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c21cdb479737324a2bf002caea6cbc35342abd72 | e0b530f1d389c1de35175643d306eb4be64445f4 | /googleyoutubereportingv1.auto/R/youtubereporting_functions.R | 0de2604ed1a621b647a2f81948eff325c3c9e463 | [
"MIT"
] | permissive | Phippsy/autoGoogleAPI | 3ce645c2432b8ace85c51c2eb932e1b064bbd54a | d44f004cb60ce52a0c94b978b637479b5c3c9f5e | refs/heads/master | 2021-01-17T09:23:17.926887 | 2017-03-05T17:41:16 | 2017-03-05T17:41:16 | 83,983,685 | 0 | 0 | null | 2017-03-05T16:12:06 | 2017-03-05T16:12:06 | null | UTF-8 | R | false | false | 9,134 | r | youtubereporting_functions.R | #' YouTube Reporting API
#' Schedules reporting jobs containing your YouTube Analytics data and downloads the resulting bulk data reports in the form of CSV files.
#'
#' Auto-generated code by googleAuthR::gar_create_api_skeleton
#' at 2016-09-03 23:23:23
#' filename: /Users/mark/dev/R/autoGoogleAPI/googleyoutuberepo... |
52bc37f9ed88afabcef8bbd7863a2a8c2e0aca7c | c48b1d1d98128cb3c3d1bdf08f917276a02b1cc1 | /sources/modules/VETravelPerformanceDL/man/OpCosts_ls.Rd | 27f1eca253a6804793b43ae8e9426f4bde9415cc | [
"Apache-2.0"
] | permissive | jslason-rsg/BG_OregonDOT-VisionEval | 5ed51d550fb97793679d94d6d169fa091a89ba31 | 045cfecf341ec9fa80eac8d9614b4e547b0de250 | refs/heads/master | 2023-08-22T08:22:54.495432 | 2021-10-12T18:01:48 | 2021-10-12T18:01:48 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,425 | rd | OpCosts_ls.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CalculateVehicleOperatingCost.R
\docType{data}
\name{OpCosts_ls}
\alias{OpCosts_ls}
\title{Vehicle operations costs}
\format{
A list containing the following three components:
\describe{
\item{VehCost_AgTy}{a matrix of annual vehicle mainte... |
185520c32ee523fe36ae5d20fbbd337d22921706 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/EMMIXskew/examples/inverse.Rd.R | 95ae840c4fae23c38ef8fc5b0ebaa82c5ab3c69c | [] | 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 | 169 | r | inverse.Rd.R | library(EMMIXskew)
### Name: inverse
### Title: Inverse of a covariance matrix
### Aliases: inverse
### ** Examples
a<- matrix(c(1,0,0,0),ncol=2)
a
inverse(a,2)
|
46889b0df3917f5e74bb1c3700fb03d2732d0366 | e0b530f1d389c1de35175643d306eb4be64445f4 | /googlespeechv1beta1.auto/R/speech_functions.R | 363c396dbb0cd3d8ee295830dc69778af96404ec | [
"MIT"
] | permissive | Phippsy/autoGoogleAPI | 3ce645c2432b8ace85c51c2eb932e1b064bbd54a | d44f004cb60ce52a0c94b978b637479b5c3c9f5e | refs/heads/master | 2021-01-17T09:23:17.926887 | 2017-03-05T17:41:16 | 2017-03-05T17:41:16 | 83,983,685 | 0 | 0 | null | 2017-03-05T16:12:06 | 2017-03-05T16:12:06 | null | UTF-8 | R | false | false | 8,162 | r | speech_functions.R | #' Google Cloud Speech API
#' Google Cloud Speech API.
#'
#' Auto-generated code by googleAuthR::gar_create_api_skeleton
#' at 2016-09-03 23:47:37
#' filename: /Users/mark/dev/R/autoGoogleAPI/googlespeechv1beta1.auto/R/speech_functions.R
#' api_json: api_json
#'
#' @details
#' Authentication scopes used are:
#' \it... |
2c6d0b823e154b5627c8e96df877bfaa13d0b0c1 | 50c0013d8dd4320d70e48fa7047f7a6d5f967aad | /1_data_cleaning.R | 41ad2c26d2ef198d12d2aa5fa1e188e2e3584715 | [
"MIT"
] | permissive | Joscha-K/cardio-project-spring-school | 2c9922dd98f72b8f0efbaf7e83388c2120709eea | a43317b01dc65ae6f0503b533c58b6cc930dee8c | refs/heads/main | 2023-07-14T17:49:28.099340 | 2021-08-26T14:05:15 | 2021-08-26T14:05:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 517 | r | 1_data_cleaning.R |
rm(list = ls())
library(dplyr)
# set the working directory to cardio-project-site-example/ with setwd
# or use here() in the library(here)
dat <- read.csv("data.csv", stringsAsFactors = FALSE)
dat$CoronaryCA <- as.numeric(dat$CoronaryCA)
dat$Age_Part <- as.numeric(dat$Age_Part)
dat$Sex <- as.factor(dat$Sex)
dat$l... |
dfa6a8928ad54b1027fbf4b64b94d28b52395f9a | 5f546a630772d4158e10db221b5c7f19fbe31c7f | /Nat-Comm-2019_TMT_QE_averages.r | 4aa8590521f34b4bedc9a33b1899000b2a4c3e20 | [
"MIT"
] | permissive | pwilmart/BCP-ALL_QE-TMT_Nat-Comm-2019 | 30d8ea99cf334032b87c7802d534b0bb492a22c5 | 646f1f4cf667df4f71aa31b3c994845e18c9a05d | refs/heads/master | 2020-05-06T20:10:59.856144 | 2019-04-11T22:08:15 | 2019-04-11T22:08:15 | 180,223,616 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,805 | r | Nat-Comm-2019_TMT_QE_averages.r |
# library imports
library(tidyverse)
library(scales)
library(limma)
library(edgeR)
library(psych)
# get the default plot width and height
width <- options()$repr.plot.width
height <- options()$repr.plot.height
# load the IRS-normalized data and check the table
data_import <- read_tsv("labeled_grouped_protein_summary... |
1d810e40876b01b7fee7fe49b38078d89d4c500a | d8b2ab2974d83987a03b9dfd4ba08431a75fd0b5 | /modeling.R | b1646b2f971ee0531d5a9fcf68a68ce5e0d254e5 | [] | no_license | jinc132/Info370_a3 | 5120cb45ac68ee610c3f60ad7e4a62da3058b96d | 89ab2c3a0304ddb9fa1504798518ffcb78a3d5ea | refs/heads/master | 2020-04-07T18:57:51.453690 | 2018-11-29T01:09:43 | 2018-11-29T01:09:43 | 158,631,095 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,468 | r | modeling.R | library(dplyr)
library(corrplot)
data <- read.csv('./cleanData.csv', na.strings = c("", "NA"))
# Question 1
# Remove outliers from the data for a more accurate model
remove_outliers <- function(x, na.rm = TRUE, ...) {
qnt <- quantile(x, probs = c(0.25, 0.75), na.rm = na.rm, ...)
H <- 1.5 * IQR(x, na.rm)
y <... |
1cf036eceafa6a94b954baa8a9c62abb0709d719 | a18fa1fb80b3c76e8b67984b354ec03a4ea61d75 | /utils/matching.R | c029856ac7c4db7cc77abbbdda02dd2f633184f5 | [] | no_license | galileukim/ego_patronage | cf96044fc51b93fc53bceba7bc665186f84efffb | da23480e7761f00759d6e319faa592829cce2a15 | refs/heads/master | 2023-04-05T11:06:03.545906 | 2021-04-14T23:10:25 | 2021-04-14T23:10:25 | 276,722,118 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,808 | r | matching.R | # aux funs for matching
blocked_fastLink <- function(year, state, df_rais, df_filiados) {
t <- year
s <- state
rais <- df_rais %>%
filter(
year == t,
state == s
)
filiados <- df_filiados %>%
filter(
(year_start < t) & (year_cancel > t) &
... |
87405e71486c60be9fb82813ea9925c1ea2b472b | 6464efbccd76256c3fb97fa4e50efb5d480b7c8c | /paws/man/globalaccelerator_list_endpoint_groups.Rd | abc4bf8355e3f9c7652452c7dd7a3f042597927b | [
"Apache-2.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | johnnytommy/paws | 019b410ad8d4218199eb7349eb1844864bd45119 | a371a5f2207b534cf60735e693c809bd33ce3ccf | refs/heads/master | 2020-09-14T23:09:23.848860 | 2020-04-06T21:49:17 | 2020-04-06T21:49:17 | 223,286,996 | 1 | 0 | NOASSERTION | 2019-11-22T00:29:10 | 2019-11-21T23:56:19 | null | UTF-8 | R | false | true | 915 | rd | globalaccelerator_list_endpoint_groups.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/globalaccelerator_operations.R
\name{globalaccelerator_list_endpoint_groups}
\alias{globalaccelerator_list_endpoint_groups}
\title{List the endpoint groups that are associated with a listener}
\usage{
globalaccelerator_list_endpoint_groups(Li... |
a071bf314295bc24415642a5c2841e10b46e1b86 | 3d73ec1a75b54fab0e36db36b3892b9de632316a | /R/Plots/dotted_BoxPlot.R | 24a33a84025c9c64fee9c7c30a73cb9e4b7caa7c | [] | no_license | fabiodorazio/ZebrafishPGCs | 80f777cf1a36f65738649f5d9aeb67dfe6d22f95 | 5bcb3ffbe8a76e8f87b25e52587c0a77b78c6f4a | refs/heads/master | 2023-02-06T01:13:12.315538 | 2020-12-09T10:44:16 | 2020-12-09T10:44:16 | 200,243,948 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 401 | r | dotted_BoxPlot.R | r2 <- read.csv('../TestforR_Average.txt', sep = '\t')
posn.d <- position_dodge(width=0.4)
my_color = rep('dodgerblue1', times = 6)
ggplot(r2, aes(x=Treatment, y=Average.per.cell, color = Embryo)) +
geom_point(alpha = 1, position = posn.d) + scale_color_manual(values = my_color) +
geom_boxplot(alpha = 0, colour = ... |
879bccbe770ea5579a675d85c9285cbc8c411ca3 | dec7da1e4189f2d66538162af20cfe333e05e8c5 | /tests/testthat/test_genomic_annotation.R | 19c77de1c497189890f17e601e6b2574405cba29 | [] | no_license | AvinashGupta/epic | 7e5fbae3c3938945e5f33bd51442f46124062de5 | b716f3561b3d9e58668c4d72d32c6ad6ea163ab3 | refs/heads/master | 2020-03-27T17:49:46.710854 | 2016-08-18T15:52:22 | 2016-08-18T15:52:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,760 | r | test_genomic_annotation.R | context("test genomic annotations")
gr1 = GRanges(seqname = "chr1", ranges = IRanges(start = c(4, 10), end = c(6, 16)))
gr2 = GRanges(seqname = "chr1", ranges = IRanges(start = c(7, 13), end = c(8, 20)))
test_that("test percentOverlaps", {
expect_that(percentOverlaps(gr1, gr2), equals(c(0, 4/7)))
expect_that(percen... |
2ae742528e95dac0568a50715190a03db2b3539e | 0fcf20436b20ecfe3ef780dfe60f591ce069510b | /R/ht2distr.R | ce45f7dca04009e377534d7da3394f5dc12ff0d4 | [] | no_license | cran/tsxtreme | 6d564f38e970eb197f0ab2caa1ddac2cd966a363 | 118b73925a316cbbcabbdad1d04721bec4ee8020 | refs/heads/master | 2021-06-13T23:02:52.708003 | 2021-04-23T20:20:03 | 2021-04-23T20:20:03 | 84,930,497 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,264 | r | ht2distr.R | ## Copyright (C) 2017 Thomas Lugrin
## Functions related to H+T model
## (Scope:) Heffernan-Tawn model fit in 2 stages
## List of functions: - p.res2
## - q.res2
##################################################
## > res: vector of reals, quantiles on which to compute the distribution function of... |
5774cfddaddf5a8193835e115be080ca1b09bee7 | d6080d3ccfa5dae000118201c3d4c7e59ad70cdd | /plot1.R | deb6f8b401cdc2dad25a64b7c9d4119a8126278d | [] | no_license | b3ckham/ExData_Plotting2 | af71dcb41bb6fc7a2c0723672210f1d4c5e39b3b | c461f746f2ac7af111c928bd7309073ed9267058 | refs/heads/master | 2020-05-30T19:08:27.249228 | 2015-08-23T22:07:42 | 2015-08-23T22:07:42 | 41,269,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 442 | r | plot1.R | library(plyr)
library(ggplot2)
## Step 1: load data
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
aggTotals <- aggregate(Emissions ~ year, NEI, sum)
## Step 2: prepare to plot to png
png("plot1.png")
barplot(height=aggTotals$Emissions, names.arg=aggTotals$year, xlab="years",... |
5ba0d16f0225675d52af1192e1ebc21727a65fde | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/tigerstats/examples/ShallowReg.Rd.R | d8ae59a6779c9548d24718af043c5c112345e612 | [] | 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 | 199 | r | ShallowReg.Rd.R | library(tigerstats)
### Name: ShallowReg
### Title: Regression Line Too Shallow?
### Aliases: ShallowReg
### ** Examples
## Not run:
##D if (require(manipulate)) ShallowReg()
## End(Not run)
|
2eac34727d2979b31051df0dbb7c736090b9dfcb | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/shiftR/examples/shiftrPrepare.Rd.R | 85a087d15fa63dd38b6f921bebd24087eae2342f | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | r | shiftrPrepare.Rd.R | library(shiftR)
### Name: shiftrPrepare
### Title: Prepare Data for Fast Circular Permutation Analysis
### Aliases: shiftrPrepareLeft shiftrPrepareRight
### ** Examples
### Number of features
nf = 1e6
### Generate left and right sets
lset = sample(c(0L,1L), size = nf, replace = TRUE)
rset = sample(c(0L,1L), size =... |
5117817bfa074d7a3bb24a658ad401bff1d01301 | 41e8adc104bea0fa43537f081b38b305df0f70b8 | /R/hotelling.trace.R | 5d5b52d1fac28bfe8a0408c6c97d10db62c2779c | [] | no_license | cran/agce | 4f46a774552f2eab39b8eaa4494b8d813f0941b6 | 0ae0afb51e93080d6b742b7312b55e9964625833 | refs/heads/master | 2020-06-04T05:00:50.321637 | 2006-02-08T00:00:00 | 2006-02-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 392 | r | hotelling.trace.R | "hotelling.trace" <-
function(X,Y,C,U)
{
n<-dim(Y)[1]
### Compute the projection matrix on V|W
PVW<-X%*%solve(t(X)%*%X)%*%t(C)%*%solve(C%*%solve(t(X)%*%X)%*%t(C))%*%C%*%solve(t(X)%*%X)%*%t(X)
S2<-t(Y%*%U)%*%PVW%*%(Y%*%U)
S3<-t(Y%*%U)%*%(diag(n)-X%*%solve(t(X)%*%X)%*%t(X))%*%(Y%*%U)
### Compute t... |
7fd8829f824d24bf390bc91559f8ac531340a001 | c87e8550ddea7f81714bb0b1153007596f11cb83 | /R/select_KM.R | be761019ca381c38efca89a443e3deff184d0484 | [
"MIT"
] | permissive | JunLiuLab/SIMPLEs | 78bced4473f929735d0d5899ed5b56b83de84d15 | 2f4289186bb84c5b2072c57731ea52d98995ca25 | refs/heads/master | 2021-07-09T03:35:23.017466 | 2021-03-10T15:44:42 | 2021-03-10T15:44:42 | 233,257,891 | 3 | 1 | MIT | 2021-03-25T09:06:19 | 2020-01-11T16:02:41 | R | UTF-8 | R | false | false | 10,248 | r | select_KM.R | #' Wrapper of SIMPLE or SIMPLE-B to select optimal K and M based on BIC
#' @param dat scRNASeq data matrix. Each row is a gene, each column is a cell.
#' @param bulk Bulk RNASeq data matrix. Should be log(1 + tpm/fpkm/rpkm). Each row is a gene which must be ordered the same as the scRNASeq data. Each column is a cell... |
770057e8737404052b8ee8eedf36613bc8513be2 | a230b553d5bc4235d23e3f87343de8c69c9ca699 | /man/corr_data.Rd | f5ebf9ed850ba9136dc28f3e79c17520c94b8624 | [] | no_license | acryland/nbastats | b184315d895d08602c5084921656d434a91d7d4e | ab24b18f4935a82abee182cb9d5ca69fa4276b40 | refs/heads/master | 2021-02-16T11:09:57.299591 | 2020-03-04T23:10:12 | 2020-03-04T23:10:12 | 244,999,218 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 470 | rd | corr_data.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/corr_data.R
\name{corr_data}
\alias{corr_data}
\title{This function selects all numeric variables for a given year and creates a correlogram.}
\usage{
corr_data(x, y)
}
\arguments{
\item{x}{deienfes the dataset}
\item{y}{difnes the year}
}
\... |
4393acc6982662d2c14935aa1b46bba8b966562e | 90f4133259de3a5990553b49651938765caa9d0e | /man/Gaussian2binary.Rd | abc0cb4e560961f67333a0b9d6977138b3cdc26f | [] | no_license | itsoukal/bBextremes | 2099fdbf868078171ab756f5876db217363afef0 | a3783d696bb07c01c4ca4f74591833d4e5dd5219 | refs/heads/master | 2023-06-25T08:24:10.153959 | 2021-07-30T12:03:47 | 2021-07-30T12:03:47 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 559 | rd | Gaussian2binary.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Gaussian2binary.R
\name{Gaussian2binary}
\alias{Gaussian2binary}
\title{Fast estimation of equivelant correlation coefficients for Bernoulli processes}
\usage{
Gaussian2binary(P, ACSn)
}
\arguments{
\item{P}{parameter of Bernoulli process}
\... |
f47addb8b21aaf00a0f917433a36c606b432ba62 | f450df6d5114f36217e8c3258cd5e77b31eec3db | /My_Code/PackageFunctions/natmapr/inat_leaflet_save.Rd | 8053fe94d51816a04c36521d19278de41bb8c4fe | [] | no_license | WendyAnthony/Code_Each_Day | 3b9c1a83a1102f078c79de9f3b093d3b0ce8e789 | e122c8e8fd1f59cf12acf5b137e4659b58ad1b32 | refs/heads/master | 2023-02-25T00:14:53.298112 | 2023-02-16T22:45:54 | 2023-02-16T22:45:54 | 232,558,449 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,278 | rd | inat_leaflet_save.Rd | \name{inat_leaflet_save}
\alias{inat_leaflet_save}
\title{
3. Save leaflet map object as html webpage
}
\description{
Save leaflet map object as html webpage, including the required .css and .js files. To save the finished map, uses inat_leaflet_save() >> inat_leaflet_save(m, filename) >> e.g. inat_leaflet_save(map3, f... |
194f9cbf3ac56a18695f3eece0e977b04852492f | fd2a324a9505ed29e6136a06216edce999fa97a1 | /man/generatePermutations.Rd | 9ef1a1b638a3e3a28b544ce50f13bf2ecb729419 | [] | no_license | cran/mixAK | 995c88ac9b1f70ab2dac51b4fc1347b9b1356eed | adc4c2229d8ad3573e560fd598158e53e5d1da76 | refs/heads/master | 2022-09-27T10:45:02.953514 | 2022-09-19T13:46:13 | 2022-09-19T13:46:13 | 17,697,529 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 623 | rd | generatePermutations.Rd | \name{generatePermutations}
\alias{generatePermutations}
\alias{C_generatePermutations}
\title{
Generate all permutations of (1, ..., K)
}
\description{
It generates a matrix containing all permutations of (1, ..., K).
}
\usage{
generatePermutations(K)
}
\arguments{
\item{K}{integer value of \eqn{K}.}
}
\value{
... |
6c3fe1d81ce8fc09fb1a78fe566b0b295ad8cc07 | ca6d02c14d9cbe93d8460f1d20853203c831eaac | /Automation/00_hydra/deprecated/US_California.R | 63f39ea6a63a0bdfc3b342bf781a5831f971f572 | [
"CC-BY-4.0"
] | permissive | timriffe/covid_age | f654bea1cdf87e9aa9cc660facfdffb9264a9f5a | 8486772b0bfc0803efab603d8ac751ffe54e4d89 | refs/heads/master | 2023-08-18T04:56:18.683797 | 2023-08-11T10:22:19 | 2023-08-11T10:22:19 | 253,315,845 | 58 | 28 | NOASSERTION | 2023-08-25T11:03:04 | 2020-04-05T19:31:26 | R | UTF-8 | R | false | false | 10,519 | r | US_California.R | #source("https://raw.githubusercontent.com/timriffe/covid_age/master/R/00_Functions.R")
source("https://raw.githubusercontent.com/timriffe/covid_age/master/Automation/00_Functions_automation.R")
library(lubridate)
# assigning Drive credentials in the case the script is verified manually
#Im changing this to not use t... |
4cadd06a3c941ce28dcedeb9af0083708a54a631 | 8b6103d2a356350d77f52d3cbd15b650b6616afa | /2_RProgramming/ProgrammingAssignment3/rankhospital.R | 795fde23a817e6c6f12af965395ab7b787eaaa21 | [] | no_license | cesarpbn1/Johns_Hopkins_University | 95f3a803f3f342d975800309bd0082128fab89f3 | 601f51834e9435f263bfa4b0d44d0af79d99e33e | refs/heads/master | 2021-07-07T04:40:40.659948 | 2017-10-04T04:53:12 | 2017-10-04T04:53:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,004 | r | rankhospital.R | outcome <- read.csv("outcome-of-care-measures.csv", colClasses = "character")
head(outcome)
ncol(outcome)
nrow(outcome)
names(outcome)
outcome[, 11] <- as.numeric(outcome[, 11])
hist(outcome[, 11])
rankhospital <- function(state, outcome, num = "best") {
## Read outcome data
data <- read.csv("out... |
340bbb0e1ad9a44ec8a4da242b1fc35a91b9a3cc | 5009212e26f354715c83200d579dc0299e134da6 | /R/nCov2019.R | 022b0e0640740c96c9213940919bda3e71789321 | [] | no_license | LiGuangming309/nCov2019 | 63d608cc05bdc6e11f0a102debc0601f84757725 | 9dbcacbbe42ca1e5429b4d6ffce0da8b8e9f2e9a | refs/heads/master | 2021-02-11T18:27:30.302844 | 2020-03-02T10:59:11 | 2020-03-02T10:59:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,679 | r | nCov2019.R | #' download statistical numbers of the wuhan 2019-nCov
#'
#' @title get_nCov2019
#' @param lang one of 'zh' and 'en', for setting language of province and city names.
#' If lang = "auto" (default), it will be set based on Sys.getlocale("LC_CTYPE")
#' @return nCov2019 object
#' @export
#' @importFrom jsonlite fromJSON
#... |
a0ff8f9b5ce86ee62843277bb2d2455220a07fc1 | ac098be0111a2a7f6788842dbd665a03e15ae1fd | /R source files/sally_vat.R | 033063e838cca89f0f62b28ea7cecb6bdb415863 | [] | no_license | sallyshi/NYPH-PROJECT | dc67d6e2959dcecacec5c8f65e2e4bb95c8da576 | 15b19b2db6539493dc2a45759f01f60718e78980 | refs/heads/master | 2020-05-27T08:21:35.622303 | 2015-09-10T00:04:01 | 2015-09-10T00:04:01 | 41,886,601 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,281 | r | sally_vat.R | ## MEng Project
## Last Modified: March 3rd 2014
library(car)
library(hydroGOF)
data <-read.csv("~/Dropbox/MEng Project/R Stuff/vat-training.csv")
test_vat <- read.csv("~/Dropbox/MEng Project/R Stuff/vat-testing.csv")
## Constructing the Model
data$ASA.f = factor(data$ASA) #convert into categorical
test_vat$ASA.f =... |
2dc31e0fc0ff004002d36756711d384745c3d949 | 9f916fe7828f79b3355bc6ff3509ff4b0a62d0b7 | /man/check_input.Rd | 29eb6503d731442de16d725145aa049d52673e9e | [
"BSD-3-Clause"
] | permissive | greenelab/ADAGEpath | 9b5d44465a09fbed025bbdd793eaed89e7ec17a2 | e556970ef334d86ddfbf533acb8753d4ddb93967 | refs/heads/master | 2023-08-01T00:11:19.677503 | 2022-05-20T16:48:18 | 2022-05-20T16:48:18 | 70,191,632 | 5 | 7 | BSD-3-Clause | 2022-05-20T16:48:19 | 2016-10-06T20:46:19 | R | UTF-8 | R | false | true | 471 | rd | check_input.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/load_data.R
\name{check_input}
\alias{check_input}
\title{Checking input format}
\usage{
check_input(input_data)
}
\arguments{
\item{input_data}{the input to check}
}
\value{
TRUE if the input_data.frame meets the requirements, otherwise FALS... |
12b85ed0e595c86915a588e1fab264863391792b | 3fdcdc91821391eb627cf198c948a70a852417f7 | /蒙特卡洛+自助抽样法.R | 10ab8c83253104d2f0911c71866e6d6438ed6393 | [] | no_license | huuuuuuuue/-r- | feff661772774b758727d67f7bbd45aed6bd2f39 | ad91882a2614e1925e8b97271e236d6a0b0003fe | refs/heads/master | 2020-05-26T20:42:57.161936 | 2019-05-31T06:46:28 | 2019-05-31T06:46:28 | 188,368,234 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,937 | r | 蒙特卡洛+自助抽样法.R | #蒙特卡洛
#不停生成随机数然后抽
library(MASS)
n = 100
alpha = c()
for (i in 1:10000) {
mu1 = c(0,0) #均值是0
sigma1 = matrix(c(1,0.5,0.5,1.25),nrow = 2)
rand1 = mvrnorm(n=100, mu = mu1,Sigma = sigma1) #生成多元正态分布的随机数
x = rand1[,1]
y = rand1[,2]
alpha[i] = (var(y)-cov(x,y))/(var(x)+var(y)-2*cov(x,y))
}
mean(alpha)
var(alpha)
sqrt(var(alp... |
1b13694dbdb8425d169973ab2b5bfcca2c4a9e12 | e4eb148f75005834704beb06bd8c966b4542d755 | /5ii_RICKER_ESTIMATION_LAB_10_8_19.R | 738942b46c48c5562f8487807717f02102432c4f | [] | no_license | chythlook/ESC_GOAL_2019 | 85025d83712d11a258923a4408c5d4e8c0cd3156 | 88a4e2b941d3a248600f31a039258559d0bbb86f | refs/heads/master | 2020-08-07T11:41:03.604327 | 2019-10-04T21:30:19 | 2019-10-04T21:30:19 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 18,390 | r | 5ii_RICKER_ESTIMATION_LAB_10_8_19.R | ############### Ricker Estimation Lab I - Simulation Model ###############
# Functions are provided here for the first section.
# Load these functions, then calculations will be
# given more explicitly in further sections.
Ricker <- function(S, lnalpha, beta) {
S*exp(lnalpha - beta*S)
}
fitRicker <- functi... |
430655e2975d6c69ca9da500eb22de18e09d72c8 | af72407b36c1ee3182f3a86c3e73071b31456702 | /tests/testthat/test-misc.R | f0f60f392386b636b9cc462edfa691d4007f6dfb | [
"MIT"
] | permissive | bcjaeger/ipa | f89746d499500e0c632b8ca2a03904054dc12065 | 2e4b80f28931b8ae6334d925ee8bf626b45afe89 | refs/heads/master | 2021-07-12T20:52:23.778632 | 2020-04-26T16:44:01 | 2020-04-26T16:44:01 | 207,016,384 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 682 | r | test-misc.R |
test_that(
"inputs work",
{
things <- 1:3
expect_equal(list_things(things),"1, 2 and 3")
things <- letters[1:2]
expect_equal(list_things(things),"a and b")
}
)
test_that('drop empty works', {
a = list(a = 1, b = integer())
expect_equal(drop_empty(a), list(a=1))
b <- list(a = 1, b = 2)... |
d9e75dfee894aa1c2d461e3e5a6716db5f562494 | 8f46bd450429179c8530783cecf736cd7b88ae3d | /plot5.R | f56301fe86edd995bf41c9fb44fcf5f63640bc07 | [] | no_license | reckbo/Coursera-ExploratoryDataAnalysis-project2 | 802cd87a1841235afcc1a2fff465f855aa9489be | 98be1d92d2e0e7c7af915fbb68d58d2d9c6f0229 | refs/heads/master | 2020-12-25T05:27:16.781528 | 2014-08-24T05:37:04 | 2014-08-24T10:31:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 994 | r | plot5.R | source('setup.R')
library(dplyr)
if (!exists('NEI')) {
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
}
fips.baltimore <- "24510"
# I confirmed that subsetting by `type=='ON-ROAD'` is equivalent to subsetting with
# `grepl('vehicle', EI.Sector, ignore.case=T)`, and I w... |
24eca3859af9e33d8807cacf687621b849d14388 | 18e1ea7f2a92537ce82660a8ed6376470539829c | /R/Load.R | eff595d03ea7bea38ad609157d7c5ada6dcb6457 | [] | no_license | fansi-sifan/Kickstarter_survivor | 519458ca4875ba71e6fc8a42fade0780bac839f9 | 2027c6914fbb62c5674a58b44d29c541c5db73e7 | refs/heads/master | 2020-03-23T02:19:31.628507 | 2018-09-06T01:06:49 | 2018-09-06T01:06:49 | 140,968,841 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,724 | r | Load.R | # Author:Sifan Liu
# Date: Fri Aug 31 22:10:53 2018
# --------------
pkgs <- c('dplyr',"RJSONIO","jsonlite",'rvest')
check <- sapply(pkgs,require,warn.conflicts = TRUE,character.only = TRUE)
if(any(!check)){
pkgs.missing <- pkgs[!check]
install.packages(pkgs.missing)
check <- sapply(pkgs.missing,require,warn.conf... |
2634ce933037258f1707fd752bca06a79daa1963 | 3bfe56a625eadfb08b8ae569595869755cf7f906 | /analysis_func.R | 175a4571a6b5419f79bcc4ef20b4a926cb968641 | [] | no_license | zuowx/IBD_analysis | 20ae1225bb0402d8bc577b72e7885b7f3ed3fd49 | 68e90a0fbcbfc92312395ee3f585a91e4cf1d5f0 | refs/heads/main | 2023-07-18T02:38:15.077861 | 2021-09-02T01:05:26 | 2021-09-02T01:05:26 | 402,243,761 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,728 | r | analysis_func.R | library(ggpubr)
library(ggplot2)
plot.shannon <- function(df, plot_title) {
p <- ggplot(df, aes(x=Phenotype,y=shannon)) +
geom_boxplot(aes(colour=Phenotype),width=0.5,size=0.5,outlier.fill="white",outlier.color="white")+
geom_jitter(aes(colour=Phenotype,fill=Phenotype),width =0.2,shape = 21,size=2)+
sca... |
83d51dd47e8c562922535be0bd6b75d4fec6e7ff | acc82a0f64be1e131967991dea0b848195b58ead | /tests/testthat/test-dist_bray.R | 9932697db6a3387ff5afb1f5b9940c5106018990 | [] | no_license | dyerlab/gstudio | 2ecc2220cfceb625395191384401ac86c3f38963 | d7a207a8d66b4cd0b38faf4f42dc5590aae1a647 | refs/heads/master | 2023-06-01T06:35:55.709942 | 2023-05-15T17:52:05 | 2023-05-15T17:52:05 | 8,651,449 | 12 | 8 | null | 2020-03-27T00:09:45 | 2013-03-08T13:27:59 | R | UTF-8 | R | false | false | 748 | r | test-dist_bray.R | context("dist_bray.R")
test_that("individual",{
expect_that( dist_bray("Bob"), throws_error() )
expect_that( dist_bray(data.frame(Pop=1)), throws_error() )
expect_that( dist_bray(data.frame(Pop=1), stratum="bob"), throws_error() )
AA <- locus( c("A","A") )
AB <- locus( c("A","B") )
AC <- locus( c("A",... |
bb770a0aa1c2eea8568ad4e02f3a4427660bc98d | 1c03917b86f5e47c4bf954afce910ce439fd552b | /data_analytic_utilities/Inferential_stats_utils/Correlation_Regression_Classification_ML/Correlation_analysis.R | aad21ebff8882e5340feec9846a0b3abca649e58 | [] | no_license | sameerpadhye/Personal-projects | d7da869de225c48ce7c7b3ece8663bc10042c55e | d04fc8bbe723d90559f64ea7c8f8ca01eae17c56 | refs/heads/master | 2022-07-31T03:30:24.966932 | 2022-07-11T15:25:25 | 2022-07-11T15:25:25 | 179,165,892 | 1 | 1 | null | 2020-03-05T15:54:40 | 2019-04-02T22:06:24 | R | UTF-8 | R | false | false | 4,009 | r | Correlation_analysis.R | #Correlation analysis
#libraries used
library(tidyverse)
library(reshape2)
#Here a sample dataset is used. The numerical data from any dataframe (correlations of which need to be determined) can be substituted as per requirement
#Data for analysis
correlation_data<-data.frame(
var_1=c(10,12,17,29,35,NA,56,89,11... |
ea88075526e60068c6c689f90c64fc83fc92b78a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/SuperGauss/examples/Toeplitz-class.Rd.R | c0392a6ea95c3a7509c39c3b6d4afd8f213f9a0e | [] | 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 | 827 | r | Toeplitz-class.Rd.R | library(SuperGauss)
### Name: Toeplitz-class
### Title: Constructor and methods for Toeplitz matrix objects.
### Aliases: Toeplitz-class .Toeplitz setAcf getAcf traceT2 traceT4
### show.Toeplitz %*% determinant solve %*%,ANY,Toeplitz-method
### %*%,Toeplitz,ANY-method determinant,Toeplitz-method
### dim,Toeplit... |
8919d5b4b747b4da3a026424674c77fc7066ab3e | f61c1ca2a35c4a4dab86725905a82b34fe2bb912 | /complete.R | 29238ad9f1b16651fab9c88b08ad5b7c47539b2d | [
"MIT"
] | permissive | ankitprakash89/R--Assignment | 25b9a37aa3aab1bb13181978338f1d16dd052299 | 4feca865840a8a96c2e39ca45128f8f50b42c2f7 | refs/heads/master | 2020-03-17T00:28:20.358045 | 2018-05-12T05:50:21 | 2018-05-12T05:50:21 | 133,117,899 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 911 | r | complete.R | library(dplyr)
dir<- "C:/Users/Administrator/Downloads/Assignment 2/OneDrive_2018-01-28" # stored as a string
complete<- function(directory, id){
dir.files <- paste(dir, directory, sep="/")# for adding directory to the dir
setwd(dir.files) # to set the working directory
files<- list.files(pattern = '\\.csv')... |
24130472a532b60f0919ae5df3b5d70cf0c033e3 | 1f82e454a2b0a9f81a0bff4946507543f6ca4527 | /ProgrammingAssignment2-master/cachematrix.R | 889931fa83f0f19a6e895dac79b9bffb9ab91215 | [] | no_license | apriljkissinger/datasciencecourserajohnhopkins | 1d7985320010b8dca9188f822edad2007823ebc7 | c12fb15da162ea33ce67ee07a52feb93acdc81f3 | refs/heads/master | 2021-05-21T02:46:17.907860 | 2020-11-27T08:18:32 | 2020-11-27T08:18:32 | 252,507,540 | 0 | 0 | null | 2020-11-21T19:51:56 | 2020-04-02T16:23:51 | HTML | UTF-8 | R | false | false | 943 | r | cachematrix.R | ## This program will take in a matrix and spit out its inverse.
rm (list =ls())
## makeCacheMatrix are the getter and setter functions that get a matrix m in and then perform the inverse calculation on it,
## and clears out any inverse that has already been computed in the cache.
makeCacheMatrix <- function(x = mat... |
0dcf2b19702d8e197d60f87d6168c2060daf20eb | 1c3394f6720e005b338a03833044cec262808a35 | /man/checkForAuxiliaryFiles.Rd | 097f63e78f3ab7b75386450eaba3938d6fc6bbcb | [] | no_license | mdsumner/reproducible | 284e5e2603c6dc394ca8e547069fa4f4bfec4802 | 99cbc13e41d462c1445dfacf69549517c13efe2f | refs/heads/master | 2022-07-30T07:30:33.754921 | 2020-05-18T18:16:45 | 2020-05-18T18:16:45 | 265,203,071 | 1 | 0 | null | 2020-05-19T09:25:30 | 2020-05-19T09:25:29 | null | UTF-8 | R | false | true | 596 | rd | checkForAuxiliaryFiles.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/prepInputs.R
\name{.checkForAuxiliaryFiles}
\alias{.checkForAuxiliaryFiles}
\title{Check a neededFile for commonly needed auxiliary files}
\usage{
.checkForAuxiliaryFiles(neededFiles)
}
\arguments{
\item{neededFiles}{A character string of fil... |
809cb700b4a6a58775f062d29da637a6442aa4eb | 55484e772eb403bfe98c0e568749a3d6be1c539a | /plot2.R | f0505f444e5f8068c85ece296347ff15c201ea28 | [] | no_license | merlandson14/ExData_Plotting1 | 0f46e82f7391c0af791182132f6dcf63930c8d9f | d4730c55f108b04b0cccd27283e9fedc2717e890 | refs/heads/master | 2021-01-16T22:26:10.355867 | 2016-01-25T01:20:49 | 2016-01-25T01:20:49 | 50,296,101 | 0 | 0 | null | 2016-01-24T16:22:43 | 2016-01-24T16:22:42 | null | UTF-8 | R | false | false | 1,287 | r | plot2.R | # This program will read in Household Power Consumption data for Feb 1 and 2, 2007.
# It will then create a line plot of the Global Active Power amounts over the two days.
library(dplyr)
zipfileUrl = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
temp <- tempfile() ... |
7c0bc740e6950c1cb12258661c75879de132c15b | c650e9bca1d76deda90309498506159c2906768e | /tests/testthat.R | 29bae8565bad5ccf8f3de9698bdadcd49ca6c1b1 | [
"LicenseRef-scancode-warranty-disclaimer",
"LicenseRef-scancode-public-domain-disclaimer"
] | permissive | nemochina2008/netcdf.dsg | 63cd9468ea1290c89fb573188e24a2942ad8287f | 0600a0c13ea0f51f33d6058ea9a754241bbab711 | refs/heads/master | 2021-06-20T11:37:27.699344 | 2017-08-01T16:21:15 | 2017-08-01T16:21:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 43 | r | testthat.R | library(testthat)
test_check("netcdf.dsg")
|
bb96c330415b717b208fe95e5b0e925ed3f0dedf | 04df0315f2208f0b009d0e1a55953cccfbb84c52 | /man/geno.afc.Rd | 3f3a6c6f54852b0decaea8519b6482f5d13d5633 | [] | no_license | cran/AssocAFC | 5d4374156a20c7c89a53ae36658587fc3278c1a2 | 82afb6d83d99bc9e2373de41396fe2ca7bf33612 | refs/heads/master | 2021-09-13T22:32:24.496241 | 2018-05-05T08:34:22 | 2018-05-05T08:34:22 | 112,373,664 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | rd | geno.afc.Rd | \name{geno.afc}
\alias{geno.afc}
\docType{data}
\title{
AFC Genotype Example
}
\description{
An example genotype file that corresponds to the the one derived in the unrun example's
first iteration.
}
\usage{data("geno.afc")}
\format{
The format is:
int [1:1800, 1:75] 0 0 0 0 0 0 0 0 0 0 ...
- attr(*, "dimnames")=Li... |
62da1dbef1a674442f295521d8d6486655242074 | 6de599efffd75ab721fb158c82e84554bcdb3b96 | /18_t_code.R | 8d5b9db31a4fbeb0028aa141b660f8a7abd498fa | [] | no_license | ybrandvain/datasets | 26f820192033194d682d0d3a92b2b07244dbb56d | a446e472640d8f769104b4c9825f46b3678f7a00 | refs/heads/master | 2022-12-05T03:51:04.453190 | 2022-12-02T06:58:11 | 2022-12-02T06:58:11 | 253,668,620 | 0 | 0 | null | 2020-04-07T02:38:33 | 2020-04-07T02:38:33 | null | UTF-8 | R | false | false | 1,543 | r | 18_t_code.R | library(tidyverse)
library(janitor)
library(broom)
temp_link <- "https://whitlockschluter3e.zoology.ubc.ca/Data/chapter11/chap11e3Temperature.csv"
temp_data <- read_csv(temp_link) %>% # load in data
clean_names()
#glimpse(temp_data)
############################
#### Plot the data -
############################
# Pl... |
81749388975c035bc9d8dce7f8c6e9219f10336a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/FiRE/examples/Rcpp_score.Rd.R | f7f6e4d666dc5a5e62d4c7653c6c29d18a9b6d5f | [] | 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 | 322 | r | Rcpp_score.Rd.R | library(FiRE)
### Name: score
### Title: Compute score on hashed sample.
### Aliases: score
### ** Examples
## Not run:
##D
##D ## Creating class object with required arguments
##D model <- new(FiRE::FiRE, L, M)
##D model$fit(data)
##D score <- model$score(data)
##D
##D
## End(Not run)
... |
acfd5cba0b8386eaa21c60447d9f5a32ab3cd94e | d48eec86caf281f065ab2b3943d52070171b2ce4 | /13-simulando-paretos.R | d91c5d60e0f64d1e40f5574f1ef926f270c28da9 | [] | no_license | djosafat/simulacion | 05c7168433cf2013225e752b4cb211a72fa8853e | 881af801138e0e46f0f2fafc97e9c9a8fb8ce161 | refs/heads/main | 2023-05-12T03:28:46.827007 | 2021-06-01T02:36:20 | 2021-06-01T02:36:20 | 344,931,452 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 720 | r | 13-simulando-paretos.R | #Caso Pareto con parámetros a=3, b=1
FF <- function(x){1-(1+x)^(-3)}
curve(FF(x),0,3,lwd=4,col='blue')
abline(h=0,v=0,lwd=4)
abline(h=1,lty=2,col=2,lwd=4)
#inversa
FFinv <- function(x){ (1-x)^(-1/3)-1 }
curve(FFinv(x),0,0.99,lwd=4,col='magenta',add=T)
curve(1*(x),add=T)
########################################
## Gener... |
4ebfffa867b802574aecf60bfac79dcdd40ea30e | 79abac7cd7496a52f2fd3a9932eecc849dd2c661 | /R/get_basis_set.R | 54243ddf9c71d3b2ac3c79bc00922bffcc5d873c | [] | no_license | achazhardenberg/piecewiseSEM | 7758a164c0fcc328266f2cd0a5fb13cdd164a149 | 7f0b2baddd4688fc5fd4663de676726e4a45ed38 | refs/heads/master | 2020-12-07T15:21:11.580966 | 2015-06-02T22:09:15 | 2015-06-02T22:09:15 | 34,168,584 | 0 | 0 | null | 2015-04-18T14:21:59 | 2015-04-18T14:21:59 | null | UTF-8 | R | false | false | 2,002 | r | get_basis_set.R | get.basis.set = function(modelList, corr.errors = NULL, add.vars = NULL) {
dag = lapply(modelList, function(i)
if(all(class(i) %in% c("lm", "glm", "negbin", "lme", "glmmPQL","pgls"))) formula(i) else
nobars(formula(i)) )
if(is.null(add.vars))
dag = dag else
dag = append(dag, unname(sap... |
6458517cb88ff94040caf9af54678a4998cb55cd | d17100a3f8887e627fb33eebd2feac71dbbf5f02 | /UniHSMM.R | 490cbf3c42d493a08499800435d59a895335e646 | [] | no_license | jeffung/coursework-cypersecurity | 28da95476c2cd94cc139def3caf7a1a58cabe5f7 | 336844efc06022de223ccc3f52232766b845a9b1 | refs/heads/master | 2020-03-28T21:55:34.250107 | 2017-08-03T23:29:03 | 2017-08-03T23:29:03 | 149,192,504 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,245 | r | UniHSMM.R | ###
#train data
traintest1 <- list (x= data.frame(uni_train$power), N=length(uni_train$Date))
traintest1$x <- data.matrix(traintest1$x, rownames.force = NA)
traintest1$x <- scale(traintest1$x)
class(traintest1) <- "hsmm.data"
# end
uni_test<- data.frame(as.numeric(test1$Global_active_power))
uni_test <- setNames(uni_te... |
3192db04c089c6e30f015a9b86e4f1a0cdee07e5 | 9735404924fe5eedbb8e2c204504d4b266351e35 | /global.R | 63b2022af3fbf64612e0833c1a87372d2a1a775b | [] | no_license | aridhia/demo-table-statistics | e2e1c31fd48aa03b9643180ad6ec09b1fcc9d151 | f93513eb7666c042ad78f66fe5d74d430d71b6f4 | refs/heads/main | 2023-04-29T02:22:03.845046 | 2021-05-14T15:26:38 | 2021-05-14T15:26:38 | 290,808,862 | 0 | 0 | null | 2021-02-05T12:12:07 | 2020-08-27T15:13:30 | JavaScript | UTF-8 | R | false | false | 8,142 | r | global.R |
library(shiny)
library(ggvis)
library(shinyBS)
source("./code/documentation_ui.R")
source("./code/config.R")
xap.chooseDataTable <- function(input, output, session) {
d <- reactive(withProgress(message = "Reading table", value = 0, {
req(input$table_name)
xap.read_table(input$table_name)
}))
## Upda... |
d591d55cec4d075e8a7495af2db874cbac10ec36 | 9491cadebf15aed8b93a6b8bde60e81febee79ae | /modelo1.R | b6e5685b108bfc277a02cff372908ecc37ed7795 | [] | no_license | pyxisdata/sript_personal | b01a7cb535832f8501c2cbc1367117ae2d496e73 | 688147512be949951a4541189316e0842e25b4a8 | refs/heads/master | 2020-07-18T01:25:10.338963 | 2019-10-06T00:06:39 | 2019-10-06T00:06:39 | 206,143,506 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 23,599 | r | modelo1.R | # Script para el modelo de evaluacion de EMA
# Librerias
# Conexion a google drive
library(googledrive)
library(googlesheets)
library(readr)
# Procesamiento de los datos
library(lubridate)
library(dplyr)
library(tidyr)
library(stringr)
library(stringi)
library(purrr)
# Desactivar la notacion cientifica
... |
6ca8967995490324a5aa9a2b21076c4c5f28c14c | 7ce35c255fe7506795ff7abc15b5222e582451bb | /2-descriptive-outcomes/stunting/13_stunt_calc_outcomes_birth_strat.R | a40070860a4578c6d590ebd93382723114cc54ae | [] | 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 | 14,166 | r | 13_stunt_calc_outcomes_birth_strat.R | ##########################################
# ki longitudinal manuscripts
# stunting analysis
# Calculate mean LAZ, prevalence, incidence,
# and recovery, repeated for fixed effects models
# and sensitivity analysis in monthly cohorts
# with measurements up to 24 months
# Inputs:
# 0-config.R : configuration file
... |
9e4d77fea0bd3bc6dbc98c5afa4f45b6d33c9793 | 68f8217845056df195a2d78356ddd5a2f9a9e44e | /R/statistics_with_R/04_Exploring_Data_with_Graphs/Script_Files/01_graph_intro.R | 35389db31760d8c6d2f571a873131aaa1f3d35de | [
"MIT"
] | permissive | snehilk1312/AppliedStatistics | dbc4a4f2565cf0877776eee88b640abb08d2feb5 | 0e2b9ca45b004f38f796fa6506270382ca3c95a0 | refs/heads/master | 2023-01-07T15:11:18.405082 | 2020-11-07T21:06:37 | 2020-11-07T21:06:37 | 289,775,345 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 472 | r | 01_graph_intro.R | # loading ggplot2
library(ggplot2)
facebookData <- read.delim('/home/atrides/Desktop/Applied-Statistics-with-R-master/statistics_with_R/04_Exploring_Data_with_Graphs/FacebookNarcissism.dat')
head(facebookData)
# initiatingplot objsct
graph <- ggplot(facebookData, aes(NPQC_R_Total,Rating))
# adding geoms
graph+geom_... |
3bd27b7f571c0fa4a155e593e92c804c8277deac | 398cb934488b6ebed4f350ef4ab7b6b52df50e7f | /plot4.R | 34f393a3d7bf5df02043370b79f4f1b8f4fb0ce3 | [] | no_license | sankusaha11/ExData_Plotting1 | 1596a68f09cdbfb0ad1ceb6fb7934f3b2362ee97 | 9fd212992cebd92018ed9d2e5d9636fd6baf103f | refs/heads/master | 2021-01-16T23:03:39.786925 | 2016-06-17T02:50:38 | 2016-06-17T02:50:38 | 61,340,723 | 0 | 0 | null | 2016-06-17T02:46:17 | 2016-06-17T02:46:17 | null | UTF-8 | R | false | false | 1,233 | r | plot4.R |
#Generating Plot#4
setwd("C:/Users/ssaha/Desktop/Personal/coursera/ExploratoryAnalysis")
rm(list=ls())
mydf <- read.csv("household_power_consumption.txt", sep = ";", stringsAsFactors = FALSE)
mydf$Date <- as.Date(mydf$Date,"%d/%m/%Y")
mydf2days <- subset(mydf, Date == as.Date("2007-02-01")| Date == as.Date(... |
3dd1231e80f0c16a352d116e48bc64a7211522eb | bcd3edb2557cf3ca80d541c6b9ed2ac4e217bb8e | /man/cr_survreg.Rd | 8761b75c384b715b3f7f502d3c926af0b62ff834 | [] | no_license | jwdink/tidysurv | a0bf281bdea6e345d8b3357e37cc9f55226ec928 | 899e26731580f1fda4586b81f4ead0660c590fcd | refs/heads/master | 2021-01-09T05:48:09.016298 | 2017-08-09T03:16:56 | 2017-08-09T03:16:56 | 80,838,538 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,912 | rd | cr_survreg.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tidysurv-package.R
\name{cr_survreg}
\alias{cr_survreg}
\title{Easy interface for parametric competing-risks survival-regression}
\usage{
cr_survreg(time_col_name, event_col_name, data, list_of_list_of_args,
time_lb_col_name = NULL, time_de... |
71b44fa5f53d2f59309dfa50ab21228990780cb9 | ae54e750598e79dbe8b16ce02c2a14dbd5b38f1a | /cachematrix.R | 3d679fb8b11308acc2b1c11509aafbcb12013cd5 | [] | no_license | bravedream/ProgrammingAssignment2 | 3b5e06a55d9c360ceda1dd3e879c23c251b9e615 | 095ebfc05d4d5ad1bf80445cb58fa6999e553013 | refs/heads/master | 2021-01-21T05:55:40.676811 | 2016-04-06T20:23:07 | 2016-04-06T20:23:07 | 46,301,167 | 0 | 0 | null | 2016-04-06T20:21:01 | 2015-11-16T20:38:54 | R | UTF-8 | R | false | false | 1,908 | r | cachematrix.R | #The file will take an input matrix (assumed to be invertible square matrix) and check
#if the matrix already exist and have been run to get the invert. If not, return the inverted
matrix; otherwise, retrieve the inverted matrix from cachedmatrix
#the function below will create a matrix object and set the Inv to nul... |
e5953149cf2a05b19f97753147d97a599f655a23 | b9d4fdc5b544ffb6158705d1c8e81f670a2931f1 | /inst/shiny/server.R | c2dbd9fc19e751040546c77e906c3302c5ac5803 | [] | no_license | cran/nph | 284e6c2ad631edfd62f46a3540c499d26c287476 | e2fa54d4e719b85ad2c40e9ce8b6b0010bea4f1c | refs/heads/master | 2022-06-17T07:47:58.897005 | 2022-05-16T22:20:05 | 2022-05-16T22:20:05 | 236,633,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 814 | r | server.R | nph.env<-new.env()
library(ggplot2)
library(dplyr)
library(formatR)
#library(nph)
server <- shinyServer(function(input, output, session) {
options(stringsAsFactors = F)
# # include logic for each tab
source(file.path("server", "srv-tab-home.R"), local = TRUE)$value
source(file.path("server", "sr... |
4c28b900ff0c2b8fd54784c2399429dc9b7f7b43 | a6f7a6a708ac52be533d18be5e64aa513725c35e | /notebooks-2021-joule/2021_06_xx_end_of_life_variation_analysis.R | da5244781caada4a17a8a1c96c94e122ed88cf62 | [] | no_license | wengandrew/fast-formation | 24ef0b9ff9e3e69cab6804ac77aec97d018bdabc | 78735c7255cd5c3db66c53a86e981dba67129f01 | refs/heads/main | 2023-04-10T00:16:49.636322 | 2023-03-04T15:39:08 | 2023-03-04T15:39:56 | 408,813,945 | 6 | 1 | null | 2022-08-31T02:24:00 | 2021-09-21T12:36:56 | Jupyter Notebook | UTF-8 | R | false | false | 4,835 | r | 2021_06_xx_end_of_life_variation_analysis.R | library(knitr)
library(ggplot2)
library(ggbeeswarm)
library(data.table)
library(magrittr)
library(dplyr)
library(cvequality)
library(outliers)
"
Determine the statistical significance of aging variability due to fast
formation. Use some statistical tests to determine if the coefficients of
variation from the two samp... |
aafdd418c8e4dc2f1efb2bf1b9b4e35e4a9847bc | 91e79fa553199db814e7e49022666ae1f4bb9b14 | /input/input_base.R | f8f2670e5abf3b64ef1b1a5b31852281243ea9e5 | [] | no_license | gloriakang/economic-influenza | 2ae2a6b20ebc05ec120fdf581f3cf614f5282403 | d8fa9a93624425ce2284e9d3c33313a1ba09b359 | refs/heads/master | 2021-06-12T08:27:59.786460 | 2019-10-27T19:39:20 | 2019-10-27T19:39:20 | 126,077,561 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 597 | r | input_base.R | # inputs = base case
## base vaccine compliance = 0%
#vax_comp_b <- 0
bc_04 <- 0
bc_519 <- 0
bc_2064 <- 0
bc_65 <- 0
## total = 1248308.16 (36.64%)
base_04 <- 74205.56 * 0.67 #33.19%
base_519 <- 419788.08 * 0.67 #65.63%
base_2064 <- 685774.72 * 0.67 #30.68%
base_65 <- 68539.8 * 0.67 #22.21%
# base case DALYs (high... |
c7b4602ed85ff1d6a14a5ca39aa3b6633233d725 | 12cfa29386d6241c8305927c2238cacff860f7bc | /starter_rpackages.R | 0cc615aa46f55b522684a59388279033dd7d35fe | [] | no_license | thomas-keller/slurm_caret_tutor | 365115d4fdb7403d49f1199a62bd937627cee16a | 6d70f231e0374e24663c39e4a57360ec80b2e932 | refs/heads/master | 2021-09-09T09:04:34.700662 | 2018-03-14T14:35:31 | 2018-03-14T14:35:31 | 125,131,501 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 431 | r | starter_rpackages.R |
lgit <- function(gitstr){
withr::with_libpaths(new='R/x86_64-pc-linux-gnu-library/3.4/',devtools::install_github(gitstr))
}
dir.create('R/x86_64-pc-linux-gnu-library/3.4/',recursive=TRUE,showWarnings=FALSE)
lgit('sjmgarnier/viridisLite')
lgit('sjmgarnier/viridis')
install.packages(c("tidyverse","caret","corrr","d... |
53f4c62323b6f6ee180929f35bdabf82e0e8f9b3 | 63a30097fd22c13170f950b314950fa99182f3d8 | /Chignik_exec_ADFG2.R | c2698b7deeb7361d79f3ba79e77d45f49ca22962 | [] | no_license | Sages11/Chig_UW_transition | 19483d875d8c4c9f055904d70529d8f8684c77e7 | f04bd932ee45156771c08e940c707c76bba1e4fa | refs/heads/master | 2020-04-21T19:05:48.771922 | 2019-02-12T19:24:17 | 2019-02-12T19:24:17 | 169,794,174 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,842 | r | Chignik_exec_ADFG2.R | #LUKAS DEFILIPPO 1/28/18
#Script for automated execution of hierarchical logistic model and inference
#load required packages
library(lubridate)
library(viridis)
library(rstan)
library(shinystan)
library(RColorBrewer)
#parallelize
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())
#Useful fun... |
fdf3b6cc4ad75b4815d2ceb04de79cc09d8e1e00 | 55686d2928596baa6bbde6b972d191f8a035f573 | /Week_12_Discussion/Discussion _Week12_V2.R | d3e8971817eeb1a1ae129c7a3c85fe3304c08337 | [] | no_license | DarioUrbina/Teacher-A-Statististical-Methods-BME-423 | 6556688a414c1b3ee404aacdbf4401324f0b2645 | 1572301100c96583da46209d08ceac4efa570024 | refs/heads/master | 2023-01-06T23:57:37.652149 | 2020-11-06T02:45:19 | 2020-11-06T02:45:19 | 288,513,280 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 818 | r | Discussion _Week12_V2.R | #Discussion week 12
## 1. Mandatory lines
# Similiar thing as clc;clear all;close all in MATLAB
rm(list = ls()); # clear workspace variables
cat("\014") # it means ctrl+L. clear window
graphics.off() # close all plots
# In-class exercise
# Example from Navarro - chico.Rdata
load("~/Desktop/Week12/chico.Rdata")
View(ch... |
fca74ccb417286003be59ee7fb55c3a370657e76 | 301d8a72cd06b4678b3b391715c2290c4a0de3f7 | /tests/benchmarks/benchmark_join/tokens.R | cee7c3a5dc78753cb447911105fe0ff95f3f1306 | [] | no_license | XiaoyueZhang/quanteda | 8adb1c702b5067ac88e4d4baee3233066e3990db | 257d9462df3fb100bb255109497b26da3c0709d5 | refs/heads/master | 2021-01-12T09:16:53.421715 | 2016-12-14T19:05:16 | 2016-12-14T19:05:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,029 | r | tokens.R | toks <- tokens(inaugCorpus)
seqs <- list(c('united', 'states'))
microbenchmark::microbenchmark(
joinTokens(toks, seqs, valuetype='fixed', verbose=TRUE),
times=10
)
seqs_not <- list(c('not', '*'))
microbenchmark::microbenchmark(
joinTokens(toks, seqs_not, valuetype='glob', verbose=TRUE),
times=10
)
se... |
1c6cf76095e9aa40d3ec2089552d8aac8796e145 | e03a758498cac958f162d2f4ad1df886f84c8d72 | /crossover_exp.R | 310e029e74d886915386d3b142ab04d85a7d9410 | [
"MIT"
] | permissive | sealionkat/wdae-differential-evolution | 1c2407999c28048bbf7aa61f4fed0a5b2acdfa32 | f6dd4434e64d6b42ab1fe69ca87ea18173cd26a6 | refs/heads/master | 2016-08-08T07:16:14.543188 | 2015-10-12T21:58:46 | 2015-10-12T21:58:46 | 33,892,805 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 340 | r | crossover_exp.R | crossover_exp <- function(Pgi, Mgi) {
Ngi <- point();
i <- 1;
while(i <= dim) {
a <- runif(1);
if(a < CR) {
Ngi$coordinates[[i]] <- Pgi$coordinates[[i]]
} else {
break;
}
i <- i + 1;
}
while(i <= dim) {
Ngi$coordinates[[i]] <- Mgi$coordinates[[i]];
i <- i + 1;
}
... |
b9f185faa9ab4195258253de8ccfb3606b400f4d | 1a68555cdacd6d8ccc567bd2c00975f5eba3682e | /R/rec_tree3.R | 343da58d6e9662adbfa7be0b7591d679d0a1b410 | [] | no_license | franciscorichter/dmea | 8708c1a2a861bc90c42e02c3d22a0e316779d1ef | e4b1af2c3ea102554f97b4a79ed1c30e56428653 | refs/heads/master | 2020-06-28T22:35:35.962945 | 2017-07-13T17:05:32 | 2017-07-13T17:05:32 | 74,467,150 | 0 | 1 | null | 2017-02-23T09:06:58 | 2016-11-22T11:45:21 | R | UTF-8 | R | false | false | 1,270 | r | rec_tree3.R | itexp <- function(u, m, t) { -log(1-u*(1-exp(-t*m)))/m }
rtexp <- function(n, m, t) { itexp(runif(n), m, t) }
rec_tree3 <- function(wt, model='dd',pars){
lambda0 = pars[1]
mu0 = pars[2]
K = pars[3]
n = 1:length(wt)
i = 1
E = rep(1,(length(wt)-1))
ct = sum(wt)
prob = 1
p=list(wt=wt,E=E,n=n)
while(i... |
3c8c900381c99f13d3e59e04c0c6c01c2bb9bc35 | 1a87d39148d5b6957e8fbb41a75cd726d85d69af | /R/plotForest.R | 44d2b9e76a5655bb7c0ea839cb5ca9a4fbd24ce2 | [] | no_license | mknoll/dataAnalysisMisc | 61f218f42ba03bc3905416068ea72be1de839004 | 1c720c8e35ae18ca03aca15ff1a9485e920e8832 | refs/heads/master | 2023-01-12T16:49:39.807006 | 2022-12-22T10:21:41 | 2022-12-22T10:21:41 | 91,482,748 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,678 | r | plotForest.R | #' @title Calculated univariate analysis and creates a forest plot
#'
#' @description The function creates a forest plot for a given
#' number of variables, expect a srv object and a data.frame containing
#' the selected variables as columns. Univariate Cox PH models
#' are fitted. A subject vector can be specified ... |
36f365c109b4c35c233cdf8e482cf1f7c9e0f9f6 | 77157987168fc6a0827df2ecdd55104813be77b1 | /palm/inst/testfiles/euc_distances/libFuzzer_euc_distances/euc_distances_valgrind_files/1612968695-test.R | 306583ad18d94bca84930c4ec79121b3943d5457 | [] | 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 | 1,067 | r | 1612968695-test.R | testlist <- list(x1 = c(NaN, NaN, 1.39065814070327e-309, 0, 2.39021688577355e-310, 1.11897728190947e+87, 2.77478592360575e+180, 2.77448002212291e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.72320665544079e+180, 2.77448001762435e+180, 2.72312157072141e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.77... |
cfccae0f097beb93666ec93c451bd3151d7ced4a | d4b0f480b0816ee82b31503052134a5a158e74dc | /cachematrix.R | 98405c879bb017110791c0a30eb9065350b4cdee | [] | no_license | WanluZhang/ProgrammingAssignment2 | 8357830b927cc8a1c8124f42c8f43416496c8f7c | dd8e2485025d089bb89c0cba6ba47963248b1f3b | refs/heads/master | 2021-05-08T07:54:06.596836 | 2017-10-14T17:16:42 | 2017-10-14T17:16:42 | 106,939,972 | 0 | 0 | null | 2017-10-14T15:40:12 | 2017-10-14T15:40:12 | null | UTF-8 | R | false | false | 1,300 | r | cachematrix.R | ## This pair of functions invert a matrix and cache its result.
## makeCacheMatrix() - create a cache for a matrix
makeCacheMatrix <- function(x = matrix()) {
m <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setsolve <- f... |
f1a5bd08051df82ecaac12c1adda9daa488bb20c | 17599442579623cb1ef00358322170787a8ecc41 | /tests/testthat/test-lang.R | df5964fa4c3560faf55e4302bc48763f22d15301 | [] | no_license | rlugojr/rlang | 792c40f51bfe527810e7e2acfd83193cd14cc669 | 73164435cc3b46069c8f451e78edb28cda1d0c83 | refs/heads/master | 2021-01-13T01:07:59.722058 | 2017-02-22T14:44:45 | 2017-02-22T14:44:45 | 81,410,996 | 0 | 0 | null | 2017-02-09T05:01:54 | 2017-02-09T05:01:54 | null | UTF-8 | R | false | false | 1,808 | r | test-lang.R | context("language")
test_that("NULL is a valid language object", {
expect_true(is_lang(NULL))
})
test_that("is_call() pattern-matches", {
expect_true(is_call(quote(foo(bar)), "foo"))
expect_false(is_call(quote(foo(bar)), "bar"))
expect_true(is_call(quote(foo(bar)), quote(foo)))
expect_true(is_call(~foo(bar... |
ec2db22f17172427e8a67bb9757b0e361094fafe | 2fa33aeef712fa0a1b8043b40261d218a37cafa2 | /R/truncnorm.R | 3bf4a6352f58e56752dd0442478f370c46d4cc93 | [] | no_license | cran/bayess | 778e3cd961acecec0ccbf0de66048543af82c98c | 30208f8c4b61bc73e5885875b8134f05a963719c | refs/heads/master | 2022-09-03T00:53:47.483683 | 2022-08-11T09:30:08 | 2022-08-11T09:30:08 | 17,694,647 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 139 | r | truncnorm.R | truncnorm=function(n,mu,tau2,a,b)
{
qnorm(pnorm(b,mu,sqrt(tau2))-runif(n)*(pnorm(b,mu,sqrt(tau2))-pnorm(a,mu,sqrt(tau2))),mu,sqrt(tau2))
}
|
dd996051a5cf6ae5dc8458a5dec5b879e9fd9ca8 | 53e5567b96e5e20f556784d7173dd90cc18eb7e5 | /R/SVI_state_functions.R | 80b06b9005993991241b1047c15c864a0cbcf9ed | [] | no_license | edroxas/svi-tools | 7deec1c8df6aecc2e199ee2347b7ed419a2a1552 | fe37e6659da0c4f1c1d458ba0e5de9d093df4c97 | refs/heads/master | 2023-03-15T15:21:34.069423 | 2018-05-11T20:33:06 | 2018-05-11T20:33:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,085 | r | SVI_state_functions.R | library(RJSONIO)
library(rgdal)
library(ggplot2)
library(ggmap)
library(scales)
library(maptools)
library(rgeos)
library(dplyr)
library(data.table)
# A script to generate functions that allow us to construct the 16 separate
# pieces of the SVI for all census tracts.
# Each function has 2 inputs:
# 1. A state
# 2. a... |
77974c90b32ca06a3359691c8aa65ca79a316de6 | 690c3c3e583094011d339d20a819b0fbe11a2bf8 | /conf_matrix.R | 242a4a421b96958b7d2aaf9a6f081e7751fa8a63 | [] | no_license | AllisonVincent/StarFM-code | a0f907e2931460b7867600bd1566cb39a600338b | eac755b6ef61af5d1925b3b65d02269c846e79e1 | refs/heads/master | 2021-06-17T15:02:43.013841 | 2021-04-20T17:19:42 | 2021-04-20T17:19:42 | 194,706,294 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,064 | r | conf_matrix.R | #### This script is for the purpose of viewing individual rasters of STARM and Landsat NDSI data, as well as creating a confusion matrix to measure the performance of STARFM against the Landsat for model validation.
library(sp)
library(sf)
library(ggplot2)
library(rgdal)
library(raster)
library(dplyr)
library(caret)
... |
aa226496011a2b3bd4e5ddb4a3f17e1f1a3a1174 | 3a22a1b42404a006f4dad390d3190650bdc94ba7 | /man/list_stations.Rd | f73ec3aaa9c2dfd15ea1fae9f476d10f41e857cc | [
"MIT"
] | permissive | spadarian/USydneyRainfall | 282932136c8249081af968b2c65b75b5a27fd764 | 87dd623a8a2b79cc2a9701ffe8ce9c89c7cfada5 | refs/heads/master | 2021-01-22T05:05:38.193767 | 2015-07-01T17:47:07 | 2015-07-02T00:57:14 | 38,396,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 228 | rd | list_stations.Rd | \name{list_stations}
\alias{list_stations}
\title{Better Rainfall Forecast for Grain Growers API connection}
\usage{
list_stations()
}
\value{
\code{data.frame}
}
\description{
This function gets list of user stations.
}
|
06e21c91cc376ec62140d1b73aa3e61d3a11494b | c2e833feb1c738737ed468b3d0da503439faa199 | /save-scripts/ttrees-write.R | 025df3735151592572e716c8853815e4036590a9 | [] | no_license | privefl/paper2-PRS | 4ea4d5d6aa4d0b422c57f4e20cedc8925f4b6497 | 3487d0d0d77e27956788ddb9ef8840c46676a7bf | refs/heads/master | 2021-11-25T23:51:21.652297 | 2021-10-27T08:14:25 | 2021-10-27T08:14:25 | 106,011,226 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 751 | r | ttrees-write.R | library(bigsnpr)
library(ggplot2)
NCORES <- nb_cores()
# data
celiac2 <- snp_attach("backingfiles/celiacQC_sub4.rds")
G <- celiac2$genotypes
n <- nrow(G)
m <- ncol(G)
CHR <- celiac2$map$chromosome
POS <- celiac2$map$physical.pos
# jdb
file.jdb <- "ttrees.jdb"
cat(rep(";", 5), sep = "\n", file = file.jdb)
big_apply(... |
b7e58f3e114632b919518af7dccd773d64bc51cf | 011ee506f52512d7245cf87382ded4e42d51bbd9 | /R/ir_calc.R | 3cc5f4907daa75659c7b43fbef0e3b4cf926a6a1 | [
"MIT"
] | permissive | emilelatour/lamisc | ff5e4e2cc76968787e96746735dbadf1dd864238 | e120074f8be401dc7c5e7bb53d2f2cc9a06dd34a | refs/heads/master | 2023-08-28T02:15:00.312168 | 2023-07-27T23:39:58 | 2023-07-27T23:39:58 | 123,007,972 | 7 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,229 | r | ir_calc.R |
#### Packages --------------------------------
library(dplyr)
#' @title
#' Calculate confidence interval for crude incidence rate
#'
#' @description
#' 7 different methods that I found online for calculating the confidence
#' interval for a crude incidence rate. Note that these are all two sided. Most
#' of these c... |
4603fe190d3dfb77f710d20c8ef18ccdd53fc1d9 | 13c225cd942a60601c6dd9c9d35e174e2112f73d | /01-relational-data.R | 0c2f8372654a73cc68bbf9b9d4fc527371b5810c | [] | no_license | deblnia/data-wrangling-relational-data-and-factors | 59fc1e9ae384def0d9ccc60d9d26c05f171ad090 | e53ac044dda1efe207f8fe1bf58a2e4799abe7e3 | refs/heads/master | 2022-12-08T08:44:41.449989 | 2020-08-31T15:48:44 | 2020-08-31T15:48:44 | 289,555,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 498 | r | 01-relational-data.R | # load required packages
library(tidyverse)
# # uncomment and run if nycflights13 is not already installed
# install.packages("nycflights13")
library(nycflights13)
# Is there a relationship between the age of a plane and its departure delays?
## consider the data frames you need to use to solve the problem
glimpse(fl... |
22a6bab6f3d4d7343cf4ff746f8e54439012b193 | 366a094282df7ec63aff057a1c415991cd5603f4 | /Problem 9.R | cd5adc5bc86afde9ff7f5d6570847db9e346cfaf | [] | no_license | spyroso/Project-Euler | ed66c89304a12de4b6524138490d36efcb98af83 | f6b48f0a9b9fbee040d306a984e8fd6ebd2f9570 | refs/heads/master | 2020-06-08T20:56:45.721009 | 2019-07-11T03:05:13 | 2019-07-11T03:05:13 | 193,305,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 199 | r | Problem 9.R | # Problem 9: Brute force approach
for (c in 1:998){
for (b in 1:(c-1)){
for (a in 1:(b-1)){
if (a+b+c == 1000 && a^2 + b^2 == c^2){
print(a*b*c)
break;
}
}
}
} |
f6705d8f235a243f5e6ec771266786d0b75452a3 | 41364e42f222803c4741c60210c3d0f5b85e83a3 | /Quantitative Primer/samples/ch9-exercises.r | 1115bc9ec4cefd4f5aea1d223e58aa2ec42e4f72 | [
"MIT"
] | permissive | bmoretz/Quantitative-Investments | 2a052e612e06afe1027fa168df54b5b9212b556f | 25d9a7199f212787dd9ae05f7af9e7407591c5bc | refs/heads/master | 2021-06-16T18:10:11.203155 | 2021-04-05T03:13:57 | 2021-04-05T03:13:57 | 186,317,831 | 7 | 4 | null | null | null | null | UTF-8 | R | false | false | 3,093 | r | ch9-exercises.r | ## Dale W.R. Rosenthal, 2018
## You are free to distribute and use this code so long as you attribute
## it to me or cite the text.
## The legal disclaimer in _A Quantitative Primer on Investments with R_
## applies to this code. Use or distribution without these comment lines
## is forbidden.
library(MASS)
library(xt... |
16fd4aae0cc78d23b7dd0ebf28a68a6d41c89331 | d9736711c9c01c91218f9bb06b5a81498014cf0b | /R/read_vcfs_as_granges.R | e2d3c8a48a5d8be6eae448928f44c942c575d647 | [
"MIT"
] | permissive | Biocodings/MutationalPatterns | 6c3819ecca88b1e51f6d41510504c3960e60f217 | 5698fb9abb7c61e54c05b37df4e7f131c1ba5c28 | refs/heads/master | 2021-01-20T07:13:39.640056 | 2017-05-01T12:08:32 | 2017-05-01T12:08:42 | 89,982,167 | 2 | 0 | null | 2017-05-02T02:16:44 | 2017-05-02T02:16:44 | null | UTF-8 | R | false | false | 9,453 | r | read_vcfs_as_granges.R | #' Read VCF files into a GRangesList
#'
#' This function reads Variant Call Format (VCF) files into a GRanges object
#' and combines them in a GRangesList. In addition to loading the files, this
#' function applies the same seqlevel style to the GRanges objects as the
#' reference genome passed in the 'genome' paramet... |
41d3eda160e85a7b594c790369438221b273fc2b | 5ceb1928a72ce2e1e3249cf6af607637c51f06d2 | /R/R03.R | 537121d4c26b65eba45ed2ae2cd43398b36fb80c | [] | no_license | doyun0916/BIG_DATA | a9f9df40a51bbf763716c8d907842f510c528bc4 | 0c17a0e7f86a7dcab69a99a3bea68f1740f3d4ef | refs/heads/main | 2023-02-26T01:35:58.121065 | 2021-01-28T00:55:53 | 2021-01-28T00:55:53 | 328,689,545 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 495 | r | R03.R | # R03.R #
# Matrix #
c(1,2,3,4)
m01 <- matrix(c(1,2,3,4))
m01
m02 <- matrix(c(1,2,3,4), nrow =2)
m02
m03 <- matrix(c(1,2,3,4), nrow = 2, byrow = T)
m03
seq(1,9)
m04 <- matrix(c(1:9), nrow=3, ncol = 3, byrow=T)
m04
m04 <- matrix(c(1:10), nrow=3, byrow=T)
m04
m04 <- matrix(seq(1,9),3,3,T)
m... |
38274dfe16a1494f215c679a30b00372d0cad512 | 41a7fe696b9339ae11bcbb8fb3d49dbef9daa186 | /Broad-Rush/mergeROSMAPMethylationData.R | 789732d4652570b236f596fe0cdac168a3759bcd | [] | no_license | alma2moon434/ampAdScripts | 88f8386fa66adaaf0772ea492ac68fa162079294 | 04e3f388b5c552283865064f2f57fe28494fb0a3 | refs/heads/master | 2020-04-06T13:45:07.562444 | 2016-04-19T04:19:46 | 2016-04-19T04:19:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,570 | r | mergeROSMAPMethylationData.R | library(synapseClient)
library(plyr)
library(dplyr)
library(reshape2)
library(rGithubClient)
synapseLogin()
## Get this script
thisRepo <- getRepo("Sage-Bionetworks/ampAdScripts")
thisScript <- getPermlink(thisRepo, "Rush-Broad/mergeROSMAPMethylationData.R")
# Function to get all files from Synapse
readData <- funct... |
da0ea9dc6b03ffe143277f9d6745195be7ba8e8a | 9eab973e373e12f170dbbba8dffbb18f2038bd54 | /papers/Rpaper1/figures/plot.chi2.R | 00b987752588455766548a4468e44ea41a82ec5f | [
"CC-BY-4.0"
] | permissive | richarddmorey/nullHistoryAMPPS | f1c36cb40d862ec1bd473cfdb31b882c7de68e64 | 2256b9fe547e957e636b0fb7a1b2b70c36a8e836 | refs/heads/master | 2021-09-13T16:24:44.933397 | 2018-05-02T07:35:01 | 2018-05-02T07:35:07 | 112,319,821 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 429 | r | plot.chi2.R | plot(0,0,ty='n',ylim = c(0,1.2), xlim=c(0,10), xaxs='i',yaxs='i',
main = expression(paste(chi^2,"(1) distribution",sep="")),
ylab = "Density", xlab="Squared deviation from expectation", axes=FALSE)
xx = seq(qchisq(.025,1), qchisq(.975,1), len = 200)
polygon(c(xx,rev(xx)), c(dchisq(xx,1), xx*0), border = NA, ... |
d8517f2b9631768b680bc529d54b3a0c0d74b735 | 4acd939710367338d5e1dc08a3b70c8b948f303a | /day1.R | f7a5797529ab795290d7ebced6645a962eb5f865 | [] | no_license | amittiwari18/R-Language | e9298f669e4c6e81f3aab4de8d73fff649a1f9b4 | 30de78027f32d3c93adc9412fa39d351a553e157 | refs/heads/master | 2020-04-28T20:48:05.639119 | 2019-03-14T07:06:21 | 2019-03-14T07:06:21 | 164,835,086 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 492 | r | day1.R |
x<- 3
y<- 4
x*y
(8/2)-(x*y)
8/(2-x)*y
#Number of sequence created
z<- 1:20
w<- 20:1
#Character vectors created using function paste
a<- "label"
k<- 1:30
paste(a,k)
#word "brown" replaced with word "red"
s<- "The quick brown fox jumps over the lazy dog"
sub("brown","red",s)
# word "fox" picked up using substr()
su... |
43fbb87264320ccfced9f8d5f1f8fe083deef111 | 3411a6cb316c664d37af1640b2b8e929aa607aa8 | /cell_annotation.R | 11464651c5230a5b7049d9a0253c7ce5979f2c39 | [] | no_license | rongfan8/DBiT-seq_FFPE | dfcc3aad43f82c53de761e6f5302e02238b6d17c | 47992a89dc7f2d1f978651f09b4fc731ef0f7765 | refs/heads/master | 2022-12-06T10:58:12.606385 | 2020-08-26T13:31:48 | 2020-08-26T13:31:48 | 290,505,811 | 1 | 0 | null | 2020-08-26T13:34:49 | 2020-08-26T13:34:48 | null | UTF-8 | R | false | false | 20,579 | r | cell_annotation.R | library(Seurat)
library(SeuratData)
library(ggplot2)
library(patchwork)
library(dplyr)
library(rhdf5)
library(Matrix)
library(sctransform)
library(plyr)
library(gridExtra)
library(magrittr)
library(tidyr)
library(raster)
library(OpenImageR)
library(ggpubr)
library(grid)
library(wesanderson)
dir <- "C... |
c1ff90f2da7a9ab0559aebac98e6fadf527363f6 | c8b76c289224a86d20c8a9e60d45777867946c26 | /PredictionFuncs.R | 4a9eec8ce5e961fe94e093e951e913ac1a9a2347 | [] | no_license | drewCo2/DS-Capstone | 0a35a830c5e7d0bc2c31445aadb013130a46a147 | e891cd5e08afe008796e3a5a4d84f94a270332ee | refs/heads/master | 2020-04-02T05:45:11.969390 | 2016-06-23T21:47:48 | 2016-06-23T21:47:48 | 60,376,073 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,095 | r | PredictionFuncs.R |
library(quanteda)
library(dplyr)
# Create a string that we can use with dplyr's 'filter_' function.
# We use this to select our ngrams from the result sets.
makeFilter<-function(tokens)
{
n<-length(tokens)
res<-sapply(1:n, function(x)
{
sprintf("t%d=='%s'",x, tokens[x])
})
res<-paste(res, collapse=... |
38c5676984b8a2e88233715f41060258309a6415 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/GSE/man/TSGS-class.Rd | 9a212dccdc9b9a310a3e52b22336f11f08662f33 | [] | 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 | 1,270 | rd | TSGS-class.Rd | \name{TSGS-class}
\docType{class}
\alias{TSGS-class}
\alias{getFiltDat,TSGS-method}
\title{Two-Step Generalized S-Estimator for cell- and case-wise outliers}
\description{Class of Two-Step Generalized S-Estimator. It has the superclass of \code{GSE}. }
\section{Objects from the Class}{
Objects can be created by cal... |
42335dd8b5097ca66c09b139408f844e83cf99f2 | 05a095367c9970e09044f1dd36e9a45321d66c64 | /DMC2/dmc2.R | 359e42fdfb30376027d74c7865359d6eebe4ae14 | [] | no_license | taimir/DMC2015-2016 | 712581957dee90dfc0ce856ecedf5c6d813ee98e | 71084f81b7a34c82725ab929975f5f891880c2c2 | refs/heads/master | 2021-01-10T03:15:06.755473 | 2016-01-19T20:55:52 | 2016-01-19T20:55:52 | 49,517,681 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 17,514 | r | dmc2.R | # Business Analytics
# Data Mining Cup Introduction
#
# Please note, that this script only has the nature of proposal. It provides useful functions for the steps of data mining but does not cover all possibilities.
# The caret package is used (http://topepo.github.io/caret/index.html)
#install.packages("caret")
librar... |
1c7b134ef9008fc049029d36f99178ecf97f9f40 | 4f0d8fff4a5910661a1e0650e1e288322fe94eae | /app.R | f0d3ca5e8c50185af62454e729b8fa90d13a129b | [] | no_license | josuejv/tripto | bb58d15dc68db8d02e98c64da501ebce9529be5e | 3b6e2b821a7c11952140e30830719abf35c5a83b | refs/heads/main | 2023-05-02T11:34:38.291771 | 2021-05-25T02:32:37 | 2021-05-25T02:32:37 | 370,523,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 43,945 | r | app.R | ## Aplicacion desarrollada por Josué Jiménez Vázquez
# Load packages ----
if(!require(shiny)) install.packages("shiny", repos = "http://cran.us.r-project.org")
if(!require(shinythemes)) install.packages("shinythemes", repos = "http://cran.us.r-project.org")
if(!require(shinydashboard)) install.packages("shinydashboard"... |
5b0f4273892d12c96d4166ef02e811850c64a0ee | a2da3f8ed1f91e1792e9f47a9f801bdd41aaa371 | /02-Learn-R.R | cecd91547ce8c37980fc61b175749db4fb5aa9b2 | [] | no_license | fatihilhan42/DATA-SCIENCE-WITH-R | fe2d25afbd7dfb07533ac3559928c460f31e32b0 | 5bfc0025edff853eedc8f56852dcad98e4f55f7a | refs/heads/main | 2023-05-12T06:37:42.207102 | 2021-06-07T10:49:16 | 2021-06-07T10:49:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 975 | r | 02-Learn-R.R | # R ÖĞRENELİM
"Merhaba Dünya!"
#<- atama operatörü
x<-5
#help() yardım için metot
help(getwd)
#Çalışma dizini ekrana yazdıralım
getwd()
#Çalışma dizini değiştirelim
setwd("C:/Users/ASUS/Desktop/demo")
#Vektör veri tipi
v<-c(1,2,3)
#Liste veri tipi
l<-list(x=c("elma","erik"),y=1:10)
#Vektörün elemanlarına ulaşma... |
5b7a60b0c43bbe5fb2e833e772960d4ef895a0dc | d62b3bbe79263098532870905d5e8e7442421dbf | /setup.R | e024d982ed812beb41f5843bea2231c60f1f16a8 | [] | no_license | doberstein/CVK-MOOC-Analysis | b051dff96392477fa6f3dae8c77145d1e5a724a4 | e9a75b868b859b6ca85010e9a9b396d3079e1011 | refs/heads/master | 2020-04-05T11:56:08.659074 | 2019-12-18T13:51:43 | 2019-12-18T13:51:43 | 156,850,802 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 264 | r | setup.R | library(purrr)
library(tidyr)
library(jsonlite)
library(dplyr)
library(ngram)
library(anytime)
library(ggplot2)
library(compare)
library(reldist)
library(rmarkdown)
library(dmm)
library(TraMineR)
library(fpc)
library(stringr)
library(apcluster)
library(data.table) |
fb725fc3f4c2eab2d44419db6cdf6a58c385d9b8 | 58b63f843ddba75a567d921f736554d978caa33d | /Rename_Files.R | ad0ad6119763974d533386694058a52ed1adf458 | [] | no_license | Broccolito/HRLR300_Tissue_Dataset | d41208453c9ed15ddd1dc48f99b422e679673533 | 2e4d8061bc8ca80df25b02836ad4cf57ba4fca5b | refs/heads/master | 2020-03-22T15:19:30.857993 | 2018-07-10T00:57:49 | 2018-07-10T00:57:49 | 140,244,144 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 230 | r | Rename_Files.R | #Run this line to rename the files in a fashion of
# 1 ~ # of files
file.rename(from = list.files(pattern = "png"),
to = paste(seq(1:length(list.files(pattern = "png"))), ".png",
sep = "")) |
a95e1f4187d8cae651ddd23246b28426ce95e27e | 98cdc943ac1f7bee868b2a485f74955abd38ae57 | /tests/testthat.R | cf8bd1cc4536f03006ae886c7d4bf80b745e3b13 | [
"MIT"
] | permissive | FoRTExperiment/PestED | f31e4aff1eb3096c9b6703d772d85f7139d58ae9 | 62f023d8fd8098649d8f671068b1409f0cd20300 | refs/heads/master | 2021-05-17T04:41:56.114938 | 2020-11-18T02:12:17 | 2020-11-18T02:12:17 | 250,627,623 | 2 | 2 | NOASSERTION | 2020-11-18T02:12:18 | 2020-03-27T19:31:21 | R | UTF-8 | R | false | false | 56 | r | testthat.R | library(testthat)
library(PestED)
test_check("PestED")
|
bedae03aef5a98bbc6eb1f65dd74c590d452c554 | 65061263ab8ea942345edc0b1a7b5771b06406c9 | /man/IV_PILE.Rd | 85be21d4f97ee2fcba329b10a1aed48b453695c8 | [] | no_license | cran/ivitr | ae438aeb16bc498713a2bd01445467a08c5b5afc | 4404053c3195c948ac9463b7d00ae36d7a4af670 | refs/heads/master | 2022-12-08T16:46:34.854825 | 2020-09-11T07:40:03 | 2020-09-11T07:40:03 | 295,361,081 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,316 | rd | IV_PILE.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/IV_PILE.R
\name{IV_PILE}
\alias{IV_PILE}
\title{Estimate an IV-optimal individualized treatment rule}
\usage{
IV_PILE(dt, kernel = "linear", C = 1, sig = 1/(ncol(dt) - 5))
}
\arguments{
\item{dt}{A dataframe whose first column is a binary IV ... |
7ee9d63d6d7086cdf58f1c84b178e2e8a1a3c05e | 08377005c504cad79e453e702725fd0cfc5ae360 | /R/Exiqon_2Colour_miRNA_Pipeline.r | 09d8a669dd75d8e19120552d3bac7e6a67addb57 | [] | no_license | AndrewSkelton/BSU_Scripts | a7c73960fa9aba754b82c2aca100e4e812790ca9 | 8a1a81fd2112ef51a9f8d30f920bbde46b95a939 | refs/heads/master | 2016-09-10T10:48:52.905124 | 2014-05-28T16:13:59 | 2014-05-28T16:13:59 | 16,342,295 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,657 | r | Exiqon_2Colour_miRNA_Pipeline.r | ##'LOAD IN GAL FILE (ANNOTATION) AND RAW DATA
biocLite("ExiMiR")
library(ExiMiR)
install.packages("miRada")
make.gal.env(galname="gal_208500,208501,208502,208510_lot35003-35003_hsa-and-related-vira_from_mb180,miRPlus.gal" , gal.path="./" )
ebatch <- ReadExi(galname="gal_208500,208501,208502,208510_lot35003-35003_hsa-an... |
1a80fb0353a84b559f9b06fcc684d19c5235c5f5 | b33611762071f9277bf18d712d3beaddb1683788 | /man/upsamplePitchContour.Rd | c18a9f1f57eda08c5ec089eadb949b88a6d524cc | [] | no_license | fxcebx/soundgen | abc6bb7d7aded02e11fe6bd88cb058ca0947f75f | 2d8ae67893509bd29d132aaa04c0e9385879ddd9 | refs/heads/master | 2020-09-06T21:51:23.464374 | 2019-10-31T17:43:11 | 2019-10-31T17:43:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,334 | rd | upsamplePitchContour.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilities_analyze.R
\name{upsamplePitchContour}
\alias{upsamplePitchContour}
\title{Upsample pitch contour}
\usage{
upsamplePitchContour(pitch, len, plot = FALSE)
}
\arguments{
\item{pitch}{numeric vector of pitch values, including NAs (as re... |
0de774875df50356e3ed7d4ffa024a116eeff800 | eca810397cfa067c4c7f8ced66c4b748b8a1e8c9 | /temp/DataPrep.R | c1ee039569a961ddcdd8fe54f8a86ca578e9c7fa | [] | no_license | PennBBL/pncPreterm | c149319dfdbb801eabf0e0acf15f9db5dc138cec | 936cb62f63f652b2adb393dbafe6bf31891c313b | refs/heads/master | 2022-06-20T18:22:41.423267 | 2020-05-06T13:12:30 | 2020-05-06T13:12:30 | 116,976,795 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,406 | r | DataPrep.R | #################
### LOAD DATA ###
#################
##Demographic data (n=1629)
data.demo <- read.csv("/data/jux/BBL/studies/pnc/pncDataFreeze/n1601_dataFreeze/demographics/n1601_demographics_go1_20161212.csv", header=TRUE)
##Environment data (n=1601)
data.environ <- read.csv("/data/jux/BBL/studies/pnc/pncDataFree... |
320bf2e73f602095f74225495c0f89c394ff1e84 | be5f7e66344c9f2e0ab2ed2af54605d7d99dd790 | /Chemostat_simulation_recovery/Optimization_algorithm_chemostat_model.R | b02539bb7b17368e0abcb722ee2200b571a29775 | [] | no_license | claycressler/deb_fitting | f4db2f78da5a9f92746f6cea0cc2d4f5e4d5e18d | cb4b4559e64f7e9f772b34e6b76dd162500cd667 | refs/heads/master | 2020-12-25T16:57:25.668226 | 2017-09-28T19:51:04 | 2017-09-28T19:51:04 | 35,557,425 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,278 | r | Optimization_algorithm_chemostat_model.R | require(pomp)
require(plyr)
## I want to use an algorithm that allows me to hone in on the best
## parameter combinations, starting from near complete ignorance. The
## algorithms of pomp, liked iterated filtering and trajectory
## matching, are really only intended to work locally. Run by
## themselves, they do not g... |
b6f6fe2c14ae6ef43b40a576c3a38af13c6a8978 | 4b5d1178e3fbc94223c974926bbdd417182c314d | /R/linreg_estimators.R | 27eb6453f0a260fbeede4ee811267fa0ff2f815b | [] | no_license | rossklin/dynpan | 6686d891af9834eaa94a4190cfb4c325b18c3b2f | 2f655de1f350e54375621edf3010b67eff37a4ce | refs/heads/master | 2021-01-10T22:01:39.875849 | 2016-08-28T15:39:39 | 2016-08-28T15:39:39 | 36,667,036 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 24,196 | r | linreg_estimators.R | ## Software License Agreement (BSD License)
##
## Copyright (c) 2014, Tilo Wiklund (tilo@wiklund.co)
##
## Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are
## met:
##
## Redistributions of source code must retain the above c... |
884d0dd0cb4d6f81269ec4aa44b1e9af35bcf0ef | 8c3ce85957132b59fab7a0489b6c90d38290ed34 | /R/genesOfInterest.R | b37f5c5f5fead15a87aac29298cf127f2f2e9b67 | [
"MIT"
] | permissive | ChristofferFlensburg/superFreq | a8e60ef211d461c1b5a148f573d550634621651e | 99584742099b33310f96a4bfb3a9fd179274d5cb | refs/heads/master | 2023-04-08T12:58:53.463534 | 2023-03-29T03:28:09 | 2023-03-29T03:28:09 | 51,901,575 | 118 | 38 | MIT | 2023-02-08T19:28:54 | 2016-02-17T06:53:45 | R | UTF-8 | R | false | false | 14,319 | r | genesOfInterest.R |
#' plots a heatmap of copy numbers, highlighting genes of interest
#'
#' @param GoI character vector. Genes of interest.
#' @param Rdirectory character The Rdirectory where the superFreq run was run.
#' @param genome character. The genome the sample is aligned to. 'hg19', 'hg38' or 'mm10'
#'
#' @details This function ... |
03b661941f973487e38e9690ba23a8d2a171e1a2 | cd2e6a05bbf1196bf447f7b447b01f0790f81e04 | /ch4-classification/4.3-multivariate-logistic-regression/quiz17.R | fcd76292a7753f449c5d89cebbfb4b7e299aaefb | [] | no_license | AntonioPelayo/stanford-statistical-learning | d534a54b19f06bc5c69a3981ffa6b57ea418418f | c4e83b25b79a474426bb24a29110a881174c5634 | refs/heads/master | 2022-04-21T07:33:00.988232 | 2020-04-22T05:44:46 | 2020-04-22T05:44:46 | 242,658,021 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 414 | r | quiz17.R | # Quiz 17
# 4.3.R1
ProbAinClass = function(B0, B1, B2, X1, X2){
# X_1 = Hours studies
# X_2 = Undergrad GPA
# Return probablility student recieves an A
numerator = exp(B0 + (B1 * X1) + (B2 * X2))
return(numerator / (1 + numerator))
}
hoursToStudy = function(Px,B0, B1, B2, X2){
return((log(Px / (1-Px)) - B... |
e60ddddab17f45d0b8b248a2184dc1825c5c4f2f | d473a271deb529ed2199d2b7f1c4c07b8625a4aa | /zSnips_R/Columns.R | 98f7fcc809a872e0723f8697c40b0d07c9d889e3 | [] | no_license | yangboyubyron/DS_Recipes | e674820b9af45bc71852ac0acdeb5199b76c8533 | 5436e42597b26adc2ae2381e2180c9488627f94d | refs/heads/master | 2023-03-06T05:20:26.676369 | 2021-02-19T18:56:52 | 2021-02-19T18:56:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,634 | r | Columns.R | # IN THIS SCRIPT:
# Add columns
# Remove columns
# Name columns
# Rename columns
# Perform operations across columns
# Test for column present
# Dynamically reference columns
#---------------------------------------------------------------------------
# ADD COLUMNS
# in this example, df has 3 rows
#------------------... |
a649ef9c4ca5b14f1babd0b2399a102f398337e4 | 4c20974447b0b474410266f3d73fec02ad47404b | /PRACTICA.R | de00c9e0e9b41d0dcd2ccd429300063a351c1c10 | [] | no_license | josebummer/ugr_estadistica | d9583f4013727d3750e7b3732d00f1a0e12e01c9 | 993b70240202c88371e0d0350c9430c7aeb032bd | refs/heads/master | 2021-12-26T15:11:43.302307 | 2021-12-17T16:06:23 | 2021-12-17T16:06:23 | 75,067,680 | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 919 | r | PRACTICA.R |
Respuestas <-
readXL("C:/Users/Jose/Google Drive/Universidad/SEGUNDO CUATRIMESTRE/Estadistica/Practicas/Practica 2/respuestas.xls",
rownames=FALSE, header=TRUE, na="", sheet="Respuestas", stringsAsFactors=TRUE)
library(relimp, pos=14)
showData(Respuestas, placement='-20+200', font=getRcmdr('logFont'), maxwi... |
7d16b0ccca067eb983dfde00a615014aee067f59 | 403dc51dcd89aa11ed7079f865bbd159e2ec35b6 | /Classiffda/R/hselect.R | fa3f79bb291d768973b9f4c1d805a16934d89a9a | [] | no_license | dapr12/Classifficationfda | c49f79ba8581c9beca12869b8245473ba1309b9f | bb9b930f0eecb48de663b6a050fb704b60e61977 | refs/heads/master | 2020-04-15T18:13:23.383393 | 2014-08-05T10:58:56 | 2014-08-05T10:58:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,084 | r | hselect.R | #' CVSelect - Select the Cross-Validation Bandwith described in (Foster, and ) for the Median of the PSE funcion based on Functional Data
#' @param bandwith
#' @param x Location of the discretization points. THis discretization points must be uniform and missing values are not accepted.
#' @param y Typically a matri... |
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