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 |
|---|---|---|---|---|---|
3a0c2a90fecde6c58d7d4070161c8659b95d28ac | YoJimboDurant/R-MET-R-MOD | compAermin.R | # compAermin reads the comparison files from aerminute and produces diagnostic plots
# using the package openair' model comparison functions
compAermin <- function(startYear, stopYear, makePDF=FALSE, outfile="compAermin.pdf", sumFilePattern="_comp_.*\\.dat"){
require(openair)
require(lubridate)
years <- seq(s... | 1,574 | unlicense |
88177a689ec3f27a013bec4c84304f6d4189c401 | taranu/ProFit | inst/example/SAMIDataPrep.R | # A quick function to fit a planar sky (should be able to use hyper.fit instead if needed,
# but SVD gives the least-squares solution right away and we don't need uncertainties)
.svdfitplane <- function(x, fitintercept=TRUE)
{
ndim = dim(x)[[2]]
stopifnot(!is.null(ndim) && ndim > 1)
medians = vector(mode="numeric... | 7,758 | gpl-3.0 |
acd2cf64aeaa54995d7f288b8ff11bd4e05cafc7 | dterror/minutiaer | R/minutiaer.R | #' Creates a String with interpolated values
#'
#' @param text A string
#' @return The text string with the substituted interpolated variabled
#' @examples
#' name <- "Kristorffeson"
#' greetin <- s("Hi there, ${name}")
#' # => "Hi there, Kristorffeson"
s <- function(text, envir=parent.frame()) {
# We grab the paren... | 1,010 | mit |
743c873cbfb3f88279cccb51d6f40a801839b19b | lawshannah/lawshannah.github.io | Other/19Sept2017.R | library(dplyr)
c(25, 10, 15, 12) %>% rank
#TRS:
#1. Rmarkdown
#2. blogdown
#these two PROJECT
#groups of 2-3. start with reading and making graphs of biking data
##################26SEPT2017
library(ggplot2)
library(dplyr)
DF %>%
group_by(Gender)%>%
summarize(MP = mean(Pay))%>%
ggplot(aes(x = Gender, y =... | 449 | mit |
8a205aa32fbea834e976f91003d6d41caf609fe9 | FESOM/spheRlab | R/sl.tracer.calculate.trajectories.R | sl.tracer.calculate.trajectories <- function(U, x.ini, t.ini = 0, grid, i.neighs=NULL, tri.cont.ini = NULL, abort.if = NULL, tgr = NULL, dt.def = 1, T.end = 10,
maxiter = 4, qfrac = 2, cart_geo= "geo", geo.gc_rad = "gc", patch.level = 10, method = "Petterssen", Rsphere = 6371){
requ... | 13,942 | gpl-3.0 |
2438bec118f466267b1b7bf91887acadd4201a28 | scrim-network/BRICK | fortran/R/daisF.R | # =======================================================================================
# DAIS-fortran90 (# estimation by calling fortran routine)
# DAIS: Simple model for Antarctic ice-sheet volume [m sle] (Schaffer 2014)
# =======================================================================================
#
# ... | 6,235 | gpl-3.0 |
a9c341c3a4877c37adc5c6ecdd01aed2e844e67f | karawoo/icetest | R/check_doc.R | ##' Check DOC values
##'
##' We originally asked for DOC data in units of ug/l, however mg/l was provided
##' by most researchers and is a more standard unit. If DOC is in the hundreds
##' or thousands, it is likely in ug/l and we should follow up with researchers
##' and likely convert their data.
##'
##' @param dat ... | 705 | mit |
2982f681be791c9e9b4706af210dcf7a2568a519 | andrewdefries/andrewdefries.github.io | FDA_Pesticide_Glossary/karvon.R | library("knitr")
library("rgl")
#knit("karvon.Rmd")
#markdownToHTML('karvon.md', 'karvon.html', options=c("use_xhml"))
#system("pandoc -s karvon.html -o karvon.pdf")
knit2html('karvon.Rmd')
| 192 | mit |
de513c011b9b3a3ad52dac3f905d77259e37855c | cgvarela/jasp-desktop | JASP-Engine/JASP/R/ttestpairedsamples.R |
TTestPairedSamples <- function(dataset=NULL, options, perform="run", callback=function(...) 0, ...) {
all.variables <- unique(unlist(options$pairs))
all.variables <- all.variables[all.variables != ""]
if (is.null(dataset))
{
if (perform == "run") {
if (options$missingValues == "excludeListwise") {
... | 14,151 | agpl-3.0 |
de513c011b9b3a3ad52dac3f905d77259e37855c | tlevine/jasp-desktop | JASP-Engine/JASP/R/ttestpairedsamples.R |
TTestPairedSamples <- function(dataset=NULL, options, perform="run", callback=function(...) 0, ...) {
all.variables <- unique(unlist(options$pairs))
all.variables <- all.variables[all.variables != ""]
if (is.null(dataset))
{
if (perform == "run") {
if (options$missingValues == "excludeListwise") {
... | 14,151 | agpl-3.0 |
de513c011b9b3a3ad52dac3f905d77259e37855c | Tahiraj/jasp-desktop | JASP-Engine/JASP/R/ttestpairedsamples.R |
TTestPairedSamples <- function(dataset=NULL, options, perform="run", callback=function(...) 0, ...) {
all.variables <- unique(unlist(options$pairs))
all.variables <- all.variables[all.variables != ""]
if (is.null(dataset))
{
if (perform == "run") {
if (options$missingValues == "excludeListwise") {
... | 14,151 | agpl-3.0 |
19af1caaee5ec04b8d5a9e7c70c8a24933b0eacf | huanzhang12/LightGBM | R-package/R/lgb.importance.R | #' Compute feature importance in a model
#'
#' Creates a \code{data.table} of feature importances in a model.
#'
#' @param model object of class \code{lgb.Booster}.
#' @param percentage whether to show importance in relative percentage.
#'
#' @return
#'
#' For a tree model, a \code{data.table} with the following co... | 2,234 | mit |
19af1caaee5ec04b8d5a9e7c70c8a24933b0eacf | huanzhang12/lightgbm-gpu | R-package/R/lgb.importance.R | #' Compute feature importance in a model
#'
#' Creates a \code{data.table} of feature importances in a model.
#'
#' @param model object of class \code{lgb.Booster}.
#' @param percentage whether to show importance in relative percentage.
#'
#' @return
#'
#' For a tree model, a \code{data.table} with the following co... | 2,234 | mit |
19af1caaee5ec04b8d5a9e7c70c8a24933b0eacf | fstonezst/LightGBM | R-package/R/lgb.importance.R | #' Compute feature importance in a model
#'
#' Creates a \code{data.table} of feature importances in a model.
#'
#' @param model object of class \code{lgb.Booster}.
#' @param percentage whether to show importance in relative percentage.
#'
#' @return
#'
#' For a tree model, a \code{data.table} with the following co... | 2,234 | mit |
0db8a4bc98ac615abec53342b8cbe46fad67d070 | mhunter1/OpenMx | inst/models/nightly/xxm-lgc.R | # http://xxm.times.uh.edu/learn-xxm/latent-growth-curve-model/
library(OpenMx)
options(width=120)
got <- suppressWarnings(try(load("models/nightly/data/reisby.wide.xxm.RData")))
if (is(got, "try-error")) load("data/reisby.wide.xxm.RData")
reisby1 <- mxModel(
"reisby", type="RAM",
mxData(reisby.wide, "raw"),
ma... | 1,726 | apache-2.0 |
360e000d3c3167eda4b56d0775fdfc060f5734c7 | hadley/purrr | R/transpose.R | #' Transpose a list.
#'
#' Transpose turns a list-of-lists "inside-out"; it turns a pair of lists into a
#' list of pairs, or a list of pairs into pair of lists. For example,
#' if you had a list of length n where each component had values `a` and
#' `b`, `transpose()` would make a list with elements `a` and
#' `b` tha... | 1,940 | gpl-3.0 |
547d9ed9aee51b229d9d3bc46f7fd8aaf26a499a | waddella/loon | R/man-roxygen/descr_layer_labels.R | #' @description Layer labels are useful to identify layer in the layer
#' inspector. The layer label can be initially set at layer creation with the
#' label argument.
| 173 | gpl-2.0 |
360e000d3c3167eda4b56d0775fdfc060f5734c7 | cran/purrr | R/transpose.R | #' Transpose a list.
#'
#' Transpose turns a list-of-lists "inside-out"; it turns a pair of lists into a
#' list of pairs, or a list of pairs into pair of lists. For example,
#' if you had a list of length n where each component had values `a` and
#' `b`, `transpose()` would make a list with elements `a` and
#' `b` tha... | 1,940 | gpl-3.0 |
cab1e6aefa91f10e0c5683d86b9c1c18e312eabf | pik-piam/magclass | R/where.R | #' where
#'
#' Analysis function for magpie objects
#'
#'
#' @param x A logical statement with a magpie object
#' @param plot deprecated. Use the function whereplot in package luplot.
#' @return A list of analysis parameters
#' @author Benjamin Leon Bodirsky, Jan Philipp Dietrich
#' @seealso whereplot in package luplot... | 1,535 | lgpl-3.0 |
839b3484a1ab4cfabe203cd647c0b1a70e3bcebc | ChristophH/Inferelator | R_scripts/vis_tfs_and_targets.R | library('Matrix')
library('ggplot2')
library('reshape2')
library('gplots')
if ('parallel' %in% installed.packages()[, 'Package']) {
library('parallel')
} else {
library('multicore')
}
# for heatmaps
my.hclust <- function(d) hclust(d, method="ward.D")
my.hclust.co <- function(d) hclust(d, method="complete")
my.hclu... | 11,725 | mit |
10c14a00af417583b79928eb0ae65beb200b1c80 | cpcloud/arrow | r/R/arrow-package.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... | 10,883 | apache-2.0 |
cab1e6aefa91f10e0c5683d86b9c1c18e312eabf | cran/magclass | R/where.R | #' where
#'
#' Analysis function for magpie objects
#'
#'
#' @param x A logical statement with a magpie object
#' @param plot deprecated. Use the function whereplot in package luplot.
#' @return A list of analysis parameters
#' @author Benjamin Leon Bodirsky, Jan Philipp Dietrich
#' @seealso whereplot in package luplot... | 1,535 | lgpl-3.0 |
afc9e04ff2dae3b3ebd9dd937ace8ee27c6aaa5f | DannyArends/CTLmapping | Rctl/R/ctl.correlation.R | #
# ctl.correlation.R
#
# copyright (c) 2010-2014 - GBIC, Danny Arends, Pjotr Prins, Yang Li, and Ritsert C. Jansen
# last modified Mar, 2014
# first written Mar, 2014
#
# Wrappers around the correlation and chisquare code
#
correlation <- function(x, y, nthreads = 1, verbose = FALSE){
if(is.matrix(y)) { ... | 4,630 | gpl-3.0 |
ba4bccacedc761a26b42ff89a9e21e7c8d835032 | elangovana/ClockLat | sourcecode/utilities.R | source("./globals.R")
calcRMS <- function(actualData, predictedData){
predictedData[ is.na(predictedData) ] <- 0
return (sqrt(clocklat.mean((actualData-predictedData)^2)))
}
compareAcutalVsPredicted <- function(actualTestDataLatLon, predictedResults){
rmsLat <- calcRMS(actualTestDataLatLon[, lat], predictedRes... | 1,046 | mit |
dd64887ffad83c3b3bb25a9b5f0b1707f4113e31 | stephlabou/ecology_under_lake_ice_repo | Scripts/Analysis-Figures/seasonal_means.R | ###################################################################################
# This script calculates seasonal means (e.g. iceon and iceoff means) and #
# standard deviations and errors for each variable and unique lake/station #
# combination. Output also includes the total number of (non-NA) obs... | 6,327 | mit |
e65abfd4459d9e60dcc73ad0a9174aa13e933b7c | KopfLab/isoreader | R/isodata_structures.R | # Structures ----
# basic data structure
make_iso_file_data_structure <- function(file_id = NA_character_) {
structure(
list(
version = packageVersion("isoreader"),
read_options = list( # records read options+defaults
file_info = FALSE, # whether file info was read
method_info = FALSE... | 14,658 | gpl-2.0 |
53c301e969642fe85672c26131bf55ecb81e85be | AlexeiSleptcov/aggi | R/commonSeg.R | #' Calculate common CNA statistic
#'
#' Calculate common CNA statistic
#'
#' @param data CBS data, found CNA from \code{thrCBS}
#' @param data2 DNAcopy data
#' @param biomart logical, if TRUE \code{\link{biomaRt}} will be used for description
#'
#' @export
commonSeg <- function(data, data2, biomart=FALSE){
if(!in... | 3,973 | gpl-2.0 |
fe7865e852e7aa3b3362f0001437c5bcb284d8c5 | ucd-ipo/agroft | examples/crd_one_var.R | # This script runs a one-way ANOVA based on one dependent continuous variable
# and one independent factor which come from a completely randomized experiment
# design. It is based on the example given on page 27 of the agricolae tutorial
# [1]. Everything displayed to the terminal and the two plots will be displayed
# ... | 5,488 | bsd-3-clause |
e8445c0ad66b4c18acf185627b5854c3d96b103a | manfredo89/ED2io | R/Bci_histogram.R | #Script to convert the BCI inventory to the array used to plot the size distribution
dbh=read.table("/Users/manfredo/Desktop/bci_size.txt")
dbh=as.double(dbh[,1])
dbh = dbh / 10.0
this.classdbh = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24)
h=hist(dbh, breaks = this.classdbh, right=T,freq = T)
#2... | 521 | gpl-3.0 |
fe7865e852e7aa3b3362f0001437c5bcb284d8c5 | ucd-ipo/aip-analysis | examples/crd_one_var.R | # This script runs a one-way ANOVA based on one dependent continuous variable
# and one independent factor which come from a completely randomized experiment
# design. It is based on the example given on page 27 of the agricolae tutorial
# [1]. Everything displayed to the terminal and the two plots will be displayed
# ... | 5,488 | bsd-3-clause |
fe7865e852e7aa3b3362f0001437c5bcb284d8c5 | iamciera/aip-analysis | examples/crd_one_var.R | # This script runs a one-way ANOVA based on one dependent continuous variable
# and one independent factor which come from a completely randomized experiment
# design. It is based on the example given on page 27 of the agricolae tutorial
# [1]. Everything displayed to the terminal and the two plots will be displayed
# ... | 5,488 | bsd-3-clause |
fe7865e852e7aa3b3362f0001437c5bcb284d8c5 | msimmond/aip-analysis | examples/crd_one_var.R | # This script runs a one-way ANOVA based on one dependent continuous variable
# and one independent factor which come from a completely randomized experiment
# design. It is based on the example given on page 27 of the agricolae tutorial
# [1]. Everything displayed to the terminal and the two plots will be displayed
# ... | 5,488 | bsd-3-clause |
2ad85410f5216927cc8e60ede0441bd52e0b1a0b | sakrejda/parse-stan | R/cmdstan-arg-tree-helpers.R |
#' Based on an argument tree ('args' object) find a model
#' and return the path. If the model file (.stan file) is not
#' found, check for the presence of a partial model file (.model file)
#' and splice it with components from args[['model_dir']]
#'
#' @param args arg-tree object (list).
#' @return path to object's... | 2,353 | gpl-3.0 |
5e0f69fdaf3c624c257e2ec47db31653e4643d3a | malachig/alexa | Array_analysis/R_bin/c_elegans_testAnalysis.R | #Test of normalization strategies on NimbleGen data
#Use data from 7 stages of C. elegans development, provided by Kim Wong
#1.) NORMALIZATION
#A.) RAW DATA
datadir = "/home/malachig/AlternativeSplicing/data_sources/C_elegans_NimbleGenData/RawData";
setwd(datadir)
#Read in the raw data files, which have the followin... | 7,040 | gpl-2.0 |
1aa9206b7305102a9c629840f59d65e07f241f02 | ColumbusCollaboratory/electron-quick-start | R-Portable-Mac/library/recipes/doc/Simple_Example.R | ## ----ex_setup, include=FALSE---------------------------------------------
knitr::opts_chunk$set(
message = FALSE,
digits = 3,
collapse = TRUE,
comment = "#>"
)
options(digits = 3)
## ----data----------------------------------------------------------------
library(recipes)
library(caret)
data(segmentationDa... | 2,298 | cc0-1.0 |
5d59cf0463389e544d132fb7cd6ddfe6ba2c7715 | imbforge/NGSpipe2go | tools/reports/shiny_scrnaseq_reporting_tool/server.R | library(rmarkdown) # for the report generator
library(knitr) # for the report generator
source("sc.shinyrep.helpers.R") # helper functions to generate the plots
# things that will run only once per session
# always in the global environment to ensure access (could be placed in global.R also)
loadGlobalVars()
addRes... | 3,986 | gpl-3.0 |
557bc03215559b371ede6cf01144fd5ac2ee9a82 | kenkellner/tutorials | 07_maximum_likelihood.R | #How can we estimate unknown parameter values from a set of data?
#Generate a set of random Bernoulli data with known p
n = 1
p = 0.3
samples = 1000
Y = rbinom(samples, n, p)
mean(Y) / 1
#Maximum likelihood estimator
#What does the probability distribution of a Binomial look like?
#Take the log; sum for each value o... | 2,869 | gpl-2.0 |
9ab44efac3a8e9e4300ec88eba71b1189a464b1d | johngarvin/R-2.1.1rcc | src/library/methods/R/makeBasicFunsList.R | ## the executable code to complete the generics corresponding to primitives,
## and to define the group generics for these functions.
## uses the primitive list and the function .addBasicGeneric
## defined (earlier) in BasicFunsList.R
.makeBasicFuns<- function(where)
{
env <- new.env(hash=TRUE, parent=as.environm... | 5,104 | gpl-2.0 |
4e07cc3c865c1821769699262bb2b5747ba31c12 | radfordneal/pqR | src/library/methods/R/SClasses.R | # File src/library/methods/R/SClasses.R
# Part of the R package, http://www.R-project.org
# Modifications for pqR Copyright (c) 2014, 2017, 2018 Radford M. Neal.
#
# 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 Fr... | 37,866 | gpl-2.0 |
8fd33128447934a7b7938a6699766eeafc6e2b96 | dankelley/oce-issues | 18xx/1808/1808c.R | library(oce)
d <- read.oce("PLNKG_2019004_1_1_Z.ODF")
# Find what's there
summary(d)
# Count by species
table(d[["taxonomicName"]])
| 133 | gpl-2.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
498ebd54f7230576cb8695d4b48db1b6f8a714db | etsakl/DasyMapR | packrat/lib/x86_64-pc-linux-gnu/3.2.3/rgeos/tests/test-translate-points.R | library(testthat)
library(rgeos)
setScale()
context("Translate Points")
test_that("translate points", {
p = readWKT("POINT(1 1)")
mp = readWKT("MULTIPOINT(1 1, 2 2, 3 3, 4 4, 5 5)")
gcp1 = readWKT("GEOMETRYCOLLECTION( POINT(1 1), POINT(2 2), POINT(3 3), POINT(4 4), POINT(5 5))")
gcp2 = readWKT(... | 2,361 | gpl-3.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | kmillar/cxxr | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
3cf9b953639f4f18755bce4a2e9e0283c84602da | jeroenooms/r-source | tests/eval-fns.R | ### Checking parse(* deparse()) "inversion property" ----------------------------
## EPD := eval-parse-deparse : eval(text = parse(deparse(*)))
## Hopefully typically the identity():
pd0 <- function(expr, backtick = TRUE, ...)
parse(text = deparse(expr, backtick=backtick, ...))
id_epd <- function(expr, control = c... | 4,250 | gpl-2.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | kmillar/rho | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | krlmlr/cxxr | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
fd24a77a3db10dc762e146b4163fe80a191d467c | skyguy94/R | src/library/methods/R/rbind.R | # File src/library/methods/R/rbind.R
# Part of the R package, http://www.R-project.org
#
# Copyright (C) 1995-2012 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 ... | 3,998 | gpl-2.0 |
3cf9b953639f4f18755bce4a2e9e0283c84602da | reactorlabs/gnur | tests/eval-fns.R | ### Checking parse(* deparse()) "inversion property" ----------------------------
## EPD := eval-parse-deparse : eval(text = parse(deparse(*)))
## Hopefully typically the identity():
pd0 <- function(expr, backtick = TRUE, ...)
parse(text = deparse(expr, backtick=backtick, ...))
id_epd <- function(expr, control = c... | 4,250 | gpl-2.0 |
fd24a77a3db10dc762e146b4163fe80a191d467c | mirror/r | src/library/methods/R/rbind.R | # File src/library/methods/R/rbind.R
# Part of the R package, http://www.R-project.org
#
# Copyright (C) 1995-2012 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 ... | 3,998 | gpl-2.0 |
0409a726cfc61ca2f761780b1684c94ef3863a51 | rho-devel/rho | src/extra/testr/filtered-test-suite/array/tc_array_20.R | expected <- eval(parse(text="structure(c(0.92317305817397+0i, 0.160449395256071+0.220125597679977i, 0.40353715410585+2.39063261466203i, -3.64092275386503+3.51619480964107i, -0.30877433127864+1.37503901638266i, -0.5590368753986+2.95994484328048i, 2.07117052177259-1.58552086053907i, 5.12796916272868+5.50114308371867i, 0.... | 7,990 | gpl-2.0 |
4a40977488147b819fbd01496d5a85b7203f1248 | iliastsergoulas/shinyapps | country/agri_econ_active_population_wb_country/app.R | # Data: Economically active population in agriculture (number)
# This R script is created as a Shiny application processing raw data downloaded from World Bank through WDI package,
# and creating plots and maps as WDI(country = "all", indicator = "EN.AGR.EMPL", extra = FALSE, cache = NULL)
# The code is available unde... | 11,162 | mit |
59a00b63a466b9288d50d5f1abd14bddd061d551 | bnaras/cvxr | R/variable.R | #'
#' The Variable class.
#'
#' This class represents an optimization variable.
#'
#' @slot id (Internal) A unique identification number used internally.
#' @slot rows The number of rows in the variable.
#' @slot cols The number of columns in the variable.
#' @slot name (Optional) A character string representing the na... | 16,425 | gpl-2.0 |
c396b3a5ea71871604046ea7714817900825ce42 | cxxr-devel/cxxr | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | jangorecki/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | mathemage/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | nilbody/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
c396b3a5ea71871604046ea7714817900825ce42 | rho-devel/rho | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
8844bd1fb8d9339aa027588796b421b1e71ddbea | lucymli/EpiGenR | R/outbreak.R | #' Line list of a simulated outbreak
#'
#' A dataset containing details of each individual infected during an outbreak
#' of an acute infectious disease.
#'
#' @format A data frame with 4,000 rows and 4 variables:
#' \describe{
#' \item{id}{Index of each infected individual}
#' \item{date}{Date that the disease was r... | 480 | mit |
c396b3a5ea71871604046ea7714817900825ce42 | ArunChauhan/cxxr | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | YzPaul3/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | h2oai/h2o-dev | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | spennihana/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
b9d833c097dfba9cd55866ecc157dd3d500c466b | debarros/RScantron | StoreItemResponses.R | #Store Item Responses
#' @title Store Item Responses
#' @description Store item response files downloaded from the TMS
#' @param responses output from GetItemResponses_1section
#' @param testpath file folder path corresponding to the desired test
#' @param classname character of length 1 holding the name of the sectio... | 1,622 | gpl-2.0 |
59a00b63a466b9288d50d5f1abd14bddd061d551 | anqif/cvxr | R/variable.R | #'
#' The Variable class.
#'
#' This class represents an optimization variable.
#'
#' @slot id (Internal) A unique identification number used internally.
#' @slot rows The number of rows in the variable.
#' @slot cols The number of columns in the variable.
#' @slot name (Optional) A character string representing the na... | 16,425 | apache-2.0 |
c396b3a5ea71871604046ea7714817900825ce42 | krlmlr/cxxr | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
c396b3a5ea71871604046ea7714817900825ce42 | kmillar/cxxr | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | h2oai/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
1fcacea67008f38a044e55174f681d3f46665ea6 | michalkurka/h2o-3 | h2o-r/tests/testdir_algos/glrm/runit_glrm_grid_iris.R | setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f")))
source("../../../scripts/h2o-r-test-setup.R")
test.glrm.iris <- function() {
Log.info("Importing iris_wheader.csv data...")
irisH2O <- h2o.uploadFile(locate("smalldata/iris/iris_wheader.csv"), destination_frame = "irisH2O")
print(summary(iris... | 1,613 | apache-2.0 |
c396b3a5ea71871604046ea7714817900825ce42 | kmillar/rho | src/extra/testr/filtered-test-suite/operators/tc_operators_150.R | expected <- eval(parse(text="structure(c(FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE... | 3,935 | gpl-2.0 |
55b739d9d262fe970329b3860aa060dcf12742de | joshgabriel/dft-crossfilter | CompleteApp/crossfilter_prec_app/birch_nls.R | library(minpack.lm)
#library(plot3D)
#library(plotly) # Load the minpack.lm package
#library(webshot)
#library(rsm)
mydata = read.csv("Rdata.csv") # Read CSV data file
x<-mydata$kpoints # Select the kpoints atom density
V<-mydata$volume
E<-mydata$energy
init_data = read.csv("Rdata_init.csv")
v0<... | 1,664 | mit |
55b739d9d262fe970329b3860aa060dcf12742de | joshgabriel/dft-crossfilter | CompleteApp/prec_analysis/birch_nls.R | library(minpack.lm)
#library(plot3D)
#library(plotly) # Load the minpack.lm package
#library(webshot)
#library(rsm)
mydata = read.csv("Rdata.csv") # Read CSV data file
x<-mydata$kpoints # Select the kpoints atom density
V<-mydata$volume
E<-mydata$energy
init_data = read.csv("Rdata_init.csv")
v0<... | 1,664 | mit |
f97ba6db3029b7def5ed7b7c1e471a9c175bb865 | ding-lab/hotspot3d | lib/TGI/Mutpro/Main/HorizClustersLines.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
y = read.table(args[1], sep = "\t")
z = read.table(args[2], sep = "\t")
RD<-y[[2]]
ID<-y[[1]]
z[z$V1==z$V2,"V3"] = 0.1 # show singletons at RD=0.1
y0<-z[[1]]
x0<-z[[3]]
y1<-z[[2]]+1
x1<-z[[3]]
Cluster<-z[[5]]
# adjust plot height according to the num... | 884 | gpl-3.0 |
55b739d9d262fe970329b3860aa060dcf12742de | joshgabriel/dft-crossfilter | CompleteApp/birch_nls.R | library(minpack.lm)
#library(plot3D)
#library(plotly) # Load the minpack.lm package
#library(webshot)
#library(rsm)
mydata = read.csv("Rdata.csv") # Read CSV data file
x<-mydata$kpoints # Select the kpoints atom density
V<-mydata$volume
E<-mydata$energy
init_data = read.csv("Rdata_init.csv")
v0<... | 1,664 | mit |
505bc10feb07c32e6d40fc173eed7236d88e1e52 | wibeasley/readr | tests/testthat/test-read-builtin.R | test_that("read_builtin works", {
skip_if(interactive())
# fails with unquoted symbol (like data(storms, package = "dplyr"))
expect_error(read_builtin(storms, "dplyr"))
# fails with error if the dataset namespace is not attached
unloadNamespace("dplyr")
expect_error(
read_builtin("storms")
)
# fail... | 642 | gpl-2.0 |
0ebe28400ebf8f253c6ad9a5c8b6b026875ab073 | psathyrella/partis | packages/RPANDA/R/fit_bd.R | fit_bd <-
function (phylo, tot_time, f.lamb, f.mu, lamb_par, mu_par, f=1,
meth = "Nelder-Mead", cst.lamb=FALSE, cst.mu=FALSE,
expo.lamb=FALSE, expo.mu=FALSE, fix.mu=FALSE,
dt=0, cond="crown")
{
if (!inherits(phylo, "phylo"))
stop("object \"phylo\" is not of class \"phylo... | 2,173 | gpl-3.0 |
66798dab48387ba9878c9e67a3c66586beeb5ae1 | graalvm/fastr | com.oracle.truffle.r.pkgs/rJava/R/converter.R | # in: Java -> R
.conv.in <- new.env(parent=emptyenv())
.conv.in$. <- FALSE
# out: R -> Java
.conv.out <- new.env(parent=emptyenv())
.conv.out$. <- FALSE
# --- internal fns
.convert.in <- function(jobj, verify.class=TRUE) {
jcl <- if (verify.class) .jclass(jobj) else gsub("/",".",jobj@jclass)
cv <- .conv.in[[jcl]... | 1,003 | gpl-2.0 |
5049bae298a609a0e6bb12436214476cffc23932 | vinhqdang/my_mooc | MOOC-work/coursera/FINISHED/compdata-004 Computing for Data Analysis/Coursera-Computing-for-Data-Analysis-master/Week3/rankhospital.R | helper <- function(data, outcome, num){
rank <- data[, 2][order(outcome, data[, 2])[num]]
rank
}
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
## Check that state and outcome are valid
## Return hospital name in that state with the given rank
## 30-day death rate
data <- rea... | 2,193 | mit |
50e826397d9e3f5e699fb9ec61887fe232cb9130 | LeonardCohen/coding | r/plot_distribution.R | require(ggplot2)
require(grid)
# binomial distribution
x1 <- 1:20
df1 <- data.frame(x = x1, y = dbinom(x1, 20, 0.5))
x2 <- 1:20
df2 <- data.frame(x = x2, y = dbinom(x2, 20, 0.7))
x3 <- 1:40
df3 <- data.frame(x = x3, y = dbinom(x3, 40, 0.5))
plot1 <- ggplot() +
geom_point(data=df1,aes(x=x,y=y),stat = "identity", ... | 771 | gpl-2.0 |
40218c532f80bc1e95fa1c492e1de4d757c95d43 | neerajsubhedar/R-scripts | Kaggle - Sberbank Russian Housing Market/Russian housing market/Russian Housing Market - xgboost - Project 16.R | ## 5/14/2017
## implementing xgboost
## using gblinear
# Clear workspace and environment
cat("\014")
rm(list = ls())
#modify memory size
options(java.parameters = "-Xmx10g" )
# Functions
Missing <- function(input.data.frame){
col.name <- colnames(input.data.frame)
list.input.nas <- lapply(lapply(input.data.fr... | 6,398 | gpl-3.0 |
67c47b31e6b318563833ed2533a86fa9974b942d | mharrod/Security | Security Intelligence/Sec EDA.R | setwd("~/Documents/Core /TELUS/security intelligence")
pkg <- c("bitops", "ggplot2", "maps", "maptools",
"sp", "maps", "grid", "car")
new.pkg <- pkg[!(pkg %in% installed.packages())]
if(length(new.pkg)){
install.packages(new.pkg)
}
library(bitops)
ip2long <-function(ip){
ips<-unlist(strsplit(ip,'.', fix... | 3,247 | mit |
a6bcc867eedf03dbddac3b303cdcb63bc2048c73 | tarasane/h2o-3 | h2o-r/tests/Utils/shared_javapredict_RF.R | heading("BEGIN TEST")
check.rf <- function(conn) {
heading("Uploading train data to H2O")
iris_train.hex <- h2o.importFile(conn, train)
heading("Creating DRF model in H2O")
balance_classes <- if (exists("balance_classes")) balance_classes else FALSE
iris.rf.h2o <- h2o.randomForest(x = x, y = y, training_fra... | 4,228 | apache-2.0 |
f16ba67d4dc6fcb4a0d8004bb6312d02a2ce08b6 | mhahsler/rBLAST | R/AAA.R | #######################################################################
# rBLAST - Interfaces to BLAST
# Copyright (C) 2015 Michael Hahsler and Anurag Nagar
#
# 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... | 1,210 | gpl-3.0 |
a6bcc867eedf03dbddac3b303cdcb63bc2048c73 | bospetersen/h2o-3 | h2o-r/tests/Utils/shared_javapredict_RF.R | heading("BEGIN TEST")
check.rf <- function(conn) {
heading("Uploading train data to H2O")
iris_train.hex <- h2o.importFile(conn, train)
heading("Creating DRF model in H2O")
balance_classes <- if (exists("balance_classes")) balance_classes else FALSE
iris.rf.h2o <- h2o.randomForest(x = x, y = y, training_fra... | 4,228 | apache-2.0 |
068c40abc1618d2c4745e2543bd5f638cdbc1114 | rlzijdeman/nlgis2 | scripts/etl/R/cedar_meets_nlgis01.R | # File: cedar_meets_nlgis01.R
# Author: richard.zijdeman@iisg.nl
# Date: October 10, 2014
# Purpose: see whether you can retrieve data from CEDAR's SPARQLE endpoint and
# plot them on map retrieved from NLGIS-2 API.
# Note: DATA SHOULD NOT BE INTERPRETED SUBSTANTIVELY (for debugging purposes only)
# Laste chan... | 3,374 | gpl-3.0 |
068c40abc1618d2c4745e2543bd5f638cdbc1114 | rlzijdeman/nlgis2-1 | scripts/etl/R/cedar_meets_nlgis01.R | # File: cedar_meets_nlgis01.R
# Author: richard.zijdeman@iisg.nl
# Date: October 10, 2014
# Purpose: see whether you can retrieve data from CEDAR's SPARQLE endpoint and
# plot them on map retrieved from NLGIS-2 API.
# Note: DATA SHOULD NOT BE INTERPRETED SUBSTANTIVELY (for debugging purposes only)
# Laste chan... | 3,374 | gpl-3.0 |
068c40abc1618d2c4745e2543bd5f638cdbc1114 | IISH/nlgis2 | scripts/etl/R/cedar_meets_nlgis01.R | # File: cedar_meets_nlgis01.R
# Author: richard.zijdeman@iisg.nl
# Date: October 10, 2014
# Purpose: see whether you can retrieve data from CEDAR's SPARQLE endpoint and
# plot them on map retrieved from NLGIS-2 API.
# Note: DATA SHOULD NOT BE INTERPRETED SUBSTANTIVELY (for debugging purposes only)
# Laste chan... | 3,374 | gpl-3.0 |
2f6e380e9c0775864e43da920b73e85aab67a538 | jkarl/SamplingTools | Shiny/OptimalErrorRate/optimal_alpha.R | #Optimal alpha t-test R code version 1.1, updated for compatibility with R version 3.x.
#Authored by Joe Mudge (questions or comments? contact: joe.mudge83@gmail.com).
library(ggplot2)
beta.t.test<-function (n1 = NULL, n2 = NULL, d = NULL, sig.level = 0.05, type = c("two.sample", "one.sample", "paired"),tails = c("tw... | 6,836 | mit |
58058177ae44c09633215f19a4dba4bc661d3aac | wStockhausen/tcsamSurveyData | R/resampledSizeComps.calcEffN.R | #'
#' @title Calculate and plot effective N from resampled size compositions
#'
#' @description Function to calculate and plot effective N from resampled size compositions.
#'
#' @param dfr - dataframe with resampled size compositions
#' @param byFacs - vector of column names for factors other than YEAR and STRATUM
#' ... | 5,197 | mit |
2f6e380e9c0775864e43da920b73e85aab67a538 | jkarl/LandscapeToolbox | Shiny_Tools/OptimalErrorRate/optimal_alpha.R | #Optimal alpha t-test R code version 1.1, updated for compatibility with R version 3.x.
#Authored by Joe Mudge (questions or comments? contact: joe.mudge83@gmail.com).
library(ggplot2)
beta.t.test<-function (n1 = NULL, n2 = NULL, d = NULL, sig.level = 0.05, type = c("two.sample", "one.sample", "paired"),tails = c("tw... | 6,836 | cc0-1.0 |
c0778052c2d046536b2bf5f9ad54a82e09209d68 | CompNet/MultiplexCentrality | data/HepatitusCVirus_Multiplex_Genetic/Dataset/conversion.R | library("igraph")
folder <- "data/HepatitusCVirus_Multiplex_Genetic/"
edge.file <- paste(folder,"Dataset/hepatitusC_genetic_multiplex.edges",sep="")
edge.list <- as.matrix(read.table(edge.file))
node.file <- paste(folder,"Dataset/hepatitusC_genetic_nodes.txt",sep="")
node.list <- as.matrix(read.table(node.file... | 1,787 | gpl-3.0 |
2f6e380e9c0775864e43da920b73e85aab67a538 | jkarl/LandscapeToolbox | OptimalErrorRate/optimal_alpha.R | #Optimal alpha t-test R code version 1.1, updated for compatibility with R version 3.x.
#Authored by Joe Mudge (questions or comments? contact: joe.mudge83@gmail.com).
library(ggplot2)
beta.t.test<-function (n1 = NULL, n2 = NULL, d = NULL, sig.level = 0.05, type = c("two.sample", "one.sample", "paired"),tails = c("tw... | 6,836 | cc0-1.0 |
de9b1977a166103dabd293e7fcad79b15b3595a6 | IQSS/gentb-site | R/buffer_chart.R | # devtools::install_github('ramnathv/rCharts')
library(rCharts)
library(jsonlite)
n1 <- rPlot(mpg ~ wt, data = mtcars, color = "gear", type = "point")
data(iris)
names(iris) = gsub('\\.', '', names(iris))
rPlot(SepalLength ~ SepalWidth | Species, data = iris, type = 'point', color = 'Species')
n1
n1$addControls("x"... | 2,733 | agpl-3.0 |
d11ddce46c70cbb5e25b679c12516a00bb2ce3bf | vladovidiulupu/labs | course1/week4_ex4.R | data(ChickWeight)
plot(ChickWeight$Time, ChickWeight$weight, col=ChickWeight$Diet)
chick = reshape(ChickWeight,idvar=c("Chick","Diet"),timevar="Time",direction="wide")
head(chick)
chick = na.omit(chick)
# We will focus on the chick weights on day 4 (check the column names of 'chick' and note the numbers).
# How muc... | 3,169 | mit |
16b4a7a58528ebb6ca3f35f95019a9de5cbcbb30 | ktoddbrown/decomPower | synthetic_data/Server_runs/simulate_and_fit_uniform.R | .libPaths('/vega/stats/users/mk3971/rpackages/')
library(rstan)
set.seed(12345)
num_rep <- 3
t_meas_all <- seq(1/360, 1, length.out = 200)
t_cap_all <- head(t_meas_all, -1) + 0.85 * (tail(t_meas_all, -1) - head(t_meas_all, -1))
t_cap_all <- c(t_meas_all[1] - (t_meas_all[2] - t_cap_all[1]), t_cap_all)
sm <- stan_model(... | 1,096 | mit |
ed2aff3aac46002ab572bcc080cae6ba2a6fe8ea | gabriel-slima/nkmodel | R/calculateFitness.R | #' calculateFitness
#'
#' Given N, K and the statesDF, it calculates the average fitness of the organism
#'
#' @param N Number of traits
#' @param K Number of other traits which have a fitness contribution of each gene or trait
#' @param sp Species id
#' @param organism Sequence of N 1s and 0s representing presence or ... | 1,116 | gpl-3.0 |
63ad5336f8c04465742251cf2f61665a2cae7513 | Guus-H/thesis | Backwards_diversity_measures.R | #===== Backwards Divesity Measures =====
#sourcing dependencies
source('functions/load packages.R') #Packages
source('functions/backwards_diversity.R')
source('functions/techdiv-techrel2.R')
require(devtools)
source_gist(4676064)
#=====Acquiring data=====
backCitOccur <- readRDS('data/RPbackCitOccur.Rds') # obtained ... | 1,015 | gpl-2.0 |
c1dfb952f12839f82f1eb21e8d0379dee8139aa6 | mengqinxue/DBNorm | DBNorm_test.R | library(DBNorm)
# load example data arrays
loadData(0)
DBdata1 <- genDistData(DArray1, 500)
DBdata2 <- genDistData(DArray2, 500)
DBdata3 <- genDistData(DArray3, 500)
DBdata4 <- genDistData(DArray4, 500)
# define distribution
DBdata5 <- defineDist(Norm(mean=0, sd=1))
# visualising distribution datasets
visDistData(D... | 2,467 | gpl-3.0 |
852a9b258bbdadb58a5e9a41cecb766bcd70eac6 | selective-inference/R | tests/test_QP.R | library(selectiveInference)
### Test
n = 80; p = 50
X = matrix(rnorm(n * p), n, p)
Y = rnorm(n)
lam = 2
soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
print(soln1)
print(soln2)
plot(soln1... | 364 | gpl-2.0 |
852a9b258bbdadb58a5e9a41cecb766bcd70eac6 | jonathan-taylor/R-selective | tests/test_QP.R | library(selectiveInference)
### Test
n = 80; p = 50
X = matrix(rnorm(n * p), n, p)
Y = rnorm(n)
lam = 2
soln1 = selectiveInference:::fit_randomized_lasso(X, Y, lam, 0, 0)$soln
G = glmnet(X, Y, intercept=FALSE, standardize=FALSE)
soln2 = coef(G, s=lam/n, exact=TRUE, x=X, y=Y)[-1]
print(soln1)
print(soln2)
plot(soln1... | 364 | gpl-2.0 |
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