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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a502f071cb8e8defc8f98548bfeef593be83e30f | 00046ef7671d5a02ea27deb30bf5657c45d45acb | /clients/r-client/run_tests.R | 86eee7bbdc3dcb658ad7f6f7777436a84f4bf2b1 | [
"BSD-3-Clause"
] | permissive | uptake/groundhog | b9a7915b5bf269dafc69ee337a3493cd1c1aa895 | 41b143f528ddcf7be581fcb2bde90e5d360cd827 | refs/heads/master | 2021-07-17T17:05:04.768249 | 2019-10-27T21:31:49 | 2019-10-27T21:31:49 | 200,898,621 | 7 | 11 | BSD-3-Clause | 2020-08-27T19:05:31 | 2019-08-06T17:48:17 | Python | UTF-8 | R | false | false | 1,192 | r | run_tests.R |
# Define a function to check testthat results.
CheckTestThatResults <- function(testthat_results){
outcomes <- unlist(lapply(testthat_results
, function(x){lapply(x$results, function(y){class(y)[1]})}))
numErrors <- sum(outcomes %in% c('expectation_failure','expectation_... |
2a41b7c5a9b78a6fd60bcedd81ca550ca84f5d46 | 11ca614b32749f369af93febbb04571b7f95748a | /statistical_inference/quizzes/quiz4.R | 4f48dac8ccbf254120c7b9ad188da91a3fb5701e | [] | no_license | richelm/course_notes | 61c525ca433d4faf90216509575432665fd7f461 | 0677708e8a05e67063a94a6dbee7e303b7b5d55a | refs/heads/master | 2021-01-18T21:51:10.181408 | 2019-12-03T22:36:29 | 2019-12-03T22:36:29 | 42,669,012 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 577 | r | quiz4.R | # question 1
b <- c(140, 138, 150, 148, 135)
w2 <- c(132, 135, 151, 146, 130)
t.test(b,w2,paired = TRUE)$p.value
# question 2
mu <- 1100
s <- 30
n <- 9
df <- n - 1
mu + c(-1,1) * qt(0.975, df, lower.tail = FALSE)*s/sqrt(n)
# question 3
n <- 4
mu0 <- 2
mua <- 3
# question 4
ppois(1787-1,.01*1787,lower.tail = FALSE)
... |
7666fd3c329adced2c70335c0e266efac36054c9 | 93730e6fee0ae4e3bb028714d6e8a5e335632121 | /man/clear_results.Rd | 2f80b15d4707073b49ee42d982772e3d2bf8e0b4 | [] | no_license | jeevanyue/PIVOT | 56ef7c2d2eb30a197b1c0387ae79e73605ab7eae | 46fa6af11f19c320ee338452ccff745aa93a1f6d | refs/heads/master | 2021-01-01T06:30:45.988532 | 2017-07-17T02:51:20 | 2017-07-17T02:51:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 231 | rd | clear_results.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\name{clear_results}
\alias{clear_results}
\title{PIVOT Data Control}
\usage{
clear_results(r_data)
}
\description{
Clean all analysis results
}
|
21beee98d8fee1f262b65d29175cb12310955502 | 11fe2a2d8bf3327517e9c76e667ee68c06cf5599 | /man/Dist_mat_edge.Rd | 66ba3f6b8ef1ea749b72f33c9cdf3e0db132e26e | [] | no_license | BenjamenSimon/EpidemicR | b07e115b42eb50d45e2ea47c87b5ee6440bb0693 | 7d0a6876affa2adf1f34c6b77dbf21bd8512c076 | refs/heads/master | 2020-09-11T05:53:27.546250 | 2020-03-16T12:47:44 | 2020-03-16T12:47:44 | 186,651,203 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,404 | rd | Dist_mat_edge.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Dist_mat_edge.R
\name{Dist_mat_edge}
\alias{Dist_mat_edge}
\title{Clustered edge population distribution.}
\usage{
Dist_mat_edge(N, xlim = 20, ylim = 20, clusters = 8)
}
\arguments{
\item{N}{The total number of individuals in the population.}... |
0ac3370da96915221d0465db17f189ac9f447ce7 | a528173483407425c55cbbdf278a2b724830a01e | /man/zapm_direct.Rd | 1aec68bfb405306a73a29a65538e5ff92d205ac4 | [
"MIT"
] | permissive | gmke/zernike | 7ea52f89dc353f7d72a8385078e03bc2853a22c1 | 397a5d2f316e2f95cc1a1209007780855da16b13 | refs/heads/master | 2023-05-28T21:58:50.075555 | 2023-05-10T15:07:23 | 2023-05-10T15:07:23 | 166,230,701 | 0 | 0 | MIT | 2021-06-18T12:00:04 | 2019-01-17T13:30:49 | R | UTF-8 | R | false | true | 973 | rd | zapm_direct.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zapm_direct.r
\name{zapm_direct}
\alias{zapm_direct}
\title{Zernike Annular polynomials from formulas}
\usage{
zapm_direct(rho, theta, eps)
}
\arguments{
\item{rho}{a vector of radial coordinates with eps <= rho <= 1.}
\item{theta}{a vector ... |
925d3015295392f7073745c267e2a177b22bf423 | 7676c2d5b77b588adde0e787a501dac27ad8efcd | /work/r데이터분석_예제파일/예제/2_09.R | b501e6cc44c458aa77535171dd49eb74a006384e | [] | no_license | bass4th/R | a834ce1e455ca2c018364a48ea8ba0fbe8bf95e9 | fb774770140a5792d9916fc3782708e09c12273d | refs/heads/master | 2020-05-05T09:02:52.210737 | 2019-04-06T19:48:37 | 2019-04-06T19:48:37 | 179,888,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 78 | r | 2_09.R | excel <- read.csv("exceldata.csv")
excel
excel[1, ]
excel[,2]
str(excel) |
c793009ed3eead786ca3996933521ec5f9f3f082 | 30bdba988524885dc0fb3487476194299d21a05c | /tests/testthat/test-print.R | 42f7250ee27818ccae00c0f17497228fd628feb4 | [
"MIT"
] | permissive | applied-statistic-using-r/valaddin | d66b01144f519c12b856b6f2ad3addbac27d3cb2 | 44a446a1f7433161a80154e1ca0d2cb22eeaacd2 | refs/heads/master | 2021-01-19T00:50:33.078839 | 2017-04-04T06:13:41 | 2017-04-04T06:13:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,611 | r | test-print.R | context("Printing")
# Like expect_output(), but accepts expectation strings with less escaping
expect_output_p <- perlize(expect_output)
# firm_closure ----------------------------------------------------------
has_xy <- map_lgl(args_list, ~ all(c("x", "y") %in% names(.)))
fs <- lapply(args_list[has_xy], pass_args)
... |
a824d13653cc1ad1a84c8ffd075ebc466533fd23 | ede1c2211638eb1b9dbf1c5e57297dd71529708c | /Data import and cleaning/Coal_Consumption.R | 30d385b40777b291ea4c0ee2c2b1aaa1874be412 | [] | no_license | sbrylka/Statistical_Review_of_World_Energy | ea7cb4d125f1c0e719f31b9fe429f875e1101b41 | 9306cdf6c415278b9b1373f321c8b09b2ebb7dcf | refs/heads/main | 2023-01-31T20:27:07.697369 | 2020-12-09T19:08:10 | 2020-12-09T19:08:10 | 312,024,263 | 0 | 0 | null | 2020-11-11T18:31:54 | 2020-11-11T16:18:17 | null | UTF-8 | R | false | false | 1,238 | r | Coal_Consumption.R | library(readxl)
library(dplyr)
library(tidyr)
#reading BP data
CC <- read_excel("bp-stats-review-2020-all-data.xlsx", sheet = "Coal Consumption - EJ", na = "n/a")
CC <- as.data.frame(CC, stringsAsFactors = False)
#fixing few names
CC[111,1] = 'OECD'
CC[113,1] = 'European Union'
CC[2,1] = 'Country' #changing JEDNOSTK... |
6e06648552aecd0f3cd09303a0b05160ba361f85 | 37184e75eddc045944b7cfe9c0834523c4ebb666 | /src/generate_deseq_object.salmon.R | 5f25ad0ce836000ad3e331fdf04684a624c2d22e | [] | no_license | TomHarrop/wasprnaseq-test | ecfd1b7d72108a0b958c58beea362125a4298adc | d583391650049a982cafbeaf5a10ad8d937dfb53 | refs/heads/master | 2023-01-05T04:12:28.424647 | 2020-11-03T23:56:22 | 2020-11-03T23:56:22 | 308,509,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,005 | r | generate_deseq_object.salmon.R | #!/usr/bin/env Rscript
log <- file(snakemake@log[[1]],
open = "wt")
sink(log,
type = "message")
sink(log,
append = TRUE,
type = "output")
library(data.table)
library(DESeq2)
library(tximeta)
transcript_file <- snakemake@input[["mrna"]]
gff_file <- snakemake@input[["gff"]]
file_list <- snak... |
b9445788ae7f5067e3d2de79560a8c998288b70a | 5e01f8ae5fa31589c85c15d482d93a1d2ae1f3dd | /R/expr.R | 68e186dc29c3d49ddf4dff84674cf859298bd7d9 | [] | no_license | Yafei611/CFGL | 719b070afa508df65da7658d26c6f96a74b40b70 | f71894cc57c273ba04fed0aeef3c9a9855dc66c8 | refs/heads/master | 2021-06-14T22:20:26.657969 | 2018-07-24T19:56:02 | 2018-07-24T19:56:02 | 141,628,393 | 2 | 3 | null | 2021-05-26T15:46:35 | 2018-07-19T20:37:08 | R | UTF-8 | R | false | false | 617 | r | expr.R | #' Gene expression data for 2 rat tissues
#'
#' A dataset containing gene expression levels for rat brain and heart tissues. The expression intensity were measured using Affymetrix Rat Exon 1.0 ST Array. 100 differential genes are selected to construct example co-expression network for 2 tissues.
#'
#' @format A data l... |
c6060d16889ea608f3bcf0a98f15612aec10bbde | 21f123043fc90b4abfd504a6bdf189bb51276e38 | /ROC curves/roc-curve.R | 561b0312b01a47a8e60ec38f9d50cec602d6f4fc | [] | no_license | EudaldTejero/MasterThesis | 722d22c1487844255ef9fdf2242f0a7e072fa6f7 | dfb638874d134f424bc509a42ccb0065263989cd | refs/heads/master | 2020-03-22T14:35:46.803426 | 2018-07-09T06:46:14 | 2018-07-09T06:46:14 | 140,191,219 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,395 | r | roc-curve.R | library("cvAUC")
category1 <- lapply("category1.txt",readLines)
category1 <- as.numeric(unlist(category1))
category2 <- lapply("category2.txt",readLines)
category2 <- as.numeric(unlist(category2))
category3 <- lapply("category3.txt",readLines)
category3 <- as.numeric(unlist(category3))
category4 <- lapply("ca... |
071f049ed041c86b1fbc57808ddc3fe090e44741 | 7a7777dae0c4d694f1e600bb3f8bdbb1284d0e6a | /man/gpdd_timeperiod.Rd | 9698142525d88cb16abefce8fa433b56ca07297a | [
"CC0-1.0"
] | permissive | ropensci/rgpdd | b8bf4340d11914a7b559bab06c0949cd2b9349e5 | 7406de253d397e7fa7485ecd4b4816ede9a25a2e | refs/heads/master | 2021-07-18T19:23:14.049930 | 2020-05-18T16:02:50 | 2020-05-18T16:02:50 | 12,953,752 | 9 | 5 | NOASSERTION | 2020-05-13T14:44:43 | 2013-09-19T16:44:52 | R | UTF-8 | R | false | false | 507 | rd | gpdd_timeperiod.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{gpdd_timeperiod}
\alias{gpdd_timeperiod}
\title{The time period table}
\description{
TimePeriod is a look-up table that provides text descriptions of
the temporal period the sample relates to such as 'Janua... |
30f2abe140116581e4579f2bdb759f6a1ce1443f | b616673f7b104256e8a7bff99cb6e11c5fff199b | /amto/array/htmlpdfr/fs_ary_basics.R | 7429b4325539a12729bfc343eed9816d0e4647f0 | [
"MIT"
] | permissive | yanliangs/R4Econ | 3fac5b2dec797297e21ee32d2a0078af78422c39 | 06f9633c523c21aed5ac744caee9d8ccd3c906cc | refs/heads/master | 2023-02-23T00:36:44.142180 | 2021-01-24T17:57:49 | 2021-01-24T17:57:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,989 | r | fs_ary_basics.R | ## ----global_options, include = FALSE----------------------------------------------------------------------------------
try(source("../../.Rprofile"))
## ---------------------------------------------------------------------------------------------------------------------
ar_a <- c(1,2,3)
ar_b <- c(1,2,3/1,2,3)
rep(0... |
e308a5b65ba92346d45e1e7ce49855f6bb991859 | fee47bdbb5917055c5c0e02d183e1767bd8db512 | /MontesReylaEmigration.R | 98acd4999c571f6414e7f56d040b0ff28330a3fa | [] | no_license | acmontes393/Assignment3MontesReyla | b8480b9e12fe038d317228613b15cad9d7ccc16d | bd357a3f38f4469f6776c13e41158b8b7b5f0161 | refs/heads/master | 2021-01-10T14:52:25.093855 | 2015-11-20T11:09:59 | 2015-11-20T11:09:59 | 45,249,459 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 12,017 | r | MontesReylaEmigration.R | ########################################################################################
########################## Collaborative Data Analysis Assigement 3 ####################
########################################################################################
# 0. Clearing the workspace
rm(list = ls())
# 1. In... |
828056d2f88cc8684bf4f110b240b89289328819 | 3adadd11d662f5f18d2e568053b1e6704aba610d | /Project 2/Problem3Sim.R | da85d2f71692ad75c9638c7f7c2ae623e81b6ea7 | [] | no_license | Helenerb/TMA4250 | 76e3baf06b696084b256bb73d095f3d625d12c6e | ca9dc67c41f8df83a2c5c8af898d66234afe069a | refs/heads/master | 2020-12-29T17:43:22.732370 | 2020-04-27T09:00:51 | 2020-04-27T09:00:51 | 238,686,601 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,865 | r | Problem3Sim.R | # NS simulation
library(matrixStats)
library(spatial)
library(MASS)
library(ggplot2)
library(gridExtra)
library(reshape2)
library(RColorBrewer)
library(datasets)
library(MASS)
torus.2d <- function(x){ #transforms to torus domain
while(x[1] < 0 | x[1] > 1 | x[2] < 0 | x[2] > 1){
if(x[1] < 0){
x[1] <- 1 + x[... |
fca28f03a7d9572cd3645fba00c1f549d4b6ebbb | 1c34da0873c513bd40028072bb525a999e4fff32 | /R/zzz.R | 10a8f0dcb1db560f5fe80d13fef5192ae5e22afc | [] | no_license | paris-appartemnt-project/apartment_project | b2378b1a1c93861a808b8ca43decf9c0bd59550c | 52b609f78f15ab0d0c571a69e9ca78b04c43e648 | refs/heads/master | 2020-04-07T10:49:04.377690 | 2018-12-14T21:37:57 | 2018-12-14T21:37:57 | 158,301,473 | 0 | 0 | null | 2018-12-10T13:35:21 | 2018-11-19T23:05:29 | null | UTF-8 | R | false | false | 452 | r | zzz.R | globalVariables(c(
# data_scraping.R
"ville", "Type", "Description", "piec.", "m2", "arrondissement", "latitude", "longitude","station_a_proximite","coord", "prix","lat", "lng", "my.env", "lextrait.n", "lextrait.stationextrait", "priority", "restaurant","decile","anoncesmini","apartment_index","anime_resto_cine_qt"... |
d42337a387f044837f31aa8743f6b6be06fdb257 | 8306bfb1438a2516d3f9e7bea51f9d805793798d | /tests/testthat/test-miss-prop-summary.R | 1ceebc368998799c689d693bf3520753087c6316 | [] | no_license | cran/naniar | 849ad432eea4e343ffc4302b3ae7612759f9a552 | 30710de1ca289d1dd994e203c650bebc62a61a0f | refs/heads/master | 2023-02-21T19:46:58.125455 | 2023-02-02T08:50:02 | 2023-02-02T08:50:02 | 99,764,801 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,625 | r | test-miss-prop-summary.R | test_that("miss_prop_summary returns a data.frame",{
expect_s3_class(miss_prop_summary(airquality),
"data.frame")
})
test_that("miss_prop_summary returns a tibble",{
expect_equal(class(miss_prop_summary(airquality)),
c("tbl_df", "tbl", "data.frame"))
})
test_that("miss_prop_summar... |
929726b02f1255d727b12cc917de2e2326214bf8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/precrec/examples/create_sim_samples.Rd.R | 8608b5b4fbc2caf41cbb745d3759ca025910727f | [] | 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 | 916 | r | create_sim_samples.Rd.R | library(precrec)
### Name: create_sim_samples
### Title: Create random samples for simulations
### Aliases: create_sim_samples
### ** Examples
##################################################
### Create a set of samples with 10 positives and 10 negatives
### for the random performance level
###
samps1 <- create_... |
a4b9489e70ee2c1dc801a1de3a22b4895df6ed93 | 246945df242f21d7730bdcb81c303eea249bff8e | /OldFiles/Aging/Code/aging1.R | 4e50b726879c4667cbcebd21e303e4ecab2fc8bb | [] | no_license | gautam-sabnis/Visual-Frailty-Index-Analysis | 229c746a69dcf838e1b0163491b45e5f434a30bc | f55f90e35d54b38d6ef43460578cb5c8fc827d4f | refs/heads/master | 2023-06-30T08:46:50.226617 | 2021-08-06T12:33:16 | 2021-08-06T12:33:16 | 278,666,207 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,678 | r | aging1.R | rm(list = ls())
libraries <- c('glmnet','leaps','caret','e1071','reshape')
lapply(libraries, require, character.only = TRUE)
setwd('/Users/sabnig/Documents/Projects/Aging/Temp')
frailty_parameters <- c('Alopecia','Loss.of.fur.colour','Dermatitis','Loss.of.whiskers','Coat.condition',
'Piloerection','Cataracts','Eye.d... |
7daf00eb7bddd3e83df82ea60aaccc3b626acef8 | 8724ef8dede95067991ba5216536c942bea20a36 | /man/format_number.Rd | 1b151acfa3103bf26d80a8a5679781f2f9406837 | [
"MIT"
] | permissive | lee269/iapdashboard | 716fb83ab0da12cbd369a8ee730a43fb9ad8dff1 | 28983023644c6af26d26817f7d0f8318679b46f8 | refs/heads/master | 2020-12-14T13:59:02.209407 | 2020-06-16T10:08:54 | 2020-06-16T10:08:54 | 234,764,891 | 0 | 0 | NOASSERTION | 2020-04-20T17:20:04 | 2020-01-18T16:42:38 | R | UTF-8 | R | false | true | 290 | rd | format_number.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{format_number}
\alias{format_number}
\title{Format numbers}
\usage{
format_number(x, format)
}
\arguments{
\item{x}{}
\item{format}{}
}
\value{
}
\description{
Format numbers
}
\keyword{internal}
|
5a79ceb9444f15b8f6c07223eba9f9c893bac1ca | 16066007ac4fccb116f4a5e255aaf62233f1ec58 | /Rcode/sesion000_creardocumento.R | 45f3afa6ee3ae1db417b76ad00e17d3b336257a2 | [
"MIT"
] | permissive | NeoMapas/Monitoreo-acustico-JBM | 9b1435e25d4fd4d2f1c858dab8774f11bf8acf15 | 8611ad0ad79596c7e1c1265a8f4bfa1fa068199a | refs/heads/master | 2023-04-11T23:47:07.079757 | 2021-08-20T03:46:47 | 2021-08-20T03:46:47 | 279,161,252 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 978 | r | sesion000_creardocumento.R | ##R --vanilla
setwd("~/tmp/CEBA")
hoy <- format(Sys.time(), "%Y%m%d")
mi.dir <- "400_InventarioJardinBotanicoMaracaibo"
mi.arch <- "Documento1_climaJBM"
titulo <- "EstacionalidadClimaJBM"
cdg.doc <- ""
mi.arch <- "Documento2_MonitoreoAcustico"
titulo <- "Ensayo_Monitoreo_Acustico_JBM"
cdg.doc <- ""
if (file.exists("~/... |
5a1fab496dce724b3278270fb93df092f962f114 | 7a36292bf7357844a8bde2d7a0cf92a722777489 | /source/workflow/18_stl_zip_plots.R | 3d15f885970938ab9494e8be9103dba9977c24c3 | [
"CC-BY-4.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | slu-openGIS/covid_daily_viz | ed573d4195f4097f5d101a0b908f9c1d0bb5bb21 | ec69fa0e5819f043fe9024af31e77f61ea4fdc57 | refs/heads/master | 2023-04-11T09:59:25.238840 | 2022-04-01T18:50:18 | 2022-04-01T18:50:18 | 249,867,864 | 9 | 9 | CC-BY-4.0 | 2020-07-05T19:03:25 | 2020-03-25T02:26:17 | R | UTF-8 | R | false | false | 15,927 | r | 18_stl_zip_plots.R | # st louis zip code level plots
# =============================================================================
# load data
regional_zip_sf <- st_read("data/MO_HEALTH_Covid_Tracking/data/zip/daily_snapshot_regional.geojson", crs = 4326,
stringsAsFactors = FALSE) %>%
st_transform(crs = 269... |
7de611a98949a6cb732d0e8c29c086bc57f2b0e0 | 69ad7c31d1bfa947acf3f35f96704a95c0c31a89 | /code/8-create-grid-panel.R | f353205a51d39da2097d291874b224e31a5fefcb | [] | no_license | Copepoda/witch-trials | f0958ed44ca1a9fcd143522b50f053a5e871f7fb | b6c77d91b9f60860ff44052bf254b83f811690e9 | refs/heads/master | 2021-06-18T11:24:54.549251 | 2017-05-19T02:04:26 | 2017-05-19T02:04:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,833 | r | 8-create-grid-panel.R | # Title: 8-create-grid-cells.R
# Description: Create a panel dataset by grid cell.
# Authors: Peter T. Leeson and Jacob W. Russ
library(readr)
library(raster)
library(dplyr)
library(magrittr)
library(tidyr)
library(maptools)
library(sp)
library(spatialEco)
library(Grid2Polygons)
library(rgdal)
library(ggplot2)
# I... |
c4fad3dc62b23eec50e9d8fe9f0568acb056f24f | 27834a88caf9870538887f398b094a246d7b62c8 | /workflow/scripts/homer_clean.R | 0ff822521d2e1eb8838e22478a2bc49c973f3f1c | [] | no_license | pd321/chipseq | 6d88a9b7e508bdbf62283c1cd2e620552c889761 | 94c990f0b212d1d55144520c70414653269c388c | refs/heads/master | 2023-05-10T07:14:34.507092 | 2023-05-04T01:47:40 | 2023-05-04T01:47:40 | 208,949,740 | 0 | 0 | null | 2023-05-04T01:47:41 | 2019-09-17T03:19:25 | Python | UTF-8 | R | false | false | 743 | r | homer_clean.R | log <- file(snakemake@log[[1]], open="wt")
sink(log)
sink(log, type="message")
# Libload
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(readr))
# Read in homer file
homer.df <- readr::read_tsv(snakemake@input[[1]])
# Remove the feature details
homer.df <- homer.df %>% mutate(Ann... |
8d78f517bc5a162ecc1025e5c456352c3937ea3f | ce5e5a07068c7054376e016722ae9a5ea88d1479 | /21.Data_Visualization_XY.R | fbc79ede304b6360988544270ab0cb85648c220c | [] | no_license | phamdinhthang/DataScience_Utils_R | 463d44a222e118d4089b9581043b3f4abfb8fc0f | 2bf97198fbd262bf8690ba6cd7d238415a5b6d56 | refs/heads/master | 2020-03-18T23:34:07.870444 | 2018-05-30T08:22:17 | 2018-05-30T08:22:17 | 135,411,024 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,769 | r | 21.Data_Visualization_XY.R | #load the neccessary package
#library(MASS)
#library(grid)
#library(ggplot2)
#library(insuranceData)
#library(robustbase)
#library(car) #qqPlot() available inside car package
#library(aplpack) #bagplot() available inside aplpack
#library(corrplot) #corrplot() available inside corrplot
#library(rpart)
#library(wordcloud... |
b2104b6bf2775449f08a09135bc69daa323de359 | 750423288021c0d0bcd0d656d09351e4f86870de | /analysis/2016/shiny_apps/snake_draft/v2_4/ui.R | f3e483ade4514775c8f89a7704f1813dc176266e | [] | no_license | johnckane/fantasy-football | b51ae061dc221ad9e17900d1915b95c231a454ad | 2ccfdb62f0011738172d774f9f4e2ba72936de2b | refs/heads/master | 2022-12-05T09:18:40.070217 | 2020-09-03T02:28:39 | 2020-09-03T02:28:39 | 106,360,628 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,451 | r | ui.R | library(shiny)
df <- read.csv("/home/john/stats_corner/2016/shiny_apps/snake_draft/v2_1/faa_projection_data.csv",
#df <- read.csv("/srv/shiny-server/stats-corner/2016/snake-assistant/faa_projection_data.csv",
stringsAsFactors = FALSE,
header = TRUE)
shinyUI(fixedPage(
titlePanel("Snak... |
900451e58ac40ffaa11a7ac16bddb044c4a1b2be | d657afbb753d08c15eb57f57f2b02b58001504ea | /Corrr.R | a2eed8a512e8df2a1ca6d61206bc0b3e16e0161c | [] | no_license | MackSkippy/datasciencecoursera | 3f7ca2f210e238032f41212e04a3c0ae6fb7fa0a | 158eedca031794cc419d504c370b986207de5a23 | refs/heads/master | 2021-01-19T15:06:19.387720 | 2017-04-30T01:15:20 | 2017-04-30T01:15:20 | 88,196,360 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,266 | r | Corrr.R | corr <- function(directory, threshold = 0){
#take all csv which meet threshold, then run correlation between sulfate and
#nitrate columns
allCases <- NULL
complete <-function(directory, id = 1:332, allCases){
#set working directory to m... |
6f3069dfb0181c4d78f627526e714ca045ab634e | ca34075095171c0fc733302a27ccf32156ccda99 | /R/computeAuxillaryParameters.R | b44e244ed4ec026259ffedbb06eac9a1ff1325d6 | [] | no_license | cran/EquiNorm | e3dbb55ae859ee1690fc7fce8c16aa44e1ee013c | 99862de9040e0f7e2479808b9be5748a15bf675f | refs/heads/master | 2020-08-09T19:48:31.706218 | 2011-03-24T00:00:00 | 2011-03-24T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,192 | r | computeAuxillaryParameters.R | computeAuxillaryParameters <-
function (data.y, data.x, alpha.hat, e.hat)
{
G <- dim(data.y)[1]
n <- length(data.x)
y <- data.y
group <- t(apply(rbind(1:G), 2, rep, each = n))
xo <- matrix(0, nrow = G, ncol = n)
xo[e.hat$o.hat == 1, data.x == 1] <- 1
xu <- matrix(0, nrow = G, ncol ... |
d47efc4911b6c19d56ca0c388752c883ff17038a | 3f3ce02d418819460d1251a83d0f8b378325ba54 | /ridge_regression.R | 2d0bc3dd9875e152d66d48f27eb9c05cc6f52057 | [] | no_license | clbwvr/Teaching | 5ac07e9a68314bc4c6bbb6f10de0d4d0aaaf6ffd | 8118deab6e5e36a75864351f1404490a4596b0b8 | refs/heads/master | 2021-06-01T02:12:27.502728 | 2016-07-10T14:50:43 | 2016-07-10T14:50:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,217 | r | ridge_regression.R | # Ridge Regression
# OLS just minimizes RSS
# Ridge regression minimizes RSS + lambda * sum(coef^2)
# Ridge improves on ols because it decreases variance in the model with shrinkage
# and thus can make a better fit when we have a large p.
# Let's compare ridge regression and plain old least squares to fit "score" usin... |
373c82bf30a6820cba923efb2376ace49285b10e | d74844d9ff34a8711d014b04fafb0cb5954e2bba | /exploration.r | 59c5347da9cc6a17f67732c39e7624a158a32413 | [] | no_license | taraeicher/ProteinAbundanceDnaRna | 3a2d3e9730f5d5d202ed3c79eacb30fd1a51c38b | 96bb786686ecda286d3171b154870e0a6ff7a033 | refs/heads/master | 2021-03-21T23:50:32.557190 | 2018-02-15T04:39:07 | 2018-02-15T04:39:07 | 121,591,588 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 313 | r | exploration.r | if (!require(corrplot)) {
install.packages("corrplot")
library(corrplot)
}
breast_dna = read.table("C:\\Users\\tarae\\OneDrive\\Documents\\PhD\\BMI7830_Integrative_Methods_of_Bioinformatics_for_Human_Diseases\\dream_challenge_2\\breast_dna_corr.rds", fill = TRUE)
corrplot(as.numeric(as.character(breast_dna)) |
169f415a81022a2c9187f8273445be2b10d05edf | 34cc3889d10a02ea1629dd2d1bcbbace8c04c5f5 | /scratch/makeMetaDataset.r | 619252cc7bc448ebb9ba38625b7e37f45f5edb82 | [
"Apache-2.0"
] | permissive | kaneplusplus/dc | 66cca7bf129c7d651f6cd2b08cd7dda0c8f20340 | 20cf923c464d75b1c78716d105784c5718c44ffc | refs/heads/master | 2020-05-17T07:43:42.303440 | 2014-04-03T14:38:45 | 2014-04-03T14:38:45 | 13,815,109 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,491 | r | makeMetaDataset.r | require(XML)
require(foreach)
doc <- xmlParse("rvf_result.xml", error=function(...){})
x <- foreach(n=getNodeSet(doc, "//PubmedArticle"), .combine=rbind) %do% {
numAuth <- length(getNodeSet(n, ".//Author"))
if (length(numAuth) == 0)
numAuth <- NA
pmid <- xmlValue(getNodeSet(n, ".//PMID")[[1]])
if (length(... |
afc6e8db030044816541d19a87ad370bcddd575d | 12e3f4c86e41b21e1aced2269c14a267d0bb09b8 | /clase_13_sept.R | dba590bce31e48dcf1fab8c1d0009a8c63a84cd8 | [] | no_license | ricardomayerb/ico8869 | ec8e5fc051b2ff819cf1d1635bfdf35fd7be13b3 | 39da698bb573cab23ca7a8a8a0ae0a5bb76af4e1 | refs/heads/master | 2020-07-05T10:09:07.678184 | 2019-09-13T17:28:38 | 2019-09-13T17:28:38 | 202,618,938 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,797 | r | clase_13_sept.R | library(fpp2)
# General id ea: evaluate forecast accuracy:
# First example from the book
# Second, two series from BCCh
# Third, download a financial series using quantmod
# Review the measures and program by hand a hardcoded evaluation of
# Naive or season naive or trend naive against an auto.arima
# Introduce sea... |
bb289dc10189ab2e4483b832d491c0417d0b52d0 | 601fbe6791e143a717066b4def5eb9a2666c8c46 | /R/tycho2.R | 313db13214feed5a3cd5486950572b997cbc5f7a | [
"CC-BY-4.0"
] | permissive | allopole/tycho2 | f08e2d81ecc67c7e48818cc48093b22ecc0b1f44 | e4648147c7dfa0ed06374c888e920986d0551fcb | refs/heads/master | 2021-06-04T00:33:27.013827 | 2019-12-10T19:51:22 | 2019-12-10T19:51:22 | 139,588,329 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,152 | r | tycho2.R | #' Get data from Tycho 2.0 database
#'
#' Calls the Tycho 2.0 database using the Tycho 2.0 web API.
#'
#' \strong{Project Tycho, a repository for global health data}
#'
#' Project Tycho is a repository for global health data in a standardized format compliant with FAIR
#' (Findable, Accessible, Interoperable, and Reusa... |
d36d3ab5f39e45ea16aca4833ada8ece66bdfcff | 1543497d67c193543a037abbe77d4f44b52889ea | /R/error_plot.R | d27e99ea8f717ca9f83d8aa6d03246457a0a58ad | [
"MIT"
] | permissive | Yuanproj/IRon | 81a2c46179f32df18559b45bf235943fc63c4799 | 2ff8f3867c432f85e25f5dd3df1b1773ad8190ce | refs/heads/master | 2023-02-26T04:44:40.210576 | 2021-02-04T21:00:00 | 2021-02-04T21:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,638 | r | error_plot.R | # ======================================================================
#' Visualization of comparison between prediction error and item's relevance
#'
#' @description The errorPlot function takes a set of target and predicted values to calculate the deviation as a function of phi and creates a plot with points and an... |
965e5e08cf49ea514a1142e5c91b592804474775 | 7ce35c255fe7506795ff7abc15b5222e582451bb | /5-visualizations/stunting/figED10-stunting-flow-line-region.R | d5f92cdaf49fab0b40e0c7b906b08df870625786 | [] | no_license | child-growth/ki-longitudinal-growth | e464d11756c950e759dd3eea90b94b2d25fbae70 | d8806bf14c2fa11cdaf94677175c18b86314fd21 | refs/heads/master | 2023-05-25T03:45:23.848005 | 2023-05-15T14:58:06 | 2023-05-15T14:58:06 | 269,440,448 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,919 | r | figED10-stunting-flow-line-region.R | ##########################################
# ki longitudinal manuscripts
# stunting analysis
# figure: line plot showing
# proportion of children who were newly stunted,
# relapsed, recovered
# includes category for mortality
# stratified by region and cohort
# inputs:
# stunt-flow-data-pooled.RDS
# stunt-flow-da... |
fce8e6bcf76cf30b00bd7830c5d4c780b499c648 | 62e90dea1e9581331331dbebb657269d2ad6dfd7 | /man/LdaFuncs-package.Rd | dff2c4bcc35982a47a656a8a96b8189417f60d38 | [] | no_license | NathanWycoff/LdaFuncs | c19fec6947730e7fabe8ff7465ca9fa6a200b003 | a18dbe6bed79e6f5cd48f723827df2562ce2a9b5 | refs/heads/master | 2021-01-06T04:01:06.265472 | 2018-03-13T16:47:28 | 2018-03-13T16:47:28 | 99,517,287 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 900 | rd | LdaFuncs-package.Rd | \name{LdaFuncs-package}
\alias{LdaFuncs-package}
\alias{LdaFuncs}
\docType{package}
\title{
LdaFuncs
}
\description{
An Implementation of the CVB0 algorithm for inference on Latent Dirichlet Allocation topic models.
}
\details{
Implements weighting for each unique term, allowing words to differ in terms of ... |
8fe1cf943e7ad06b445b56c5f25ad08cf0213f9a | d4626615378e45ad5d14e4d1e3b631278475d165 | /plot3.R | dc313848f379f5974645d46c73723c6663aff78c | [] | no_license | bhurat/ExData_Plotting1 | 04a8a6f375934a455e694e10411e963140c30e62 | d8644a7693decd9dc1e83d6d48b116a9c5c5e421 | refs/heads/master | 2021-01-18T00:14:40.999275 | 2014-11-09T20:12:28 | 2014-11-09T20:12:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 979 | r | plot3.R | plot3<-function() {
datsnames<-read.csv("household_power_consumption.txt", TRUE, ";", nrows=1)
dats<-read.csv("household_power_consumption.txt", TRUE, ";", skip = 66636, nrows = 2880)
colnames(dats)<-colnames(datsnames)
dats[,2]<-as.numeric(dats[,2])
for (i in 1:nrow(dats)) {
... |
8b66bad3ae56bc5df166e2a7635ad47bcbab0856 | 70b343f159f3f94043ebc9c116088e04b689a2f4 | /analyze_predictmrna.R | a257df3523f0fb1e9132c9455ac3b0ebee17949b | [] | no_license | xwang234/predictmrna | 579dfcc3ffa7f10dc4a81f80721450f166bee234 | 3c04a536c6364b34e1c21c449673ea58a88ee51e | refs/heads/master | 2021-01-10T01:23:03.143600 | 2016-03-24T03:48:59 | 2016-03-24T03:48:59 | 53,985,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,900 | r | analyze_predictmrna.R | #!/usr/bin/env Rscript
setwd("/fh/fast/dai_j/CancerGenomics/Ovarian")
load("./mrna_copynumber_methylation_mutation.RData")
library(gdata)
clinic=read.xls("/fh/fast/dai_j/CancerGenomics/Ovarian/TCGAdata/TCGA-OV-Clinical-Table_S1.2.xlsx")
clinic <- clinic[order(clinic[,1]),]
mrnacluster <- mrnacluster[order(mrnaclus... |
dbf488443723b79f37e7be8d6c812d3e1d6b02dc | 6ee8e9566a0df86ace46e06fe2778e07bf6e3224 | /app_latest.R | 14bf65151311e7990c59a5137a3bab6e92369513 | [] | no_license | TianaLiang/Palmer-Penguin-Project | 7553b3e1fe96f0dc715dfbf7918b54f13313271e | 26ea34446770fc50f49d32ee8eb39e4fbe8f7a3d | refs/heads/main | 2023-06-10T15:01:59.064287 | 2021-06-17T07:16:20 | 2021-06-17T07:16:20 | 377,052,550 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,668 | r | app_latest.R | library(shiny)
library(dplyr)
library(ggplot2)
origindata <- readRDS("origindata.RDS")
enrollmentdata <- readRDS("enrollmentdata.RDS")
ui <- fluidPage(
tags$head(tags$style(
HTML('
form.well{
font-family: "Arial";
background-color: rgb(105, 232, 251);
}
... |
2d0bba29c2d7b3e8127e19335af9039379422659 | c9e0c41b6e838d5d91c81cd1800e513ec53cd5ab | /man/gtkTextIterForwardChars.Rd | fe488831eb564c029e15ae1baa62ec705f339c19 | [] | no_license | cran/RGtk2.10 | 3eb71086e637163c34e372c7c742922b079209e3 | 75aacd92d4b2db7d0942a3a6bc62105163b35c5e | refs/heads/master | 2021-01-22T23:26:26.975959 | 2007-05-05T00:00:00 | 2007-05-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 859 | rd | gtkTextIterForwardChars.Rd | \alias{gtkTextIterForwardChars}
\name{gtkTextIterForwardChars}
\title{gtkTextIterForwardChars}
\description{Moves \code{count} characters if possible (if \code{count} would move past the
start or end of the buffer, moves to the start or end of the
buffer). The return value indicates whether the new position of
\code{it... |
85c15f71497a00c3415d06482c6571020676d9c8 | db0ae95504a72594012fb708bb2df4014540a319 | /server.R | edae2cd71a277cf5d0896a9aa6b729b9bdf6ae10 | [] | no_license | RCollins113/Capstone_RShiny | 07d17570e586c545efce403244d32ca0cf94d746 | 16b6fe805f6750fc681b42c17d517790024fbb54 | refs/heads/main | 2023-06-21T14:56:19.611444 | 2021-07-27T20:55:30 | 2021-07-27T20:55:30 | 388,106,689 | 0 | 1 | null | 2021-07-29T05:41:33 | 2021-07-21T12:17:39 | R | UTF-8 | R | false | false | 3,682 | r | server.R | # Install and import required libraries
require(shiny)
require(ggplot2)
require(leaflet)
require(tidyverse)
require(httr)
require(scales)
# Import model_prediction R which contains methods to call OpenWeather API
# and make predictions
source("model_prediction.R")
test_weather_data_generation<-function(){... |
944bc27a3fa39c6d82d51c4505cc33d0dc97d16e | c59a47a59fcafc82249e40c58950db9a9fbf1250 | /R/b2DownloadFileById.R | e2f07688e57ef138dfdcd25d981279b1f0942636 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | phillc73/backblazer | dcd5a9804b9fb3a3a048c67c402e6bd7df0b9169 | 9a1d4fa9998174938b425ff79bda842fe7685195 | refs/heads/master | 2021-07-22T01:26:17.566419 | 2016-11-16T20:47:47 | 2016-11-16T20:47:47 | 49,987,062 | 8 | 2 | NOASSERTION | 2021-06-30T20:25:09 | 2016-01-19T22:17:01 | R | UTF-8 | R | false | false | 3,321 | r | b2DownloadFileById.R | #' Download B2 File by ID.
#'
#' \code{b2DownloadFileById} downloads a file from the user's account on the
#' Backblaze B2 cloud storage product.
#'
#' This function downloads a file from the user's account on the Backblaze B2
#' cloud storage product using the file's unique ID only. Files of the same name
#' may have ... |
414dc4993724db301e50cef7cf1d8175f82643b8 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/CondIndTests/examples/InvariantTargetPrediction.Rd.R | e5941c948e00316eb8528b04faaf25f75e44d881 | [] | 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 | 876 | r | InvariantTargetPrediction.Rd.R | library(CondIndTests)
### Name: InvariantTargetPrediction
### Title: Invariant target prediction.
### Aliases: InvariantTargetPrediction
### ** Examples
# Example 1
n <- 1000
E <- rbinom(n, size = 1, prob = 0.2)
X <- 4 + 2 * E + rnorm(n)
Y <- 3 * (X)^2 + rnorm(n)
InvariantTargetPrediction(Y, as.factor(E), X)
Invari... |
fbc588ac673aed077269d7b70cef639f755f3264 | df712880b661e0148f7173d2e5e26fc69f84a07f | /R/AcqFunctionSmsEgo.R | fa0ecef7dd6db8e4e53cbf00c2211b39a031ff4f | [] | no_license | mlr-org/mlr3mbo | 006de583ae52232e32237e7a8986f65e6bde5cc9 | 520622de10a518b4c52a128b79398960dc7e6e09 | refs/heads/main | 2023-06-21T23:12:53.325192 | 2023-06-20T08:01:40 | 2023-06-20T08:01:40 | 212,462,939 | 23 | 1 | null | 2023-08-23T21:20:31 | 2019-10-02T23:44:40 | R | UTF-8 | R | false | false | 6,712 | r | AcqFunctionSmsEgo.R | #' @title Acquisition Function SMS-EGO
#'
#' @include AcqFunction.R
#' @name mlr_acqfunctions_smsego
#'
#' @description
#' S-Metric Selection Evolutionary Multi-Objective Optimization Algorithm Acquisition Function.
#'
#' @section Parameters:
#' * `"lambda"` (`numeric(1)`)\cr
#' \eqn{\lambda} value used for the confi... |
38412ce35d348a4c8b6636d57d0f7aad380786ed | bd8c41ccf973b2602f01ef2cf772c26ee058e7cb | /R/validData.R | a2f5a8e380d5e60c2036a9b0737b78ade679e545 | [] | no_license | cran/RghcnV3 | 5759f30fe96b386faec9e1667c1ede43aca0d72c | d2d3dfadbddf5acd9e6c8d05d797d8d8b4b085d2 | refs/heads/master | 2021-01-15T22:29:03.926304 | 2012-06-02T00:00:00 | 2012-06-02T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 573 | r | validData.R | validData <- function(Data){
if(isArray(Data) | isMts(Data) |is.zoo(Data)){
if(sum(duplicated(getStations(Data))) !=0){
print("duplicated stations")
return(FALSE)
}
if(!validMonthCount(Data)){
print("Invalid Month count. USE trimHeadtail")
return(FALSE)
}... |
4724f58c9cb99a99159a114ede841671da0fbe12 | 806ca2e424e11a5b7eab6c947fc47b5aee8a6c84 | /R/df-util.R | 7349b7fef8b17a164f96cad080ae078bd39ea52f | [] | no_license | dfyj/wm | cefcb9f3c11bd37bef69e4a9e2a24b93f180bb60 | ecf27eb4349400717a8fdee16754c253c8543ec7 | refs/heads/master | 2020-03-24T22:34:01.845300 | 2019-01-26T04:02:12 | 2019-01-26T04:02:12 | 143,091,352 | 0 | 2 | null | 2018-08-01T02:52:48 | 2018-08-01T02:04:38 | R | UTF-8 | R | false | false | 1,365 | r | df-util.R | # data frame related utils ------------------------------------------------
#' group + top_n + sum
#'
#' @param df data frame
#' @param gp group_by col
#' @param n top n
#' @param value value vol to sum
#'
#' @return
#' @export
#'
#' @examples
#' df_group_top_n_sum(df, date, 10, weight){
df_group_top_n_sum <- function... |
1fbeeb91b8b3df12480491d05b57e56bf2488187 | 2df4b0fcee1964c9f37401e50d7abfff7460ccdc | /615-Final_Shiny.R | 43741e2be309c579a17ef7b37e7c2840d7c43564 | [] | no_license | DonghaoXue/615-Final_Project | 062c51bc38a867e0370bdd2e3909a3d02f464609 | 0cb55f024cdc1fbd9978821f76efa5ce27b8f39f | refs/heads/main | 2023-02-03T15:14:37.789135 | 2020-12-14T17:27:56 | 2020-12-14T17:27:56 | 321,418,438 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,703 | r | 615-Final_Shiny.R |
library(shiny)
library(knitr)
library(tidyverse)
library(lubridate)
library(bizdays)
library(tidyquant)
table_stock = tq_get(c("AAPL","GOOG","AMZN"), get = "stock.prices", from = '2020-07-01',to = "2020-12-02")
#get individual asset returns grouped by asset
stock_returns <- c("AAPL", "GOOG", "AMZN") %>%
tq_get(g... |
07412220bd79d3947412a625abb89680fcce9bed | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /ffstream/man/getExistsErrorMessage.Rd | 9af1e22bc9f95ea92bf148b7c907a69538744dfd | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | true | 842 | rd | getExistsErrorMessage.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/errorMessage.R
\name{getExistsErrorMessage}
\alias{getExistsErrorMessage}
\title{Create the error message for a certain parameter that does not exist}
\usage{
getExistsErrorMessage(paramName, value = NULL, algoName = NULL,
functionName = ""... |
9ffacbcce47207ffe8e5916220a44b3f57b21073 | b68f039d3e9446439e322a7e5557b6f9df720673 | /man/FindOverlap.Rd | aca55ae9a590ad0c1ccd6f61100176d96b962dcd | [] | no_license | kieranrcampbell/SpatialStats | 9ca0b231b5f0307320239b3e4feb6edff395dd00 | 49ea65afdfcab88f04b5f678765afdeee67b2901 | refs/heads/master | 2021-01-18T11:17:23.450562 | 2014-08-29T13:52:49 | 2014-08-29T13:52:49 | 23,077,488 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,104 | rd | FindOverlap.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{FindOverlap}
\alias{FindOverlap}
\title{Given a list of two pathway results, find the overlap between them}
\usage{
FindOverlap(results, remove = c("same", "different"))
}
\arguments{
\item{results}{A list containg two 'pathway' matrices}
\item{remove}{Which r... |
e847c19c3c63fcfd350a07468b26f2eb209e3de3 | 6cfede497caf67b5a1e4745b56b029e5ccce128f | /2020/2020-05 SCS Regional Forecast/R/household_units.R | 286efb6f008c416ec1acefccf3dea5d053c31dca | [] | no_license | SANDAG/QA | 3bce623e269c745cd7c60933be8d81bab14a0e27 | 37edb55a7e79f205d44b67eb18e6474689268477 | refs/heads/master | 2023-08-19T10:47:05.145384 | 2023-08-17T15:57:01 | 2023-08-17T15:57:01 | 138,326,659 | 6 | 3 | null | 2023-02-24T22:07:34 | 2018-06-22T16:48:58 | Jupyter Notebook | UTF-8 | R | false | false | 6,024 | r | household_units.R | #
# This report summarizes select QA findings of the 2020 SCS Forecast review (2020-05).
#
# Tests included in this report are:
#
# Test #3: Household Units (Performance Analysis)
#
# Thorough descriptions of these tests may be found here:
# https://sandag.sharepoint.com/qaqc/_layouts/15/Doc.aspx?sourcedoc={17b5... |
b35b9d98349de3f214bc5bb77a8d1f45b388f9f0 | c88b0cbeda0edf9e745e324ef942a504e27d4f87 | /Budongo cognition/fileLoader.R | a34055ea49bdb603170ce132b92076d5f98cdef8 | [] | no_license | Diapadion/R | 5535b2373bcb5dd9a8bbc0b517f0f9fcda498f27 | 1485c43c0e565a947fdc058a1019a74bdd97f265 | refs/heads/master | 2023-05-12T04:21:15.761115 | 2023-04-27T16:26:35 | 2023-04-27T16:26:35 | 28,046,921 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,360 | r | fileLoader.R | # script for importing excel files
library(Rcpp)
library(plyr)
rbind.all.columns <- function(x, y) {
x.diff <- setdiff(colnames(x), colnames(y))
y.diff <- setdiff(colnames(y), colnames(x))
x[, c(as.character(y.diff))] <- NA
y[, c(as.character(x.diff))] <- NA
return(rbind(x, y
,he... |
0cf6053810f81b7648ff328b18b9cf6bb6b4ffdb | 5c724066f1c548acd17007702056982b12e5f7bf | /data/pbdb_3TPub_make.R | 3e31b35d6946e821d16d0259984453e81f2f7572 | [] | no_license | ajrominger/extra_paleo_sstat | b403f03d20ba5241de73312a59f0e1f45391ff94 | c923d0eb97decbb682020d0e672f29e03ffbbb21 | refs/heads/master | 2020-10-02T02:47:52.470484 | 2019-12-12T19:52:27 | 2019-12-12T19:52:27 | 227,478,075 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,463 | r | pbdb_3TPub_make.R | # **this script produces diversity estimates per family per time bin from PBDB data
# corrected by the '3 timers method' and for possible publication bias**
# source function to produce a matrix of time by taxon with cells
# of corrected diversity
source('R/make3TPub.R')
# load other needed funcitons
# source('code/... |
a64f04667659283b64e98f4cbade990e434f42b5 | 391ad5a8f32ea0d0076f63885b995fb25603e2ad | /R/covcor_design.R | 114154939cd6a1b0d91f4fb3b54572ce39407fec | [] | no_license | cran/metan | a0d951dff5a151bd5a8b0b7a0a699fde0dddb50c | b3637d40a2fc9d955b928120d61399ba1bf6e11d | refs/heads/master | 2023-03-20T17:17:24.469173 | 2023-03-05T21:00:15 | 2023-03-05T21:00:15 | 236,625,199 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,578 | r | covcor_design.R | #' Variance-covariance matrices for designed experiments
#' @description
#' `r badge('stable')`
#'
#' Compute variance-covariance and correlation matrices using data from a
#' designed (RCBD or CRD) experiment.
#'
#'
#'@param .data The data to be analyzed. It can be a data frame, possible with
#' grouped data... |
09af4c1b7c4fe7e2633fe8b9bae0f253786455ab | 80b5dc2575bbc4ce13ae760ab90aa6447e036fd7 | /20190924.R | 48935e1fb32b7f70fd5151ee5a1614c7b689f341 | [] | no_license | WestlakeData/Data_Course_PORTER | de56feda5be2fcf0d4c6da7a64db95741984ee3b | 86f123590a268b904a6ffef0185ec620088df152 | refs/heads/master | 2020-07-07T18:59:09.811909 | 2019-12-12T21:20:24 | 2019-12-12T21:20:24 | 203,446,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 496 | r | 20190924.R | dat <- DNA_Conc_by_Extraction_Date
library(dplyr)
library(ggplot2)
D_Upstairs <- dat%>%
filter(Lab == "Upstairs") %>%
select(starts_with("D"))
?summarise
Lab <- dat %>%
group_by(Lab) %>%
summarise(Ben = mean(DNA_Concentration_Ben),
Katy = mean(DNA_Concentration_Katy))
Avg.GPA <- GradAd %>%
f... |
c066af88ac56c23d470995d32c1e3a75c2b6ae2b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/quantoptr/examples/abso_diff_est.Rd.R | 7c80b2a5cf972d784c54b44b763529b619648880 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,320 | r | abso_diff_est.Rd.R | library(quantoptr)
### Name: abso_diff_est
### Title: Estimate the Gini's mean difference/mean absolute
### difference(MAD) for a Given Treatment Regime
### Aliases: abso_diff_est
### ** Examples
library(stats)
GenerateData.MAD <- function(n)
{
x1 <- runif(n)
x2 <- runif(n)
tp <- exp(-1+1*(x1+x2))/(1+exp(-1... |
21bca21435da18de114bd24b53352294d1b06bd1 | cf606e7a3f06c0666e0ca38e32247fef9f090778 | /test/integration/example-models/bugs_examples/vol2/cervix/post.R | b7386a545b237b53e1a7c50a771829e158695ee8 | [
"BSD-3-Clause",
"LicenseRef-scancode-free-unknown"
] | permissive | nhuurre/stanc3 | 32599a71d5f82c759fd6768b8b699fb5f2b2d072 | 5612b357c1cd5a08cf2a57db97ce0e789bb87018 | refs/heads/master | 2023-07-05T02:27:08.083259 | 2020-11-12T15:37:42 | 2020-11-12T15:37:42 | 222,684,189 | 0 | 0 | BSD-3-Clause | 2019-11-19T11:50:39 | 2019-11-19T11:50:38 | null | UTF-8 | R | false | false | 409 | r | post.R | library(coda)
library(BUGSExamples);
post <- read.csv(file = 'samples.csv', header = TRUE, comment.char = '#')[, -(1:3)]
summary(as.mcmc(post))
pars <- c("q", "beta0C", "beta", "phi")
ex <- list(name = "Cervix", parameters = pars,
nSample = 10000, nBurnin = 1000, nThin = 1,
nChain = 3)
# jag... |
762ea136c7e6fcb370d4df27240e6efeff58a6e8 | 866fba40d302b2fd52bbd92a4fc4c31af3fee6a9 | /man/atualizar_dados.Rd | 147fee1894fa7d8603ae0d1bc5ba0cf21e4c06a2 | [
"MIT"
] | permissive | curso-r/tidynetflix | 7f498cff9d8e964294347667f6b111f6ba49ba35 | f64b8e247374e430f6edee021e8ba3a30f96c204 | refs/heads/master | 2023-04-06T11:38:41.277488 | 2021-04-22T01:03:22 | 2021-04-22T01:03:22 | 360,337,508 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 669 | rd | atualizar_dados.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/atualizar-dados.R
\name{atualizar_dados}
\alias{atualizar_dados}
\title{Atualizar base de dados do Tidytuesday}
\usage{
atualizar_dados(file, ...)
}
\arguments{
\item{file}{Caminho para salvar a base (deve ser CSV)}
\item{...}{Outros argueme... |
e2a886b7b76d4152a565f9dedda679009d3d57e2 | c734ca3b2f2eda498461049be11c2c007e99d367 | /tools/Rscripts/Best_blast.R | e62c200d9925845dcb8a0f074ba10bed4de56b26 | [] | no_license | nnalpas/Proteogenomics_reannotation | 95b7387f3c81dbe1c9c26409fc5118b2572c79cb | b054de55f29bd095001f37db09adfda8b155f2f2 | refs/heads/master | 2023-08-22T14:38:55.164844 | 2023-08-08T10:33:31 | 2023-08-08T10:33:31 | 111,789,188 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,905 | r | Best_blast.R | #!/usr/bin/env Rscript
# This script identifies the best blast hits from blasting results
# for target entries
### Environment set-up ---------------------------------------------------
# Start with clean environment
rm(list = ls())
# Define current time
date_str <- format(Sys.time(), "%Y-%m-%d")
print(paste("Sta... |
f4a3456cbe4844d2ac212a65b290a220a522b000 | 7a124a0bd8997e7f08f2f8599a1eb307eca4adcd | /R/SL.glm.R | eb53858bc3d661e0cf3a89797ca40d87ab7ab9ef | [] | no_license | tedwestling/SuperLearner_Old | e4303cba6c1d9ee0348dbe8ca049c4f93eb582aa | 3e328a5c830785b34c4b5d7ee5c2a1352b923a82 | refs/heads/master | 2021-05-27T01:38:20.763620 | 2011-05-26T04:32:51 | 2011-05-26T04:32:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 958 | r | SL.glm.R | # generalized linear regression
SL.glm <- function(Y.temp, X.temp, newX.temp, family, obsWeights, ...){
fit.glm <- glm(Y.temp~., data=X.temp, family=family, weights = obsWeights)
out <- predict(fit.glm, newdata=newX.temp, type="response")
fit <- list(object=fit.glm)
foo <- list(out=out, fit=fit)
class(foo$fit) ... |
c461c89ed15c7615fd5fc8926f21a42a95d7cedd | f44a214a714ce68e1cbb8fd8cd01fd608fe073f1 | /Subject_Statistics/intro6.R | 1fe8ad39062c5ba738b631d38de8a308fd812e49 | [
"MIT"
] | permissive | mahnooranjum/R_Programming | cdc4cffb3decabb07a6ed2e37515cdd055eb2bde | 3227d95323d7c33644edeb6d21264d50f18a2725 | refs/heads/master | 2023-02-17T13:35:11.697968 | 2021-01-18T12:22:12 | 2021-01-18T12:22:12 | 257,075,397 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 189 | r | intro6.R | # load in the dataset
cars = read.csv("http://s3.amazonaws.com/assets.datacamp.com/course/uva/mtcars_semicolon.csv", sep = ';')
# print the first 6 rows of the dataset
print(head(cars))
|
665149879a8162cddeb62062ae50b3480a0a266c | 2de4b01d976daf4b4c34f035ea1943b3af0351fa | /R/drop_na_rows.R | 67a0c733355ce5b106bf198583211b17bcf1ce5a | [
"BSD-3-Clause"
] | permissive | cytomining/cytominer | 4143e84f6d87bee70002e046320d5e0f0dc08924 | d911eb5bc152e0fc8e382ee0492356fd939d2488 | refs/heads/master | 2023-07-06T08:08:54.859029 | 2023-06-29T20:02:36 | 2023-06-29T20:02:36 | 39,411,869 | 45 | 21 | NOASSERTION | 2023-06-29T19:57:20 | 2015-07-20T22:38:45 | R | UTF-8 | R | false | false | 1,604 | r | drop_na_rows.R | utils::globalVariables(c("key", "value", "rowname_temp", "rowid", "coalesce"))
#' Drop rows that are \code{NA} in all specified variables.
#'
#' \code{drop_na_rows} drops rows that are \code{NA} in all specified variables.
#'
#' @param population tbl with grouping (metadata) and observation variables.
#' @param variabl... |
67fe6f39c02e6325ed8c02b4d04f6aceed315daf | 298fc5c4e90603412b6ab568a6406d058df95fcd | /man/reccsim-package.Rd | 5682896926bab210e48c80798800ac8b6329f522 | [] | no_license | yadevi/reccsim | 0334ef24c696cbad1aaa8c245dd4b09c67fa4eb0 | 03e4b6ba67d79a5d8e1367c03fcaba9230780351 | refs/heads/master | 2021-05-26T16:43:24.904127 | 2012-04-20T00:00:00 | 2012-04-20T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,832 | rd | reccsim-package.Rd | \name{reccsim-package}
\alias{reccsim-package}
\alias{reccsim}
\docType{package}
\title{
Simulate case-control studies.
}
\description{
This package allows you to simulate case control-studies from a known
population with binary exogenous variables and a binary endogenous variable.
}
\details{
\tabu... |
39391162b8c3dadd4d7243747fe42abbb2ae18de | c4eac36ff94b667c3465a6a44556db8a615331e0 | /R/get_hclust.R | 2e5df20b45b8f6a3493005ffffaab5766997a1e9 | [
"MIT"
] | permissive | gtonkinhill/fastbaps | 647b1b15f9a2b9afb95ddd213e7055a812c2d222 | 1c322182eab509c647a0584e9d55df7d50c21823 | refs/heads/master | 2022-10-16T03:32:54.563347 | 2022-09-18T13:09:50 | 2022-09-18T13:09:50 | 137,083,307 | 40 | 10 | null | null | null | null | UTF-8 | R | false | false | 1,745 | r | get_hclust.R | #' as.phylo.hclust.node.attributes
#' get_hclust
#'
#' Function to generate hclust object. Uses initial kmeans if dataset is too large.
#'
#' @import ape
#' @import Matrix
#'
#' @param sparse.data sparse data object generated using
#' @param quiet whether to supress additional printing information
#' @param method whic... |
a827d25a8e8537c226886e21da2c80a3da73d005 | f5a57fa0d904f9ca6844e72e75d46699852aac5d | /Code/07a-vector_evaluation_100k_50d.R | cc76ba6fe760a1ecec54c0564489ce6091ce3b9b | [] | no_license | rymc9384/Track2Vec | 2b11b2708b2f121e7888f8c231b136e97f1da9c9 | 850fccc9772c7721ce606ab114fd0d41bb195283 | refs/heads/master | 2021-03-16T10:56:47.993501 | 2017-07-21T19:23:17 | 2017-07-21T19:23:17 | 82,110,816 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,255 | r | 07a-vector_evaluation_100k_50d.R | ## Author: Ryan McMahon
## File: "~/Track2Vec/Code/07a-vector_evaluation_100k_50d.R"
## Date Created: 03/07/2017
## Date Last Modified: 03/07/2017
##
## Purpose: Explore the vectors produced by a GloVe model with the most frequent
# 100k (99,997) tracks on Spotify; embedding dimension is 50.
#
#
## NOTES:
# ... |
b8506d04f843d4931f98226770cf4e4d9e8fbd89 | 51e5e5eca9abc684f9b4d93488af410b456b98d2 | /script.R | a558031b51fd5d668e60e346604bb8a469906269 | [] | no_license | cndesantana/relatorio_brandwatch | 376a61db56400d59c90b1414b962e1b594bfd201 | 39f7ac670fd96e53438a4dad4238c8519be021b9 | refs/heads/master | 2022-12-25T04:52:21.588321 | 2020-09-30T00:03:04 | 2020-09-30T00:03:04 | 298,928,076 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 27,958 | r | script.R | library(tidyverse)
library(plotrix)
library(stringr)
library(quanteda)
library(readtext)
library(tm)
library(dplyr)
library(scales)
library(qdapRegex)
library(grDevices)
library(treemap)
library(stylo)
library(tidytext)
library(tokenizers)
library(rtweet)
library(readxl)
library(lubridate)
library(treemapify)
### Vari... |
e84b8123e653f5c76bf38d4490472d3de997a2a9 | 4ac943bff7e252c3086ead0030847f4c52bc06af | /R/calc_rho.R | a42b6ca74e77a0975a6243bf37674afa196e7efa | [
"MIT"
] | permissive | fboehm/xu2015 | be2717a6e46acfac29c466b4a225f154b6a30f4f | edf08e79adf13ce82f91ab518166cc26d663a66b | refs/heads/master | 2021-01-10T12:12:16.816133 | 2016-02-14T20:18:09 | 2016-02-14T20:18:09 | 50,595,752 | 8 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,895 | r | calc_rho.R | #' Calculate rho(omega_current, omega_proposal)
#'
#' @param omega_big parameter vector from model with bigger $K$
#' @param omega_small parameter vector from model with smaller $K$
#' @param y a data vector
#' @param theta DPP hyperparameter
#' @param tau DPP hyperparameter
#'
#' @export
calc_rho <- function(omega_sma... |
aea7070c7c888ed26c495cf2daa869ab1858af56 | 759251b2620a2ad8674f157e2ff9a66603f28b39 | /run_analysis.R | 9a7103583498a4c33a89b3f96ce2f1edb15ebfd5 | [] | no_license | lutfor3737/Tidy-data | e152447dcdc908d8f565d419676f156fedc0c600 | 8178345e4f1d588e835e0eb8862c1ef928728c2e | refs/heads/master | 2021-01-10T01:02:23.084504 | 2015-08-23T21:05:42 | 2015-08-23T21:05:42 | 41,262,964 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,161 | r | run_analysis.R | setwd("~/R/Getting and Cleaning data")
if (!require("data.table")) {
install.packages("data.table",dependencies = TRUE)
}
if (!require("reshape2")) {
install.packages("reshape2",dependencies = TRUE)
}
require("data.table")
require("reshape2")
#Merges the training and the test sets to create one data set.
X_tr... |
fcb1bfe8e1542b6669c0b749d2fccbff0f425e7f | 3925dcd72c626dd7979b65a3bc02dc997bdf52f1 | /run_analysis.R | 3bfe15bcc1b1f68604de3dac89bf60dda13d57f2 | [] | no_license | visheshtayal/ProjectCourse3Week4 | ed01c3c28db429543e583ce84246849682d0e265 | dd2102242878fa2efc2531853c60451838bbf809 | refs/heads/master | 2023-06-09T00:12:19.737040 | 2023-05-29T01:51:21 | 2023-05-29T01:51:21 | 286,048,788 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,153 | r | run_analysis.R | #Loading packages and getting the data
install.packages("reshape2")
library(data.table)
library(reshape2)
path<-getwd()
url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url,file.path(path,"datafiles.zip"))
unzip("datafiles.zip")
#Loading activity and features
... |
459489ebdaa711582ce08b1cb34a0e8b67e8b0be | 878f7f98050ff52ef50a168e7a2452e03c1f5bac | /R/get0.R | 1fd7b144a59f1af11011275631c358782e69c201 | [] | no_license | dmurdoch/backports | c87654f818830114f87a5978ee2f96d6c3156039 | 4b85d90a3ad11a5ee4e61d1c113991526b4d1cc2 | refs/heads/main | 2023-04-11T12:15:23.749190 | 2023-03-20T13:02:45 | 2023-03-20T13:02:45 | 284,331,893 | 0 | 0 | null | 2020-08-01T20:13:42 | 2020-08-01T20:13:42 | null | UTF-8 | R | false | false | 1,024 | r | get0.R | #' @title Backport of get0 for R < 3.2.0
#' @rdname get0
#'
#' @description
#' See the original description in \code{base::get0}.
#'
#' @keywords internal
#' @rawNamespace if (getRversion() < "3.2.0") export(get0)
#' @examples
#' # get function from namespace instead of possibly getting
#' # implementation shipped with... |
7d64a22504b343627f71ffbda5ec6c9a8f3b0572 | e26420970229a0c55ec092168797ed6e42f5708f | /tests/run_tests.R | 6c3aaa82f9ba44eb3f3ef9196fd1d84246a699df | [
"MIT"
] | permissive | kcha/psiplot | cd26b3d39301d20d82e453e4882b60a214afb600 | 7c840f69f7b321b8690e9f3f03191a0cd699a371 | refs/heads/master | 2022-06-03T02:54:17.403767 | 2022-03-18T15:30:05 | 2022-03-18T15:30:05 | 27,505,866 | 2 | 1 | MIT | 2022-03-18T15:30:06 | 2014-12-03T20:20:07 | R | UTF-8 | R | false | false | 81 | r | run_tests.R | #!/usr/bin/env Rscript
library(testthat)
library(psiplot)
test_check("psiplot")
|
9a9d59906ffcc47d0996840777444b5791248ab9 | c799d05e76440b5224e2960946b80c567571eaeb | /pp_qrf.R | 8f60a25af77cb62aeb0e557f96b11a062aaf4fd2 | [
"MIT"
] | permissive | XuetongWang-DRR/paper_pp_wind_gusts | 6c035786efbe8bc90bc5234e83e2d8dcd660f730 | 7aa4a2f9f69dd4474d7291f40213f73e8ccc4a0c | refs/heads/main | 2023-08-28T00:36:12.464457 | 2021-10-27T07:08:55 | 2021-10-27T07:08:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,622 | r | pp_qrf.R | ## File containing functions for postprocessing ensemble forecasts via QRF
#### Import ####
# Import basic functions
source(paste0(getwd(), "/fn_data.R"))
source(paste0(getwd(), "/fn_eval.R"))
#### Prediction ####
# Function for prediction based on QRF #
qrf_pred <- function(X, qrf_train, pred_vars,
... |
18edf2afba05498057f03d0bd9cbc2e70487840c | d0108c3f8d99cf84d0227c57b10dbd8236585192 | /Fig.1D.venn.diagram.R | ca366a1d9580693f0bb792940f1aba0f7822d89b | [] | no_license | Webb-Laboratory/Maybury-Lewis_et_al_2021 | f2ef2f9e4571427571bfbf83f5242cc88254b51a | 77f353d6c5ae91fa0cb260abe0d674fbed946bc4 | refs/heads/main | 2023-06-05T10:24:03.584835 | 2021-06-30T19:55:13 | 2021-06-30T19:55:13 | 346,836,008 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 390 | r | Fig.1D.venn.diagram.R | ###################################################
# venn diagrams for AQ, AA, stable peak #s
# R version 3.6.1
# venneuler
###################################################
setwd("~/Dropbox/Desktop/ATAC-seq-PEAKS/DiffBind/results.diffbind.2019/v2_AvsQ")
library(venneuler)
# venn diagram for Activated vs Quiesce... |
791a0da09dd99558be2d6f824166257f202171c0 | ab5efddbc1a7d58c8666a5ae17faf6b8420f6dcd | /inst/runit_tests/runit-throw.R | 24c337f85df65beeea582eda71d553acf2128114 | [] | no_license | cran/fakemake | c509062cae30a17645d3d86fc8e3d152e71cb39a | 05aabfdfd2c8449f650701eac6be1d6cabbd5f21 | refs/heads/master | 2023-08-17T14:49:30.136027 | 2023-08-15T22:10:01 | 2023-08-16T01:26:42 | 111,586,682 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 93 | r | runit-throw.R | test_exception <- function() {
RUnit::checkException(fakemake:::throw("Hello, error"))
}
|
76061df711d21ae9f0a78df4b71360e9c132d67a | 54bd3fd47a43afce71a194225486325f1a1488ff | /R/fars_functions.R | a39e8258b5b0326b9af71dedb50c2f74168d7142 | [] | no_license | shaowei72/MyFirstPackage | 5c5589a660bc733523d1dad63deb98c9b7f54bf2 | df35e1dca53740024580658edcdfa8cff81513de | refs/heads/master | 2022-11-12T08:00:02.235744 | 2020-06-20T09:13:30 | 2020-06-20T09:13:30 | 271,950,089 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,290 | r | fars_functions.R | #' Read data from file into a tibble
#'
#' Function that reads in data from a csv file and returns a tibble
#'
#' @param filename Name of the csv file
#'
#' @return This function returns a tibble.
#'
#' @details Function aborts and prints error message if the file filename cannot be found
#'
#' @examples
#' \dontrun{
#... |
a2843d58ee3366e19a62e86d7a36396f7611714c | 1e4c9ffd2979c793edf1312b220abeecb3d1a25b | /man/plotLocalMethylationProfile.Rd | e59878335d7942bf19dea1c7e42cec19e0b1fa8d | [] | no_license | nrzabet/DMRcaller | e21e2919f191fbb9b5db4a8a91be15526c288c22 | 6191d9e68dee6e0a2daedc07485bdc939e84e14e | refs/heads/master | 2020-03-11T13:32:07.018510 | 2019-02-15T18:16:10 | 2019-02-15T18:16:10 | 130,027,757 | 5 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,614 | rd | plotLocalMethylationProfile.Rd | % Generated by roxygen2 (4.1.0): do not edit by hand
% Please edit documentation in R/localProfile.R
\name{plotLocalMethylationProfile}
\alias{plotLocalMethylationProfile}
\title{Plot local methylation profile}
\usage{
plotLocalMethylationProfile(methylationData1, methylationData2, region,
DMRs = NULL, conditionsName... |
6211fad128f61ebe47d7fde5774a6b43d12a390b | 3ee34bf19343757c8661ee4a0eeb8d6ca33f1208 | /R/PLNmixture.R | dea6e9afff33002d6b2c8c83da457a1731aa7606 | [] | no_license | cran/PLNmodels | 48c0b954fb9aca6b8824d89a348bc9b923216621 | 57733cce7d9c585feada02a6ee99c21f2da667a7 | refs/heads/master | 2023-09-03T14:47:28.812155 | 2023-08-24T15:10:02 | 2023-08-24T16:35:05 | 187,254,106 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,700 | r | PLNmixture.R | #' Poisson lognormal mixture model
#'
#' Fit the mixture variants of the Poisson lognormal with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets).
#'
#' @param formula an object of class "formula": a symbolic description of the model to be fitted.
#' @param data an optional dat... |
09bd74896d053a20a9e7a008e40b24170f8015ed | 0a997668786ab68d7bed3c4b5aec0fa78899f124 | /.Rprofile | c8e6b1e4e10fe0ea0878d4b60a917918b72a2b4e | [] | no_license | noamross/reprotemplate | 70578bd7e9ec9e42de04487356c5b310305f5283 | 2cb5cb2706e6967a29870636d6ba23f566f04269 | refs/heads/main | 2023-07-15T15:54:02.946685 | 2021-08-28T19:23:03 | 2021-08-28T21:55:50 | 389,652,556 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,613 | rprofile | .Rprofile | if (file.exists(".env")) {
try(readRenviron(".env"))
} else {
message("No .env file")
}
# Put the project library *outside* the project
Sys.setenv(RENV_PATHS_LIBRARY_ROOT = file.path(normalizePath("~/.renv-project-libraries", mustWork = FALSE)))
if (file.exists("renv/activate.R")) {
source("renv/activate.R")
} ... |
f7fa25c741c741f2a761c82f15cc222a6144fcbc | 44f613b633e9b6e797cddab9a1e9f825b601c8f4 | /R/check.R | c887b2cd3f73b2e9200de5f7dd4f1dfc3196848c | [
"MIT"
] | permissive | ktargows/rhub | 5e7801c774b2384f9dbbdee193cc8aaf5c96e727 | 84476e5e935471a2e25a29571f4e8b397c03e48b | refs/heads/master | 2020-12-30T12:55:38.296861 | 2016-10-04T18:55:34 | 2016-10-04T18:55:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,130 | r | check.R |
#' Check an R package on r-hub
#'
#' @param path Path to a directory containing an R package, or path to
#' source R package tarball built with `R CMD check`.
#' @param platform Platform to build/check the package on. See
#' [platforms()] for the available platforms.
#' @param email Email address to send notificat... |
68119b33ee3946369602f09db10c4ec54d980882 | c2b840bbebe54492a64f91d16cc7023998a686d0 | /import_questemp.R | 4729788530de62143c823adcdc5baff5ffe71945 | [
"MIT"
] | permissive | yutamasuda/heatillness | 29886cdccff2dc665833dba435a20c1e84382850 | d6626655158d6bb7f3995db29608786933794377 | refs/heads/master | 2021-05-03T10:26:26.617892 | 2018-09-19T20:50:46 | 2018-09-19T20:50:46 | 120,534,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,713 | r | import_questemp.R | # Import core body temperature (Questemp) data
# Repository: https://github.com/yutamasuda/heatillness/
# License: MIT - See the LICENSE file for more details
# ------
# Setup
# ------
# Clear workspace of all objects and unload all extra (non-base) packages
rm(list = ls(all = TRUE))
if (!is.null(sessionInfo()$other... |
341c596e8f6a310905bf82944c196ead0b36c203 | 206aab419371d80bab70ab01ef9a7f78e3d8232f | /inst/examples/ui.R | 482d2bef8bb8b2e7cd475cb3bd1ca8e6e1153458 | [] | no_license | anubhav-dikshit/rLab5 | c09b5e4913887b38849b5e6deddefc983191c969 | c6ab9f3dc2381206960b2b6221fdbd3670652b80 | refs/heads/master | 2020-03-29T23:54:20.340409 | 2018-10-01T10:01:19 | 2018-10-01T10:01:19 | 150,486,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 515 | r | ui.R | #' Ui function that sets the ui
#'
#' @importFrom shiny fluidPage sidebarLayout sidebarPanel textInput mainPanel tableOutput
#'
#'
ui <- shiny::fluidPage(
# Sidebar layout with a input and output definitions
shiny::sidebarLayout(
shiny::sidebarPanel(
# Inputs
shiny::textInput(inputId = "c... |
8c10428322304e04e27b916303641d803c9d7d0b | 29585dff702209dd446c0ab52ceea046c58e384e | /IntClust/R/FindElement.R | 9fe15b8e2a56867151734377f6537f3e22520a25 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,485 | r | FindElement.R | FindElement<-function(What,Object,Element=list()){
#str(Object)
if(class(Object)=="data.frame"){
#search in columns
if(What %in% colnames(Object)){
Element[[length(Element)+1]]<-Object[,What]
names(Element)[length(Element)]=paste(What,"_",length(Element),sep="")
}
else if(What %in% rownames(O... |
8feb2559415b7a3d0b0e4ed583f3f7724fd53dd8 | 95e50cf75ded646b9664ff08977e95726301272b | /ARIC/split.ARIC.R | 1a94070de11f435128aa2984b20149d3de59de2a | [] | no_license | gracekatherine/hacking-NHLBI | 206b4b93298922c83e197f3d5e901c1eb2e905a3 | 674a49bde39f51cab34be43e9aab70820c2b106a | refs/heads/master | 2021-09-06T00:16:58.506092 | 2018-01-31T22:56:43 | 2018-01-31T22:56:43 | 119,752,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,051 | r | split.ARIC.R | ## Split up large ARIC files into smaller ones
csv_name <- "phs000280.v3.pht004208.v1.p1.c1.UBMDALL.HMB-IRB.csv"
data <- read.csv(csv_name)
data <- data[,1:1330]
name_length <- nchar(csv_name) - 4
csv_name_short <- substr(csv_name, 1, name_length)
csv_name_1 <- paste(csv_name_short, "_1.csv", sep = "")
csv_name_2 <- p... |
e23306f27715d53031197978002ea338cf623a79 | 2f4917898af7f531467820a9ed8b878a091e856d | /scripts/network_plot.R | 6daf9472c14d08e129b5fc0d67bc8d715ffb13e8 | [] | no_license | more1/NETWORK_MA_FRONTIERS_TUTORIAL | 52cfbd6af45600892975b798939d347d1a6ff726 | a047edad0bd957e5e7d86806d187d00b90b25c8a | refs/heads/master | 2023-06-02T16:18:32.039042 | 2019-12-13T20:56:53 | 2019-12-13T20:56:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,640 | r | network_plot.R | ## plot the network. You can set the node text size and node size and add plot title
## Input:
## MTCdata: MTCdata file
## map_txt: a dataframe which maps each treatment to a number
## dataType: the type of your MTCdata, available options are “Arm” and “Contrast”
## for other arguments, please see mtm.netw... |
92cdc396ea9dd02d344de038de4c8603ae9521f3 | 7a6a8c1980d0d61e984cbd6474513520621ccad6 | /kaggle-pakdd2014-initial-research.R | d1a9ea610b5852f285a18312a168d5f041e3cd32 | [] | no_license | Convalytics/kaggle-pakdd2014 | e2d33aa32897be9b33b87e16db1d87150642ebb9 | b62b647f7d9e97af33ff9f87f366114b3dd247e6 | refs/heads/master | 2016-09-05T18:47:37.969058 | 2014-03-28T21:22:36 | 2014-03-28T21:22:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,181 | r | kaggle-pakdd2014-initial-research.R | ######################################################
# Kaggle PAKDD 2014 ASUS Challenge
# Jason Green
# March 18th, 2014
# https://github.com/Convalytics/kaggle-pakdd2014
# Last Updated: 3/18/2014
######################################################
#install.packages("Hmisc")
# Load Packages
library(plyr)
lib... |
a2451db81517cf61edec1e8521b67e784396fa3a | 62e9ec62c910bac5eeb77c6cf9719a1fb30f566e | /R/GraphP1.R | 5c5b8fe63f7befc8dab815062daa8bba52642191 | [
"MIT"
] | permissive | bhupendpatil/Practice | 25dd22ccca706359aabe135a3cbfb813e2611cef | cae96a3636527100e4b98880e5d3e9ce9399335a | refs/heads/master | 2023-09-01T13:58:53.857928 | 2023-08-21T06:16:41 | 2023-08-21T06:16:41 | 112,185,402 | 5 | 1 | MIT | 2023-08-21T06:16:43 | 2017-11-27T11:02:33 | Jupyter Notebook | UTF-8 | R | false | false | 522 | r | GraphP1.R | library(igraph)
library(igraphdata)
dir = graph(edges = c(1,2,3,4,2,3,1,3,4,2,4,5,2,5,6,2,2,7),n = 7,directed = T)
undir = graph(edges = c(1,2,3,5,2,3,2,4,5,6,3,2,5,4,3,4,5,8,6,4,4,2,4,6,4,2,1,7,4,2),n=8,directed = F)
plot(dir)
plot(undir)
# i) number of edges
gsize(undir)
ecount(undir)
# ii) number of nodes
vcount... |
229013074a188fc6844aa4fbacbc7676df3ed0df | 407350d22d3a58e93dd9b02bdb5e2213d8c55cc8 | /Lab 1/Solutions/assignment2.R | 4795189831fbd6842b24002251c4d816bbb1d734 | [] | no_license | vonElfvin/Machine-Learning-Practice | e679fa2b111c8178273c9b3e375abe528ceac213 | 65334ae79d29673c7ae9e5e4833728cf309be22e | refs/heads/master | 2021-05-14T16:23:43.328273 | 2018-11-23T19:46:05 | 2018-11-23T19:46:05 | 116,019,701 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,932 | r | assignment2.R | # APPENDIX 2
# returns the loglikelihood value for given θ and vector X
loglikelihood = function(x, θ){
return(length(x)*log(θ)-θ*sum(x))
}
# returns the argmax θ of the logilikelihood the given distribution estimation
max_loglikelihood = function(x){
return(length(x)/sum(x))
}
# returns log value of the bayesia... |
4107a8e4350c658d9ba0000379a56d48161caffe | 7eb915ede2984068148d50b538c6a4c67ae103bd | /man/wig2rle.Rd | f9a0853e2997b54196aec8afd32e269a3847c2e7 | [] | no_license | lbarquist/norclip | 8867b946a128016e97722e422c31bc9c91023cd0 | 28e6297a9ed402e03be2b82166c95ac8f9bd7f3a | refs/heads/master | 2020-05-22T00:04:20.781365 | 2016-11-23T11:00:44 | 2016-11-23T11:00:44 | 60,686,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 727 | rd | wig2rle.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/wig2rle.R
\name{wig2rle}
\alias{wig2rle}
\title{Import wig files into rle coverage vectors.}
\usage{
wig2rle(forward_path, reverse_path)
}
\arguments{
\item{forward_path}{Path to forward strand wig file.}
\item{reverse_path}{Path to reverse ... |
6b5ba587fb6aebe8836ded5fcb32723ff38d167f | a9b4425bb81547dd57939a29f1a635ce5f653da8 | /Plot1.R | 28ed61640d3f30d03a59213a90572957c19518c4 | [] | no_license | ScJavier/ExData_Plotting1 | c688bef85f5611f07eaadb5da66063cc9824f9ed | 6f1e047b043438cffcf6979f27d3972307388d5d | refs/heads/master | 2021-01-14T10:54:49.617907 | 2014-09-07T00:58:03 | 2014-09-07T00:58:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 662 | r | Plot1.R | #Plot 1
#Read and subset the data
dir<-"C:/Users/demyc 13/Google Drive/Coursera/Data Science Specialization/04 - Exploratory Data Analysis/Prog.Assign1/ExData_Plotting1/"
file<-"C:/Users/demyc 13/Google Drive/Coursera/Data Science Specialization/04 - Exploratory Data Analysis/Prog.Assign1/household_power_consumption.t... |
15ed99f2863d1632f0f1688c1b44207e98cd51c7 | 2b2aee3352f8a10c121fe74036eddec01b3ee595 | /man/slim_extract_genlight.Rd | 1634f2f617f5977b6264c30d2ac6bd8c8e7a28b6 | [
"MIT"
] | permissive | rdinnager/slimr | 56f1fef0a83198bce292dd92dc1014df87c2d686 | e2fbb7115c7cca82dabd26dc6560e71a8cd0958b | refs/heads/master | 2023-08-21T14:00:36.089104 | 2023-07-31T03:11:09 | 2023-07-31T03:11:09 | 226,999,099 | 8 | 1 | NOASSERTION | 2023-08-03T05:44:32 | 2019-12-10T01:04:16 | R | UTF-8 | R | false | true | 536 | rd | slim_extract_genlight.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/slim_extract.R
\name{slim_extract_genlight}
\alias{slim_extract_genlight}
\title{Extract data into a genlight object}
\usage{
slim_extract_genlight(x, ...)
}
\arguments{
\item{x}{\code{slimr_results} object containing data generated by \code{... |
459369dec5c28285355d0a6e08650dc297ac7b94 | 9b76f92dfecfc84e2a43da24b9e4aa678a2de356 | /bootcamp/022RBasicsExercise.R | a65c9f5e9f16ffdce33e6d4279dcb0c7c85e0eb8 | [] | no_license | rathanDev/r | fa9d82582a83271b1f771c3bc9dd4348b0b28f73 | 2c4871f13de7cde82df7e0e63a253fa4a575a23b | refs/heads/master | 2022-12-13T17:56:46.669651 | 2020-09-10T12:32:49 | 2020-09-10T12:32:49 | 264,051,092 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 473 | r | 022RBasicsExercise.R | # R basics exercise
# /udemyR/R-Course-HTML-Notes/R-for-Data-Science-and-Machine-Learning/TrainingExercises/Exercises/RBasicsExercise.html
2 ^ 5
stock.prices <- c(23,27,23,21,34)
stock.prices
daysOfWeek <- c('Mon','Tue','Wed','Thu','Fri')
names(stock.prices) <- daysOfWeek
stock.prices
mean(stock.prices)
over.23... |
b607703b96eb80b406e6f6a3a9dd8869ed7c5976 | a2718fd2bab9eb1b86b77b8c9b0d776973d11315 | /extras/basicShinyApp/ui.R | 6427cbf00eea934bb990dbe65ad770928e478250 | [
"Apache-2.0"
] | permissive | OHDSI/SelfControlledCaseSeries | df61f7622fa19bb7b24a909a8da4f18c626cf33c | 28cd3a6cb6f67be5c6dfba0682b142ff3853e94c | refs/heads/main | 2023-08-31T02:00:48.909626 | 2023-04-13T11:42:06 | 2023-04-13T11:42:06 | 20,701,289 | 13 | 12 | null | 2023-09-07T04:50:44 | 2014-06-10T20:53:47 | R | UTF-8 | R | false | false | 8,394 | r | ui.R | library(shiny)
library(DT)
shinyUI(
fluidPage(style = "width:1500px;",
titlePanel("SCCS Evidence Explorer"),
tags$head(tags$style(type = "text/css", "
#loadmessage {
position: fixed;
top: 0px;
... |
69d92ff9dcf0a2d6e936d61b0ad2ed88ee284174 | c7f693112b1019b0017f94fcdb3f1dba79749016 | /Rfunction/CellSIUS_final_cluster_assignment.R | 18ef7133f7bba5e1884c62b0cd49cc00cf892048 | [] | no_license | fabotao/GapClust-rge-paper | d08d13dfc14d730a6c77459a1a353f5c7de4443b | bc845328fe1196f2b4aac380a8f0192372481f10 | refs/heads/master | 2022-12-09T08:47:44.750004 | 2020-09-06T12:58:22 | 2020-09-06T12:58:22 | 291,741,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,368 | r | CellSIUS_final_cluster_assignment.R | ############################################
# OPTIONAL: Final assignment to unique clusters
# Note: This is different from the previous subcluster asignemnt, where a cell can potentially be
# a member of multiple subgroups.
############################################
#' Final assignment to unique clusters
#'
#'
#'
#... |
3aa24f6b7bdd0dd29144b7ba4ab70864f1995aba | 1fe8e63f792ec46de431b05f8a43ef85d4374a69 | /scripts/Merged quantification and stats/Spectre - merged quantification and stats.R | 233d87526c3da339ea83da5e39f3d5cb8675e505 | [
"MIT"
] | permissive | tomashhurst/Spectre | 4b470b7f366037e9b9b00b6c75af2cafd1c64236 | b24cf382ea9b0d98c28fc4b5d60ab3339447d220 | refs/heads/master | 2021-07-16T18:07:55.542076 | 2021-06-27T04:42:54 | 2021-06-27T04:42:54 | 199,840,725 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,414 | r | Spectre - merged quantification and stats.R | ##########################################################################################################
#### Spectre discovery workflow - C - differential quantiative and statistical analysis
##########################################################################################################
# Spectre R p... |
e9f2d6f32e326f08f4fab55da123db5ee28e9279 | 7f3f81bdde73f1aa8f2f5a5baf208659c35925a8 | /plot1.R | cb90cbff95ab56420852b810264ff48aa9be0a1b | [] | no_license | eranmanor/Exploratory-Data-Analysis---Course-Project-1 | f64e4db4e18e32e18689852a99aff5cf8b0c12a5 | 6c135b10f85c1e4916972374a67eefa7fb23f628 | refs/heads/master | 2020-12-03T14:59:02.320312 | 2016-09-01T08:22:31 | 2016-09-01T08:22:31 | 67,109,578 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 837 | r | plot1.R | # Reading Power Consumption Data into a table
hhPowerConsum <- read.table("household_power_consumption.txt", skip = 1, sep = ";", na.strings = "?")
names(hhPowerConsum) <- c("Date","Time","Global_active_power","Global_reactive_power","Voltage","Global_intensity","Sub_metering_1","Sub_metering_2","Sub_metering_3")
# Su... |
6a1dc24c46b02081ade7b1504ee0a4d91a22c1e3 | 7f4bbb0dd5be6a704b618cf0fbf0c80fff699235 | /man/load_tab_rachat.Rd | 25d4dafa390c542755e2f6731e765d74d18097f4 | [] | no_license | DTichit/ALModel | 67c7b041a84dd9fdaec3c084d3cbd1f322b5a827 | 820faab78ff29d404b522b737170393930132c77 | refs/heads/master | 2023-03-08T09:40:48.628155 | 2021-02-10T18:24:52 | 2021-02-10T18:24:52 | 337,813,431 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 789 | rd | load_tab_rachat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TabRachat-load.R
\docType{methods}
\name{load_tab_rachat}
\alias{load_tab_rachat}
\title{Fonction \code{load_tab_rachat}.}
\usage{
load_tab_rachat(address)
}
\arguments{
\item{address}{est un objet de type \code{character} indiquant le dossie... |
f0688457e784fc4006bba2b76ef3b51d243442dc | 0392e3ed22ce0e014c04634f2adb018325c0025b | /inst/docs/examples_for_paper.R | f2ffef7808666a9c420e13035dd6d95543ab88c4 | [] | no_license | swang87/fc | cff78468ac1cdb1ed2922f94a29fd2e4eb682c92 | f031539b06b26016560f47303ebef28dec90808d | refs/heads/master | 2021-03-27T14:53:00.290061 | 2019-10-20T22:53:12 | 2019-10-20T22:53:12 | 114,667,105 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,735 | r | examples_for_paper.R | ### Benchmark
library(fc)
library(purrr)
library(magrittr)
library(microbenchmark)
make_res_tbl <- function(x, units = "us") {
N <- 10000 # num of its, for SE calc
if (units == "us") {
div <- 10^3
} else if (units == "ms") {
div <- 10^6
} else {
div <- 1
}
res <- paste0("$", round(aggregate(x$t... |
f423aea1ad117efd8696dd2a9568f9e6801748c8 | 574a4b779bc2fe69eb19113cf7130f9ae9100ee6 | /man/SCDC_prop.Rd | 57e67fe44748553a93a6708dd3d06147e1016043 | [
"MIT"
] | permissive | meichendong/SCDC | 472a3bc0bd676446f57795ce2106d5de40e49dce | 890c604eebd7fffa4a08d7344fbd516df6efcf8d | refs/heads/master | 2023-01-27T12:42:41.558530 | 2023-01-09T02:31:48 | 2023-01-09T02:31:48 | 187,677,545 | 40 | 14 | null | 2022-06-09T07:51:14 | 2019-05-20T16:33:20 | R | UTF-8 | R | false | true | 1,569 | rd | SCDC_prop.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Deconvolution.R
\name{SCDC_prop}
\alias{SCDC_prop}
\title{Proportion estimation}
\usage{
SCDC_prop(
bulk.eset,
sc.eset,
ct.varname,
sample,
ct.sub,
iter.max = 1000,
nu = 1e-04,
epsilon = 0.01,
truep = NULL,
weight.basis = ... |
0e7dc72e79e9714f4140cb035bf9145b645b3dc4 | 411beb364f61f99d7ac3c3747cf73a864fc71d26 | /EstimatingActivity_example_Nepal2019.R | 2abc25842930e78a1123fe2905ecc45dbdd8a158 | [] | no_license | GBFerr/CameraTrapActivity_tutorial | 6a67c87543da4cbd0f64a75b2c2d0e4badbc4f46 | 16df1ba1c6167b747f2c64c8cb9d14ef46066698 | refs/heads/main | 2022-12-30T23:27:34.571775 | 2020-10-23T11:01:15 | 2020-10-23T11:01:15 | 306,605,422 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,194 | r | EstimatingActivity_example_Nepal2019.R | ########################################################################
# Estimating activity level and activity pattern from camera trap data #
# Guilherme Ferreira - 14/July/2020
########################################################################
# Key papers:
# 1) Rowcliffe et al. Methods in Ecology a... |
e841f7dfbd5ce2bccb7b77984264df8a46165094 | c79acbf4db62c5e2fecde2931a437ae626766633 | /apply family.R | 8b572e03550dfb65126c6b8c70cd51e5488cd8a6 | [] | no_license | gonggaobushang/R-Base | 5e999750f49227051f6d890cad25a7ce53b7540e | a5c266af6bf59a8c55e79a6d1eae7ffeefeb5439 | refs/heads/master | 2020-05-31T13:49:50.510290 | 2019-11-26T08:36:48 | 2019-11-26T08:36:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,427 | r | apply family.R | ########################################################################apply
#apply(X, MARGIN, FUN, ...) MARGIN: 按行计算或按按列计算，1表示按行，2表示按列
#矩阵、数据框、数组(二维、多维)，按行或列进行循环计算
x<-matrix(1:12,ncol=3)
apply(x,1,sum)
apply(x,2,mean)
x <- cbind(x1 = 3, x2 = c(4:1, 2:5)); x
myFUN<- function(x, c1, c2) {
c(sum(x[c1],1)... |
957e9a777ce2f7950ba5cf9e5dfc8bdab7dac15b | 3b65ab1fc7af3b4711085c05f21dd28ef4bcb98a | /original.R | a96688d0f5b9f12a7acd449ce7304795b7466752 | [] | no_license | llecowen/Mark-Resight-Recovery-HMM | 56a16e9e03c157291f09e08a936486ba23be45cb | 662a186c2e4f486db5f351ac722a2748075f0d85 | refs/heads/master | 2020-06-11T07:30:03.262890 | 2018-07-13T21:57:20 | 2018-07-13T21:57:20 | 75,733,909 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,475 | r | original.R | #This is the version of the HMM model that works with the constant paramters.
devMULTIEVENT <- function(b,data,eff,e, nh,km1){
# data= encounter histories, eff= counts/frequency
# e vector of dates of first captures
# garb vector of initial states
# km1 number of recapture occasions
# nh number individuals
# ... |
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