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080850b8e98bccd0346df091c3f44f79b41cfb52 | 5b7a0942ce5cbeaed035098223207b446704fb66 | /man/lsAPI.Rd | 9eed417d8ad533f24c69ed486007e2b51f957f50 | [
"MIT"
] | permissive | k127/LimeRick | 4f3bcc8c2204c5c67968d0822b558c29bb5392aa | a4d634981f5de5afa5b5e3bee72cf6acd284c92a | refs/heads/master | 2023-04-11T21:56:54.854494 | 2020-06-19T18:36:05 | 2020-06-19T18:36:05 | 271,702,292 | 0 | 1 | null | 2020-06-12T03:45:14 | 2020-06-12T03:45:14 | null | UTF-8 | R | false | true | 772 | rd | lsAPI.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lsAPI.R
\name{lsAPI}
\alias{lsAPI}
\title{Low level API calls}
\usage{
lsAPI(method, params = NULL, lsAPIurl = getOption("lsAPIurl"))
}
\arguments{
\item{method}{The API method name}
\item{params}{A list of \code{method}'s parameters (make s... |
776e13ab668ee97d8ef7ab8511c628ede3a3b833 | 64718fae9f573e3d56f81500ba1466b47c8441c0 | /Importing and Cleaning Data/readExcel.R | e94bf760494d23b4ab75b1187a2e7213aee9c968 | [] | no_license | sanswons/Datacamp-courses | 4576366727a9ccda93bc82e5bbe5e89c9fd2062e | 080442379be67dad1de4b8f9a30866ab05cbd38e | refs/heads/master | 2020-12-31T00:19:38.221742 | 2016-02-24T09:36:46 | 2016-02-24T09:36:46 | 50,502,807 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 550 | r | readExcel.R | # Load the readxl package
library(readxl)
# Find the names of both spreadsheets: sheets
sheets=excel_sheets("F:/sanjana/books/CSE/Machine Learning/Data Camp/Practice/Datacamp-courses/Importing and Cleaning Data/latitude.xlsx")
# Print sheets
sheets
# Find out the class of the sheets vector
class(sheets)
# The rea... |
89366bb7301ca73a69e1048dd16c964f07a00ed8 | e99928f515a755bf448e12e08dd616918356cbfb | /Ragusa2018b/source/data.R | bd1efc622e5fdf6add9a0a8ae238d11985e0310b | [] | no_license | jragusa/Publications | 8c6056dd5c7cd67214c0d3ac43202f6b92208773 | 006007a4f7a5770c4b2c92e786d28ad46d1f79d2 | refs/heads/master | 2022-06-28T05:32:39.031277 | 2022-06-18T15:36:51 | 2022-06-18T15:36:51 | 88,986,000 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,334 | r | data.R | ####
# author: Jérémy Ragusa
# date: 2018-05-22
# description: prepare dataset for Ragusa2018b
####
# library ####
library(cowplot)
library(dplyr)
library(factoextra)
library(ggplot2)
# library(ggtern)
library(magrittr)
library(matrixStats) # colCounts for GrainSize
library(compositions)
# library(reshape2)
library(ti... |
5ba1d03bf2203f83192528a778f2a66a590a71bb | 6cf4e77cb8a08649c133a0477ca72c38724f8677 | /R/cyr.R | dd580638216e6b6415c7c8a569ba0370f10a5264 | [
"MIT"
] | permissive | nemochina2008/cyr | ac1fa3d4e5466693b3837d581778a0614734e0d9 | 8cad3410708305ecdfa964c680dac72436353915 | refs/heads/master | 2021-06-15T07:24:03.733087 | 2017-04-20T20:27:29 | 2017-04-20T20:27:29 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,372 | r | cyr.R | port_number <- '1234'
cytoscape_url <- paste("http://localhost:", port_number, "/v1", sep="")
cytoscape_network_url <- paste(cytoscape_url, 'networks', sep = "/")
table_type_node <- 'defaultnode'
table_type_edge <- 'defaultedge'
table_type_network <- 'defaultnetwork'
table_column_selected <- 'selected'
table_column_... |
442bf8b95cbfd5b8f8aa87636e83b1a6e0256f61 | 18720a0366eddff4bf0a68874b749dedf2c5df31 | /activity7/activity7_script.R | 151f53060a5832e1e26a8e8a954a99b750e58dcf | [] | no_license | cschwartz1/GEOG331 | d1edf08ac1f2f4041d03a4e0edd94ad833cdb234 | 079fdbe56e79a31675de0cb723f0a32b0e9aaabf | refs/heads/master | 2020-12-20T10:20:37.143814 | 2020-06-30T20:39:30 | 2020-06-30T20:39:30 | 236,040,461 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,029 | r | activity7_script.R | #loading necessary packages
library(raster)
library(sp)
library(rgdal)
library(rgeos)
library(mapview)
library(caret)
library(randomForest)
library(nnet)
#set up directory for oneida data folder
dirR<- "/Users/carlyschwartz/Documents/Colgate/ColgateRound4/GEOG331/activity7/oneida"
#read in Sentinel data
rdatB2 <- ras... |
64eaa169be9d1c4c6cfe81b38d68e2e0a55439a6 | 36a25a9052d14520300e7f5613730a3a9606a8c9 | /Generator/kf_ekf.R | a452fef3ae643f28bcc679da62702913a29d3c2c | [] | no_license | cyrulnic/NoStRa | 20fbe84dd2c3a7f43bc8e9c39bc025d35c0e50c9 | 83e9776158503fbdf5b5a23aa7a23c5ead53691f | refs/heads/master | 2020-03-29T04:09:25.821312 | 2019-09-18T13:23:15 | 2019-09-18T13:23:15 | 149,518,116 | 2 | 1 | null | null | null | null | UTF-8 | R | false | false | 9,644 | r | kf_ekf.R | KF_EKF = function(ntime_filter, n, dt, stride, ind_obs_space, ind_time_anls, Rem,
UU, rrho, nnu, ssigma,
F_Lorenz, J_Lorenz, sd_noise,
R_diag, m, OBS,
X_flt_start, A_start,
model_type, filter_type,
pre... |
fa0dc59801c7e54733b34bd3a899f9793b102014 | b0f13e8af99c895b56436a8a8570f090b611ccbd | /coenocytic_growth_synchrony.R | 575a09bd7855caff27d0ae9c360d1200b4080b80 | [] | no_license | andrejondracka/coenocytic_growth_synchrony | b0e5a08245f1af44161d95b25e3b96765977c60b | 00d99caaee9852cd9574e491c2c349247e762ecf | refs/heads/master | 2020-03-09T10:59:30.062156 | 2018-04-10T20:22:55 | 2018-04-10T20:22:55 | 128,750,392 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,690 | r | coenocytic_growth_synchrony.R | library(dplyr)
initcon <- c(rep(0,328),rep(1,1116),rep(2,270),rep(3,12),rep(4,3),rep(5,1),rep(6,1),rep(7,2)) ###sets a pool of initial conditions from the distribution identical to t=0 in the real data
vari <- 0.50
mean <- 11
###function that generates truncated normal distribution (to avoid having negative durations... |
e4a9435475348b0971e9d28f47f33a822aa4f6b9 | c2149b76357f5f962db0d967b2a22b5a6c3ab622 | /glm_script.R | 436d3f3646ffa99baa51f349c7c835959bf7d458 | [] | no_license | Pereirajpf/R_statistics_learning | 6c3388889a087654e54acc47cc269e29aaea9c7f | df1d8793dd2a2bae0b9ea7dfdff46a53b53f8b9b | refs/heads/master | 2023-03-30T23:02:48.857376 | 2021-04-12T11:53:56 | 2021-04-12T11:53:56 | 357,177,565 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,091 | r | glm_script.R | # Learning Generalized Liner Model (GLM)
## url: https://www.guru99.com/r-generalized-linear-model.html
library(dplyr)
data_adult <-read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/adult.csv")
glimpse(data_adult)
#ERROS CORRETION
##convert the chr var to factors
data_adult <- data_adult %>%... |
8a275cfe46276199159ab3d02140229ddf6c74bd | 4ed27ad1a562ae48ec160c1182d49420a384eb5e | /slowtests/fastshap-parallel-16cores-ames.R | f5951906f25adc2dcc810c8bb57ceb55ea4f6267 | [] | no_license | huitmj/fastshap | 21033983db4eea6b1916d9cd9ee4083117b11d1d | 9de31b88b25af6eb6570c3f2f9cbfaab5ae26a06 | refs/heads/master | 2023-03-11T05:00:13.600636 | 2021-03-03T02:13:05 | 2021-03-03T02:13:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,058 | r | fastshap-parallel-16cores-ames.R | # Load required packages
# library(AmesHousing)
library(fastshap)
library(ranger)
# Load Ames housing data
ames <- as.data.frame(AmesHousing::make_ames())
X <- subset(ames, select = -Sale_Price)
# Fit a random forest
set.seed(102)
rfo <- ranger(Sale_Price ~ ., data = ames, write.forest = TRUE)
# Prediction wrapper
... |
4c4406b042e456180390eeb026148b5c425cd5c0 | 6292bd85e787a05b5c49aca6646de02c7a8d9dcd | /graphs.R | d40ae8a91da05e0c66d40f1b743cb7ece2a9cbb2 | [] | no_license | haututu/rentalCosts | 367ec06792772e9bf1768889a0f4517ee44041fc | f740704b3e1c8adce7f6ea3d324e9e08ee1f37f8 | refs/heads/master | 2021-05-10T10:14:58.657970 | 2018-11-08T08:36:16 | 2018-11-08T08:36:16 | 118,376,575 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,563 | r | graphs.R | <<<<<<< HEAD
=======
#Cleans and loads the base data
>>>>>>> 91d6f3851a19aa52f5f7351b31ccfb09babc3be8
source("setup.R")
library(ggplot2)
library(plotly)
###Data for graphs is generated in setup.R
#Plot for december months, geometric mean
meanDecPlot <- ggplot(filter(rentData, year(Month) > 2008), aes(as.Date(Month),... |
1a47ed9e621b2973809b4258b0d0b22617aaea18 | eeb9196b365641c5353dd5b4194952b1f32fb247 | /MicroMetabolism/man/get_species_text.Rd | 6000c14c2678bd687e68a68c86f7f4d7a4796951 | [
"MIT"
] | permissive | thackmann/MicroMetabolism | 41f39f906c9e94155f08ec5288198fc4a034ffb4 | 35111f1a28f5d7ffcb537a8a2af541c245f42fc8 | refs/heads/main | 2023-08-29T23:31:33.031096 | 2021-10-14T23:57:10 | 2021-10-14T23:57:10 | 300,643,560 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 642 | rd | get_species_text.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/get_text.R
\name{get_species_text}
\alias{get_species_text}
\title{Get Species Text}
\usage{
get_species_text(url_list, text_full, text_tables)
}
\arguments{
\item{url_list}{A character vector of url names for articles}
\item{text... |
63b10464e520c386487c3c2358e122df07c34110 | 92e39a5004ee1efa759644a09296640f83404a87 | /combine_files.R | dc943826f52c0712f12e101f3820b56b48ad025f | [] | no_license | sagelinae/Brant-Data-Entry | 68dd4e43b95bf9d7e6eb30bf36c90de785d7eb22 | 2c6571d4d41ae3d2dc88a084261c01f71b5c9297 | refs/heads/master | 2020-08-05T04:44:25.148301 | 2020-02-26T16:35:54 | 2020-02-26T16:35:54 | 212,400,542 | 0 | 0 | null | 2020-02-25T18:52:42 | 2019-10-02T17:26:27 | R | UTF-8 | R | false | false | 2,676 | r | combine_files.R | ###########
#Script to Combine Data entered on multiple devices
###########
date_time <- paste0(substr(Sys.time(), 6,10), "_",sub(':', '-', strftime(Sys.time(), "%H:%M"))) #Figures out the date to add to backups
#***Set A Directory to save Backups to
NestBackupdir <- "C:\\Users\\sellis\\Desktop\\Brant-Data\\Ba... |
cfb972c69ae5a568c6a4c7decdb6885e6f102136 | d33d116b02f9e993d286ee6e2953410f85809aee | /plot2.R | b5c68a2f9170d19680259af15a94f37580e48199 | [] | no_license | tyz910/ExData_Plotting1 | bfd56ba19685e6bfed0032b60f3ef2784a853e68 | 42f883f4939dd40d408fd9fbfe8eecdd6f04d6d3 | refs/heads/master | 2020-12-29T01:00:00.932079 | 2015-01-10T14:45:14 | 2015-01-10T14:45:14 | 29,058,068 | 0 | 0 | null | 2015-01-10T13:32:38 | 2015-01-10T13:32:36 | null | UTF-8 | R | false | false | 206 | r | plot2.R | source('get_data.R')
png(filename = "plot2.png", width = 480, height = 480)
plot(hpcdata$DateTime, hpcdata$Global_active_power, type = "l", xlab = "", ylab = "Global Active Power (in kilowatts)")
dev.off() |
e01163851b2dc5f8dbb860f950f778212702612e | d55c03b0f4a1a8a7c757ee653198d28b62f43f41 | /global.R | d140cdc241752c7addb324be2bf6780eeed17954 | [] | no_license | fataltes/herRingShiny | 58c4aa251ea9d57546622ef9c6d657b288d2ef66 | 049fa42b3a81d2224a5b7049960d7d29e912bc18 | refs/heads/master | 2021-07-24T01:03:41.327790 | 2017-10-09T05:35:29 | 2017-10-09T05:35:29 | 96,694,420 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,335 | r | global.R | rm(list=ls())
allres<-readRDS("allres.rds")
dispNames<-read.csv('AllResNameDecoder.csv')
uniqueCR = unique(allres$CR)
uniquePropc = unique(allres$Propc)
uniqueFcapprop = unique(allres$Fcapprop)
uniqueFracBmsyThreshLo = unique(allres$FracBmsyThreshLo)
uniqueFracBmsyThreshHi = unique(allres[c('FracBmsyThreshHi', 'FracBm... |
f8503725b54c160292acaf12d0ff1d1ac9f0fa45 | 1fc421ae8d2d0cc87944ec21ea53b37b1ef02544 | /R/MackNet_Fit.R | 25f5fa67280a3b1fd047892865b07a81c2541418 | [] | no_license | EduardoRamosP/MackNet | 5f3df28a30385e83c4d3de0eb10606a416499c92 | 1281f90ccad86df2f496b6e1a33aeab18cf81807 | refs/heads/master | 2022-12-18T22:17:47.097987 | 2020-09-21T20:30:55 | 2020-09-21T20:30:55 | 296,931,038 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,077 | r | MackNet_Fit.R | #' @title MackNet_Fit
#' @description This function fits the ensemble of RNNs required for the payments MackNet model. The optimum weigthed decay is obtained by selecting the configuration that minimizes the test error.
#' @param Cumulative.T Cumulative payments triangle.
#' @param Incurred.T Incurred cost triangle.
#'... |
4c15e575a44ffcb677d8558e82efeb240751ab1f | 20ba561c94011548361ec18b916bdf5cec44eb82 | /man/vigencia.Rd | ffbabc2f8c1533a7bd4959880c97f1d94ca128c7 | [
"MIT"
] | permissive | paloosa/idealisto | 5e99f0251be4188c3a690ee3f2542af2026b772b | 5a82b64bb736c1313a12c0117883bca1e60708b1 | refs/heads/master | 2020-07-16T20:05:18.532208 | 2018-04-23T11:57:11 | 2018-04-23T11:57:11 | 205,859,155 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 790 | rd | vigencia.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vigencia.R
\name{vigencia}
\alias{vigencia}
\title{Enrich the csv file generated by idealisto function.}
\usage{
vigencia(ruta)
}
\arguments{
\item{ruta}{A valid path in your computer that leads to an idealisto created csv file.}
}
\value{
It... |
f72ff0f834688698b644c3a81f2271bc2f421e6f | 251302c5cc0a0ebfd6b8ea4b355ed349697a331d | /polynomial/build_poly.R | befdbc717c953ee5891b2af45f83ac9c5d0926e8 | [] | no_license | richiemorrisroe/grad_descent | e77c5e7c1f90d24137fad9c251344aaa5ffb525a | 9e6e8bd02762fcda5e1c70592c2cf139a57b2b2b | refs/heads/master | 2021-01-12T08:45:40.016571 | 2017-07-08T19:13:01 | 2017-07-08T19:13:01 | 76,680,127 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 290 | r | build_poly.R |
setwd('~/Dropbox/Code/Stats/polynomial/')
devtools::setup(".", rstudio=FALSE)
devtools::use_build_ignore("^#")
devtools::use_build_ignore("build_poly.R")
devtools::use_testthat()
devtools::use_package("stringr")
devtools::document()
devtools::check()
devtools::build()
devtools::install()
|
015b94a0599c7e5f385b39539db775e3e1071ab9 | 646cf417b48d7ba3a363b74230a9045394520ae3 | /src/brooks_pitchfx_scraper.R | 44897241c81e1924b6d35176ec30c03b0e06097e | [] | no_license | PrestonEn/RockyHelium | ff5ddd401f780a12115b546168a44da8a19214a5 | bb9954b3f0bc758fe1de0f802f323860541a53d2 | refs/heads/master | 2021-01-10T16:08:30.315631 | 2016-04-30T05:16:28 | 2016-04-30T05:16:28 | 55,182,069 | 0 | 0 | null | 2016-04-29T21:50:52 | 2016-03-31T20:49:37 | TeX | UTF-8 | R | false | false | 1,660 | r | brooks_pitchfx_scraper.R | library(XML)
library(tidyr)
library(dplyr)
library(magrittr)
library(reshape2)
pitchers <- tbl_df(read.csv("data/control_pitchers_mlbamid.csv"))
pitchers.keys <- pitchers$key_mlbam
#pitchers.keys <- c(506560)
var_list <- c("mph", "maxmph", "pfx_x", "pfx_z", "hloc", "vloc", "bway")
#var_list <- c("mph", "maxmph")
pitch... |
857b464839335fe3412cf6940d485b9579a53098 | 32a5b9ec56f8cac3053fb630903c3685ffd85c6c | /R/easy_fun.R | 88615e3608779fed0b4676e1c9dc0654b3d2f3f3 | [] | no_license | cloud-brain/backtest | 86500bb639bf3ea07912a33b528935f88ae1df30 | 7713dc0f67decc7a2fa1462dfc00cd9cb6a2c1d7 | refs/heads/master | 2020-12-13T21:48:45.511327 | 2020-02-04T04:08:44 | 2020-02-04T04:08:44 | 95,461,479 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 300 | r | easy_fun.R | #' @import lubridate
##date translate to char
dt_to_char <- function(x)
{
if(is.null(x))
{
return(x)
}
return(format(ymd(x),'%Y%m%d'))
}
##combine char list
comb_char <- function(x)
{
paste0("'",x,"'", collapse = ',')
}
fill_na <- function(x, fill = 0)
{
ifelse(is.na(x), fill, x)
} |
d1e53e420e006ef12872dff21dd23da185634bc4 | 1e9c9f2a9639db7cdb032aae69cb4d99aef1d3a5 | /dataCamp/openCourses/dataAnalysisAndStatisticalInference/7_inferenceForCategoricalData/13_whatAboutIndia.R | ed48dec177d76b3d3afe80db080bcc3d5568da7f | [
"MIT"
] | permissive | sagarnikam123/learnNPractice | f0da3f8acf653e56c591353ab342765a6831698c | 1b3b0cb2cff2f478006626a4c37a99102acbb628 | refs/heads/master | 2023-02-04T11:21:18.211654 | 2023-01-24T14:47:52 | 2023-01-24T14:47:52 | 61,184,927 | 2 | 1 | MIT | 2022-03-06T11:07:18 | 2016-06-15T06:57:19 | Python | UTF-8 | R | false | false | 778 | r | 13_whatAboutIndia.R | # What about India?
#######################################################################################################################
#
# Using the inference() function, now calculate the confidence intervals for the proportion of atheists
# in 2012 in India.
#
# First, make sure to note whether the conditions f... |
a243aa3d6388eed9c880730f7403035a5f1e3a40 | 5ec06dab1409d790496ce082dacb321392b32fe9 | /clients/r/generated/R/ComDayCqWcmDesignimporterImplEntryPreprocessorImplProperties.r | d8d179f05430b10bbca06abd366a0b8d0a8b113e | [
"Apache-2.0"
] | permissive | shinesolutions/swagger-aem-osgi | e9d2385f44bee70e5bbdc0d577e99a9f2525266f | c2f6e076971d2592c1cbd3f70695c679e807396b | refs/heads/master | 2022-10-29T13:07:40.422092 | 2021-04-09T07:46:03 | 2021-04-09T07:46:03 | 190,217,155 | 3 | 3 | Apache-2.0 | 2022-10-05T03:26:20 | 2019-06-04T14:23:28 | null | UTF-8 | R | false | false | 3,707 | r | ComDayCqWcmDesignimporterImplEntryPreprocessorImplProperties.r | # Adobe Experience Manager OSGI config (AEM) API
#
# Swagger AEM OSGI is an OpenAPI specification for Adobe Experience Manager (AEM) OSGI Configurations API
#
# OpenAPI spec version: 1.0.0-pre.0
# Contact: opensource@shinesolutions.com
# Generated by: https://openapi-generator.tech
#' ComDayCqWcmDesignimporterImplEnt... |
3043bfa972ed541af84d8cc6415a5ac577ba345b | 1367e80139e7cf4072aa1aed5413530476ba9ab0 | /external/actions_navpanel.R | fd0d8989345ed8ff8ed62939ba98e9da66a47bf8 | [] | no_license | xinofekuator/ShinyFirstProject | 73471ca9b6496553d3e3228394d323f23ab1f308 | c06d018c9c5a23f8fa6f4889eb5bcb0814f3506b | refs/heads/master | 2021-01-10T01:21:07.804541 | 2015-12-07T18:47:43 | 2015-12-07T18:47:43 | 47,571,742 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | r | actions_navpanel.R | output$carPlot <- renderPlot({ plot(cars, main = 'cars dataset')})
output$otherPlot <- renderPlot({ plot(faithful, main = 'faithful dataset')})
output$info <- renderText({
paste0("x=", input$plot_click$x, "\ny=", input$plot_click$y)
})
output$info2 <- renderText({
xy_str <- function(e) {
if(is.null(e)) return... |
14174d8a1b4af960f6d9113cb483fab51caa515c | 2b16b0bb4b607ba7f18c87f0d3642e3a2a12c8f2 | /CO2/CO2 calc/sub functions/General/01_SetConstants.R | 5c7ecf3abcf13aab781fa1d8bec9984dd43afe40 | [] | no_license | low-decarie/Useful-R-functions | 4f195cc23806ef54c0f180eac9cc0ab5c6d2fa7d | 1dfca0de951d9f7a208aace26bf66f80188c5c13 | refs/heads/master | 2016-09-07T17:11:16.883656 | 2014-03-27T20:53:18 | 2014-03-27T20:53:18 | 3,854,769 | 3 | 2 | null | null | null | null | UTF-8 | R | false | false | 613 | r | 01_SetConstants.R | #Setting constants
R = 82.05784 #The gas constant (R) in cm^3 atm K^−1 mol^−1
#or library(marelac); R=100*Constants$gasCt1
alpha = .00001 #volume expansion coefficient for borosilicate glass
#for dry air (this assumes xCO2 is .00036)
... |
ba51d6b0f3a07bca39eaecaa45d0379dc745ac0a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/MixMAP/examples/mixmapTest.Rd.R | 31e04374664576943301f2793b3b87057ffb8dcb | [] | 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 | 538 | r | mixmapTest.Rd.R | library(MixMAP)
### Name: mixmapTest
### Title: Implements the MixMAP algorithm using hypothesis testing
### framework.
### Aliases: mixmapTest
### Keywords: GWAS Mixed Models Genetics
### ** Examples
library(MixMAP)
#Load data
#This data has been prepared to be used as input to the MixMAP function
data(MixMAP_e... |
464daa24977b619d6a534f2f0e2a9927fbf15679 | 1f5489b5171979817c01b09f550a266e92edfc35 | /Plot1.R | 4c4263216739c948064c015119d7d1fbba6645af | [] | no_license | sumitraBinu/ExData_Plotting1 | 53a7a4bb323205797aea2cd905b60b2ab4b6d699 | 45b9785e6538c1180e5eb69da9395633ea611fb5 | refs/heads/master | 2022-07-04T02:21:27.421601 | 2020-05-15T08:46:37 | 2020-05-15T08:46:37 | 264,104,340 | 0 | 0 | null | 2020-05-15T05:30:18 | 2020-05-15T05:30:17 | null | UTF-8 | R | false | false | 1,252 | r | Plot1.R | #Plot1
#Downloading and unzipping the file if it doesn't already exist
if(!file.exists('pocon.zip')){
url<-"http://archive.ics.uci.edu/ml/machine-learning-databases/00235/household_power_consumption.zip"
download.file(url,destfile = "pocon.zip")
}
unzip("pocon.zip") # This code is for unzipping the file pocon.zip ... |
27f46c8148c26d2390a48f17f8373542fe4d3ae3 | 9ea93143b1c8c1f34f991c2c1dd1446c042aefc3 | /R/reroute.R | 996455de39a63ddc9cd7b6b676a7005e31ed3ca5 | [] | no_license | inambioinfo/tidygraph | 2ecf217e087c428636982e08be03c190747f93ae | 08f6f569d18629aa97f020540fb25d343678fab3 | refs/heads/master | 2021-06-21T17:22:38.349146 | 2017-08-18T20:46:22 | 2017-08-18T20:46:22 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,426 | r | reroute.R | #' Change terminal nodes of edges
#'
#' The reroute verb lets you change the beginning and end node of edges by
#' specifying the new indexes of the start and/or end node(s). Optionally only
#' a subset of the edges can be rerouted using the subset argument, which should
#' be an expression that are to be evaluated in ... |
94284d083738019400039c523ea91ceeba89f082 | 265d146eba2dd4d001262f547f852b464cc5d525 | /man/multicast.Rd | 43eb1ac772f1e583794e7fff1fc32f42b6aa9545 | [] | no_license | cran/multicastR | f0160834ce0f36fb4fff1b9d6030b35a5318dbc1 | c2621c1ceda8ed8b1fe3969abfb6765eb035b56c | refs/heads/master | 2021-06-11T18:19:39.382533 | 2021-02-22T18:20:02 | 2021-02-22T18:20:02 | 137,462,924 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 3,520 | rd | multicast.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/multicast.R
\name{multicast}
\alias{multicast}
\title{Access Multi-CAST annotation data}
\usage{
multicast(vkey = NULL)
}
\arguments{
\item{vkey}{A four-digit number specifying the requested version of the
metadata. Must be one of the version... |
6c28547bb81a957cc4e5c9acc335ebf06d49efb5 | fbbc021e6029baf5899c0e0f668d9f69f82eaa19 | /man/joinRtData.Rd | 5fb1a215701dd1a867dc1c09f1445f4e00406b58 | [
"MIT"
] | permissive | RichardMN/RtD3 | a54aee2a7e56ba6e436f34d43cddc626c7cc5c34 | 07e1c4e77a95f99a1882a567e34f0e3c5e441116 | refs/heads/master | 2023-01-08T14:35:38.712936 | 2020-11-08T17:16:33 | 2020-11-08T17:16:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,082 | rd | joinRtData.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/joinRtData.R
\name{joinRtData}
\alias{joinRtData}
\title{Join RtData}
\usage{
joinRtData(rtData, rtData2)
}
\arguments{
\item{rtData}{A nested list as required by \code{summaryWidget}}
\item{rtData2}{A nested list as required by \code{summar... |
e60f1a08cb9ea914197236251aed7443017e00b3 | e6a401ae8cc996ed76881d9d7467a5b6167d5b7b | /Scripts/ICEWS_Sources_Git.R | 406dbbb1e0287edf78bc356c88a895b5251e4886 | [] | no_license | ZacharyST/ICEWS | 1d40b7a7b7ad0963e72c7c62e54ef34e3b34c355 | 5379e41755287a94f8da7d0acb33f2b2a5105153 | refs/heads/master | 2016-09-08T01:12:30.043360 | 2015-06-13T04:10:15 | 2015-06-13T04:10:15 | 33,836,773 | 6 | 0 | null | null | null | null | UTF-8 | R | false | false | 619 | r | ICEWS_Sources_Git.R | '''
This script loads 2 years of ICEWS data and reads the source column. The goal is to create a list of all sources used in ICEWS.
'''
#Load data
data <- read.csv('/Data/ICEWS/events.2010.20150313084533.tab',header=TRUE,sep='\t')
data <- rbind(data,read.csv('/Data/ICEWS/events.2011.20150313084656.tab',header=TRUE,... |
84dfafda11bf17b13396777087e692670a785ead | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.internet.of.things/man/iot_update_audit_suppression.Rd | a882b83f1c124821839f53167d9cce8559c15c94 | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,371 | rd | iot_update_audit_suppression.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/iot_operations.R
\name{iot_update_audit_suppression}
\alias{iot_update_audit_suppression}
\title{Updates a Device Defender audit suppression}
\usage{
iot_update_audit_suppression(checkName, resourceIdentifier,
expirationDate, suppressIndefi... |
171a616f90a2675aec4d300eb0b2ef613335dc99 | 5e64e69fc69cb20dca1d497a8b2022dc66190456 | /man/genericTest.Rd | 846e76c5602cbf106601022cc94ec5c1e12c0934 | [] | no_license | wahani/aoos | 4f058332b2ed8c1aa400c427c69043764a22a9a0 | 232e0f930fd3e16f7531cbf16fd6cf0032d0d83f | refs/heads/master | 2020-05-20T13:59:15.248798 | 2017-05-06T17:46:38 | 2017-05-06T17:46:38 | 26,126,717 | 4 | 1 | null | 2015-01-14T08:14:51 | 2014-11-03T16:07:51 | R | UTF-8 | R | false | true | 508 | rd | genericTest.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/S4-generics-test.R
\docType{methods}
\name{.genericTest}
\alias{.genericTest}
\alias{.genericTest,numeric-method}
\title{Generic Test}
\usage{
.genericTest(x, ...)
\S4method{.genericTest}{numeric}(x, ..., methodParam = function() 1)
}
\argum... |
59f73b42511680a6ef9cc7438466d41861008afa | 5764dcff9c201b8d889f5bb8608f35643fecac53 | /class 10 Student Data Regression/eda.r | e209bd19728c0fb89fa959d79e5d740f9511f444 | [] | no_license | goforaditya/R-for-Statistics-and-Data-Science | 2c87987ac97c825dd78d71ddfa607fd373c7fe58 | f7eb3eb3de98d7040471ca6589c2ed823b65a0c7 | refs/heads/master | 2022-12-14T07:13:00.353114 | 2018-05-01T18:48:25 | 2018-05-01T18:48:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 822 | r | eda.r | setwd("D:\\MS BDA I SEM\\Computing For Data Science\\R\\class 10 Student Data Regression")
df <- read.csv('student-mat.csv',sep = ';')
str(df)
head(df)
summary(df)
# Checking for NAs
any(is.na(df))
# Exploratory Data Analysis
library(ggplot2)
library(ggthemes)
library(dplyr)
# Correlation
# grab only ... |
bd4f995107b40b5fb1490909bfea3ddb3e3a2d39 | 4d60c4ae06f4a53690ed1fbb30f123d2ea6c87d6 | /generate-eqtl-ranef-data | f4978a5af1c2995341ada09130df6af7146c744f | [] | no_license | antoniofabio/eqtl-ranef | 67841e6f204b99b1d821a76f58b5b8a209d1acc2 | 602ce8a77b22e50accab3827945622166efdb8c0 | refs/heads/master | 2021-01-02T09:32:47.661398 | 2016-09-22T11:44:32 | 2016-09-22T11:44:32 | 21,175,477 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,980 | generate-eqtl-ranef-data | #!/usr/bin/env Rscript
suppressMessages({
library(RSQLite)
library(methods)
library(optparse)
library(reshape)
library(plyr)
})
options(warn = 1)
option_list <-
list(
make_option("--regressors", help = "# regressors [default: 2]", default = 2),
make_option("--outcomes", help = "# outcom... | |
f3959344ce94db7ff10ef542c58ec2b36fff526b | c8eb502f925a9b9d8f25420a9f57d26ad218c8d7 | /HFI.02B.Expertise.calculation.R | dbb0314e8152052c6d99c6c749005905b265c566 | [] | no_license | victorcazalis/sensitivity_paper | a319704508f54da925b3e092fafe5fb90a59cf1a | 55edf1449c4695894c381540b923a3e49b9e0c86 | refs/heads/main | 2023-04-08T06:55:12.008934 | 2021-06-21T05:17:01 | 2021-06-21T05:17:01 | 378,814,180 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,843 | r | HFI.02B.Expertise.calculation.R |
### Charge all checklist files and merge
chlist1<-readRDS(paste0(GBrow, "0.Data/1.Exported4Expertise/Qmerged/chlist.expertise.Q1merged.rds"))
chlist2<-readRDS(paste0(GBrow, "0.Data/1.Exported4Expertise/Qmerged/chlist.expertise.Q2merged.rds"))
chlist3<-readRDS(paste0(GBrow, "0.Data/1.Exported4Expertise/Qmerged/chli... |
91c2612a35184f0d67aca6650320c08b25e796eb | 9ced058004c19ba00d837a8e456817d56a565c9d | /tests/testthat/test-oc_bbox.R | 2a978d5b5b36447f3b0b1782c44a92e8b5011bb8 | [] | no_license | cran/opencage | 84594102736a8d97869cceb15ec774c5d7af0f41 | 11a46b26ae7b13a3eca36a2b4a42fa3c998a4361 | refs/heads/master | 2021-05-15T01:06:06.777397 | 2021-02-20T00:00:02 | 2021-02-20T00:00:02 | 58,643,210 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,121 | r | test-oc_bbox.R | # Test oc_bbox ------------------------------------------------------------
test_that("oc_bbox works with numeric", {
bbox1 <- oc_bbox(-5.6, 51.2, 0.2, 51.6)
expect_type(bbox1, "list")
expect_s3_class(bbox1[[1]], "bbox")
expect_equal(
unlist(bbox1),
c(xmin = -5.6, ymin = 51.2, xmax = 0.2, ymax ... |
fe2b98dab306402365e1bb220adeb0e0b32cffa8 | 4f60f5253e9fb3129309c9f65b8b0358da9edc49 | /server.R | e2321c9c2a2ca78e02b31a53fd86ec3c7b73a36f | [] | no_license | drwo/CellarMasters | 5c2c338021ee6922e1cb3035e160e8cc64028c7b | e327840471098e2e9dabcc125c5a272056706e4e | refs/heads/master | 2020-06-04T10:58:30.771965 | 2020-01-12T19:17:15 | 2020-01-12T19:17:15 | 191,992,997 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,670 | r | server.R | shinyServer(function(input, output, session) {
# print(test.run)
loginModal <- function(failed = FALSE) {
modalDialog(
textInput("user.id", "Enter user ID and password",
placeholder = "user ID"),
passwordInput("password", label = "", placeholder = "password"),
if (failed)
... |
71b4db6f759404257dc14a5ed61242b2626606f2 | 3f172286547e4e01c2fbf3345a3f6f7150cdab3d | /man/core_compare_functions.Rd | 2fa4e5e2eccbf5cd6fb9ca92b051718b6d2a30a5 | [] | no_license | sujeetp97/compare-functions | 8b0a546377fd957aba5ada57a389ca4df89bf001 | d55c85239ab9ebe18840dd48950a5c3f723c862d | refs/heads/master | 2021-07-22T10:50:39.157845 | 2017-10-31T08:38:41 | 2017-10-31T08:38:41 | 104,383,661 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,445 | rd | core_compare_functions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compare_functions.R
\name{core_compare_functions}
\alias{core_compare_functions}
\title{core_compare_functions}
\usage{
core_compare_functions(functions, param, size)
}
\arguments{
\item{functions}{A vector of single parameter functions. Do n... |
11343d12baddba3ecacd6e8e0f0abe7006ed3e76 | e0036043d155f01a659af2e821dcccf9fc819ee8 | /12-3 test for dependent p.R | 84e2b1489f6871cb64d3bfbf42969a484865b3da | [] | no_license | azambesi/STA4173 | e52cc02b6fcc9dfa2ed7c2a40274b41ab5c112fb | 8e7040f1ae0b51280c5d89f117c00178c74d88f2 | refs/heads/master | 2023-08-14T13:52:59.022642 | 2021-09-14T14:34:59 | 2021-09-14T14:34:59 | 411,345,926 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,284 | r | 12-3 test for dependent p.R | ##### EXAMPLE 1 #####
# enter counts in a matrix
observed_table <- matrix(c(293, 43,
31, 103),
nrow = 2, ncol = 2, byrow = T)
# I prefer to include breaks to make it look like the table given
# just for checking purposes
# give names to the rows and columns
rowname... |
d11a2d9796ab44a8126d506b9aec60bfaa941197 | 13d600b6e0d7fa0d81cc6fe3b366b1b3da319a34 | /man/zinedown.Rd | 95f9f4a03c4044447b5247de2690f5a7d2101c7d | [] | no_license | Robinlovelace/zinedown | 37c7ea98ca861c2f62fc6933116ecda41aa851f1 | 2868e7a92618b0f7f4cf8c968e2614aa5ebd4497 | refs/heads/master | 2020-04-09T03:52:17.609461 | 2018-12-02T20:44:19 | 2018-12-02T20:44:19 | 160,000,291 | 8 | 2 | null | null | null | null | UTF-8 | R | false | true | 610 | rd | zinedown.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/zinedown.R
\docType{package}
\name{zinedown}
\alias{zinedown}
\alias{zinedown-package}
\title{zinedown: A package for creating zines using R Markdown}
\description{
zinedown: A package for creating zines using R Markdown
}
\section{zine_gitbo... |
d43d20f7415104e52bd1f6d04b3940c7dcad1a52 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/bcpa/examples/ChangePointSummary.Rd.R | a5ccac269ec854783bb28aa8365d3fa331eca43b | [] | 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 | 463 | r | ChangePointSummary.Rd.R | library(bcpa)
### Name: ChangePointSummary
### Title: Obtain summary of BCPA analysis
### Aliases: ChangePointSummary
### ** Examples
if(!exists("Simp.VT")){
data(Simp)
Simp.VT <- GetVT(Simp)}
if(!exists("Simp.ws"))
Simp.ws <- WindowSweep(Simp.VT, "V*cos(Theta)", windowsize = 50, windowstep = 1, progress=TRUE)
#... |
f78d53e3a5d61a6af5c6a6e8a8aa275e26564861 | f3aac55a8582aa2b9ec92389a1a8aee72e197db9 | /man/calc_myreg_mreg_logistic_yreg_logistic.Rd | cd17d980945050168ecc549267f0c35c08d73f37 | [] | no_license | kaz-yos/regmedint | fe620ae12014996497d559713bc960400279e185 | e3c3ffea5d99c00bae2b42f7ab87f57e1bb99a74 | refs/heads/master | 2022-05-17T18:21:57.259579 | 2022-04-06T17:02:41 | 2022-04-06T17:02:41 | 245,831,454 | 24 | 6 | null | 2022-02-03T21:24:47 | 2020-03-08T14:41:41 | R | UTF-8 | R | false | true | 2,503 | rd | calc_myreg_mreg_logistic_yreg_logistic.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/07_calc_myreg_mreg_logistic_yreg_logistic.R
\name{calc_myreg_mreg_logistic_yreg_logistic}
\alias{calc_myreg_mreg_logistic_yreg_logistic}
\title{Create calculators for effects and se (mreg logistic / yreg logistic)}
\usage{
calc_myreg_mreg_log... |
000e05153c6eb0af19cc60cd4915caadf2c8b927 | b363cf1145275571fe5f490d3d7cae9d357d843b | /R/old/map.loc.asreml.R | e7c1138eb33866c678aa8c5e3a1484a1c996fb58 | [] | no_license | behuang/dlmap | 93b31b020866fa5db64615f9df2b45bb0879c48e | 81cfc6efddf7a32a91ac99296519edae85053147 | refs/heads/master | 2020-04-01T19:48:49.328751 | 2015-04-15T23:50:43 | 2015-04-15T23:50:43 | 15,329,196 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,295 | r | map.loc.asreml.R | `map.loc.asreml` <-
function(input, s.chr, chrSet, prevLoc=NULL, ...)
{
dfMrk <- input$dfMrk
dfMerged <- input$dfMerged
envModel <- input$envModel
nphe <- input$nphe
map <- input$mapp[[s.chr]]
mrk <- grep(paste("C", s.chr, "M", sep=""), names(dfMerged))
chr <- sort(c(mrk, grep(paste("C", s.chr, "P", sep... |
65910bcbbd57d5944e9930e6f49f4b60ba3cc863 | ffe87a0a6134783c85aeb5b97332b201d50aca9d | /MINI_2015/prace_domowe/PD_11/pd_11_sudol.R | df7036dc9de45a6bb05cb734741e530be19d64c1 | [] | no_license | smudap/RandBigData | d34f6f5867c492a375e55f04486a783d105da82d | 4e5818c153144e7cc935a1a1368426467c3030a5 | refs/heads/master | 2020-12-24T15:51:11.870259 | 2015-06-16T08:50:34 | 2015-06-16T08:50:34 | 32,064,294 | 0 | 0 | null | 2015-03-12T07:53:56 | 2015-03-12T07:53:56 | null | UTF-8 | R | false | false | 833 | r | pd_11_sudol.R | f = function(){
# wylosuj dane, 2 kolumny, 10000 wierszy
df <- data.frame()
for (i in 1:10000) {
df <- rbind(df, data.frame(x=rnorm(1), y=rnorm(1)))
}
# policz modele regresji na probach bootstrapowych
resx <- numeric()
resy <- numeric()
inda <- NULL
for (i in 1:500) {
ind <- sample(1:nrow(df), replace = TRUE)
... |
c861be4dc18c7e8b360389a0938739022e53ef4e | 6b084409ce9e23028d3749a6508d9f842f186b24 | /Logistic.R | 26da2ee5cd1f20ce1582d33303555a61a45faa0b | [] | no_license | avigoud89/credit-analysis | 9cf5494696a819c1938cca46f7be4139209dd284 | a6721e82c03b47466f611d0d17f258a4ca7a85e1 | refs/heads/master | 2021-01-15T12:48:24.679060 | 2015-11-13T01:41:25 | 2015-11-13T01:41:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,841 | r | Logistic.R | setwd("~/Downloads/MISC")
credit <- read.csv("credit3.csv")
str(credit)
credit$NPV <- gsub(",","",credit$NPV)
credit$NPV <- as.numeric(credit$NPV)
head(credit$NPV, 20)
credit$CAN <- rep("Yes", nrow(credit))
credit$CAN[credit$NPV <0] <- "No"
credit$CAN <- factor(credit$CAN, levels = c("No", "Yes"))
str(credit$CAN)
credi... |
5930bca41c3bb1e9212140ff2ec0850920a0c9bf | ff816cf9be953573639964769fff362fd8a1b9d3 | /R/sampling_reference_set.R | 6bbcef44538f61f22568a30ee6419e1dacf37481 | [] | no_license | BEAST-Community/weifang-sarscov2 | e1a4f8dc3e026930d9e258937614b454cf3aca1d | 38488632becbbdafb79117bc6b0e0c1b2d96edeb | refs/heads/master | 2023-01-05T12:25:57.541500 | 2020-11-05T12:02:39 | 2020-11-05T12:02:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,671 | r | sampling_reference_set.R |
# sarscov2Rutils package required: https://github.com/emvolz-phylodynamics/sarscov2Rutils
# devtools::install_github("emvolz-phylodynamics/sarscov2Rutils", ref = 'sarscov2Rutils')
# GISAID database and metadata required: gisaid.org.
# place these files in a /data folder
require(ape)
library(lubridate)
require(sa... |
545f6f82c5a31f7bfad3f28d60a55734ad556fb1 | baaa21c343d251555c3ddd21427bef14b9b8af58 | /BiomarkerSelectionMethods.R | ce9986eb85554eccb3e134319a94ca496043574a | [] | no_license | amcrisan/UnnamedBiomarkerProject | 8cfcdab7639b66220cd0a64b82ab60f4392a6337 | 1f9e3d73105226b6f945da67e4ecc51dea7b49c5 | refs/heads/master | 2020-04-10T21:06:34.836018 | 2015-09-03T00:03:36 | 2015-09-03T00:03:36 | 31,673,859 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 30,647 | r | BiomarkerSelectionMethods.R | library(biom)
library(scales)
library(plyr)
library(dplyr)
library(rms)
library(RColorBrewer)
library(reshape)
library(ggplot2)
library(e1071)
library(nnet)
library(pROC)
library(vegan)
library(glmnet)
source("SupportingBiomarkerMethods.R")
set.seed(1)
########################################
# Loading and wrangling ... |
daf87f955f80dbcf2cc253297165ec1e2599dc13 | e3c9a095241d3eb7c02544aad3a14fbd5f1a2ba6 | /cachematrix.R | 7824a72a38c3384d702cf588d68dff0d3d0be8f2 | [] | no_license | JotaRX/ProgrammingAssignment2 | 055f769d646f24a3dc51aaccba425b855fbb763f | 7c95471f057ec17f3761aec3f3911f2d40dd56b8 | refs/heads/master | 2022-11-22T12:14:23.425721 | 2020-07-18T18:50:02 | 2020-07-18T18:50:02 | 280,716,893 | 0 | 0 | null | 2020-07-18T18:36:29 | 2020-07-18T18:36:28 | null | UTF-8 | R | false | false | 971 | r | cachematrix.R | ## Assignament 2
## The objective that this functions is create a matrix, save it in the caché and
## then calculate his inverse without use a lot of resources
## Set matrix and clear his inverse
makeCacheMatrix <- function(x = matrix()) {
i<-NULL
setmatrix<-function(y){
x<<-y
i<<-NULL
}
getmatrix <- ... |
fcad1c14e5273d893968eb978657c823200ecee0 | fde9c70b67e2ea092f0a3966c8b098b08ad0ffcc | /man/sortCrit.Rd | 61845231700eb6dd45f255fdf038b41a77d85900 | [] | no_license | hazaeljones/geozoning | 41d215022b34e6944e4ba7395dc0778c2c49ba48 | c8310ca97a775c4d55807eb3ac3ab1ae73da5334 | refs/heads/master | 2021-01-20T12:37:46.187798 | 2018-02-23T09:44:47 | 2018-02-23T09:44:47 | 90,385,766 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,290 | rd | sortCrit.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sortCrit.R
\name{sortCrit}
\alias{sortCrit}
\title{sortCrit called by correctionTree}
\usage{
sortCrit(qProb, crit, cost, costL, nz, mdist, listOfZ, map, disp = 0,
SAVE = FALSE)
}
\arguments{
\item{qProb}{probability vector used to generate... |
def65b0d0db71acba01abad4cd51af40f2b30efa | 57edf42dab2d6ce0d0400daf40573ad4d63cf843 | /global.R | c11acb7cf776b3d0f03496988a37a8a442b52f06 | [] | no_license | MikeLeeMcLau/Capstone_Info | ca42f50a044495d235f8091691f39e496f81ac21 | 266cefa2ab4efb5afe7a349c3a9e109038fdeaf7 | refs/heads/master | 2021-01-25T00:57:17.667130 | 2017-06-18T19:33:22 | 2017-06-18T19:33:22 | 94,708,211 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,602 | r | global.R | Bi_Data <- read.csv("Bi_Data.csv")
Tri_Data <- read.csv("Tri_Data.csv")
Quad_Data <- read.csv("Quad_Data.csv")
Pent_Data <- read.csv("Pent_Data.csv")
Sext_Data <- read.csv("Sext_Data.csv")
checkforFiveWords <- function(getCheckString) {
wordCount <- sapply(gregexpr("\\W+", getCheckString), length) +... |
c06130bf145f74b38abacac16fdf8b95553870a9 | 1443e812411278d1f776f8f7d1196add8e2dcc31 | /man/seurat_small.Rd | 5b2d6bdb91c44b0d0550d01ba770d686bc9f725d | [
"MIT"
] | permissive | WeiSong-bio/roryk-bcbioSinglecell | e96f5ab1cb99cf1c59efd728a394aaea104d82b2 | 2b090f2300799d17fafe086bd03a943d612c809f | refs/heads/master | 2020-06-15T23:38:23.802177 | 2018-07-03T21:01:07 | 2018-07-03T21:01:07 | 195,422,697 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 532 | rd | seurat_small.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/datasets.R
\docType{data}
\name{seurat_small}
\alias{seurat_small}
\title{Seurat Example}
\format{An object of class \code{seurat} of length 1.}
\usage{
seurat_small
}
\description{
Seurat Example
}
\examples{
show(seurat_small)
}
\seealso{
O... |
4489d04f889e5912dc0bb1a75c1630e89f7b4593 | f928b334d4fdde7fceeb8e21773f0ad85af9b2f3 | /preliminaries.R | fc9c75b4e603d3c74554f9a9273e7079b5bd0552 | [
"Apache-2.0"
] | permissive | KirstyLHassall/ACE | 596eeaf09bd8ac0a131a6c5bfb4fb07c2519c0a6 | 5c169c833b62d4bf7bb9f676784559609f30cf36 | refs/heads/master | 2020-04-22T11:45:32.348515 | 2019-02-12T21:51:34 | 2019-02-12T21:51:34 | 170,351,902 | 6 | 2 | null | null | null | null | UTF-8 | R | false | false | 5,990 | r | preliminaries.R |
# Create all auxilliary files
# Creates initial Network diagram and saves the coordinates
# Creates initial CPT files
library(igraph)
library(shape)
library(RColorBrewer)
appName <- "ACE"
allcontexts <- read.csv(paste(appName, "//nodeTables//NetworkNames.csv", sep=""))
contexts <- as.character(a... |
40662b03a131028d4a8ee1cb956184093caa6333 | 41cbddf0dca2eb8d7d766c73c824f7ea0e9d643a | /man/find_colnames.Rd | 904c7c68d6ddb07e04fe3b36f190cec122234fb3 | [
"MIT"
] | permissive | jfontestad/dmtools | d3db3380e729e1bc6bf1f7e0b0822b7191ee61fb | 5e8ad2305600daa4bdd5b393391875ffc2e20678 | refs/heads/master | 2023-02-10T05:27:38.535802 | 2020-12-19T18:57:23 | 2020-12-19T18:57:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 421 | rd | find_colnames.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/abstract.R
\name{find_colnames}
\alias{find_colnames}
\title{Find column names}
\usage{
find_colnames(obj, dataset, row_file)
}
\arguments{
\item{obj}{An object for check.}
\item{dataset}{A dataset, a type is a data frame.}
\item{row_file}{... |
7455d06300f492c2ca443a00d86d0f5e0813e2a9 | ba628e6c0bbdfba914a2c6c693ec85d2f5aa027a | /Getting and Cleaning Data/assignment1.r | 1d0f56b5d42d4053a49b38d65807d2ed47a9012b | [] | no_license | jlpeng75/Coursera | b7abe959bcf327b5aeb463348b3a916f9f25500f | adf84ee26c48085af0fd32d798bad6d3b1317f45 | refs/heads/main | 2023-07-18T04:15:06.784508 | 2021-09-01T17:58:03 | 2021-09-01T17:58:03 | 402,151,347 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,714 | r | assignment1.r | setwd("C:/Users/jpeng11/coursera")
course <- "Getting and Cleaning Data"
if(!file.exists(course)) {
dir.create(course)
}
setwd(course)
fileUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
download.file(fileUrl, destfile = "IdahoHousing.csv")
dateDownloaded <- date()
dateDownloa... |
d309ae3a8234c79e0ab91c7faf9c58fe4fed5522 | b24b769f98bd9b5dd8a12b4cbe62f472e5c53318 | /R/match_fluency.R | ffb9347fd36706907b0b5f18d833c0bb16931e0b | [] | no_license | dwulff/memnetr | b77f37c163d7e2abe5bcc961432d25ed2fb673fd | babf658870f8c0d5344253fbba049ae9d2950661 | refs/heads/master | 2020-04-01T03:10:24.616086 | 2019-08-01T14:29:08 | 2019-08-01T14:29:08 | 152,811,963 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,244 | r | match_fluency.R | #' Match group fluency data
#'
#' Prune fluency data of two groups so that the groups'
#' distributions of number of productions match.
#'
#' @param data a data frame containing (at least) fluency productions,
#' subject id, and a grouping variable.
#' @param labels a character vector containing the labels of variabl... |
6ff7ad639f1201b77843f6b8e746a5e6d1c72dfc | 869f3b10cafa55e1667186a404b400110fc44ac3 | /nhanes_data_analysis.R | ba7a349797f3955369e453c2b5a72f8af31c8a5d | [] | no_license | Allisterh/BRSE | 2c0969cef9edd3f189d6ebd7c9f4f04f2bfead7c | c63c2d756dcbff13f425ee9a469bfaa147fa2600 | refs/heads/main | 2023-04-17T04:28:21.830643 | 2021-05-07T05:14:11 | 2021-05-07T05:14:11 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,061 | r | nhanes_data_analysis.R | library("sandwich")
library(R2jags)
library(dplyr)
library(xtable)
library(ggplot2)
library(ggpubr)
set.seed(4)
dat <- readRDS("nhanes_subset.rds")
n <- nrow(dat)
x <- dat[, c("MALE", "RIDAGEYR")]
y <- dat[, "BPXSY"]
## exploratory plot
plot(BPXSY ~ RIDAGEYR, data = dat, col = MALE + 1)
dat$gender <- ifelse(dat$MA... |
c492dd0784c85f69817155fc6a505e1c4304631b | d7caf224ef89ac88d9f1df99b1270db81d4065d8 | /ui.R | e2e2992e276a0d8753ac0d65536f5ef52ac1bed2 | [] | no_license | emilopezcano/probfreq | c92086d1603f7a250a57523ee7d205fade560a90 | 5da55ca5adfd0596f35f817cb99b346fc14e608d | refs/heads/main | 2023-08-27T07:10:10.850844 | 2021-10-24T10:40:10 | 2021-10-24T10:40:10 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 742 | r | ui.R |
# This is the user-interface definition of a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#
library(shiny)
shinyUI(fluidPage(
# Application title
titlePanel("Relative frequency approach to Probability"),
# Input text boxes
sidebarL... |
0da5174e4902e869f5d8096232f7300fe852a3c4 | 172288efc3ea342e191e503f372a6b5b8b5e7465 | /man/extractExperimentInformation.Rd | d9fd60ebdd06ab93a2565a2e1e3ac19bcd243413 | [] | no_license | guypwhunt/r_shiny_geo2r_visulisation_package | 062d14e0fd9500bd3133a45828f56f9db498500c | afa27c0a97f8ab9488005160981d61c0bfb76128 | refs/heads/main | 2023-04-09T17:03:27.130019 | 2021-04-07T14:58:47 | 2021-04-07T14:58:47 | 355,532,616 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 641 | rd | extractExperimentInformation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/geoIntegrationFunctions.R
\name{extractExperimentInformation}
\alias{extractExperimentInformation}
\title{A GEO Function to Convert the Experiment Information Object into HTML}
\usage{
extractExperimentInformation(experimentData)
}
\arguments... |
81568663156740ba73a38cd56e4f6e2c0775a2c4 | 8879118eb01708b3c9d914406809fe6b828d5b1c | /plot4.R | 56a2373de95fcb75f89a5bd872625e599438f676 | [] | no_license | senaus/ExData_Plotting1 | b6ef237d0e101028b9bcbd6da595390480fb8ed6 | 9eac5cf83045fe654e79ddfad0591a30bf5f8ebf | refs/heads/master | 2020-04-07T13:15:08.709293 | 2018-11-20T15:14:49 | 2018-11-20T15:14:49 | 158,399,405 | 0 | 0 | null | 2018-11-20T14:08:38 | 2018-11-20T14:08:38 | null | UTF-8 | R | false | false | 1,360 | r | plot4.R | ##
## Plot 4
##
# Downloading data
if (!file.exists("HWdata.zip")) {
download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", "HWdata.zip")
}
# Unzipping data
if (!file.exists("household_power_consumption.txt")) {
unzip("HWdata.zip")
}
# Reading data
header <-... |
76762f7cc608200ad37c840b2f6660a02bc63c84 | e641a149924ecc8cc6bc55102ea93aa3046796a9 | /R/pair.R | adc0b722315f05bd71c422883991ff80539aced9 | [] | no_license | cran/cooccur | a866dba2efb77771da40faa7162a4edd1e358441 | c900eb2120fd0644c4f3734830d8a3969b8c6907 | refs/heads/master | 2020-12-24T13:16:04.060161 | 2016-02-09T20:53:56 | 2016-02-09T20:53:56 | 17,695,239 | 0 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,485 | r | pair.R | pair <-
function(mod,spp,all=FALSE){
ptab <- mod$results
if (all==T){
alpha <- 1
}else{
alpha <- 0.05
}
if (is.numeric(spp)){
p1 <- ptab[ptab$sp1 == spp & (ptab$p_gt <= alpha | ptab$p_lt <= alpha),c("sp2","sp2_inc","obs_cooccur","prob_cooccur","exp_cooccur","p_lt","p_gt")]
p2 <- ... |
765460d0d02efaf12a8880e987722e8d4949e2e4 | a3ba5a0582f119daad1e832147b295387fc5bcfb | /4_PCA/47.R | f416ce77eff6b91106a7c9357688b6d3f89162f8 | [] | no_license | myoshimu/MV | d57c32e4838fec482e5e236cd755adda83272475 | 749d2c4422d4d4f0ac03f83f08d3b8ae97f9f8bf | refs/heads/master | 2022-02-19T03:55:24.651461 | 2019-09-01T14:53:51 | 2019-09-01T14:53:51 | 101,030,387 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 381 | r | 47.R | #prcompによる主成分分析
d<-read.csv("Ramen.csv",fileEncoding = "cp932")
d
#行名の書き換え（biplotで見やすいように）
rownames(d)<-d$店名
d
#prcomp実行
pr<-prcomp(d[,-1],scale=TRUE)
pr
#主成分得点
pr$x
#主成分負荷量
t(t(pr$rotation)*pr$sdev)
#累積寄与率
cumsum(pr$sdev^2)/3
#バイプロット
par(family="HiraginoSans-W3")
biplot(pr) |
0e13870d84df63cf3396ec63161561ff0c391288 | 73ce0fdac6b3bca74a5a74eac0d25de24011033e | /tests/testthat/test-lforce.r | f52431dd2e30f33094dc3420dc6acc984436743e | [
"MIT"
] | permissive | TobCap/lazystreamr | 4011082f2ed4bc9229a9dd4e356ab905b0c7d3ac | ff77c65a7dc765f55e1577074fe318b74f1c1016 | refs/heads/master | 2021-07-02T12:29:43.454616 | 2017-02-04T08:39:21 | 2017-02-04T08:39:21 | 34,054,194 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 584 | r | test-lforce.r | context("test for lforce")
test_that("test", {
x1 <- 1L %:% (2L %:% lempty)
x2 <- (1 %..% 2) %:% ((3 %..% 4) %:% lempty)
x3 <- llist(llist(1L, 2L), llist(3L, 4L))
x4 <- 1L %:% 2L
expect_identical(lforce(x1), list(1L, 2L))
expect_identical(lforce(x2), list(list(1L, 2L), list(3L, 4L)))
expec... |
6719e4f4f21dacba2a1debe9c5d0769434967373 | 62995111781a92641244bd84fb8c5348a9a7b6c9 | /R/A_br_war.R | 0ac1d8ec55a38f0f963df1df7b4c5641166642ea | [] | no_license | MicroWeaR/MicroWeaR | 172618e8a23d4fc270f3f9487e8365fd652e8875 | caf512e44360d85b7e8a7b8589da7fda3e68c13c | refs/heads/master | 2023-06-23T16:30:20.006060 | 2023-06-09T17:18:54 | 2023-06-09T17:18:54 | 119,274,504 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 263 | r | A_br_war.R | #' @title example dataset
#' @description Working area of the picture of Anoiapithecus brevirostris.
#' @name A_br_war
#' @docType data
#' @author Antonio Profico, Flavia Strani, Pasquale Raia, Daniel DeMiguel
#' @keywords MicroWeaR
#' @usage data(A_br_war)
NULL
|
c1e79ac6779dd319459608533e66a99a7a2d54be | 442e79fd0d33f74087de7c538b95dba3ac41f9dc | /tests/testthat/test_pcl_range.R | 90dab38590f8f0ab244f1fd335165a4b22e9dfe1 | [
"CC-BY-4.0"
] | permissive | atkinsjeff/pcl | 2ef7f2c9ee6ce813b682962c9d522fe85eab57bf | d16e7d42b2f58c50b7083a0966c2905972496f15 | refs/heads/master | 2023-04-11T20:17:04.679401 | 2022-04-12T14:37:24 | 2022-04-12T14:37:24 | 260,939,433 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 192 | r | test_pcl_range.R | context("pcl")
test_that("pcl", {
dat <- pcl
# checks for outliers
expect_true(all(dat$can.max.ht < 60))
expect_true(all(dat$rugosity < 100))
expect_true(all(dat$vai.max <= 8))
})
|
8603cb85cacca6cfef66a4394f49e403224aa46c | ba6b15d209cd71dfdea4cb1d26f278c794e26323 | /QMJ_VAL_regressions.R | 355e5ec32f03f359bc221331a887444faabc90da | [] | no_license | Patrick-J-Close/Finance_tools | f5879f4100fe92bee9193e8a2a79e97c9ccd7c8a | e0289a50ae6b771718a9a6e09fc01690ce6e1fc8 | refs/heads/master | 2016-08-12T15:10:14.879102 | 2016-02-28T13:38:15 | 2016-02-28T13:38:15 | 52,722,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,861 | r | QMJ_VAL_regressions.R | ## loop through all files in directory
# http://www.r-bloggers.com/looping-through-files/
path = "S:/Neptune Investment Team/Analyst_Patrick/Dev/Projects/QMJ/OutputFilesAVG"
setwd(path)
out.file <- ""
QUAL_file.names <- dir(path = path, pattern = "QUAL.csv")
VAL_file.names <- dir(path = path, ppattern = "VAL.csv")
RA... |
9dc034e63b2f267d5067191ec8f3fbb95aa85397 | d33d903d5cce614e41d8fd05ea5a722996523769 | /R-scripts/Density.R | bd6f4f90adcebbbedd345942d229dd6a014f8cb6 | [
"Apache-2.0"
] | permissive | DennisRippinger/spade | 10bea53608a497cfd6efdd699699aa1cee34f136 | 59023fd9e863518e23e71288bd7a23da865a85f6 | refs/heads/master | 2023-01-19T21:02:20.639294 | 2023-01-16T08:32:21 | 2023-01-16T08:32:21 | 17,828,071 | 0 | 3 | null | 2016-03-09T19:26:31 | 2014-03-17T13:03:36 | Java | UTF-8 | R | false | false | 386 | r | Density.R | library(RMySQL)
con = dbConnect(dbDriver("MySQL"), user="root", password="root", dbname="spade", host="localhost")
density = dbGetQuery(con,"SELECT densityFunction FROM `Spade`.stylometry;")
df <- data.frame(density = as.numeric(density$densityFunction))
df$density = round(df$density, digits = 1)
table <- xtabs(df)
p... |
fcc69ca0e3c3e53708f41aba7c6f3a8cb5144be7 | 737c608948fb450f11786cc758091d31ee62f059 | /man/add_cls-methods.Rd | c4d969a16e0084ebca1cbce2f967ac43344bf841 | [
"MIT"
] | permissive | isglobal-brge/omicRexposome | 01302d3d1d009b6f4c4acaa1b1ae156ccd319321 | c8716b177556bd7e082b269d2708eb43184e74fa | refs/heads/master | 2021-10-24T06:34:29.461874 | 2021-10-15T09:36:15 | 2021-10-15T09:36:15 | 84,179,294 | 1 | 3 | null | null | null | null | UTF-8 | R | false | true | 996 | rd | add_cls-methods.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/01_AllMethods.R, R/MultiDataSet-add_cls.R
\docType{methods}
\name{add_cls}
\alias{add_cls}
\alias{add_cls,MultiDataSet,ExposomeClust-method}
\alias{add_cls}
\title{Method to add an ExposomeClust to a MultiDataSet}
\usage{
add_cls(object, clsS... |
7343ac21b23a1a61cd799604a010fd40b28c9b73 | 22dc322d68a8bfaecf3c57be5ec99a433f0a95a8 | /man/mice.impute.2l.glm.norm.Rd | 86a80829bb9b75dd827ae71cf9ada34d01eeee6c | [] | no_license | cran/micemd | 19a1acfeb69da9e62d1639265a518ecebeb1f3a5 | e5adbe076babd9f6c9aa3926eaabdd73d76dd69f | refs/heads/master | 2023-06-09T21:04:43.211056 | 2023-06-01T11:00:04 | 2023-06-01T11:00:04 | 91,136,501 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,861 | rd | mice.impute.2l.glm.norm.Rd | \name{mice.impute.2l.glm.norm}
\alias{mice.impute.2l.glm.norm}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
Imputation of univariate missing data using a Bayesian linear mixed model based on non-informative prior distributions
}
\description{
Imputes univariate missing data using a Baye... |
759f1d1e09eb75fa2294633bf29b8caca77e72c5 | 9c308a99d7c2d5c4b6212ee9f5f15759d44994aa | /man/records-class.Rd | 665b516d35a427f9da6898abf3bf2fd9ec805fc7 | [] | no_license | FranzKrah/rMyCoPortal | d1a20217aca46a8e7544271158910af2874ebec9 | 710e32c5d2b4c61f63e5336fead20fa039b78c53 | refs/heads/master | 2020-03-31T13:09:36.542099 | 2018-12-30T11:41:25 | 2018-12-30T11:41:25 | 152,243,980 | 6 | 2 | null | null | null | null | UTF-8 | R | false | true | 845 | rd | records-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/records-class.R
\docType{class}
\name{records-class}
\alias{records-class}
\title{An S4 Class to represent query result from the function \link{mycoportal}}
\description{
\code{mycodist} holds a records table together with the query meta data... |
5eaa64735a65c599199fdbf948bd8d31887662a3 | 29585dff702209dd446c0ab52ceea046c58e384e | /eha/R/summary.coxreg.R | a68398b12125233ebea532dceb91601b7a487f95 | [] | 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 | 54 | r | summary.coxreg.R | summary.coxreg <- function(object, ...) print(object)
|
ae28ce63ea9340b34ad9d9a8cbe1f7c290576e81 | 48c1dea7ee94ff1f8cf7077ff1f6189e9300c97a | /March 29_2 Binomial Tree.R | a0d178d218bb476a44a53d224fe837877617f616 | [] | no_license | LeafmanZ/Financial-R | c153e3a733e0abf2ed6b5a81b039f68eaa094cdf | 98fde9375699950479182c7c3f12a9d74c576d90 | refs/heads/main | 2023-04-11T10:45:23.383347 | 2021-04-09T20:58:27 | 2021-04-09T20:58:27 | 353,474,026 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 352 | r | March 29_2 Binomial Tree.R | library(fOptions)
library(xlsx)
tree<-BinomialTreeOption(TypeFlag="ca", S=50, X = 55,
Time=4, r=0.01, b=0.00, sigma = 0.2, n = 4)
t <- BinomialTreePlot(tree, cex=.8,
xlab = "period", ylab = "Option Value", digits=3)
title(main="Option Tree")
print(tree)
write.xlsx(tr... |
66b9cdbf3d33626d8648d1d84cc396000ee2808e | f0efd62dc5565eb0664ca06c7d8e450dc3158669 | /pipeline/code/norm.clusters.R | d731c861bd44ba78fc970704c2b939771e6b0fd7 | [] | no_license | hjanime/small-RNA-analysis | 61c65799a875ed5511db1818f753b0173b73c63d | c93d814f082c004eb1deefbfe5faa6a547e3f1aa | refs/heads/master | 2021-01-18T13:55:49.466840 | 2015-07-20T23:19:03 | 2015-07-20T23:19:03 | 42,481,459 | 1 | 1 | null | 2015-09-14T22:50:33 | 2015-09-14T22:50:33 | null | UTF-8 | R | false | false | 2,014 | r | norm.clusters.R | library(data.table)
library(DESeq2)
library(qvalue)
library(ggplot2)
library("RColorBrewer")
library("gplots")
library(gtools)
table<-read.table("../../fasta/prepare_18_35_10/res/counts.tsv",sep="\t",header=T,row.names=1)
ann<-table[,1]
names(ann)<-row.names(table)
table<-table[,2:(ncol(table))]
table <- table[,mixed... |
4b69558f0b5d72b2d8cef7716ff53f76b9ab85a0 | 235df06a8ef93f21e0af979affa4f08ae89fd9e3 | /Springleaf_functions.R | 7731a6c0c7711b6d0bcdb4cb44b5145dc247d8f8 | [] | no_license | snassimr/springleaf | 63a87682ea2ce8eb9422be97adfb17ae90242b7f | 7577058c046843b847395f9eb515b2deb1ac21fa | refs/heads/master | 2021-01-10T03:34:38.111598 | 2015-10-19T07:32:27 | 2015-10-19T07:32:27 | 43,256,762 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 20,877 | r | Springleaf_functions.R | perform_data_preparation <- function()
{
library(readr)
#READ MODELING DATA
SYSG_ME_DATA_FILENAME <- "train.csv"
setwd(SYSG_INPUT_DIR)
me_input_data <- read_csv(SYSG_ME_DATA_FILENAME)
#READ PREDICTION DATA
SYSG_P_DATA_FILENAME <- "test.csv"
p_input_data <- read_csv(SYSG_P_DATA_FILENAME)
... |
c5a28098da3d99c5c82a5ec01e0100bfbe51c2b8 | bdcb7b9d3e48e828934e6019ebdadc06c617e8b5 | /data-raw/original_events_code.R | cee8dbc3b7746c9d9d6dbc5a7916a3c8c6714ffd | [
"MIT"
] | permissive | jfontestad/farr | 8dd97a0b3465ba2c954677153144f3eafb75dd03 | 95a99b69522c700030c48dac4d8e95f9569e5ecd | refs/heads/main | 2023-08-25T23:41:40.867831 | 2021-11-05T02:23:14 | 2021-11-05T02:23:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,667 | r | original_events_code.R | # Load up the PostgreSQL driver, create a connection to the database
library(RPostgreSQL)
# The following function takes a list of permnos and event dates, then for each
# calls the function above to get event returns for each PERMNO-event date
# combination.
getEventReturns <- function(permno, event.date, days.before... |
e01c8aabe0e6cab8955bcf03876c2b4c3862839f | c42ac5d274e19dfe2ea47dec3cf3dd64b87b8fcc | /air_pollution.R | 276b9ea64f8a65c09e4eb9f9c1e73da4e086f5dd | [] | no_license | jomaghacot/air_pollution | c935c8a641be8eae483b17e546862f59009dabc8 | e8280f6605c3596153ba356f7c79770fa6b40b30 | refs/heads/master | 2023-01-24T18:47:52.274558 | 2020-12-15T08:13:33 | 2020-12-15T08:13:33 | 321,594,727 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 28 | r | air_pollution.R | setwd("D:/R/air_pollution")
|
8ccfe1fb71d16f5fc4070769c3638e527981ce5c | bb80c7add11e2ab33414f5af9172e4755c215ace | /src/01_import_spotify.R | 9ab89ce511ed6e82eb604fcdf7963b3a4974ecde | [] | no_license | paulapereda/tayloR-old-version | 1e0b477952534b154cea47b65262bba7a64622e1 | 17667cbfd657973c5c6684259cef211c8b1bdfe5 | refs/heads/master | 2022-02-18T10:35:43.632530 | 2019-08-28T14:13:35 | 2019-08-28T14:13:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,395 | r | 01_import_spotify.R | library(tidyverse)
library(spotifyr)
source('token.R')
# (!) REVISAR, a algunos álbumes le faltan las tres canciones finales:
############# (i) Fearless
############# (ii) Speak Now
############# (iii) Red
############# (iv) 1989
spotify_df <- get_artist_audio_features('taylor swift') %>%
filter(album_name != "19... |
d1960459aa576fcc731a721add3f4aa3a8f470c7 | 768c84c5314c42cad91f3b53c5fb8ede09bb4a68 | /content/code/R/fizzbuzz.R | 98d37652f5f6930ead6cef06c310f5c3939606f6 | [
"CC-BY-4.0"
] | permissive | coderefinery/testing | 6842146c0dd1af94ce0e65c320583026b8277736 | 00affc88985ceac299bb4ec4a56971e9a9afaffd | refs/heads/main | 2023-05-27T05:19:59.943039 | 2023-05-15T19:28:30 | 2023-05-15T19:28:30 | 76,298,304 | 9 | 41 | CC-BY-4.0 | 2023-08-24T21:06:03 | 2016-12-12T21:46:14 | C++ | UTF-8 | R | false | false | 810 | r | fizzbuzz.R | # define the function
fizz_buzz <- function(number){
if(!number%%1==0 | number < 0) {
stop("non-integer or negative input not allowed!")
}
if(number%%3 == 0 & number%%5 == 0) {
return('FizzBuzz')
}
else if(number%%3 == 0) {
return('Fizz')
}
else if (number%%5 == 0){
return('Buzz')
}
e... |
f0787db602d7211669089e7aec77d32066bfbb9d | 7182a132d3dac38e52d833a0ac0f4e6792b326d0 | /R/fileio-package.R | 860636c2199212beab26530a6b36b41d0335c678 | [] | no_license | hrbrmstr/fileio | 29e1ccb173f20aad942d21e656977c96456b7dc2 | 0a0ebdabfa7cc754825a2e5d3862fd27340bb498 | refs/heads/master | 2020-03-15T06:40:14.332798 | 2018-05-04T01:42:52 | 2018-05-04T01:42:52 | 132,012,660 | 23 | 3 | null | null | null | null | UTF-8 | R | false | false | 506 | r | fileio-package.R | #' Post Files, Text or R Data to 'file.io'
#'
#' The 'file.io' <file.io> service enables ephemeral, convenient
#' and anonymous file sharing. Methods are provided to upload existing files,
#' R data objects or text messages to this service.
#'
#' @md
#' @name fileio
#' @docType package
#' @author Bob Rudis (bob@@rud.is... |
a97f38eb9f87f0b288370b72bc2cde2e59ebf311 | cce66c207e90a9b977fb7a4b17f930e58542bded | /code/nationwide.R | ab9befe89a02f086041620f0943c1de9fd021623 | [
"MIT"
] | permissive | STRIDES-Codes/Examination-of-COVID-19-s-impact-on-maternal-health-disparities | 6f5abaa88f5966782215879d65dca8061e2887ed | 20fca4f3ab12e89ce1bfd3d977686c6d4745dab4 | refs/heads/main | 2023-05-31T22:47:10.783917 | 2021-06-25T13:19:49 | 2021-06-25T13:19:49 | 375,821,512 | 0 | 2 | MIT | 2021-06-22T18:53:01 | 2021-06-10T20:22:31 | null | UTF-8 | R | false | false | 3,285 | r | nationwide.R | # Looking at entire covid dataset bnation-wide
# This is looking at representatio in COVID cases in general
# similar code to state_picker.R but looking at US as a whole - see
# state_picker.R for now for mode commented code
library(tidyverse)
library(maps)
library(ggthemes)
library(ggeasy)
entire_covid_data <- re... |
e40e26aae810b8f28fbcbf3f7bbdd301b53bbb1d | e34c6b5b46a16501607a472b78a82cc631fa65a9 | /Practicas_TareasU2/Practica4.r | 4540b27384f8ae7ed6e564c732e18c5ce84d1d6e | [] | no_license | manuelorozcotoro/Mineria_De_Datos | 379598c8045dbf14aa03141f1ee37b3c8cdebd2f | 595aedb734c045c1e2f804817d016242d3fd756c | refs/heads/development | 2020-12-30T01:54:25.851040 | 2020-06-17T03:44:40 | 2020-06-17T03:44:40 | 238,821,758 | 0 | 3 | null | 2020-06-17T03:44:41 | 2020-02-07T01:42:13 | R | ISO-8859-1 | R | false | false | 4,901 | r | Practica4.r | # El archivo csv se busca desde su ruta
getwd()
setwd("/Users/Dell/Desktop/DataMining-master/MachineLearning/LogisticRegression")
getwd()
# Se importa el conjunto de datos con el que trabajará.
dataset <- read.csv('Social_Network_Ads.csv')
# Se seleccionan los campos con los que trabajaremos.
dataset <... |
f25eea4c66a9cabc723b5eb3e9223eea02805f1b | 3a073957ba775b8457d58760ec43029c12303856 | /run_analysis.R | 08aefb57c514a45a8c482deb6b28135fa78dabc9 | [] | no_license | morsta/coursera_get_clean_data | bef18e7913edd6ddf20814c508b4df9f0bec7b32 | e47ca2e7c70305564193728aede47d0d091384f6 | refs/heads/master | 2021-01-01T05:29:29.038075 | 2016-05-22T22:04:06 | 2016-05-22T22:04:06 | 59,412,389 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,477 | r | run_analysis.R | #read the variable names from the given file:
var_names <- read.table("features.txt", stringsAsFactors = FALSE)
dat_test <- read.table("test/X_test.txt", col.names = var_names[,2])
dat_train <- read.table("train/X_train.txt", col.names = var_names[,2])
merged <- rbind(dat_train, dat_test)
#subs <- grep(".([Mm]ean|... |
2763c18ba7ce8274c5c0f4cd5dd2886eb0d0679d | 4cecc8cc52436a08674442d4df18b25234e0cbfa | /R/3_distances_disc.R | b3c836e20c673fb7f2585d755cbbec96c67f17ae | [] | no_license | anjaweigel/mixComp_package | 3be8e19eff9a943dadb3e2bb755954f21219d3c4 | eb27f7ec39fc1e5bdaf5fe4a6e4b2a8f29a16254 | refs/heads/master | 2022-12-07T17:35:08.432093 | 2020-08-26T09:07:31 | 2020-08-26T09:07:31 | 279,328,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,429 | r | 3_distances_disc.R |
## Purpose: returns the L2 distance function corresponding to parameters x
.get.fmin.L2 <- function(dat, dist, formals.dist, ndistparams, j, n.inf, N, dist_call){
function(x){
w <- x[1:(j - 1)]
# first term of difference: sum over values up to "n.inf"
theta.list.long <- vector(mode = "list"... |
3535c4c624659d2e29493db5e86a8acdd6c39b3a | dcda07c019c48d7a64149bde01a0bad7b1982d10 | /R_code.R | b3b399a0cd23870e5b58b4701dfda267623ad3e8 | [] | no_license | Shrunket/cab-fare-predict-ds | 8a01185999450f633c65c0e84181600159ff8cab | cf3de207272b7d44d4654d111e0d214727a75b5a | refs/heads/master | 2020-11-30T10:24:39.819058 | 2020-01-03T09:34:48 | 2020-01-03T09:34:48 | 230,377,046 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,064 | r | R_code.R | rm(list=ls())
getwd()
setwd('C:/Users/Samruddhi/Desktop/Edwisor Project 1')
train_data= read.csv('train_cab.csv')
test_data= read.csv('test.csv')
head(train_data)
head(test_data)
str(train_data)
str(test_data)
train_data$fare_amount= as.numeric(as.character(train_data$fare_amount))
num_col= c('fare_amount... |
b139ac441fcbbaf9775a0637b21cc1865f40ad4a | 103f61b6cbdd466a88528aa6e3b76f196f637ad3 | /R/allele_hist.r | 7e682feb0f73df3f1efba4b88e50f26d68e72119 | [] | no_license | cran/genomatic | 304e6f31443f3b874d5d40c7b2754aa81db1abab | 9e96f6e987377ce10bd650fd50449c7a8824ea48 | refs/heads/master | 2020-05-01T06:14:48.685106 | 2010-01-05T00:00:00 | 2010-01-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,000 | r | allele_hist.r | allele_hist <- function(gmtc.a, allele.df, xlim=NULL)
{
# gmtc.a is a locus (two columns) of a genomatic file.
#
# allele.df is a data.frame cotaining bin information.
locus <- names(gmtc.a)[1]
locus <- substr(locus, 1, nchar(locus)-1)
#
allele.df[,8] <- (floor(10*allele.df[,8]))/10
allele.... |
e6f9bd50777d56245146b8a85aa974488cfb507f | 82130086817e8fa9291ad9cf095af6817a523282 | /testing_scripts/scripts/test_update_B.R | 6f9752067d452fe5c3b1cc84e1e94a9d1caf50c7 | [] | no_license | skdeshpande91/GAM_SSL_SSGL | 0b01d95f38ac80d68029708a97588aa3cc53fe3e | 0b086919eacb2d0ca91c01111e9ac89ae64b02d4 | refs/heads/master | 2022-07-18T14:56:35.557757 | 2020-05-15T22:41:14 | 2020-05-15T22:41:14 | 263,340,646 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,093 | r | test_update_B.R | # First test of update_B
library(Rcpp)
library(RcppArmadillo)
library(MASS)
library(splines)
source("scripts/prepare_X_Phi.R")
set.seed(129)
n_train <- 100
n_test <- 100
p <- 200
X_train_orig <- matrix(runif(n_train*p, 0,1), nrow = n_train, ncol= p)
X_test_orig <- matrix(runif(n_test*p, 0,1), nrow = n_test, ncol = p)... |
9f3541f1cecce3ebb06325eeda082679c9e21536 | 29585dff702209dd446c0ab52ceea046c58e384e | /metaSEM/R/pattern.n.R | 373560aeb27b1334bbaadcd7a37cc7e76cad27cd | [] | 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 | 126 | r | pattern.n.R | pattern.na <- function(x, show.na=TRUE) {
out <- Reduce("+", lapply(x, is.na))
if (show.na) out else length(x)-out
}
|
28dcaf4083078655f5394527d59f3ff0d1e807dc | bbc3754d8900e36146bf80d3cc98f5c36c450cd2 | /man/get_lambda.Rd | f96c17bda9eed7d2704c6b8afbcf552b42dcc09b | [] | no_license | netterie/cantrance | e9782750337bffd0476b76bfed627df0dddf6c5f | a2b29ed44c6dfd50858be06ec03accb2264b85fa | refs/heads/master | 2021-01-25T07:34:45.788607 | 2015-08-26T02:51:45 | 2015-08-26T02:51:45 | 41,400,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 569 | rd | get_lambda.Rd | \name{get_lambda}
\alias{get_lambda}
\title{Takes in parameter info for an exponential and returns the rate(s)}
\usage{get_lambda(param, values, k = NA)}
\arguments{
\item{param}{String specifying the type of parameter to be used
in the construction of the exponential curve. May
be "rate", "median", "mean",... |
0c3b4b4f86da2004a5410bb801a07d8b78395160 | 6782c085e03463fc96f87f344296509244f7245a | /calibration_toy.R | bd0fa4c951e794ac7d2bd1df3b3dedcc1e2af184 | [] | no_license | jake-coleman32/research_code | 83f88b768a254d2679312ddd89590b4eb7e3c0be | 2ddf9dbc49490d94fb1eb512d14f3bc944a469cd | refs/heads/master | 2020-05-30T06:09:59.859710 | 2018-02-14T01:37:14 | 2018-02-14T01:37:14 | 82,625,230 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 898 | r | calibration_toy.R |
t = c(.11,.432,.754,1.077,1.399,1.721,2.043,2.366,2.688,3.010)
y = c(4.73,4.72,4.234,
3.177,2.966,3.653,
1.970,2.267,2.084,
2.079,2.409,2.371,
1.908,1.665,1.685,
1.773,1.603,1.922,
1.370,1.661,1.757,
1.868,1.505,1.638,
1.390,1.275,1.679,
1.461,1.157,1.530)
t_rep <... |
b0d4aceae922b53f30f9483cd462eaf8e6195c07 | a3195ac49f9899167e2406bc83df197fe594906f | /sim_setup_ES_Factorial.R | dbe468246d303711e071b5e697ff611cb2675920 | [] | no_license | katiecoburn/effect_size_proj | d751cb4808f92482845f0924c43d0b3f7f9c1e92 | cc0a30b440c134f964f16820a021bfa7fbe18641 | refs/heads/main | 2023-02-25T03:29:25.925122 | 2021-02-02T22:24:55 | 2021-02-02T22:24:55 | 323,428,730 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,991 | r | sim_setup_ES_Factorial.R | library(MASS)
library(tidyverse)
library(mvtnorm)
grandmean=0
alpha= 0.5
#Case with m=2
i=2
j=2
k=10
var_error= 0.5
var_block=0.5
var_inter=0
var_total=1
beta= rnorm(j, 0, var_block)
interaction= rnorm(i*j, 0, var_inter)
error = rnorm (i*j*k, 0, var_error)
Y_trt_1= c()
Y_trt_2= c()
Y_cnt_1=c()
Y_cnt_2 = c()
#note... |
8a22e8936ac6de7f2ca4b2a34d8399a7e303d906 | 67e4c0d407570dc986de96c92b9d92ec9da43eaf | /scripts/row.extractor.R | 19f4f9e8692770f3bab8481e9566034673dc8433 | [] | no_license | mrdwab/2657-R-Functions | eb1890121a95d41504544d819ec06ae91f86a6f4 | 881cdd962a54228845fb02c880940d1d2ad5e2e7 | refs/heads/master | 2021-01-21T07:53:38.561129 | 2013-01-01T08:36:39 | 2013-01-01T08:36:39 | 4,145,944 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,419 | r | row.extractor.R | ## @knitr rowextractor
row.extractor = function(data, extract.by, what="all") {
# Extracts rows with min, median, and max values, or by quantiles. Values
# for --what-- can be "min", "median", "max", "all", or a vector
# specifying the desired quantiles. Values for --extract.by-- can be
# the variable ... |
7c6876342adce5194e732075f79e21c334c50643 | 29585dff702209dd446c0ab52ceea046c58e384e | /aroma.affymetrix/inst/testScripts/system/chipTypes/Mapping50K_Hind240,Xba240/21.CRMA,paired.R | 5c1ce6ed19e7dff9c43aecc06b393bdf82465771 | [] | 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 | 1,561 | r | 21.CRMA,paired.R | library("aroma.affymetrix")
log <- Arguments$getVerbose(-4, timestamp=TRUE);
dataSet <- "HapMap,CEU,testset";
chipTypes <- c("Mapping50K_Hind240", "Mapping50K_Xba240");
#chipTypes <- chipTypes[2];
# Expected sample names
sampleNames <- c("NA06985", "NA06991", "NA06993",
"NA06994", "NA0700... |
15a312f1d670c1fb7299658b31b905baa919cef1 | d859d67e16df9d322481bbecdd7aacec357356f7 | /btp.R | 49333a6f79fed1bd8bdc4dddd7d918a66150cdab | [] | no_license | sanant854/BTP_final | bf6645f6b515fe7ee84724efebfb77215f5d4475 | 4eac574b025c563c00643d778b2536f97d0d0566 | refs/heads/main | 2023-02-19T16:09:38.300741 | 2021-01-15T10:03:10 | 2021-01-15T10:03:10 | 329,873,647 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,291 | r | btp.R |
total<-100000
lambda<-runif(total,min=0,max=10)
miu<-runif(total,min=0,max=1)
r_wifi<-32
r_lifi<-runif(total,min=2900,max=3200)
t_h<-0.00833
data<-cbind(lambda,miu,r_lifi)
data<-as.data.frame(data)
k1<-r_wifi
k2<-(1-miu)*(r_lifi)
k3<-1:total
for(i in 1:total){
k3[i]<-(max(miu[i]-(lambda[i]*t_h),0)... |
ad1bd6da547f1d333a27bce7d2d323318047bf0d | 5c318b3d88082918477ea3c8ab24e53c0ba12e25 | /negbin_trig_run.R | 319a5782299af7b4ae6df578a6ec685d1b6d3520 | [] | no_license | alxymitr/Carpenter | cc9cd3c6fd6c4cb615c150a04927b79414d2cf55 | e8e5ae9d71a3ac1bd42b9dd0e3c989585e9dde37 | refs/heads/master | 2020-03-20T00:55:12.129322 | 2018-06-12T21:45:14 | 2018-06-12T21:45:14 | 137,060,710 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,272 | r | negbin_trig_run.R |
library(rethinking)
library(MASS)
N <- 50L
tt <- 1:N
xcos <- cos(2*pi*tt/N)
xsin <- sin(2*pi*tt/N)
alpha <- 2
beta_cos <- 0.5
beta_sin <- -0.3
theta <- 2
mu <- exp(alpha + beta_cos * xcos + beta_sin * xsin)
y <- rnegbin(N, mu = mu, theta = theta)
# Function to generate random outcomes from a Negative Binomial di... |
cb8399131c953a65fbd766be8ce8475277518930 | 1c062257e940aa272a30c2f5cf4675d219e9690d | /math4753/man/mypvalue.Rd | c407a88f31ec5bdc8f264f8aecb46d44f7cfb371 | [] | no_license | taoxu-zhu/math4753 | 4139ac34f1d2bc55d5cdc86bb6c0a5cb8b0f282b | 09095268386cef6ea2331f43d2b22cda6dd2e370 | refs/heads/master | 2022-11-10T03:31:14.189666 | 2020-06-30T03:37:06 | 2020-06-30T03:37:06 | 275,980,505 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 430 | rd | mypvalue.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mypvalue.R
\name{mypvalue}
\alias{mypvalue}
\title{course 4753}
\usage{
mypvalue(t0, xmax = 4, n = 20, alpha = 0.05)
}
\arguments{
\item{x}{to find the p value}
}
\value{
value of p value
}
\description{
to find the p value.
}
\examples{
set.... |
c853dddd1455362a9324adc57ce36ae208a20df4 | 41d46c7c39adecfbfbaddb6bd18632c48fcf4de5 | /man/calculate_win_probability.Rd | 603322b705b156c72fe6501ee7ebf2ec03d24eac | [
"MIT"
] | permissive | Vinnetou/nflfastR | 50d9cfc77c40cc9ca8c046037a3710bcc2e15857 | c124e3ac4cb17746d2962ec957eabaff4d7e8882 | refs/heads/master | 2023-02-08T19:26:54.954165 | 2021-01-01T12:55:03 | 2021-01-01T12:55:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,595 | rd | calculate_win_probability.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ep_wp_calculators.R
\name{calculate_win_probability}
\alias{calculate_win_probability}
\title{Compute win probability}
\usage{
calculate_win_probability(pbp_data)
}
\arguments{
\item{pbp_data}{Play-by-play dataset to estimate win probability ... |
f57534445391c9da8c4d3789af4d2dbca1b90b4d | 4314b3de52f8ce39629b7c0887796d895b4a761c | /code_R/sentiments.R | eeb3f3ca4449cb341869f2f82afe9c18a9ae23fb | [] | no_license | kln-courses/corpustextanalysis | b04992e11ba449df125f8524f2792bd378e181e2 | 79e707fe9638d073351f9a71b244ae9f6b42b40d | refs/heads/master | 2021-01-11T04:39:17.821553 | 2017-03-08T14:12:27 | 2017-03-08T14:12:27 | 71,120,794 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,612 | r | sentiments.R | # sentiment analysis
rm(list = ls())
wd <- '/home/kln/Documents/education/tm_R/some_r'
setwd(wd)
source('util_fun.R')
###### word level lexicons/dictionaries
# handheld with some help
matt.v <- paste(scan('data/kjv_books/Matthew.txt', what = 'character', sep='\n', encoding = 'UTF-8'), collapse = " ")
# sentence token... |
6e14fa045eb896281750cc79ade88e16a777debb | cece5aa1c01160ee3880a6b88a882bcac1aa96d7 | /Q4.R | 0a23fb915392a0c4719bcaf596ec625cb0212a4b | [] | no_license | vik235/Multivariate-Statistics | c29c16da0f713b326b1ba5fd34579946c2ffb0fa | 780875c2a35c5a47041e0dfca655adf20ef78e61 | refs/heads/master | 2021-09-09T04:42:28.880183 | 2018-03-13T23:40:20 | 2018-03-13T23:40:20 | 125,128,010 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,520 | r | Q4.R | library(MVN)
library(alr3)
sweat <- read.csv('sweat.csv', header = TRUE)
head(sweat)
n = nrow(sweat)
p= ncol(sweat)
nu= n-1
alpha=.05
c= sqrt((nu*p/(nu - p +1) )*qf(1 - alpha, p,nu - p +1 ))
plot(sweat)
##Solving for b
S = cov(sweat)
SInv= solve(S)
eigens=eigen(S)
eigenvectors=data.frame(eigens$vectors)
eigenvectors
... |
dc00b58498a7c9f7a6199944a36ff25f0cfbfc44 | e9518dced01a5de45d3405218e753db547464904 | /cachematrix.R | 8e90491b826f4d03456e1b71ddfa930ede01b512 | [] | no_license | normbyer/ProgrammingAssignment2 | 91295b6c313efb79efd83b2b1afb7a96963f8418 | b1960015122419ad803895e88bbe66610d626ee6 | refs/heads/master | 2021-01-18T13:00:31.307738 | 2015-12-27T14:31:22 | 2015-12-27T14:31:22 | 48,381,436 | 0 | 0 | null | 2015-12-21T16:13:56 | 2015-12-21T16:13:55 | null | UTF-8 | R | false | false | 1,401 | r | cachematrix.R | ## These functions are used for when one needs to invert a matrix
## and get the result multiple times without recalculating.
## "makeCacheMatrix" sets up the matrix and needed functions
## "cacheSolve" returns the inverted matrix
## returns a list that can be used with the "cacheSolve" function
## a matrix can be pas... |
f529a64f7a85a4d0745f645d171fa6402cf905db | ef1d6fa0df37fa552c4c4625e6e9cb974e8482f0 | /R/ovcCrijns.R | d9d0a98eac5c52e40be21c15aff84fdca792c262 | [] | no_license | bhklab/genefu | 301dd37ef91867de8a759982eb9046d3057723af | 08aec9994d5ccb46383bedff0cbfde04267d9c9a | refs/heads/master | 2022-11-28T09:22:02.713737 | 2022-05-30T15:35:53 | 2022-05-30T15:35:53 | 1,321,876 | 17 | 15 | null | 2022-11-07T11:52:05 | 2011-02-02T21:06:25 | R | UTF-8 | R | false | false | 4,915 | r | ovcCrijns.R | #' @title Function to compute the subtype scores and risk classifications
#' for the prognostic signature published by Crinjs et al.
#'
#' @description
#' This function computes subtype scores and risk classifications from gene
#' expression values using the weights published by Crijns et al.
#'
#' @usage
#' ovcCrijn... |
58af675e5355e681f55d60ec09fbda816c666427 | a985793831427b9706752ae6ea9454e22960aa59 | /test.r | 90b355b5e8ade2c7f1a6524d641860c99d84b1cc | [] | no_license | kabirahuja2431/SchizophreniaClassification | 2df893d725fadb79afe2d8cf1c1db19a55f4851d | 33e9dba173aa723c7373955822190faae1313320 | refs/heads/master | 2021-07-07T12:27:02.356318 | 2017-10-05T20:18:11 | 2017-10-05T20:18:11 | 105,685,813 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 126 | r | test.r | fun1 <- function(x){
x = x+ 10
print(x)
}
fun2 <-function(y){
fun1(y)
}
x <- 15
y <= c(100,100,100)
fun1(y)
print(y) |
5d079659c5fd03fcfe2336c66784e71c32c04579 | 29585dff702209dd446c0ab52ceea046c58e384e | /gdimap/R/simul.simplefield.R | c8e7b3a131a4cb2b1b53896558529db98433f129 | [] | 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 | 10,586 | r | simul.simplefield.R | simul.simplefield <-
function(gdi="gqi", b=3000, sigma=NULL, clusterthr=0.6, logplot=TRUE,
savedir=tempdir(), fmask="m1", ang=NULL, ...)
{
## glyph field mask
tfmask=c("m1","m2","m3","mx1","mx2","mx3")
## default angles in masks
amask =c(60,60,60,60,90,45)
mfun <- match(fmask, tfmask)
if(is.null(ang))
... |
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