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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
362c832e3df85b176627fdd40a320062ceca8858 | 14ab055cf7c88ebe702c16c594dbc65b7c542181 | /alluvials/defect_rate.R | 9f93b58c562df960b3517cee41c38362381fe64e | [] | no_license | ryanburge/cces_panel | d9f83d1808d31bc8d649f23f4d1585cecb23ddbb | 9f5a12c5ea7ab5e35124fa5abe9b226114383baa | refs/heads/master | 2021-09-09T13:44:01.072012 | 2018-03-16T16:55:32 | 2018-03-16T16:55:32 | 109,161,371 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,195 | r | defect_rate.R |
## Create the Defection Rate Bar Chart
test <- cces %>%
select(caseid, religpew_10, religpew_12, religpew_14)
count <- test %>% group_by(religpew_10) %>%
count(religpew_14) %>% mutate(pct = prop.table(n)) %>% ungroup(religpew_10)
defect <- count[count$religpew_10==count$religpew_14,]
defect <- defect %>%
... |
59b0a7c44f3174d70f271c2e4277f02b688936a7 | a5bd747b4d1ac2800d355b61adee8765d1cc663f | /R/setPriors.R | 43bc055e9def053b6085298f880e504e8c99ef07 | [] | no_license | bonata/HMSC | d97cdf0a597565cbe9cf08ed7fe93c35f620a320 | 567f2f7cd118f6c405a833e56b9b5f64a9e2abf2 | refs/heads/master | 2020-04-03T13:56:05.314479 | 2018-09-30T16:17:52 | 2018-09-30T16:17:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,477 | r | setPriors.R | #' @title setPriors
#'
#' @description Sets or resets priors to the Hmsc object
#'
#' @param V0 scale matrix in the Wishart prior distribution for the Beta parameters
#' @param f0 number of degreees of freedom in the Wishart prior distribution for the Beta parameters
#' @param mGamma mean for the prior multivariate Gau... |
7d816391dfb9499b215825a0193ac068ff88351e | 50b816887e74e050ff9e3ad03da691835e6be6a2 | /r_scripts/work/hw4.R | ffd273e910489e27dbfb0b701cea0491adbce8b6 | [] | no_license | alexsalo/time-series-class | d79964abf5e472d9f88b0e8179e6dd597f8ca692 | f65f957074745c35732ea8fffce973d40452d604 | refs/heads/master | 2020-12-07T17:09:33.692052 | 2016-01-19T16:35:18 | 2016-01-19T16:35:18 | 41,764,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,146 | r | hw4.R | source('C:/Dropbox/Baylor/TimeSeries/R/ts_hw/ts_hw/timeslab.R')
dyn.load('C:/Dropbox/Baylor/TimeSeries/R/ts_hw/ts_hw/timeslab2015_64.dll')
install.packages('knitr', dependencies = TRUE)
library(knitr)
### C4.2
gen_wn_corr <- function(n, nreps=20, m=25, sig2=1)
{
data = vector()
for (i in 1:nreps){ # Creating the Ens... |
82e773c0efb3e7a27fc7d7c67e170969b3fa2b90 | 77157987168fc6a0827df2ecdd55104813be77b1 | /palm/inst/testfiles/euc_distances/libFuzzer_euc_distances/euc_distances_valgrind_files/1612968324-test.R | 3f9480b831eca5bff2c9d45139d7ed43154c9839 | [] | no_license | akhikolla/updatedatatype-list2 | e8758b374f9a18fd3ef07664f1150e14a2e4c3d8 | a3a519440e02d89640c75207c73c1456cf86487d | refs/heads/master | 2023-03-21T13:17:13.762823 | 2021-03-20T15:46:49 | 2021-03-20T15:46:49 | 349,766,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 748 | r | 1612968324-test.R | testlist <- list(x1 = -1.50493695303394e+206, x2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), y1 = c(2.81773963954832e-202, 5.18365505937542e-130, NaN, 3.32535613919497e-93, 2.77448001743148e+180, 3.07594210200033e-304, 1.27654717701454e+74, NaN, NaN, NaN, 1.38523893523259e-309, 0, -5.4861... |
45376ebab3b4818da9f75fc5764bf1506b335a7a | 90b16bff7561d50820e03bc0704d1e1f681dd570 | /scripts/collect_resid_for_assoc.R | 69c099775031422ff68bfa7cc06dbd3148865282 | [] | no_license | e-macdonald-dunlop/catalogue_omics_clocks | d08a177a21e9fdebc94f93100e54fcf180167ee5 | 67533bbe8d5fe2c3c85c04a4c987e7f4dcd4700b | refs/heads/main | 2023-03-19T06:26:39.548487 | 2021-03-05T11:01:18 | 2021-03-05T11:01:18 | 333,200,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,887 | r | collect_resid_for_assoc.R | library(data.table)
library(yaml)
library(ggplot2)
library(plyr)
library(reshape2)
library(RColorBrewer)
library(gdata)
library(Hmisc)
args<-commandArgs(T)
if(!file.exists(args[1])){dir.create(args[1])}
setwd(args[1])
source("/exports/igmm/eddie/wilson-lab/projects/prj_107_omics_clock_pipeline/scripts/eddie/pipeline_... |
bb9f9c57754d10876f7609366d8cf4ce4d1f62b6 | 91b827e48e510661d90d81d0155130b2703ae51d | /analysis/2-binary-adjusted/2c-PR-prev-ipcw.R | eff209b0c065e5733c37e542fe0fb4b90020b448 | [] | no_license | LaKwong/WBB-STH-Kato-Katz | 2f0cf1a906ced8badb35b18df72adbcf41013534 | d4d97093fb4e04f6cb7bcf919e41c42ea25cff83 | refs/heads/master | 2020-09-02T05:37:17.925364 | 2019-04-24T19:43:26 | 2019-04-24T19:43:26 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,680 | r | 2c-PR-prev-ipcw.R | ##############################################
# WASH Benefits Bangladesh STH Kato-Katz Study
# Primary outcome analysis
# STH IPCW analysis
# by Jade Benjamin-Chung
# jadebc@berkeley.edu
##############################################
rm(list=ls())
source(here::here("0-config.R"))
#--------------------------------... |
44a39e8291d641dee3c6e66a9a321bcea032fa3f | 031d1fc59f9c6b4e66d38d799e4039ecf4be235a | /R/cluster/scr_move_DA_rsig.R | 9327d3b37ce3f2ebf3a15705ffb84dc231b00582 | [
"MIT"
] | permissive | rbchan/scr-move | cc76c59795996843b63efd312e3901525354cc2f | 30d3ed9f8c3f554b6867f6dc923a6fa143de4551 | refs/heads/main | 2023-04-11T11:57:30.973400 | 2021-10-31T14:11:56 | 2021-10-31T14:11:56 | 355,622,082 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,010 | r | scr_move_DA_rsig.R | ## Fit SCR+move model in JAGS and using custom Gibbs sampler
## Must include model for capture of telemetered guys
## Otherwise, some of them would have all zero encounter histories, which
## isn't consistent with the (S)CR likelihood
## First we have to build the R package on the cluster
print(olibs <- .libPaths())
... |
949ab8fca114c8f467258dde240a1a9f4e006ef7 | effe14a2cd10c729731f08b501fdb9ff0b065791 | /paws/man/s3_get_bucket_notification.Rd | b940414eab451c82e7f89798b5d3cbbe11499b1c | [
"LicenseRef-scancode-unknown-license-reference",
"Apache-2.0"
] | permissive | peoplecure/paws | 8fccc08d40093bb25e2fdf66dd5e38820f6d335a | 89f044704ef832a85a71249ce008f01821b1cf88 | refs/heads/master | 2020-06-02T16:00:40.294628 | 2019-06-08T23:00:39 | 2019-06-08T23:00:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 897 | rd | s3_get_bucket_notification.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/s3_operations.R
\name{s3_get_bucket_notification}
\alias{s3_get_bucket_notification}
\title{No longer used, see the GetBucketNotificationConfiguration operation}
\usage{
s3_get_bucket_notification(Bucket)
}
\arguments{
\item{Bucket}{[required... |
c41ef438baf688fc8e9ab41a3949f219b3c7c97c | 467dd55648968dfe4ca81cfafb9ff233a122f59f | /Machine Learning/Classifiers kit/Part II/NaiveBayes.R | ca862ba584dbd3ce5056a4a60116ae09aeccd464 | [] | no_license | aminamp/University-Projects | f745b8518936d34b30c9231ec9335cae18f80b2b | e7c7ef3c74b56dc574cd49dcc4efae53336006d9 | refs/heads/master | 2021-01-18T01:25:48.195700 | 2016-09-20T17:04:32 | 2016-09-20T17:04:32 | 68,672,488 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 502 | r | NaiveBayes.R | library(MASS)
library(class)
library(e1071)
pima <- read.table("http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data",header=F,sep=",")
for (i in 1:10 ) {
indexes = sample(1:nrow(pima), size=0.1*nrow(pima))
test = pima[indexes,]
train = pima[-indexes,]
model... |
22222c37740dd8b0991adb9d1cbd0107ca812f8a | 9716a6d75781d99a1cfdfc09cb55b7f53ea46b81 | /AUD/bop/tot/code/tot.r | 102dbaa02d956a5e520c118546e01e492cdd40ad | [] | no_license | ricardianambivalence/Rproject | 17ae2017de25cb8e934fbce5bc4619b86ce49194 | a7136c6cf654cc8c1eda4d74d98764e3037b5764 | refs/heads/master | 2021-01-01T06:04:47.397872 | 2014-01-26T23:37:43 | 2014-01-26T23:37:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,536 | r | tot.r |
# {{{ packages and functions
require(ggplot2)
require(reshape2)
require(gridExtra)
require(xts)
source('~/R/Rhelpers/helperFuncts.r')
source('~/R/Rhelpers/RAcolorpal.r')
# }}}
# {{{ PATHS
projectPATH <- "~/R/aud/bop/tot/"
codePATH <- file.path(projectPATH, "code")
dataPATH <- file.path(projectPATH, "data")
plotPATH <... |
cac81a2d2d1dab6b249091cdcc422c6d3524da74 | 537404b0e7cc3794e2fb767573eafb3a8b245efa | /Package Installer.R | 5df223858d2e433b4dd67130aaa09d672c430b18 | [] | no_license | anhnguyendepocen/Data-Science-3 | b58620111f743c4d2654c38778bea4deb7d31261 | a3e27011f3d9ee005bbb3a058b289e2f6a46dd4b | refs/heads/master | 2022-02-18T08:04:04.370157 | 2019-10-04T01:25:08 | 2019-10-04T01:25:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 510 | r | Package Installer.R | install <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[, "Package"])]
if (length(new.packages))
install.packages(new.packages, dependencies = TRUE)
sapply(packages, require, character.only = TRUE)
}
# usage
required.packages <- c("ggplot2", "dplyr", "reshape2", "devtool... |
cbb1bb7983d12c52c627ef8f992c0b84dd2bf074 | b79b7709a0cef8482b70f98ebd54022895bd7e73 | /bootstrapping_exe.R | 957003cc8df855a314f4cc214543cf95cb4ef03e | [] | no_license | jeku7901-CU/Barcode_07_28_2020 | 07e0b549830d4390611f669946cabed6e04da504 | 52c724b8338be6ae57b12c52785a9d4782620984 | refs/heads/master | 2023-07-01T01:59:04.583589 | 2021-08-05T12:37:52 | 2021-08-05T12:37:52 | 283,261,887 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,399 | r | bootstrapping_exe.R | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args[1])
print(args[2])
#setwd("/Users/jeku7901/olwin_dowell_labs/reanalysis_09_26_20/bootstrapping/")
pruned.merged.tab.noTRs.numeric <- read.csv(args[1], header = T, row.names = 1)
pruned.merged.tab.noTRs.numeric <- data.matrix(pruned.merged.tab.noTR... |
8dacdc24a74d6e8391a699fd7914a0549669cd61 | 81449b3859ba496fa486875122321f4572c77d10 | /cachematrix.R | 10aaa5a18b97024581a587eea2fcc9f12e5986d3 | [] | no_license | ravinan/ProgrammingAssignment2 | a3de7c4f8bc78921ad1b1edb42fa864e3c6c9da8 | 64cddbe8b14a81931c5572ba0163c157a453a0fc | refs/heads/master | 2022-12-12T03:07:20.434880 | 2020-09-07T16:33:50 | 2020-09-07T16:33:50 | 293,095,964 | 0 | 0 | null | 2020-09-05T14:50:27 | 2020-09-05T14:50:26 | null | UTF-8 | R | false | false | 1,512 | r | cachematrix.R | ## Two short R functions that:
## 1) store a square matrix and it's inverse (makeCacheMatrix),
## and
## 2) compute and return the matrix inverse as well as store the matrix inverse
## for future use (cacheSolve).
## The function makeCacheMatrix stores a square matrix in the local variable x and cache's
## it's inv... |
e0a81ec2756cc8f505be03887c8110501642a7d3 | f23b0f652e57bc759259ba57028fda8c10fc436c | /man/f7Select.Rd | f0168ec18d14dd8a4c2eeddcbbfe1de7e4e65b98 | [] | no_license | CristianPachacama/shinyMobile | 57e2d48018196ec999a9ae5207c248283a963b76 | 7e6ef87b42e6f23fe776b9ad9f182a1363c77d04 | refs/heads/master | 2020-09-27T19:55:35.522503 | 2019-12-07T00:45:55 | 2019-12-07T00:45:55 | 226,598,443 | 1 | 1 | null | 2019-12-08T01:26:08 | 2019-12-08T01:26:08 | null | UTF-8 | R | false | true | 889 | rd | f7Select.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/f7-inputs.R
\name{f7Select}
\alias{f7Select}
\title{Create an f7 select input}
\usage{
f7Select(inputId, label, choices)
}
\arguments{
\item{inputId}{Select input id.}
\item{label}{Select input label.}
\item{choices}{Select input choices.}
... |
bff24f245d7daa5b9093bf97d74e60e14ea3fefb | 2adc513c87e5f60db34f991e8634a1d141b0eccc | /tests/testthat/test-CLR.r | 315ac72ae378d5f497ebc22570ed84e100f86906 | [
"MIT"
] | permissive | hyunseungkang/ivmodel | 663608701e58d37ee6347c58b07eace83a0b06d5 | 825fa71c52961bc8b1066f6acb1bd24e93abcbba | refs/heads/master | 2023-04-14T12:29:29.634364 | 2023-04-10T16:04:52 | 2023-04-10T16:04:52 | 71,493,810 | 9 | 2 | MIT | 2020-01-03T19:24:12 | 2016-10-20T18:48:54 | R | UTF-8 | R | false | false | 4,232 | r | test-CLR.r | context("Check accuracy of CLR using Card data")
test_that("CLR test without exogeneous covariates and intercept, single-IV", {
Y=card.data[,"lwage"]
D=card.data[,"educ"]
Z=card.data[,"nearc4"]
foo = ivmodel(Y=Y,D=D,Z=Z,intercept=FALSE)
teststat_out = CLR(foo)
expect_equal(as.numeric(teststat_out$ci),... |
21c5c1eb90136f5b02e04dc14af66ea129453661 | 70ceafccb7ed3005e64521551eae6657385118e5 | /R-Portable/library/Matrix/tests/Simple.R | 721bbd2fcf9ea32dac61c70e90a2e4bbcab68c65 | [
"GPL-2.0-only",
"LicenseRef-scancode-unknown-license-reference",
"LGPL-2.0-or-later",
"GPL-3.0-only",
"GPL-1.0-or-later",
"LicenseRef-scancode-public-domain",
"CC0-1.0"
] | permissive | ksasso/Electron_ShinyApp_Deployment | 6e63686b27bc38607bca1e5b50ed7cd58f6a4a3b | 1402f4d6bbb4a415bce07ebfddc8b76704f11f97 | refs/heads/master | 2023-07-06T11:48:21.413305 | 2020-04-30T12:53:11 | 2020-04-30T12:53:11 | 120,221,429 | 100 | 24 | CC0-1.0 | 2023-06-25T03:17:43 | 2018-02-04T20:42:11 | HTML | UTF-8 | R | false | false | 40,260 | r | Simple.R | #### Currently a collection of simple tests
## (since 'Matrix' takes long to load, rather have fewer source files!)
##-------- *BEFORE* attaching Matrix: --------------------------------
str(Matrix::Matrix)# -> load the namespace
T <- new("ngTMatrix", i=0L, j=2L, Dim = c(2L,6L))
T
as(T, "CsparseMatrix")
## gave Error ... |
18bb7c04846ccedea73bc7f962a0f8cab38c35b8 | 36e231c2defb96012d7e75269654b43ac859d714 | /Analysis_after_BAM_Scripts/positionOverlay_Poolseq.R | ac4b0a09ad4345bc2a44867605eabbdd42ddb5b1 | [] | no_license | PaulKnoops/Experimental_Evolution_Sequence_Repo | 179b9b4124f19b707a604aa20d27a2b822953cc7 | 11f6af2ec5634181b11469f4a7f9cebf4e1ed5fe | refs/heads/master | 2020-03-12T16:01:25.734641 | 2018-05-04T18:51:18 | 2018-05-04T18:51:18 | 130,705,291 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,626 | r | positionOverlay_Poolseq.R | #Overlay Plots
require(data.table)
require(tidyverse)
xcs <- fread('../Data/Positions/candidatePos.csv')
#Positions
require(tidyverse)
require(data.table)
Zxc_sig <- fread('../Data/poolseq_outputs/poolseq_FDR_Sigpos.csv')
Zxc_length <- fread('../Data/poolseq_outputs/SelCoef_Full_LENGTHS.csv')
aa <- Zxc_length$x[1]
b... |
267b2a7e3904ea5b7abc1946bd694156d7c5f71d | dca44395dbf60e1743c65bced7b26838bd676781 | /KCDC/imputation.tool/imputation.tool.memory.check.R | d8a05b6935ab48d71df03516626046ff549a5225 | [] | no_license | ksmpooh/SungminCode | 1b550c375125ea7869917de337aa093160aa03eb | 33b266b80389664282a2d4d6eb9c2db593442a5f | refs/heads/master | 2023-08-03T16:22:35.085299 | 2023-07-31T16:12:33 | 2023-07-31T16:12:33 | 106,177,934 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,373 | r | imputation.tool.memory.check.R | setwd("c:/Users/user/Desktop/KCDC/imputation.tool/")
#################TEST
loglist <- read.table("memory.check/impute4/log.list.txt",header = T)
head(loglist)
df <- read.table("memory.check/impute4/100000001_105000000.log",header = T)
head(df)
table(df$COMMAND)
process_list <- data.frame(table(df$COMMAND))$Var1
out... |
f23abcae41b4412914b0afd57da767a5310c0749 | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/rstudioserver_analysis/WKM_and_BM_together/LDA_TSS_downstream_BM_checks.R | 5fd18c366dc4df6c3aa490d608ad33cf64783cad | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,571 | r | LDA_TSS_downstream_BM_checks.R | # Jake Yeung
# Date of Creation: 2020-06-23
# File: ~/projects/scchic/scripts/rstudioserver_analysis/WKM_and_BM_together/LDA_TSS_downstream.R
#
rm(list=ls())
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
library(topicmodels)
library(hash)
library(igraph)
library(umap)
library... |
7d5f64b9321262a4823ed30524da51455b698dfb | de4f8bf5c5f873508d950dd500828e2173428ab8 | /scripts/Script 1 Preparing and Analyzing Phenotypic Data.R | a04319fa9dfa16039c35c854f2049fe90d3423b1 | [] | no_license | kraltendorf/IWG_Yield_Components | 289ec03902881bf59e106bda7839a9462e67b01c | 4e84ca1146889cf196fa8b8bbd616552043df382 | refs/heads/master | 2023-02-04T13:14:14.381873 | 2020-12-17T21:44:18 | 2020-12-17T21:44:18 | 304,757,777 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,938 | r | Script 1 Preparing and Analyzing Phenotypic Data.R | # Project: IWG_Yield_Components
# Script 1 - Preparing and Analyzing Phenotypic Data
# Author: Kayla R. Altendorf
# Date: 05/21/2020
# Required Pacakges:
library("dplyr")
library("tidyr")
library("lme4")
library("lmerTest")
library("emmeans")
library("stringr")
library("reshape")
library("plyr")
library("multcompView... |
7c83e2b2f0694d3512dc5acef66e8546c84667e9 | f2a7697627b6848a1de1f39a966abd8cd4e26b97 | /man/predict.dawesModel.Rd | 914c5167ee4e724c4c1df06932132a2e2786f5df | [
"MIT"
] | permissive | dnlbrkc/heuristica | 954203a8fcbc6b99fb98bbb4c93c6f05b494218c | d4125158e72b8165bca137b1ae791d70fe019df1 | refs/heads/master | 2020-12-24T22:29:05.291133 | 2015-08-01T15:19:13 | 2015-08-01T15:19:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 645 | rd | predict.dawesModel.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/heuristic_classes.R
\name{predict.dawesModel}
\alias{predict.dawesModel}
\title{Generates predictions for Dawes Model}
\usage{
\method{predict}{dawesModel}(object, ...)
}
\arguments{
\item{object}{A dawesModel.}
\item{...}{Normally t... |
60307e1b9a51564e80edd6d25138768f37ec0b6b | 98fd03ebd9de52038f06cd89200a460432f9cc5c | /man/metric_score.pkg_metric_has_news.Rd | fbb5e5eea423df883a00da5e9edf1851dcf7c712 | [
"MIT"
] | permissive | pharmaR/riskmetric | 51d3b067da6db6ad1252f3ba706db1d922b5df64 | 3d1501880edc07cff5cd72129c0df0899db83029 | refs/heads/master | 2023-07-26T07:33:56.471690 | 2023-05-31T14:58:21 | 2023-05-31T14:58:21 | 173,354,970 | 148 | 32 | NOASSERTION | 2023-09-12T20:41:31 | 2019-03-01T19:11:16 | R | UTF-8 | R | false | true | 642 | rd | metric_score.pkg_metric_has_news.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/assess_has_news.R
\name{metric_score.pkg_metric_has_news}
\alias{metric_score.pkg_metric_has_news}
\title{Score a package for the presence of a NEWS file}
\usage{
\method{metric_score}{pkg_metric_has_news}(x, ...)
}
\arguments{
\item{x}{a \co... |
ac5865e08c7818f4e22b9dc6767757936f32965f | 900b5811701444c45e39d1506c36f39a1e3d7205 | /removeFeatures.R | 26a28b7935fa46efb117f3f100a4c8322288ffbf | [] | no_license | svicente99/DataScience_Capstone_Report | f03ef3633f22ad0779d6b6d1cef0fc4679f5abb2 | fe05ad155cc61c8251e5060a09d2a537fbcb51ee | refs/heads/master | 2021-01-10T03:09:29.187869 | 2015-12-31T00:39:56 | 2015-12-31T00:39:56 | 48,718,767 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,571 | r | removeFeatures.R | # ---------------------------------------------------------------------
# Coursera.org - Data Science Specialization - Capstone Project
#
# >> Explore dataset files (clean up refinement, remove features)
# author..: Sergio Vicente (@svicente99)
# data....: 26th Dec. 2015
# ---------------------------------------------... |
f4e89d5bd54abb44e5cf51f0b8527f8332f06cf7 | be58961b8970ff0d19ff8b5d1ed1fd5b398a6adf | /metahit_script.R | 90c91357c365d8622a0cdba9c9c50954e6c2c0ad | [] | no_license | metahit/mh-execute | 0b7f880363ad82efcb94a9c041c1b3b2d7b5dc49 | 4d2a571621a1294552a5dedec941927f74696e8a | refs/heads/master | 2022-05-18T11:28:19.719126 | 2022-03-29T15:45:38 | 2022-03-29T15:45:38 | 194,102,830 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 50,918 | r | metahit_script.R | rm(list=ls())
library(ithimr)
library(splines)
require(tidyverse)
require(knitr)
require(kableExtra)
# require(citr)
library(compiler)
library(earth)
library(future)
library(future.apply)
library(doFuture)
library(stringr)
registerDoFuture()
plan(multisession)
# Increase maximum size of global variables
options(futur... |
98a2ba49a61c4a1722838f8cf3228f790757923f | a2ba22a5bb7f38a214d40b769cbc8f6378426750 | /R/calculateTiTv.R | e3f375a42d4584988b8fe061efd0fb93ab944cdb | [] | no_license | huklein/R453Plus1Toolbox | faa368f57e1f27b2f45249ce12d95ed72610be8b | 539c22e575530ea93c2f2e95166bd6ef646a2a8d | refs/heads/master | 2021-08-30T02:00:13.385605 | 2017-12-14T01:55:16 | 2017-12-14T01:55:16 | 113,723,920 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,947 | r | calculateTiTv.R | .calculateTiTv <- function(object) {
if (!is.element("variantBase", names(fData(object))) |
!is.element("referenceBases", names(fData(object)))) {
stop("Column variantBase or referenceBases is missing.")
}
df = fData(object)
indSNP = nchar(df$referenceBases) == 1 & nchar(df$variantBase) == 1
df = ... |
7ae14af848d34fee466d5f5fceed4786fbd3e6bd | d10541def727e478bdf637b43bc48cf52111d1c5 | /01_simple.R | 717198a7480b86ed4a5f8a902c701fb8d50cd676 | [] | no_license | mfoos/step-by-step-shiny | fff337c686a1b2418669651955732ebdc92b8cb0 | c28a55245c2044cffd9eea1deff5f797d9d2d3a8 | refs/heads/master | 2020-05-05T13:01:56.596112 | 2019-09-22T22:19:03 | 2019-09-22T22:19:03 | 180,056,150 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 649 | r | 01_simple.R | library(shiny)
ui <- fluidPage(
fluidRow( # Determines that this app will use the "fluid page" (grid) layout method
column(width = 12,
# Creates the input widget
textInput("textinputlabel", "What's up?"),
# Designates the place-holder for the output generated
text... |
2b8c533a8ce199fa231b2d81a76dc903af5ce843 | 15e79da5728a9d3aea403753a19487e5fc05ca67 | /DataAnalysisStatisticalInference/labs/3/lab3.R | 8831d4c093eba8a898b8193a847050c27486689b | [] | no_license | jose-dev/coursera | d690289e77c00d218461af64626cf047f5038c6e | a644badb9a56b4293affad936fcb906c64de31fe | refs/heads/master | 2021-01-17T15:02:33.592741 | 2017-03-28T16:46:55 | 2017-03-28T16:46:55 | 23,797,568 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,653 | r | lab3.R | #
# https://www.datacamp.com/courses/data-analysis-and-statistical-inference_mine-cetinkaya-rundel-by-datacamp/lab-3a-foundations-for-inference-sampling-distributions?ex=1
# https://www.datacamp.com/courses/data-analysis-and-statistical-inference_mine-cetinkaya-rundel-by-datacamp/lab-3b-foundations-for-inference-confid... |
9bf0b8305cfca98c6d476d8983ea99bbc4257d0d | 212f8d17d4e2a21ad33ebeee49571ec8764ab3e6 | /R/read-meteologica2.R | 4c7a3c2226889383e173a2c968167f4ad2954500 | [] | no_license | rte-antares-rpackage/antaresWeeklyMargin | 50db90d81dee445dc223e6a37c223c6f91f7a0c4 | 31ca059a2d80ce82ed079cb7f75147b4a5c9415e | refs/heads/master | 2021-05-11T10:41:49.555037 | 2019-07-24T15:29:38 | 2019-07-24T15:29:38 | 118,109,627 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,447 | r | read-meteologica2.R |
#' Read Meteologica file
#'
#' @param path path to a directory containing forecast files or a single file.
#' @param country filter files to read only one country.
#' @param date filter files to read by date(s), using a \code{Date} or a character with format \code{\%Y-\%m-\%d}.
#' @param time filter files to read by t... |
1028fba4b6a94b6af9f256a1b036c89d6f43ad67 | fca19c9d85b9e37203868fbce35694f645092daa | /tv-ratings/04_categorize.R | e72ecf931f9c147a57ac721ad2c1cd8e6b3704de | [] | no_license | ryanvmenezes/futbol | 4592e2fcf78eb4c36852aca35f0923af89a9c89c | 9729af696be09ee2f70190f666dabf644c7a619f | refs/heads/master | 2020-08-02T23:13:41.645638 | 2020-01-05T07:47:46 | 2020-01-05T07:47:46 | 211,540,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 838 | r | 04_categorize.R | library(lubridate)
library(tidyverse)
library(googlesheets4)
raw.ratings = read_csv('ratings-uncleaned.csv')
raw.ratings
raw.ratings %>% arrange(-viewers)
comps = raw.ratings %>%
group_by(comp) %>%
summarise(
countgames = n(),
totalviewers = sum(viewers),
avgviewers = totalviewers / countgames
)... |
4242341f4f874f63a6cf14f75b1505beb67087de | 7de54d346a101f2620058f14dc3eaf0fa415750c | /code/R/man/rhls.Rd | f4804b660fa874715da3da44624071c7147e357c | [] | no_license | a3a/RHIPE | 4e4d82d5e50e6eccc9aa603dc196dc2411a5f5ae | e5a9d3b261cd31a9ab01002b4551b5955948e858 | refs/heads/master | 2021-01-19T06:03:08.989299 | 2013-04-11T00:05:58 | 2013-04-11T00:05:58 | 9,531,318 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,125 | rd | rhls.Rd | \name{rhls}
\alias{rhls}
\title{List Files On HDFS}
\usage{
rhls(folder = NULL, recurse = FALSE)
}
\arguments{
\item{folder}{Path of directory on HDFS or output from
rhmr or rhwatch(read=FALSE)}
\item{recurse}{If TRUE list all files and directories in
sub-directories.}
}
\value{
vector of file and director... |
99cd0bf0951150ada8eae21589a25074a35d8804 | 9bd25d71e6b745f7ba96bd3447dddf0579e97704 | /man/write_gifti_component_of_cifti.Rd | 135632405e67f24b19ff43c878ff79a0ee2aef3e | [] | no_license | noguchit/ciftiTools | a7a4cbe562835478b8725d4c9508a1405bb51293 | a858c524520a5a1cdf1db1ebc05e3adf90e83d04 | refs/heads/master | 2022-12-07T13:40:00.144630 | 2020-08-14T01:04:22 | 2020-08-14T01:04:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 865 | rd | write_gifti_component_of_cifti.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/write_cifti.R
\name{write_gifti_component_of_cifti}
\alias{write_gifti_component_of_cifti}
\title{Write out a GIFTI File}
\usage{
write_gifti_component_of_cifti(data, out_fname, datatype = NULL, ...)
}
\arguments{
\item{data}{A data matrix to... |
738feb279e95ef3491b802f044110ae738773884 | 60edbb04067fed891c5aba36bef5dba769c650b6 | /CYTOF_base_population_plots.R | fd6f142c0a7194cc69cb1f0c7c7fe004f2551c3c | [] | no_license | am794/CyTOF_Analysis | a2704402b8555d054985bcae1b4d0d2631532e51 | c779c0bca4298fe0ebb59329bb3df45703b4edf6 | refs/heads/master | 2022-12-25T16:12:13.620992 | 2020-09-30T06:25:09 | 2020-09-30T06:25:09 | 283,353,191 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,949 | r | CYTOF_base_population_plots.R | #########################
# Base population plots #
#########################
library("openxlsx")
library("reshape2")
library("ggplot2")
pertussis_events <- read.xlsx("/Users/amandava/Desktop/Pertussis/New_Donors/Events/Analysis_3/Events.xlsx",sheet=2)
#config <- read.table("/Users/amandava/Desktop/Pertussis/New_Donor... |
88280e82cf3a95559ea163571b4c569a074c4839 | dcabe9a688420893a01ff7c2000b0ca2ae4687c0 | /R/10-Test_robust_residuals.R | e947b537446d6f65ccab74922f116d89d311f1d9 | [
"CC-BY-4.0",
"MIT"
] | permissive | nicebread/PSE-Database | 30ceccaaa0a051e7815e5122405d77c79dae1c54 | cac7584bed80871f2bd4217c5f56c26977b13760 | refs/heads/master | 2020-11-23T21:06:00.618381 | 2020-02-03T08:24:08 | 2020-02-03T08:24:08 | 169,381,395 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,774 | r | 10-Test_robust_residuals.R | ## This source code is licensed under the FreeBSD license (see "LICENSE" file)
## (c) 2018 Felix Schönbrodt
## ======================================================================
## Explore the difference between OLS correction for word count
# (current standard practice) and the newly recommended robust regression... |
ab80910b497ce671412f8adfb5c6f934fed0b3e3 | f2e4096c2e4f1150bc38bde846964ae8c36f44a7 | /man/DEM.Rd | b966836a409efec69e8b7018b62bf69e7ab79bbd | [
"Apache-2.0"
] | permissive | bschumac/perfectPeak | 6510cea3575e8810e4379235cbfb7cfd86ffb7c5 | 37b49f187fce77314df4ffc5084afc5c3a5b90f4 | refs/heads/master | 2020-07-29T04:42:06.812365 | 2016-02-03T17:47:05 | 2016-02-03T17:47:05 | 209,674,012 | 1 | 0 | Apache-2.0 | 2019-09-20T00:47:48 | 2019-09-20T00:47:48 | null | UTF-8 | R | false | true | 1,059 | rd | DEM.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/perfectPeak-package.R
\docType{data}
\name{DEM}
\alias{DEM}
\title{Demo Digital Elevation Model DEM}
\format{Arc ASCII Grid}
\source{
Data source: \url{https://www.tirol.gv.at/data/datenkatalog/geographie-und-planung/digitales-gelaendemodell-... |
f5b4228a81759de1d0d3b0d7c19c170a8fa3b19d | c9de7552a5bbf42969eec9d2f742d95dd3758897 | /in_class_mar_03.R | 01b8f4afe82e90a74cfdddc45bf7459ad53dc47f | [] | no_license | michaelfrancenelson/NRC290B_2020 | d24c0439306fda034c701a9a42a0ac421806e8a1 | da60d6b0ef47b2479344f8d704d7a7e014568d27 | refs/heads/master | 2021-03-06T16:59:45.845597 | 2020-11-09T15:32:57 | 2020-11-09T15:32:57 | 246,211,139 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,033 | r | in_class_mar_03.R | # I need to tell R that the 'Spp' column contains names.
# I want these row names of the data frame rather than a column of data.
# The 'row.names = "Spp"' is needed to tell r that it should be row names and not data
# on my computer, the data file is in a subdirectory called 'data' so I have to
# tell R to look there... |
b4ac4f355b3056d093e76f08b392f358f884b37c | 98f14939c2588d57de7025434b6934dcb076208c | /R/13_projections.R | 94b378a130b1db777cad5ed2befa4f6be4e68c88 | [] | no_license | larenaud/OWPC | 997ce88a1f974528201dc5eb90ddf73825c48808 | 773e6ece1735ea1047c428a0564e9bd9c0307e5b | refs/heads/master | 2021-10-28T03:55:32.968774 | 2021-10-19T21:11:04 | 2021-10-19T21:11:04 | 238,471,426 | 0 | 1 | null | 2020-02-05T17:13:10 | 2020-02-05T14:36:54 | R | UTF-8 | R | false | false | 49,153 | r | 13_projections.R | # code to simulate demographic rates - lambda - under different scenarios of temperature and phenology
# created by S. C. Beaumont
# modified by L. Renaud # 2020-04-23
# modified by S. C. Beaumont # 2020-06-01
# modified by J Van de Walle # 2020-07-16
# modified by S. C. Beaumont # 2020-07-20
####=================... |
b9facf1e206b65b7eae3f1565961edd226aadad4 | 85476eebe615722cf1759286db14e54725d15c2b | /R/genomic/chronology/league_model.R | 6bb378b243d035d192a9fd28e530ef30eacdebcf | [
"MIT"
] | permissive | matahi/lib | 6b51799d86196f87c3a2df7422504f6898ae3bdf | d5f665db5cfefa05c68950311d0f016bcf9ef690 | refs/heads/master | 2021-09-22T16:04:13.496704 | 2018-09-11T18:53:43 | 2018-09-11T18:53:43 | 103,403,531 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,121 | r | league_model.R | league_model <- function (chronology.df, n.repeat=1000, type="score", win=2, tie=1, loss=0, cutoff.occurrence=5, add.bars=T, group.genes=NULL) {
# cutoff.occurrence <- 5
# n.repeat <- 1000
# win <- 2
# tie <- 1
# loss <- 0
# add.bars <- T
# group.genes <- Reorder... |
32ed73242b379fb5b76c98d946338fbe53eb33f0 | 41b50c2e16db59df993603387ee33df698e5fdd3 | /code/prepare_data_MeDeCom_CCSP.R | dae80f6ce71e761f639a09045271a0297db5edb6 | [] | no_license | tkik/Lung_CoO_2 | f407d8f7c124fa0f3d29b11671840e9b9b78f4e9 | 94a415c26b97ea8e8aa37fc851b3be0d04e9b858 | refs/heads/master | 2023-06-08T18:15:56.333197 | 2021-07-01T08:24:11 | 2021-07-01T08:24:11 | 349,067,228 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,413 | r | prepare_data_MeDeCom_CCSP.R | library(methrix)
library(data.table)
library(HDF5Array)
library(SummarizedExperiment)
library(ggplot2)
library(rtracklayer)
library(limma)
library(knitr)
library(pheatmap)
library(MeDeCom)
library(ggsci)
###########libraries and functions#############
if (grepl("Windows", Sys.getenv("OS"))){
PATH ="V:/"} else {
... |
55148bbfbb37f16828aa402f9d08e0cefb7043c1 | c2bbe165858014ea7fd226710fa3dc1f4af36fe8 | /man/gwa.Rd | c941c642f433723a566c29c5e1c6fbe0d8f4543d | [] | no_license | tonymugen/GWAlikeMeth | 5781bb71e0ac79d6772d405a025f4379a52453cc | 8245a7224ba4253681f54aaedd17a60b4520bedf | refs/heads/master | 2020-04-16T00:34:56.464885 | 2020-01-31T22:28:18 | 2020-01-31T22:28:18 | 165,144,017 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,472 | rd | gwa.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/interfaceFunctions.R
\name{gwa}
\alias{gwa}
\title{Single-marker genome-wide association}
\usage{
gwa(Y, K, X = NULL, repFactor = NULL, snps, nPerm = NULL,
nThreads = NULL)
}
\arguments{
\item{Y}{matrix of phenotypes; phenotypes should be i... |
6e94697e93d39bf9a74e5de5621255ebac0290b6 | 43cf53d4395e950b16ac065c9badff519fd77f9f | /plot5.R | 13d150dc7f68a75d2f15c418b2bf2d40a2710051 | [] | no_license | DEHDailey/ExData_Project2 | 567ab77193ebf263a48473f1b1122c8493fb2ebc | f5e90b53a25d71daf17c000eafde38f6d1315da2 | refs/heads/master | 2016-09-11T09:53:39.997608 | 2014-07-11T17:09:51 | 2014-07-11T17:09:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,086 | r | plot5.R | ## Coursera Exploratory Data Analysis (exdata-004) Course Project 2
## plot5.R: Creates an exploratory plot for Question 5:
## 5. How have emissions from motor vehicle sources changed from 1999–2008 in
## Baltimore City?
## Between 1999 and 2008, vehicular emissions of PM2.5 in Baltimore City have
## decreased marke... |
7585e0897c1237c04f37a8c86838e9b7acba7b11 | 424710e3b6366696935e29f8b0f6220a29e2d545 | /rsm-msba/connections/rsm-msba-postgres.R | d0bbd49f27f93957ca52caf898c8373c540a99ee | [
"BSD-3-Clause"
] | permissive | imccommons/docker | 661111548eee1a8d89ac8610e32507325feffc16 | 80be5876cb88917d196316011dca65550d9c211e | refs/heads/master | 2020-04-08T20:25:17.010484 | 2018-11-28T22:07:26 | 2018-11-28T22:07:26 | 159,698,519 | 1 | 0 | null | 2018-11-29T16:49:15 | 2018-11-29T16:49:15 | null | UTF-8 | R | false | false | 328 | r | rsm-msba-postgres.R | library(DBI)
library(RPostgreSQL)
con <- dbConnect(
dbDriver("PostgreSQL"),
user = "postgres",
host = "postgres", ## use when establishing a connection between containers
# host = "127.0.0.1", ## use when connection to postgres from local Rstudio
port = 5432,
dbname = "postgres",
password = "post... |
eceac028dd7eff3479452077ea64707bb861dba7 | 52b84546a64b4f31245eb0bfaa68bfa489c90534 | /sta141a/2016/discussion10.R | 64a50a187ede002e5923a4a10e20c17fc82e4533 | [
"CC-BY-NC-SA-4.0"
] | permissive | nick-ulle/teaching-notes | 6cb48d874ef4c8c99402b9987e58b2958adff056 | 12e388f626f415bd39543bfed99c44e4130a065b | refs/heads/master | 2023-02-20T12:55:06.521649 | 2023-02-05T02:53:22 | 2023-02-05T02:53:22 | 86,759,329 | 31 | 33 | BSD-3-Clause-Clear | 2019-01-15T15:44:11 | 2017-03-30T23:49:53 | Jupyter Notebook | UTF-8 | R | false | false | 4,563 | r | discussion10.R | # discussion10.R
# Week 10
# -------
# How the Singapore Circle Line rogue train was caught with data:
#
# https://goo.gl/Raz9FB
#
# Please fill out TA evals!
# The Bootstrap
# -------------
library(readr)
library(broom)
elm = read_tsv("../data/elmhurst.txt")
mod = lm(gift_aid ~ family_income, elm)
coef(mod)
# ... |
07131491f88d21d31d97c45e082ec4aa6575e6b7 | d8aa7961d1b6a9b4c95031095625915fea43a5b5 | /R/tuneLearn.R | 12f01b29d1e4693881d28a7f87bfd7b07f054c01 | [] | no_license | mfasiolo/qgam | 4b74cdbdc193f198424f3dcc0ffb5ad3f045aa5a | 70fe0b2013c1d4b395bf6f3a53ba15a8d206db49 | refs/heads/master | 2023-02-27T07:19:28.703301 | 2023-02-06T23:30:10 | 2023-02-06T23:30:10 | 56,142,623 | 27 | 8 | null | 2019-02-06T13:26:50 | 2016-04-13T10:28:50 | R | UTF-8 | R | false | false | 10,790 | r | tuneLearn.R | ##########################
#' Tuning the learning rate for Gibbs posterior
#'
#' @description The learning rate (sigma) of the Gibbs posterior is tuned either by calibrating the credible intervals for the fitted
#' curve, or by minimizing the pinball loss on out-of-sample data. This is done by bootrapping... |
47af63e9a6909bae62efc4d40d0491bd7b3eb32c | 08c20460437f3922026b18836fc4f9e940086ba4 | /R/theme_report.R | 106210e58fb8b080a8cf6ebc393367904a375ace | [] | no_license | purplezippo/ggpkt | 5f0d96196ed14c5a5b2b30766d507c12171ff762 | c13fc9d31c1c665fdadacb3f72952c63708269c3 | refs/heads/master | 2020-04-06T16:52:35.254481 | 2019-04-02T14:23:12 | 2019-04-02T14:23:12 | 157,637,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,365 | r | theme_report.R | #' Theme for reports
#'
#' This theme is used for create figures in data analysis reports.
#'
#' @param font.size.legend.title Font size of legend title.
#' @param font.size.legend.text Font size of legend text.
#' @param font.size.axis.x Font size of axis x.
#' @param font.size.axis.y Font size of axis y.
#' @family t... |
694d81200249722b9e2638d71c867bed317ca437 | ba34b341270e79fdc6e3cffe50a6bc25c0506de3 | /R/checks.R | 0b799446ad4ad06038ed85f1def007673a468532 | [] | no_license | ekstroem/DKcpr | b804fd81c3fc6a22653ef3de869547f575f5c9ee | 0ee38fc6393c66768ac4ec77f7cd962fe0844162 | refs/heads/master | 2020-04-08T21:42:56.798282 | 2019-03-27T00:31:27 | 2019-03-27T00:31:27 | 159,755,540 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,445 | r | checks.R | #' Check the modulo 11 rules for CPR numbers
#'
#' @param cpr A vector of CPR number strings
#' @return A logical vector with values TRUE (CPR number ok), FALSE (CPR number not ok), or NA (cannot check)
#' @export
check_mod11 <- function(cpr) {
cpr <- clean_cprstring(cpr)
anyna <- is.na(date_of_birth(cpr))
... |
f68af76b19102a146280e146da9296ac76fa5dd0 | 6ec7d30e63bab3594c8f80f5cbc0ac763d74b4f6 | /RPackage/MeshDataSimplification/demo/example.R | 91256a8b94dd0f5c9eebb750127cb93546f16600 | [] | no_license | Abdelilah-Majid/meshsimplification | 1a85ccec3a333c002de83fe52e65d05fea6ee434 | 99cf4da098c17f0f684bc13349371629fbae9ad8 | refs/heads/master | 2021-12-14T15:07:43.374196 | 2017-05-03T16:51:54 | 2017-05-03T16:51:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,268 | r | example.R | #' Driver running the simplification process within R.
#'
#' @param dataset name of dataset storing the mesh as an object of class SURFACE_MESH:
#' - pawn: pawn geometry, 2522 nodes;
#' - bunny: Stanford Bunny geometry, 34834 nodes;
#' - brain: cortical surface, 40962 nodes.
#' @param n1,n2 two different ... |
5409cb12a4487d70bc56c21918c28c11cad1b35c | 832f70afec26f748ce8b3f5ba337d235227dd40d | /R/srrr_fit.R | 25d67392a0ef94a23ff70d32912022a4b19bef18 | [] | no_license | zsaijun/StatComp18032 | 8238e56fa3a467b0d48a1af74e4a15f9c8a44e56 | 41599a40a62c64fb3067187d8191d2276b73453f | refs/heads/master | 2020-04-17T06:49:44.302974 | 2019-01-18T04:13:30 | 2019-01-18T04:13:30 | 165,175,872 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,474 | r | srrr_fit.R |
#' @title Fitting Sparse Reduced-Rank Regression
#'
#' @description Given a response matrix and a covariate matrix, fits sparse reduced-Rank
#' regression for a specified rank.
#' @import remMap
#' @param Y a matrix of response (n by q)
#' @param X a matrix of covariate (n by p)
#' @param lam penalty parameter for B
#... |
4cbded349901a949b5b64e0faf7185742f697076 | ab9315145932e1d0edcb1b52df1cc3ed078a317c | /man/format_raw_results.Rd | df975c7ee4c85b80ab17bb21dc4caa2a3f59787c | [] | no_license | johnchower/flashreport | cfcbefc1afe5d77da6857ae21323d48ed3a6db96 | c23dca38331dc9632c251983a2db59cf95dae664 | refs/heads/master | 2020-07-28T12:31:42.252620 | 2017-05-03T16:39:10 | 2017-05-03T16:39:10 | 73,411,648 | 0 | 0 | null | 2017-05-03T20:53:11 | 2016-11-10T18:54:18 | R | UTF-8 | R | false | true | 1,302 | rd | format_raw_results.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/query_functions.r
\docType{methods}
\name{format_raw_results}
\alias{format_raw_results}
\alias{format_raw_results,auQuery-method}
\alias{format_raw_results,notificationsQuery-method}
\alias{format_raw_results,paQuery-method}
\title{A generic... |
c2e8567ae9907ab5b3bbd2f3d9208226e48d4fa1 | 2f94e0f8911055053c30f9ab88e2f66691ca719d | /R/detect_duplicate_genomes.R | 1488ea29d218ceaca45d0f67a0a7a8ae3b908886 | [] | no_license | anne-laureferchaud/stackr-1 | 4e4dd971dd83a8b238a858d6e1026ab2c6e271ed | 55837d3005d4e4f13ce5706284e8137c98e8501a | refs/heads/master | 2020-06-17T10:01:15.282997 | 2016-11-28T16:49:44 | 2016-11-28T16:49:44 | 75,013,926 | 0 | 0 | null | 2016-11-28T21:02:47 | 2016-11-28T21:02:46 | null | UTF-8 | R | false | false | 19,572 | r | detect_duplicate_genomes.R | # Detect duplicate genomes
#' @name detect_duplicate_genomes
#' @title Compute pairwise genome similarity or distance between individuals
#' to highligh potential duplicate individuals
#' @description The function can compute two methods
#' to highligh potential duplicate individuals (1. distance between individuals... |
f9a843d337db0239f14adc2056800e26ee6064d3 | 2854264f4db84d49e75a4b214f6121b664a9971c | /dataset_creation/R/Old/tests.R | 953d66f04940a527c246bd3c60daa1304fd9a57f | [] | no_license | kajdreef/IN4334-MSR | 6e256218072b484293da1f15fd49826c993d7570 | deb8e80fa8ad0f5f0a1178f476ca06c53f070858 | refs/heads/master | 2021-01-10T09:37:24.734735 | 2016-01-27T21:29:51 | 2016-01-27T21:29:51 | 46,335,107 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,760 | r | tests.R | # Clear history
(rm(list=ls()))
# Import libraries
library(dplyr)
library(ggplot2)
#CHANGE THESE TWO PARAMETERS
project = "lucene"
threshold = 0.05 #To distinguish minor and major
corr_cutoff = 0.75
sample_size = 2000 #Check the output of table(metrics$implicated) before setting
#SAMPLE SIZE MUST ALSO... |
2a278351e253f0dfe4ec2107dd61f3507620947d | 97f1e3e6e908a83489e4243268ba539316196176 | /R/antsImageHeaderInfo.R | 3e91562022f702d3a63e1daf1e19e23f5fe7889b | [
"Apache-2.0"
] | permissive | ANTsX/ANTsRCore | 1c3d1da3bea84859da7d18f54c34ae13d2af8619 | 8e234fd1363c0d618f9dc21c9566f3d5464655a2 | refs/heads/master | 2023-05-24T23:53:30.886217 | 2023-05-22T02:52:39 | 2023-05-22T02:52:39 | 83,897,912 | 8 | 22 | null | 2023-05-22T02:52:40 | 2017-03-04T14:09:48 | C++ | UTF-8 | R | false | false | 1,341 | r | antsImageHeaderInfo.R | #' Read file info from image header
#'
#' Read file info from image header
#'
#'
#' @param filename name of image file to scan for info
#' @author Duda JT
#' @examples
#' antsImageHeaderInfo( getANTsRData("r16") )
#'
#' @return outputs a list containing:
#' \itemize{
#' \item{pixelclass: }{Type of pixel (scalar, vec... |
832630a14e0caecf8b33dc199f5696734c5fc007 | be0dcd3a7ff4df3a352a15a26cf5611bcc25eae5 | /man/REE_plus_Y_Elements.Rd | c944167d49529c8e173e32b499f7b52ec85b9797 | [
"MIT"
] | permissive | cicarrascog/imputeREE | 90b0ef70126c8dfc70705d075b0a188ed2e18e98 | 2f6c0594482391ae730220ad2c2d1b8d2df92c14 | refs/heads/master | 2023-07-07T09:16:39.443365 | 2023-06-25T23:30:25 | 2023-06-25T23:30:25 | 409,395,192 | 4 | 0 | null | null | null | null | UTF-8 | R | false | true | 370 | rd | REE_plus_Y_Elements.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/REE_plus_Y_Elements.R
\docType{data}
\name{REE_plus_Y_Elements}
\alias{REE_plus_Y_Elements}
\title{Rare earth element list}
\format{
Rare earth element + Y list
}
\usage{
REE_plus_Y_Elements
}
\description{
A string vector containing the elem... |
1ac6824896f24cba9afaa98e237e27afbd2d922d | b26b28874f761ea2b085db8d60c994af57d99cd5 | /man/two.ways.stepfor.Rd | 283bcb1e934d617209df8cb09bfced2446e8ba73 | [] | no_license | mjnueda/maSigPro | de1f90885fafc23b38585bb75aec40aeee9ed89f | 000885f3103c02880fb9bd1e69f0c679e08ae580 | refs/heads/master | 2021-01-22T12:37:54.848463 | 2020-12-08T17:53:08 | 2020-12-08T17:53:08 | 102,353,792 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,368 | rd | two.ways.stepfor.Rd | \name{two.ways.stepfor}
\alias{two.ways.stepfor}
\title{Fitting a linear model by forward-stepwise regression}
\description{
\code{two.ways.stepfor} fits a linear regression model applying forward-stepwise strategy.
}
\usage{
two.ways.stepfor(y = y, d = d, alfa = 0.05, family = gaussian(), epsilon=0.00001 )... |
0b9223502f1d0a958d1422788c585cf3440e0b09 | 4dcd47cbd1468df4004c62f23f0abb635b718b8c | /R/fitted.R | 843ad67890f7427f35baaaeefe1c36c2b624d3fc | [] | no_license | peter1328/brms | ebbce7e0bb6f7c0ed1316377009f7f99f849d519 | a5dbbb6a32c87976e6c6e59d62913a10bcd2ae40 | refs/heads/master | 2021-07-13T08:11:31.938842 | 2017-10-18T00:11:56 | 2017-10-18T00:12:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,979 | r | fitted.R | # functions in this file have the same arguments structure
# Args:
# mu: untransformed linear predictor matrix
# draws: A named list returned by extract_draws containing
# all required data and samples
# Returns:
# transformed linear predictor representing the mean
# of the response distribution
fitte... |
d7cb68e7f6b02ceee20be5585cf4f83e8260b4d9 | 514ca787ec8ef69fdcf173831989d8563bc9c1ee | /R/scripted-utilities.R | 92a339f66613a85b33828325e09e9a4d7ba0fa19 | [] | no_license | sakrejda/scripted | 6b6fd24d4bd8e9c5dcabb39bdec181c5c71d1153 | 32e5ff7e12db77bccf267e847e5e0949add9b424 | refs/heads/master | 2020-03-28T17:47:21.526575 | 2018-11-02T17:03:50 | 2018-11-02T17:03:50 | 148,821,168 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,317 | r | scripted-utilities.R |
#' Create a .check file name for the script
#'
#' This is not a fail-safe procedure.
#'
#' @param target_dir directory to look in.
#' @return single-string character vector of the file name.
check_file_path = function(target_dir, stub) {
found_path = dir(path = target_dir, pattern = paste0(stub, ".*\\.check"),
... |
5d0e78e6a8ffa4151a6633707cfe11abe16267aa | baa3ca16ece08d8af0b085b58167c0876fb55c96 | /Cuarto/Simulacion/Ejercicio5/mm1.r | 812f1e705963a9501fa7d3117cada726d2b32afa | [] | no_license | ZhijieQ/inf-uam | 851d66b1484a63cdf6e2f35209075abf07bdb830 | 38079fdd5da5bb0ea35fa3265e813e0fe1a3bdd1 | refs/heads/master | 2021-06-01T10:20:44.917316 | 2016-08-23T18:24:12 | 2016-08-23T18:24:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,016 | r | mm1.r |
#DES application: M/M/1 queue,arrival rate 0.5,servicerate 1.0
# initializes global variables specific to this app
mm1initglbls <- function(apppars) {
mm1glbls <<- list()
#simulation parameters
mm1glbls$oldpercent <<- 0.4
#Ventanilla, distrib normal, media 5 desviacion tipica 5
mm1glbls$srv1avg <<- 5... |
b9fcce32bdba161347c8552b0a128ffe3d49126b | 9f983a6b5cec8fdee6ec13fc84d834634b762287 | /man/partitionATSDir.Rd | 3ea9096e46dd6d75c9897cd3791a36f184f6d69d | [] | no_license | cbergmeir/tsExpKit | f30939015eb0ca8de33ef647d69bf06cf434af9d | c61a83697e942889cef9ed2b815b3cb01b7b2285 | refs/heads/master | 2021-03-12T20:06:10.572459 | 2013-01-29T18:12:20 | 2013-01-29T18:12:20 | 7,897,910 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 958 | rd | partitionATSDir.Rd | \name{partitionATSDir}
\alias{partitionATSDir}
\title{Partition (to training and test sets) all time series in an ATS directory}
\usage{
partitionATSDir(inPath,
partitionParameters = list(type = c("blockedCV"), order = 0, numPartitions = 5, ratioValSet = 0.2),
outPath = NULL, savePlots = TRUE)
}
\arguments{
... |
1d3b3dc8f7fb6127422f0d3daa3b2bcdd1fc5dae | c84f4694c07cd32674abee7235e1d31353e225a3 | /Distributions/eachGraphs/extremeValueDistribution_2(FrechetDistributionwithLocationParameter).R | 56ee591ed1e0cd155f8e93ffdb4da366de358074 | [] | no_license | praster1/Note_SurvivalAnalysis | c1e2fee2865432fa4be89a4000c20e38ef1bef3e | db50363874c69fea6d4d47a24409c387c5276f84 | refs/heads/master | 2020-03-26T23:00:24.345471 | 2018-12-04T10:34:49 | 2018-12-04T10:34:49 | 145,502,914 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,279 | r | extremeValueDistribution_2(FrechetDistributionwithLocationParameter).R |
source("colorPalette.R")
require(VGAM)
##### lfrechet Distribution
### parameter
location = c(-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1)
shape = c(0.25, 0.5, 0.75, 1, 2, 4, 8)
scale = c(0.25, 0.5, 0.75, 1, 2, 4, 8)
### input varialbe
x = seq(0.1, 10, length.out = 1000)
# m : shape / c
# beta : scale / b
# mu :... |
ec5a7203b21a2bdaefca1f0670fb68532c30a368 | 1faf1557d278511830c2bf8ff83538c3544377ec | /cachematrix.R | 01a0bc06e50db16049efa96c76882ae2522c253a | [] | no_license | bryonjacob/ProgrammingAssignment2 | 51aaf7ce93e800703edbb3244181cfdef3e31c93 | 56914fd420244c669b87b9e6674c9cea736e6aa7 | refs/heads/master | 2021-01-15T10:52:44.586550 | 2014-09-14T20:47:21 | 2014-09-14T20:47:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,263 | r | cachematrix.R | ## This file contains functions for optimizing matrix operations by caching
## the results of potentially expensive computations. Caching of the "solve"
## method is implemented, to compute and cache the inverse of an invertible
## square matrix.
## Construct a cacheMatrix object given a "normal" R matrix - this cac... |
abc0550bebfc28e9c6ef1a7463ebf1e48c57030f | 77157987168fc6a0827df2ecdd55104813be77b1 | /MGDrivE/inst/testfiles/calcCos/libFuzzer_calcCos/calcCos_valgrind_files/1612732889-test.R | dc7c8c7cdf959ef5e85ffd0a3b234ca37fe9108a | [] | no_license | akhikolla/updatedatatype-list2 | e8758b374f9a18fd3ef07664f1150e14a2e4c3d8 | a3a519440e02d89640c75207c73c1456cf86487d | refs/heads/master | 2023-03-21T13:17:13.762823 | 2021-03-20T15:46:49 | 2021-03-20T15:46:49 | 349,766,184 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 184 | r | 1612732889-test.R | testlist <- list(latLongs = structure(c(1.39988503525853e-306, 1.43400496860732e-314 ), .Dim = 2:1), r = 3.59560005352873e+252)
result <- do.call(MGDrivE::calcCos,testlist)
str(result) |
449397251a1453ddfb8b73a70fcfea70fda4830e | b3962eff09caad325e910526023892e71cb51e9a | /man/ensebl_id.Rd | 4fbec06feadf29ff3ca1ab6462d4441b7e67142e | [] | no_license | ybai3/SCQC | d3b5e17d506842a2ce2245079abca0d58f14453b | 3050d97b19508c2866dabff59166bcac5b52cb22 | refs/heads/master | 2020-03-29T12:51:38.294165 | 2018-09-24T02:13:19 | 2018-09-24T02:13:19 | 149,924,302 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 713 | rd | ensebl_id.Rd | \name{ensebl_id}
\alias{ensebl_id}
\docType{data}
\title{Ensemble_id of housekeeping gene list
%% ~~ data name/kind ... ~~
}
\description{Housekeeping gene list provided by Eisenberg E and Levanon EY in Human Housekeeping genes, revisited.
%% ~~ A concise (1-5 lines) description of the dataset. ~~
}
\usage{data("ens... |
49255876ce464555366e1a45f5b21d02e1d04c4a | 5e1fdf6e9e63c8c8d09f3bdf0a1fb519d3770318 | /ml_titanic.R | d278bf4529145addd83d66982f5e660bdc91c125 | [
"MIT"
] | permissive | yerlandana/R_project | 410a215b408abfa484f385bfe1408af812709228 | 8cd004939da2c0650e70203261d84f8f8fa75afc | refs/heads/master | 2020-06-15T21:08:46.355401 | 2020-01-12T15:28:14 | 2020-01-12T15:28:14 | 195,392,812 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,164 | r | ml_titanic.R | library(rpart)
install.packages('DMwR')
library(DMwR)
install.packages('rpart.plot')
library(rpart.plot)
dft <- read.csv('C://Users/dyerl/Desktop/Titanic dataset.csv')
Ttree<- rpart(Survived~Pclass+Sex+Age+SibSp+Parch+Fare+Embarked, data= dft, method="class", control = rpart.control(minbucket = 25))
prp(Ttree)... |
b269934ef5f0815eae42555d591485bd3e482327 | 4999d197a87922bbbed19b2512419ddf05201b19 | /atividade_6.r | 4f045e49c0485f0db412da32260ed9eea7f6c900 | [] | no_license | bahbbc/RP-Atividades | 05e1d6d94b437f069d70dfb4de3c9664d6c089b4 | 0e5c3fea436da991c176c15b9d4d8a29c5e39a23 | refs/heads/master | 2020-05-20T13:37:35.066453 | 2014-05-06T01:48:41 | 2014-05-06T01:48:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,010 | r | atividade_6.r | split_data = function(data, k){
split = floor(nrow(data)/k)
left = nrow(data) %% k
sizes = 0
# por enquanto soh diz quanto vai ter em cada subconjunto
#acredito que temos que dividir o conj. original, soh num sei se eh agora ou na prox. fase
for(j in 1:k){
sizes[j] = split
}
for(i in 1:left){
#substituir ... |
a2f5ee2ff236243865251cac960c44c0b9f0f3f0 | 12a9bea8cfff9e5dcd44651102a5e0adf477164a | /explorations/passes.R | 8e1e6961c605e2bb4c8243c7128d0b9b4bedabde | [] | no_license | duncantl/RLLVMCompile | 2b98a04f1f7e71f973a281b40457f5730e38f284 | 7fad5bd394a6f74ace0f6053a5d08e4f15cf3a1f | refs/heads/master | 2021-01-19T01:42:02.316459 | 2017-03-07T00:49:31 | 2017-03-07T00:49:31 | 3,894,344 | 32 | 3 | null | 2015-03-03T13:27:54 | 2012-04-01T18:04:28 | R | UTF-8 | R | false | false | 1,348 | r | passes.R | library(RLLVMCompile)
diffModule =
function(a, b)
{
if(is(a, "Module"))
a = strsplit(showModule(a, TRUE), "\\n")[[1]]
if(is(b, "Module"))
b = strsplit(showModule(b, TRUE), "\\n")[[1]]
c(setdiff(a, b), setdiff(b, a))
}
# This is a place to explore the effects of some of the compiler Pass c... |
5a1d635bb0d556786592c8abf47eb65b64bd6ff7 | 3807c0d642d7c4789f3284f1b0429711973aa370 | /R/radf-methods.R | 2bf0c06ca1f4e4e5c91749423083e6525dd69a39 | [] | no_license | cran/exuber | e938e35fdb5e0a4708228234a20089e23cd40fc3 | 453054b85ca14707ee86774a979e95312c3ba627 | refs/heads/master | 2023-04-06T08:29:47.039261 | 2023-03-22T22:10:02 | 2023-03-22T22:10:02 | 137,660,134 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,649 | r | radf-methods.R |
# summary -----------------------------------------------------------------
#' Summarizing `radf` models
#'
#' \code{summary} method for radf models that consist of `radf_obj` and `radf_cv`.
#'
#' @param object An object of class `radf_obj`. The output of \code{\link[=radf]{radf()}}.
#' @param cv An object of... |
9a65a1c6a2c05fd2db6b769744f6212818bf4d7c | 7cf81a9e617f6e77624ce45ef5b5c51a01cd0c28 | /plot2.R | b051bf94af10913e71a395b01b696d4f0e334819 | [] | no_license | jhunnius/ExData_Plotting1 | d15d934bd2d57d3a6a8f3252841c49b105b49abc | 418e84713931dde7957254d6f54f5b8dfced3e77 | refs/heads/master | 2021-01-18T12:54:05.364633 | 2015-01-07T12:01:09 | 2015-01-07T12:01:09 | 28,904,903 | 0 | 0 | null | 2015-01-07T08:09:16 | 2015-01-07T08:09:16 | null | UTF-8 | R | false | false | 1,872 | r | plot2.R | ## This function downloads the household power consumption data set from the internet
## and plots a line graph of global active power (in kilowatts) vs. time for the
## available data from 2007-02-01 to 2007-02-02.
plot2 <- function() {
## Set the locale to English for correct weekday abbreviations
Sys.... |
f498e1c36d96cbec046a166bf0f0f9df01dd8691 | 1c5957fd06781762a5d3d14c6e088b0788738ff4 | /ui.R | aa88f4c1e5ccfd25bfc111d98603290f1fe6d88d | [] | no_license | sor16/BayesianRCmodel | a2759930b89575329c683765ded2733bdeefbbc0 | 3abe5b797e1787d7c34074d69915a371c93ab909 | refs/heads/master | 2021-01-15T13:01:41.816611 | 2015-10-04T02:35:59 | 2015-10-04T02:35:59 | 38,426,271 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,102 | r | ui.R | suppressPackageStartupMessages(library(shinydashboard))
suppressPackageStartupMessages(library(googleVis))
dashboardPage(
dashboardHeader(title='Bayesian Rating Curve'),
dashboardSidebar(
sidebarMenu(
menuItem("Instructions", tabName = "instructions", icon = icon("info-circle")),
menuIte... |
4a96bedc65b86ce5feb7dc1e57f20c7a7b056269 | aa25c9ba3b36f795d768d924cddce3c9c1e2cdf5 | /dash-simulator/evaluate-all.r | d31ee4be7040e2d3dc7def72a5b6d2f64d80ad3d | [] | no_license | nus-mmsys/QUETRA | 1b21aa296d394318b19fce9ee3acd54b29cddbbf | 7b71dfec179655d00ff6e7cf5df7a12e71f93675 | refs/heads/master | 2021-01-02T08:53:58.867677 | 2017-07-29T13:46:36 | 2017-07-29T13:46:36 | 99,086,279 | 9 | 5 | null | null | null | null | UTF-8 | R | false | false | 1,564 | r | evaluate-all.r | #!/usr/bin/Rscript
library(ggplot2)
args <- commandArgs(trailingOnly=TRUE)
if (length(args) < 1) {
cat("\nusage: evaluate-all.r <benchmark file name> [<path to optimal results>]\n\n")
quit()
}
optimalpath <- ""
optdata <- data.frame()
if (length(args) == 2) {
optimalpath <- args[2]
}
benchdata <- rea... |
8c94ec5a7ab643579968ac38839b4bf5e1214a6b | 4a80a4a1c06ccb28efd613098a7dd40df311de5e | /createNetAdults.R | 51289916e8ead02625780bb0d3eef7bb2615c5c0 | [] | no_license | dnatheist/Ch5GenomicDiversityAndSpatialStructure | 9e6c4ffa40fd033d5dc5229ff737042158d8f5ab | b74dccb88452d416106021d1526a61b076d22c13 | refs/heads/master | 2020-06-14T13:50:35.979108 | 2016-11-30T21:51:32 | 2016-11-30T21:51:32 | 75,175,716 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,626 | r | createNetAdults.R | ## @knitr createNetA
require(igraph)
relData<-output$relatedness[,c(2,3,estimator)]
#hist(relData[,3]) #to see how the data is distributed
#Makes links DF
lrelData<-relData
colnames(lrelData)[1] <- "from"
colnames(lrelData)[2] <- "to"
colnames(lrelData)[3] <- "weight"
lrelData$type<-"probRel"
relDataNoRows<-nrow(re... |
20da39f26125587368ecfb74d5f0a6460a3b0ba4 | 6b915ba9db1de8d26bec39589c77fd5d225e1fce | /stanlba/hierarchical_noncentered_empiricalpriors_extend_with_init_vals_12c.R | 4f7d13cd65928faa0f6b1f1f859a7fa292c2e36c | [
"Apache-2.0"
] | permissive | bjsmith/reversallearning | c9179f8cbdfdbbd96405f603e63e4b13dfc233af | 023304731d41c3109bacbfd49d4c850a92353978 | refs/heads/master | 2021-07-09T23:42:22.550367 | 2018-12-08T01:40:54 | 2018-12-08T01:40:54 | 100,329,149 | 0 | 2 | null | 2017-09-05T04:27:41 | 2017-08-15T02:21:25 | R | UTF-8 | R | false | false | 10,571 | r | hierarchical_noncentered_empiricalpriors_extend_with_init_vals_12c.R | library(LaplacesDemon)
#the informative priors model took 4.45 hours compared to 12.70 hours for the weakly informative priors model.
#so it may have sped up the model, although this is hard to judge because:
#(1) the server might be doing other things;
#(2) random chance for the very first starting values
print("exte... |
7301537b0d14bc83ce1df07f4e330e5a66ca5c29 | 88592a2a30f76e3276f55614097cd5b28667f11d | /main.R | 3ef4a9b46e0bfed041e9a472b1e1cfcf3bca7758 | [] | no_license | krabby-k/datasciencecoursera | a4907f0b62c77e966d8a23947d2d84c3f5d8642e | 089007a8eb4be5080cc886313acbcd4ea6619bb4 | refs/heads/master | 2023-01-24T08:26:30.326151 | 2020-12-09T08:11:52 | 2020-12-09T08:11:52 | 319,884,187 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 84 | r | main.R | # Title : TODO
# Objective : TODO
# Created by: brian
# Created on: 11/18/2020
|
4545748cae69abfd4a2d3f756b1a86ffba3849d6 | 2f15b2dc16de0471e7bee43f6739b6ad8522c81d | /tests/testthat/test-set_dimension_type_conformed.R | e997c5768d756f2a4e68f9550af37acff741cdbd | [
"MIT"
] | permissive | billster45/starschemar | 45566be916c95778727a3add3239143d52796aa9 | 5f7e0201494a36f4833f320e4b9535ad02b9bdc1 | refs/heads/master | 2022-12-20T13:45:06.773852 | 2020-09-26T03:44:12 | 2020-09-26T03:44:12 | 298,796,838 | 1 | 0 | NOASSERTION | 2020-09-26T11:10:30 | 2020-09-26T11:10:30 | null | UTF-8 | R | false | false | 225 | r | test-set_dimension_type_conformed.R | context("test set_dimension_type_conformed")
test_that("set_dimension_type_conformed works", {
d <- st_mrs_age_test$dimension$when
d <- set_dimension_type_conformed(d)
expect_equal(is_conformed_dimension(d), TRUE)
})
|
2947199a2240bd41a91324f34407d6a74bca44de | 399e5a660591a8906796b8cb6af5cdd2d2dc92fc | /R/max_norm.R | 69a48a9c3141d43925ac37cd9925d767871c01be | [] | no_license | leontavares/BRDF | cc3b9d2c7833fc9d996a4126fb945744d4ebb4ec | ba1f4bb9be42db440db3cd4198f5fb34212d606f | refs/heads/master | 2022-01-13T17:37:18.203575 | 2019-07-12T03:21:24 | 2019-07-12T03:21:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,300 | r | max_norm.R | #' Compute the relative proximity of a solution in relation to an uncertainty.
#'
#' \code{max_norm} Determines where a norm (residual or solution) is within a certain tolerance, iteratively narrowing down a list of lambda values.
#'
#' @param norm_result Result of doing the gsvd_norm function
#' @param uncertainty Tar... |
c2931219ff9a07b6edbf735f38ad7914854034fd | 983585773a5f29526e764ff9b35c9a00bbe993c4 | /ellipse.R | 9fe93850902d6b580c06246d92d365526906effa | [] | no_license | arjunchandrashekara/R-codes | 3361f23b5867e4130df8a327ba1c0931cb73c2d3 | f9fcdd275c77c639900823ab6a22c6234473857f | refs/heads/master | 2023-03-26T09:05:51.945927 | 2021-03-29T13:48:19 | 2021-03-29T13:48:19 | 294,035,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,738 | r | ellipse.R | library(shiny)
library(shape)
ui <- fluidPage(
actionButton("create","create 4 random numbers"),
wellPanel(tableOutput("Random_Numbers")),
fluidRow(column(3,"1"),column(8,plotOutput("A"))),
fluidRow(column(3,"2"),column(8,plotOutput("B"))),
fluidRow(column(3,"3"),column(8,plotOutp... |
78fed9519c2b33877b86563e0f377cae17e53ce9 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/FRESA.CAD/man/reportEquivalentVariables.Rd | 74bc7dded56dcff18022da6400feaaeaa3be802b | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,877 | rd | reportEquivalentVariables.Rd | \name{reportEquivalentVariables}
\alias{reportEquivalentVariables}
\title{Report the set of variables that will perform an equivalent IDI discriminant function}
\description{
Given a model, this function will report a data frame with all the variables that may be interchanged in the model without affecting its classif... |
2208fa5a158a370dcdbd845cf0237d5a9ee71913 | 1c17c81a2f3014e861fbfe51ade2a9c351e11a12 | /Boxplot_6_immune cells_gut_116.R | 34a25fe04e38874afd468f8e540281f17f91b1f6 | [] | no_license | Bennylikescoding/existing_R_code | 818c36508dadfc8f10f3132ac983a2bef0ecac8a | ae50e9061e2436547e84234269cf64c859bfbf36 | refs/heads/master | 2020-11-26T03:48:12.355220 | 2020-03-24T02:50:33 | 2020-03-24T02:50:33 | 228,956,798 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,400 | r | Boxplot_6_immune cells_gut_116.R | # 0. data type
# project order_No. group sample T CD4
# AD-116 1 WT B3527 65 13
# AD-116 1 WT B3528 75.4 10.1
# AD-116 1 WT B3529 66.7 12
# AD-116 1 WT B3530 61.6 8.33
# AD-116 1 WT B3531 60.9 12.2
# AD-116 1 WT B3552 60.3 10.4
# AD-116 1 WT B3553 43.6 0.33
# AD-116 1 WT B3554 41.8 13.2
# AD-116 1 WT B3555 56... |
2517df0d7c31f5cca070aaaf5aaf8af8b454b17f | 8d0eb323a84a89fbb81045c492c8c7ed72c7cc84 | /RNA-seq-without-ref/step5-venn-compare.R | 5632ae57bf8ee6fc0d708618007310cdbef7044b | [] | no_license | reedliu/NGS-codes | e23157e1bf4a244b9e1ec0fc5b356fe562ea026d | d31dd42874789e9f0bf6979e47a7cd804f9bd5f4 | refs/heads/master | 2020-04-12T01:05:37.111875 | 2019-11-10T13:58:51 | 2019-11-10T13:58:51 | 162,221,667 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,949 | r | step5-venn-compare.R | ### ---------------
###
### Creator: Yunze Liu (Reed Liu)
### Date: 2019-3-3
### Email: jieandze1314@gmail.com
### Blog: https://reedliu.github.io/
### CAAS/AGIS/SDAU
###
### ---------------
rm(list = ls())
options(stringsAsFactors = F)
# for salmon
load('DESeq2-salmon.Rdata')
load('edgeR-salmon.Rdata')
load('method_... |
290895470ec220e356173258ce7871b2a6de8c4d | fb0cbf6db81ee5ff6dfe73b618e2d649251b8b77 | /Model2.R | 4a4f07b3ce2f30cf8e0cde679d9a90e80a6b217a | [] | no_license | PGC-PTSD-EWAS/PGC-PTSD-Longitudinal-Analysis | 6a5841a9a2a33388959851e555a608315611b677 | 8ff7f5832bccac3343f5fefc3ee1d408885edeee | refs/heads/main | 2023-08-12T00:50:22.291579 | 2021-10-11T14:49:57 | 2021-10-11T14:49:57 | 416,046,475 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,087 | r | Model2.R | ################################################################################
# Model 2: Linear model to evaluate CpGs associated with change in PTSS in each cohort
################################################################################
library(lmerTest)
library(data.table)
# Scale pheno
pheno <- read.csv(... |
4daa58f9a25c9ba13068a7a08ce220a28189261d | cc9707494002db22ce5797b63326602a99009e9a | /R/sg.R | ecda8b4a0a5183eb2a5e49fd505ada11e275885b | [
"MIT"
] | permissive | CIP-RIU/sbformula | 54fae73fc69c9009ed74d8d6221ccffa907a5e58 | f22517e474411910c890083103f07346ab79c8a8 | refs/heads/master | 2021-06-21T07:55:37.290766 | 2017-08-18T21:34:01 | 2017-08-18T21:34:01 | 33,208,831 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 933 | r | sg.R | #'Formula for calculating the Specific gravity (SG)
#'
#'@param twa Tuber weight in air
#'@param tww Tuber weight in water
#'@return Return the specific gravity
#'@author Omar Benites
#'@details Formula for calculating the specific gravity
#'@references International Potato Center (CIP).2014: Procedures for Standard Ev... |
1ffc98816f44b9b73eb9b999a7d29a5b50973b87 | 1ab3fe36ec133cb90fcfc4071c15b37edc1d1c79 | /Seccion 06 - Regresion/078 - Regresión Lineal.R | bfe33693ca913878012e794143562d7e3950dc05 | [] | no_license | achiola/r | 419d182bd6ec546af4ef0dc10b7b59678ada561b | 08a5c2b78b58193d7fdbbf0fa612c52ec21df925 | refs/heads/master | 2020-07-06T14:24:46.427229 | 2020-01-07T21:43:38 | 2020-01-07T21:43:38 | 203,048,278 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 1,698 | r | 078 - Regresión Lineal.R | library(caret)
auto <-read.csv("Seccion 06 - Regresion/auto-mpg.csv")
auto$cylinders <- factor(auto$cylinders,
levels = c(3,4, 5,6,8),
labels = c("3 cyl", "4 cyl", "5 cyl", "6 cyl", "8 cyl"))
set.seed(2018)
t.id <- createDataPartition(auto$mpg,
... |
290a3c1df2798fa4ae54b1929db72ea71fabc681 | 5f7bddcf382d0ef974ebb3748e79b1b9eb30cbfb | /Tutorials/tukey_code.R | 3f96761b2efa487ea64c6505f6aa559835b871d5 | [] | no_license | vmoprojs/GeoModels-page | b1a1dcd4cbf66c1fca901156bccc73d9b2c5c026 | 3832d083d77504148e051a8d76ead5da6be6a478 | refs/heads/master | 2023-09-03T15:46:07.570852 | 2023-08-29T03:45:55 | 2023-08-29T03:45:55 | 122,678,188 | 5 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,106 | r | tukey_code.R |
rm(list=ls());
require(GeoModels);
require(fields);
model="Tukeyh"; # model name in the GeoModels package set.seed(989);
set.seed(818);
N=1500;
coords=cbind(runif(N),runif(N));
plot(coords ,pch=20,xlab="",ylab="");
corrmodel = "Matern";
scale = 0.2/3;
smooth =0.5;
nugget=0;
tail=0.1; # tail parameter
sill= 1... |
50ee7560d8b8791cd7810761e0c844e11bff2546 | fdc19ee9ca0e79837c139b47423a3369f203ea95 | /plot1.R | ee3784610b17c670dc398a8724b82cad0c6758b6 | [] | no_license | M0nd4/ExData_Plotting1 | dea54b570517e36964353b1b4615fd1126725ff7 | 64d722b57ba7a91c55b09bc2e932d81f501711dc | refs/heads/master | 2021-01-20T23:24:20.904851 | 2014-08-11T00:27:06 | 2014-08-11T00:27:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 531 | r | plot1.R | #### Reference
#### https://github.com/maurotrb/ExData_Plotting1
#### Read data.
datos <- read.table("household_power_consumption.txt", header=TRUE, sep=";", na.strings = "?")
#### Subset of filtered data with related days.
datos<- subset(datos, (datos$Date == "1/2/2007" | datos$Date== "2/2/2007"))
#### Plotting
... |
8c47244ee9716bb248cc88d54da4ce1c4224e8d3 | 2fc6db1958f1c3d18c1d2a248ba2be2023c98406 | /visualize/Make_Effects_Graph - Multiple SPIs.R | d001619b1085a80fa177a93e1bd428419bdec909 | [] | no_license | mcooper/ftf | 7c0ceed9cdbb036afbe85cd18bdff987d821a5b9 | a45f4fb2507e38e1ede0723f1f548456e0e327c2 | refs/heads/master | 2020-03-26T04:08:19.957676 | 2019-11-06T05:02:42 | 2019-11-06T05:02:42 | 144,488,349 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,229 | r | Make_Effects_Graph - Multiple SPIs.R | setwd('G://My Drive/Feed the Future/Cloud Model Results')
library(spdep)
library(dplyr)
library(ggplot2)
alpha <- 0.05/30
z <- qnorm(1 - alpha/2, 0, 1, lower.tail=T)
##############################
#Ghana
##########################
gha <- data.frame()
for (spi in c('spi12', 'spi24', 'spi36', 'spi48', 'spi60')){
... |
6c4163977cad7fdd5071a7dd639c30b253b89f82 | dc8d1b4cfa60cd851435543dd42e73626aaf92e4 | /comparing_models.R | 8a04cc19f9e2360634f6ad5d7958d7ed44a8f0a0 | [] | no_license | PeerChristensen/Churn_autoML | 3a7874b3451cdf756f553bc95707b4f094e24991 | a893f0d5dd4c63b783ff624b3491a4863b11f677 | refs/heads/master | 2020-09-06T12:38:36.701803 | 2020-01-27T15:37:24 | 2020-01-27T15:37:24 | 220,426,164 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,739 | r | comparing_models.R | # comparing models
# "2019-11-06"
# Peer Christensen
Sys.setlocale("LC_ALL","English")
library(tidyverse)
library(h2o)
library(modelplotr)
library(ggthemes)
h2o.init()
test_data <- read_csv("preprocessed_data/test_data.csv")
test_hf <- as.h2o(test_data)
###########################################################
... |
4599e3c851e0bd93bf99603b9a7620b2ba5c2bb2 | 4e5916f94bdbc6dd87c394067388f539ec702310 | /merge_files.r | 84613fefa36b94495cb0f70c8b49775ada62c401 | [
"Apache-2.0"
] | permissive | vijaybarve/tpt-siphonaptera | 37faaac8dbddca6f0c81ed33c09d5a61153c9550 | 7b281964d2af8005216089b975c8c9006afbcbd0 | refs/heads/main | 2023-04-12T05:01:55.563601 | 2021-04-26T23:12:36 | 2021-04-26T23:12:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,541 | r | merge_files.r | # add libraries
library(data.table)
library(readxl)
# define function: is not in
'%!in%' <- function(x,y)!('%in%'(x,y))
# compare FMNH to NMNH
NMNH_Siphonaptera <- read.csv("~/GitHub/tpt-siphonaptera/output/NMNH_DwC.csv", na = "NA") # read in cleaned NMNH review file
FMNH_Siphonaptera <- read.csv("~/GitHub/tpt-siphon... |
c13ba8e92f1aed1fe567d7ac66f9a99e2d74c43d | 5820b1f46b64c2bb47ae81e38fbd233543c164e9 | /scripts/load_dataset.R | 8acbfa45b04de14999d751f46db17a1e79133489 | [] | no_license | Victinyx/EXPAI-test | 10d8e359ed552dc8d66ea1979de0950659071021 | 33b8acafc809fd08883997079e911c3b08ffe588 | refs/heads/main | 2023-06-29T03:02:23.885414 | 2021-08-02T08:18:19 | 2021-08-02T08:18:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | load_dataset.R | setwd('./data')
dataset <- read.csv('heart.csv', stringsAsFactor = TRUE)
setwd('..')
colnames(dataset) <- c('age',
'sex',
'chestPain',
'bloodPressure',
'cholesterol',
'bloodSugar',
... |
8a484210b2e179227ec0261f28cbe03d0070e016 | 0ca78ef5a8670fbdab55409eecda579cec2baf68 | /DM/deeplearning/prevent_overfit.R | 7141ea2e6b70b5d64fce34ebb529a9045d27b1ee | [] | no_license | zhurui1351/RSTOCK_TRAIL | ab83fdef790778a1e792d08a876522ef13a872e6 | 2396c512c8df81a931ea3ca0c925c151363a2652 | refs/heads/master | 2021-01-23T09:01:36.814253 | 2019-05-17T15:26:19 | 2019-05-17T15:26:19 | 23,482,375 | 9 | 4 | null | null | null | null | UTF-8 | R | false | false | 5,914 | r | prevent_overfit.R |
################################################################################
## ##
## L1 Penalty ##
## ... |
080f87d33f7447acfe7bfbfde84811251b782696 | 29589aa37a7f3c31d168521bd8174adfc89c1d5d | /Part_A_Analysis_Richards_2008_plotting_binomial.R | c98317aa86ae1aee9044b2f0df4f2bc188060599 | [] | no_license | LoganHowell/Honours | a60f0343b98f2dfe67e8d1959e1dd39afc9210f7 | 4ef732f419819133b164ecb62f1babadb2f6def8 | refs/heads/master | 2023-05-04T20:35:18.167085 | 2021-05-26T00:01:55 | 2021-05-26T00:01:55 | 340,608,976 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,561 | r | Part_A_Analysis_Richards_2008_plotting_binomial.R | library(tidyverse)
library(Rfast)
library(epitools)
library(extraDistr)
library(foreach)
library(doParallel)
library(parallel)
library(stats4)
library(fAsianOptions)
library(maxLik)
library(matrixStats)
library(ramify)
library(purrr)
library(doSNOW)
library(latex2exp)
library(ggrepel)
library(data.table)
library(plotly... |
3f278a3a46d04d564cfa0aa3a49190f5083ffd46 | c53e367a5a155cfb1ee3a41e8b0351aeaa8d331d | /mlmRev/doc/MlmSoftRev.R | 0d21e893df165346828bf8e02ecc8fae5278f82b | [
"MIT"
] | permissive | solgenomics/R_libs | bcf34e00bf2edef54894f6295c4f38f1e480b3fc | e8cdf30fd5f32babf39c76a01df5f5544062224e | refs/heads/master | 2023-07-08T10:06:04.304775 | 2022-05-09T15:41:26 | 2022-05-09T15:41:26 | 186,859,606 | 0 | 2 | MIT | 2023-03-07T08:59:16 | 2019-05-15T15:57:13 | C++ | UTF-8 | R | false | false | 10,684 | r | MlmSoftRev.R | ### R code from vignette source 'MlmSoftRev.Rnw'
###################################################
### code chunk number 1: preliminaries
###################################################
options(width=80, show.signif.stars = FALSE,
lattice.theme = function() canonical.theme("pdf", color = FALSE))
l... |
239df01bc44dd61345066c629e02e6f892388b87 | 5065f337e3ffb1793b855e6b2264dfde4467fb97 | /R course June/05 Rice code 2.R | 0fdcca3fcf6200c63fd8b4a1a7a505cf12fc1f77 | [] | no_license | Reicharf/R-SAS.Introductory.Courses | faed787ec5c0eaa76d55dc476e6142f27c24ce6f | 69ec001f8961d5f1c4d1387f80249daa4cbc159b | refs/heads/master | 2022-06-16T23:21:33.677994 | 2020-04-26T13:50:08 | 2020-04-26T13:50:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,645 | r | 05 Rice code 2.R | rm(list=ls())
setwd("D:/Hohenheim/R-SAS.Introductory.Courses/Datasets")
library(data.table)
dt <- fread("05 Rice.txt") # directly import as data.table format
# splot plot design - two factor effects
### Split Plot Design
# When some factors (independent variables) are difficult
# or impossible to change in your ex... |
2ddcf4aa9f8ef784d51a24ea6a2733bd7bc387fd | 9f9155f936f7a800547fe6ffda440880534e93f3 | /cachematrix.R | 3801e9134f991717e64f962aee72ce59cea76c37 | [] | no_license | sarahfinder/ProgrammingAssignment2 | 375d0bd347bde7042dbe33eba5365507a6477c68 | 72da6b2f31e9921803cfe53fd4a86128878945db | refs/heads/master | 2021-01-17T08:13:08.428426 | 2015-06-19T11:16:31 | 2015-06-19T11:16:31 | 37,710,853 | 0 | 0 | null | 2015-06-19T08:30:37 | 2015-06-19T08:30:34 | null | UTF-8 | R | false | false | 1,520 | r | cachematrix.R | ## makeCacheMatrix is a function that creates a special "matrix"
## object that can cache its inverse. cacheSolve is a function that
## computes the inverse of the special "matrix" returned by the above
## function.
## makeCacheMatrix is a function that stores a list of functions: set,
## get, setinverse, getinve... |
c0f7a921be3c459c2fc8de92dec9c138831d2215 | 7124857f88de282565f0e53dfd56034323064944 | /cachematrix.R | af27560b7a328fce288972f94ec4c1e8a190e449 | [] | no_license | tipu-zlatan/ProgrammingAssignment2 | 657bddc5604f8b1bf44f8a7e4f66cacda8e6c6e1 | 211fdf6d865793d8850ef6c7e7ea933d79e90c4c | refs/heads/master | 2022-09-23T03:08:48.463674 | 2020-06-03T14:38:54 | 2020-06-03T14:38:54 | 269,087,681 | 0 | 0 | null | 2020-06-03T12:53:28 | 2020-06-03T12:53:27 | null | UTF-8 | R | false | false | 883 | r | cachematrix.R | ## A pair of functions that checks and sets the cache of inverse matrix.
## This function creates a special "matrix" object that can cache its inverse.
## Write a short comment describing this function
makeCacheMatrix <- function(x = matrix()) {
invers_e<-NULL
set<-function(y){
x<<-y
invers_e<<-NULL
}
... |
73dbf5cacc7e266c5d1b25fdd3f9374add4babd1 | e32b591d7e9ca79bf90e0adae17d664da0fde1df | /R/simField.R | 14c7d5fad9a2ec0f2144363a4e38c159c9426627 | [] | no_license | nmmarquez/DRU5MR | 6d41368448397abc9860f951bdd6b6f6caaafae4 | e71ed7908b1cceb0527f38df1f3c21cd9bcf872b | refs/heads/master | 2020-04-22T12:22:45.709306 | 2019-09-01T20:02:08 | 2019-09-01T20:02:08 | 170,369,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,936 | r | simField.R | #' Simulate A continous 2D spatial field evolving over time
#'
#' @description Calculates a continous underlying spatial field for the
#' Dominican Republic over time and across ages for probability of mortality
#' for one of 7 age groups. The final observed field is a linear additive model
#' as described in Wakefiel... |
239f13ed1c88aeb5d6afe756d5f7b95104b5d7c7 | b9a05537e3cd702891305c8c958171d02050a72e | /packages/rethinking.pkgs/R/packages_import.R | 5f1f56c425a62245c9bdc2449d4a03c39c5d57f4 | [] | no_license | anhnguyendepocen/statistical_rethinking-rsuite | fd2bdd7487426a553439796089c560ca9748259c | 6d6794bec2666b618b7d26c431e5313b0486b3a5 | refs/heads/master | 2022-02-14T08:33:50.665896 | 2019-08-19T22:12:27 | 2019-08-19T22:12:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 242 | r | packages_import.R | #----------------------------------------------------------------------------
# rethinking.pkgs
#
# Global package definitions and imports
#----------------------------------------------------------------------------
#' @import logging
NULL
|
819f1c4432b5142c430187c482e77aa320f1cc89 | 9f7baaad4b6028ab5ed2fd20d8d1ca18045ffff0 | /man/AtaImpact.Rd | 82c931d7ba15cc01688a4c58575e1847ce0ac1ae | [] | no_license | ArnaudBu/ReservingLad | d294adde36af6d498d02be023ed46a87715c4ff2 | 59e9854d90825e2acf0e7770e7e3ca9ce19c6519 | refs/heads/master | 2021-09-26T08:54:56.473788 | 2021-09-19T15:19:04 | 2021-09-19T15:19:04 | 102,781,474 | 4 | 1 | null | null | null | null | UTF-8 | R | false | true | 726 | rd | AtaImpact.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AtaImpact.R
\name{AtaImpact}
\alias{AtaImpact}
\title{Age to Age factors impacts}
\usage{
AtaImpact(triangle, mode = "value")
}
\arguments{
\item{triangle}{Cumulated triangle as a matrix}
\item{mode}{Character. The way to display the impact:... |
6d4e01131132be27cdcbb3ad9bf837a1ceb76a09 | a163da6d35190a6461455e38f9be1b4ed88af943 | /cachematrix.R | 43b18fe05f25c2aeb0482fca5cf060040194de37 | [] | no_license | matwgmkw/ProgrammingAssignment2 | 5813369973f6b8faabe611d5aaf08d4687cf0b44 | 9aab3628420c150f07bdd657d771ee19cc91de86 | refs/heads/master | 2022-10-18T08:42:59.013316 | 2020-06-10T16:55:59 | 2020-06-10T16:55:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 493 | r | cachematrix.R | makeCacheMatrix <- function(x = matrix())
{
inve <- NULL
set <- function(y) {
x <<- y
inve <<- NULL}
get <- function() x
setinve <- function(inverse) inve <<- inverse
getinve <- function() inve
list(set = set, get = get,
setinve = setinve,getinve = getinve)
}
cacheSolve <- function(x, ...)
{... |
3c5aac73e86a6a75b6ff48ea52f89fbc83849d18 | 4850bb228c6eb7956c7afbd128dabf0370f9b561 | /man/dtables2latex.Rd | beaa871151a9b4b4a98db3f5aec7a1f68de02697 | [] | no_license | renlund/descripteur | 830b64f5f4a6543f7ee27f2ea8dc0314032d0163 | 1d987cdc7f01c0d52b9f7065d415846e3c063de1 | refs/heads/master | 2023-02-19T14:41:01.819745 | 2023-02-09T15:42:37 | 2023-02-09T15:42:37 | 61,724,091 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 937 | rd | dtables2latex.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dtables.R
\name{dtables2latex}
\alias{dtables2latex}
\alias{dtables2latex_ungrouped_helper}
\alias{dtables2latex_grouped_helper}
\title{dtables to latex code}
\usage{
dtables2latex(
dt,
format = TRUE,
format.param = as.list(NULL),
n =... |
72f8876429fe14c37c1b86f3ab827344ebab731c | edae1ad20b35fd8c14cfc2442cdd7eae206d279e | /R/gkde_fun.R | 524626f19519f2635ba620cadb72d2815e1c0e92 | [] | no_license | yangxhcaf/rasterExtras | 54623642888dcd3e55b1b1df09a587e23ed8b136 | 44124f68e5b04208bff1c9ad936a5b676d817361 | refs/heads/master | 2020-07-07T15:19:02.308670 | 2019-06-05T15:33:37 | 2019-06-05T15:33:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,565 | r | gkde_fun.R | #' @useDynLib rasterExtras
#' @importFrom Rcpp sourceCpp
#' @import raster
#' @import parallel
#' @import stats
#' @import snow
NULL
#' Geographic Kernel Density Estimator using linear or Haversine distances
#'
#' This function calculates a kernel density estimation for raster objects.
#'
#' @param grid A raster obj... |
ad29c7474c5a3e3b0d3c4e31ea54bafc1a890855 | 0bfcd397ed2041f9b2589165cc9b00eee48b9b94 | /aphid_anomalies.R | 6be6f849532f43f2205b71fe3a29a18aa3b6081a | [] | no_license | YDAckerman/Chlorpyrifos | 9f229a7826f44ff6b7d33df1170a09d2e4b9daf6 | 9f2df38ffd7b6c0dfa3734dbd0d5f6952ea79950 | refs/heads/master | 2021-01-10T14:40:37.619335 | 2016-02-03T00:58:21 | 2016-02-03T00:58:21 | 43,094,239 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,169 | r | aphid_anomalies.R | library(plyr)
library(dplyr)
source("~/Documents/ZhangLab/R/Chlorpyrifos/assemble_final_dataset.R")
findAnom <- function(tab){
if(!is.data.frame(tab)){return(NA)}
rel_cols <- attr(attr(tab, "info")$allRanges, "relevantCols")
cols <- unlist(llply(rel_cols, function(x){
attr(tab, "info")$allRanges[[... |
3820f0373bae7b4387a50b1aa11f711c8c8d0a28 | 58b0cfbf14d5adcf8b84bedf827d7cbde1c3e1dd | /R/ef_scaled_euro.R | 7664ffb0c1486e92321a1a42b407240316b2466b | [] | no_license | ipeaGIT/gtfs2emis | 5c40fcb787eaa72a6b4e67c9199c5220a7542d10 | d68ffe805a66229f73df76de1e9b42c39d981688 | refs/heads/master | 2023-07-19T16:04:46.727586 | 2023-07-13T15:31:34 | 2023-07-13T15:31:34 | 218,305,263 | 23 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,407 | r | ef_scaled_euro.R | #' @title
#' Scale local emission factors in order to make emission estimates a function
#' of speed.
#'
#' @description
#' Scale emission factors to account for vehicle speed based on values from the
#' emission factor model by the European Environment Agency (EMEP/EEA). Emission
#' factor estimates are expresse... |
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