0), .Dim = c(8L, 1L), .Dimnames = list(c(\"2\", \"3\", \"6\", \"7\", \"8\", \"9\", \"14\", \"17\"), \"x\")))"));
do.cal... | 391 | gpl-2.0 |
9bc97bfbf4abbe37558b0939ef3401178a42f46f | krlmlr/cxxr | src/extra/testr/filtered-test-suite/isatomic/tc_isatomic_11.R | expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(0, -0.0555555555555556, 0.02, 0.0625, 0.0625, 0.04, 0, 0), .Dim = c(8L, 1L), .Dimnames = list(c(\"2\", \"3\", \"6\", \"7\", \"8\", \"9\", \"14\", \"17\"), \"x\")))"));
do.cal... | 391 | gpl-2.0 |
9bc97bfbf4abbe37558b0939ef3401178a42f46f | kmillar/cxxr | src/extra/testr/filtered-test-suite/isatomic/tc_isatomic_11.R | expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(0, -0.0555555555555556, 0.02, 0.0625, 0.0625, 0.04, 0, 0), .Dim = c(8L, 1L), .Dimnames = list(c(\"2\", \"3\", \"6\", \"7\", \"8\", \"9\", \"14\", \"17\"), \"x\")))"));
do.cal... | 391 | gpl-2.0 |
9bc97bfbf4abbe37558b0939ef3401178a42f46f | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/isatomic/tc_isatomic_11.R | expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(0, -0.0555555555555556, 0.02, 0.0625, 0.0625, 0.04, 0, 0), .Dim = c(8L, 1L), .Dimnames = list(c(\"2\", \"3\", \"6\", \"7\", \"8\", \"9\", \"14\", \"17\"), \"x\")))"));
do.cal... | 391 | gpl-2.0 |
9bc97bfbf4abbe37558b0939ef3401178a42f46f | rho-devel/rho | src/extra/testr/filtered-test-suite/isatomic/tc_isatomic_11.R | expected <- eval(parse(text="TRUE"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(0, -0.0555555555555556, 0.02, 0.0625, 0.0625, 0.04, 0, 0), .Dim = c(8L, 1L), .Dimnames = list(c(\"2\", \"3\", \"6\", \"7\", \"8\", \"9\", \"14\", \"17\"), \"x\")))"));
do.cal... | 391 | gpl-2.0 |
e9ed05bbdd437ee540d03ec72790d6913c5e189e | CTLife/SomeRecords | meRIP-seq_QC/Trumpet.5.R | library(Trumpet)
getwd()
## Collect the path of all the aligned MeRIP-seq data files in BAM format.
f1 <- "/media/yp/yongpeng16TB/PsychoENCODE/m6A-seq/IP/H_samples/5-finalBAM/3_STAR/B81.IP.bam"
f2 <- "/media/yp/yongpeng16TB/PsychoENCODE/m6A-seq/IP/H_samples/5-finalBAM/3_STAR/B82.IP.bam"
f3 <- "/media/yp/yongpeng1... | 3,485 | gpl-3.0 |
7336b2f1b9481b71db99a314d80fb629d3bea7ba | lpantano/DEGreport | batchx/deg-patterns/run.deg-patterns.R | library(getopt)
library(DESeq2)
library(DEGreport)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(plotly)
# arguments
spec <- matrix(c(
'deseq2Object', 'd', 1, "character", "DESeq2 object (required)",
'qValue', 'q', 1, "numeric", "Q-value threshold to filter elements from the DESeq2 object (required)"... | 10,234 | mit |
60cbe13e6569b4dbb03251fb43ffbcb6f0d66280 | gfegan/pwani_tab_stats | rfiles/stuff_from_laz/Tues/sp4.R | ## Remember to change to your working directory by typing "setwd()"
# setwd("C:/kilifi_course/data")
## To know your current working directory, type "getwd()"
## Your working directory should have the datasets and the R-Scripts
## R Practical 1
getwd()
setwd("/home/thoya/Documents/kemri/MYDATA/kilifi_course/data")
d... | 2,169 | gpl-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | jangorecki/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | mathemage/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | nilbody/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | h2oai/h2o-dev | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | YzPaul3/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
d557e229cec639a99099e85438076abe878f1df4 | campsych/concerto-platform | src/Concerto/TestBundle/Resources/R/concerto5/R/concerto.session.update.R | concerto.session.update = function(){
concerto.log("updating session...")
sql = sprintf("UPDATE TestSession SET
status = '%s',
timeLimit = '%s',
error = '%s',
updated = CURRENT_TIMESTAMP
WHERE id='%s'",
dbEscapeStrings(concerto$connection, toString(concerto$session$status)),
dbEscapeStrings... | 612 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | h2oai/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | spennihana/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
2360b6fd572a8620da51100bcb6e0d48a1722be7 | michalkurka/h2o-3 | h2o-r/tests/testdir_munging/binop/runit_binop2_gteFrame.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.gte.frame <- function() {
hex <- as.h2o(iris)
Log.info("Expectation is a frame of booleans")
Log.info("Try hex >= 5 : ")
hexGTEFive <- hex >= 5
print(head(hexGTEFive))
Log.info... | 643 | apache-2.0 |
40476e2755f9033434ff89018ce70c60af654d02 | wangjue444/programming-for-fun | Fencang510/Fencang/utils/FastKNN.R | #'k-Nearest Neighbors
#'the \code{k.nearest.neigbors} gives the list of points (k-Neigbours) that are closest
#'to the row i in descending order.
#'@param i is from the numeric class and is a row from the distance_matrix.
#'@param distance_matrix is a nxn matrix.
#'@param k is from the numeric class and represent the n... | 4,759 | lgpl-3.0 |
d1cfe72e1788a427150c1874e46e5caf6fdcbf54 | lebebr01/shinyApps | CLT/server.R | library(shiny)
library(ggplot2)
library(gridExtra)
rbimod <- function(n, mean = c(-2, 2), var = c(1, 1), num.dist = 2){
if(length(mean) != num.dist) stop("length of mean must equal num.dist")
if(length(var) != num.dist) stop("length of var must equal num.dist")
if(length(n) > 1) {
unlist(lapply(1:num.dist... | 3,845 | gpl-2.0 |
40476e2755f9033434ff89018ce70c60af654d02 | wangjue444/Mine | Fencang510/Fencang/utils/FastKNN.R | #'k-Nearest Neighbors
#'the \code{k.nearest.neigbors} gives the list of points (k-Neigbours) that are closest
#'to the row i in descending order.
#'@param i is from the numeric class and is a row from the distance_matrix.
#'@param distance_matrix is a nxn matrix.
#'@param k is from the numeric class and represent the n... | 4,759 | lgpl-3.0 |
ef6400d92088819bf2c20673d4657bdfd7c4913d | mexicoevalua/incidenciaDelictiva | abreviaturas_estados.R | ### Agregar abreviaturas para los nombres de los estados
### Cargar rgdal
require(rgdal)
# Cargar abreviaturas
codes <- read.csv("data/state_names.csv", encoding="utf8")
codes$state_code <- sprintf("%02d", codes$state_code)
codes <- codes[,-3]
# Load shapefile using "UTF-8". Notice the "." is the directory and t... | 1,019 | mit |
39a60576fbe58efdd4f96b37483ea27bdb862065 | droglenc/FSAdata | R/Casselman1990.R | #' @title Instantaneous growth rates for two calcified ageing structures.
#'
#' @description Instantaneous growth rates (percent change per day) for body growth and two calcified ageing structures from age 1-4 female Northern Pike (\emph{Esox lucius}) from Wickett Lake, Ontario.
#'
#' @name Casselman1990
#'
#' @docType... | 1,784 | gpl-2.0 |
6c5fa49f8212b869eb207e81c4d093fd5dfa778b | stephenslab/EbayesThresh | inst/code/EB_Update_CompVal.R | install.packages("EbayesThresh")
library(EbayesThresh)
options(digits=12)
printres <- function(par_vec){
list_par = as.list(par_vec)
return(do.call(paste, c(list_par, sep=",")))
}
# beta.laplace
x <- c(-2,1,0,-4,5)
printres(beta.laplace(x))
# postmean
x <- c(-2,1,0,-4,5)
printres(postmean(x, w = 0.5))
# postmed
x... | 1,292 | gpl-3.0 |
06686bcee10ed37cb5726e745fc0ec611a68cbb1 | dhduncan/ConoceR | pathtofile.R | require(swirl)
.pathtofile <<- function(course_, lesson_, file_){
if(as.character(packageVersion("swirl")) > "2.2.21"){
file.path(get_swirl_option("courses_dir"), course_, lesson_, file_)
} else {
file.path(find.package("swirl"), "Courses", course_, lesson_, file_)
}
} | 283 | gpl-2.0 |
ddf21ab2dc81171d1bce2aeb215c92e3f7048b82 | bdilday/poz100analytics | poz100r/R/carter.R |
library(dplyr)
library(RPostgres)
library(DBI)
library(lme4)
library(stringr)
library(ggplot2)
library(htmlTable)
pl_lkup = Lahman::Master %>%
select(playerID, retroID, bbrefID, nameFirst, nameLast)
pl_lkup$nameAbbv =
paste(stringr::str_sub(pl_lkup$nameFirst, 1, 1), pl_lkup$nameLast, sep='.')
top_seasons_rate ... | 4,874 | bsd-2-clause |
b5cb467c9dfde73c65d6faa9ef648601af00f7b3 | h2oai/h2o-3 | h2o-r/tests/testdir_algos/naivebayes/runit_naivebayes_segment.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
check.naivebayes_segment <- function() {
iris_hex <- h2o.importFile(locate("smalldata/junit/iris.csv"))
models <- h2o.train_segments(algorithm="naiveBayes", y="petal_wid", training_frame=iris... | 509 | apache-2.0 |
b5cb467c9dfde73c65d6faa9ef648601af00f7b3 | michalkurka/h2o-3 | h2o-r/tests/testdir_algos/naivebayes/runit_naivebayes_segment.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
check.naivebayes_segment <- function() {
iris_hex <- h2o.importFile(locate("smalldata/junit/iris.csv"))
models <- h2o.train_segments(algorithm="naiveBayes", y="petal_wid", training_frame=iris... | 509 | apache-2.0 |
04b73ed0de43c3ae9f48eb0c439bcfd08416ddd2 | peter19852001/decomp | tmp.R | ###
## To test our algorithm on synthetic data previously generated.
##
# assume test.n, test.p, test.m, test.max.n are defined
source("sim.R");
source("infer.R");
#
print.grn <- function(g) {
# all use 0-based indices
n <- nrow(g);
for(i in 1:n) {
cat("To:",g$to[i]-1, "From:",g$from[i]-1, "Delay:",g$delay... | 934 | gpl-2.0 |
292bf4c807eb40565b9746fe8c904754f953209e | bikash/h2o-dev | h2o-r/tests/testdir_jira/runit_NOPASS_hex_1613_cm.R | ######################################################################
# Test for HEX-1613
# Bad confusion matrix output for mixed inputs
######################################################################
setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
options(echo=TRUE)
source('../h2... | 863 | apache-2.0 |
cd73233971d4cefcfcd40570eeb599fae9615c5a | julienmoeys/macroutils | pkg/macroutils/tests/macroInFocusGWConc.R |
library( "macroutils" )
# Maximum differences acceptable in conc
maxConcDif <- 1.6e-06
maxPercDif <- 1.7e-04
# Path to the file to be read
( filenm <- system.file( "bintest/MACRO001_20151005.BIN",
package = "macroutils", mustWork = TRUE ) )
res <- macroInFocusGWConc( x = filenm )
res
attr( res, "more"... | 2,585 | agpl-3.0 |
58bec891aed53994fe6275f1116570a90ab787cf | GeoscienceAustralia/ptha | misc/probabilistic_inundation_tonga2020/gauges/nukualofa/spectral_highpass_filter.R |
#' Remove low-frequencies (below cutoff_frequency) from a time-series
#'
#' This offers a reasonable method to remove non-tsunami components from a
#' signal -- just remove everything with frequency less than a cutoff of (say) 3
#' hours or similar. Beware that unless the signal is periodic, this function
#' lead to s... | 2,434 | bsd-3-clause |
04bf9cc3ac75b058ba192f84d26569b3219de813 | SoftwareIntrospectionLab/FixCache | graphs/rscripts/month_vs_rate.R | #!/usr/bin/env Rscript
# month vs hitrate
rates <- read.csv("ratesfile", comment.char="#")
attach(rates)
plot(Month, HitRate, type="p", ylim=range(0,100), xlim=range(1,max(Month)), xaxt="n")
axis(at=Month, side=1)
abline(h=max(HitRate), lty=2)
abline(h=min(HitRate), lty=2)
mtext(side=4, text=min(HitRate), las=1, at=min... | 772 | bsd-3-clause |
d5909af32ab474453bfd80c9d27ab401e377892c | arnejohannesholmin/TSD | R/zeropad.R | #*********************************************
#*********************************************
#' Pads numerics with zeros at the beginning.
#'
#' @param x is a numeric vector.
#' @param n is the number of characters in the returned strings.
#'
#' @return
#'
#' @examples
#' \dontrun{}
#'
#' @export
#' @rdname zeropad
... | 1,458 | lgpl-3.0 |
03dee5f920249001a301ee2f93094227b2151a0f | ThinkRstat/ThinkR | R/is_full_na.R | #' Predicate for full NA vector
#'
#' @description is_full_na test if the vector is full of NA's
#' @param . a vector
#'
#' @return a vector of boolean
#' @export
#'
#' @examples is_full_na(c(NA, NA, NA))
is_full_na <- function(.) {sum(is.na(.), na.rm = TRUE) == length(.)}
| 275 | gpl-3.0 |
64a3dea5bb6ce396941b6fa7d3a2f7921389862a | maxheld83/pensieve | tests/testthat/helper_psOpenSorts.R | # Creation ====
# you can combine individual sorts into a list ====
los <- psOpenSorts(open_sorts = list(lisa = lisa, peter = peter, rebecca = rebecca))
# or create psOpenSorts from a more convenient input ====
# recreate messy format from canonical form (don't do this at home)
ass <- pensieve:::make_messy(open_sorts ... | 581 | agpl-3.0 |
aef95ec44b665a7ddfddecea24ee98fbbae908bc | karawoo/icetest | R/check_neg.R | ##' Check for negative values
##'
##' Checks that numeric columns are never negative. These columns (with the
##' exception of stationlat, stationlong, and airtemp which can be negative)
##' should not contain values less than zero.
##'
##' @param dat Data frame to be tested.
##'
##' @author Kara Woo
##'
##' @export
... | 1,099 | mit |
bc68347689cc23d47da363d52a92f09cf721d257 | ecjbosu/fSEAL | PerformanceAnalytics/R/chart.StackedBar.R | #' create a stacked bar plot
#'
#' This creates a stacked column chart with time on the horizontal axis and
#' values in categories. This kind of chart is commonly used for showing
#' portfolio 'weights' through time, although the function will plot any values
#' by category.
#'
#' This function is a wrapper ... | 14,756 | gpl-2.0 |
426976c175390b891f9aa08c3bcc2664103bbc3c | twareproj/tware | benchmarks/r/logreg/logreg.R | sigmoid <- function(x, a) {
1 / (1 + exp(-a*(x)))
}
download_start <- proc.time()
data_file = read.csv("") #read csv file with name "data.csv"
download_end <- proc.time()
process_start <- proc.time()
data_length <- length(data_file[,1])
num_features <- (length(data_file[1,]) - 1)
labels <- data_file[,(num_featu... | 947 | apache-2.0 |
82efa19bb314cddeff874fb5b59a762727e94559 | basilrabi/mansched | tests/testthat.R | #https://github.com/luckyrandom/cmaker/commit/b85813ac2b7aef69932eca8fbb4fa0ec225e0af0
Sys.setenv("R_TESTS" = "")
library(testthat)
library(mansched)
test_check("mansched")
| 175 | gpl-3.0 |
426976c175390b891f9aa08c3bcc2664103bbc3c | feiyanzhandui/tware | benchmarks/r/logreg/logreg.R | sigmoid <- function(x, a) {
1 / (1 + exp(-a*(x)))
}
download_start <- proc.time()
data_file = read.csv("") #read csv file with name "data.csv"
download_end <- proc.time()
process_start <- proc.time()
data_length <- length(data_file[,1])
num_features <- (length(data_file[1,]) - 1)
labels <- data_file[,(num_featu... | 947 | apache-2.0 |
efd2a38fc884755912bedad409a49f09f49afcdd | e-sensing/sits | tests/testthat/test-cube_copy.R | test_that("Downloading and cropping cubes from BDC", {
cube <- tryCatch(
{
sits_cube(
source = "BDC",
collection = "CB4_64_16D_STK-1",
tiles = c("022024", "022025"),
bands = c("B15", "CLOUD"),
start_date = "2018-01-0... | 6,016 | gpl-2.0 |
e0a98e1bb63aedd9c0d76c5b7a6029439f151304 | JoeyBernhardt/photosynthesis | R/03_k-temp-figures.R |
library(tidyverse)
library(cowplot)
library(stringr)
flux_rates_raw <- read_csv("data-processed/flux_rates.csv")
flux_rates <- flux_rates_raw %>%
filter(temperature.x != 19) %>%
filter(temperature.x != 22) %>%
gather(key = flux_type, value = rate_estimate, gross_photosynthesis, gross_photosynthesis_corr, respi... | 2,941 | mit |
1686bafc07a81b07656fb12c6306f7fbb20c3d8a | cran/rv | R/rvmultinom.R | #' Generate Random Variables from a Multinomial Sampling Model
#'
#' Generates a random vector from a multinomial sampling model.
#'
#' The length of \code{prob} determines the number of bins.
#'
#' The vector \code{prob} will be normalized to have sum 1.
#'
#' If \code{length(prob)} is two, \code{rvbinom} is calle... | 1,676 | gpl-2.0 |
2d84bf0584501757116098e9c482601c024cc5a4 | RevolutionAnalytics/RRO | R-src/src/library/tools/R/index.R | # File src/library/tools/R/index.R
# Part of the R package, https://www.R-project.org
#
# Copyright (C) 1995-2015 The R Core Team
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either v... | 10,001 | gpl-2.0 |
ac37580c6b3736ac836f8cfd2c5a44dcb7aea66a | ahnqirage/spark | R/pkg/R/mllib_fpm.R | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | 9,895 | apache-2.0 |
57794c3c058a92d450f3e75615913c55be865ac4 | CTTV/ropentargets | scripts/association_examples.R | # Interested in Inflammatory Bowel Disease and all the associations for this disease currently held in CTTV.
# Firstly, we need to find the EFO identifier for "Inflammatory Bowel Disease".
ensemblGeneID <- 'ENSG00000073756'
efoID <- 'EFO_0003767'
assocObj <- ropentargets::Association$new(ensemblGeneID, efoID)
assocDeta... | 423 | apache-2.0 |
143131015f6f0c9994ad549d16689f73edd64093 | cavios/coastMDT | coastMDT/R/iterativeAveSmootherBoot.R | #' Iterative box filter
#'
#' The function \code{iterativeAveSmoother} is a simple average filter applied nit number of times. The size of the filter in the E-W direction is scaled according to the latitude.
#' @param dat An object as returned by the function 'getSubGrid', which includes a list containing a matrix ... | 2,269 | lgpl-3.0 |
f184ece77820ec3ccd410a64c203eae368fa679d | walterxie/ComMA | R/UtilsCombine.R | # Utils
# Author: Walter Xie
# Accessed on 29 Nov 2016
#' @name UtilsCombine
#' @title Utils to combine data frames or matrices into the required data format
#'
#' @details
#' \code{getTriMatrix} converts pairwised comparison result
#' into a symmetric triangular matrix.
#' The pairwised comparison result is stored... | 10,083 | gpl-3.0 |
a4620fb06862dbcc6566016d5d4c1dd809bd9c9e | sanoke/hetviz | R/forestPlot-plotFcns.R | #' Generates figure for 'Forest Plot' tab
#'
#' \code{forestPlot()} is an internal function that
#' takes a dataset and returns a plot.
#'
#' @param ds Any object that can be coerced into a \code{data.frame},
#' that contains data needed for plotting. This dataset is
#' of a very specific structure, as
#' defined... | 13,888 | gpl-3.0 |
c152585df53a209b0a52a94e9f3a33b6186f973e | lordbitin/CIM-2017 | src/3-Markovian_Comparison-Rank_Aggregation.R | #
# This script performs a Rank Aggregation process over a grid of evaluated DCMMs in order to create a final ranking
# that achieve a compromise between the predictability and the interpretability of the model.
#
# Requirements:
# The object "gs" must exist, as a result of executing the script "2-Markovian_Compar... | 8,201 | mit |
ee2ed57de249a3b64dbb50156a14b922a671887e | cpcloud/arrow | r/R/parquet.R | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 22,330 | apache-2.0 |
8d7bfaf60764cd97ab42e1f01a204a3380cec448 | Polgy/DataCleaningAssignment | chk_packages.R | #load packages
# credit Roger Peng course
local({
message("checking if depending packages are loaded. This may take a minute or two...\n")
checkPkgs <- function(pkgs) {
pkg.inst <- installed.packages()
#pkgs <- c("data.table")
have.pkg <- pkgs %in% rownames(pkg.inst)
if(any(!have.pkg))... | 723 | mit |
8ae973b8411810890fd1b99667880649fbc8b6bb | scheidan/adaptMCMC | adaptMCMC/R/Adaptive_MCMC.R | ## =======================================================
## (Adaptive) Metropolis Sampler
##
## Implementation of the RAM (robust adaptive Metropolis)
## sampler of
## Vihola, M. (2011) Robust adaptive Metropolis algorithm with
## coerced acceptance rate. Statistics and Computing.
## [online] http://www.springerlink.... | 8,366 | gpl-2.0 |
eca78c3029c1989b21ff1754f15b863ea38e374f | cuttlefishh/papers | cyanophage-light-dark-transcriptomics/code/rpkmClust.R | #source required functions
source("cluster_funct.R")
#read in data
geneInfo <- read.delim("genes_med4phm2.tsv",header=FALSE,stringsAsFactors=FALSE,sep="\t",quote="")
countsTable <- read.delim("med4phm2_sense_R.tsv",header=TRUE,stringsAsFactors=FALSE,sep="\t",quote="")
#store gene size array
sizebp<-geneInfo$V2
sizekb... | 4,114 | mit |
4a97627f9e23f9b41b444548c095a3285274fe8e | griffithlab/GenVisR | R/compIdent_format.R | #' Format readcount tables from compIdent
#'
#' Format readcount tables from compIdent for input into compIdent_buildMain
#' @name compIdent_format
#' @param x Named list of data frames with rows of the data frame corresponding
#' to target locations.
#' @return Formated data frame
#' @noRd
compIdent_format <- functi... | 998 | cc0-1.0 |
16cb85e70b99299a8b77f369e3c80e293de2edea | dankelley/oce-issues | 06xx/678/678.R | library(oce)
file <- "POS-ECH-P10.ctd"
d <- read.oce(file)
| 59 | gpl-2.0 |
228694f9be7e9b4868d823e37535f1b59ca3139c | llrs/WGCNA | R/matchLabels.R | # Relabel the labels in source such that modules with high overlap with those
# in reference will have the same labels
# overlapTable ####
#' Calculate overlap of modules
#'
#' The function calculates overlap counts and Fisher exact test p-values for
#' the given two sets of module assignments.
#'
#'
#' @param lab... | 8,640 | gpl-3.0 |
4a97627f9e23f9b41b444548c095a3285274fe8e | griffithlab/GGgenome | R/compIdent_format.R | #' Format readcount tables from compIdent
#'
#' Format readcount tables from compIdent for input into compIdent_buildMain
#' @name compIdent_format
#' @param x Named list of data frames with rows of the data frame corresponding
#' to target locations.
#' @return Formated data frame
#' @noRd
compIdent_format <- functi... | 998 | cc0-1.0 |
be3f2143c7772724f639e9d0bccb66763246f2db | zettsu-t/cPlusPlusFriend | cppFriendsRcpp.R | library(Rcpp)
library(BH)
Sys.setenv("PKG_CXXFLAGS"="-std=gnu++14")
sourceCpp('cppFriendsRcpp.cpp')
split_double_components(c(0.0, 1.0, 1.5, -3.5, Inf, -Inf))
| 166 | mit |
f15d7e5e033fa59508a1bb563ddaae07e9d9420b | dpastoor/shinystan | inst/ShinyStan/ui_files/help.R | # This file is part of shinystan
# Copyright (C) Jonah Gabry
#
# shinystan is free software; you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation; either version 3 of the License, or (at your option) any later
# version.
#
# shinyst... | 3,960 | gpl-3.0 |
72c1b59257f1362f5b5105b5fb6b547ccd614dd8 | vinaywv/mlr | R/RLearner_regr_cforest.R | #' @export
makeRLearner.regr.cforest = function() {
makeRLearnerRegr(
cl = "regr.cforest",
package = "party",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", lower = 1L, default = 500L),
makeIntegerLearnerParam(id = "mtry", lower = 1L, default = 5L),
makeLogicalLearnerParam... | 3,065 | bsd-2-clause |
72c1b59257f1362f5b5105b5fb6b547ccd614dd8 | tijoseymathew/mlr | R/RLearner_regr_cforest.R | #' @export
makeRLearner.regr.cforest = function() {
makeRLearnerRegr(
cl = "regr.cforest",
package = "party",
par.set = makeParamSet(
makeIntegerLearnerParam(id = "ntree", lower = 1L, default = 500L),
makeIntegerLearnerParam(id = "mtry", lower = 1L, default = 5L),
makeLogicalLearnerParam... | 3,065 | bsd-2-clause |
f15d7e5e033fa59508a1bb563ddaae07e9d9420b | jhsiao999/shinystan | inst/ShinyStan/ui_files/help.R | # This file is part of shinystan
# Copyright (C) Jonah Gabry
#
# shinystan is free software; you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation; either version 3 of the License, or (at your option) any later
# version.
#
# shinyst... | 3,960 | gpl-3.0 |
4c26782b39ae98be7ddc0a6cf7a849097dab9945 | kirillseva/explain | R/pending.R | pending <- function() {
TRUE
}
| 33 | mit |
8f772c41c6a607b0e694440c15a668c58b19798b | sammorris81/extreme-decomp | markdown/fire-analysis/fit-gsk-5-5.R | rm(list=ls())
source(file = "./package_load.R", chdir = T)
# Number of bases: 5, 10, 15, 20
process <- "gsk" # ebf: empirical basis functions, gsk: gaussian kernels
margin <- "gsk" # ebf: empirical basis functions, gsk: gaussian kernels
L <- 5 # number of knots to use for the basis functions
cv <- 5 ... | 1,324 | mit |
397c0a6e69935351f2bbc970ef51a76030c51100 | NovaInstitute/Rpackages | novaAI/R/nr.plot.report.R | #' Stand Number Plot QC Function
#'
#' Match a list of stand numbers to the stand numbers in a geografic
#' file (like a SpatialPolygonsDataFrame) and prints a summary of whether
#' a suburb is plotted or not
#'
#' @param x Data frame containing the stand numbers
#' @param standnumbervar The name of the variable... | 1,112 | mit |
333464900ecf18809c5240400b7c0cc65eb1dea3 | pstessel/medicare | censusVis/ui.R | # ui.R
shinyUI(fluidPage(
titlePanel("censusVis"),
sidebarLayout(
sidebarPanel(
helpText("Create demograpic maps with
information fromthe 2010 US Census."),
selectInput("var",
label = "Choose a variable to display",
choice... | 720 | mit |
26ecb7933c5e5d1e73678399b392a1571919f937 | cchacua/m1-vcf | scripts/useless.R |
wiot.2000 <- open.rdata(wiot.files[1])
wiot.2001 <- local(get(load(wiot.files[2])))
wiot.2002 <- local(get(load(wiot.files[3])))
wiot.2003 <- local(get(load(wiot.files[4])))
wiot.2004 <- local(get(load(wiot.files[5])))
wiot.2005 <- local(get(load(wiot.files[6])))
wiot.2006 <- local(get(load(wiot.files[7])))
wiot.2007 ... | 8,274 | gpl-3.0 |
ec0d6c923d19b5b675fab5410aa14c3ac19db0c1 | nhejazi/methyvim | R/zzz.R | .onAttach <- function(...) {
packageStartupMessage(paste0(
"methyvim v",
utils::packageDescription("methyvim")$Version,
": Targeted, Robust, and Model-free Differential Methylation Analysis"
))
}
| 212 | mit |
78f32732fbeb873a19db47ec303b471a7316ade0 | johngarvin/R-2.1.1rcc | src/library/grid/R/layout.R |
is.layout <- function(l) {
inherits(l, "layout")
}
# FIXME: The internal C code now does a lot of recycling of
# unit values, units, and data. Can some/most/all of the
# recycling stuff below be removed ?
valid.layout <- function(nrow, ncol, widths, heights, respect, just) {
nrow <- as.integer(nrow)
ncol <- a... | 4,912 | gpl-2.0 |
324b41fab5efa6bb3c6b4aca3530356e217c2413 | ISRICWorldSoil/SoilGrids250m | profiles/Russia/rw_Russia.R | # title : rw_Russia.R
# purpose : Reading and writing of Russian profiles (234 profiles);
# reference : Russian SOIL REFERENCE PROFILES from LAND RESOURCES OF RUSSIA CD-ROM [http://webarchive.iiasa.ac.at/Research/FOR/russia_cd/guide.htm]
# producer : Prepared by T. Hengl
# last update : In ... | 3,038 | gpl-2.0 |
019cf89a70832911b4fb1167ac1d7c7a5ad8f20b | krishnaiitd/Rprogramming | complete.R | complete <- function(directory, ids = 1:332) {
## 'directory' is a character vector of length 1 indicating
## the location of the CSV files
## 'id' is an integer vector indicating the monitor ID numbers
## to be used
## Return a data frame of the form:
#... | 1,524 | mit |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | sanuj/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
b132954b651456b57820796ce0c9c771517f17f1 | aappling-usgs/mda.streams | tests/testthat/test-combine_ts.R | context('combine_ts')
test_that("combine_ts works", {
xy <- download_ts(c('suntime_calcLon', 'doobs_nwis', 'wtr_nwis', 'baro_nldas'), 'nwis_01467087', version='rds', on_local_exists="replace")
dim(base <- read_ts(xy[1]))
dim(same <- suppressWarnings(read_ts(xy[2])))
dim(more <- read_ts(xy[3]))
dim(offset <-... | 3,964 | cc0-1.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | elkingtonmcb/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | shangwuhencc/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | Ialong/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | youssef-emad/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
b132954b651456b57820796ce0c9c771517f17f1 | USGS-R/mda.streams | tests/testthat/test-combine_ts.R | context('combine_ts')
test_that("combine_ts works", {
xy <- download_ts(c('suntime_calcLon', 'doobs_nwis', 'wtr_nwis', 'baro_nldas'), 'nwis_01467087', version='rds', on_local_exists="replace")
dim(base <- read_ts(xy[1]))
dim(same <- suppressWarnings(read_ts(xy[2])))
dim(more <- read_ts(xy[3]))
dim(offset <-... | 3,964 | cc0-1.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | AzamYahya/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | rho-devel/rho | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | jondo/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | arasuarun/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | cdawei/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | youprofit/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | krlmlr/cxxr | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | Saurabh7/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | mit |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | curiousguy13/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | chenmoshushi/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | rcurtin/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | kostajaitachi/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | pavel-odintsov/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | abhiatgithub/shogun-toolbox | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | kmillar/cxxr | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
40a5382ed461719e2cc271b445cb0b3f9b81ab6e | kmillar/rho | src/extra/testr/filtered-test-suite/sum/tc_sum_4.R | expected <- eval(parse(text="6.63795852562496"));
test(id=0, code={
argv <- eval(parse(text="list(structure(c(-1.94895827232912e-306, 0, 9.36477567902783e-210, 3.61651164350633e-272, 0, -6.24957292845831e-288, 8.01866432306958e-186, 8.68951728615672e-228, -4.51587577314873e-307, 3.4482... | 1,845 | gpl-2.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | sperka/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
5dacd1e646ae08ab059eca0aee43fbfc4640c605 | lukw00/shogun | examples/undocumented/r_static/classifier_perceptron.R | library("sg")
size_cache <- 10
C <- 10
epsilon <- 1e-5
use_bias <- TRUE
fm_train_real <- as.matrix(read.table('../data/fm_train_real.dat'))
fm_test_real <- as.matrix(read.table('../data/fm_test_real.dat'))
label_train_twoclass <- as.double(as.matrix(read.table('../data/label_train_twoclass.dat')))
# Perceptron
print... | 621 | gpl-3.0 |
d28a04db12d2784f611405a67ea01cdccb881d50 | moocunsw/FL-dashboard | 04_futurelearn-dashboard/Demographics_GeographicalDistribution/App.R | # ************************************************************************************************
# ***************** FutureLearn Analytics dashboard. (Educators' view) *********************************
#
# The project is developed to provide re-usable analytics building blocks supporting the sense-making ... | 8,182 | agpl-3.0 |
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