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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
f6d055fa4838ad1d43b643048c3b60f85b481af7 | 10395d3e481332b1afade1b52b7e23d1f09b2380 | /Quiz4.R | 2cc999ca62f8dafc69c15555ad38c3664ac55907 | [] | no_license | sparklezzz/coursera-model-regression-sparklezzz | ed7b50215b75198a1b1ebd74df0d491e526180e4 | 529e46e83797d373953e9c1c46eb955d94206665 | refs/heads/master | 2016-09-05T15:38:15.190527 | 2014-11-03T02:14:06 | 2014-11-03T02:14:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 900 | r | Quiz4.R | #q1
library(MASS)
fit <- glm(use ~ wind, family = binomial, data = shuttle)
exp(fit$coef)
#q2
fit2 <- glm(use ~ wind + factor(magn), family = binomial, data = shuttle)
exp(fit2$coef)
#q3
y1 = c()
y1[shuttle$use == 'auto'] = 0
y1[shuttle$use != 'auto'] = 1
fit3 <- glm(y1 ~ shuttle$wind, family = binomial)
summary(fit3... |
1da9bdfd2a184acbf40b78612de09cddc49d058b | a0fc3f6312e9bba3d7b370fc98adacd4c1c1b70b | /man/fcount2.Rd | bad3b8bc290724907c85f3f3ae3f9a77c1c1a666 | [
"MIT"
] | permissive | yusifang/foofactors | b30716f9c75cad8ea7d562d0a1c122cc4ee4a572 | df10f08fa0629911bcbdcfacc9da93fce61a7286 | refs/heads/main | 2023-03-17T16:04:23.136687 | 2021-03-05T01:33:39 | 2021-03-05T01:33:39 | 344,342,199 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 224 | rd | fcount2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fcount2.R
\name{fcount2}
\alias{fcount2}
\title{Title omit}
\usage{
fcount2(x)
}
\arguments{
\item{x}{x}
}
\value{
x
}
\description{
Title omit
}
|
1ba3aabe16fdcec091fff7f9a081d4540938d466 | b2fecdade5403ea4eea7bb3d4dfb4b400d96f43e | /data_analysis/words.R | 35f4a9df45ff9bef808e84e0a7b1a34531c58ff7 | [] | no_license | stankorenromp/StaticTwitterSent | 63be537e11fddd4e5a73aa10c7e551446963277f | f27b5fee1aedc9a2eb3241d81911cc27879f521a | refs/heads/master | 2021-01-20T15:04:08.476286 | 2013-10-23T20:18:21 | 2013-10-23T20:18:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,569 | r | words.R | setwd("Dropbox/thesis_exp/")
metalex<-read.csv(file="metaLex.csv",header=T,sep="\t")
metalex<-metalex[metalex$OPFIND!="neutral"&metalex$OPFIND!="both",]
metalex<-metalex[metalex$SWN3!=0,]
NCRneutral<-metalex$Nanger==0&metalex$Nanticipation==0&metalex$Ndisgust==0&metalex$Nfear==0&metalex$Njoy==0&metalex$Nsadness==0&me... |
a8a1c50b1e89f5e5b6ee04b2911ee7626af115d7 | 670d56d2eed0d4d99eef45cd880f6ba15d65f7ba | /chapter/01/R/recipe1b.R | 486214e49482434874fe379db7dc42ad7b032bee | [] | no_license | Keniajin/graphsCookbook | 72ec324554ae4c689529fbc685e04b79f44ad6f9 | 6bd582ef2491019a4e291059ab305d8066c53370 | refs/heads/master | 2021-01-21T16:00:39.696560 | 2012-02-12T03:12:23 | 2012-02-12T03:13:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 949 | r | recipe1b.R | source("comboPlot.R")
base.recipe.1b <-function() {
plot(cars$dist~cars$speed,
main="Relationship between car \ndistance \\& speed",
xlab="Speed (miles per hour)",
ylab="Distance traveled (miles)",
xlim=c(0,30),
ylim=c(0,140),
xaxs="i",
yaxs="i",
col="red",
... |
cca0c3e717875451085466a9ed7648219aed2be2 | 02ad61c1562483bd72a90b9a0baa1f5d7817b6e1 | /test_size.R | 762fbcafca90d0e6bad2b99b67d4d461d2d1023b | [] | no_license | postgrespro/spatial_benchmark | 0c954699fdc261f79707c9d272aef4d59f0feed8 | 88563adf73d2ef9d819c818f8d27d79fd7626ea4 | refs/heads/master | 2023-06-23T05:20:22.560407 | 2018-09-13T08:35:48 | 2018-09-13T08:35:48 | 143,134,460 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 722 | r | test_size.R | require(sfsmisc)
setwd('~/compwork/postgres/spherical')
o_pg = scan('out_size_pg', list(t=0, n=0, sr=0, hbread=0, hbhit=0, ibread=0, ibhit=0))
png('fig_size_number_time.png', width=1024, height=768)
plot(o_pg$n, o_pg$t, type="b", pch=2, col="red", log='xy', cex=0.5, xlim=c(1e4,1e8), ylim=c(1e-2,1e2),
xlab="Numb... |
149aa99ad2fdf01acd4c71e179051c0979926122 | cfda292acf2c2d4ad18b6bf1768ce7c4b4e2bc58 | /tests/testthat.R | 212a584d394d0233ffa2dd72f8c8052317747ce9 | [] | no_license | briandconnelly/acewater | 8f0cac27c5eed4b8e6d11418e409480e192373e6 | 323478e54f217dcb846e057c5b9c279b753184bd | refs/heads/master | 2021-06-06T10:01:01.136160 | 2021-04-25T02:13:00 | 2021-04-25T02:13:00 | 92,439,185 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | testthat.R | library(testthat)
library(acewater)
test_check("acewater")
|
72f965c86c7f343405bf75d5e6e1b5ec43c590c1 | aaec91363846b2cd9db733aa27a34b059bd24b00 | /man/matrix-classes.Rd | fe132842b9df0429a6fd083373d90d84d415f2bf | [] | no_license | cran/orientlib | 2497db6c76472e79cc545a29cb227e1536eaeaf1 | 7978fe7f10a4a8f23c0618b5f93efc3b3755ad3e | refs/heads/master | 2023-04-20T22:21:33.385297 | 2023-01-10T15:20:02 | 2023-01-10T15:20:02 | 17,698,124 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,299 | rd | matrix-classes.Rd | \name{matrix-classes}
\docType{class}
\alias{matrix-classes}
\alias{rotmatrix-class}
\alias{skewmatrix-class}
\title{Matrix orientation classes }
\description{ An orientation represented by 3 x 3 SO(3) matrices or 3 x 3 skew symmetric matrices }
\section{Objects from the Class}{
Objects can be created by calls of the f... |
a342442d9c69c5a81afe2c9d49c61660f472b38a | af84f4fb4bd7c41432482cce6170da3e6af3a130 | /PE/18.R | b763c886617deaded0603250005fa31017008ba2 | [] | no_license | tkmckenzie/pan | a65fc375eea8171c9b64f8360c5cf7b152830b7d | 5337e4c1d09f06f2043551e1dd1ec734aab75b49 | refs/heads/master | 2023-01-09T11:54:29.636007 | 2022-12-21T22:26:58 | 2022-12-21T22:26:58 | 156,240,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 700 | r | 18.R | rm(list = ls())
s = "75
95 64
17 47 82
18 35 87 10
20 04 82 47 65
19 01 23 75 03 34
88 02 77 73 07 63 67
99 65 04 28 06 16 70 92
41 41 26 56 83 40 80 70 33
41 48 72 33 47 32 37 16 94 29
53 71 44 65 25 43 91 52 97 51 14
70 11 33 28 77 73 17 78 39 68 17 57
91 71 52 38 17 14 91 43 58 50 27 29 48
63 66 04 68 89 53 67 30 7... |
2b510e366f774cc99834abec69e41067092b6a8c | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sptemExp/examples/inter2conOpt.Rd.R | 5084874e483d8c9f45a0a8f3f92dbc50ad8c385b | [] | 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 | 820 | r | inter2conOpt.Rd.R | library(sptemExp)
### Name: inter2conOpt
### Title: Batch Interpolation of the Missing Values for Time Series Using
### Constrained Optimization.
### Aliases: inter2conOpt
### Keywords: models parallel
### ** Examples
## No test:
# Here is the sample for the first 500 locations.
# In practice, you may need more... |
c02608c70f336cacd2791d773211a8e58c966735 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ropercenter/examples/read_ascii.Rd.R | e83bc2896dd06e9b20fd48c06218b5f6fe88e2e3 | [] | 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 | 433 | r | read_ascii.Rd.R | library(ropercenter)
### Name: read_ascii
### Title: Read ASCII datasets downloaded from the Roper Center
### Aliases: read_ascii
### ** Examples
## Not run:
##D gallup9206 <- read_ascii(file = "roper_data/USAIPOGNS1992-222054/USAIPOGNS1992-222054.dat",
##D total_cards = 4,
##D var_names = c("q24", "weight")... |
93fd1c23ba08b9a24e7834d35cd8c3ffbea39a30 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/crisp/examples/crisp-package.Rd.R | 9a93898e5ccb46bd2f21332a278a3c610f987e76 | [] | 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 | 2,023 | r | crisp-package.Rd.R | library(crisp)
### Name: crisp-package
### Title: crisp: A package for fitting a model that partitions the
### covariate space into blocks in a data-adaptive way.
### Aliases: crisp-package
### ** Examples
## Not run:
##D #general example illustrating all functions
##D #see specific function help pages for detai... |
37dbfcac18d060df0ba45672ae4f072ef56519bc | 6a1d84304d1f4d45e3e417a4ca772a17aa03e559 | /Part2/part2_prediction.R | af468d9eece1eab960566d56dc8d84d0f779cf7a | [] | no_license | VivekKumarNeu/ADS-Midterm | 59a77edbb833926917f842a7be2663c8159bfb11 | e8fb279ab63a6b161b81f89ee64c1ebbc5a3b407 | refs/heads/master | 2020-07-03T07:55:54.170670 | 2016-11-21T04:48:36 | 2016-11-21T04:48:36 | 74,187,285 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,412 | r | part2_prediction.R | # Algorithms on the entire model.
install.packages("forecast")
install.packages("devtools")
install.packages("broom")
install.packages("ROCR")
install.packages("FNN")
install.packages('neuralnet')
library(dplyr)
library(tidyr)
library(grid)
library(MASS)
library(neuralnet)
library(FNN)
library(forecast)
##############... |
b01df0c9fb80b3249b6526da5259d3f943f17e88 | ed10f88f5e57500f49e64c57986eb6bf3027b490 | /plot1.R | 783245c7d88170d9070675113ead787de621323e | [] | no_license | solis/ExData_Plotting1 | d70a8f37fd0e2aad82775243954d7fcf15188c75 | e35110a7fba96c8eaa8dfd24db369ec171496ccc | refs/heads/master | 2021-01-20T17:09:25.382789 | 2015-08-06T11:24:13 | 2015-08-06T11:24:13 | 39,191,277 | 0 | 1 | null | 2015-07-16T10:37:12 | 2015-07-16T10:37:12 | null | UTF-8 | R | false | false | 185 | r | plot1.R | source("read.R")
# Get data
df <- read()
png("plot1.png")
hist(df$Global_active_power, col = "red", main = "Global Active Power", xlab = "Global Active Power (kilowatts)")
dev.off() |
375aeaa088c19d0b35c90ce80ea3fb7198cd1b63 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lidR/examples/readLAS.Rd.R | d5a1fc5e0e66d068b8a1194d66b987fb7e099027 | [] | 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 | 503 | r | readLAS.Rd.R | library(lidR)
### Name: readLAS
### Title: Read .las or .laz files
### Aliases: readLAS
### ** Examples
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile)
las = readLAS(LASfile, select = "xyz")
las = readLAS(LASfile, select = "xyzi", filter = "-keep_first")
las = readLAS(LASfil... |
69307b67d677a9c6f7ca301f24ded78298ad3efb | 6584c3f2f7f5ec2aea9d0ddfafb2c4d717a743ee | /DataPreprocessing.R | c108614fc113dde7fe317c5993012f4985a620c3 | [] | no_license | rishikesh21/data-merger | f81006d126218c4e5588425471161dff461ae8c6 | 814dd08309c536b873cf5120718230976f2036e7 | refs/heads/master | 2020-04-06T18:10:24.368315 | 2018-11-21T11:23:19 | 2018-11-21T11:23:19 | 157,688,101 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 335 | r | DataPreprocessing.R | library(data.table)
library(dplyr)
setwd("D:\\IS5126\\RestaurantReviewTripAdvisor\\")
files = list.files(pattern="*.csv")
DT =tryCatch({
rbindlist(lapply(files, fread))
},
error=function(e){
print(e)
}
)
DT=DT[!duplicated(DT)]
fwrite(DT,file="D:\\IS5126\\trip_advisor_results.csv",sep=',',col.names = TRUE,row.... |
52053b8d537f5da119d6b60c0ae1e3519f9124f6 | f1b5dab47082ad795250ae3882bab5d0352e61c7 | /man/comparison_test_data_perfect_prediction.Rd | 9e056f10d4b026e6830516a05c74219a70aafbb5 | [] | no_license | johnchower/gloobtyd | 79a20071b4f3637b12f6d885946fbab94740ce99 | 7c39258924eb51364850a9f3158dc0bfa787e24e | refs/heads/master | 2021-01-18T05:09:16.842040 | 2017-03-24T15:51:20 | 2017-03-24T15:51:20 | 84,277,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 440 | rd | comparison_test_data_perfect_prediction.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_doc.r
\docType{data}
\name{comparison_test_data_perfect_prediction}
\alias{comparison_test_data_perfect_prediction}
\title{Data frame for testing the output of the calculate_sps function}
\format{data.frame}
\usage{
comparison_test_data_... |
a67e37ea4c593fcae864c092f204c2a3827a3289 | 63b9f4ab3f858a53dababfb814f543b4f76f4556 | /man/coeffRV.Rd | 88c8f9c58fb10fe947b4699cc14638a1d751b132 | [] | no_license | cran/FactoMineR | 81eaa31984e57e8f8836bd7bdc16a2a3f0fc629c | 46df41770a7c7599fc28b0bf7351fb8034ba0c1f | refs/heads/master | 2023-04-07T02:53:01.777853 | 2023-03-27T07:50:02 | 2023-03-27T07:50:02 | 17,679,199 | 14 | 16 | null | 2018-05-31T16:57:54 | 2014-03-12T18:53:31 | R | UTF-8 | R | false | false | 1,976 | rd | coeffRV.Rd | \name{coeffRV}
\alias{coeffRV}
\title{Calculate the RV coefficient and test its significance}
\description{
Calculate the RV coefficient and test its significance.
}
\usage{
coeffRV(X, Y)
}
\arguments{
\item{X}{a matrix with \emph{n} rows (individuals) and \emph{p} numerous columns (variables)}
\... |
9b319679fc79fb8c988b76bc283e4e1568f71c0b | 1dd001af7b9a6d69c6af484dc64ce95a60acf5e8 | /Code/UD_buf_GPS.R | 3290bcd6c2332ef75451e796bde5dc3c1a0990e0 | [] | no_license | Geethen/where_the_wild_things_are | c1fc394d53a685873a341f991c75b6c30c8b0282 | d5ad3faa82d746241824abbb2e052cbeb476f473 | refs/heads/main | 2023-07-26T18:27:33.342127 | 2021-08-26T14:29:28 | 2021-08-26T14:29:28 | 371,052,132 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 429 | r | UD_buf_GPS.R | library(ctmm)
library(parallel)
data(buffalo)
# fit models for first two buffalo
GUESS <- mclapply(buffalo[1:6], function(b) ctmm.guess(b,interactive=FALSE))
FITS <- mclapply(1:6, function(i) ctmm.fit(buffalo[[i]],GUESS[[i]]) )
names(FITS) <- names(buffalo[1:6])
UDS <- akde(buffalo[1:6],FITS)
meanUD = mean(UDS, weig... |
d3dee9cda291c3b0f83b5cde3efc8a42a0523f3b | 6deb321a1918055852ab2f9a5d221fc5356ea2af | /man/defineOnset.Rd | 07e6592e33b7c00023c8ebd9ec742ac6f903dd8a | [] | no_license | kemacdonald/lwl | 6c01faf2feb6271dedffea4c5dcbf72fe4b96c67 | 2be012b0f215bb531d1319c4876d689744070d6c | refs/heads/master | 2021-01-11T00:04:08.983943 | 2016-10-13T05:29:29 | 2016-10-13T05:29:29 | 70,760,114 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 522 | rd | defineOnset.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/define_onset.R
\name{defineOnset}
\alias{defineOnset}
\title{Define critical onset in iChart}
\usage{
defineOnset(iChart, critonset = 300, includeAways = FALSE)
}
\arguments{
\item{iChart}{the iChart}
\item{critonset}{a number}
\item{includ... |
eeb1b832477503f3cdd838debaefbab3b41060f3 | 9dfbd575e53169f9327b5b24ed52fa3014972d19 | /Descripcion de Datos/Count of time by projects.R | 79685dcc9361706057f5932933f7abc42f4aee74 | [] | no_license | davidDacR/Thesis | 15a7ec7a7a984c7c3282906109673c71f1cade7b | 54e7c8a151d9c8329e1683a106e122dd6e2bb377 | refs/heads/master | 2016-09-06T02:19:25.118686 | 2015-09-02T22:48:38 | 2015-09-02T22:48:38 | 40,221,748 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,658 | r | Count of time by projects.R | #################
##Time/Projects##
#################
library(plotly)
# set driver
m<-dbDriver("MySQL");
#connect to the DB in MySQL
con<-dbConnect(m,user='root',password='admin',host='localhost',dbname='tsppacedb');
options(max.print=1000000);
vectorT<-c();
vectorX<-c();
vectorY<-c();
#get count of projects
sqlc... |
b4436fa6f724af5314b317f1de9232994e7cf7f3 | fb0196d11088962ece6a9e50e617416f5de5606e | /man/plotPredictions.regres.Rd | 23c64c116bbac49842054f6a8600230c8c72a7da | [] | no_license | zeehio/mdatools | 97b9ff3fbe5885232df6a8e9b36d278e0aa01fad | 4dc3920700536a521b9e42efc67ddddb69235e7f | refs/heads/master | 2021-01-12T19:43:28.567829 | 2015-12-01T07:10:29 | 2015-12-01T07:10:29 | 57,378,371 | 0 | 0 | null | 2016-04-29T11:04:35 | 2016-04-29T11:04:35 | null | UTF-8 | R | false | false | 1,197 | rd | plotPredictions.regres.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/regres.R
\name{plotPredictions.regres}
\alias{plotPredictions.regres}
\title{Predictions plot for regression results}
\usage{
\method{plotPredictions}{regres}(obj, ny = 1, ncomp = NULL,
main = "Predictions", xlab = NULL, ylab = NULL... |
311a1ec4babe7b774563858041db497d545d92b1 | 1b99276280112438a5487cd7d7d1eb3070dd3a47 | /inst/templates/brapi_POST_callBody.R | 28d36019a5252385f4c09ff49e4a48ef94101da9 | [] | no_license | mverouden/brapir-v2-gen | 357ef012dda26fc512ce8d7af0546ef01347a7e2 | d2d201c17e3029b847da7dddd1c2921623ad8f1b | refs/heads/main | 2023-08-24T09:56:17.561428 | 2021-10-31T10:52:51 | 2021-10-31T10:52:51 | 309,704,622 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,870 | r | brapi_POST_callBody.R | #' Helper function to create a call body for a POST call
#'
#' @author Maikel Verouden
#'
#' @noRd
#' @keywords internal
brapi_POST_callBody <- function(usedArgs, reqArgs) {
if (is.null(usedArgs[["con"]])) {return(NULL)}
## Preprocess required arguments
if (grepl(pattern = ", ", x = reqArgs)) {
reqArgs <- str... |
3d71b7322b11dfce16ae0c03de470b5f8e50d69f | b28f74d681bb5dfbf34549c82a8c932f77c1b0a8 | /man/prnMDS.Rd | de03c8a46d7c810a52f7f80c5b6f70311cfc9ba6 | [
"MIT"
] | permissive | sailfish009/proteoQ | b07e179e9fe27a90fd76cde2ed7caa55e793e9d6 | e6a4fe79a21f9a9106a35d78c2ce42d59e9d82e2 | refs/heads/master | 2022-12-25T20:06:40.340740 | 2020-10-15T20:18:14 | 2020-10-15T20:18:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 18,525 | rd | prnMDS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mds.R
\name{pepMDS}
\alias{pepMDS}
\alias{prnMDS}
\title{MDS plots}
\usage{
pepMDS(
col_select = NULL,
col_group = NULL,
col_color = NULL,
col_fill = NULL,
col_shape = NULL,
col_size = NULL,
col_alpha = NULL,
color_brewer = NU... |
ace14f603ee2a536d37ad06b6ddd61dc58bc41bb | 91a77be68e5ad1aa16e9a2681ba6fb090c118e4d | /man/as.tdm.Rd | fd19aaf46953b7f3dd7155120cdce26da2a74edf | [] | no_license | cran/qdap | e42f194e98a38eb02084eb6ac92dd587024b8540 | 5f032a6a8bf41255cd2547b11325ed457a02a72a | refs/heads/master | 2023-05-25T03:10:36.324940 | 2023-05-11T05:10:02 | 2023-05-11T05:10:02 | 17,698,836 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 16,012 | rd | as.tdm.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/as.tdm.R
\name{as.tdm}
\alias{as.tdm}
\alias{as.TermDocumentMatrix}
\alias{as.dtm}
\alias{as.DocumentTermMatrix}
\alias{as.tdm.Corpus}
\alias{as.tdm.default}
\alias{as.tdm.character}
\alias{as.dtm.Corpus}
\alias{as.dtm.default}
\alias{as.dtm.... |
fe65c973893c92cd6918ea47c970fcd87dea88d4 | b9b96aee722f984edf62eeabe0fb32ebbdc2598f | /R/f1d.irt.R | 40933e4a32289352fce55db2d163a6f792700e6c | [] | no_license | daniloap/sirt | 46690dca1382385d0fdfc39c12f15d7dd2adf291 | 8c4cb12ffafd70c14b28c9ca34bfd28c58734e83 | refs/heads/master | 2021-01-17T10:49:37.611683 | 2015-03-03T00:00:00 | 2015-03-03T00:00:00 | 35,530,314 | 1 | 0 | null | 2015-05-13T05:38:46 | 2015-05-13T05:38:46 | null | UTF-8 | R | false | false | 3,935 | r | f1d.irt.R |
#########################################################
# Functional Unidimensional Model (Ip et al., 2013)
f1d.irt <- function( dat=NULL , nnormal=1000 , nfactors=3 ,
A=NULL , intercept=NULL , mu=NULL , Sigma = NULL , maxiter=100 ,
conv=10^(-5) , progress=TRUE ){
if ( ! is.null(dat) ){
# estimate ... |
9c13050ab7e613b768e3861854799dfb2e48c666 | 8516967f77aa4437daa77f038444e6929e4dfd35 | /man/loglikeSP.Rd | 37e710bee5dfd9c829fa05b2fe03f847af5ad675 | [] | no_license | bomeara/phybase | 52a7c5872809f7dff283f4e8c8a0d3693cfe2d85 | 05949f49b2bac58de81113e81d6515c20340142d | refs/heads/master | 2016-08-11T20:09:33.013441 | 2016-02-02T21:29:32 | 2016-02-02T21:29:32 | 50,945,737 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,523 | rd | loglikeSP.Rd | \name{loglikeSP}
\alias{loglikeSP}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ loglikelihood of the species tree, i.e., Rannala and Yang formula }
\description{
This function calculates the loglikelihood of a species tree from a set of gene trees using the Rannala and Yang formula
}
... |
3fde62a700c7a44f0af10bd78d70af636924f4b0 | 57fb0239edbfecef2e28545e0976d5d54cb26ba9 | /cachematrix.R | b14712a5e1fc0041edfb4ccb4daceb6e305ffaf9 | [] | no_license | llself/ProgrammingAssignment2 | a75083c132446f11d14dcf44b9f576d7b4c030f6 | 0e4e74ba1a2284b345127ffedfa87a4ee0c0b9c5 | refs/heads/master | 2021-01-21T23:54:34.467087 | 2014-10-23T16:26:17 | 2014-10-23T16:26:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,192 | r | cachematrix.R | # Leslie Self
# R Programming Class
# Programming Assignment #2, Lexical Scoping and getting the Inverse of a Matrix
# Date: October 23, 2014
## I have two functions, makeCacheMatrix and cacheSolve. The makeCacheMatrix manages my cached value.
## I will set it initially to null and then will create functions to get... |
0264cb653ec9234bda22e91ddf5521ba28c0b301 | f7546999748d00b74db8551ed65e02cc564f7f4f | /man/A0SD.BAF.Rd | 95ce5fc13b332e4f338f43a31f91f992ee201639 | [] | no_license | cran/CHAT | b5887ac9eb97d1deace91cd638477e5d7bf56319 | 1819354a80335e6d92384002b90899b70c60719f | refs/heads/master | 2021-01-19T08:15:18.756721 | 2014-02-10T00:00:00 | 2014-02-10T00:00:00 | 19,303,604 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 481 | rd | A0SD.BAF.Rd | \name{A0SD.BAF}
\alias{A0SD.BAF}
\docType{data}
\title{
B-allele frequencies of selected chromosomes for TCGA sample A1-A0SD.
}
\description{
This is partial level 2 bi-allele copy number data selected from The Cancer Genome Atlas Breast Carcinoma 800K Genome-wide SNP 6.0 array for sample A1-A0SD in the format of B-all... |
348f51912fd9c29f372bb6026c4b8fdfe5a5be7d | 0fdc85c0cced444a2210e65523a0dd0b339319db | /R/spiderplot.R | 8e0bd464ac82c399ce98c454ce6f5d5c8f72e075 | [] | no_license | jaroyle/oSCR | 769730d56085d856437ddbb4d7b870509805bd41 | 250323186d9cef173be929667b618bbb79dc96c7 | refs/heads/master | 2023-04-14T00:24:00.143601 | 2023-04-06T11:40:40 | 2023-04-06T11:40:40 | 42,339,391 | 9 | 11 | null | 2020-10-30T14:19:41 | 2015-09-12T01:10:02 | R | UTF-8 | R | false | false | 2,670 | r | spiderplot.R | spiderplot <- function(scrFrame = NULL, session=1, y=NULL,
traplocs=NULL, add=FALSE, return.stats=FALSE){
if(!is.null(scrFrame)){
traplocs <- scrFrame$traps[[session]]
y <- scrFrame$caphist[[session]]
}
dither <- FALSE
dx <- max(traplocs[, 1]) - min(traplo... |
de18cb71b3b2ad1a3a680ed6f7e85299f5d73a07 | d50f7b303f6b7c0c8b6878d51bc5e3323d7b4a6e | /Code.R | de1c0874c3ea354ac0be5867a3dc5f34d768d292 | [] | no_license | ShravyaKadur/gdp-net-debt-analysis | 5fc7c312ead39dab5df359a35c278bccde676e7a | 6f7b8cbeae628869d9f3cbffc61a7c1ed9f61703 | refs/heads/master | 2020-12-31T22:50:12.185586 | 2020-02-08T03:22:01 | 2020-02-08T03:22:01 | 239,062,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,996 | r | Code.R | #Setting the directory
setwd("C:/Users/Shravya/Documents/College Stuff/Data Analytics/dataAnalyticsProject/Cleaned")
#Loading the data
gdpnc <- read.csv("GDPInPercent.csv")
netdebtnc <- read.csv("netDebtToGDP.csv")
#Converting to transpose
trgdpnc <- t(gdpnc)
trnetdebtnc <- t(netdebtnc)
#Converting ... |
dcc06b849eece64a79c72a440734f2e73c1c930b | 6d7a5a343804d41d36224b3cdac78821af3a291f | /Section04/Section04_Tables.R | 74c5fdb6c6cff4caca3317bf5b51ea492cf677a5 | [] | no_license | AI-Ahmed/Big-Data-Analytics-Using-R | eca4b339d8ed2d0df154eb2bc6efa46b2c9fd2dc | 44d4876bfbd5cb54eddbc8d35414a8f7c6d9c3af | refs/heads/main | 2022-12-29T17:45:23.269562 | 2020-10-16T04:24:34 | 2020-10-16T04:24:34 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,244 | r | Section04_Tables.R | #Import File
mytable <- read.table(file = file.choose(),header= T, sep=",")
#alt Import File
mytable <- read.table(file = "E:\\Education\\Terms_Components\\Term07\\BigDataAnalysis\\Lab\\Practics\\Section04\\empData.csv",header= T, sep=",")
#To Run it -> view(mytable)
#Summary the data
summary(mytable)
#to se... |
2e335563c5b9d3dbd3014558066b11ca662e2999 | 580524d16c446d195dbc75234f43e5635db37c86 | /random_trial.R | 5b4b937cf4c2f15e379cbcfd84768ebb58f6a36a | [
"MIT"
] | permissive | BrennaS/SURGE | 967d1244e6246c82dbc1ae80b463a8d0564bbec4 | 11163eb74e608ac49042111a869447738a6c6dca | refs/heads/master | 2021-01-15T18:04:18.088204 | 2016-05-03T23:32:35 | 2016-05-03T23:32:35 | 52,685,373 | 0 | 1 | null | 2016-02-27T19:44:16 | 2016-02-27T19:44:16 | null | UTF-8 | R | false | false | 880 | r | random_trial.R | ## Random Walk Historical Surge Time Series
library(rjags)
y=y
RandomWalk = "
model{
#### Data Model
for(i in 1:n){
y[i] ~ dnorm(x[i],tau_obs)
}
#### Process Model
for(i in 2:n){
x[i]~dnorm(x[i-1],tau_add)
}
#### Priors
x[1] ~ dunif(-1,10)
tau_obs ~ dgamma(a_obs,r_obs)
tau_add ~ dgamma(a_add,r_add)
}
"
data <- list... |
35582b77bb456602057ca641d81e59a57cca72ce | f797c1fa91d8072cc7f4cfcbcfee1d02b0c6b6c2 | /listings/header_roxygen.r | 13750d97525592d2fcfd8722d1e70b240c049410 | [
"CC-BY-4.0"
] | permissive | outheis/programmierleitfaden | 0887b999c07ca464a58ad7a4772cc477a4f209ba | adf12e13f7bf4ebd51a17f3669dc16c1461e850f | refs/heads/master | 2021-01-21T06:10:30.793615 | 2015-05-04T11:57:02 | 2015-05-04T11:57:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 311 | r | header_roxygen.r | #!/usr/bin/Rscript --vanilla
#' Jahrestagung_ForstBW_CU.R
#'
#' create graphics for presentation at the 2014 annual convention of ForstBW.
#'
#' @author Dominik Cullmann, <dominik.cullmann@@forst.bwl.de>
#' @section Version: Tue Apr 14 10:22:59 CEST 2015
#' @docType data
#' @name Header
NULL
# ROXYGEN_STOP
|
09e7839a24708e28e22352669c7fba0bb884f1ec | 4183488ba1d91a21cc7e80e385351a805bb1c37b | /man/sampleDirs.Rd | a6acece6c378ab600ef5b64cdd28bc50b9407a1c | [
"MIT"
] | permissive | roryk/bcbioBase | f6f88b2b1324bb6291cd6b4e86941fe95bd62b18 | ec7b4053c9bfed4189fd326ede0d7dad6facf6fb | refs/heads/master | 2021-05-02T18:17:37.380306 | 2018-08-19T19:36:33 | 2018-08-19T19:36:33 | 120,660,171 | 0 | 0 | null | 2018-02-07T19:18:51 | 2018-02-07T19:18:51 | null | UTF-8 | R | false | true | 659 | rd | sampleDirs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sampleDirs.R
\name{sampleDirs}
\alias{sampleDirs}
\title{Sample Directories}
\usage{
sampleDirs(uploadDir)
}
\arguments{
\item{uploadDir}{\code{string}. File path to bcbio run upload directory.}
}
\value{
Named \code{character} containing sam... |
31a1a272dc8263f35d379fd65c2656b8317b3070 | b08b7e3160ae9947b6046123acad8f59152375c3 | /Programming Language Detection/Experiment-2/Dataset/Train/R/n-queens-problem-1.r | 3332e6a59544d7460d32f376c1069b8757c09f60 | [] | no_license | dlaststark/machine-learning-projects | efb0a28c664419275e87eb612c89054164fe1eb0 | eaa0c96d4d1c15934d63035b837636a6d11736e3 | refs/heads/master | 2022-12-06T08:36:09.867677 | 2022-11-20T13:17:25 | 2022-11-20T13:17:25 | 246,379,103 | 9 | 5 | null | null | null | null | UTF-8 | R | false | false | 538 | r | n-queens-problem-1.r | queens <- function(n) {
a <- seq(n)
u <- rep(T, 2 * n - 1)
v <- rep(T, 2 * n - 1)
m <- NULL
aux <- function(i) {
if (i > n) {
m <<- cbind(m, a)
} else {
for (j in seq(i, n)) {
k <- a[[j]]
p <- i - k + n
q <- i + k - 1
if (u[[p]] && v[[q]]) {
u[[p]]... |
8cb2913c204052fa9085fb706f65e77a17dad8a7 | 839640e98e37f8b668e58e61f86453270d5fcc57 | /R/sim-coef.r | 2284a71dc4fa49863daadf75b42a6ca1f833ec46 | [] | no_license | dan410/sfdasim | f7f06ce63747c717b3ac279ead0b484f8fcdd436 | 7c39d423d10c29cdfd62a57b6f4290f43cc0ebfc | refs/heads/master | 2021-01-21T12:40:38.159809 | 2014-08-07T07:23:27 | 2014-08-07T07:23:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,179 | r | sim-coef.r |
#' simulates the coefficients of the basis functions as independend Gaussian random fields
#'
#' simulates a Gaussian random field for each basis function given user specified covariance function
#' and covariance parameters. The model specification corresponds to GeoR package.
#'
#' @param nfields number of fields ... |
0ad296f9259aff272146ecf3e7b34b2c9cf29590 | e0f41a9688e53c450a93831466f6768ee0679145 | /2 R programming/week3/scoping_function.R | 8cb07ea50a05001333ec6cd1512f3f85e6dc864a | [] | no_license | roeeorland/Data-Science | 7f4652ef4d9d433483a314641df912fc35589daa | 5b3184c429e1cc010349045e7560a7a751676dac | refs/heads/master | 2021-01-22T22:33:58.851268 | 2017-04-24T11:49:51 | 2017-04-24T11:49:51 | 85,548,290 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 65 | r | scoping_function.R | y<-10
f<-function(x)
{
y<-2
y^2+g(x)
}
g<-function(x)
{
x*y
} |
9510f2a08f188a542a8d06a1252ab4008f10b161 | 1f2ed7e0778776371702499954ab1b11d3ad3a4c | /man/ecbit.Rd | e599f28961b760445d4b574254f302a79410544a | [] | no_license | cran/VGAMdata | 1e3b653b5a9d4921535fb7d2e6d4191aa2d9201a | fbbb0beb0bf79fff712d1b994cf51de5cb3b176b | refs/heads/master | 2023-04-07T05:39:02.437835 | 2023-01-11T19:20:02 | 2023-01-11T19:20:02 | 17,694,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,586 | rd | ecbit.Rd | \name{ecb06.it}
\alias{ecb06.it}
\alias{ecb14.it}
\docType{data}
\title{Italian Household Data for 2006 and 2014}
\description{
%% ~~ A concise (1-5 lines) description of the dataset. ~~
Part of the data collected
at two time points (2006 and 2014)
by the Bank of Italy, as part of
the European Central Banks ... |
963e28ff93f660757501203df7b5f2ccb85f54f8 | f26ea5d9d7488fd41ea17c17bf52ae3cf4f093c7 | /man/getScale.Rd | cbd8057b6e15401edc8364aaf5d1989a1fde6391 | [] | no_license | cran/robustHD | b62265881b537151dd5990d64dc0a63379d52030 | f348809d3fc47b880050183225863908867aca05 | refs/heads/master | 2023-01-30T18:45:24.631406 | 2023-01-18T13:50:02 | 2023-01-18T13:50:02 | 17,699,287 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 2,557 | rd | getScale.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/accessors.R
\name{getScale}
\alias{getScale}
\alias{getScale.seqModel}
\alias{getScale.sparseLTS}
\title{Extract the residual scale of a robust regression model}
\usage{
getScale(x, ...)
\method{getScale}{seqModel}(x, s = NA, ...)
... |
a8466570e5addadfde2c16007831267a7ac15638 | 1f91bd700defe89b61f8d3f31e69fcf082948085 | /Programação em R/Básico Programação R/aula3_For_loops.R | 276adcbed58a597ae092a4e8b2951697d71711f2 | [] | no_license | Soaresolavos/Data-Science-and-ML-with-R | f94a62e526099bac1157ae02abce7a211804a68f | ceab8c36db0f26f8b53377578deae0cc83cee943 | refs/heads/main | 2023-05-15T01:07:26.020931 | 2021-06-08T21:46:27 | 2021-06-08T21:46:27 | 375,152,113 | 0 | 0 | null | null | null | null | WINDOWS-1252 | R | false | false | 431 | r | aula3_For_loops.R | v <- c(1, 2, 3, 4, 5)
for (temperatura in v) {
resultado <- temperatura + 1
print('A temperatura + 1 é igual:')
print(resultado)
}
mat <- matrix(1:25, nrow = 5)
for (num in mat) {
print(num)
}
mat <- matrix(1:25, nrow = 5)
for (linha in 1:nrow(mat)) {
for (coluna in 1:ncol(mat... |
c39a54d8750cb444131a8573b81bb9cf7b8ce092 | 0ccb2ef7d5d608d9c33ec1b68c176c17a7a3d888 | /discovery_SNP/conditional_analysis_novel/code/conditional_analysis_2M_known.R | 8faa76b493c96c20f34364b33c4826522d8c7c37 | [] | no_license | andrewhaoyu/breast_cancer_data_analysis | dce6788aa526a9a35fcab73564a457e8fabb5275 | d84441e315e3ce135149e111014fa9807228ee7c | refs/heads/master | 2023-05-31T20:59:44.353902 | 2023-05-15T19:23:30 | 2023-05-15T19:23:30 | 103,444,023 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,912 | r | conditional_analysis_2M_known.R | #install_github("andrewhaoyu/bcutility",args = c('--library="/home/zhangh24/R/x86_64-pc-linux-gnu-library/3.5"'))
args = commandArgs(trailingOnly = T)
i1 = as.numeric(args[[1]])
library(data.table)
library(bcutility)
setwd('/data/zhangh24/breast_cancer_data_analysis/')
discovery_snp <- as.data.frame(fread("./data/disco... |
fc4f69fefa620766d1eafc8381c53c65fff81c32 | b309bd451ea093e366c91dde005f60895e0adb00 | /munge/03_norm_eval(duplicate).R | fad3c49d94d8dec102d0369ee52f6971ed2b1101 | [] | no_license | WaverlyWei/FA-meth450k | 0ea3e28aa5f8799b610ff2772ff7a9d303651e23 | 706de94aca5da072e0438e69154793c9a2538f2e | refs/heads/master | 2020-04-16T21:45:17.153018 | 2019-04-22T23:41:51 | 2019-04-22T23:41:51 | 165,938,713 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,788 | r | 03_norm_eval(duplicate).R | ################################################################################
# In this script we estimate the correlation between technical replicates as a
# way to evaluate the normalizations.
################################################################################
# Set up workspace
library(here)
library... |
b87edc553cc29f4df43d75469ae0a4f4edd9fdec | cab1356eaf5956e0b9d8d6e72583f74160840cc1 | /man/quantileCI.Rd | 21c1d199043b972fa416c14d370209670bdf928a | [] | no_license | genome-vendor/r-cran-mkmisc | b6f3f9a0a2a70012706f2f045a3e8ed3a8eee74a | c072292326cc728de8eb8c51a483ab4f033bfb56 | refs/heads/master | 2021-01-10T19:24:11.540172 | 2014-10-31T19:21:46 | 2014-10-31T19:21:46 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,922 | rd | quantileCI.Rd | \name{quantileCI}
\alias{quantileCI}
\alias{medianCI}
\title{ Confidence Intervals for Binomial Proportions }
\description{
This functions can be used to compute confidence intervals for binomial proportions.
}
\usage{
quantileCI(x, prob = 0.5, conf.level = 0.95, method = "exact", na.rm = FALSE)
medianCI(x, conf.lev... |
3a8159d56c4e6423acb352d8f4c62dedf49fbf75 | 230f40513fd7169f354f7995919023321eecfd93 | /R/AdjSpec_AllPoints_curve.R | 157740c041ef1a0f5ec41b2998aafb94d57f4107 | [] | no_license | cran/caROC | 3f665f7d0cfeb305d8cffff85addc9e2d9e3465b | 6c243aa38c14806fac04fefb5fb164e189fc920a | refs/heads/master | 2023-03-30T16:32:56.914227 | 2021-04-02T07:20:03 | 2021-04-02T07:20:03 | 354,063,180 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,407 | r | AdjSpec_AllPoints_curve.R | AdjSpec_AllPoints_curve <- function(diseaseData,
controlData,
userFormula) {
outform <- ParseuserFormula(userFormula = userFormula)
M0 <- eval(parse(text = paste("controlData$",outform[[1]])))
M1 <- eval(parse(text = paste("diseaseData$"... |
cab56abb1e389c005d42cfebc5e5cec4b6ed9060 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/TwoStepCLogit/tests/bison_example.R | b1cf8e90f95996fded320dcea324a5402aa1cd56 | [] | 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 | 829 | r | bison_example.R | # Some descriptive statistics about the data set:
ddim(formula = Y ~ strata(Strata) + cluster(Cluster), data = bison)
# Model 1: covariates meadow, biomass and biomass^2
# Random effects in front of biomass and biomass^2
# Main diagonal covariance structure for D
Fit1 <- Ts.estim(formula = Y ~ meadow + biomass ... |
526f0656bcbe96f2bba7bfcfd3858739e7043691 | 01e953fe3ca03c2b20a8cfdfce3debe0e95e8a27 | /install.R | 433f29a4288ee2c794d8184cf30c8a65e4ab8c08 | [] | no_license | abotzki/datasc_mofa | 110fe05bd9c0f1886c78f62c39390a28a5a6f285 | 52cd13c971e58ef811c3c7ed6e2ceef033bc6885 | refs/heads/master | 2020-03-14T07:18:35.409594 | 2018-04-29T18:47:43 | 2018-04-29T18:47:43 | 131,502,142 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 88 | r | install.R | install.packages("devtools")
devtools::install_github("PMBio/MOFA", subdir="MOFAtools")
|
f4ac6607c0a9cf87a317c9c7c6a789ed60c6660f | 73c2e9b0af9f7e9fc5299cde844b3527e0d79489 | /R/openmi.om.runtimeController.R | be94f5b2e9dd5ad6f34b8447468bbf5c3f7ad3c3 | [] | no_license | HARPgroup/openmi-om | 0907fc362423a48d67f704511b9a112424bc465d | 166d76ca5f964bf6efcee105f917e1af07182722 | refs/heads/master | 2023-03-08T17:27:17.716249 | 2023-02-27T14:47:31 | 2023-02-27T14:47:31 | 149,749,781 | 0 | 0 | null | 2023-02-27T14:47:33 | 2018-09-21T10:41:26 | R | UTF-8 | R | false | false | 1,771 | r | openmi.om.runtimeController.R | #' The base class for meta-model simulation control.
#'
#' @description Class providing a runnable model controller
#' @details Will iterate through time steps from model timer start to end time, executing all child components
#' @importFrom R6 R6Class
#' @return R6 class of type openmi.om.runtimeController
#' @seealso... |
17f1c857b9e64193e5750fca6e84c490b4455752 | b7dbc8fa280edb6215a6260e1401e0f83b9954b0 | /OpenDataGroup/cpir/man/subindices.Rd | 60e2f280714415221dc4536bdca04ca1f96d8c10 | [] | no_license | cwcomiskey/Misc | 071c290dad38e2c2e6a5523d366ea9602c4c4e44 | 1fad457c3a93a5429a96dede88ee8b70ea916132 | refs/heads/master | 2021-05-14T18:10:35.612035 | 2020-02-17T15:09:51 | 2020-02-17T15:09:51 | 116,065,072 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 425 | rd | subindices.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{subindices}
\alias{subindices}
\title{Example Subindices (ECs)}
\format{An object of class \code{data.frame} with 100 rows and 69 columns.}
\usage{
subindices
}
\description{
The subindices, and associated date, us... |
bd11676ac2fa7ef7dce315ded19be3a06b35c67c | 3c3d1ea5eadf294bf0b8275b35d71efdea13835f | /R/scrape_chart_ts.R | cc13329d869f9ce883f51a575b086652d611fbf6 | [] | no_license | ces0491/webScrapeR | 45bd4921a015353f8e4dfe5c2938ecbd9070ecbc | f5be62f4c11c2cc807cdbff4a031c9ee933d6f14 | refs/heads/master | 2023-06-18T20:50:17.521530 | 2021-07-15T17:34:52 | 2021-07-15T17:34:52 | 290,697,723 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 845 | r | scrape_chart_ts.R | #' scrape timeseries data from highcharts
#'
#' @param pjs_session phantom JS session object
#'
#' @return \code{data.frame} containing date and value
#' @export
#'
scrape_chart_ts <- function(pjs_session) {
pg_source <- unlist(pjs_session$getSource())
source_data_link <- stringr::str_match(pg_source, "jQuery.getJ... |
da7ef5a284720677570c2ef82026fb51b0320a92 | ca21e692e45f0cafe76cb864aad5c1f363ef8123 | /gym/R/gym.R | b486dbad5cdc36871c131c9e83af3221a0eda499 | [] | no_license | hal2001/gym-R | 48b4d3ea3faaecdd4dc35cf6d894601b6bd056aa | 52237a6de5d57dcf7dde2164435e910cba2287cb | refs/heads/master | 2020-04-16T11:34:08.471198 | 2017-07-01T19:46:13 | 2017-07-01T19:46:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 87 | r | gym.R | #' gym: Provides Access to the OpenAI Gym API
#'
#' @docType package
#' @name gym
NULL
|
f8c3324e5d43c4ab5e7db28e910038b1645018ac | 949b3e51e617484c889a410913a822f424562e28 | /K Means Segmentation in R/KMeans_Segmentation_v1.R | 277d489f5d68aee372052703563f2dfdfb03fe4f | [] | no_license | Shafquat/Machine-Learning-Tensorflow | ab3e9e7ad5fc9d100725e3aa1ef60697b2cfb6b1 | 6d49d3a09c072274e8ed242b117b128afca8043f | refs/heads/master | 2020-09-26T11:42:44.628031 | 2019-12-06T05:39:43 | 2019-12-06T05:39:43 | 226,248,284 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,811 | r | KMeans_Segmentation_v1.R | ##################################
# Code for Segmentation
#################################################################
# ###########################################################
# Analysing the Grocery Store data
# ###########################################################
# load from the file location
# ... |
894ab86c954fc8a65262b690aa202279e1b590b9 | fbe83f5d30aef93e099354294ca0784ad3e99274 | /man/check_form3.2_box7_abdo.Rd | 86160b13a192cd2507287c06b70fe46e8cceed65 | [] | no_license | EddieZhang540/INORMUS | fcbf1b424cc9209a82b4b38d7b51df9e53385afd | 155dea53b00bc488dc8061ae0b4c0aa4fc60f2a8 | refs/heads/main | 2023-06-04T09:34:00.792724 | 2021-06-06T01:30:29 | 2021-06-06T01:30:29 | 376,072,158 | 0 | 0 | null | 2021-06-11T15:44:47 | 2021-06-11T15:44:47 | null | UTF-8 | R | false | true | 451 | rd | check_form3.2_box7_abdo.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/checks.R
\name{check_form3.2_box7_abdo}
\alias{check_form3.2_box7_abdo}
\title{Filters out invalid rows for the abdomen box 7 of form3.2}
\usage{
check_form3.2_box7_abdo(form)
}
\arguments{
\item{form}{form3.2}
}
\value{
a form containing pts... |
0f007fd8c718f95f9f2761c486b9bd82f3ef0e5a | 820289a6667b00d3a4c7c35d4cd4b9b43b6b1ebf | /code.R | 64fe07ab2c40e56b7ef661d00a69076d696b12b4 | [] | no_license | Butrait/Loan-Prediction | c0b392032bd89fec40767984cb9a1c82f0d3bfae | 1eefc0839b82a4e6b4ee4e73acb6d927a630f08b | refs/heads/master | 2020-04-29T14:05:59.687579 | 2017-06-25T10:15:45 | 2017-06-25T10:15:45 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,935 | r | code.R | setwd("C:/Users/Divya Sharma/Desktop/myprojects/loan")
customer <- read_csv("C:/Users/Divya Sharma/Desktop/myprojects/loan/customer.csv")
str(customer)
head(customer)
any(is.na(customer))
#Dependent Variables
customer$dti<-customer$debts/customer$income
customer$loan_decision_status <- ifelse(customer$loan_decision_ty... |
ee3bf7dfd3556ab577f09d946abc09408cca99ef | f4460d114a82aaf3ae848d2ec110abab2bae4d08 | /Lab 3/ex_2.6.r | ee3b78be1b77a1f7181bd55350249964eae032bb | [] | no_license | UTBM-AGH-courses/agh-data-mining | 9bb84b0eea20f756cb0a06778188b0a601ed2465 | ba19a6b68e43c426fdd07d56bf53f9bb6788f65e | refs/heads/master | 2023-02-09T19:50:45.972503 | 2021-01-11T14:56:01 | 2021-01-11T14:56:01 | 307,353,451 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 605 | r | ex_2.6.r | #!/usr/bin/env Rscript
florist <- read.table("florist.txt", header = T, sep = ",")
florist_by_week <- aggregate(florist$price, by = list(florist$bouquet, florist$week, florist$year), FUN = sum)
names(florist_by_week) <- c('bouquet', 'week', 'year', 'total')
florist_by_week <- florist_by_week[order(florist_by_week$yea... |
6806250e5ec0be0be3ccbe950afe84307c6981c6 | ea166fae9420fbbfd99b965e8e5db1d05a772b81 | /utils.R | 43cac384f6d6ca439560f2f48be1ddd1138a082e | [] | no_license | mkukielka/survival_analysis | d4bf9e544c2a0f45f43aa8f5b658effe876aa466 | d95c526d62b3ec83393494edf1e465c5f013217f | refs/heads/main | 2023-03-29T02:09:52.956769 | 2021-04-07T10:24:19 | 2021-04-07T10:24:19 | 355,501,707 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,979 | r | utils.R | source('constants.R')
load.packages <- function() {
suppressPackageStartupMessages({
library("tidyverse")
library("Xmisc")
library("survival")
library("dplyr")
library("survminer")
library("mltools")
library("tidyr")
library("dplyr")
library("stringr")
library("caret")
lib... |
009b9cc3b13e7aa611a7b44cf40a5c50d845de76 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/MRS/man/mrs.Rd | c6d4edd3532e475f24a990543da2b05e6cd21653 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,561 | rd | mrs.Rd | % Generated by roxygen2 (4.1.1): do not edit by hand
% Please edit documentation in R/mrs.R
\name{mrs}
\alias{mrs}
\title{Multi Resolution Scanning}
\usage{
mrs(X, G, n_groups = length(unique(G)), Omega = "default", K = 6,
init_state = NULL, beta = 1, gamma = 0.3, delta = NULL, eta = 0.3,
alpha = 0.5, return_global... |
e1fabe9505c8ab67609739cc288793ee3e02afef | c787ef4c899aeb5ae61366c788cebeec3e173753 | /analyze-bootstraps.R | 778f0331c7e342320dc20b0259156d2d2e1cfa52 | [] | no_license | openbiome/fmt-public-health-model | be7b1e3f5af772120c20061136d55063c9a0e901 | 8ee892be7645b8aab872aa1699227759de3d6870 | refs/heads/master | 2021-03-16T16:11:12.537079 | 2020-04-02T14:54:24 | 2020-04-02T14:55:04 | 246,922,459 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,675 | r | analyze-bootstraps.R | #!/usr/bin/env Rscript --vanilla
library(tidyverse)
source("util.R")
source("model.R")
set.seed(5)
n_plot_points <- 1e3
max_nnt <- 10
# Load variates
estimates <- readRDS("cache/estimates.rds")
variates <- readRDS("cache/variates.rds")
# Point estimate model
us <- c(0, 0.50, 1.0)
point_estimates <- tibble(u = us)... |
e74b944c987a0950bb5a1c69ec9cc49c8e9a858e | 2bffd1e59a1ee5606bf28003ab6d423c8d868b2e | /proyecto_nlp.R | 6dcd48bc0a5e3094fcf326932a0522498851ab5c | [] | no_license | luispalomo97/ProyectoNLP | c471f1ee6bceccb9a9d16a4740e92659ab5ff999 | 012dccd311c8dd9580c3c8338f43d6c043ba61a3 | refs/heads/main | 2023-02-11T08:22:35.399345 | 2021-01-07T20:13:32 | 2021-01-07T20:13:32 | 326,761,150 | 0 | 0 | null | null | null | null | ISO-8859-1 | R | false | false | 10,702 | r | proyecto_nlp.R | #-------------------------------------------PASOS PREVIOS-------------------------------------------------------
#instalación de paquetes
install.packages("ggplot2")
install.packages("tm")
install.packages("wordcloud")
install.packages("RColorBrewer")
install.packages("sentimentr")
install.packages("tidytext... |
ed149573d59a269fe32712343bb1fded225cb981 | d73d30ad8c323904788266f94bf4c99081e80b3a | /clean.r | 7bb8b8e184ebf3118006807d672abe17e2c16bfa | [
"MIT"
] | permissive | phoebewong/tsw-hackathon | fb5a0ecd9864b1ff0ee60b206f4e0f326d3a5766 | 24db62399aea6017f298b1286b00fe8c1ed8a2f8 | refs/heads/master | 2021-05-10T20:50:29.693312 | 2018-01-22T04:37:02 | 2018-01-22T04:37:02 | 118,207,686 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,174 | r | clean.r | hospital.df$full_address <- paste(hospital.df$Address.1, hospital.df$Address.2, hospital.df$Address.3)
hospital.df$full_address <- paste(hospital.df$full_address, hospital.df$City, hospital.df$State, sep = ", ")
hospital.df$full_address <- gsub("NA, ", "", hospital.df$full_address)
hospital.df$pop_content <- paste(se... |
12910a283e903ae1378fee8d375181b076a6dc39 | 29585dff702209dd446c0ab52ceea046c58e384e | /cope/R/MBContour.R | 3474d5d404ec94528ca09e6b7cba5cbd92559d91 | [] | no_license | ingted/R-Examples | 825440ce468ce608c4d73e2af4c0a0213b81c0fe | d0917dbaf698cb8bc0789db0c3ab07453016eab9 | refs/heads/master | 2020-04-14T12:29:22.336088 | 2016-07-21T14:01:14 | 2016-07-21T14:01:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,279 | r | MBContour.R | #' Computes Multiplier Bootstrap realizations of the supremum of a Gaussian
#' field on a contour.
#'
#' @param x x-Coordinates of the grid on which the data is observed.
#' @param y y-Coordinates of the grid on which the data is observed.
#' @param R An array of dimension c(length(x),length(y),n) containing the
#' ... |
1ef183f36cf238dd97095bb2459a32ba24def401 | 459fc933c9da3c2ccc8456ee3bba8441fe3cb208 | /code/Economics/median_income/median_income_lineGraph.R | 388e2f058b6fb4da0b0163b002935615f61f309b | [] | no_license | DSPG-Young-Scholars-Program/dspg21hampton_roads | a8847a252474815cce9196a771f16ec3e8542610 | 8189465458901f7b986f94b41e44e5c32c4bd674 | refs/heads/main | 2023-06-29T07:01:06.204889 | 2021-08-04T19:48:41 | 2021-08-04T19:48:41 | 392,808,859 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 19,923 | r | median_income_lineGraph.R | library(tidycensus)
library(tidyverse)
library(dplyr)
library(ggplot2)
library(readr)
library(grid)
library(gridExtra)
#need to convert data from 2010-2019
#VA data
va_table <- function(varcode, year){
data.frame(get_acs(geography = "state", state = 51,
table = varcode,
yea... |
06c2c257f7282ba1edb7f103b7cadda25db3b251 | 2e2c21e3103526a82fcf7cd76f5839be27f74745 | /R/CESAnalysis.R | 3ef37ed56593d8f4f6daf82ac6828ebee36b107a | [] | no_license | Townsend-Lab-Yale/cancereffectsizeR | a65e1e8d91848902124ba72d987aa18e825b52e2 | 52ebab17ad8cc616fcc9728c528681ed0f4a63c2 | refs/heads/main | 2023-08-11T21:16:07.022309 | 2023-07-31T16:33:41 | 2023-07-31T16:33:41 | 140,888,382 | 24 | 12 | null | 2022-10-17T19:31:02 | 2018-07-13T20:03:10 | R | UTF-8 | R | false | false | 27,772 | r | CESAnalysis.R | #' Create a cancereffectsizeR analysis
#'
#' Creates a CESAnalysis, the central data structure of cancereffectsizeR.
#'
#' @param refset Name of reference data set (refset) to use; run \code{list_ces_refsets()} for
#' available refsets. Alternatively, the path to a custom reference data directory.
#' @return CESAna... |
c019bcb6f5069218353e1479c31035b3d4da4b32 | c8daf8580452d43e76da593a44a983beb94fae70 | /sendjs/ui.R | e198855fea57d74a4940656015aac2fddead10de | [] | no_license | marcionicolau/testapp | 2e537e8296b2669bb2758116862a2947c7fedca0 | c182950ad15fb9b8e924794b54a469a4a5504557 | refs/heads/master | 2021-01-23T20:57:03.182316 | 2013-05-20T18:13:02 | 2013-05-20T18:13:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 235 | r | ui.R | shinyUI(pageWithSidebar(
headerPanel("Shiny Widgets"),
sidebarPanel(
sliderInput("controller", "This slider controls other inputs:",
min = 1, max = 20, value = 15)
),
mainPanel(
h2("Filler text")
)
))
|
f8dc7582ef95563d093c22c8135c3467595b54d5 | e521a623151f7a838e7a3c79efa52370f61d0251 | /R/BATD.R | 634f0da86eac74d129a5af823606139727a7bdeb | [] | no_license | HeJasonL/BATD | 0a1128d5fb6254b66b54e2b3e290de8800c2dcd0 | 5bf5f667ca1c1a34b0b8a8045c0914f1c2b97c78 | refs/heads/master | 2023-08-29T13:42:26.723249 | 2023-08-05T07:08:53 | 2023-08-05T07:08:53 | 241,194,462 | 3 | 2 | null | 2023-09-05T15:22:14 | 2020-02-17T19:51:44 | R | UTF-8 | R | false | false | 1,073 | r | BATD.R | #' Batch analysis of tactile data 'BATD' package
#'
#' The scripts contained within this packages are used to analyze data recorded by Cortical Metrics software.
#' The scripts for the initial extraction are entitled BATD_extract_XX, with "XX" being either NF, referring to data collected from the CM4(?) or OF, referrin... |
4091ad94e42e2b4aee561d411079ff1b63eb825b | 255bde5d965a626504175d1800d22896f6820eed | /R/NI.Array.R | 24a172d54f90afb38ae3befa28cc3f652ccfd239 | [] | no_license | cran/binGroup | f0345d60454b835216ba4d29bbcbd193f3557f81 | 288a6aa03e33afa530fc1fd1eb3ad39f5b25427d | refs/heads/master | 2021-01-22T11:59:06.761427 | 2018-08-24T10:24:26 | 2018-08-24T10:24:26 | 17,694,764 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,499 | r | NI.Array.R |
# Start NI.Array() function
###################################################################
#' @title Find the optimal testing configuration for non-informative
#' array testing without master pooling
#'
#' @description Find the optimal testing configuration (OTC) for
#' non-informative array testing wi... |
d386a8e315143cf6f7e3d2dbe3d9d3e75c4e5289 | f39bfc0af440bdffdc56f9bc3aa34f1eaf07ee52 | /ca_labels/ca_labels.R | 3846e23531cfa1b6c25807c765937eaa084b0199 | [] | no_license | bazini627/Randas | 708e8591960b4472090677c5d720419d233c1db4 | ebc48709135c66391150696a4547e9bb453b1273 | refs/heads/master | 2021-05-26T22:14:09.573151 | 2020-04-09T23:06:59 | 2020-04-09T23:06:59 | 254,176,636 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,299 | r | ca_labels.R | library(dplyr)
library(sf)
library(tmap)
# Read in CA counties GeoJSON
ca <- st_read("../data/caCountiesNoChannelIslands.geojson")
head(ca)
# Drop and rename some columns in ca dataframe, only want to keep the 3rd and 6th column
ca <-select(ca,-c(1,2,4,5)) %>%
rename("county" = "NAME")
head(ca)
# Cre... |
1aa85f60921f7966fdf5e45137aace388344e170 | d257d0b55a7dd4569c419b4196ff79d5f1fde132 | /Aimee/DownLoadData/server.R | c6a9e3452a6e7bc1cb5567f1c8f8ee254c449d32 | [] | no_license | RobHayward/Shiny | 4720e7444ded2a6de0ed6412f4fd1bb404333f1d | bef01e4e92044da73443c29e60f46d684e1abcd8 | refs/heads/master | 2021-01-09T05:50:42.200166 | 2018-09-20T09:34:44 | 2018-09-20T09:34:44 | 22,253,800 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 879 | r | server.R | library(shiny)
function(input, output){
simData <- reactive({
dates <- seq(input$dateRange[1], input$dateRange[2], by = "day")
nDates <- length(dates)
simData <- switch(input$distribution,
"Normal" = rnorm(nDates),
"Uniform" = runi... |
71bf42537d60cb425adf7ce3f8d4faf0fb7c1298 | e3a0b7f672cb73c5e06dc97fb43c79d240ccf398 | /R/optimRegModel.R | 4d3cb594434f88be34eb842778d02ba540c3576f | [] | no_license | jhorzek/regmx | 621c6401b49d5ff2686a964be984523d96ffca6a | 0a3e66cb914b690faa5d1cdf6a08699e6b93f06a | refs/heads/master | 2022-10-01T08:57:18.297613 | 2022-09-14T13:14:21 | 2022-09-14T13:14:21 | 195,298,982 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,283 | r | optimRegModel.R | #' optimRegModel
#'
#' Note: regmx is based on the R package \pkg{regsem}. Because of the early status of regmx, it is recommended to use regsem instead!
#' optimRegModel creates a range of regularized models from an mxModel. It automatically tests different penalty values and returns the best model
#'
#' @param mxMode... |
52962c2fa888aa4b90a9aed0dfbecfb20a8efc3c | 7a24a59293bafdd638d2fb9cdc10aa00359f5797 | /R/angsize.R | 6a1f06d742070aea5dd5b07c6e1fcaa01122d1c2 | [] | no_license | cran/astro | c0c92d6ff150a68ce81cc7f75e1888ee6498c36f | 19710691a4a1a7d18aab7ccd31a7be31e0fe2b2a | refs/heads/master | 2021-01-13T01:30:32.071603 | 2014-09-08T00:00:00 | 2014-09-08T00:00:00 | 17,694,512 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,949 | r | angsize.R | angsize = function(z = 1, r = 1, inp = "arcsec", out = "kpc", c = 3E8, H = 70, M = 0.3, L = 1-M, K = 1-M-L){
# Apparent Angular Size RA
# redshift, radius of object, speed of light (m/s), hubble constant (km/s/Mpc), DM, DE, curvature, input units (m, pc, kpc or Mpc), output units (deg, rad, arcsec)
# FROM: ... |
522a478d04c76e016a1d271f055527e133c6959d | c8d24378e70933c30a99f4cb0097d9eeccca2b30 | /code/1_getData/getTaxData.R | 92acbb35c13745386c8fc45e3ed6668d9dd79913 | [] | no_license | emallickhossain/OnlineShoppingSalesTax | 83fe86d18b8b3755261f8fac3cf0dcf75b3896c0 | d7f26f0dc1dd0760c3d1cb42717808fecf8bc953 | refs/heads/master | 2021-01-14T01:14:21.098275 | 2020-07-09T19:10:30 | 2020-07-09T19:10:30 | 242,553,500 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,275 | r | getTaxData.R | # This takes the TDS data and puts it into one file. I only get the total sales tax
# and total use tax for each ZIP code and city. I append the year and the month as well
# I'll decide if I want to use the min or max sales tax later.
library(data.table)
library(stringr)
library(purrr)
library(tidyr)
year <- c(rep(200... |
35d73e7329a0054bc15b3a59e81116b9e1d44e9f | 9716896c5047d31239e87242481c9f950ba8f7ca | /man/HOZscale.Rd | 1d3caf24ca1e56af7fcb2d3ef5ee63ec02d4ce5b | [] | no_license | cran/RTOMO | 3917a8e81969a983ef0d6b9b2ccd2005fffd30b1 | 71cceddb2ea61812734ff6d33a313c1a287b36d6 | refs/heads/master | 2021-01-23T20:55:07.961375 | 2018-01-18T11:32:54 | 2018-01-18T11:32:54 | 17,682,949 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 791 | rd | HOZscale.Rd | \name{HOZscale}
\alias{HOZscale}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{add horizontal color scale}
\description{
Add horizontal color scale to existing plot.
}
\usage{
HOZscale(z, col, units = "", SIDE = 1, s1 = 0.4, s2 = 0.95)
}
%- maybe also 'usage' for other objects documented here.
... |
eddf9509f2bf7a15c4f9f6396d86efb5543faa39 | cfa28306f5662022b3700cc9ab244bc7e277eebb | /20200825_Chopped_clean.R | 2dab33443f75c83bf53de98ba43b2c7af677f6fd | [] | no_license | vanderpoolrr/TidyTuesday | 715ad73a393b49c307855934aa8d3019be9cf671 | 2d0985de14c0cb72871e4fb0ed208a26acf87eef | refs/heads/master | 2023-01-30T14:02:16.268833 | 2020-12-10T02:06:02 | 2020-12-10T02:06:02 | 285,155,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,984 | r | 20200825_Chopped_clean.R | ### TidyTuesday 8/24/2020 -- Chopped
## Rebecca R. Vanderpool
## vanderpoolrr@email.arizona.edu
## 2020-08-24
#install.packages("stringdist")
#install.packages("ggsci")
#install.packages("gridGraphics")
library(tidyverse)
library(stringdist)
library(ggpubr)
library(viridis)
library(ggsci)
library(cowplot)... |
72b305010f49b175fcf11d97234c7644ec592b8b | c6a8c13031488c4927dcd2de9b5fc0a960bb81f6 | /R/cosine-similarity.R | 656dffa7df4794cc4730c32feaf66ae416b17373 | [
"MIT"
] | permissive | pmcharrison/hrep | dcfe44f5f9dc5eced687011db59addeecb429836 | 9132b7a937063097ef0b7d91c2c7fcc5c0f83366 | refs/heads/master | 2023-08-31T10:48:06.875846 | 2023-01-12T09:51:52 | 2023-01-12T09:51:52 | 158,516,625 | 8 | 2 | NOASSERTION | 2023-03-11T14:26:31 | 2018-11-21T08:38:18 | R | UTF-8 | R | false | false | 362 | r | cosine-similarity.R | #' Cosine similarity
#'
#' Computes the cosine similarity between two numeric vectors.
#' @param x Numeric vector 1.
#' @param y Numeric vector 2.
#' @return Cosine similarity, as a numeric scalar.
#' @export
cosine_similarity <- function(x, y) {
numerator <- sum(x * y)
denominator <-
sqrt(sum(x ^ 2)) *
sqr... |
1b03208063c712014eef74fa3e824e418d0472b4 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lrmest/examples/alte2.Rd.R | 9d99ba6eebca8bc08ad2f4c9e874bf9b363bdb63 | [] | 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 | 884 | r | alte2.Rd.R | library(lrmest)
### Name: alte2
### Title: Type (2) Adjusted Liu Estimator
### Aliases: alte2
### Keywords: ~kwd1 ~kwd2
### ** Examples
## Portland cement data set is used.
data(pcd)
k<-0.1650
d<--0.1300
aa<-c(0.958451,1.021155,0.857821,1.040296)
alte2(Y~X1+X2+X3+X4-1,k,d,aa,data=pcd) # Model without the interc... |
bc17dc9e59ce7cae87960491c7f6eccf11827730 | 37ce38ba0eff95451aebea810a1e2ab119f89a85 | /man/dat.interp.Rd | e8341508ca621a513252722f7e0e61b349576441 | [
"MIT"
] | permissive | SwampThingPaul/AnalystHelper | 39fdd58dc4c7300b6e72ff2713316809793236ce | eb570b69d7ea798facaf146d80bc40269a3d5028 | refs/heads/master | 2023-07-21T00:19:21.162374 | 2023-07-11T17:24:36 | 2023-07-11T17:24:36 | 179,672,539 | 1 | 0 | MIT | 2020-03-21T20:05:31 | 2019-04-05T11:53:19 | R | UTF-8 | R | false | true | 593 | rd | dat.interp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dat.interp.R
\name{dat.interp}
\alias{dat.interp}
\title{Nutrient Load: Linearly interpolates data to conduct nutrient load calculations}
\usage{
dat.interp(x)
}
\arguments{
\item{x}{numeric}
}
\value{
one of two functions used to calculate d... |
6e36f4200283b876dc0d1dc02f4698a16916defc | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /rrecsys/inst/doc/b5_wALS.R | 5d971d375463f24e197431b34d49076485974175 | [] | no_license | akhikolla/InformationHouse | 4e45b11df18dee47519e917fcf0a869a77661fce | c0daab1e3f2827fd08aa5c31127fadae3f001948 | refs/heads/master | 2023-02-12T19:00:20.752555 | 2020-12-31T20:59:23 | 2020-12-31T20:59:23 | 325,589,503 | 9 | 2 | null | null | null | null | UTF-8 | R | false | false | 453 | r | b5_wALS.R | ## ---- eval=FALSE---------------------------------------------------------
# wALS <- rrecsys(smallML, "wALS", k = 5, lambda = 0.01, scheme = "uni", delta = 0.04)
# wALS
## ---- eval=FALSE---------------------------------------------------------
# setStoppingCriteria(nrLoops = 10)
## ---- eval=FALSE---------------... |
9a07e46dbe2a58bbe4703a9bb2d11a4fa6202be5 | e43ccc719a5df63664598db7614d7b10e3b4d4fb | /tests/testthat/test_1.R | 234fdc6f66e874ef676bc4dcc8eedf8eb2f8d4ab | [] | no_license | opisthokonta/goalmodel | 58fa2236e894df745f4f5985e16c863e55fd6272 | 55a33c620a1c36b51ad634f0e47abf402766cf56 | refs/heads/master | 2023-09-03T09:30:18.823581 | 2023-08-29T08:39:50 | 2023-08-29T08:39:50 | 153,664,398 | 98 | 21 | null | 2019-12-28T23:10:27 | 2018-10-18T17:49:21 | R | UTF-8 | R | false | false | 29,098 | r | test_1.R |
require(engsoccerdata)
require(dplyr)
require(Rcpp) # Should not be necessary to load Rcpp like this, but sometimes it fails if not.
# Load data from English Premier League, 2011-12 season.
engsoccerdata::england %>%
dplyr::filter(Season %in% c(2011),
tier==c(1)) %>%
dplyr::mutate(Date = as.Date(Date),
... |
f1ee24695246da7ff6b7dddd60c8eb50b314894f | 4327c535795eabb956eecaf154fa476de41e3b86 | /R/rigid_Viterbi.R | a60be26549565d7a745c6ee374089c69302760bd | [] | no_license | rfael0cm/RTIGER | 5e02b13ed6282b82f0df34753a89d29ccb21cac9 | d1ce04c748510269551cf0d6091726f7024e3464 | refs/heads/master | 2023-03-30T22:33:02.866958 | 2021-08-25T11:58:29 | 2021-08-25T11:58:29 | 347,925,332 | 2 | 5 | null | 2022-08-17T10:00:34 | 2021-03-15T10:27:17 | Julia | UTF-8 | R | false | false | 4,865 | r | rigid_Viterbi.R |
### Efficient rigid Viterbi decoding
# Input: The emission matrix psi_k(o_t)
# The transition matrix A=(a_jk), possibly the initiation vector pi = (pi_k)
# rigidity parameter "rigid": The minimum length of a stretch of the same state
# before a transition may occur
# Output: The r-Viterbi path and its log-likelihood... |
67d7f853f789dc9cb8566027d17a352eef712b03 | 0d3acd434222c88e4aa761c0d2d63e1b233edc0f | /man/edit_distance.Rd | e818500adfa4502dc0109fb90f4d2f42609b6809 | [] | no_license | EngleLab/englelab | fdc1e041e49945d7005c67f3ceafa19621de9fd2 | 7b85d9e2edd148c3570b1c380a977739e6d450f9 | refs/heads/main | 2023-05-24T21:23:29.756190 | 2023-05-10T01:10:58 | 2023-05-10T01:10:58 | 151,780,396 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 692 | rd | edit_distance.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/edit_distance.R
\name{edit_distance}
\alias{edit_distance}
\title{Calculates EditDistance.unit and EditDistance.load scores}
\usage{
edit_distance(x, target = "MemoryTargets", recall = "Recalled")
}
\arguments{
\item{x}{dataframe}
\item{targ... |
d41acd6b791a8d236812861f50313875224eb34f | 9b9445f48eba04344f84f04588671eec991c7f07 | /R/final_size.R | 330662beef2ba9ac782998d0fb2bd5728d774c97 | [] | no_license | jameshay218/episize | 232677ce381f30a1ac732a32ce6ec3f5774e58ea | 8af8e097e6e766b62320ecc7108db0b5a713ddb8 | refs/heads/master | 2021-01-10T10:34:32.310211 | 2015-12-09T18:28:55 | 2015-12-09T18:28:55 | 47,708,152 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,606 | r | final_size.R | #' Mutliple age and titer class final size equation system
#'
#' Given an attack rate matrix, R0, contact matrix, population proportions and immunity, gives the difference of the final size equation(see Kucharski PLoS Pathogens 2014; Andreasen 2011 and Wallinga 2006). This should return zero if the attack rate matrix ... |
8407409e53591a1b905db4f721f7f9331853a332 | 8f56589e11ae57523a7d8011386738a53999a4e1 | /R/fy.year.R | 7f8ccf314a34a3c5966325339baf88030e6b8480 | [] | no_license | grattaninstitute/grattan | b7b5c738401850fc67f03f4003f3715f11b27406 | 56fac9813308cf695d850be84adddad61b50d774 | refs/heads/master | 2020-05-20T22:23:58.720935 | 2015-06-27T02:12:23 | 2015-06-27T02:12:23 | 38,221,337 | 0 | 0 | null | 2015-06-29T01:28:33 | 2015-06-29T01:28:33 | null | UTF-8 | R | false | false | 722 | r | fy.year.R | #' Convenience function for creating FY
#'
#' @param yr_ending ()
#' @return the financial year ("YYYY-YY") corresponding to June of \code{yr_ending}
is.fy <- function(fy.yr){
!grepl("^.*([12][0-9]{3}).?[0-9]{2}.*$", fy.yr) |
# Are the years consecutive?
((as.integer(gsub("^.*([12][0-9]{3}).?[0-9]{2}.*$... |
9abea98b54454cd182d725c9d1fe8d5777c03272 | da8dae69e597072bc616936d1d72a96f65e4efa0 | /code/oldversions/v3_20180424/model/Z1Results.R | d322cef315a1a1285d4c2f821006ab3b160c8473 | [] | no_license | UCL/provis | 71e82c383cd9414840e57c2a2867826d6b4ee3e6 | 86a287c7bc705d4aeffb9bbcf96747e97e6d688b | refs/heads/master | 2020-08-01T04:08:20.198284 | 2019-11-08T12:09:43 | 2019-11-08T12:09:43 | 210,310,151 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,369 | r | Z1Results.R | # create table of model 1 results
plotflag<-2
nregions<-11
datastub<-"m11"
m2flag<-5
regnames<-data.frame(region_id=seq(1:nregions),
region=c("CaMKOx", "CornwallDevon",
"EastMid", "EastEng",
"London", "NE",
... |
5e8eabf8a964ef4f7ff3c577975540da0a748630 | ee58ee5a25e89b90d133aa0a29ccf04ed7576828 | /Code/CellTypeAdjustment/refactor.R | 1d45a343f1a56fa93df32199939239b1e1d9cad5 | [] | no_license | JessMurphy/WFMethylation | c3535d7b58cea51e6cd0563712b87896685fcabf | 989a78e88e815879fb1314bf8d87957aac174c4c | refs/heads/main | 2023-04-12T12:32:53.158170 | 2021-05-09T04:08:30 | 2021-05-09T04:08:30 | 343,962,243 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,370 | r | refactor.R | ############################################################
## Women First Trial: Guatemala Methylation Analysis (Cell Type Adjustment)
## Written by Jessica Murphy
## Last edited on July 10, 2020
## This script performs the ReFACTor method for cell type adjustment. The
## original code was downloaded f... |
4bada030b8235773a1ca854cbdbc904830d1682f | dc0115022f8c862310d9bb8c59d95373f237cb1a | /2. Subsetting data.R | 1d4dbe894bdb941f16175012e5cbe1f4f1df11f1 | [] | no_license | Sandhya-RK/Social-Media-Analytics | af1ea8773714ca82371a3535ea552f66a045d2a8 | 6341c344b6070807fef8d5485dfd5d093785c1ac | refs/heads/master | 2022-06-01T01:58:38.370420 | 2020-05-04T09:51:27 | 2020-05-04T09:51:27 | 261,135,968 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 818 | r | 2. Subsetting data.R | library(data.table)
setDT(final.df)
final.df[1:3, c(screen_name, source)]
final.df[1:3, .(screen_name, source)]
#Subseting data tables
final.df[country == "Ireland" | country == "United Kingdom"]
final.df[(country == "Ireland" | country == "United Kingdom") & source == "Instagram"]
final.df[retweet_count>10, (retwee... |
e34aaa2e4570a4bf47d94427e376e8667f62251f | db2695a8bc14d14a2d7dae9b097d12da41ca1100 | /Run_analysis.R | 830f151aad55bd8523df83d8df5193415a1b97ea | [] | no_license | Juspi/Getting-and-Cleaning-Data | d6d0a5bbc9ec6ada301b5761d02e0ca389e0a630 | 4aa5c937004a6e4bdee35a1a5353af3c353ad914 | refs/heads/master | 2021-01-20T07:15:08.897835 | 2014-11-23T18:28:06 | 2014-11-23T18:28:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,836 | r | Run_analysis.R | #Reads and binds data
train<-read.csv("UCI Har Dataset/train/X_train.txt", sep="", header=FALSE)
trainlabel<-read.csv("UCI Har Dataset/train/y_train.txt", sep="", header=FALSE)
trainsubject<-read.csv("UCI Har Dataset/train/subject_train.txt", sep="", header=FALSE)
test<-read.csv("UCI Har Dataset/test/X_test.txt", sep=... |
2f6123d97bb8676bbad5fed9757251d9446a9a91 | 8fe397730828ba49422d8fd0ef444de54e99f879 | /setup.R | 9ca29d9573c14b167cfcc2c6cdc2c3263392735b | [
"MIT"
] | permissive | albertsalgueda/GeoLift_execution | 92d0632803f2958deb62e524f2bc677beced7580 | 4b11e88e56cebd7b39429959848fddf9edee5ce7 | refs/heads/main | 2023-06-19T08:29:53.136208 | 2021-07-22T14:01:17 | 2021-07-22T14:01:17 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,950 | r | setup.R | # File finds what the best treatment group and test duration for a certain country and dataset is.
######################
## Install packages ##
######################
# install.packages("devtools")
# library(devtools)
# install_github("ArturoEsquerra/GeoLift", force=TRUE)
# install.packages("dplyr")
################... |
341bf82b1c322a1a2ede4fc7591a655bfd25db0a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/psychomix/examples/mptmix.Rd.R | e4f4ddbac937820f53d23511fe66f56bdc328aa9 | [] | 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 | 643 | r | mptmix.Rd.R | library(psychomix)
### Name: mptmix
### Title: Finite Mixtures of Multinomial Processing Tree Models
### Aliases: mptmix FLXMCmpt
### Keywords: mixture model
### ** Examples
## No test:
## Data
data("PairClustering", package = "psychotools")
pc <- reshape(PairClustering, timevar = "trial", idvar = "ID",
... |
f7d70815af7b767c3e705bcab0ff91b225ea04e7 | 608ef56860ee4ad73620fd589fa076c854c76184 | /man/evalR2_XPASS.Rd | 4da23ac8b0b0358da1996e81dda361dc522c977f | [] | no_license | RitaWang0427/XPASS | 315479a8905fdd22d682380c7cac869a174af8da | dc42e5814936055576b60e41f0a2b1fdb88eb7c2 | refs/heads/master | 2023-04-29T14:20:32.856905 | 2021-05-27T16:13:26 | 2021-05-27T16:13:26 | 371,375,233 | 0 | 0 | null | 2021-05-27T13:07:24 | 2021-05-27T13:02:23 | null | UTF-8 | R | false | true | 771 | rd | evalR2_XPASS.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict_XPASS.R
\name{evalR2_XPASS}
\alias{evalR2_XPASS}
\title{Evaluate the approximated predictive R2 using an independent GWAS summary statistics.}
\usage{
evalR2_XPASS(pm, file_z_pred, file_predgeno)
}
\arguments{
\item{pm}{The output `mu... |
0e58151637527493b6e9df4a086c8289f3303939 | 4c1d09a11ddb28e1c8e2a42465b6353455980a22 | /data-raw/raw_tables.R | 0741cf015390f1eefed13df22be3f25fd23afaa9 | [] | no_license | a2research/rempreq | 2154560b1563ee94990f5ff70ef32843552a27b0 | 7c6ca499a4c1bdbaef336d2182feaa85ec3a50b3 | refs/heads/master | 2021-06-01T09:43:13.776739 | 2016-08-10T15:55:18 | 2016-08-10T15:55:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,197 | r | raw_tables.R | # raw_tables.R -------------------------
library(devtools)
TABLE_YEARS <- 1997:2014
TABLE_NROWS <- 206
LATEST_YEAR <- max(TABLE_YEARS)
# Read in raw BLS domestic and total real valued (2009 USD) Employment Requirements Matrix Files
# BLS sector numbers and names
sectors <- read.table('data-raw/sect313.csv', stringsAsF... |
7baad2765510f4a1af4413b8d76bc1ffcf055cf7 | 25ebb294d1feae6a4ff6b17f227129170685f905 | /plot3.R | 0fd410aeeac4a73ccb7c4b296b0c9ad8552f40d7 | [] | no_license | Wabram/EDA | c1f4da1cc6387b088864b27bef2261e5b48f7e5e | 3e0f0d89c3ab9e20b7df6dad3d2418bc78beab83 | refs/heads/master | 2020-04-04T16:27:49.410937 | 2018-11-04T14:43:54 | 2018-11-04T14:43:54 | 156,079,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 740 | r | plot3.R | # read hpc file
hpc <- read.csv("H:/Projects/Courses/R/EDA/ExploratoryDataAnalysis/project_1/hpc.txt", sep="")
head(hpc)
# plot data from three columns
with(hpc, {
plot(Sub_metering_1,type="l", xaxt="n", xlab="", ylab="Energy sub metering")
lines(x=Sub_metering_2, col="red")
lines(x=Sub_metering_3, col="blue")
... |
b431d684a7f193c5a202af5513ed1987a8052a0b | f409f2b563deb8a9570bd64c10e622ac2bb89c03 | /R/main.R | 4866d4807871dfeaecb0187548d78e751f263ed6 | [
"Apache-2.0"
] | permissive | chrisknoll/CohortManager | 5be0f0916b753d5e3a1cd14de2211467c7ab2c81 | 0a350df155a873241fedcbb9ef12f8b7e3578ab8 | refs/heads/master | 2021-01-19T10:52:12.976747 | 2017-02-28T18:03:00 | 2017-02-28T18:07:02 | 82,221,186 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,364 | r | main.R | #' @export
cacheEnv <- new.env()
#' @export
buildCsvDef <- function (name, columns) {
csvDef <- {};
csvDef$name <- name;
csvDef$columns <- columns;
return(csvDef);
}
#' @export
resolveConcept <- function(conceptId) {
jsonlite::fromJSON(paste(cacheEnv$webAPI, "vocabulary", cacheEnv$sourceKey, "concept", con... |
a12884e6169f868f0ad7a4bab1f31904466c9c67 | 2441f634ad77edc68aef7ba1370cf9c44cd3232e | /man/stripe_list_subscriptions.Rd | ede6ae06aeb68fcd5767a89e175048201cb1a729 | [] | no_license | muschellij2/RStripe | bee390283e5ed2fa718daaccf3f7d70c741ab4fc | 2456061256a927a4711b7267942a8af01393040f | refs/heads/master | 2021-01-21T21:07:02.455296 | 2017-05-24T17:12:22 | 2017-05-24T17:12:22 | 92,309,551 | 0 | 0 | null | 2017-05-24T15:45:46 | 2017-05-24T15:45:46 | null | UTF-8 | R | false | true | 848 | rd | stripe_list_subscriptions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/subscriptions.R
\name{stripe_list_subscriptions}
\alias{stripe_list_subscriptions}
\title{List Subscriptions}
\usage{
stripe_list_subscriptions(customer_id, args = NULL, api_key = NULL)
}
\arguments{
\item{customer_id}{The id of the customer ... |
e6451e37d8b58b7ebc0b25ec6f45c8b2be12d51f | 5bd80de7ff59e8d1ea755cfd10cc61a264593c18 | /src/speedtest.R | 171bcffab0c630843e801dc9eccb8de4e5512c3c | [] | no_license | andymd26/coding_questions | c1a4a8d17ecc5db475c024813957b310b2439331 | 82d987b461e0dad7642ac15766513e1fba53028c | refs/heads/master | 2021-01-18T13:02:26.517185 | 2017-03-27T19:02:52 | 2017-03-27T19:02:52 | 84,334,443 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,096 | r | speedtest.R | install.packages("dplyr")
rm(list=ls())
options(digits=15)
options(scipen=999)
require(dplyr)
path.processed = "C:/Users/ablohm/Documents/earth_network/data/processed/"
# Change to the folder with the list_logrnml.rds dafile
setwd(path.processed)
dist.price.v2 = readRDS(file = "logrnml_price.rds")
dist.load.v2 = re... |
c8e5e563334ddbf6a64001549a9b3e560dde8d95 | 8b04b06555f2b4f0f101fe7a81370490ea4bc716 | /src/functions/3_signal_drift_correction.R | 150fb1b781aa6c7838bb9545ab38a60c1537c786 | [
"MIT"
] | permissive | morchalabi/COMPARE-suite | d16f3aa6ccb9d4fb3376349ac274c35126d465e2 | df2feaf6aa982e0f6f077eb85f26acce6bb61063 | refs/heads/master | 2023-08-30T12:00:12.962203 | 2021-02-25T13:28:32 | 2021-02-25T13:28:32 | 256,939,470 | 1 | 0 | MIT | 2021-01-27T19:07:32 | 2020-04-19T07:25:21 | R | UTF-8 | R | false | false | 26,085 | r | 3_signal_drift_correction.R | # This module removes intra- and inter-plate signal drift bias. Running it with no request for correction reveals possible sources of bias.
# Possible sources of bias are like edge effect, signal drift, cell viability, auto-fluorescence and carry-over effect.
# To remove signal drift bias, it needs to know the directio... |
4058049c12853428c747a7cb427e8d07868c13d3 | 1a27aa8d067869d7079bc6fb5732dfa1a413c76a | /man/DataSet.Rd | b648ec06c5c38227034657d9f7479a3ebb4b8160 | [] | no_license | YuLab-SMU/clusterProfiler | 60cff769b53943f61f535d58967c76432f0c029c | e7abcb5c666df11806f4b798edfea152e93d8a56 | refs/heads/devel | 2023-08-30T18:29:32.320507 | 2023-08-15T04:03:55 | 2023-08-15T04:03:55 | 20,360,040 | 565 | 150 | null | 2023-09-08T15:47:44 | 2014-05-31T16:34:32 | R | UTF-8 | R | false | true | 574 | rd | DataSet.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{DataSet}
\alias{DataSet}
\alias{gcSample}
\alias{kegg_species}
\alias{kegg_category}
\alias{DE_GSE8057}
\title{Datasets
gcSample contains a sample of gene clusters.}
\description{
Datasets
gcSample contains a sampl... |
e37b407a73fb3dd08f554a039d6e66c321fb02d6 | 956f493986a2e4836cd7d5565fb23be636645a24 | /man/func_train.Rd | 0ecdf89d4533cb70bf9c8805409004480fe9602e | [
"MIT"
] | permissive | Bhaskers-Blu-Org2/CNTK-R | d2fcb0eab33a5f6a9134fa20937622b308590b4a | 5e2b8f5320a48dc492fa7dd1654532dd9e3e856a | refs/heads/master | 2021-08-20T10:36:09.764093 | 2017-11-28T22:55:53 | 2017-11-28T22:55:53 | 276,122,540 | 0 | 1 | MIT | 2020-06-30T14:28:11 | 2020-06-30T14:28:10 | null | UTF-8 | R | false | true | 4,812 | rd | func_train.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ops-function.R
\name{func_train}
\alias{func_train}
\title{Train Function Model}
\usage{
func_train(func, minibatch_source, minibatch_size = 32, streams = NULL,
model_inputs_to_streams = NULL, parameter_learners = c(),
callbacks = c(), pr... |
602bf0c0463cfb0250f714173d4f64bbef918d5c | 3228c0f11eea3ee9c9101c42cd81c48f4a4c557c | /cachematrix.R | a6f5661034f27a523cbef5312a9aeb3c959d60a5 | [] | no_license | nishantgupta95/ProgrammingAssignment2 | 8c1654e7629d195b11a26fc7e7845d1a78d4dac5 | dd29f3184cede26428fcf6dc88bae32c8980d4b6 | refs/heads/master | 2020-05-27T11:37:28.574106 | 2019-05-25T20:04:39 | 2019-05-25T20:04:39 | 188,603,621 | 0 | 0 | null | 2019-05-25T19:14:18 | 2019-05-25T19:14:18 | null | UTF-8 | R | false | false | 939 | r | cachematrix.R | ## Both functions cache the inverse of the matrix and calculates the inverse of matrix in case of
## matrix doesn't changed.
## Creates A Special Matrix Object that can cache its Inverse.
makeCacheMatrix <- function(x = matrix()) {
inv <- NULL
set <- function(y) {
x <<- y
inv <<- NULL
}
... |
b0fb94face67f36af3a91f36d67d0a07f5f66e1d | cc092e75fd111024be2e6c17d5cff76e59c51283 | /man/multiBed3sp.Rd | c9186aa9381c1662d61283c63d058023311a1bab | [] | no_license | yingxi-kaylee/ssCTPR | 4c319a2a5cb69fc7160746997facccd8a2cec7ca | 3930134f13e914f249a31d35f90cee762fa945de | refs/heads/master | 2023-08-28T20:51:35.414297 | 2021-11-14T01:28:01 | 2021-11-14T01:28:01 | 287,055,174 | 0 | 1 | null | null | null | null | UTF-8 | R | false | true | 768 | rd | multiBed3sp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R
\name{multiBed3sp}
\alias{multiBed3sp}
\title{Multiply genotypeMatrix by a matrix (sparse)}
\usage{
multiBed3sp(
fileName,
N,
P,
beta,
nonzeros,
colpos,
ncol,
col_skip_pos,
col_skip,
keepbytes,
keepoffset,
... |
c2c15d04979ec168b69d0da76cbdba45a7d515f7 | 2b45f547209112cb8040dc2c07492ad847e5c8c3 | /resequencing/phylogenetics/snapp_treemix/reformat_plink.R | b53e2a66e2e9e11fc36d2c48c176893f2ab70b4a | [] | no_license | erikenbody/Bioinformatics_Scripts | 2ecaef2437c169385b9f40ced621d7f41eb9b292 | 0aa3edfb0a5ebd9a848bb8e5eb1105eb54299fe6 | refs/heads/master | 2022-06-07T11:38:17.824937 | 2022-05-26T18:50:56 | 2022-05-26T18:50:56 | 114,473,616 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 423 | r | reformat_plink.R | #editing plink file
#plink<-read.table("data/plink_subset.fq",header=TRUE)
plink<-read.table("wsfw_plink.frq.strat",header=TRUE)
#head(plink)
#plink$SNP2<-rep(1:(nrow(plink)/5),length.out=nrow(plink),each=5)
plink$SNP<-NULL
plink$SNP<-rep(1:nrow(plink),length.out=nrow(plink),each=5)
plink<-plink[c(1,8,2:7)]
#head(plink... |
bbeda055446f0f1ca504ae8a6b097605bc8d5c9b | 45d332bd2e7b81ef8345b6339ed8d60a7540f3d9 | /src/test4.R | 90b040eda2a5e81eaf55e8f112b4b4d9afbbfd17 | [] | no_license | Chaitany1729/GSoC_SLOPE_tests | 5d104255463678a807b1c3685dc820db4b1196ce | 95ffc9f999c6d983ca51ea7891df238649444b5f | refs/heads/master | 2023-03-29T12:08:00.668774 | 2021-04-02T14:05:06 | 2021-04-02T14:05:06 | 353,074,052 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 598 | r | test4.R | library('Rcpp')
library('SLOPE')
sourceCpp('FastProxSL1.cpp')
length_list = as.integer(runif(1, 100, 10000))
time_fastprox = c()
time_proxsort = c()
for (vector_length in length_list){
y = runif(vector_length, 0, 1000)
y = sort(y, decreasing = TRUE)
lambda = runif(vector_length, 0, 1000)
lambda = sort(lambda,... |
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