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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
066ee8a3941c60fa6c8b5f90a5a701c4287b3f8b | b6c263edf819e3cda7264f964bcbbe3a857fd140 | /R/loc_est_bw.R | 4ed3aaa5900f9ff7c0b1abc4fdb21e0196b9ff5c | [] | no_license | cran/npbr | d2da3bfa7c43c255e70651b031de24be85a5d4cf | fb273a2137bc9dbd3efe0ce35aa2c6c2fd6fc638 | refs/heads/master | 2023-04-10T12:02:30.030340 | 2023-03-22T08:00:05 | 2023-03-22T08:00:05 | 17,697,969 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,183 | r | loc_est_bw.R | loc_est_bw <- function(xtab, ytab, x, hini, B = 5, method = "u", fix.seed = FALSE, control = list("tm_limit" = 700)){
# verification
stopifnot(length(xtab)==length(ytab))
# initialiasation
h_loc_min <- max(diff(sort(xtab)))
h_loc_max <- (max(xtab)-min(xtab))/2
h_loc_range <- seq(h_loc_min, h_loc_ma... |
6b4ca402b5a648eada95f0da1c38841626b48f7c | 7ea08d762a5cfad1ff672199b1431ac9e9449a3f | /blog-2023/Blog-4-submissions/charchit/ozone-map.R | 1ad86591c9640549639cd55cdd34ef1bc09185f1 | [] | no_license | Stat585-at-ISU/Stat585-at-ISU.github.io | 54a0d08c274522914aabd2d74e71259d5253b326 | 33d38ef0d67047d83fa8b7a216598e162a5f2400 | refs/heads/main | 2023-07-06T14:48:30.349444 | 2023-06-26T20:24:22 | 2023-06-26T20:24:22 | 78,376,998 | 3 | 3 | null | 2017-04-17T04:23:52 | 2017-01-08T23:18:27 | HTML | UTF-8 | R | false | false | 1,277 | r | ozone-map.R | library("ggplot2")
library("maps")
outlines <- as.data.frame(map("world",xlim=-c(113.8, 56.2), ylim=c(-21.2, 36.2), plot=FALSE)[c("x","y")])
map <- c(
geom_path(aes(x=x, y=y, fill=NULL, group=NULL, order=NULL, size=NULL), data = outlines, colour = alpha("grey20", 0.2)),
scale_x_continuous("", limits = c(-114.8, -55... |
9463d8e1a5ac4f18cfe3118a7fcd605bf0836fe5 | cbfa01cb81d4aa3684655ed93b7e179819057083 | /tests/testthat.R | d011b841a13e6a774e2ec3e108abff37a90a99f4 | [
"MIT"
] | permissive | dirkschumacher/lazyseq | b1d00d3603ae845acd495788304af66d8ad30fb1 | 00bde2c17f21a8708c9d6a98666435f7c1f871e1 | refs/heads/main | 2023-02-26T14:15:19.264720 | 2021-02-02T20:42:39 | 2021-02-02T20:42:39 | 335,417,878 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 58 | r | testthat.R | library(testthat)
library(lazyseq)
test_check("lazyseq")
|
b5b5f98a971832d5368ecc6b4fc83f5b5b267675 | b798dcec9242b0656453186201aa2411f65fe28d | /man/createProfileMatrix.Rd | fd6601f481f257f686c254e98c00cac48ec4e8b5 | [] | no_license | frosinastojanovska/Bioinformatics | 481367484796e04fc89ae2675644652355d88729 | 65620c37026336f816d4d981addfc92b15a2d488 | refs/heads/master | 2021-01-17T08:42:15.789577 | 2017-03-24T20:08:31 | 2017-03-24T20:08:31 | 83,944,435 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 537 | rd | createProfileMatrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/createProfileMatrix.R
\name{createProfileMatrix}
\alias{createProfileMatrix}
\title{Creates profile matrix for given aligment matrix.}
\usage{
createProfileMatrix(aligmentMatrix)
}
\arguments{
\item{aligmentMatrix}{A matrix indicating the ali... |
6cf98ade83431b0e5e5e728adfabcde688d9d20d | 24975c66d61805ffd50147890b9fc34769f18324 | /Notes_scripts_bank/ex3_binomial-uniform_multiple_control_JAGS.R | 43d5bfd580af59871877c1d8ad62400cf2a66aff | [] | no_license | npetraco/MATFOS705 | 53081de4e38a1aae8e0d67bf093a1bcd6b9f0258 | dc54407b7b13ebf8315282cbaf0c9b742212884d | refs/heads/master | 2023-05-28T03:08:46.839815 | 2023-05-16T14:47:15 | 2023-05-16T14:47:15 | 121,066,670 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,352 | r | ex3_binomial-uniform_multiple_control_JAGS.R | library(bayesutils)
# Data
n <- rep(50, 5)
participant.errs <- c(0, 0, 20, 0, 1)
s <- participant.errs
dat <- list(
"N" = length(n),
"s" = s,
"n" = n,
"a_hyp" = 0,
"b_hyp" = 1
)
inits <- function (){
list(ppi=runif(length(n)))
}
#Run the model:
fit <- jags(d... |
57da56bac26b30b5c61af0cfa6ff79342b64cb42 | 24d5d85c21f8843f7c9c61e00222522fde104e7d | /percentlabelv2.R | 8322c2d37624286fabf594b137a392d3b152fb0b | [] | no_license | cassierole/ms_percentlabel | deab302aee17274f2cfd407aae2e7429609a13ca | 25cd2408668979e56f4765e3897cc3b381823a46 | refs/heads/master | 2021-01-10T13:30:26.909309 | 2016-04-11T23:19:49 | 2016-04-11T23:19:49 | 52,817,206 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,023 | r | percentlabelv2.R | # This function accepts as argument a csv file (ex."file.csv") containing raw quantitative
# mass spec data and produces a file in the working directory containing the percentage of
# C13-labeled phospholipid for each individual species.
# Experiments carried out on Acinetobacter baumannii fed 2-C13 acetate
perce... |
ac065990bb0a476637a982b3d38e77c84571e1fc | 799f724f939763c26c4c94497b8632bad380e8f3 | /man/as.tokens.Rd | c1ad9479f2599038451fba137887d45206cbc3fd | [] | no_license | chmue/quanteda | 89133a7196b1617f599e5bba57fe1f6e59b5c579 | aed5cce6778150be790b66c031ac8a40431ec712 | refs/heads/master | 2020-12-01T02:59:48.346832 | 2017-11-22T18:35:37 | 2017-11-22T18:35:37 | 50,363,453 | 2 | 0 | null | 2016-01-25T16:18:50 | 2016-01-25T16:18:50 | null | UTF-8 | R | false | true | 3,635 | rd | as.tokens.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tokens.R
\name{as.tokens}
\alias{as.tokens}
\alias{as.tokens.list}
\alias{as.tokens.spacyr_parsed}
\alias{as.list.tokens}
\alias{unlist.tokens}
\alias{as.character.tokens}
\alias{is.tokens}
\alias{+.tokens}
\alias{c.tokens}
\title{coercion, c... |
c4e710346981ed9c21da1c4701381fdd96659e9d | f97f007dc8fab3d266fa9426f1dc612ba23754e7 | /man/high_value_terms.Rd | 221a9c828c2be02401b00a5f0723d75f088d3375 | [] | no_license | anpatton/overdoseR | 6e5c7b8f66d9664ee455379ab23392918d138530 | e84e15a027b18fff9530950aaef23fcc350e2455 | refs/heads/master | 2022-11-16T11:09:32.289312 | 2020-07-07T16:38:51 | 2020-07-07T16:38:51 | 276,998,525 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 686 | rd | high_value_terms.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{high_value_terms}
\alias{high_value_terms}
\title{High Value Terms}
\format{
A data frame with 189 rows and 4 variables:
\describe{
\item{token}{actual string of high value term}
\item{freqYES}{Frequency of ter... |
8b037e9259022527bcc68a473bceedb6f854b9c6 | 7f3d289e75c1faf4a44d50621d969dc70658259b | /2-StackFiles.R | e7dddddc2ec26fee5809d6330040f4c48275d23c | [] | no_license | alessiobocco/IFPRI_Ethiopia_Drought_2016 | 89ccaf8bbbdbe9cee57925ed4e41c8bded0f99f5 | 5f08c729c1e567adc9c629162d10b90e7ec09470 | refs/heads/master | 2020-05-27T21:09:39.770253 | 2017-02-01T14:25:57 | 2017-02-01T14:25:57 | 83,603,162 | 1 | 0 | null | 2017-03-01T21:23:58 | 2017-03-01T21:23:58 | null | UTF-8 | R | false | false | 14,948 | r | 2-StackFiles.R | # This script takes outputs from DownloadMODISFTP_Rcurl.R and stacks them
# Run the following in bash before starting R
if [ -e $HOME/.Renviron ]; then cp $HOME/.Renviron $HOME/.Renviron.bkp; fi
if [ ! -d $HOME/.Rtmp ] ; then mkdir $HOME/.Rtmp; fi
echo "TMP='$HOME/.Rtmp'" > $HOME/.Renviron
module load proj.4/4.8.0... |
faa1d0ae0f21926af9977941fa71f7a0658f2d76 | 77109777ecf9aa8b467b7225589daecd9f309148 | /Projeto2.R | e741f0e766ca95947928273632630c5c6c22b153 | [] | no_license | edufrigini/prevendo_estoque | 5bf889d44cb1e60438433e9853d75c26e4f835cf | bf6477529175c58edac3f1a9bae6efc8b1443161 | refs/heads/master | 2020-05-25T00:00:19.835408 | 2019-05-19T20:46:54 | 2019-05-19T20:46:54 | 187,526,262 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,906 | r | Projeto2.R | # DSA - DATA SCIENCE ACADEMY
# FORMACAO CIENTISTA DE DADOS
# LIGUAGEM R COM AZURE MACHINE LEARNING
#
# PROJETO 2, Prevendo Demanda de Estoque com Base em Vendas
# ALUNO: EDUARDO FRIGINI DE JESUS
#
# Goal: Maximize sales and minimize returns of bakery goods
# Data fields
# Semana — Week number (From Thurs... |
67225c32124b38f49e03a442d7fbfd4a6dc3ffd1 | b29688e753d4ae51662783aa23198292369f72b8 | /R/methMatrixManipulation.R | 19c52129b9cafe8a21438d1ad50d0174c9085490 | [] | no_license | jsemple19/methMatrix | 0de45df9e5d6c9911d4e1a0356f25e618c55f993 | 9af39085680235b9f380347b52fcc322ade51c0e | refs/heads/master | 2022-08-27T16:32:14.539253 | 2022-08-16T11:56:03 | 2022-08-16T11:56:03 | 165,834,604 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 36,963 | r | methMatrixManipulation.R | ############ functions for working with methylation matrix lists ##################
# the methylation matrices for individual genes by TSS have the following structure:
# a list (by sample) of lists of matrices (by TSS)
# e.g
# [1] sample1
# [1] TSS1 matrix of reads x Cpositions
# [2] TSS2 matrix of reads x ... |
2231a4293ef5cfcaa22324f4bdd0b958043a5ce8 | fc214123e9c4caca64b4a063e67df94584277771 | /0_GraphEffects.R | 180d3e44b60b4bc742e1229d91a1e993f1de5e8a | [] | no_license | alexanderm10/canopy_class_climate | e6ecc886ba668be3ef969af2227365c3f76d3971 | ebc780ec0cda97492beb9c531b933ca95cadce42 | refs/heads/master | 2021-05-09T20:09:12.334030 | 2020-11-04T20:05:55 | 2020-11-04T20:05:55 | 118,674,596 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 16,979 | r | 0_GraphEffects.R | # Making functions so that all models can get graphed using the same parameters
cbbPalette <- c("#009E73", "#e79f00", "#9ad0f3", "#0072B2", "#D55E00")
# plotting the size effect; standard dimensions = 8 tall, 5 wide
plot.size <- function(dat.plot){
ggplot(data=dat.plot[dat.plot$Effect=="dbh.recon",]) +
# ggtitle... |
5f075ed8daf8b88d2a27da3d3b5c18c2531882b4 | 06c93d110bc8441b6fafba6e2df030224e87cb2b | /man/create_course_assigment.Rd | 333d36c826eff3b853c9b0beaaccd1e1178a09a6 | [] | no_license | wsphd/rcanvas | 57162905a0d860b80ac0cc548cab259a72a8fd6e | ad8929935f410d196a9891a07f537e4b6c8d3ab2 | refs/heads/master | 2020-03-25T05:49:58.661865 | 2018-08-03T20:04:54 | 2018-08-03T20:04:54 | 143,467,846 | 0 | 0 | null | 2018-08-03T19:51:25 | 2018-08-03T19:51:25 | null | UTF-8 | R | false | true | 6,207 | rd | create_course_assigment.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/uploads.R
\name{create_course_assigment}
\alias{create_course_assigment}
\title{Create a course assignment}
\usage{
create_course_assigment(course_id, name, position = NULL,
submission_types = NULL, allowed_extensions = NULL,
turnitin_ena... |
467a5cb825e22f212b5a78ea30c33d81750a7033 | 1f6d79658ce351eafa3bf83cf38949d82b58de2f | /man/diffnet_check_attr_class.Rd | 036294892729f14b1260efbf1df6e3d7e2f102ad | [
"MIT"
] | permissive | USCCANA/netdiffuseR | 3dd061f8b9951f7bdc5ec69cded73144f6a63cf7 | 7c5c9a7d4a8120491bfd44d6e307bdb5b66c18ae | refs/heads/master | 2023-09-01T08:26:19.951911 | 2023-08-30T15:44:09 | 2023-08-30T15:44:09 | 28,208,077 | 85 | 23 | NOASSERTION | 2020-03-14T00:54:59 | 2014-12-19T00:44:59 | R | UTF-8 | R | false | true | 728 | rd | diffnet_check_attr_class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/diffnet-indexing.r
\name{diffnet_check_attr_class}
\alias{diffnet_check_attr_class}
\title{Infer whether \code{value} is dynamic or static.}
\usage{
diffnet_check_attr_class(value, meta)
}
\arguments{
\item{value}{Either a matrix, data frame ... |
42e69cb0f2aed966673789ba384d84b1758e4571 | e8577e571531992fa56a9173a19c55529716502b | /tests/testthat/test-golem.R | 7a39a39b69320843a697c95583a75bd0ae150bfe | [
"MIT"
] | permissive | DivadNojnarg/packer | bcd2c3a13da1308761fcef6cc124aeabfc478b2b | 332e01964835bf4cdf3d5dfc1eb0f2c43d3db107 | refs/heads/master | 2023-08-29T14:59:35.402851 | 2021-10-17T12:27:30 | 2021-10-17T12:27:30 | 416,646,602 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,166 | r | test-golem.R | source("../fns.R")
skip_on_cran()
test_that("Golem Bare", {
# keep working directory
wd <- getwd()
# test bare
pkg <- create_tmp_golem()
setwd(pkg)
on.exit({
setwd(wd)
delete_tmp_package(pkg)
})
expect_output(scaffold_golem(edit = FALSE))
expect_error(scaffold_golem(edit = FALSE))
expect... |
affa1b8754127a55c4d0e31899f70c5a9c75cae2 | 1ea6b75a27e313ec0a0386e28352f390a6915677 | /Examples/4_DefaultCreditCard.r | 1740766c57eed3f983e319e08ecaa2da37014a40 | [] | no_license | NQuinn27/NCIY4_R_Labs | 7cb160943d64868282e9df9e7ad6fd75f3d560bf | ce9f062b5bc9ed022fc59344ed9beaa4ddd9c2ff | refs/heads/master | 2021-01-11T02:19:03.436797 | 2016-10-15T09:46:10 | 2016-10-15T09:46:10 | 70,979,428 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,660 | r | 4_DefaultCreditCard.r | install.packages(c("e1071", "C50", "ggplot2", "hexbin","descr", "caret", "e1071"))
library(e1071)
library(hexbin)
library(ggplot2)
library(caret)
library(descr)
library(C50)
setwd("Developer/NCI/Data Application Design")
# First remove first row (i.e., X1, X2 etc.) and export the data as .csv from Excel
data <- read.... |
a6e3d1820e9f78af549205514fe4336d2d17661d | f898801224c1f17ba62089b28f3f69c7c525e766 | /binomial/man/bin_probability.Rd | b149aac2493a72bde00e98ddac62ae3ae437c2cc | [] | no_license | stat133-sp19/hw-stat133-nadia1212 | 44079944e7b5ab9dffdddbbb3fb82033d2de79a9 | 57ba3ab524660f9d3e8162f1b53a6d030eac6dd6 | refs/heads/master | 2020-04-28T12:33:00.104710 | 2019-05-03T19:14:44 | 2019-05-03T19:14:44 | 175,279,474 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 447 | rd | bin_probability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{bin_probability}
\alias{bin_probability}
\title{bin_probability}
\usage{
bin_probability(success, trials, prob)
}
\arguments{
\item{success}{number of success}
\item{trials}{number of trials}
\item{prob}{probability of suc... |
dfa4f2bc4f44c54fec8d7db3de36d71c672e0c72 | 72d9009d19e92b721d5cc0e8f8045e1145921130 | /spass/inst/testfiles/mlFirstHExp/libFuzzer_mlFirstHExp/libfuzzer_logs/1609981123-inps.R | 8a1d6fcad90d0447c14a8fc2ea60b5c6155bd97f | [] | 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 | false | 109 | r | 1609981123-inps.R | list(kf = 0, tp = 0L, type = 0L, y = numeric(0))
testlist <- list(kf = 0, tp = 0L, type = 0L, y = numeric(0)) |
3c66d0a8d5998ca3f88b983fa45d685f9fcc5974 | 8849921ce5655845b566e5f740fba1a399432fe6 | /R/CUSUM.R | 0602c64cba83915bbb70637a23497a89b38b1940 | [] | no_license | cran/spcadjust | 3ad36af53c3eb6c0541f81656445177f3ea67b78 | de5d69322dcb921c7755154e30e5cbd659730f80 | refs/heads/master | 2021-01-22T23:53:18.673892 | 2016-09-29T11:37:35 | 2016-09-29T11:37:35 | 17,699,983 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,440 | r | CUSUM.R | ########################
## Basic CUSUM Charts ##
########################
#' @include main.R model.R CUSUMlib.R
NULL
#' CUSUM Charts
#'
#' Class extending SPCChart with a basic CUSUM charts implementation.
#'
#' The only slot this class contains is the data model. This data
#' model should already incor... |
6d435a409f3ef5bcabef5dd4a40b2aca3ce00406 | 04eb50424b3fa3e24fff24fbcab6c7b7fcb22f65 | /scripts/merge_1000.R | 7d0991d1ef4f4a572f6f926481694db71d5396fc | [] | no_license | jonathanperrie/bigham_climber_dump | 070992955761d26e33009e71a6b420f3c611c33f | 46884c0c975fb2782f7e70af7751873c1c2071d9 | refs/heads/main | 2023-06-21T14:33:00.647978 | 2021-07-29T17:24:49 | 2021-07-29T17:24:49 | 389,523,877 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,955 | r | merge_1000.R | library(VariantAnnotation)
setwd("~/Bigham/v2/")
data <- read.table("igsr_samples.tsv",sep="\t",header=T)
x <- scan("intersect_set.txt", what="", sep="\n")
setwd("~/Bigham/v2/1000")
geno1000_list<-read.table("affy_samples.20141118.panel",sep="\t",header=T)
matches_2 <- data[data$Sample.name %in% geno1000_list... |
50e959bf19c7434a6dadfc261542068458af2b32 | 9ec240c392225a6b9408a1636c7dc6b7d720fd79 | /packrat/src/backports/backports/man/file.size.Rd | 0a5914342e5f922492c72c18f7979a62a068119f | [] | no_license | wjhopper/PBS-R-Manual | 6f7709c8eadc9e4f7a163f1790d0bf8d86baa5bf | 1a2a7bd15a448652acd79f71e9619e36c57fbe7b | refs/heads/master | 2020-05-30T17:47:46.346001 | 2019-07-01T15:53:23 | 2019-07-01T15:53:23 | 189,883,322 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 795 | rd | file.size.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/file.mode.R, R/file.mtime.R, R/file.size.R
\name{file.mode}
\alias{file.mode}
\alias{file.mtime}
\alias{file.size}
\title{Backports of wrappers around \code{file.info} for R < 3.2.0}
\usage{
file.mode(...)
file.mtime(...)
file.size(...)
}
\... |
9139adf60c030d3435badf6065649dfe0cc263b7 | 79aa103b7b35ae807444be74805e42ed4e57acc5 | /R/ggscatter.R | ecd70c1d37ca72f981baa5fe34adf14dd4bbd870 | [
"MIT"
] | permissive | UBC-MDS/ggexpress | 47ac0252e35ae6f7ee0cc52743bd303b45219d09 | 76e9692c62054a71534d8da1c98f03c424e3a4cc | refs/heads/master | 2021-01-15T06:03:34.739464 | 2020-03-26T22:45:36 | 2020-03-26T22:45:36 | 242,897,067 | 2 | 1 | NOASSERTION | 2020-03-26T22:45:38 | 2020-02-25T03:06:09 | R | UTF-8 | R | false | false | 1,872 | r | ggscatter.R | library(dplyr)
library(ggplot2)
#' Create a scatterplot and calculate correlation values for two numerical variables
#'
#' Creates a ggplot scatterplot object containing two numerical variables. Arguments in the
#' function will control whether correlational values and log transformations are calculated for the input... |
74cdbc5eec50d85ca2197607cafb639dead4b79f | d11fadd13f28e7223c37a8395661cc650159c817 | /R/urbn_geofacet.R | 7d895f16d4526fdae40acf784efe2f54c15d263c | [] | no_license | scoultersdcoe/urbnthemes | 4f5a7dd3a76bc68a1b2709fc866e737503a49062 | 8d88a618c8bf50a4c8c4a80eef3bfd5f5f4e9200 | refs/heads/master | 2023-08-01T02:59:26.110414 | 2021-09-03T20:58:11 | 2021-09-03T20:58:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 284 | r | urbn_geofacet.R | #' Urban Institute geofacet template
#'
#' @format Data frame with columns
#' \describe{
#' \item{row}{Row in geofacet}
#' \item{col}{Column in geofacet}
#' \item{state_abbv}{State abbreviation}
#' \item{state_name}{State name}
#' }
"urbn_geofacet"
#' @importFrom tibble tibble
NULL
|
5f8d01ae57a3d97ee718f3c859ea198761d04b46 | 95f82fae345c99b0ac48f7080306617d8af109da | /run_analysis.R | f9f3a464ea2681b8df13035dcd6acdec40e8c837 | [] | no_license | PavelMAA/Getting-and-Cleaning-Data-Course-Project | 681ac6a7e06a68081f5a05012bbfdbb4ebf10a26 | a965fcf2ee7b4b69b420fc4e7090f47760ba20e3 | refs/heads/master | 2023-06-28T02:00:42.938567 | 2021-08-06T00:58:28 | 2021-08-06T00:58:28 | 393,202,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,974 | r | run_analysis.R | rm(list = ls())
##install library
install.packages("reshape2")
install.packages("dplyr")
library(reshape2)
library(dplyr)
## define directory
setwd("C:/Coursera/GettingData")
#Get data(data> UCI HAR Dataset)
if(!file.exists("./data")){dir.create("./data")}
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%... |
441abfb264f145b9b801ab942b3c3de5f2183669 | f53c62df2e61aa215870b40ce90265bf97705960 | /RNA_FISH/AC16_Tp53_Results.R | 87667bd8ee5e3c168aa519f5f95d7b676ccd7404 | [
"MIT"
] | permissive | hjf34/Cold | 058b3ac970a20ef7902909d3d910261a784b6dbc | 32c70926e4646e8db62f0e6752efe73dc8a19bc1 | refs/heads/master | 2023-02-23T03:06:37.524385 | 2021-01-19T15:32:03 | 2021-01-19T15:32:03 | 272,039,850 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,911 | r | AC16_Tp53_Results.R | setwd("/path/to/Results.csv/directory/")
lf = list.files()
lf1 = lf[grep("Results.csv", lf)]
filenamestem = as.vector(sapply(lf1, function(n) strsplit(n, ".dv")[[1]][1]))
Nr1d1NuclearDots = rep(NA, length(lf1))
Nr1d1CytoplasmicDots = rep(NA, length(lf1))
NuclearArea = rep(NA, length(lf1))
WholeCellArea = rep(NA, lengt... |
44b23c880aa8e07ec657d656bc673732b05d359c | 58ec8c9b97ea1bd69aed7d55c994f026aad420a2 | /man/myLinearRegression.Rd | 9ad23a596fc9d193f55a1314881351bfa4377c23 | [] | no_license | msalmon7/myLinearRegressionPackage | 6a888f366a806a30809e86e9549b39d9dee53cdc | de07ff8619f8b9483515380a022df738b9c43da6 | refs/heads/master | 2022-06-12T07:36:05.779333 | 2020-05-01T15:39:08 | 2020-05-01T15:39:08 | 260,494,542 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,716 | rd | myLinearRegression.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/myLinearRegression.R
\name{myLinearRegression}
\alias{myLinearRegression}
\title{Perform linear regression and create scatterplots of each pair of covariates.}
\usage{
myLinearRegression(y = myData[, 1], x = myData[, 2:4], sub = c(1:20))
}
\a... |
621cb6f6284d288722ea7ba040069c127d0a5f22 | 10af39cbdd712d0bd786232d41089b71005788aa | /data_raw/results_exp_ols_mc.mvn_ci.R | 6f0099a4c0792962599f3c4d218eb4dd738c63f1 | [
"MIT"
] | permissive | jeksterslabds/jeksterslabRmedsimple | 1f914691b75c44f14ea6701bdbec776908997da5 | 4a14bc41892bfa670ebc56f691262475986d48fb | refs/heads/master | 2022-12-30T23:59:12.475315 | 2020-10-16T05:48:55 | 2020-10-16T05:48:55 | 287,948,188 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,123 | r | results_exp_ols_mc.mvn_ci.R | #' ---
#' title: "Data: Simple Mediation Model - Exponential X lambda = 1 - Complete Data - Monte Carlo Method Confidence Intervals with Ordinary Least Squares Parameter Estimates and Standard Errors"
#' author: "Ivan Jacob Agaloos Pesigan"
#' date: "`r Sys.Date()`"
#' output:
#' rmarkdown::html_vignette:
#' toc:... |
49be68b4186cc4bf35bf4254668cd9f1872b2a94 | 7d5d8492c2d88b88bdc57e3c32db038a7e7e7924 | /PhD/0007-crop-modelling/scripts/cmip5/06.bc_rain-functions.R | 5d07755a84e26bc7b9652dddcd94f375b24e3cb3 | [] | no_license | CIAT-DAPA/dapa-climate-change | 80ab6318d660a010efcd4ad942664c57431c8cce | 2480332e9d61a862fe5aeacf6f82ef0a1febe8d4 | refs/heads/master | 2023-08-17T04:14:49.626909 | 2023-08-15T00:39:58 | 2023-08-15T00:39:58 | 39,960,256 | 15 | 17 | null | null | null | null | UTF-8 | R | false | false | 13,291 | r | 06.bc_rain-functions.R | #Julian Ramirez-Villegas
#UoL / CIAT / CCAFS
#Oct 2012
#final wrap
wrap_bc_wthmkr <- function(k) {
source(paste(src.dir,"/0007-crop-modelling/scripts/cmip5/06.bc_rain-functions.R",sep=""))
lmts <- bc_rain_wrapper(k) # bias correct the data
ctrf <- make_bc_wth_wrapper(k) # generate the wth files
}
... |
6d2a5deb4e11cad50cf670d0ba58a0623088554a | 09b581d3c65d6c9687684aa5e538e3b650f130d2 | /Cross-validation.R | 77257f341da16f357b6ca3d518d576c2eaf55abf | [] | no_license | seth127/statToolkit | a9fe2886341eb609def4cf9497fdcb4413a9f3d0 | e220cd3d760cadd6bce663e92eb72f5fa9a82625 | refs/heads/master | 2021-01-11T04:40:10.916944 | 2016-10-26T04:36:00 | 2016-10-26T04:36:00 | 71,141,777 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,273 | r | Cross-validation.R | setwd("~/Documents/DSI/notes/2-STAT-6021/team assignments")
train <- read.csv("teamassign05train.csv", header=T, stringsAsFactors = F)
## cross validate with the lm function
cv.lm <- function(vars, train, k=5) { ## vars should be a character vector of variable names/combinations
# the function to do the cross-valid... |
76353d72611753e502853ff241975c07be5c40ad | 85df0b56b85eb23536cd7f95160b816884148238 | /bnlearn.R | 8731c0b2162e3edfe5ce0eba5f35b745b676ae7a | [] | no_license | dgod1028/Bayesian-Network | 5167c7c48256446af39f3e86d15446c1820bddc7 | 46da7afa9175af0321a82f06dbe470b2b432a142 | refs/heads/master | 2021-01-10T11:58:38.012575 | 2016-04-08T05:52:37 | 2016-04-08T05:52:37 | 54,442,688 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,819 | r | bnlearn.R |
## install.packages("bnlearn")
## 関連文献 http://arxiv.org/pdf/0908.3817.pdf
library(bnlearn)
library(Ecdat)
data(Fair)
head(Fair)
data <- Fair[,c(-2,-3,-9,-7)]
data[] <- lapply(data,as.factor)
colnames(data) <- c("性別","子供","宗教","教育","幸福")
bn.gs <- gs(data)
bn.gs
bn.hc <- hc(data,score ="aic")
bn.hc
par(mfrow = c(1... |
0baf50d068a94b514b364113d45941efb6debeb9 | bb789bd6b0649fae85c747710354122fd8ff7e25 | /man/summary.pmlr.Rd | 846a7cc219ab2298662057035554ca7df9777c4a | [] | no_license | jshinb/pmlr | 8efb9cf4e213f117db51ee4152747d9503408403 | d000cc72192113356a84dd0ff501dae5fa98320f | refs/heads/master | 2016-08-10T14:19:18.869369 | 2016-02-20T00:12:23 | 2016-02-20T00:12:23 | 49,685,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,376 | rd | summary.pmlr.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/summary.pmlr.R
\name{summary.pmlr}
\alias{summary.pmlr}
\title{Summarizing Penalized Multinomial Logistic Model Fits}
\usage{
\method{summary}{pmlr}(object, ...)
}
\arguments{
\item{object}{an object of class \code{"pmlr"}, a result of a call... |
665e33bf4046de16fa0ff58b2c6f7ddc6002c1ab | fecb973f3ed39663ddeafeacac4b4e272c53f6fc | /R/mhealth.validate.R | 53cce8389666033fbd64e3e84da8ec70fefe47f8 | [
"MIT"
] | permissive | qutang/mHealthR | 79c173db45ea34b67566fbdd38fddc133b2e8621 | 4b3fef1fa24249a1c3c09d9721cd1148ae5c98a4 | refs/heads/master | 2018-12-09T04:44:39.771468 | 2018-09-11T20:09:01 | 2018-09-11T20:09:01 | 70,870,269 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,621 | r | mhealth.validate.R | #' @name mhealth.validate
#' @title Validate filename or dataframe against mhealth specification
#' @param group_cols numeric or character vector specify columns to be validated as group variable. Only feasible for dataframe validation. Default is NULL, meaning there is no group columns in the dataframe.
#' @import str... |
af4a6ca8503482c042057826e8357c41876676a5 | 176deb3e42481c7db657cd945e2b53ad0dab66ca | /man/seg.Rd | ae4d2e4a51e74c2814c88ae688293f1c9a3336d7 | [
"LicenseRef-scancode-public-domain-disclaimer",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | katakagi/rloadest_test | a23d4ba635eadf00cebf5f9934217b3c5e16e0fd | 74694ab65c7b62929961c45fdfa7eabf790869ef | refs/heads/master | 2023-07-28T20:15:56.696712 | 2021-10-09T01:46:54 | 2021-10-09T01:46:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 567 | rd | seg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/seg.R
\name{seg}
\alias{seg}
\title{Load Model}
\usage{
seg(x, N)
}
\arguments{
\item{x}{the data to segment. Missing values are permitted and result
corresponing in missing values in output.}
\item{N}{the number of breaks in the segmented ... |
3e9b9ba36a41b00a8da687c2364d59bb41652cf2 | 4ba00ffc6623cdfc2d61f7b5e13918a889ee2472 | /R/rbindfill-spdf.R | 6448ed906db6aa61d41ed4adda8a728ff2e42398 | [] | no_license | fdetsch/StackExchange | acfe10041443c351a0336e38d62c72b0072ad90f | 52dbd089da4fb9a59fcc3b40ebfcc2d129133b3e | refs/heads/main | 2022-09-10T04:06:42.964264 | 2022-09-06T07:36:44 | 2022-09-06T07:36:44 | 99,312,967 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,092 | r | rbindfill-spdf.R | ### Answer to 'How to use rbind with SPDFs when the number of columns of arguments do not match?' -----
### (available online: https://gis.stackexchange.com/questions/267284/how-to-use-rbind-with-spdfs-when-the-number-of-columns-of-arguments-do-not-match/267355#267355)
library(raster)
France_map <- getData(name = "GA... |
f0c57c6658d5effb36425ca452561d4c5bd8b404 | d4edb03b1bf3b0a0fbc58208b6437fda4dbd3d6f | /inst/simulation_and_plotting_scripts/compare-methods_auRC-auPRC(discrete).R | 749954556350da61ee528df407411960562edcb9 | [
"MIT"
] | permissive | insilico/npdr | 245df976150f06ed32203354819711dfe60f211b | 1aa134b6e82b700460e4b6392ef60ae2f80dfcfc | refs/heads/master | 2023-06-23T20:24:58.581892 | 2023-06-10T00:53:23 | 2023-06-10T00:53:23 | 163,314,265 | 10 | 5 | NOASSERTION | 2021-09-21T18:15:34 | 2018-12-27T16:16:52 | R | UTF-8 | R | false | false | 14,556 | r | compare-methods_auRC-auPRC(discrete).R | # compute auRC and auPRC for NPDR, Relief, and Random Forest from 30 replicated data sets
# Discrete (SNP) Data
library(npdr)
library(CORElearn)
library(randomForest)
library(reshape2)
library(ggplot2)
library(PRROC)
show.plots = T # probalby want F for num.iter > 1 iterations
save.files = F # T for subsequent auPRC ... |
711d25eca26fc012f7ba6995d5ffb63627896e21 | 3758b9c36518ec91a374660bc94745a2a615f675 | /cmd/testsite/golden/load/test-library-4.0/fansi/tests/unitizer/_pre/funs.R | cff2b418d0422cd12a78c60f08533a4777a52597 | [] | no_license | metrumresearchgroup/pkgr | ac35bce7c4ae384daba0d6738fd52ef7b3ba16e8 | 27c581cb81b353769b88ea742e9649fbcc1b533d | refs/heads/develop | 2023-06-08T20:07:04.191824 | 2023-06-06T14:00:06 | 2023-06-06T14:00:06 | 150,817,179 | 33 | 5 | null | 2023-08-30T14:06:50 | 2018-09-29T02:45:53 | R | UTF-8 | R | false | false | 240 | r | funs.R |
## Helpers to extract the condition message only due to instability in
## C level error/warning in displaying the call or not
tce <- function(x) tryCatch(x, error=conditionMessage)
tcw <- function(x) tryCatch(x, warning=conditionMessage)
|
6f13a5979d7a7955926728bf01ebb12ff8f9e063 | 472eb2a35e6e58adddaf24dbd0b9aebf6aa61ad8 | /02_nova_codificacao.R | 02676227f26dad5ef6bf644f2ddb3cb50af46f48 | [] | no_license | neylsoncrepalde/midia-e-esfera-publica | 99c04ce9c2074aedd12a4a56408beb908ed7f9d7 | d08072cd592415b5e4f5f02c0d3ffacf426da86d | refs/heads/master | 2021-09-15T07:03:49.802502 | 2018-05-28T07:23:23 | 2018-05-28T07:23:23 | 103,695,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,350 | r | 02_nova_codificacao.R | ##############################
# Mídia e Esfera Pública
# Rousiley, Gabriella Hauber
# Script: Neylson Crepalde
##############################
setwd('~/Documentos/Rousiley')
list.files()
library(xlsx)
dados = read.xlsx("Nova_codificacao.xlsx",1)
View(dados)
nomes = names(dados)
nomes
nomes2 = gsub("X", "", nomes)
n... |
60fa77a12c131eab107a8be989aab79fefefafd5 | 31698075d1580c6dc455adde3b30b636f0c87e70 | /man/get_pdp_predictions.Rd | 5c97d7bdfedc404424db58ff7d2d9c629bf75e4c | [] | no_license | erblast/easyalluvial | 11d10267e31ed44400f99c2e8d5df982ec90f468 | df22644596db1eaa8e66f05e66b343de338d0d1f | refs/heads/master | 2022-07-29T20:10:19.912023 | 2022-07-09T07:06:21 | 2022-07-09T07:06:21 | 149,339,634 | 101 | 10 | null | 2022-07-09T07:06:22 | 2018-09-18T19:13:25 | R | UTF-8 | R | false | true | 3,757 | rd | get_pdp_predictions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/alluvial_model_response.R
\name{get_pdp_predictions}
\alias{get_pdp_predictions}
\title{get predictions compatible with the partial dependence plotting method}
\usage{
get_pdp_predictions(
df,
imp,
m,
degree = 4,
bins = 5,
.f_pred... |
4ba924dd9ed1e2bcd7e365f02cc8153f51670cf7 | 2f7eb4978331ab3585a57347f19a6a4323460191 | /R/get_icc.R | 962110b9cb99abc1f46465c797dd8e83c90308a0 | [
"MIT"
] | permissive | ernestguevarra/sampsizer | 09ee112e7f9dc84cabab619167ceda4df0761a3e | 801108c9c131b895465f3f328f04f642721953bd | refs/heads/master | 2020-03-27T06:14:05.216847 | 2019-09-07T05:08:50 | 2019-09-07T05:08:50 | 146,090,847 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,039 | r | get_icc.R | ################################################################################
#
#'
#' Function to calculate the intracluster correlation coefficient of an
#' indicator collected from random cluster survey (RCS). This is a wrapper of
#' the \code{deff()} function in the \code{Hmisc} package.
#'
#' @param x variable t... |
5d12096453c1c4d5ab8e2d156204435b6ba68949 | 5397b2f52030662f0e55f23f82e45faa165b8346 | /R/j_get_meta.R | 46c4c6392256e31d80e03c8d8bee3fd907a42d22 | [
"MIT"
] | permissive | data-science-made-easy/james-old | 8569dcc8ce74c68bcbb81106127da4b903103fcd | 201cc8e527123a62a00d27cd45d365c463fc1411 | refs/heads/master | 2023-01-12T21:16:23.231628 | 2020-11-19T13:46:17 | 2020-11-19T13:46:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 86 | r | j_get_meta.R | #' @export j_get_meta
j_get_meta <- function(index) {
j_get(index, what = "meta")
}
|
342508343a5374389c1d68fa1d7e81b659cf13f1 | 4ff46765a3b93d40632c9c94d154b8b23e638fdd | /cba/machine-learning/codes/batch.R | 37e7016c0f1aed3c613eae8a4f274ad976cb6556 | [] | no_license | harakonan/research-public | e5bedaa225aba44d1da624e9816434abe05529ac | 5ea6a58e8603a08130095ad028c92ca791cbed3f | refs/heads/master | 2023-06-28T04:27:35.057728 | 2023-06-08T11:47:55 | 2023-06-08T11:47:55 | 129,033,826 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,323 | r | batch.R | # Batch file for data cleaning and analysis
# Do not forget to change set_env depending on the environment
# set_env <- "test"
set_env <- "main"
# Path to working directories
if (set_env == "test"){
source("~/Workspace/research-private/cba/machine-learning/codes/pathtowd/pathtowd_test.R")
} else if (set_env... |
9fcc4793e8a7fdb8e83911aaf5c655161344402a | ac0a2b0c0dcbe8ea08d0baa42b66a38b2ffe6e37 | /mean_substitution_rate_by_pango.R | 8f2e6a84b30345c6d9836b849e4659e460632f4f | [
"CC0-1.0"
] | permissive | cednotsed/ditto | 3957e0fcb11ec505570bf7c6862a99c1916bb507 | 8a27ec1a06c2363ae36a6674457f3e7e2e1b9c9a | refs/heads/main | 2023-04-17T18:43:09.591358 | 2022-05-08T03:50:28 | 2022-05-08T03:50:28 | 430,035,274 | 1 | 0 | CC0-1.0 | 2022-05-08T03:50:30 | 2021-11-20T07:13:17 | R | UTF-8 | R | false | false | 12,373 | r | mean_substitution_rate_by_pango.R | rm(list = ls())
setwd("../Desktop/git_repos/ditto/")
require(tidyverse)
require(data.table)
require(ape)
require(ggplot2)
require(see)
require(foreach)
prefixes <- list.files("results/human_animal_subsets/V5/dating_out/")
ant_df <- read.csv("data/metadata/netherlands_humans_anthroponoses.txt", header = F)
p... |
2907d4a548e7eed2a183281696420f5cebbb6704 | 50131539a2e92a690f43aea65b0c8ff10b90bbc0 | /R/theme_coffee.R | 7733fec4e244824f1792cbf47f85028c6cc88808 | [
"MIT"
] | permissive | RMHogervorst/coffeegeeks | 904bc083fec2fc19e16151020795f19c33648698 | 23deb87e58029c6d345a5f1aa0e0ec07e96b86ca | refs/heads/master | 2021-01-16T19:46:37.509578 | 2017-09-16T14:56:04 | 2017-09-16T14:56:04 | 100,188,183 | 1 | 1 | null | 2017-09-12T17:41:59 | 2017-08-13T16:01:48 | CSS | UTF-8 | R | false | false | 2,344 | r | theme_coffee.R | theme_coffee <- function(base_size=12, base_family="sans"){
ggplot2::theme(rect = element_rect(colour = "black", fill = "white"),
line = element_line(colour = "black"),
text = element_text(colour = "black"),
plot.title = element_text(face = "bold",
... |
a3b3a40f870cbb34eded25f2b6edc0d190a243e0 | 770b14ae44e4991d444f0a0b1af124396bf2960f | /pkg/man/memisc-deprecated.Rd | c781641bbc0811b5941a701f27834785bd8ca0b0 | [] | no_license | melff/memisc | db5e2d685e44f3e2f2fa3d50e0986c1131a1448c | b5b03f75e6fe311911a552041ff5c573bb3515df | refs/heads/master | 2023-07-24T19:44:10.092063 | 2023-07-07T23:09:11 | 2023-07-07T23:09:11 | 29,761,100 | 40 | 10 | null | 2022-08-19T19:19:13 | 2015-01-24T01:21:55 | R | UTF-8 | R | false | false | 1,615 | rd | memisc-deprecated.Rd | \name{memisc-deprecated}
\alias{memisc-deprecated}
\alias{fapply}
\alias{fapply.default}
\title{Deprecated Functions in Package \pkg{memisc}}
\description{
These functions are provided for compatibility with older versions of
\pkg{memisc} only, and may be defunct as soon as the next release.
}
\usage{
fapply(formul... |
a64bc677622f253be64e2a4663447c5f9c2fbb99 | 32ca51d8fb4de3e520b4aa49b545acefed74c3b2 | /binder/install.R | 303d08bd98f60deda45709203c155a7beaac6011 | [] | no_license | cannin/repo2docker-test | 639211141beda5979a56536aaf653c1ea9eca445 | 1e7671acfbbfaa6855bdc19ec8a2f6de96306b2a | refs/heads/master | 2020-05-18T09:33:28.996626 | 2019-08-05T17:54:24 | 2019-08-05T17:54:24 | 184,327,815 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 191 | r | install.R | install.packages("devtools")
install.packages("rJava")
#source("https://gist.githubusercontent.com/cannin/6b8c68e7db19c4902459/raw/installPackages.R")
#installPackages("r-requirements.txt")
|
23590832b02d5139b5f6749c34fdb49d1afbc9d2 | 7f9d20b9e57be5dad4feeabf3e7f5fc6448c04e5 | /WB_Data_Africa.R | bfe2b7a7c39f611a2792e5368938aaa08493c651 | [] | no_license | monmon1994/TB_COVID19_Africa | 3de5e9d03cdea9c3c459be7271d9f7e814ac7220 | 2c4d01c2d211b9ed0cef4ac36c49580e785d23be | refs/heads/master | 2022-07-20T22:12:59.814109 | 2020-05-13T08:48:22 | 2020-05-13T08:48:22 | 263,574,765 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,767 | r | WB_Data_Africa.R | # COVID Data and World Bank Data for Africa
library(wbstats)
library(tidyverse)
library(tidycovid19)
library(scales)
library(ggrepel)
# World Bank Data
# Load the data from the stats
wb_africa <- wb(country = c("DZA", "AGO", "BEN", "BWA", "BFA", "BDI", "CV", "CMR", "CAF", "TCD", "COM", "COD",
... |
5bb7c25110290707bcbd78341103071b8449100a | 0e6f03600e49c8a9f6bfe1c4e0ed2fb423d9690d | /Real_study/r/1_lacpd_wadi.R | aad41b3144521915e53bcfa547bc74ae9e2ad778 | [] | no_license | mmontesinosanmartin/LACPD_Article | bd1688dea09d993dcd859b01741a9c3449d28ee5 | b8acd76eae1ea704c21cf8c9306dc34403e4f071 | refs/heads/master | 2023-02-16T04:15:10.784437 | 2021-01-12T17:48:11 | 2021-01-12T17:48:11 | 259,288,942 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,680 | r | 1_lacpd_wadi.R | ###############################################################################
# R CODE: Locally adaptive change point detection
###############################################################################
# Moradi, M., Montesino-SanMartin, M., Ugarte, M.D., Militino, A.F.
# Public University of Navarre
# License: ... |
0b5fcd5e86a49b416e21482e56be05a22beecd1e | 2970a3fe4634b1f8fb24243730b8becce4b5da42 | /week_1/5_friday/Phylacine/commands.r | cf867f8bf0490e9bef76d4e80bbfa95f400f5335 | [
"MIT"
] | permissive | chase-lab/biodiv-patterns-course | 21b38d5b99e3a90cc18e020c2fb74db9a5a8966b | 8212e9fabb2db9187c510e6e15ce8e94e83f960e | refs/heads/master | 2022-04-05T06:07:53.110753 | 2020-02-21T16:11:04 | 2020-02-21T16:11:04 | 235,298,888 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 467 | r | commands.r | library(rgdal)
library(sp)
library(raster)
# ------------------------------------------------------------------------------
sh <- readOGR(dsn= "Shapefile", layer = "land_boundary")
rs <- raster("Environment/Elevation.tif")
plot(rs)
plot(sh, add=T)
# projections
BEHRMANN <- CRS("+proj=cea +lon_0=0 +lat_ts=30 +x_0=0 +... |
17b8ef6f3dcae7519cebb932a446dd011848db4a | 0accbe1994d882fdeb49cad9398519cfb4e40a15 | /r_scripts/170220_pol2_profiles_meta_meta.R | 1a3219ce6cc7ffb18f3588d907aa5547ab544545 | [
"MIT"
] | permissive | linlabbcm/RASMC_Phenotypic_Switching | 026da5f96dc0aef3705eb0218fd7d2d3990f025e | 0636cf9154bae385c998e1a44c360f726e09ea56 | refs/heads/master | 2020-03-22T01:05:36.876041 | 2018-06-30T23:43:42 | 2018-06-30T23:43:42 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,457 | r | 170220_pol2_profiles_meta_meta.R | pol2_0_TSS = read.delim('C:\\Users\\rhirsch\\Documents\\rasmc_docs\\mappedFolder\\RN6_TSS_ALL_-3000_+0\\RN6_TSS_ALL_-3000_+0_RASMC_POL2_UNSTIM_NEW.gff',header=TRUE)
pol2_0_TXN = read.delim('C:\\Users\\rhirsch\\Documents\\rasmc_docs\\mappedFolder\\RN6_TXN_ALL_-0_+0\\RN6_TXN_ALL_-0_+0_RASMC_POL2_UNSTIM_NEW.gff',header=TR... |
37ea4ccc462b754f2191118194d7cffb4b481bd4 | 0fd0fb1ada1b966357fe55bfab6540fc7358b62a | /man/count_consonants.Rd | 4ca4dda86f3b6c74710c323dcbf4ba7e610b72f2 | [
"MIT"
] | permissive | nelsonroque/ruf | f1f8598964ade0085ad9ecc77b92b99a4c544a4a | dbf86c97ce5ad03e417cd379c47a410c4dbd0566 | refs/heads/master | 2021-06-26T18:31:48.057608 | 2021-02-28T14:04:12 | 2021-02-28T14:04:12 | 206,207,103 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 285 | rd | count_consonants.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/count_consonants.R
\name{count_consonants}
\alias{count_consonants}
\title{ruf}
\usage{
count_consonants(str)
}
\arguments{
\item{str}{class: string}
}
\description{
ruf
}
\examples{
count_consonants(str)
}
|
bdfa3f1d3ba92174c9c8200bb1a59c53aa6f4581 | 3c8ba871dfa3dc3673c2fbd0e4838c35e09caf2a | /댓글 크롤링.R | d032fb9b2c911276c7bed9d3b2898129a2c097b2 | [] | no_license | JinsaGalbi/webtoon-recommend-system | bc580fb61f61802254cf9836eae9a0e7707edf52 | 9983935d1ec26f5723e3859f6c933b08d0886b81 | refs/heads/master | 2022-12-15T03:02:37.297373 | 2020-09-08T04:56:15 | 2020-09-08T04:56:15 | 293,414,785 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,027 | r | 댓글 크롤링.R | ## 댓글 크롤링
setwd('c:/Users/USER/Desktop/주제분석/웹툰크롤링')
comment_url <- read.csv('comment_url.csv')
library(rvest);library(tidyverse);library(magrittr)
library(RSelenium)
rD <- rsDriver(port = 4445L,browser = 'chrome',chromever = '78.0.3904.70')
remDr <- rD[["client"]]
# comment_final <- data.frame(nickname=NULL,id... |
f463c9693a65af962d8d8bba38aa927377cde04a | 474c866367bda19aa16c88961e34dde81a17786f | /housing-market/new-home-sales/new-home-sales.R | c2ced9c14db2a14738d5b9ae464dfb4dbc0af4f1 | [] | no_license | davidallen02/economic-market-commentary | fefde0a6e46a5f92eac8dfe9969d004769a34f2e | 2eef6f4d7fbbff1db39c3323bb7246f6263dff3d | refs/heads/master | 2023-06-08T10:36:40.946661 | 2021-06-22T21:04:09 | 2021-06-22T21:04:09 | 281,976,227 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,320 | r | new-home-sales.R | library(magrittr)
lay <- pamngr::set_layout(11)
title <- pamngr::set_title("New Home Sales")
new_home_sales <- pamngr::run_and_load("new-home-sales", "new-home-sales") +
ggplot2::theme(legend.position = "none", plot.caption = ggplot2::element_blank())
northeast <- pamngr::run_and_load("new-home-sales", "new-home... |
2528244c1ab6f5a52508444bafeab1cf52eeada3 | 2d12c1d9fff0c57acc87fb1fcbd9d189854c3d79 | /programs/ggplottrain.R | 7427ba1d880cf60c2d7ff4663c157c2283d67d28 | [
"MIT"
] | permissive | lfthwjx/DataAnalytics | 8cbab6bf7006d46f6e8ab34bbc86d7907d9ed177 | 68aaf7a936a5a7599e2e2039f6fe26b0a90c14e9 | refs/heads/master | 2021-01-13T12:35:18.255673 | 2016-11-01T21:52:37 | 2016-11-01T21:52:37 | 72,579,217 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 585 | r | ggplottrain.R | library(ggplot2)
data("diamonds")
p <- ggplot(data = diamonds, mapping = aes(x = carat, y = price, color = cut))
summary(p)
p
p + layer(geom = "point", stat = "identity", position = "identity")
#p + geom_point()
p <- ggplot(data = diamonds, mapping = aes(x = carat))
summary(p)
p
p2 <- p + layer(geom = "ba... |
2588cb153675ee6087016b010a4cfa5fb084ffe1 | 49e30ad80df564f40b163938f0fd1c00658ff1da | /Fig_1B/limma_main_Fig1B.R | 22fcba4ca58f78a26bcfa550642040b240d5dec5 | [] | no_license | wasimaftab/IMS_Shotgun | ca583f16aa72198ef4ce174c69de7196d528cbc3 | 32bb13f21df969d9562b966f1e44ff9436d3e159 | refs/heads/master | 2021-08-18T02:48:43.082139 | 2020-12-27T11:46:04 | 2020-12-27T11:46:04 | 237,218,411 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,580 | r | limma_main_Fig1B.R | ################################################################################
## This code is to reproduce the data and image of Figure 1B
## Since the missing values are imputed randomly (from normal distribution),
## One can notice minute changes in numbers associated with fold change and p-values
## across ... |
f9764567b463f5354b76ef002822db1fba0361e3 | afa997ac0246d2ede8e3fde18dc3ae780fa16d2f | /8kyu/litres.r | 5876ad0a46bca7e20fc663e7ef83f7baabc57e12 | [] | no_license | supvolume/codewars_solution_in_R | 20fc2332afd5b33fd3e15ba716a1e54af955e761 | 1c3912e6b7c60ced07a9879405fad43ada5ba24a | refs/heads/master | 2021-11-27T16:58:02.949777 | 2021-11-26T08:48:05 | 2021-11-26T08:48:05 | 169,952,519 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 99 | r | litres.r | # solution for Keep Hydrated! challenge
litres <- function(time) {
return(as.integer(time*0.5))
}
|
d58f1f833c12d8b6a5a0f0b0473bce3d289ce808 | ab38fcf1040a66038f81e2963d3ab96cc513a4df | /man/lpnet.Rd | 22628f8581c3cd958e40fc6db696c306d5473c33 | [] | no_license | yukimayuli-gmz/lpnet | a1d271864508886939ede89dac3d73d1d9d3a1f1 | 5cbb3af788c1a742e4448450efca1e1344d81f20 | refs/heads/main | 2023-04-19T04:50:32.524818 | 2022-02-17T18:20:03 | 2022-02-17T18:20:03 | 343,287,614 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,529 | rd | lpnet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lpnet.R
\name{lpnet}
\alias{lpnet}
\title{Consruct a circular network based on a tree by Linear Programming}
\usage{
lpnet(
M,
tree.method = "unj",
lp.package = "Rglpk",
lp.type = "B",
filename = "lpnet.nex",
taxaname = NULL
)
}
\... |
21838bc23052db1f85d945de07e536f808b1a525 | f256fd8f31ea589cae179c155f44b7e4f8de744a | /R/check_collinearity.R | 163bfcc3d401005f4a9d0d19c7e9a87840471383 | [] | no_license | cran/performance | 9578b76fd8981e5896d25d036cb8439e8b01c24a | c44285dff9936445c56ec8b83feb7ff9cae3fa81 | refs/heads/master | 2023-06-08T08:42:02.230184 | 2023-06-02T10:30:02 | 2023-06-02T10:30:02 | 183,289,241 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,547 | r | check_collinearity.R | #' @title Check for multicollinearity of model terms
#' @name check_collinearity
#'
#' @description
#'
#' `check_collinearity()` checks regression models for
#' multicollinearity by calculating the variance inflation factor (VIF).
#' `multicollinearity()` is an alias for `check_collinearity()`.
#' `check_co... |
f016d45af91e02c9c900332fe90a551519a3522a | 5db2dac679963587ac50ad850ea3a2ccb508465a | /phd-scripts/R/zeta_plot.R | 575804f6dcd92b8239de7789259cd8f1a01b7996 | [
"MIT"
] | permissive | softloud/simeta | be88fe336eeee9610086823adce839493781c0ef | 2a7e979077c57812a7d29c3e23e8c00080e1cb03 | refs/heads/master | 2023-04-16T23:27:16.936986 | 2023-03-25T11:49:23 | 2023-03-25T11:49:23 | 200,359,586 | 2 | 2 | NOASSERTION | 2020-01-28T09:55:16 | 2019-08-03T09:56:12 | HTML | UTF-8 | R | false | false | 1,525 | r | zeta_plot.R | #' plot the distribution of the proportion allocated to the intervention group
#'
#' @family vis_tools
#' @family reporting Functions and tools for reporting simulation results.
#'
#' @export
zeta_plot <- function(mu, epsilon) {
# check numeric args
neet::assert_neet(mu, "numeric")
neet::assert_neet(epsilon, "nu... |
f9ca07dcd19963980785a8e19f5ac735565975cd | 5e67544bd977d277ea24050d1aafa1c9bed9cf86 | /2-analysis/old/adoption.R | 574966b90e8e35102c8ab9efc418e202775850ff | [] | no_license | balachia/Currency | 8d08a1c11a6472e7b019c641afb64ad90c7e1b7b | ff46e4b042eb176cb7787ba524c52d21303cd5ce | refs/heads/master | 2021-01-17T04:46:31.851629 | 2016-07-14T22:45:06 | 2016-07-14T22:45:06 | 15,240,711 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,994 | r | adoption.R | # let's run an adoption model viz coxph
# What do we need to do here?
# Create a data set containing events by user/currency
# split spells on ...
# every day with observed friend or self returns?
library(rms)
library(survival)
library(data.table)
library(ffbase)
library(reshape2)
library(texreg)
library(parallel)
... |
4fd2c388a19e7e3a237edacd3c7114b175d47838 | 1f10c23dcc48836a6f731a5dd3a1ba1279efd343 | /R_scripts/3_fit_models.R | b3fa668dcf28667726fd47226066597dd9797aae | [
"CC-BY-2.0"
] | permissive | LeanaGooriah/ISAR_analysis | fcab2596754ca3ba7a75b7499ec749effaf94b2c | 2827a402db297464ace5c29222f7bf735bcc007c | refs/heads/master | 2021-07-15T02:44:43.837907 | 2020-05-31T22:24:44 | 2020-05-31T22:24:44 | 147,322,370 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 692 | r | 3_fit_models.R | library(tidyr)
library(purrr)
library(dplyr)
library(broom)
dat1 <- read.csv("ISAR_DATA/diversity_indices/allstudies_allscales_allindices.csv", stringsAsFactors = F)
names(dat1)
by_index <- dat1 %>%
group_by(Study, Scale, index) %>%
nest()
by_index2 <- by_index %>%
mutate(model = map(data, ~ lm(log(value) ~... |
7dc5a5425d33c2fd427c9d304f72075f9e33c339 | 4d2c711b308db9eeefd2faf1f13d4c5db3ab70b1 | /plot1.R | b0e4ca019c5a70ec1fa13f01068e731cabd3b833 | [] | no_license | kimhale/ExData_Plotting1 | 1ad185478180f4b969fb7093d92616ac2d9fcf48 | 53014e056de9524bc888444ff2368a9d9d271aa1 | refs/heads/master | 2020-03-23T02:11:07.152949 | 2018-07-14T22:41:53 | 2018-07-14T22:41:53 | 140,961,037 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,480 | r | plot1.R | # Set working directory
setwd("/Users/kimberlyhale/Documents/Coursera/DataScienceCert/C4_EDA/ExData_Plotting1")
# Load packages
library(data.table)
# Download file if data doesn't exist
if (!file.exists("data/household_power_consumption.txt")){
dir.create("data")
fileURL <- "https://d396qusza40orc.cloudfront.net/... |
3dd29b0e9348838f15d725dff2952d512be0a7cb | 7e1d6c1822045ee656a6a41c063631760466add3 | /man/appendVerticalTab.Rd | 113dd66ba17e586cb312a4e3dc34c7214f741149 | [
"MIT"
] | permissive | jcheng5/shinyWidgets | c784a3c9e4da76c0c7f23e8362fe647044beb6d2 | f17f91f6c2e38ee8d7c6be6484ccb474ebef6417 | refs/heads/master | 2020-04-29T18:20:46.195001 | 2019-03-18T16:10:00 | 2019-03-18T16:56:59 | 176,321,196 | 3 | 0 | NOASSERTION | 2019-03-18T15:59:35 | 2019-03-18T15:59:35 | null | UTF-8 | R | false | true | 1,350 | rd | appendVerticalTab.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vertical-tab.R
\name{appendVerticalTab}
\alias{appendVerticalTab}
\alias{removeVerticalTab}
\alias{reorderVerticalTabs}
\title{Mutate Vertical Tabset Panel}
\usage{
appendVerticalTab(inputId, tab,
session = shiny::getDefaultReactiveDomain()... |
f88419bd06c98c170a02b78d9937aff2a86fcd8d | 83d7c5a5d018752961787e7ddaee30e005ab36aa | /man/bbx_pad.Rd | fab8f72bc6f95f6a186b0037aae8412c55bc4df3 | [
"MIT"
] | permissive | dmi3kno/bbx | 09f9997463987144b0c3a541ae96215598a96405 | c805c50bc1ebbab04c035b80e4c5f835979d6f91 | refs/heads/master | 2022-06-22T03:44:02.044482 | 2020-05-07T08:22:31 | 2020-05-07T08:22:31 | 261,409,960 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,028 | rd | bbx_pad.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/transform.R
\name{bbx_pad_width}
\alias{bbx_pad_width}
\alias{bbx_pad_height}
\title{Functions for padding bbx
These functions can "pad" (increase size of) bbx}
\usage{
bbx_pad_width(bbx, n = 1, word = NULL, side = "both")
bbx_pad_height(bbx... |
9a362c140c0f61805bf401a7c3c87046f4f53bd8 | c9a0e70c007ab6f1f6495dbaef44258e18959f17 | /climate_comp_plots_AGUV_GUV_IVT_AGU2020poster.R | a7eff26545bf9cc74904cd038cc65112062b70eb | [
"MIT"
] | permissive | LizCarter492/MW_P_STG | e51e10ef872222fbc644f6ca88836e48dd13cb8c | 06701a69c29201eb717638baecd4ec1c0e25e88c | refs/heads/main | 2023-03-06T18:34:00.191059 | 2021-02-20T01:11:07 | 2021-02-20T01:11:07 | 340,173,894 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,232 | r | climate_comp_plots_AGUV_GUV_IVT_AGU2020poster.R |
library(lubridate)
library(ncdf4)
library(fields)
library(maps)
library(maptools)
library(raster)
library(data.table)
library(RColorBrewer)
library(viridis)
library(colorRamps)
#setwd("D:/DELL_PHD/Box Sync/AFRI_Aexam/R scripts")
source("filled.contour3.R")
source("filled.legend.R")
in_path<-("D:/DELL_... |
fdd28f2ca586a74ae6acf749263d5fe263120948 | b75b290b2dd161e4c850858ecf7abee486bdede8 | /man/calc_cgp.Rd | 7589849018715d0f17fccd34f387a42f88fc3405 | [] | no_license | rabare/mapvizieR | 7d7bffb8691f6045e678d822f9e461e748038cd3 | 1a344ec1376ee41e85dcda0407f8bc63cfb95a82 | refs/heads/master | 2020-12-25T16:14:33.669385 | 2015-07-14T21:24:27 | 2015-07-14T21:24:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 930 | rd | calc_cgp.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/cgp_prep.R
\name{calc_cgp}
\alias{calc_cgp}
\title{calc_cgp}
\usage{
calc_cgp(measurementscale, grade, growth_window, baseline_avg_rit = NA,
ending_avg_rit = NA, sch_growth_study = sch_growth_norms_2012,
calc_for = c(1:99))
}
\arg... |
6c7f8064efec1b49029180adb0f75d1594ae282d | 2171f3867747a89929b1ad396afb855b09896309 | /man/yan_2013.Rd | 832f7396b51bb9b54906ccc61faac0d2f994aa83 | [
"MIT"
] | permissive | aelhossiny/rDeepMAPS | 4c1a0a0bcdad4bf6240984b9a47da55250c1fa8e | ec6b742f9f42dc4f1a40a392ddfe8d3610ab9e63 | refs/heads/master | 2023-06-09T20:02:19.866887 | 2021-07-07T02:56:57 | 2021-07-07T02:56:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 504 | rd | yan_2013.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{yan_2013}
\alias{yan_2013}
\title{Data of yan_2013}
\format{
A list with expression matrix and metadata:
$meta:
\describe{
\item{$expr}{a data frame with 20214 genes (rows) amd 90 cells (columns)}
\item{$meta$Clus... |
215ba762b7cc4a8e6ab9bc81badb0841c5fe9406 | b54ab392f5b31b6d665521d7f32d9b77ccae8728 | /aprioritestingCosmetics.R | 9f428d5a243942c07d466365d65feb820d52063c | [] | no_license | snehavishwanatha/Apriori_on_Cosmetics_Dataset | 59cd97bdf5600e5a52b479faf165b7adbf5a88b9 | dd145ddc4e5c1487fdbc368d31ab864df4d0652c | refs/heads/master | 2020-05-15T21:45:01.909665 | 2019-04-23T07:12:51 | 2019-04-23T07:12:51 | 182,508,244 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,272 | r | aprioritestingCosmetics.R | library(arules)
library(arulesViz)
library(RColorBrewer)
f=file.choose()
f1=read.csv(f)
#summarizing data
data<-f1
#data=na.omit(f1)
head(data,n=10)
str(data)
summary(data)
#inspecting rules
rules<-apriori(data,parameter=list(supp=0.5,conf=0.8,target="rules"))
rules.sorted <-sort(rules, by="confidenc... |
eef2d94954c515dd5df756cec2f12b4c4fd722dc | 8c07cac2a097b87ab88301826126271165bdd261 | /november/data/nadieh/Step 1 - Get the top 100 Fantasy authors from Amazon.R | 8bfe38970bd69a1b6fde013c0a10793e220212aa | [] | no_license | Minotaur-zx/datasketches-gh-pages | d53dc7e7d3afac9e0a279eb95cb44463c69950f1 | b63dcef736d67a52da69d70092f5eb25be4e8fb5 | refs/heads/master | 2022-12-31T08:06:05.991245 | 2020-10-23T05:35:06 | 2020-10-23T05:35:06 | 306,538,550 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,093 | r | Step 1 - Get the top 100 Fantasy authors from Amazon.R | #Get the top 100 fantasy authors from Amazon
#Taken on November 7th, 2016 at 13:24 Amsterdam time
#Web scraper explanation
#https://blog.rstudio.org/2014/11/24/rvest-easy-web-scraping-with-r/
library(rvest)
library(stringr)
library(tidyr)
#https://www.amazon.com/author-rank/Fantasy/books/16190/ref=kar_mr_pg_1?_encodi... |
26abe9f81a64e8c4bcd8f7696660a6d584701f9f | a8b27b2a52ed577253ecc30b969ee8f4d5b0dca3 | /man/opchar_admissable.Rd | 629134e020523462c65f0786d20e6582a9d67964 | [] | no_license | mjg211/singlearm | 51d2a0c3141ef4861a0156e0b29d57c23815100b | ad0c23a7780b902b1d5028915b8948d208999ba6 | refs/heads/master | 2021-06-03T14:25:29.390692 | 2021-05-04T14:57:48 | 2021-05-04T14:57:48 | 133,361,647 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,486 | rd | opchar_admissable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/opchar_admissable.R
\name{opchar_admissable}
\alias{opchar_admissable}
\title{Determine the operating characteristics of admissable group sequential
single-arm trial designs for a single binary endpoint}
\usage{
opchar_admissable(des, k, pi, ... |
cc14e1cf8319f8a967c45ffb049e2f54c126321b | 4c671b09b63895596debc68f8a1eee4de7507356 | /1_Names_Load Data Functions.R | 17f53331e930c06952424ca882fb3f2133a35ce9 | [] | no_license | ryantimpe/Kerasaurs | 80782e631f4a5df29895348bc840da9c9ebdfdfa | c289e61ce2d2da7bdee437ffd7cd622681e581c5 | refs/heads/master | 2021-05-03T23:22:23.042740 | 2018-12-13T21:51:51 | 2018-12-13T21:51:51 | 120,399,442 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,703 | r | 1_Names_Load Data Functions.R | #this file loads the packages and creates the functions that will be used in the model
require(readr)
require(stringr)
require(dplyr)
require(purrr)
require(tokenizers)
require(keras)
# This function loads the raw text of the _saurs
get_saurs <- function() {
readRDS("data/list_of_extinct_reptiles.RDS") %>%
str_r... |
83bbbbd00c095f9d751b7f0e90199a1583281456 | 5e968d1fa5ae2f0b3f15693561d61571a0b27a77 | /R Code/Producemobile Time Series Analysis/producemobile_tsa.r | c9087445dccea46c37fb7e7718ba6db32ad373e7 | [] | no_license | adityagi1/everybody-eats-summer-2020 | a4dadce45728122f046c926bbdfcbb96b85f2e5c | fbd17660736eae447a447bbb594bd1dea19e8da9 | refs/heads/master | 2022-12-28T22:06:29.840546 | 2020-10-20T02:50:39 | 2020-10-20T02:50:39 | 305,567,747 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,331 | r | producemobile_tsa.r | library(tidyverse)
library(readxl)
library(lubridate)
library(imputeTS)
pm = read_excel("../../Data/ProduceMobile/Producemobile_historical_data_clean.xlsx",
sheet = "Sheet1")
#rename column names
names(pm) = c("date", "in_out", "amt_food_received","num_guests_signed_in",
"num_individuals_served", "t... |
c01b07e4222a9a434cffcda6fe0dab792bd5099a | 7188afce97d4674ec52fe33aeaf27b38e0889ac0 | /plot2.R | fd59ad7864091381ec92e4c4f10f2163e9ef9280 | [] | no_license | joski/ExData_Plotting1 | b7aaa92f0faa94bd343e0e7bf239595a9cd49f9d | a0392e468ac403d04d8727b08f57dacdb5b1bedd | refs/heads/master | 2020-03-07T05:28:33.147234 | 2018-03-29T18:24:57 | 2018-03-29T18:24:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 243 | r | plot2.R | source("load_data.R")
data<-load_data()
png("plot2.png", width=400, height=400)
with(data,plot(Time,Global_active_power,
type="l",
xlab = "",
ylab = "Global Active Power (kilowatts)"))
dev.off()
|
fae3732706ea29acd8534118f8ff641aba989bb2 | d175a3870bbe9086724b614aaf681eda6002fa83 | /server.R | a2a9e45db6ee1a729cdb7417ed1666669ff4e60c | [] | no_license | MFKiani/ShinyAppCode | 22bec721bdf85a026a050e4388627de5ade785ef | 652981e69fc5cb35dffec5c555e96620c58c1c5e | refs/heads/master | 2021-01-10T17:00:19.093394 | 2015-05-23T18:31:37 | 2015-05-23T18:31:37 | 36,120,606 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,229 | r | server.R | library(shiny)
# We tweak the "am" field to have nicer factor labels. Since this doesn't
# rely on any user inputs we can do this once at startup and then use the
# value throughout the lifetime of the application
# Define server logic required to plot various variables against mpg
shinyServer(function(input, outp... |
6622733770139bc3c00ca3772d78cc176b5e736b | 985015b0c5399a82a1c874e1d68406aeb8042c72 | /dockerfiles/haloplex-qc/CoveragePlots.R | 006e36bb92d74a3ed93949795cb5af36c90c5a51 | [] | no_license | genome/cle-myeloseq | f27f48c8e2fc868ac6a0ee3178c0f180be0871dc | fedd71f8daed318f3e4794e398797d038c002350 | refs/heads/master | 2021-10-10T00:53:51.361753 | 2021-10-08T16:34:19 | 2021-10-08T16:34:19 | 180,651,790 | 1 | 2 | null | 2021-05-04T19:04:01 | 2019-04-10T19:40:08 | Perl | UTF-8 | R | false | false | 2,966 | r | CoveragePlots.R | require(scales)
sample.name <- commandArgs(T)[1]
pdf(height=8.5,width=11,file=paste0(sample.name,".coverage_qc.pdf"))
layout(matrix(c(1,1,2:5),nrow=3,byrow = T),heights = c(.2,1,1))
op <- par(mar=c(2,2,2,2))
plot.new()
text(0,.5,labels = sample.name,cex=2,font=2,xpd=T,pos=4)
par(op)
cov <- read.table(paste0(sample.... |
1eadbb12f1feb597ecc4417e001eef1e1fb63f42 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/hawkes/examples/simulateHawkes.Rd.R | eda387c2ed25579725f6550cfc8ef0fa8e42b585 | [] | 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 | 457 | r | simulateHawkes.Rd.R | library(hawkes)
### Name: simulateHawkes
### Title: Hawkes process simulation Function
### Aliases: simulateHawkes
### ** Examples
#One dimensional Hawkes process
lambda0<-0.2
alpha<-0.5
beta<-0.7
horizon<-3600#one hour
h<-simulateHawkes(lambda0,alpha,beta,horizon)
#Multivariate Hawkes process
lambda0<-c(0.2,0.2)
... |
41fe9f7b6f7180e00a09a589546b81344b98e8c6 | 20c506f33d3bfe2322d9be7a894d64bd6a179fff | /R code | 3a8e10a2c2c3e308682852e15fbf60fd4a6ded9a | [] | no_license | johnukfr/IDaSRP | ccdb75eab5bd7a42b63b2fd6a7f998d68599bbce | 329bbd0260bcc62dacf5614ee7abcd642c189c9e | refs/heads/master | 2020-07-04T18:04:08.063693 | 2020-05-15T13:57:21 | 2020-05-15T13:57:21 | 202,365,845 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 201,845 | R code | #!/usr/bin/Rscript
# sink("IDaSRP_077.3.txt", type = c("output", "message"))
#####---------------------------------------------------------------------------
##### R code for Disertation 'Information Demand and Stock Return Predictability'
##### University of Essex
##### Written by: Jonathan Legrand
#####--------------... | |
b4f6013695118af9d13122f2cc3d6c72adf49ab7 | d5eef5ca98115b14d13345c3e104fe4d9448b721 | /man/print.SDMfit.Rd | 6cfe153b07a41dc16c9f27755da43401b32d11ed | [] | no_license | giopogg/webSDM | b6e3e40fc0ee82f87b2cee3a4c2167555fc3e19a | 9b011d1dcb58b9e2874f841af4874db752c73fff | refs/heads/main | 2023-04-19T02:10:10.368235 | 2023-03-15T07:23:58 | 2023-03-15T07:23:58 | 359,931,820 | 5 | 1 | null | 2023-03-14T09:38:33 | 2021-04-20T19:39:24 | R | UTF-8 | R | false | true | 884 | rd | print.SDMfit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/print.SDMfit.R
\name{print.SDMfit}
\alias{print.SDMfit}
\title{Prints a SDMfit object}
\usage{
\method{print}{SDMfit}(x, ...)
}
\arguments{
\item{x}{A SDMfit object, typically obtained with trophicSDM() and available in the field $model of a ... |
15f687e7462a61df6dd3a85ee05cbbbcce4955dc | e10ccafdbc900072e638285141eab217f241ae2f | /man/GeomTimeLineLabel.Rd | a0c80ab32c8d9d2f587fce7b01c4775a6e0dfb26 | [] | no_license | pvisser82/earthquakedata | 04e545d00a022e4b1fc794c67552b7782e2e1571 | e04ab6d870acc5f4307a5fc8c8388b196c740d7f | refs/heads/master | 2021-05-06T05:28:06.046277 | 2018-01-02T09:51:34 | 2018-01-02T09:51:34 | 115,095,231 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 617 | rd | GeomTimeLineLabel.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/timeline.R
\docType{data}
\name{GeomTimeLineLabel}
\alias{GeomTimeLineLabel}
\title{GeomTimeLineLabel proto}
\format{An object of class \code{GeomTimeLineLabel} (inherits from \code{Geom}, \code{ggproto}) of length 6.}
\usage{
GeomTimeLineLab... |
1bc9ff81d4270477ae3f7a898c6b9cb1201ce8d8 | be232f39144fcd136ce0ba20549124e50d1f495a | /1-Exploratory_Data_Analysis.R | 5ca54fc23c1b24da8260b4c538f0555d33585d7f | [] | no_license | davidsalazarv95/Resolve_test | f86311e406014e310ba298acc126ce6a424c9176 | 9cb31e331aab7d89b26df084c8b8ddfca1e44fba | refs/heads/master | 2021-07-22T23:11:46.095043 | 2017-11-01T21:10:11 | 2017-11-01T21:10:11 | 109,029,268 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,459 | r | 1-Exploratory_Data_Analysis.R | ################ Univariado Valor ################
library(tidyverse)
library(corrplot)
library(hrbrthemes)
library(corrr)
library(viridis)
library(gridExtra)
library(grid)
histograma_valor <- function(datos, binwidth = 50) {
medidas_centrales <- datos %>% select(valor) %>% summarise(mediana = median(valor),
... |
66488a4355c805221849ff67597c3bda2834700f | e6f4e3afd16a7ee5c7a8fb61f7ed697ce88ef4c4 | /Pro3/R_p3/Subsampling_final_example.R | 9ed5c50e625af4ca62d0632c0697f24044eba1bb | [] | no_license | xl0418/Code | 01b58d05f7fae1a5fcfec15894ce0ed8c833fd1a | 75235b913730714d538d6d822a99297da54d3841 | refs/heads/master | 2021-06-03T21:10:31.578731 | 2020-11-17T07:50:48 | 2020-11-17T07:50:48 | 136,896,128 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,707 | r | Subsampling_final_example.R | library(DDD)
library(ggplot2)
library(ggridges)
library(ggthemes)
library(viridis)
library("RColorBrewer")
library(grid)
library(gridExtra)
library(ggtree)
library(ape)
source(paste0(getwd(),'/g_legend.R'))
# source('E:/Code/Pro3/R_p3/barplot3d.R', echo=TRUE)
source('E:/Code/Pro3/R_p3/multi3dbar.R', echo=TRUE)
moviedir... |
7ea35b0a1016904316bebadfef3fd5cbd1eef18c | 54833a5f2af934ba192600b0c2550f0ce2e4d97f | /in-action/apis/analysis.R | 2cd34a49d3c53e55a5e61f04e57846f28789cd56 | [
"MIT"
] | permissive | HOXOMInc/programming-skills-for-data-science | 2e47fb2a445e71bcfe3d7acc2a881fda217e4a2e | 5aa1e954434674789c5df9518f8f137c3b91358b | refs/heads/main | 2023-06-27T02:03:13.023455 | 2021-07-25T05:00:55 | 2021-07-25T05:00:55 | 319,144,846 | 2 | 4 | null | 2021-01-17T07:56:40 | 2020-12-06T22:28:03 | null | UTF-8 | R | false | false | 2,300 | r | analysis.R | # APIs in Action: シアトルのキューバレストラン
# 必要なパッケージをロードする
library(httr)
library(jsonlite)
library(dplyr)
library(ggrepel)
# ggmapの開発中のバージョンをインストール・ロードする
library(devtools) # GitHubからパッケージをインストールする
devtools::install_github("dkahle/ggmap", ref = "tidyup")
library(ggmap)
# Google API Keyを登録する
# https://developers.google.com/map... |
603b4e282ca403212d04bd1f9473358524542273 | 63e1231faa30a4cea6dd9f25e87c2372383aa2f4 | /man/L2A.Rd | d8b36f47d29d7c36320ae8a050356a14ec4ac7b2 | [] | no_license | cran/MSEtool | 35e4f802f1078412d5ebc2efc3149c46fc6d13a5 | 6b060d381adf2007becf5605bc295cca62f26770 | refs/heads/master | 2023-08-03T06:51:58.080968 | 2023-07-19T22:10:23 | 2023-07-20T01:47:18 | 145,912,213 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 661 | rd | L2A.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Misc_Exported.R
\name{L2A}
\alias{L2A}
\title{Length to age conversion}
\usage{
L2A(t0c, Linfc, Kc, Len, maxage, ploty = F)
}
\arguments{
\item{t0c}{Theoretical age at length zero}
\item{Linfc}{Maximum length}
\item{Kc}{Maximum... |
4f678fec2c41c02a12223a5edc4836843ffa880c | 15f26ad8f0fef7e64ab39a39fcfcf501ae15c5f1 | /man/zipf_race.Rd | 5340181924bedcc822f0696f7fff4eda1ab04098 | [] | no_license | seqva/RcappeR | 56120272f9b39287e579134346e268800fd78bf6 | e84bdf0b386898fc4b88850cb298efaa442cdca0 | refs/heads/master | 2020-07-02T15:13:55.485347 | 2016-02-26T00:02:30 | 2016-02-26T00:02:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,437 | rd | zipf_race.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zipf_race.R
\name{zipf_race}
\alias{zipf_race}
\title{Handicap a race using one race}
\source{
Article by Simon Rowlands explaining use of Zipf's Law:
\url{https://betting.betfair.com/horse-racing/bloggers/simon-rowlands/simon-rowlands-on-ha... |
3fc057d66e362395b0c16524e5dd5b34ef1fa2ee | eec115235405a54b642d3286863dcca14786c9e8 | /experiments/experiment_variance_pvalue_plotter.R | 62f1cd1ef596df03b50c504aea082e213e20eb55 | [] | no_license | linnykos/selectiveModel | 5f5c95f84c006dc8bd83fdc2032ea251a9f4d3e6 | 75e42567a9a4fe1daf4d80a54cc15e47e549f60d | refs/heads/master | 2023-01-21T18:24:22.665232 | 2020-12-01T16:45:43 | 2020-12-01T16:45:43 | 110,089,332 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 550 | r | experiment_variance_pvalue_plotter.R | png("../figures/pvalue_stability.png", heigh = 2000, width = 2500, units = "px",
res = 300)
par(mfrow = c(2,2))
hist(res_vec_known_0, breaks = seq(0, 1, length.out = 50), col = "gray",
xlab = "P-value", main = "Delta = 0 (Null), Known sigma")
hist(res_vec_unknown_0, breaks = seq(0, 1, length.out = 50), col = "... |
ea6a9546894cf4023fe51412c984a1663c14c325 | 2a6dc4ea444f5522291923ef172865314c4bc9e8 | /R/sgdWt_convexLinCom.R | f01dace307c7cc895353b5db58bb578fc93a64f7 | [] | no_license | benkeser/onlinesl | e482e8606268a546011d6bc01019bb5e51c30ad3 | acf1935523cf5bdf4c0408f55f7930e74bc7f384 | refs/heads/master | 2020-12-26T04:27:31.876193 | 2016-09-22T15:41:39 | 2016-09-22T15:41:39 | 66,727,503 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,228 | r | sgdWt_convexLinCom.R | #' Perform first order stochastic gradient descent update of super learner
#' weights
#'
#' This function performs a single step of gradient descent on the weight
#' vector for the super learner weights and projects the resulting vector onto
#' the L1-simplex via the internal function .projToL1Simp. The functio... |
bebc5f4c8b628eb1df2a6ed9317a3a70edfb8f75 | d966e9fe4e0e667b20cad92c1cd21a55ccf401a9 | /R/TitanicAnalysis01_lowlevel.R | 7153869eccee011ae6bffcff697c456e0f92c140 | [] | no_license | darsa881r/titanic | 0efbb92d408958de5860e431206319403e87e71a | f519ab108bf7eede6c9f5126cc33d5228062ae4b | refs/heads/master | 2022-11-26T17:03:12.945916 | 2020-08-10T20:52:28 | 2020-08-10T20:52:28 | 286,527,686 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,361 | r | TitanicAnalysis01_lowlevel.R | # Loading default Libraries
library("ggplot2")
library("datasets")
library("graphics")
library("grDevices")
library("methods")
library("stats")
library("utils")
# Loading data
train <- read.csv("/Users/sabbirhassan/Dropbox/ML_stuff/titanic/train.csv", header = TRUE)
test <- read.csv("/Users/sabbirhassan/Dropbox/ML_s... |
af508b56e662505139cb4fb1e644d181fc2827d6 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/pvsR/examples/CandidateBio.getAddlBio.Rd.R | 07197f4146c46d8fc42ce7babb9d1f7db4003692 | [] | 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 | 569 | r | CandidateBio.getAddlBio.Rd.R | library(pvsR)
### Name: CandidateBio.getAddlBio
### Title: Get a candidate's additional biographical information
### Aliases: CandidateBio.getAddlBio
### ** Examples
# First, make sure your personal PVS API key is saved as character string in the pvs.key variable:
## Not run: pvs.key <- "yourkey"
# get additional b... |
96f35caf5fe5aef409fde95604fc8d7aa484b630 | bb9140e05d2b493422d65084bc9df4fb6ae88ba9 | /R/R_cookbook/data_structures/factor_example.R | d5ef13aeccd8ecbe771712427ffbb0d25e3d0e37 | [] | no_license | 8589/codes | 080e40d6ac6e9043e53ea3ce1f6ce7dc86bb767f | fd879e36b6d10e5688cc855cd631bd82cbdf6cac | refs/heads/master | 2022-01-07T02:31:11.599448 | 2018-11-05T23:12:41 | 2018-11-05T23:12:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 68 | r | factor_example.R | f <- factor(c("Win","Win","Lose","Tie","Win","Lose"))
print(f)
wday |
fbecfa8cdb8c24b2c4d79dd2c2439d6f93ffa635 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /distr6/inst/testfiles/C_EmpiricalMVPdf/libFuzzer_C_EmpiricalMVPdf/C_EmpiricalMVPdf_valgrind_files/1610036757-test.R | 9dfed72e18972c277e4bfadc27f585d7957ce119 | [] | 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 | 330 | r | 1610036757-test.R | testlist <- list(data = structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(10L, 1L)), x = structure(c(1.82391755146545e-183, 1.82391755146545e-183, 8.0988077346472e-179, 5.4674514851239e-304, 4.88059051526537e-312, 0, 8.48798316386109e-314), .Dim = c(7L, 1L)))
result <- do.call(distr6:::C_EmpiricalMVPdf,testlist)
s... |
5b7ba371257b5b0f2a2965eb0b04362cbf50a895 | 32a77ca7d4f4acbc71e8d4c1cdb5fadb9e2a0eef | /zarzar_hw06.R | 3add4fc355bdc8f4bfed9c4b886264d564040d89 | [] | no_license | ChrisZarzar/quantitative_methods | dd2673d839b03206f2ce978f6b1d1c16a16713f0 | 17a4e867c38e32646adbd767b912a5a4be33f848 | refs/heads/master | 2020-04-05T09:35:28.117681 | 2018-11-08T20:36:52 | 2018-11-08T20:36:52 | 156,753,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,112 | r | zarzar_hw06.R | #a)
#I am going to plot the PDF
quantiles <- seq(0,5, by =0.01)
pdf.exp <- dexp(quantiles, rate=(1/2))
plot(quantiles, pdf.exp, type='l', main = "PDF: Beta = 2")
#I am going to plot the CDF
quantiles <- seq(0,5, by =0.01)
cdf.exp <- pexp(quantiles, rate=(1/2))
plot(quantiles, cdf.exp, type ='l', main = "CDF: Beta = 2... |
3cd36d7534bbe2b2de1d4e7e3c05e3c1fc0f586c | 9b5483c96399f5accf4ee2f8758899f7b41cb5cf | /man/clusterability.Rd | e6c650723f4668b8ff2cfb3f78cfc070dd061c04 | [] | no_license | cran/clusterability | a7f070fa7b8ddcdd0a9e67b230435c7b3a3ef7d8 | 57a774c8c63c7920b0af55a62db0ea5daa707c85 | refs/heads/master | 2020-12-21T21:46:43.838786 | 2020-03-04T10:40:07 | 2020-03-04T10:40:07 | 236,572,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,247 | rd | clusterability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clusterability.R
\docType{package}
\name{clusterability}
\alias{clusterability}
\alias{clusterability-package}
\title{clusterability: a package to perform tests of clusterability}
\description{
The \code{\link{clusterabilitytest}} fun... |
aad51b1e6f1515aa0c3af3013e39afaa3acd0035 | 8e8fe47449384105bd58ef569aa509a834494501 | /Goodies/man/PHS.bxp.Rd | 4dbb5628637719c0d7892326dc0661fcaffc85f7 | [] | no_license | Kuvelkar/TEST | 761d20692383052559081f67abd50293512e875c | d06f8c49dfcff128f98ca79f6477c1b1c15a6b11 | refs/heads/master | 2021-01-10T16:48:09.896028 | 2016-02-03T08:53:29 | 2016-02-03T08:53:29 | 50,986,445 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,564 | rd | PHS.bxp.Rd | \name{PHS.bxp}
\alias{PHS.bxp}
\title{Draw Box Plots from Summaries}
\description{Draw box plots based on the given summaries in z. It is usually called from within bowplot.}
\usage{PHS.bxp(z, notch = FALSE, width = NULL, varwidth = FALSE, outline = TRUE,
notch.frac = 0.5, log = "", border = par("fg"), pars = N... |
365192ce355be889b2dcd0dfada7c68ea3c785fb | 187337b1f53c771c1537f2dc3b5c6dde99519b02 | /man/eclat.Rd | a58da3317845800ae850374aacfd3619b497725f | [] | no_license | lgallindo/arules | 8da9dabe54a7351afa3e7bec12c0c11bbc9de741 | 887184cd80068e04531b0c13bc231ecbd1afddfb | refs/heads/master | 2023-07-24T02:56:14.691724 | 2018-01-10T19:08:50 | 2018-01-10T19:08:50 | 117,311,210 | 0 | 0 | null | 2018-02-17T08:13:46 | 2018-01-13T03:38:40 | C | UTF-8 | R | false | false | 2,612 | rd | eclat.Rd | \name{eclat}
\alias{eclat}
\title{Mining Associations with Eclat}
\description{
Mine frequent itemsets with the Eclat algorithm.
This algorithm uses simple intersection operations for equivalence
class clustering along with bottom-up lattice traversal.
}
\usage{
eclat(data, parameter = NULL, control = NULL)
}
\ar... |
f7d4c936c395e5cc69130c02bde5f88053d3cb9c | 570f57c6d5355d2064d123e4452094090f16d5b9 | /Assignment 2 code.R | 08b00730becf8df9ff892a2e766ec5dda8cf5a4a | [] | no_license | R-pidit/R-projects | 7227aa6d5f45d841e53289f7a41eb9daed1491d2 | 6ac3f271fed69ce54b135b4b554a37085099ffee | refs/heads/master | 2021-01-22T14:55:51.562359 | 2017-09-07T11:09:20 | 2017-09-07T11:09:20 | 102,372,084 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,475 | r | Assignment 2 code.R | install.packages("titanic")
install.packages("rpart.plot")
install.packages("randomForest")
install.packages("DAAG")
library(titanic)
library(rpart.plot)
library(gmodels)
library(Hmisc)
library(pROC)
library(ResourceSelection)
library(car)
library(caret)
library(dplyr)
library(InformationValue)
library(rp... |
ece440a44978c2ff0932d186df772f1bd402e513 | 8bde36c00a458f1d3b3c81eea824c56bc72b26e2 | /approximate_UMVUE.R | cf57a4d4fd65278e0e72e433d3927c47406a6cea | [] | no_license | snigdhagit/Bayesian-selective-inference | ea186c866700510cf5206977e936e6224dbc5dd2 | a4e436aef0636c6acc6385e75f40109ead722d21 | refs/heads/master | 2021-07-08T07:45:00.242158 | 2017-10-02T08:47:00 | 2017-10-02T08:47:00 | 105,512,054 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 544 | r | approximate_UMVUE.R | #computes approximate UMVUE under additive Gaussian randomization with variance tau^2 (in the univariate case)
UMVUE.compute<-function(y, sigma, tau)
{
objective<-function(z,alpha)
{
(((-alpha*z))+((z^2/2)+log(1+(1/z))))
}
objective1<-function(z,alpha)
{
(((-alpha*z)/tau)+((z^2/2)+log(1+(1/z))))
... |
f3a5851977bbf960cc10042ff7fcd3375fd35624 | bccaf9ca75d67fef6bec733e784c582149a32ed1 | /plagiat/R/jclu2bup.f.R | 89e1438d0b95b2f91986de64fd67d7cdb98df4c2 | [] | no_license | brooksambrose/pack-dev | 9cd89c134bcc80711d67db33c789d916ebcafac2 | af1308111a36753bff9dc00aa3739ac88094f967 | refs/heads/master | 2023-05-10T17:22:37.820713 | 2023-05-01T18:42:08 | 2023-05-01T18:42:08 | 43,087,209 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,050 | r | jclu2bup.f.R | #' JSTOR Clustering 2 Bimodal Unimodal Projection Illustration
#'
#' @param jclu
#' @param s
#'
#' @return
#' @export
#' @import igraph magrittr data.table
#'
#' @examples
jclu2bup.f<-function(jclu,s=300){
par(mar=c(0,0,1,0))
bs<-igraph::as_edgelist(jclu$b) %>% data.table %>% setnames(ec('j,l')) %>% .[sample(1:.N,s... |
7a8a8347270b7c5bfd44f40a1de964a87bab6a5f | bb767f6a07340c0c313c79587ea6c96ce5e17f33 | /R/data.r | da1aa212d98a75fe535d3ca11d467ba2b7815e10 | [] | no_license | psychobas/corpustools | 82694086aa0b3d861e38624a5cf17a53ce61e23e | e9c1ac2011234a62b2fc2c7b46ab01dd9159e4ac | refs/heads/master | 2023-05-04T01:35:30.735603 | 2021-05-25T10:41:30 | 2021-05-25T10:41:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 808 | r | data.r | #' State of the Union addresses
#'
#' @docType data
#' @usage data(sotu_texts)
#' @format data.frame
'sotu_texts'
##save(sotu_texts, file='data/sotu_texts.rda', compression_level = 9)
#' coreNLP example sentences
#'
#' @docType data
#' @usage data(corenlp_tokens)
#' @format data.frame
'corenlp_tokens'
#' A tCorpus w... |
d165b85aafed9b2a16ca2e7d132720bc7eca0e02 | eff4f65785cdd0f198245b46876720ee4cf40bba | /Intro to R II.R | fb055ab99c6c1967bde355baa1e7f89b8fa3be7e | [] | no_license | PRATIKSHIRBHATE/r_training | afc84e0f5a24ded8c4cbb00c8499bd5eb1605e14 | d11dbe97ac602eaca0c37c1285dcdd4cfe623f47 | refs/heads/master | 2020-09-25T15:53:36.079049 | 2019-12-05T11:13:50 | 2019-12-05T11:13:50 | 226,038,433 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,153 | r | Intro to R II.R | ## Introduction to R - Part II
## This file contains all of the same code that is contained in the Markdown
## version of the guide. This document serves to demonstrate how a regular R
## script looks and how to run code in this format.
# Loading the packages and data as shown on the previous guide
# "Intro... |
38ac95555fe279ef8ae6c775d78ed81c1f14e47d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/HelpersMG/examples/modeled.hist.Rd.R | 271dde27ceb26963b9b570054bf6ba2ea8b265bf | [] | 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 | 615 | r | modeled.hist.Rd.R | library(HelpersMG)
### Name: modeled.hist
### Title: Return the theoretical value for the histogram bar
### Aliases: modeled.hist
### ** Examples
## Not run:
##D n <- rnorm(100, mean=10, sd=2)
##D breaks <- 0:20
##D hist(n, breaks=breaks)
##D
##D s <- modeled.hist(breaks=breaks, FUN=pnorm, mean=10, sd=2, sum=100)... |
dcc6d38fe84ec6562e1230189687a50a97624fa0 | ef424746a3ea4ed6e167f03d359b39da48a0fc21 | /man/colLuminosity_utility.Rd | a6badc261d6881eba0e7c11abd380faa9f24fb26 | [] | no_license | smitdave/MASH | 397a1f501c664089ea297b8841f2cea1611797e4 | b5787a1fe963b7c2005de23a3e52ef981485f84c | refs/heads/master | 2021-01-18T18:08:25.424086 | 2017-08-17T00:18:52 | 2017-08-17T00:18:52 | 86,845,212 | 0 | 3 | null | 2017-08-17T00:18:52 | 2017-03-31T17:42:46 | R | UTF-8 | R | false | true | 671 | rd | colLuminosity_utility.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/MICRO-Landscape-Utilities.R
\name{colLuminosity_utility}
\alias{colLuminosity_utility}
\title{Brighten or Darken Colors}
\usage{
colLuminosity_utility(color, factor, bright, alpha = NULL)
}
\arguments{
\item{color}{vector of hcl colors}
\ite... |
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