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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
bf2d530a32196991dc5896d285d10cc361f60ab1 | 9262e777f0812773af7c841cd582a63f92d398a4 | /inst/userguide/figures/Covar--Covar_sec6_01_set-up-seasonal-dat.R | ec6b474e5e5fe30e80e18d653878dd4825db0fee | [
"CC0-1.0",
"LicenseRef-scancode-public-domain"
] | permissive | nwfsc-timeseries/MARSS | f0124f9ba414a28ecac1f50c4596caaab796fdd2 | a9d662e880cb6d003ddfbd32d2e1231d132c3b7e | refs/heads/master | 2023-06-07T11:50:43.479197 | 2023-06-02T19:20:17 | 2023-06-02T19:20:17 | 438,764,790 | 1 | 2 | NOASSERTION | 2023-06-02T19:17:41 | 2021-12-15T20:32:14 | R | UTF-8 | R | false | false | 478 | r | Covar--Covar_sec6_01_set-up-seasonal-dat.R | ###################################################
### code chunk number 11: Covar_sec6_01_set-up-seasonal-dat
###################################################
years <- fulldat[, "Year"] >= 1965 & fulldat[, "Year"] < 1975
phytos <- c(
"Diatoms", "Greens", "Bluegreens",
"Unicells", "Other.algae"
)
dat <- t(fulld... |
267de738fbff8a82401188dec5a3f9431af94aa5 | 82d9da6f33a3e8165850e05ab8fb3296b5623bba | /scripts/pipeline/import_plates.R | 9333d196be41b4378f01ca6068b0f8120a24dd17 | [] | no_license | tanaylab/tet-gastrulation | 6d7da1145f4d3b36ae27a17bfd8f3d7d9d16e83f | 12360727690611199a6b680ca86facb380524ca1 | refs/heads/main | 2023-04-07T01:02:55.146723 | 2022-06-24T09:51:34 | 2022-06-24T09:51:34 | 500,408,241 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,985 | r | import_plates.R |
import_plates <- function(mat_nm, metadata, base_dir = "data/umi.tables/") {
metadata$Amp.Batch.ID <- metadata$plate
metadata$Seq.Batch.ID <- metadata$plate
metadata$Batch.Set.ID <- metadata$plate
write.table(x = metadata, file = paste("config/key_", mat_nm, ".txt", sep = ""), quote = F, sep = "\t"... |
5b1912d097181dc092f2bed141cd3d9a91408f1c | b49fb76ade4a6bfcc163436857833b2fe9bc29c3 | /man/plot.apc.Rd | c1a38aa1f6d77e51b2595d5baecf5522b63d0e04 | [] | no_license | volkerschmid/bamp | 8659109d5f399e8609cea05475261871a6bfd249 | f89888f9874f83a8fe6046d4ca4fbd8624d6f07f | refs/heads/master | 2023-02-23T18:11:51.839627 | 2023-02-15T09:39:17 | 2023-02-15T09:39:17 | 116,521,258 | 7 | 0 | null | null | null | null | UTF-8 | R | false | true | 848 | rd | plot.apc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plot_apc.R
\name{plot.apc}
\alias{plot.apc}
\title{Plot apc object}
\usage{
\method{plot}{apc}(x, quantiles = c(0.05, 0.5, 0.95), ...)
}
\arguments{
\item{x}{apc object}
\item{quantiles}{quantiles to plot. Default: \code{c(0.05,0.5,0.95)} is... |
cf23b1c672297c55b8b36358dcf24f5a24c5b10d | 7539525778c356cafc44b7ac1f1b412004812ac1 | /r/scripts/probit_logit.R | 0632c116142e04f136492675c42edb5147f3fc8b | [] | no_license | rotabori/project_ad_analisis_datos | c4fa834fdb12eff351f4389b261b7e874ee3a06d | d92f09bc0f2d09e86748118657dd33ca4f13d2d5 | refs/heads/master | 2023-09-01T20:04:25.312240 | 2023-08-24T23:05:40 | 2023-08-24T23:05:40 | 195,309,809 | 3 | 3 | null | 2021-02-03T21:46:35 | 2019-07-04T23:11:41 | Stata | UTF-8 | R | false | false | 2,941 | r | probit_logit.R | ## PROJECT: ANALISIS DE DATOS
## PROGRAM: probit_logit.r
## PROGRAM TASK: REGRESION LINEAL
## AUTHOR: RODRIGO TABORDA
## AUTHOR: JUAN PABLO MONTENEGRO
## DATE CREATEC: 2020/06/02
## DATE REVISION 1:
## DATE REVISION #:
####################################################################;
## #0 PROGRAM SETUP
... |
823397a87da547764ce581e7969ba436db217490 | 3da3895c22be687f0a079877e1c52e9dea283e96 | /R Programming/Week 4/Week 4.R | 95ba920999091f014d6f374d31c414243b3b8834 | [] | no_license | meethariprasad/Data-Science | b165a04031a5efb266e9e79e0074547e3930e062 | 70abd81773b78bf4e597fb6afd1ed879af5a28d6 | refs/heads/master | 2021-01-17T23:23:09.036056 | 2020-03-30T08:38:30 | 2020-03-30T08:38:30 | 84,219,764 | 0 | 0 | null | 2017-03-07T16:10:42 | 2017-03-07T16:10:42 | null | UTF-8 | R | false | false | 1,172 | r | Week 4.R | # Generating random variables from normal distribution
x = rnorm(10) #generate random numbers from normal distribution
x
summary(x)
x = rnorm(10, 20, 2) #generate random numbers from normal distribution with
# mean = 20, sd = 2
x
summary(x)
# Generate random numbers from a linear model, where... |
7236f9f55bb37c0ee14b499644b55dc31e7c35a6 | ab73d60d1734a8d08eec9c410744b4441750ad95 | /man/checkYearValidity.Rd | 46e7793ff2f0ac9468115f144b76dfa5b2cf4c2c | [] | no_license | gsimchoni/yrbss | d608ef13375c3a4a5901b4aef99709e534e0c476 | c181dc14e7429ef37c65da4ca8db5903e3fa4d37 | refs/heads/master | 2020-09-05T05:25:36.680035 | 2017-06-18T16:14:41 | 2017-06-18T16:14:41 | 94,413,209 | 2 | 1 | null | null | null | null | UTF-8 | R | false | true | 529 | rd | checkYearValidity.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checkYearValidity.R
\name{checkYearValidity}
\alias{checkYearValidity}
\title{Check the validity of a year}
\usage{
checkYearValidity(year)
}
\arguments{
\item{year}{a four-digit number representing the desired year}
}
\value{
an ERROR if the... |
654c13d1a1f5d67253e946c4c6116f897f40b1fa | 263646b68232338dc2c2a637de9e7f6a5f9e1195 | /plot4.r | adf373613ab43fc1681499670525c8da54e6d540 | [] | no_license | antekai/ExploratoryDataAnalysis | 0572e0748c7133ac4feb4bfbc7efaffd5b3004d5 | 7e12c470c570b73f9941b1c78bd9e13d9b37d3af | refs/heads/master | 2021-01-22T14:24:39.795944 | 2014-07-12T16:03:55 | 2014-07-12T16:03:55 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,888 | r | plot4.r | ## Getting data (reading the txt file data as powerDF,
## note for R: T==TRUE,F==FALSE)
powerDF <- read.table( "c:/household_power_consumption.txt", header = T, sep = ";",
na.strings = "?", stringsAsFactors = F,
colClasses = c("character","character","numeric","numeric"... |
76e439b8b09a7c59b914fec79bd11b07bdc73ce9 | 28615c76fdbaf72c3e5ffefa89c413fa57f554af | /steam/scripts/colourspace_transform.r | 48a923d7ef71be9b0b739a313fe3cb98deebfaf8 | [] | no_license | emcake/emcake.github.io | a1e7472dc7093a90be578d9dc8dc61550c622c9c | 780aad391886ba3a6eb3f78f35ffe7ea55c4af66 | refs/heads/master | 2020-12-30T15:22:09.610945 | 2017-05-12T22:43:41 | 2017-05-12T22:43:41 | 91,133,571 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,956 | r | colourspace_transform.r | xyzpiv <- function( n )
{
#return (n > 0.04045 ? Math.Pow((n + 0.055) / 1.055, 2.4) : n / 12.92) * 100.0;
x <- if (n > 0.04045) ((n+0.055)/1.055)^2.4 else n/12.92;
return (x * 100);
}
rgb2xyz2 <- function( r, g, b )
{
rp = xyzpiv( r );
gp = xyzpiv( g );
bp = xyzpiv( b );
X<- rp*0.4124 + gp*0.3576... |
a794f642a4567dce489e910c6e51661d06b2ec97 | 61048c416c7b2e1a6750536f30b6a0c6c0025b92 | /r-packages/omop-utils/man/typedTbl.Rd | 5d0da14c88d35a89aaee00ecddce33428d9c3bf2 | [] | no_license | terminological/etl-analysis | 81481af7cc2c0c02e4228bdcd2b26dc25968e652 | 4978f1211ffe33231b1edf1fdc4c487d9fe17163 | refs/heads/master | 2020-06-18T05:07:38.117358 | 2020-03-12T23:44:13 | 2020-03-12T23:44:13 | 196,173,803 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,611 | rd | typedTbl.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utilityFunctions.R
\name{typedTbl}
\alias{typedTbl}
\title{#' Normalise an omop dataframe to a consistent format to construct a single feature set accross
#'
#' with the following columns:
#'
#' * cohort_person_id,
#' * cohort_entry_datet... |
9b32d17cdaca4077c5a1346cd2e63d25429ebf4d | 1f666e790464c93210443ff9c1456391aae7bc9b | /שיעור2.R | 4228a82858755eb9d1101c99ccf49cf147822f3b | [] | no_license | yosefLuzon/EXmacine | 470a9652b8d4db0bc42db06f41b998963ef49468 | 0510e8a5dcc31aaf93bafced9cb0fe08416acb75 | refs/heads/master | 2020-03-10T19:12:34.135798 | 2018-09-14T13:48:19 | 2018-09-14T13:48:19 | 129,543,158 | 0 | 0 | null | null | null | null | WINDOWS-1255 | R | false | false | 2,425 | r | שיעור2.R | v1<-c(1,2,3,4,5,6)
v2<-c(7,8,9,10,11,12)
mat1 <- rbind(v1,v2)
mat2<-cbind(v1,v2)
mat1
class<-(mat1)
#random numbers
# normal distu
dnorm(0,0,0)
qnorm(0.5,mean=0,sd=1)
#הסתברות שהתוצאה תהיה קטנה או שווה ל2
pnorm(1,0,1)
#דגימה מיתוך התפלגות נורמאלית
rnorm(10,0,1)
# דגימות-10, התפלגות נורמאלית-ממוצע 0
mean(rnorm(1000000,... |
7f3592159f6f3f9fb63554cdd615def0ef776cd4 | 8097fb5b06d4a1b8fbc53359c2c2d676cb5c3b4a | /man/coalesce_values.Rd | cfa8ea83e08ef96550a6bcda1bb646e72c14c0bf | [] | no_license | ying14/yingtools2 | 03e2e64f04754a842ee7684d6605d6e0ef1c67ee | d3056b6fe8a906311639c75c0dd456f18fe0ff7c | refs/heads/master | 2023-08-16T21:14:22.291830 | 2023-08-14T20:02:58 | 2023-08-14T20:02:58 | 54,286,753 | 40 | 13 | null | 2020-07-01T16:55:13 | 2016-03-19T20:54:56 | R | UTF-8 | R | false | true | 915 | rd | coalesce_values.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/yingtools2.R
\name{coalesce_values}
\alias{coalesce_values}
\title{Coalesce values into one summary variable.}
\usage{
coalesce_values(..., sep = "=", collapse = "|", omit.na = FALSE)
}
\arguments{
\item{...}{variables to coalesce together.}
... |
7403c442c7a984b86e6656ad6d876029ff767c48 | 50fadb4afb4eab0c32165b3fff0b1a0f2048e9f4 | /inst/templates/bs4Dash/boilerplates/controlbar.R | 28f54aff3c75288611d9fe479f8b897f241b668d | [] | no_license | RinteRface/RinteRfaceVerse | 65ce34771754dd613b34712196ba97f7365337b2 | 70c7f618db45d129dff4390bed0cb4d51ef2c3b7 | refs/heads/master | 2020-05-06T14:07:12.335036 | 2019-04-10T14:18:44 | 2019-04-10T14:18:44 | 180,173,469 | 10 | 3 | null | null | null | null | UTF-8 | R | false | false | 73 | r | controlbar.R | controlbar <- bs4DashControlbar(skin = "dark", title = NULL, width = 250) |
b14586677da2ca644bb0b7dc82baa7f5a51b06c5 | 6a0170ee4cfcf02221cfc483df49c4523f4dbb5f | /scratchpad/cluster_old.R | f09dba49ab85ff3354d3845edf3363037b8c1b18 | [] | no_license | emilieea88/beer-data-science | 0f85cf00259b5465d6b027025814bab9640c0677 | b9992760195f85559bb88aa66792c06c006c3639 | refs/heads/master | 2022-01-08T05:21:04.217954 | 2018-06-04T10:06:58 | 2018-06-04T10:06:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,668 | r | cluster_old.R | # source("./run_it.R")
# source("./read_from_db.R")
source("./most_popular_styles.R")
library(NbClust)
# ------------------- kmeans ------------
# only using top beer styles
# select only predictor and outcome columns, take out NAs, and scale the data
beer_for_clustering <- popular_beer_dat %>%
select(name, sty... |
ad412cd707a04637bc2f1686e2fa05400535abf2 | 157252491600a136fff59d5c5d4f3e00003cddfe | /resources/geneAnnotations/Exome/b37/getCanonicalTargets.R | 90ed397eb6b3a3074b3ad87967771f1010caa419 | [] | no_license | soccin/seqCNA | 76137d35dc6500d0424a2114bb5ed358e135b423 | 8cc0cb28c45b3345f2029a08ba9951e6ec2ab804 | refs/heads/master | 2023-09-03T08:45:57.762043 | 2023-08-18T23:34:59 | 2023-08-18T23:34:59 | 72,948,490 | 0 | 3 | null | 2022-10-15T19:52:57 | 2016-11-05T19:26:50 | R | UTF-8 | R | false | false | 1,669 | r | getCanonicalTargets.R | library(tidyverse)
library(data.table)
library(magrittr)
library(stringr)
ISOFORM0="/opt/common/CentOS_6-dev/vcf2maf/v1.6.12/data/isoform_overrides_at_mskcc"
ISOFORM1="/opt/common/CentOS_6-dev/vcf2maf/v1.6.12/data/isoform_overrides_uniprot"
isoform0=read_tsv(ISOFORM0,
col_names=c("TID","gene_name","refseq_id","c... |
5f42d446eca9db61ab35fc63557364bd01cb4e10 | adc2f9e770264b77610c8a974c1e947aebfecd3c | /run_analysis.R | 74ebb42e4448fe0f74f489a84b79ad1fb2263d41 | [] | no_license | sdronava/data_cleaning_proj | 1ebdb3ab90987941830a3f2e0891c6dac646c388 | b5ba8c8d09e8330ef1ce6ebe4bc0f2c440f22de7 | refs/heads/master | 2020-05-31T06:54:17.592793 | 2015-02-22T14:50:58 | 2015-02-22T14:50:58 | 31,162,943 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,876 | r | run_analysis.R | library(dplyr)
runAnalysis <- function() {
# load the train data set with just the means and stds
trainSet <- createTrainDataSet ()
# load the test data set with just the means and stds
testSet <- createTestDataSet ()
# join the two data sets with just the means and stds
completeSet <- rbind(trainSet, testS... |
292a21f1318eff73ac19d3f8ae06ead4ae99c7e6 | ebcb5cdc14c62b6009e73e5abfb27b13e301e63a | /dlt.caret.smda.R | ba1bac8def0c8f78920e0b791699a6fc7a608c3a | [] | no_license | dlt-lee/dlt | 06cb40dd4cfad26a7052b58a48502a5231ce16ac | 242f9cf53720474d39d1997174e1a395731aa7f7 | refs/heads/master | 2021-12-24T12:07:20.073163 | 2021-11-18T05:50:41 | 2021-11-18T05:50:41 | 135,522,395 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,978 | r | dlt.caret.smda.R | data<-dlt
count<-dim(dlt)[1]
#dlt.mda <- function(data,count) {
library(caret)
library(sparseLDA)
library(mda)
library(rda)
trains_1 <-tail(data,count)[1:(count-3),]
trains_2 <-tail(data,count)[2:(count-2),]
trains_3 <-tail(data,count)[3:(count-1),]
results<-tail(data,(count-3))
tests_1<-tail(data,coun... |
5fee8ce0b1b7710bc3f296852f7b0223b7b3bc2b | 3b3674cc7cf9a06c1926533f532ccc091bac2f14 | /30_mirna_seq/02_r_code/02_comparisons/03_literature_mirs.R | e32d6475a7e1e3f1c6277375273515425a1a349d | [] | no_license | slobentanzer/integrative-transcriptomics | 8618c6eef9b58da9c31a188e34ff527f3f9f8d04 | e9e0a7b6f7ed7687f40fbea816df9d094ba293a2 | refs/heads/master | 2022-07-18T10:32:51.331439 | 2021-01-19T15:17:52 | 2021-01-19T15:17:52 | 214,313,249 | 2 | 2 | null | 2022-06-29T17:42:35 | 2019-10-11T00:57:06 | R | UTF-8 | R | false | false | 5,469 | r | 03_literature_mirs.R | #LITERATURE MIRS IN SCZ AND BD####
rm(list=ls())
home= '~/GitHub/'
rootdir = paste(home, "integrative-transcriptomics", sep="")
setwd(rootdir)
library(ggplot2)
library(venn)
library(RColorBrewer)
library(RNeo4j)
graph <- startGraph("http://localhost:7474/db/data/")
mir.matrix <- readRDS(file = "working_data/mir_de_... |
b8652f2225407965c0070b6a70b0f2a3afa66602 | 187842c58b7690395eb7405842ac28bc4cafd718 | /man/vcov.nlstac.Rd | f000f36b02975c798472d4193f8633334e26df1e | [] | no_license | cran/nlstac | d5e38b819795e2862e1b8c7e3e94d0a9af8fbc2f | 298e5206c29e091929eb76091ba2cb67a22e8316 | refs/heads/master | 2023-04-13T16:09:33.706071 | 2023-04-11T14:20:02 | 2023-04-11T14:20:02 | 310,516,792 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,050 | rd | vcov.nlstac.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vcov.nlstac.R
\name{vcov.nlstac}
\alias{vcov.nlstac}
\title{Calculate Variance-Covariance Matrix for a nlstac Fitted Model Object}
\usage{
\method{vcov}{nlstac}(object, ...)
}
\arguments{
\item{object}{An object of class \code{"nlstac"} obtai... |
c24caa6675b880736c866fe9c568d0c33033db57 | 678ea8ca41e724e5bc9f7fbfeeeeb4278565784e | /inst/util/mkRPPATumorDataset.R | 067902c993afc5463fa5575f6ca81a5cf319db0e | [] | no_license | rmylonas/SuperCurvePAF | 3b5bddd6c86ecfee161e1da4790f856352d4e0d9 | d55a7b5017ae96704b11dba0a671baef79a65e99 | refs/heads/master | 2021-01-23T22:37:10.698784 | 2015-06-10T11:40:11 | 2015-06-10T11:40:11 | 38,314,177 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,306 | r | mkRPPATumorDataset.R | ###
### $Id: mkRPPATumorDataset.R 947 2015-01-21 17:44:54Z proebuck $
### (Re)creates 'rppaTumor' dataset object found in 'data' directory.
###
local({
##-------------------------------------------------------------------------
makeRPPAs <- function(antibody,
filename,
... |
18d6146200463cdfd05fc7d5a066bbbef35960a4 | 3100f891537e474960e9c1f6e986a811eec7efe0 | /cachematrix.R | 9854f5f8b2d1fd0a66488249789de066891bbc1b | [] | no_license | jleonard7/datasciencecoursera | 492741cdf2b5b103499b36769b848b96a5555043 | d059034dd8e5d774370bcc05cffb58da2b112397 | refs/heads/master | 2021-01-17T09:11:08.913244 | 2017-05-06T19:22:14 | 2017-05-06T19:22:14 | 83,981,041 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,725 | r | cachematrix.R | ## The purpose of these functions are to allow the user to
## cache the inverse of an invertible matrix. This is useful
## to reduce execution time of programs and calculations
## The function below defines 4 functions which are then
## stored in a list. The list is used in second function to
## cache inverse of a m... |
84ae5c47b9af8dfb04e0f4e042cf6466df459878 | 6c6e0a5f91591b5fd0a0bfc194a36ae6ba846112 | /plot6.R | 207d8bcb5adbddf63d862719bda3cc98abf3fb8f | [] | no_license | TarushiRMittal/Exploratory-Data-Analysis-Week-4 | 9209a2a5fa55f991df1976343be7b988350cdd91 | 0a0c390fd46da3d331e84bdd217b63656d108bf9 | refs/heads/master | 2020-06-16T18:58:27.132626 | 2019-07-07T22:47:24 | 2019-07-07T22:47:24 | 195,671,592 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 700 | r | plot6.R | emiss_stats <- readRDS("summarySCC_PM25.rds")
classification_code_source <- readRDS("Source_Classification_Code.rds")
baltimoreLA_cars <- subset(emiss_stats, emiss_stats$fips=="24510" | emiss_stats$fips=="06037" & emiss_stats$type=="ON-ROAD")
baltimoreLA_cars_annual <- aggregate(baltimoreLA_cars$Emissions, by=list... |
8040ac0f28693b6d810c305a739715f8a869a882 | 6caaaa04f0227d61c3a3795050e0e358cd49cf5b | /R/altitudeSysDef.R | 0f2ee477b399ad1adc6a9dc5e89a2969ca59e8ae | [
"MIT"
] | permissive | cboettig/build.eml | 29df6528845efc9a0347ff0ca8f2838db6d7a41e | 1195dc7f109c75448200047c2918e226adbbe75a | refs/heads/master | 2020-04-08T17:53:24.909984 | 2018-12-03T19:57:42 | 2018-12-03T19:57:42 | 159,584,932 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,037 | r | altitudeSysDef.R | #' altitudeSysDef
#'
#' altitudeSysDef
#'
#'
#' @inheritParams common_attributes
#' @param altitudeDatumName The identification given to the surface taken as the surface of reference from which altitudes are measured. See [altitudeDatumName()]
#' @param altitudeResolution The minimum distance possible between two ad... |
d9830b64055735236a3ae412ab6d9ff928040d7b | b694dcea2899a28c6d23cfd0539c17bba84b2bc1 | /octane_blog_01_eda.R | 771a45bba6167be303c22663fcb99d9a0a5a26e6 | [] | no_license | jeffgriesemer/r-project | 78fee3b8d17b3956e6ffe2a78ca628e74e310cd9 | ad4c5724396c8de904d14d7f8514c187e7ee1f8a | refs/heads/master | 2020-12-24T10:57:06.852179 | 2019-05-03T02:28:36 | 2019-05-03T02:28:36 | 73,116,509 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,175 | r | octane_blog_01_eda.R | library(tidyverse)
library(dplyr)
library(lubridate)
# visual
library(ggplot2)
library(ggrepel)
library(scales)
library(themes)
#install.packages("googlesheets")
library(googlesheets)
rm(list=ls())
df_stats <- gs_title("golf_stats")
df_2018_drive <- df_stats %>% gs_read(ws = "2018 driving accur")
df_2018 <- df_2018... |
a221fc6d9d200266948c2336878f9381de1b667f | 5490e86ba63d4123a2686ee36121f718f883a11f | /R/OUwieAvg.R | c7067a0629266781b32c064d30ff74e737e4d624 | [] | no_license | willgearty/pcmtools | 359bab4dc30167e075ab076cdba97048a03cd2dd | 14fb49ce08d1c677971a756bb699554070482253 | refs/heads/master | 2020-08-07T12:22:04.595425 | 2019-10-29T22:52:05 | 2019-10-29T22:52:05 | 213,449,197 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,941 | r | OUwieAvg.R | #' Calculate AIC weights
#'
#' This function takes a vector of AIC (Akaike Information Criterion) values and returns a vector of AIC weights using the formula from Burnham and Anderson (2002).
#'
#' If \code{na.rm = FALSE} and any values in \code{AIC} are \code{NA}, all returned values will be \code{NA}.
#'
#' @param A... |
350a4040aa194e0277a79fdd274aeded541cade6 | 4dfae026a7c16a91e0eee543fbc1404009246db2 | /tests/testthat/test-order_cells.R | 7da8873efefdf53aa10f06b915d14507dfc46b61 | [
"MIT"
] | permissive | cole-trapnell-lab/monocle3 | 2d32dddb777ba384470f3842b0fd7d27b857cd5b | 2b17745d949db1243e95e69e39d2b4b1aa716c09 | refs/heads/master | 2023-09-03T07:06:43.428228 | 2023-08-18T22:50:49 | 2023-08-18T22:50:49 | 167,440,342 | 280 | 110 | NOASSERTION | 2023-01-24T21:25:37 | 2019-01-24T21:26:18 | R | UTF-8 | R | false | false | 11,896 | r | test-order_cells.R | context("test-order_cells")
skip_not_travis <- function ()
{
if (identical(Sys.getenv("TRAVIS"), "true")) {
return(invisible(TRUE))
}
skip("Not on Travis")
}
cds <- load_a549()
set.seed(100)
test_that("order_cells error messages work", {
skip_on_travis()
expect_error(order_cells(cds), "No dimensionality... |
b34cf74a262617329b73d6662888cd9cea0c2e7c | 4ab888da78d52fcacb6a22affa53f09f9e0da9a8 | /inst/doc/Francais.R | 84b676387582701af53971a9c2b9b56c62660348 | [] | no_license | MarionLi0/antaresFlowbased | a291ead418fe29f99baa0cad9dbb181b2e9ff0b8 | 9207cd7564b4f821f4d25acf30ba7d1f09d5e286 | refs/heads/master | 2021-01-21T18:24:48.240411 | 2017-05-16T12:25:39 | 2017-05-16T12:25:39 | null | 0 | 0 | null | null | null | null | WINDOWS-1250 | R | false | false | 599 | r | Francais.R | ## ---- eval=FALSE---------------------------------------------------------
# antaresRead::setSimulationPath("D:/exemple_test", 0)
#
# # initialisation de l'étude flowbased
# initFlowBased()
## ---- eval=FALSE---------------------------------------------------------
# # chemin du solver antares
# setSolverAntar... |
714b8712934f4e07ff098fbdc1bf1a7ec742e1cc | 4b8dddc2ced41524396dd1b035fbcdebf08cdef8 | /final/zhihu_tfidf_score.R | fb005cb1af79ef89da35bfb63a22ba9891351938 | [] | no_license | OOmegaPPanDDa/shiny_dsr_zhihu | 8ecfecff3f9e79382c9d66ff9d7187c06b610517 | 5e728b560d9da8a6baca54f8d4623564c9ff14e0 | refs/heads/master | 2021-01-11T22:50:07.659968 | 2017-01-13T04:49:39 | 2017-01-13T04:49:39 | 78,510,048 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,533 | r | zhihu_tfidf_score.R | # source('~/dsr/script/zhihu_preprocessing.R')
# library(dplyr)
# library(rJava)
# library(tm)
# library(tmcn)
# library(SnowballC)
# library(slam)
# library(XML)
# library(RCurl)
# library(Rwordseg)
# library(Matrix)
# if (!require('tmcn')) {
# install.packages('tmcn',repos = 'http://R-Forge.R-project.org')
# }
... |
7178133a620f8fd91c5c6f3cd2f7a040468866ec | 3733c7ac7146a6cbd6454d7af07b6265ae70e8e8 | /tidytextapp_applications.R | 815e35b00b7fe45c4c1fcde67d196ecee0908ebc | [] | no_license | aashishkpandey/tidytextapp | 830556b5edcda663665038aa5f1ab5a25f6110e7 | acf75037587b14b856f7efb97b0a66432b73abde | refs/heads/master | 2021-04-26T23:54:19.237943 | 2018-05-29T13:25:36 | 2018-05-29T13:25:36 | 123,877,983 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,335 | r | tidytextapp_applications.R | source('https://raw.githubusercontent.com/aashishkpandey/tidytextapp/master/tidytextapp_functions.R')
#--------------------------------------------------------#
# Create DTM of BD
#--------------------------------------------------------#
system.time({
years = 2015:2016
for (year in years) {
bd.df = read... |
c55d66a9a6e03e3305877e524125a3dbd4af27b4 | 60491b8d44eaa4ee02c7ae9d90d9d6991febbcd6 | /code/24_7_study/cgm/cgm_data_overview.R | d45a921bddec9368d2244d8e05dc37b4eec770f8 | [
"MIT"
] | permissive | jaspershen/microsampling_multiomics | ae2be38fe06679f43b980b76ea152109bbdd8fce | dea02f1148e5aad3243c057a98f565f889be302f | refs/heads/main | 2023-04-14T17:44:20.010840 | 2022-09-05T23:23:26 | 2022-09-05T23:23:26 | 469,214,924 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 5,906 | r | cgm_data_overview.R | ##cgms
no_function()
library(tidyverse)
###cgm
masstools::setwd_project()
rm(list = ls())
source("code/tools.R")
load("data/24_7_study/cgm/data_preparation/expression_data")
load("data/24_7_study/cgm/data_preparation/sample_info")
load("data/24_7_study/cgm/data_preparation/variable_info")
load("data/24_7_study/summ... |
87a30a1beb7f7e43862df1db4a4bacd430ba88d1 | b5dcfcde6c991b0e1272562d95ac1d8bb3eff9b8 | /man/parameters.Rd | 602f29636c591be25c272ab673d913a66bbc93f4 | [] | no_license | jkennel/aquifer | 080b0b25c77ebe3c09bd4c1719fefc072e2c7649 | 8a5b812906953c046443035196f89912c7048545 | refs/heads/master | 2022-08-09T04:43:35.876031 | 2022-07-25T18:09:20 | 2022-07-25T18:09:20 | 80,688,849 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,849 | rd | parameters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parameters.R
\name{parameters}
\alias{parameters}
\title{parameters}
\usage{
parameters(frequency, period, omega, alpha_w, storage_aquifer,
storage_confining, specific_yield, transmissivity_aquifer,
diffusivity_vadose, diffusivity_aquifer... |
c46e31d252910d588d52c6d43ab4d75825755315 | b588e0a4df002a71bc1948f660b0f033bab57858 | /ag.R | a2c9949dc7d581c2dbbf716835673e9b8524ea47 | [] | no_license | ccqa86/Proyecto-Maestria | 2eacb09dee422c255d722c22f6922106e6eead64 | 29bf33516abc74280b763c70c473a1e7defd2fcd | refs/heads/master | 2023-01-03T10:12:34.574582 | 2020-11-02T00:30:02 | 2020-11-02T00:30:02 | 294,197,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,601 | r | ag.R | ##### Algoritmo genético ######
source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/crearpob.R")
source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/fitnessind.R")
source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/fitnesspob.R")
source("C:/Users/Carmen C/Documents/R/Proyecto-Maestria/seleccionarind.R")
so... |
97a57701fce1713d589eb8ec146f95b43a795d00 | 6fe2e5bc5971de72c47e61f0702753d6803f8af4 | /man/add_clusters.Rd | 4577c83711ac2520d16b685aafba2cd5cce25648 | [
"MIT"
] | permissive | babasaraki/gggenomes | 19946a9c4c3a1803b4d75d923046fba7e63c7d27 | 92dd85720b185f78680a1f4989496b2933616e8a | refs/heads/master | 2023-02-24T08:03:59.381942 | 2020-07-13T17:22:33 | 2020-07-13T17:22:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 238 | rd | add_clusters.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sublinks.R
\name{add_clusters}
\alias{add_clusters}
\title{Add gene clusters}
\usage{
add_clusters(x, parent_track_id, ...)
}
\description{
Add gene clusters
}
|
ceb28ff943ece6aff7ab3dca4df231dbbc0bc0db | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/DMRMark/examples/MakeGSoptions.Rd.R | 43dc99b827ff639f68299878cfab7efd0c4a2b8a | [] | 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 | 222 | r | MakeGSoptions.Rd.R | library(DMRMark)
### Name: MakeGSoptions
### Title: Encapsulate prior parameters and Gibbs Sampler (GS) control
### parameters
### Aliases: MakeGSoptions
### ** Examples
# MakeGSoptions
opts <- MakeGSoptions()
|
5033b09639056fa3e24c461aa04cc7702bf53b3c | c6a6b77f3b71ea68f1281b043dd60f17dd85381c | /inst/unitTests/test_conversions.R | 0232772d66ee6a3db8dede4bffa43d8d980e5660 | [] | no_license | benilton/oligoClasses | df76a4ee4d755342ae32b07c9acb5355153e3f4f | be0e1088c52ee8827c86f061e80ffe9b44982a88 | refs/heads/master | 2021-01-10T21:40:35.903511 | 2019-11-23T12:22:08 | 2019-11-23T12:22:08 | 1,779,156 | 3 | 1 | null | null | null | null | UTF-8 | R | false | false | 837 | r | test_conversions.R | test_conversions <- function(){
p <- matrix(runif(20), nc=2)
integerRepresentation <- as.integer(-1000*log(1-p))
int2 <- p2i(p)
checkTrue(all.equal(integerRepresentation, int2))
}
test_oligoSnpSet <- function(){
data(oligoSetExample)
checkTrue(validObject(as(oligoSet, "SnpSet2")))
}
test_makeFeatureRanges <- fu... |
5abf744989f857531f304e80eee5bee6aa357be6 | a61f32d6d17b43240abe8d0e7424c13cd9ada27f | /exercise_10_3.R | b7af3d5ef93db67758fc05021e1b04c4012d4b50 | [] | no_license | synflyn28/r-lessons | a393a16724c6fe574a78bc83e8a1003d1083f90d | 6f6294c1414cfb78af77da34ca15fcdcd769f0e7 | refs/heads/master | 2020-05-23T08:02:11.738606 | 2018-10-23T01:40:39 | 2018-10-23T01:40:39 | 80,488,399 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,088 | r | exercise_10_3.R | #Question A
loopvec1 <- 5:7
loopvec2 <- 9:6
foo <- matrix(NA, length(loopvec1), length(loopvec2))
for(i in 1:length(loopvec1)) {
foo[i,] <- loopvec1[i] * loopvec2
}
#Question B
chars <- c("Peter","Homer","Lois","Stewie","Maggie","Bart")
num_vals <- rep(NA, times=length(chars))
for (i in 1:length(chars)) {
nu... |
9880eeeae4e98e9e3dd27917568f3e3736b6df54 | 3763e5f2b164e831fd870c1ddd1cf6ff33b2c1a0 | /yapay-sinir-aglari.R | baa8aef901b4c60a73f8efa3fe7da1646eabfd50 | [] | no_license | alzey73/R-Programming-for-Data-Science | d27f85784f7650a97431b63e92d6e0adc6d8f68c | ed385c3bcfcfebdb66d09f522f1ea257482c52c9 | refs/heads/master | 2022-10-20T00:49:18.923517 | 2020-05-27T17:48:33 | 2020-05-27T17:48:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,754 | r | yapay-sinir-aglari.R | # YAPAY SİNİR AĞLARI
### İlk önce bütün girdileri temizleyelim
rm(list = ls())
## Kütüphaneler
library(caret)
library(tidyverse)
library(AppliedPredictiveModeling)
library(pls) #kismi en kucuk kareler ve pcr icin
library(elasticnet)
library(broom) #tidy model icin
library(glmnet)
library(MASS)
library(ISLR)
library(P... |
0e12d55ed6633ee628b187cd408112b73afb6cd6 | 079921b991ba463dc449bf5cd42cece21e2cb022 | /R/est.R0.AR.R | d1709625f1c3253d00ba4c1040ca9f4c6885e271 | [] | no_license | cran/R0 | 6356959c0252ebd3d535a6e1b5605246a8337418 | ba0053a2b1c3feda26e946202c35735ea8b117f1 | refs/heads/master | 2022-09-26T11:50:43.225873 | 2022-09-05T14:10:07 | 2022-09-05T14:10:07 | 17,681,766 | 4 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,453 | r | est.R0.AR.R | # Name : est.R0.AR
# Desc : Estimation of basic Reproduction Number using Attack Rate method
# (derived from SIR model), as presented by Dietz.
# Date : 2011/11/09
# Author : Boelle, Obadia
###############################################################################
# Function declaration
est.R0.A... |
b23ea94353e2d780290ecc99352e0bda21570a70 | 9125098458ffb2c97389767fc021369ba6bc417e | /R/transDT.R | 1a06c31f50e661052a2946a82153852fbeecea78 | [] | no_license | Dave-Clark/ecolFudge | d22dd02ee5fd094a7775e58334c1d8b7ce22e29f | 1f15001229acad3bba31cc1bf185661190aeca9e | refs/heads/master | 2021-07-05T09:52:57.486820 | 2020-07-29T10:19:32 | 2020-07-29T10:19:32 | 146,623,289 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 813 | r | transDT.R | #' A function for quickly transposing data.table objects
#'
#' This function allows you transpose data.table objects very rapidly, whilst maintaining control of row and column names.
#' @param dt The data.table object you wish to transpose.
#' @param transCol The name of the column that you wish to pivot on. Values in ... |
e2c8e1a12566f625099272671fea6c4a19e51428 | d746fef241f9a0e06ae48cc3b1fe72693c43d808 | /tesseract/rotate/d7js34-015.r | 9dbed9fa0485308d67d09678dcc592dfaf672d65 | [
"MIT"
] | permissive | ucd-library/wine-price-extraction | 5abed5054a6e7704dcb401d728c1be2f53e05d78 | c346e48b5cda8377335b66e4a1f57c013aa06f1f | refs/heads/master | 2021-07-06T18:24:48.311848 | 2020-10-07T01:58:32 | 2020-10-07T01:58:32 | 144,317,559 | 5 | 0 | null | 2019-10-11T18:34:32 | 2018-08-10T18:00:02 | JavaScript | UTF-8 | R | false | false | 195 | r | d7js34-015.r | r=0.84
https://sandbox.dams.library.ucdavis.edu/fcrepo/rest/collection/sherry-lehmann/catalogs/d7js34/media/images/d7js34-015/svc:tesseract/full/full/0.84/default.jpg Accept:application/hocr+xml
|
378cd3d340aee43dcef9499182e91375329121d0 | ee5e3fcc38b89b49ac6de735ad9e7c6ead222758 | /R/MSEdata.R | 2cb64c8d7337018c0e22cf54582c20e6eb03603b | [] | no_license | OlivierBinette/dgaFast | 2dea5aaa89578a4bf981c8eb13975db3656edc3c | 82a7d7599814d04f0a116f1ed52fc1838b8fb9e5 | refs/heads/master | 2022-12-29T18:51:46.501827 | 2020-10-20T15:08:38 | 2020-10-20T15:08:38 | 299,384,279 | 1 | 0 | null | 2020-10-05T14:12:43 | 2020-09-28T17:33:26 | R | UTF-8 | R | false | false | 4,290 | r | MSEdata.R | #' MSE data format
#'
#' The function \code{MSEdata()} transforms an existing dataframe to the "MSE" format,
#' ensuring it contains a "count" column and that the other columns refer to
#' inclusion (1) or exclusion (0) on a set of lists.
#'
#' Zero counts of unobserved capture patterns are added and duplicates capture... |
e5413be8cd53699addda82f50a4bb8ff1c664166 | cb826d1f9ad59f0c44cbd2c5d884d47c8723ded9 | /MyPackage/R/impuArima.R | c14465fc173f9942d69f5adb979b771b4ab95b2d | [] | no_license | sophiaaading/NCSA_Rpackage | 701fdc90a037d7b46305142111bca4f579343226 | ff5dc9ca9056c15718b1076b86aa35f120d0b1c4 | refs/heads/master | 2021-07-09T06:30:36.442878 | 2020-11-13T17:57:39 | 2020-11-13T17:57:39 | 210,968,558 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 701 | r | impuArima.R | #' Imputation with ARIMA Model
#' @param individualDataset individual dataset
#' @return The dataset after imputed by ARIMA Model
#' @example impu_arima(aList(clean(dataset))[[1]])
#' @export
# install.packages("forecast")
# library("forecast")
impu_arima <- function(individualDataset) {
impu <- naInsert(individual... |
8613ec820b601f2ee895a3b0dd7a14ee77cfcec9 | 1b840a4c27f41d4dfdf719c572908b452aedeffa | /R/geolevel_get_empty_geometry.R | 2744d04dfec52c3532b603a8b5a369e8744d35fe | [
"MIT"
] | permissive | josesamos/geodimension | 7da26e55b64ed11969ce600f49137fe48e509bb9 | 8eda23973a70d96d78140b6469674104754047ce | refs/heads/master | 2023-01-14T01:30:37.651220 | 2020-11-27T13:13:10 | 2020-11-27T13:13:10 | 314,524,426 | 0 | 0 | NOASSERTION | 2023-09-11T21:24:02 | 2020-11-20T10:45:47 | R | UTF-8 | R | false | false | 1,365 | r | geolevel_get_empty_geometry.R | # empty geometry ----------------------------------------------------------
#' Get empty geometry instances
#'
#' Get the instances of the data table that do not have associated geometry for
#' the specified geometry type.
#'
#' @param gl A `geolevel` object.
#' @param geometry A string, type of geometry of the layer.... |
a97549b2e4c8b59892ee27497e5c2c030b87ee64 | 7682ad70789d6c01b608260e6280fd6a696ec397 | /0509-4-CART.R | 577f123e0b58e7f3cf90b86f53763fc518576dc2 | [] | no_license | tkionshao/r-datamining-example | e952cefd77ccab4b3b7302bb8a0fb7b11904c607 | 1f95aec93609e214363dc9995306458ae0091446 | refs/heads/master | 2020-03-14T05:03:29.711479 | 2018-06-28T14:15:44 | 2018-06-28T14:15:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 878 | r | 0509-4-CART.R | setwd("E:\\MegaSync\\MEGAsync\\R\\tryByself")
data <- read.table("babies.csv", header = TRUE, sep = ",")
# Missing compentsation.
for(i in 1:7){
n_null <- is.na(data[,i])
n_mean <- mean(data[,i], na.rm = TRUE)
data[n_null,i] <- n_mean
}
# Slipt to Train and Test.
n = 0.3*nrow(data)
n_tes... |
b672bcfbec0daa3e3271125ea15352121db1b2eb | c80d09a871404fe135fa72549c6875806e24ded9 | /inst/shiny/fire_viewer_db/model.R | 383521ad77f138c3e05c0322cfcab3c352b82528 | [] | no_license | raffscallion/goesfire | 6ecd35dda7221b416ebd0b3a2277024206428a2d | b9784e4b92cd3d599ed05194be61b1c5dc672967 | refs/heads/master | 2022-07-13T00:27:11.284327 | 2022-07-05T18:33:30 | 2022-07-05T18:33:30 | 134,614,089 | 1 | 2 | null | 2019-12-21T00:56:10 | 2018-05-23T19:05:28 | R | UTF-8 | R | false | false | 6,348 | r | model.R |
# Take pixels from map viewport and produce bluesky input
model_inputs <- function(file, data, name, size, type, tz) {
# Convert all times to local time as specified by user
data <- mutate(data, StartTime = lubridate::with_tz(StartTime, tz))
hourly <- get_hourly_data(data)
profile <- get_diurnal_profile(hour... |
18b1d1b0d1dea36a25f31264bd798a621cf3bd9e | 87842e166d17bb1c11d957bf0eeeb67d5422c186 | /coursera programs/profile_most_probable_kmer.R | 8fc84a49ce759d42ff2bfa5094bbe6d3d56d2a51 | [] | no_license | girija2204/Bioinformatics-I-Finding-Hidden-Messages-in-DNA | 1c7d9d9401ded89c151ff57874eea85d089fc1c6 | 0b24e4c66b1ec2884a8638e4136708edacea5938 | refs/heads/master | 2021-06-03T21:49:33.314182 | 2021-05-14T10:19:51 | 2021-05-14T10:19:51 | 88,222,712 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 863 | r | profile_most_probable_kmer.R | source("/home/giri/Downloads/MTech Thesis/coursera programs/find_probability_and_best_probabilty.R")
profile_most_probable_kmer = function(text, k, profile_matrix){
probability = 0
pmp_kmer = 0
for (i in 1:(nchar(text)-k+1)) {
kmer = substr(text, i, i+k-1)
pp = find_probability(kmer, profile_matrix)
i... |
e514551676d5b67ee69f17cfb4c0a6c741d7729b | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/likelihoodExplore/examples/likcauchy.Rd.R | 808657ff15bc4afe751701fa49b9aa25d4cefd6d | [] | 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 | 165 | r | likcauchy.Rd.R | library(likelihoodExplore)
### Name: likcauchy
### Title: Cauchy Log Likelihood Function
### Aliases: likcauchy
### ** Examples
likcauchy(x = rcauchy(n = 2))
|
3feba0c53e7d9f0f2cbd553cebc73f2ffc98a2a3 | b2f61fde194bfcb362b2266da124138efd27d867 | /code/dcnf-ankit-optimized/Results/QBFLIB-2018/E1+A1/Database/Kronegger-Pfandler-Pichler/dungeon/dungeon_i10-m10-u5-v0.pddl_planlen=106/dungeon_i10-m10-u5-v0.pddl_planlen=106.R | 3d12bd02efddbc613daaf5affc1913c0c734698d | [] | no_license | arey0pushpa/dcnf-autarky | e95fddba85c035e8b229f5fe9ac540b692a4d5c0 | a6c9a52236af11d7f7e165a4b25b32c538da1c98 | refs/heads/master | 2021-06-09T00:56:32.937250 | 2021-02-19T15:15:23 | 2021-02-19T15:15:23 | 136,440,042 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 91 | r | dungeon_i10-m10-u5-v0.pddl_planlen=106.R | 5d39dce903943a535f7a9ae1bebe45ef dungeon_i10-m10-u5-v0.pddl_planlen=106.qdimacs 18263 56860 |
17dc5da7dcf70b5487ad1110d5d4c17f9d42e505 | 68af5b4db04bde1c466c9e9ada5605f940f565b0 | /R-code/RNA-Seq/Radigoraphic_groups/Progression_vs_No_Progression/Progression.R | 85cad6e3b6513608fa6a021567b528f96152ad31 | [] | no_license | shrumin/Rheumatoid_Arthritis---Analysis-and-visualisation-of-autoantibody-profiles-of-rheumatoid-arthritis- | 2e887992eade4d6dc30fae0a2b9f3037d3434462 | f15e2984a2f5e3f1a3d7346824f2436603763efa | refs/heads/main | 2023-07-14T17:31:25.169110 | 2021-08-25T08:12:46 | 2021-08-25T08:12:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,189 | r | Progression.R | setwd("~/Desktop/RA")
dat <- read.csv(file="Progression_data.csv", row.names=1)
#filtering and naming genes
library(biomaRt)
ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
nonprotein=biomaRt::getBM(attributes = c("ensembl_transcript_id", "transcript_version", "ensembl_gene_id", "external_gene_name", "en... |
19724017e042d28382f4b8c45890e8b74a800657 | 53755dcb7c54fa6059cc060e4638a743d3ef9f1b | /plot2.R | 1f9f1769be0257780c82b40782fd433fec3246fd | [] | no_license | sabank/ExData_Plotting1 | 7b561add8c0d75fe92358c2fda13ec2b833d22c3 | 6e2362e489fe205e4a681b6c312a9059bfc94ac9 | refs/heads/master | 2021-01-15T09:24:04.136437 | 2015-04-11T07:36:11 | 2015-04-11T07:36:11 | 33,553,181 | 0 | 0 | null | 2015-04-07T15:56:59 | 2015-04-07T15:56:59 | null | UTF-8 | R | false | false | 2,021 | r | plot2.R | ### This program describes the sequence of actions undertaken to plot data as scatter plot.
### It takes 3 arguments, start date, end date, variable column number.
plot2 <- function(x="2007-02-01",y="2007-02-02",z=3){
## defines path to and reads txt file and replace "?" by NA
path <- file.path(getwd(),"03_dat... |
a7c4205970f31deadf817a696b4c04c59f0526f2 | 099552f15fc47a5b988684d22126ef799d578e03 | /R/get_node_attr_from_selection.R | c2a6beff6928952f98d08aeb1a3ac45eba6b45bd | [] | no_license | statwonk/DiagrammeR | 261ff3f9e74035172ad5029b0e13b16813db16a5 | 75253f412daeb87c1c04f69659e4a6e05b681df8 | refs/heads/master | 2021-01-23T18:46:54.192760 | 2015-11-08T16:42:44 | 2015-11-08T16:42:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,406 | r | get_node_attr_from_selection.R | #' Get node attributes based on a selection of nodes
#' @description From a graph object of class \code{dgr_graph}, get node
#' attribute properties for nodes available in a selection.
#' @param graph a graph object of class \code{dgr_graph} that is created
#' using \code{create_graph}.
#' @examples
#' \dontrun{
#' lib... |
f478ef27da949e77375110cca8a8ac93e5a99be1 | df443ce148759af76cbd558cd7401fbbc72506a6 | /1 linear regression plots Wk2.R | 9780e783b8ed7d3c5dcb38fcf3b7786f7ce344f0 | [] | no_license | wrona-42067898/Linear-Regression | 9e6bbf976c13ef952fd4fc5d14f8c0877d6813e4 | 1a69cdb6eca370dac354728ac974b51380507a4b | refs/heads/master | 2020-04-15T01:21:56.400491 | 2019-01-06T04:11:16 | 2019-01-06T04:11:16 | 164,273,368 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,418 | r | 1 linear regression plots Wk2.R | ###Linear Regression 2 discrete variables###
library(statsr)
library(dplyr)
library(ggplot2)
data(mlb11)
#Ask whether variable x predicts variable y (y is response variable)
#Scatter plot with linear model overlayed
ggplot(data = mlb11, aes(x=runs, ?=new_onbase)) +
geom_point() +
stat_smooth(method... |
843505b4e2cbef767f2c7a77768f3f5a013c3f03 | 8ee250e304ccdc04181a66ee99e694ee96036822 | /app.R | bc024093188c284c7e6bc4c047a285d3de90b5aa | [] | no_license | ajay-aggarwal01/capStoneProject | f6c9e7130311b88832f81e6e53735c78bd412d39 | 3b404760ed38b14e06144a1dffeaccb458f28cf6 | refs/heads/master | 2020-06-23T03:20:54.595736 | 2019-08-01T01:46:44 | 2019-08-01T01:46:44 | 198,492,316 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,158 | r | app.R | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define UI for application that draws a histogram
ui <- fluidPage(
titlePane... |
f0c75b12d713b4bcd988f672f4a36979b0551e2a | da2f2493a2611fb980f59234100305f4e38d9068 | /R/hello.R | dcf5088e5dcf1c5a71a8fae76345ec7bd6be66ce | [] | no_license | davesteps/insertPipe | dc7030cb804f8e2941e2aa120bb9af10b70f4d45 | 6fffabe69d48512b83e2e305876cf5eda6aea4f6 | refs/heads/master | 2020-12-24T06:41:46.859142 | 2016-06-13T16:52:39 | 2016-06-13T16:52:39 | 61,054,018 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 141 | r | hello.R | insertPipe <- function() {
rstudioapi::insertText(" %>% \n")
}
insertFun <- function() {
rstudioapi::insertText("<- function(){\n}")
}
|
e25e859f8facf94966f3a04c57f4109b24e875f7 | 8e0989c127fa440b1356606c5b1616703d76c06d | /man/resetOptions.Device.Rd | ad2d210921b6920af1d8bb80564e8c584f466311 | [] | no_license | HenrikBengtsson/R.graphics | 8da678165bd6cfad546faf71b78d768a44b3165e | c92a1761f82806ecf1b761d58a59ab55532aa518 | refs/heads/master | 2021-01-20T11:44:10.433776 | 2014-06-19T04:04:58 | 2014-06-19T04:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,226 | rd | resetOptions.Device.Rd | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Do not modify this file since it was automatically generated from:
%
% Device.R
%
% by the Rdoc compiler part of the R.oo package.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\name{resetOptions.Device}
\alia... |
85581a11825c12ead96069d1008cdc4efce8c7d8 | c8e71af48d925c34d1cb9f4dad262c970e8968d5 | /man/Leukemia.Rd | 75e08ca5f3e1924cce3057701a36fc21dbd3cc74 | [
"MIT"
] | permissive | tessington/qsci381 | 43c7cd323ab64cf28ba738be35779157c93e62cf | b981f0bd345b250d42ff5f1c0609e5e61f5911f7 | refs/heads/master | 2022-12-24T20:56:56.045374 | 2020-09-24T20:50:29 | 2020-09-24T20:50:29 | 284,817,926 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,226 | rd | Leukemia.Rd | \name{Leukemia}
\alias{Leukemia}
\docType{data}
\title{Responses to Treatment for Leukemia}
\description{
Treatment results for leukemia patients
}
\format{
A data frame with 51 observations on the following 9 variables.
\tabular{rl}{
\code{Age} \tab {Age at diagnosis (in years)}\cr
\code{Smear} \tab {Diffe... |
21a09b6466b75db4c13156301a291c213d3f9730 | 3d3a99fc6f571e0d977d81401dcec0d93b9b8a50 | /man/getBackbone.Rd | fdbe38745590e4dd4cdf6a2674c2f6b4235e88b9 | [] | no_license | cran/genBaRcode | cda9cde11003dbccc60bcef762a8320663bcf551 | e579b731f96c6628fb114e82bdedea0f79c2a156 | refs/heads/master | 2023-04-01T02:34:08.761473 | 2023-03-15T22:50:05 | 2023-03-15T22:50:05 | 104,376,933 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 428 | rd | getBackbone.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/BCdata-class-methods.R
\name{getBackbone}
\alias{getBackbone}
\title{Accessing the Barcode Backbone slot of a BCdat objects.}
\usage{
getBackbone(object)
}
\arguments{
\item{object}{a BCdat object.}
}
\value{
A character string.
}
\descriptio... |
5341edceea621ab244b65ec7fc13cebaecebb236 | ce1b08611df0fff10fbf5c79f8d79fd170cdb08f | /windows/server.R | 89a7ba246b4d9ec3587ea9796622d43ac2d9843f | [
"MIT"
] | permissive | xiaodaigh/shinydistro | b0824d8f6618b0492e4ba064ab6fe6e5603d7c96 | cd795bff774873f6dc3f68f818a9979a6f4eeb35 | refs/heads/master | 2021-01-10T20:22:01.340395 | 2014-03-26T16:38:01 | 2014-03-26T16:38:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 206 | r | server.R | # it recommended that you put the below code inside your shinyServer() function so that when PortableChrome Closes it will also close the underlying R session
session$onSessionEnded(function() { q("no") })
|
96d649672e5704c4a031bf82ddb84cb673765f87 | c262ebf6ac6dbd85ce1e786158ffca258744a73f | /ExploratoryDataAnalysis/Week4_ProgrammingAssignment/Plot5.R | e1615cdea7341d330b32cd3c6c0289c97795abd4 | [] | no_license | imreyes/datasciencecoursera | 47bc09b12f090b42c077db55b326d5f4377f443d | 7b35668a3174645f62c9ad575516b4b505c52253 | refs/heads/master | 2021-01-12T12:21:17.517449 | 2016-12-09T22:50:12 | 2016-12-09T22:50:12 | 72,453,655 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,330 | r | Plot5.R | # DSS Coursera.org
# Exploratory Data Analysis
# Week 4
# Programming Assignment
# Data source: EPA air pollution data - fine particle pollution.
# Unwrapping and loading data.
library(ggplot2)
unzip('exdata-data-NEI_data.zip')
# The below variables are used per instructed.
NEI <- readRDS("summarySCC_PM25.rds")
SCC ... |
07d06bbc78de7e0b6c7441775751a39c7c851496 | 5e3011b1de8bbb6e2a0e092eb01b0b1ce678a4d6 | /man/bootstrap_fs_perc_change.Rd | 2a46da6a476c8fdc58a146b6eb9fd56e705b225a | [
"MIT"
] | permissive | evanjflack/cfo.behavioral | a95a81bd89903f4b876522331860b607fa08b83b | b10e451026910c48a08c3bdda011bde039250959 | refs/heads/master | 2023-02-23T09:08:59.557128 | 2020-10-06T19:13:57 | 2020-10-06T19:13:57 | 227,146,208 | 0 | 0 | NOASSERTION | 2020-08-03T16:20:28 | 2019-12-10T14:50:50 | R | UTF-8 | R | false | true | 863 | rd | bootstrap_fs_perc_change.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fit_first_stage_elasticity.R
\name{bootstrap_fs_perc_change}
\alias{bootstrap_fs_perc_change}
\title{Bootstrap SE Percentage Change Models}
\usage{
bootstrap_fs_perc_change(DT, form, y, x_main, x_int, B, quiet)
}
\arguments{
\item{DT}{a data.... |
40ae5723b7ba7d4b2b13cfae04e25d6f6a725c9f | 781d9f53df5cbc291d8e7a10435106d1f0c69e5b | /2-Parallel-Job/parallel-job.R | 13c2d521d2a285c290efbce0967ebb3b655d51d7 | [] | no_license | syanthonyadam/Mercury-Tutorial | f922a779a8cda4b7787db07e68367f480ca9ead5 | dc62fab0f8a111819be7bd4a207cac10ed530737 | refs/heads/master | 2023-07-29T00:26:24.766522 | 2021-09-19T22:42:58 | 2021-09-19T22:42:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 751 | r | parallel-job.R | # parallel-job.R
# Runs a parallel job in R
library(parallel)
# Get number of cores from the SLURM_JOB_CPUS_PER_NODE enviromental variable
num_cores <- as.integer(Sys.getenv("SLURM_JOB_CPUS_PER_NODE"))
print(paste0("I have ", num_cores, " cores ready in R."))
# Run a parallel job
print("Using the two cores in paral... |
b36e6994a55185cb8a27cc9d9eb6fec5c0b93c11 | 2477434cc1b95634c5b15f558669e39ec2e963a2 | /man/adjustOne.Rd | 74a451c830533625a41931d11d4098d893b1da00 | [] | no_license | pariswu1988/proteomics | 4e4b273d04490a9f3279553dd889d870e504b62f | 5e50c3e344068130a079c9e6c145ffdbf5651ca2 | refs/heads/master | 2021-01-13T03:55:36.847983 | 1977-08-08T00:00:00 | 1977-08-08T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 294 | rd | adjustOne.Rd | \name{adjustOne}
\alias{adjustOne}
\title{Adjust for confounding -- In one single experiment only}
\usage{
adjustOne(dwide)
}
\arguments{
\item{dwide}{iTRAQ data in wide format.}
}
\description{
Simple code when only one iTRAQ-experiment has been
performed. (Code not used anymore.)
}
|
fc010d80e65ee41d3f98b81125fea8dfeffcc3c5 | 4c8dd8169fae247b71b26ff6b64db46f4174e73b | /5_Convert_LatLong_to_Postcode.R | fa8bf6a39177c8d106c40a064d33edc8ec6c2a44 | [
"MIT"
] | permissive | git-2-it/diss_g | c03b76d63937a665e9b505fd04671558c4fccd0a | ab4a4c2a2968afee5d4b4a5aa5ac3690ce80be34 | refs/heads/main | 2023-02-08T23:23:33.615634 | 2021-01-02T17:08:52 | 2021-01-02T17:08:52 | 325,520,551 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,552 | r | 5_Convert_LatLong_to_Postcode.R | # ca4_ni
# Creates summarised data per practice, per month
# Total items
# For each file, crate equivalent summarised version
# Unzips, processe and cleans-up the extracts
# Init required libraries
library("readr")
library("tidyr")
library("dplyr")
library(PostcodesioR)
library(sf)
datapath <- "../../../data"
datap... |
6e6839323028259b384694cb246695ff7621ee79 | ff59d2a5ae2fa5790e82d74244f4601ba89d7026 | /scripts/defineCohorts.R | 1b7cfcb886c59b033a3ca4836268eea1d252a37a | [] | no_license | laderast/OHDSIqueries | 13e854df3f97ced01044050c1b2c4cbfa8a62924 | 3b150e836a83c8548d6bab71d93797de64edf22c | refs/heads/master | 2021-01-22T18:43:50.890491 | 2017-05-16T17:02:53 | 2017-05-16T17:02:53 | 85,112,830 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,060 | r | defineCohorts.R | dbListTables(mydb)
?dbFetch
#step 1: pull every person
sqlStatementall <- "
select
pr.person_id, pr.year_of_birth, pr.month_of_birth, pr.day_of_birth, pr.race_source_value as race,
pr.ethnicity_source_value
from person pr
"
rs <- dbSendQuery(mydb,sqlStatementall)
fetch(rs, n=-1)
dbClearResult(rs)
#step 1.b : query c... |
980e29b80415db2c7a34be0dd728bf0d7b2cba6a | 344529cba1ea472905140fc8b4dcaf9ad8929fa8 | /Logistic_Regression.R | 7aee41d1b8c02976ed9a3673b34f95d6de24344c | [] | no_license | fall2018-saltz/cs_project1 | 01be370863a7b7c83b6aea38234d250110557597 | bb552d4c1b19db3a82743419a494000a3a9b2f1e | refs/heads/master | 2020-03-30T01:59:53.651688 | 2018-12-08T03:34:04 | 2018-12-08T03:34:04 | 150,606,761 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,965 | r | Logistic_Regression.R |
library(jtools)
#install.packages('pscl')
library(pscl)
#install.packages("broom")
library(broom)
#install.packages("ggstance")
library(ggstance)
#install.packages("effects")
#library(effects)
#install.packages("lattice")
#install.packages("caret")
library(caret)
#install.packages("e1071")
library(e1071)
str(data)
#C... |
1b3179bd401226a355e7e6f9aa2137a45ec81330 | 9fe4998982a9b52a66a6746d8f138fd9a3a895fb | /2d5.R | 09d156a050cb131158ccdf4d107cb0a31a2c9aaf | [] | no_license | avegac1996/Estadistica-en-R | effef846090888aa0e31892b18a2dbb79aca2e87 | 29d989ec541f5c4e547dce9eb9dd81ce3ad0c772 | refs/heads/main | 2023-06-01T22:05:07.795016 | 2021-06-21T01:15:07 | 2021-06-21T01:15:07 | 371,099,714 | 0 | 0 | null | 2021-06-14T02:07:36 | 2021-05-26T16:24:27 | R | UTF-8 | R | false | false | 545 | r | 2d5.R | #-----------------a--------------------
x = c(9,32,18,15,26)
y = c(10,20,21,16,22)
reg = lm(y~x) #Regresion estimada que relaciona y con x
summary(reg)
0.11719 <=0.05
#------------------b----------------
x1 = x^2
reg2 = lm(y~x+x1)
summary(reg2)
b00 = reg2$coefficients[1]#Coeficente b0
b11 = reg2$coeffici... |
cc27c7cdb0974eead33cd67e83ab8971bce3e563 | a9c540d94681b5e4ffb2300fd320d6c16eab3040 | /man/Cascade_confidence.Rd | b6c69a9e435fce0fa31616a769b7c7eeef949691 | [] | no_license | fbertran/SelectBoost | fd5716b73cb07d05cc3c67bbc75b8915672c1769 | 5610332658b95a71dacdbccea721d00a88a9742f | refs/heads/master | 2022-12-01T07:52:52.171876 | 2022-11-29T22:18:18 | 2022-11-29T22:18:18 | 136,206,211 | 6 | 2 | null | 2021-03-21T16:43:02 | 2018-06-05T16:35:45 | R | UTF-8 | R | false | true | 884 | rd | Cascade_confidence.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{Cascade_confidence}
\alias{Cascade_confidence}
\alias{net_confidence}
\alias{net_confidence_.5}
\alias{net_confidence_thr}
\title{Confidence indices}
\format{
A \code{network.confidence} object with four slots ... |
b4b5737d44647cc6a34a9f2af24c7d723f03620f | d03924f56c9f09371d9e381421a2c3ce002eb92c | /man/internalGenerics.Rd | 863e9136597726467ccee1c5ef36b0529bcef725 | [] | no_license | cran/distr | 0b0396bbd5661eb117ca54026afc801afaf25251 | c6565f7fef060f0e7e7a46320a8fef415d35910f | refs/heads/master | 2023-05-25T00:55:19.097550 | 2023-05-08T07:10:06 | 2023-05-08T07:10:06 | 17,695,561 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 828 | rd | internalGenerics.Rd | \name{internalGenerics}
\alias{internalGenerics}
\alias{distribution}
\alias{samplesize}
\alias{samplesize<-}
\title{Internal: Common Generics 'distribution' and 'samplesize', 'samplesize<-'}
\description{
In order to be able to use packages \pkg{distrSim} and \pkg{distrMod}
resp. \pkg{RobAStBase} independe... |
99660094e6e8c4e2b789c0294705731fa7a8fd6b | 7f129731f177fa696af7574aee5e16304648759a | /unused-code/negbinomial_mixture.r | c769bf8dfb71895ce6f0b4a66adbff1bff4405b7 | [] | no_license | rohanmaddamsetti/STLE-analysis | 82e906ccad71425132d9b2533c3a5edba225ffd7 | 85cadef141135d04244b61f64b88afe8b6787943 | refs/heads/master | 2021-01-17T17:02:07.320741 | 2017-12-15T23:22:58 | 2017-12-15T23:22:58 | 63,113,993 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,165 | r | negbinomial_mixture.r | full_ngb_mixed_model = """
data {
int<lower=0> N; #samples
real<lower=0> mean1; # mean counts inferred
real<lower=0> tau1;
int<lower=0> y[N];
}
parameters{
real<lower=0> mean2;
real<lower=0> tau2;
real<lower=0> alpha;
real<lower=0> beta;
real<lower=0,upper=1> pi;
}
model {
pi ... |
c21aef46ee05fe0bf3c8a8370e36d61012b8307f | 2d44449f9a0021ad81b927c2bf64fe580ab4c5de | /man/Spherical.Rd | 6284493d6c72179a2d516678529864d8371ec8ac | [] | no_license | jbrowell/Dynamic-Covariance | 58c4cabb5a0e3c48e72c62d0839433c6045a5535 | c2af77f96f0a73b2b61c13f539f39656b8c9115d | refs/heads/main | 2023-04-15T09:24:23.839923 | 2021-09-30T14:45:47 | 2021-09-30T14:45:47 | 358,193,873 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 834 | rd | Spherical.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CovarianceFunctions.R
\name{Spherical}
\alias{Spherical}
\title{Spherical Covariance Function}
\usage{
Spherical(r, params = list(sigma = 1, theta = 1))
}
\arguments{
\item{r}{Vector or matrix of separation distances}
\item{params}{A list of... |
53140b6317498768dff289eff951a9b1891d7a57 | 1523184d172fca9c0562a358e30a66b3ce07c02c | /IC/Dashboard/Funcoes/Graficos/Profissional/GraficoTrabalhaAreaEvasao.r | 6d246ac2777b635226b010ea107f16002dd4edbf | [] | no_license | guidinhani/IC2019 | 7d523dd37cf1018ecd25f430bc8d77d4b8a636f6 | f60dee874522f6d920d9d2ab06f6eeefccd6d3e1 | refs/heads/master | 2020-08-21T05:35:07.561411 | 2019-10-24T22:35:49 | 2019-10-24T22:35:49 | 216,102,653 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,856 | r | GraficoTrabalhaAreaEvasao.r | GraficoTrabalhaAreaEvasao <- function(trabalhaAreaEvasaodf) {
# ==============================================================================
# CONSULTA - QUANTIDADE DE EVADIDOS QUE TRABALHAM NA ÁREA DE EVASÃO
# ==============================================================================
quantidadeTrabal... |
470a58b6e57ffbd6a7affc6bfa489a4c0dc5295c | c529e1776d0073d1c122ee88def416f6f69d6c87 | /man/stat_qq.Rd | e04b6b799eb5ad37f930fad5217d4d5c62ead386 | [] | no_license | lixinyao/ggplot2 | db919577d8c53bc0522b7c47d9d56cd10ff28452 | 7be4c8944bca845c9b9e189ec8c44231f6b4dc2b | refs/heads/master | 2021-01-18T18:22:59.926529 | 2016-01-31T22:22:40 | 2016-01-31T22:22:40 | 50,810,838 | 1 | 0 | null | 2016-02-01T03:18:43 | 2016-02-01T03:18:43 | null | UTF-8 | R | false | true | 2,727 | rd | stat_qq.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/stat-qq.r
\name{stat_qq}
\alias{geom_qq}
\alias{stat_qq}
\title{Calculation for quantile-quantile plot.}
\usage{
stat_qq(mapping = NULL, data = NULL, geom = "point",
position = "identity", ..., distribution = stats::qnorm,
dparams = list(... |
4e0e2c94d72873dc53c4c4767e57ab13ccb57960 | d1129b1d416e283a9c5bc6e9f57d407b3afa06c1 | /R/as_numeric_dot_default.R | 6e846a136aa0c06ce3b92eb8d620448597b15f70 | [] | no_license | yaboody/my_package | bcc8794217e08e8e9b5f2b05aefc3f05c014e1fd | f2b82a86e58be4fbf2962805ce7171989d955fba | refs/heads/master | 2022-11-18T11:25:33.787707 | 2020-07-09T19:02:12 | 2020-07-09T19:02:12 | 278,447,441 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 405 | r | as_numeric_dot_default.R | #' Default Numeric Object Coercer
#'
#' @description by default, as.numeric works fine for coercing objects into numeric objects.
#'
#' @param x an object
#'
#' @details just a wrapper for as.numeric really
#'
#' @return x as a numeric object
#' @export
#'
#' @examples
#' as_numeric("4")
#' as_numeric(4)
... |
35f5ca01bf01e8380dcbf0d9bab0d9e0c493f66a | a4b67ea46787badabc054665407cb8b90f7e2819 | /tests/testthat/test-load-data.R | 8056fdc77a921f660abf58d1aa011fda694fe11b | [] | permissive | vikwato/datim-validation | 2745486588b70b23ee257385d1c8230ebfb8985d | f206c43ea7710917936c1627fa0da02ba5771832 | refs/heads/master | 2020-03-31T17:53:56.344120 | 2019-10-16T14:19:02 | 2019-10-16T14:19:02 | 152,438,101 | 0 | 0 | BSD-3-Clause | 2019-10-16T14:19:09 | 2018-10-10T14:33:42 | R | UTF-8 | R | false | false | 10,589 | r | test-load-data.R | context("Parse CSV data")
with_mock_api({
test_that("We can read a CSV file coded with IDs", {
config <- LoadConfigFile(test_config("test-config.json"))
options("maxCacheAge"=NULL)
expect_type(config,"list")
d<-d2Parser(filename=test_config("test-data.csv"),
type="csv",
organisation... |
5cece9d1c8c4e9a7ac63ca6a58a79c6693ee5ba1 | 1492ba730dd6c25d527b4c66a6a9c46357cdedb8 | /man/track_circos.Rd | df7132be6ae18b49b61b5a621c0d545f9d7d2000 | [] | no_license | tankbuild/postpsassR | 11417d7bddce3bc27f2386a97251dea82d7dfea7 | c2d135ab4bc0313e37f9c44dd7c674161479a5e9 | refs/heads/master | 2020-05-21T10:46:38.483966 | 2019-05-10T16:56:25 | 2019-05-10T16:56:25 | 186,015,419 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 810 | rd | track_circos.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/draw_circos_plot.R
\name{track_circos}
\alias{track_circos}
\title{Drawing circos track}
\usage{
track_circos(data, track_label, bg.col = "white", ylim = c(0, 1.025 *
max(data[[3]]) + 0.01), top.track = FALSE, point.size = 0.1,
color.poin... |
0316ee40e8ef23a52d58e6b9e32bf4571fdb46b4 | c5b441921f14d4ed8faa4c8770a069d6c800ee7c | /Flight_duration.R | afcab51223a48cbbf09325f06f76f27b68354b14 | [] | no_license | judyh97/Timezone_conversion | 7e5dd5ea3ad5680d4336a2a4a33c54e93373aa6f | 11d7de2fd32b9d62295dfc544851930b612f0dbb | refs/heads/master | 2020-12-02T22:08:23.421084 | 2017-07-03T07:21:25 | 2017-07-03T07:21:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 901 | r | Flight_duration.R | #' Calculate flight duration.
#'
#' @param arrival_time Arrival time at the destination.
#' @param arrival_tz Timezone of the destination.
#' @param departure_time Departure time at the origin.
#' @param departure_tz Timezone of the origin.
#' @return Time elapsed between the departure time and arrival time in the time... |
1aee876d0fa2a7af3934b95953081bfee633e4bb | 5d7d36ca81276b1a858d423584e43a1f2c58f12a | /archive/comp.plot.gene.R | 22bcb947daea25617e1d97514bd76fa353e90d0d | [
"BSD-2-Clause"
] | permissive | orionzhou/rmaize | 592778ee81f3d0d6a8dc00dee46cd27d1d610d05 | 8970eecbf8ebe9deab4321c73503d41b334683ea | refs/heads/master | 2022-01-20T07:11:57.528459 | 2022-01-10T20:38:21 | 2022-01-10T20:38:21 | 159,260,801 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,178 | r | comp.plot.gene.R | require(rtracklayer)
require(xlsx)
source("comp.fun.R")
source("comp.plot.fun.R")
dirw = file.path(Sys.getenv("misc3"), 'comp.plot.gene')
fl = file.path(dirw, 'loci.xlsx')
tl = read.xlsx(fl, sheetIndex = 1, header = T, stringsAsFactors = F)
fid = file.path(Sys.getenv("misc3"), "comp.ortho.hm", "01.ids.tbl")
tid = rea... |
01d94e9d41e6ac25835ecbdb5da7514d3aba155b | 80e29403e7e9b3dcb11a076a31a930ce0a89b132 | /hate_crime_EDA.R | ba3cbb289d0378282e8addcdf408f2a3b6ea82d3 | [] | no_license | tvanichachiva/Hate_Crime_EDA | 2882f0cd7b427f8c032d30633c7f601232d8eb63 | 822fc1b7adf1d00f1a7d5efc944bcd09d2572dd7 | refs/heads/master | 2020-03-23T19:02:29.417321 | 2018-07-23T02:27:49 | 2018-07-23T02:27:49 | 141,950,225 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,751 | r | hate_crime_EDA.R | #Hate Crime EDA
library(tidyverse)
library(urbnmapr)
library(fivethirtyeight)
hc <- fivethirtyeight::hate_crimes
#Joining fivethirtyeight data with mapping data
state <- urbnmapr::states
names(state)[names(state) == "state_name"] <- "state" #Changing state_name to state to match hc data
hc_state <- left... |
443c2ed5c962a418d76c6fc9c9fa5278ee06ee32 | 6e6202e97b13bead3f40ab7a141c2bc4fe8e9345 | /sr-ch12.R | 1892114e1fb6e5c28f11368679b98a33c7832355 | [] | no_license | loudermilk/bayesian-stats | 24b074d9b3775a2e193acb509c66b8ba3550417b | ca9840314183423e15ee80666b97fac43ee0b4d1 | refs/heads/master | 2021-01-09T20:13:57.391662 | 2016-09-19T12:46:39 | 2016-09-19T12:46:39 | 62,754,526 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,998 | r | sr-ch12.R | ## sr-ch12.R
## Chapter 12 - Multilevel Models
## Remember features of each clusteer in the data as they learn about all
## the clusters.
## (1) Improved estimates for repeat sampling - when more than one observation
## arises from the same indiv, loc, or time, then traditional single-level
## models either maximally ... |
41f7403e3657e7ff8dad1545f1de69d9452b204b | 03fb214812a36c4408fd59107b333f144f4de1f8 | /man/SharpeRatio.deflated.Rd | 0f85f2ce2d2a970d1fa43de1cab2c5a8cadc19b7 | [] | no_license | braverock/quantstrat | e8a911fac4fd73d1dc6623706a3bcbec72c81d69 | 3e660300b322bb63dcb7659a26304fe4e8d4a693 | refs/heads/master | 2023-02-07T16:28:50.251525 | 2023-02-04T20:29:30 | 2023-02-04T20:29:30 | 58,736,659 | 282 | 132 | null | 2022-06-25T02:20:08 | 2016-05-13T12:07:31 | R | UTF-8 | R | false | true | 2,401 | rd | SharpeRatio.deflated.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/deflated.Sharpe.R
\name{deflatedSharpe}
\alias{deflatedSharpe}
\alias{SharpeRatio.deflated}
\alias{.deflatedSharpe}
\title{Calculate a Deflated Sharpe Ratio using number of trials and portfolio moments}
\usage{
deflatedSharpe(
portfolios,
... |
0f067ff3cbd80f806e6e8e9fb6764aa8aabb8ab2 | fe77966452f50926681c2790aad028666e41e0a8 | /R/make_plot_gwas_catalog.R | 46ffeeb966d3773a3c64cc52e2101760800bf012 | [
"MIT"
] | permissive | tbaghfalaki/CheckSumStats | 282ddd7e24fccf06ffac7e0d9617819cd96a68b8 | 6ab4f518341536a5ab322cfa53c26c8395f730e6 | refs/heads/main | 2023-08-13T01:54:49.378641 | 2021-09-21T16:41:28 | 2021-09-21T16:41:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,755 | r | make_plot_gwas_catalog.R | #' Plot comparing the test study to the GWAS catalog
#'
#' Make a plot comparing signed Z scores, or effect allele frequency, between the test dataset and the GWAS catalog, in order to identify effect allele meta data errors
#'
#' @param dat the test dataset of interest
#' @param beta name of the column containing the... |
4761670f574723b06eaaf8ce1706fe6ad8bbe9f4 | 30431c11955b3028736d8ac66e3b5a80ed05eeb1 | /man/simple.imputer.Rd | 589c188f20d403ffeeadca5ec0a4cde97aa1418a | [
"MIT"
] | permissive | johncollins/fastimputer | da22c03fdeaeb4ae9b8b6083472022f7ed88c20c | 5de99c83c359868af6660cb5e6f57fbfc6158b50 | refs/heads/master | 2020-05-17T17:25:02.192779 | 2014-07-16T03:43:18 | 2014-07-16T03:43:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 608 | rd | simple.imputer.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{simple.imputer}
\alias{simple.imputer}
\title{simple.imputer}
\usage{
simple.imputer(df)
}
\arguments{
\item{df}{Data frame for all data including missing and non-missing values}
}
\description{
I use really simple models to predict missing values.
For continuo... |
100f5a6e88b24c1b2f5dd19f42bc5a5e790e4e07 | 3cb52d718c7f563b7a420810c5ef0a16c524e362 | /Project/MLProject_Evaluate.R | aa9c7f66020edc04ec616c0c8c6922522cf69e77 | [] | no_license | leoyuchuan/MachineLearning | 1e6b2d6f10def6b8c9c890cd0123b452ab03cc7e | afd994a04b4052301caa94a114a41828f46195a8 | refs/heads/master | 2021-01-19T08:05:34.295155 | 2017-04-08T01:47:37 | 2017-04-08T01:47:37 | 87,599,981 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 13,145 | r | MLProject_Evaluate.R | library(h2o)
library(data.table)
library(Metrics)
options(warn = -1)
###### Read Data Path ######
args = commandArgs(trailingOnly = TRUE)
if(length(args)<1){
stop("Please provide path of training data & testing data")
}
path = paste(dirname(file.path(args[1])), "/", sep = "")
###### Random Forest ####... |
58b3e0bdcef3306e47441610fd13171aca2eb0a0 | d423cea9d1263ba4afe2509c95efa1e8ea18d6e8 | /R/MaxPrecip.R | 14d384075abfae1aa6d14c237e5e6a7da62949a4 | [] | no_license | nburola/climateimpacts | 50eceb87d6bdd0af7401e47582a41cc191b10efd | 38773f77d5a1b84a288bd4a3b1247076b5be5ef2 | refs/heads/master | 2021-03-14T05:50:16.466561 | 2020-03-11T02:40:41 | 2020-03-11T02:40:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 859 | r | MaxPrecip.R | #' Function to find the greatest recorded precipitation values in a daily rainfall dataset for a particular location during the given period
#'
#'
#' @param precip precipitation in inches/day
#' @param station describes the number or name of the precipitation gauge or station
#' @return the greatest recorded rainfall v... |
57d7cfe6836dd2e55941295fb51b1867c15541be | 8c82a703ee4661feb4db1f456f94ee468b5f6459 | /man/diagt.o.Rd | 6cdb826f81779834868e6403cb8a7801602a6b3a | [] | no_license | gvdovandzung/thongke | b8d93d6aa3fa08c91a1360ccec3c683dd6e8cc2c | 26936ff9587953e8601c59179e25860fa75894a7 | refs/heads/master | 2022-08-29T08:19:57.995573 | 2020-05-22T16:01:28 | 2020-05-22T16:01:28 | 266,149,251 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 916 | rd | diagt.o.Rd | \name{diagt.o}
\alias{diagt.o}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Gia tri cua xet nghiem }
\description{
Gia tri cua xet nghiem khi biet ket qua xet nghiem (nhi gia) va benh (nhi gia)
Su dung khi muon ket hop gia tri cua nhieu xet nghiem thanh mot bang
}
\usage{
diagt.o(D, btest, dig... |
d0264df5d1290b16e13cd18328f382690617c57d | 6792f8a34ceeb2a3dddaec74271c9a998f2b200e | /DRHMCcodes/lingauss_statespace/kalman_filters.R | b7fe0ce2441e4cec7b8faf67efebdb9131cb169c | [] | no_license | torekleppe/DRHMCcodes | 0ae2c7851197fcc748393e29091375ed7aad11f4 | 32c79ed707d13b24be8825a0c07501d36fafe6d3 | refs/heads/master | 2020-03-19T03:15:17.966655 | 2018-06-01T11:48:22 | 2018-06-01T11:48:22 | 135,710,089 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,027 | r | kalman_filters.R | require(CIPlib)
require(stats)
posterior_kernel <- function(lam_x,lam_y,omega,y){
T <- length(y);
phi <- tanh(CIP_AR1_psi(omega,T)$psi)
sigmav <- exp(-0.5*lam_x);
sigmay <- exp(-0.5*lam_y);
margstd <- sigmav/sqrt(1.0-phi^2);
# kalman filter
aa <- 0.0;
PP <- margstd^2;
ll <-... |
89b941212176ee9f923f26eee3bb1780000ca976 | 8f7bb94c9b21e944a82c5cffc4ec11d476211f89 | /man/oshka-package.Rd | 2810054386ffb3551a96d01f4df182b00efea1ff | [] | no_license | brodieG/oshka | 5f988be714628a8921f52964d467cbd99ae682d3 | b7f73552aba09252df74e8780ac3b4d6948a5d13 | refs/heads/master | 2021-01-23T21:55:29.615977 | 2017-10-17T00:19:21 | 2017-10-17T00:19:21 | 102,914,120 | 14 | 0 | null | null | null | null | UTF-8 | R | false | true | 642 | rd | oshka-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/oshka-package.R
\docType{package}
\name{oshka-package}
\alias{oshka-package}
\title{Recursive Quoted Language Expansion}
\description{
Expands quoted language by recursively replacing any symbol that points to
quoted language with the languag... |
1cdd58fa6b76068f5dfce5e4b4a349d654790c83 | 326a197c0f0a6852e129bce9f642e5fe71b67760 | /server.R | 68094a56be8ac5578abbb67df6e89b0e4f5c0a97 | [] | no_license | blueshadowz12/DevelopingDataProduct | 415d6cdc9149e8f0fad547f79c311dfeb53524a5 | d37a214b7967ff37a5a4a2f2a2ba055d8d2b5cd0 | refs/heads/master | 2020-04-20T15:51:03.661799 | 2019-02-03T12:44:23 | 2019-02-03T12:44:23 | 168,943,065 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,831 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
# Define server logic required to get denomination
shinyServer(function(i... |
94e3439c2dce4bd78bd76374eefedf7ec7dec9c5 | 4411d901964c67610469962d0c106d218153b4db | /cachematrix.R | c7f377accf922542da58fb8656265db48552b4cd | [] | no_license | mpancotti/ProgrammingAssignment2 | 4ae75f725bc3e2667d5ca372923ec04abf0f446b | cb56dc23537bf4c34ecfe907e6d5a04c12a18456 | refs/heads/master | 2021-01-24T21:42:24.797671 | 2014-11-22T15:27:49 | 2014-11-22T15:27:49 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,353 | r | cachematrix.R | ## The functions do more or less the same actions that the example does, only using the
## solve function instead of the mean function
## this function return a list of methods (get, setinverse and getinverse) that will be used
## by the cacheSolve function.
##
## The getInverse function return the cachedInvertedMat... |
9d78f5ab308c50e2128c75c085804ee48876e44e | 9f85709553f4e38fdb5725b9b3d5d6d661b905bd | /NumberTheory/man/is.spsp.Rd | 1431405a2892122d613847ad8c87d9616ae04192 | [] | no_license | michallbujak/NumberTheory | b78075254270827d69a04b34a382023bcaf0343a | efc483ccbb130e9512649d98cd854fcdd029499f | refs/heads/master | 2022-12-20T01:15:17.322787 | 2020-09-30T12:36:45 | 2020-09-30T12:36:45 | 297,791,298 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 349 | rd | is.spsp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions.R
\name{is.spsp}
\alias{is.spsp}
\title{Strongly pseudo-prime}
\usage{
is.spsp(a, n)
}
\arguments{
\item{a}{number to be checked}
\item{n}{modulus}
}
\value{
boolean value
}
\description{
Check whether the number a is strongly pseu... |
acb6ad8388902e5616adf2a2a67307c307fde5f7 | ddfe7b5c2b8a2f95e010fbe347ebb035fb7bb48d | /statistics.R | 81adc3edd00e5ba473e4b19d7bad8eec820fa555 | [] | no_license | sandy149/analytics_01 | 5da9ebd65be27f4c5031e7a845ac6ce42d6cd310 | 9fadad4161e674553c000a1899e1938abef09e08 | refs/heads/master | 2020-03-26T20:06:33.788159 | 2018-08-21T17:44:26 | 2018-08-21T17:44:26 | 145,305,136 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 526 | r | statistics.R | #Basic stats
x <- ceiling(rnorm(10000, mean = 60, sd = 20)) # create data for normal distribution
mean(x)
median(x)
#no mode function
table(x)
sort(table(x), decreasing = T )
library(modeest)
mlv(x, method = 'shorth')
quantile(x)
quantile(x, seq(.1,1,by = .1)) #decile
quantile(x, seq(0.01, 1, by = 0.01)) #percenti... |
719b6a2961c2bf88e191a3865e92850fba6824a7 | 371e4e296cd48efed22de583bb90fdb1030d523f | /R/fitArCo.R | 6d50885af307dd57f6876451199cbe26f8fb38aa | [
"MIT"
] | permissive | rmasini/ARCO | e461c61c9e01214e2594bed1299f0e136b372938 | 116e8d00d1516ce714be061d1434a51f568fb6ce | refs/heads/master | 2021-01-22T21:27:22.687936 | 2017-03-16T14:32:20 | 2017-03-16T14:32:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,966 | r | fitArCo.R | #' Estimates the ArCo using the model selected by the user
#'
#' Estimates the Artificial Counterfactual unsing any model supplied by the user, calculates the most relevant statistics and allows for the counterfactual confidence intervals to be estimated by block bootstrap.
#'
#' @details This description may be usef... |
859493187027392a2e326c759346229c7633d7eb | f9e73c8e325d98a1c5933142ce2f8c041f514208 | /R/plotting.R | 7ce397b6724b482d1ed9420648cc7617552d7340 | [] | no_license | shaoyoucheng/monaLisa | 836dff6fa63e807ed9b0f45b68ad1c92d236b77a | e4d2d6c070f7be958f4d044d87c76c58ca86430a | refs/heads/master | 2023-08-19T06:05:17.036710 | 2021-09-10T15:51:20 | 2021-09-10T15:51:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28,288 | r | plotting.R | #' @importFrom grDevices colorRampPalette
#' @importFrom graphics axis hist lines par plot rect rug segments barplot matplot abline legend text
#' @importFrom stats density dist hclust
#' @importFrom S4Vectors isEmpty
NULL
#' @title Get colors by bin.
#'
#' @description Get colors for elements according to their bin.... |
1dcf00b94b11029ecf9dda6a55c3b2337a4c183a | 32105d2c12935dbba0e0af2e84077e69bb08b627 | /man/lalgp_graph.Rd | 47b86428bbc9153593a3728396d2116ff16c643a | [] | no_license | atusy/LLfreq | f40be9fe9cdc92c9db006dec9b1acb21ae3d16cc | 6f944ec2e8d5cf183ede4761020359eb1dc2f49b | refs/heads/master | 2020-05-28T08:10:28.919975 | 2019-05-25T10:39:51 | 2019-05-25T10:39:51 | 188,932,685 | 0 | 0 | null | 2019-05-28T01:36:45 | 2019-05-28T01:36:45 | null | UTF-8 | R | false | true | 3,288 | rd | lalgp_graph.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lalgp_graph.R
\name{lalgp_graph}
\alias{lalgp_graph}
\title{Frequency graph}
\usage{
lalgp_graph(data, x_min = NA, x_max = NA, y_max = NA,
uncertainty = TRUE, uncertainty2 = TRUE, curve = TRUE,
rug_plot = 1, graphic_theme = 1, grid = 1, x... |
a56ba84d0d7a4ab2b7ce29a36f874e85c411c79e | b530cb3f49e020f0e6a0d6969aa48c4e284a3bb4 | /Projects/DMP/heritability_analysis_v2/DMP_heritability_v10_mockdata_plotresults.R | 2947776f59f851a8813fba437ed4bd4a88f831c7 | [] | no_license | GRONINGEN-MICROBIOME-CENTRE/Groningen-Microbiome | 614916d08d2b2748af2fe228ebc73d961960a90c | 5b13765ee003dc7b65a18981b2171540d28d1a68 | refs/heads/master | 2023-03-09T23:21:48.188107 | 2023-02-23T16:38:10 | 2023-02-23T16:38:10 | 200,232,505 | 45 | 44 | null | 2021-06-09T07:41:43 | 2019-08-02T12:40:18 | HTML | UTF-8 | R | false | false | 10,988 | r | DMP_heritability_v10_mockdata_plotresults.R | # ========================================================================
# By: R.Gacesa, Weersma Group, UMCG (2020)
#
# Script plots results of heritability analysis
# (Panels A & B in Figure 2 in main DMP manuscript )
# NOTE: These codes are implemented for mock data heritability models
# constructed u... |
0f004aa33d6f491d8b33440b5b7bb03c97c41318 | 0f3aa5b26df6061ccf85a29fd2686749c3dadb2d | /cov_vsvb.R | 9242924e8211a8401c8c9fb81b63265bbda373cc | [] | no_license | Sutanoy/Covariate-dependent-Graph-Estimation | 1f18d7910615b033a03c5020bde80a4ab838a69c | c8450783b90e6df69656cda99cc6228147f5e271 | refs/heads/main | 2023-04-20T11:56:41.378570 | 2021-05-01T00:37:31 | 2021-05-01T00:37:31 | 360,359,816 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,800 | r | cov_vsvb.R | ##The core function that calculates the variational parameter updates and returns the final variational estimates
##for a single regression. So in the arguments, y plays the role of the response, i.e the j th variable whose
##conditional distribution given the remaining variables is being calculated.
##This calls ... |
5bae6966c327262ab2a71d14c5ff44f2ff45fb3d | b1c1e9d146157d14c142d24a9e02b95b3a31f584 | /IPAM 2016/Dados da Spera por municipio.R | 24c1cb20ae328f3e4e4b00c287538bf3b116a840 | [] | no_license | Eduardoqm/Science-Repository | 1ef37904f290cbbea3c060c0a4cf37265f60b699 | d655a12fb833a9dd128672576c93cc6f9303f6ea | refs/heads/master | 2023-07-17T08:24:52.460738 | 2023-07-05T17:22:07 | 2023-07-05T17:22:07 | 200,397,253 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,942 | r | Dados da Spera por municipio.R | ## corrige eerro em ler polygon dataframes
# muda acentos para pontos
Sys.setlocale(category = "LC_ALL", locale = "C")
# carregar library
library(ggmap)
library(raster)
library(maptools)
library(spatial.tools)
library(snow)
# Diretorio do shape
setwd("/home/eduardo/Documents/public-ipam/data_geo/shapes/municipios_IBG... |
78aff0c995623bf3f375d875554b94485c2cb6d1 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/BNPdensity/examples/enzyme.Rd.R | e737718f830d4951717d0a7f2f6dbd6b60d26e12 | [] | 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 | 155 | r | enzyme.Rd.R | library(BNPdensity)
### Name: enzyme
### Title: Enzyme Dataset
### Aliases: enzyme
### Keywords: datasets
### ** Examples
data(enzyme)
hist(enzyme)
|
c9f45f22067dbc7d5a6065a34403040429b125b5 | 0e45743c43c89c504446112f081fcea949299dec | /man/sfilter.Rd | 9c0131853bb4fa87832ebf496dd04c7f8ee87ea0 | [] | no_license | bmcclintock/crwHMM | 99ac74eb7f77bebd2d68800dd1516b4228efc342 | 95d5755ecedae60bdacbab2dc95fb121249a730e | refs/heads/master | 2020-05-19T23:06:50.678045 | 2019-05-06T19:33:24 | 2019-05-06T19:33:24 | 185,260,803 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,804 | rd | sfilter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sfilter.R
\name{sfilter}
\alias{sfilter}
\title{fit the state-space model to \code{prefilter}-ed data}
\usage{
sfilter(x, model = c("rw", "crw"), time.step = 6, parameters = NULL,
fit.to.subset = TRUE, optim = c("nlminb", "optim"),
verbos... |
0ea483995e407b70d82920cd0a7053472b73cf23 | 9b84ccc884052b90bb577372f0896db6855439f1 | /backup_code.R | 977bd65512cb741686eabdfff8d45b7b47b291fc | [] | no_license | gaojingyuusa/CEM_Tool | 34ad72c1aaa5bbe137a1fd008fe3bdd9e641631d | da2056ca8be74960760ad360ab323f25bfec7d76 | refs/heads/master | 2020-07-14T22:26:21.344490 | 2019-09-06T21:21:46 | 2019-09-06T21:21:46 | 205,416,380 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,080 | r | backup_code.R |
# Test data query from data
# Comparators+Target
isocode <- c("CHN", "JPN", "USA", "CHL", "BRA", "RUS", "CHL", "IDN", "MYS", "MEX", "COL", "SGP","KOR")
group <- c("Target", "Stuc_1","Stuc_2","Stuc_3","Aspr_1","Aspr_2","Aspr_3","High income","High income","ASEAN","ASEAN","OECD","OECD")
basis <- data.frame(
is... |
d1a2bb93ed9c70deb412cbc1415e087adb37eb5a | da5ee9a7b322b05d3e99c2a0a51b1bf2078415a8 | /man/mmf.Rd | 220942b40819bbe1481efd237f83fd8e2996392b | [] | no_license | cran/growthmodels | 4140464c0d457a8bdbed1ae3cbb845077c384c95 | dd212a6ad56212c2feb34fdba3d08667906785a6 | refs/heads/master | 2023-05-25T02:10:25.242190 | 2023-05-22T18:00:02 | 2023-05-22T18:00:02 | 17,696,516 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 954 | rd | mmf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mmf.R
\name{mmf}
\alias{mmf}
\alias{mmf.inverse}
\title{Morgan-Mercer-Flodin growth model}
\usage{
mmf(t, alpha, w0, gamma, m)
mmf.inverse(x, alpha, w0, gamma, m)
}
\arguments{
\item{t}{time}
\item{alpha}{upper asymptote}
\item{w0}{the val... |
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