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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0c69ea683638698c016e99957ec711ef3150859b | 91c6539e8f0cdbbe087024d4427c710a97f2baba | /Code/undetermind.R | 72d69a1cb55ffd38c738f5d4fcf39dc42854640e | [] | no_license | kevinkr/kaggle-allstate | fa3dd5475a1c34387bfe87ac1c4f2351191cac64 | d1b76b10c7758e02af7dba02dcde6202263a08b1 | refs/heads/master | 2021-01-10T23:55:38.630232 | 2016-12-14T13:20:37 | 2016-12-14T13:20:37 | 70,800,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,398 | r | undetermind.R | # Allstate Kaggle competition
# Start: 10-13-16
source("packages.R")
#############################
# Merge data sets
#############################
#
#Create dummy variable in test
test<- mutate(test, loss = "none")
#Create sorting variable dataset before combining
test <- mutate(test, dataset = "testset")
train <... |
8c8bc7fc39cc76d211a2270e7ca490d1691e41cb | 7917fc0a7108a994bf39359385fb5728d189c182 | /cran/paws.security.identity/man/kms_list_aliases.Rd | 06a21bceab74144d46ddd9e0b8c9f540face5552 | [
"Apache-2.0"
] | permissive | TWarczak/paws | b59300a5c41e374542a80aba223f84e1e2538bec | e70532e3e245286452e97e3286b5decce5c4eb90 | refs/heads/main | 2023-07-06T21:51:31.572720 | 2021-08-06T02:08:53 | 2021-08-06T02:08:53 | 396,131,582 | 1 | 0 | NOASSERTION | 2021-08-14T21:11:04 | 2021-08-14T21:11:04 | null | UTF-8 | R | false | true | 3,849 | rd | kms_list_aliases.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kms_operations.R
\name{kms_list_aliases}
\alias{kms_list_aliases}
\title{Gets a list of aliases in the caller's AWS account and region}
\usage{
kms_list_aliases(KeyId, Limit, Marker)
}
\arguments{
\item{KeyId}{Lists only aliases that are asso... |
df200bbe7edd7af69fcbb8e4277bce057f931636 | 1a08f81a8ebee2753b42333e77735f9416f4c396 | /R/tag_by_regex.R | be7f0f164e57451f8a74d38893103d75db38ce8b | [] | no_license | gaospecial/biblioreport | 1404645207c667e71c0bd11c36af2fe9120ff124 | ab407fad6223c8ab8c5eecd80d21bb7c0471d082 | refs/heads/master | 2023-09-05T19:31:05.171230 | 2021-11-09T11:40:29 | 2021-11-09T11:40:29 | 334,099,411 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,179 | r | tag_by_regex.R | # 根据检索词对文献进行分类
#' Tag record by regular expression search
#'
#' @param x is the search content
#' @param pattern a list of regular expression, which can be obtained by [keywords_from()]
#' @param pattern.names is a human readable abbreviation name
#' @param sep default is ";"
#'
#' @return
#' @export
#'
#' @examples
ta... |
280a3f13cdc4c1ee4f3be09d4426c63982a1e5ac | 6e012caa8d23f76498892dd4a5053c618e74a0d5 | /scripts/exploratory data analysis/L0-descriptive_stats_0.1.R | 2616d50d53c2f07885a993c2377d92cdf64ce8dd | [
"MIT"
] | permissive | duttashi/learnr | 4bc973fed064fe496673fc53d65cb797db7ae120 | 3d7e17d044dfd2e4ce1996d1a2a5a02dc7e4db45 | refs/heads/master | 2023-06-29T01:39:40.940465 | 2023-06-16T05:00:07 | 2023-06-16T05:00:07 | 62,351,661 | 81 | 51 | MIT | 2023-06-16T05:00:08 | 2016-07-01T00:56:08 | R | UTF-8 | R | false | false | 1,469 | r | L0-descriptive_stats_0.1.R | # This script is in continuation with L0-descriptive_stats_0.0.R
# Data Formatting
# load the required libraries
library(stringr)
library(chron)
# see the help pages for str_pad, substring, paste, chron, head
help("str_pad")
# prints the first five rows of the dataset.
head(flights[miss_name]) # The date format need ... |
fe6dc93ca943b828d83b4a77acbf1bf6553f4f6a | 68562f46424bf312d5fe070990243ae03ed1454e | /man/labels2matrix.Rd | fd50b08987af43193692adc67824399f3ec44949 | [
"Apache-2.0"
] | permissive | ANTsX/ANTsR | edb12114bc3d143c59ebd3947301de705ec51b63 | 8deb4d897fdb295a0213ca59e3bf1846f62ce99a | refs/heads/master | 2023-06-24T14:48:05.362501 | 2023-06-24T11:15:10 | 2023-06-24T11:15:10 | 5,782,626 | 86 | 32 | Apache-2.0 | 2023-06-17T12:15:50 | 2012-09-12T16:28:03 | R | UTF-8 | R | false | true | 1,118 | rd | labels2matrix.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/labels2matrix.R
\name{labels2matrix}
\alias{labels2matrix}
\title{Convert label image to a matrix}
\usage{
labels2matrix(img, mask, targetLabels = NULL, missingVal = NA)
}
\arguments{
\item{img}{input label image}
\item{mask}{defines domain ... |
3c918eeb90cf54db623d44d85f538ce30c1a200d | f3818f31a2452e60e849a76652a013429ff64be7 | /scripts/TPF_family_dataset.R | 982936ba88e1421bd156a3538e00fbe66a5a7b49 | [
"MIT"
] | permissive | tfausten/tfdatalab | 85d3ab2443fbb644d9e257878b5b58eab9467f32 | 2a3c8c9785c71c1f4828a540e55152fa61e89fa4 | refs/heads/master | 2020-03-23T03:24:55.085592 | 2018-11-09T08:15:13 | 2018-11-09T08:15:13 | 141,029,151 | 0 | 0 | MIT | 2018-11-06T07:56:59 | 2018-07-15T13:23:10 | HTML | UTF-8 | R | false | false | 3,774 | r | TPF_family_dataset.R | #create a single-entry family-id dataset with relevant values for further analysis
load("./datasource/TPF/201803_TPF_Core.RData")
tpf_families <- subset(tpf_core, select = c(Family_id, USPTO_app_first, EPO_app_first, JPO_app_first,
PCT_app_first))
rm(tpf_core)
#tranform ap... |
e9bf4990657119e808b4c54c5ef0960df09f6a41 | 535d05d170267fe6076945f8f6f9e226b51fe50d | /global.R | fb6faa4bdb1389cc1f412c8bad0ebacef31cb4fb | [] | no_license | djepstein87/menu_scrapy | b6f6db16252d5a30eda420ecd5a5d3585f02822f | d910713238c31926a25dedc90f1ecd98cce17ba8 | refs/heads/master | 2021-01-11T14:16:41.994949 | 2017-04-10T21:50:41 | 2017-04-10T21:50:41 | 81,286,254 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 52 | r | global.R | #menus_reduced = readRDS('./data/rds_menus_reduced') |
7ff4ba647d3dc065ad20cae106cb84ac393d69ac | 28291c2ab8e0ae8b9392dd9ce921ff9d0c727f37 | /models/analise_modelos.R | 514cbc65446f1b2fd978576f016c4077039ce5f5 | [] | no_license | pbizil/predict_ipca | 009eb3018b5b60dd4e9b4158f55ed82e97351258 | 65665b2bb94310e10d1e3925ded1297dcb09cf13 | refs/heads/main | 2023-07-07T00:18:27.265783 | 2021-08-14T19:30:55 | 2021-08-14T19:30:55 | 394,425,863 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 14,628 | r | analise_modelos.R | library(DBI)
library(ggplot2)
library(rjson)
library(anytime)
library(naniar)
library(Metrics)
# target
con <- dbConnect(RSQLite::SQLite(), "/Users/pbizil/Desktop/tcc_pos/data/app_db.db")
dados <- dbReadTable(con, "preds_l1")
colnames(dados)[3] <- c("expectativas_ipca")
dados$date <- as.Date(strptime(anyti... |
bbc17553a37734f8a181a153172d16361fe57682 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/errint/examples/print.measure.Rd.R | a889b385cdae7a75a843e73a6d048e116bdf0a9d | [] | 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 | 152 | r | print.measure.Rd.R | library(errint)
### Name: print.measure
### Title: Printing Measures
### Aliases: print.measure
### ** Examples
res<-measure(0.1,0.7)
print(res)
|
3c31edabb4586f3410510c037e3ca9413f491d86 | e30bb95df470e1c177b825c7bb16d825be1c4cf0 | /Building Variable/Type I Variable.R | 76907011669cc7ba8a0e12f76f8ae6c6a8106aea | [] | no_license | Libardo1/Credit-Card-Payment-Supervised-Fraud-Detection | 316f290dba9776384e6f4a4d9c599c571b4a8b39 | c9c8f54132f6d1f3cc5245429876170d0f6f6339 | refs/heads/master | 2020-03-17T04:41:45.119460 | 2018-03-18T22:12:52 | 2018-03-18T22:12:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,750 | r | Type I Variable.R | load("Card Payments_Cleaned.rda")
library(dplyr)
############################# Type I Variable ######################
# historical count with 3 days time window
############## Subset trial #############
data_Jan = filter(data, date < "2010-02-01")
current = data_Jan[6748,]
row = 6748
###### time window ... |
b7b768f457a7348129f79f8d6ec039374709224d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/ashr/examples/mixcdf.Rd.R | 4baca8553cec29c8b2c4d7651ade2ade0691e387 | [] | 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 | 158 | r | mixcdf.Rd.R | library(ashr)
### Name: mixcdf
### Title: mixcdf
### Aliases: mixcdf
### ** Examples
mixcdf(normalmix(c(0.5,0.5),c(0,0),c(1,2)),seq(-4,4,length=100))
|
3d0db0e357e36a1f1fd19e6e23f37cd20994bb3d | 73ebcf788041071a87add04bec5b08675573b78f | /cuda/hw3.R | b9f44a46585202a2c4849c8eaaba0fce278d1710 | [] | no_license | rachan5/ECS158HW2 | 3c721b23f0dcf6830531ca57b75d71ef32b56919 | 443e21edcb3a3622a2f6ed913ee86d7bf5fdcbe3 | refs/heads/master | 2020-04-10T00:08:04.302301 | 2015-03-21T06:51:37 | 2015-03-21T06:51:37 | 30,328,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 274 | r | hw3.R |
x <- c(1, 1,2,2, 3,3, 4,4, 5,5, 6,6, 7,7, 8,8, 9,9, 10,10)
y <- c(11,11, 12,12, 13,13, 14,14, 15,15, 16,16, 17,17, 18,18, 19,19, 20,20)
smoother <- function(x,y,h) {
meanclose <- function(t)
mean(y[abs(x-t) < h])
sapply(x,meanclose)
}
print(smoother(x,y,2))
|
5e259bd0ca26b1975064087a4b1a3edccd0ff638 | 4ca76a3cef4af592ba8ab121aae35f5eccb59670 | /man/write_hrc2.Rd | 36f8cf3b50181b8ef531f6c0665a6d0594377826 | [
"MIT"
] | permissive | InseeFrLab/rtauargus | 5e9405d3453a534adc235ec71f5d559de00f8f62 | f3810aff361d2eb7aa31d47e38fe1943f42733ad | refs/heads/master | 2023-08-18T22:24:22.967560 | 2023-07-20T16:33:36 | 2023-07-20T16:33:36 | 442,119,707 | 4 | 4 | MIT | 2023-09-05T11:56:35 | 2021-12-27T09:57:18 | R | UTF-8 | R | false | true | 11,385 | rd | write_hrc2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/writehrc.R
\name{write_hrc2}
\alias{write_hrc2}
\title{Creates a hrc file from correspondence table}
\usage{
write_hrc2(
corr_table,
file_name = NULL,
sort_table = FALSE,
rev = FALSE,
hier_lead_string = getOption("rtauargus.hierlead... |
c225dc14bd82e26f163054b9ff652193f7fd3130 | 74d1d03ce2ec81c7f34d6ee0246a7d41db9568f9 | /simulate_functions_vsv.R | 38fe1d03beb61d054a3595f40f7b6efba6dae9ab | [] | no_license | ruslana-tymchyk/accuracy_simulations | 3ad3d441c904b93cfd90480a19c78888fed210a9 | a9c6bd67f304af02cf49270466c8d198d2ac8e93 | refs/heads/master | 2020-07-03T05:41:16.078903 | 2019-08-11T19:56:47 | 2019-08-11T19:56:47 | 201,804,926 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,736 | r | simulate_functions_vsv.R | #-------------------
#----DESCRIPTION----
#-------------------
#Distributions: Normal & T (truncated or not)
#Analysis: GLM & ANOVA (optional - Bayesian Anova)
#Design: Between-Subjects
#This set of function can be used to simulate and analyse the datasets using Drift Diffusion Model.
#The data is being sampled from a... |
10c8de2b650330c5d1e6e27882e6445473358397 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/genius/examples/add_genius.Rd.R | 2aa6b4455a0be5f9f8db39c4b409c25d84fb4c61 | [] | 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 | 444 | r | add_genius.Rd.R | library(genius)
### Name: add_genius
### Title: Add lyrics to a data frame
### Aliases: add_genius
### ** Examples
## No test:
artist_albums <- tribble(
~artist, ~album,
"J. Cole", "KOD",
"Sampha", "Process"
)
artist_albums %>%
add_genius(artist, album)
artist_songs <- tribble(
~artist, ~track,
"J. Cole",... |
7cb3d3d0c125dde714ceec9b31f9a8ee152cf689 | ca4cc9c323fe000df7189a448dd59618f70b8c2f | /man/imputeMinDiv2.Rd | 633eb9b3a008f6560378e2430345a7953dc563f3 | [
"BSD-2-Clause"
] | permissive | PNNL-Comp-Mass-Spec/RomicsProcessor | 235c338d2192f385d408e55c302868e37ff9dc06 | 72d35c987900febc3e6c6ed416d4d72dc5820075 | refs/heads/master | 2023-03-18T08:14:48.098980 | 2023-03-15T16:50:14 | 2023-03-15T16:50:14 | 206,400,976 | 4 | 2 | BSD-2-Clause | 2022-12-05T21:55:36 | 2019-09-04T19:49:26 | HTML | UTF-8 | R | false | true | 889 | rd | imputeMinDiv2.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/04_Manage_Missing.R
\name{imputeMinDiv2}
\alias{imputeMinDiv2}
\title{imputeMinDiv2()}
\usage{
imputeMinDiv2(romics_object)
}
\arguments{
\item{romics_object}{has to be an romics_object created using romicsCreateObject()}
}
\value{
The functi... |
9a0483e7e947510fb15c657c97c22b6cd7f55088 | 7706cfba17c70548436fb190add59e7ed6e14199 | /man/crossv_kfold.Rd | 08477bf7bbf135659db6a352891d7bf1c1adf806 | [] | no_license | jrnold/resamplr | 0997d9076f9635963bea867559de742592fbd18b | 72242df726e87fc3bd9e5c6a93bbdb5e4f9851d1 | refs/heads/master | 2021-01-19T22:01:15.942589 | 2018-07-22T21:39:32 | 2018-07-22T21:39:32 | 83,233,025 | 36 | 5 | null | 2018-03-15T10:15:21 | 2017-02-26T19:04:07 | R | UTF-8 | R | false | true | 3,254 | rd | crossv_kfold.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/kfold.R
\name{crossv_kfold}
\alias{crossv_kfold}
\alias{crossv_kfold.data.frame}
\alias{crossv_kfold.grouped_df}
\title{Generate cross-validated K-fold test-training pairs}
\usage{
crossv_kfold(data, K, ...)
\method{crossv_kfold}{data.frame}... |
fe68be34da26f20214579c9e941d3f29903fcadf | 75a635ec3af04c3898867cca8ea5cf11c6409533 | /Chapter04/glove.R | b414e714d9fb59c026fda14bf1a6a78337d85135 | [
"MIT"
] | permissive | shantanu1402/R-Machine-Learning-Projects | dd644f1fff8f0ed3cd29f7c5fba55bfc78e693b2 | 2f5c1a6a2b6a2e75edfcea6d60feb1ca95d24c85 | refs/heads/master | 2023-02-17T11:52:03.583511 | 2021-01-15T05:44:26 | 2021-01-15T05:44:26 | 464,884,312 | 1 | 0 | MIT | 2022-03-01T12:29:47 | 2022-03-01T12:29:46 | null | UTF-8 | R | false | false | 3,051 | r | glove.R | # including the required library
library(text2vec)
# setting the working directory
setwd('/home/sunil/Desktop/sentiment_analysis/')
# reading the dataset
text = read.csv(file='Sentiment Analysis Dataset.csv', header = TRUE)
# subsetting only the review text so as to create Glove word embedding
wiki = as.character(text$... |
7700a32b4bc9596b2dbf49b69a966b50808a1ae9 | bf943775e1f3d22300fa315fc53a0f1895c72902 | /makePlots.R | 198a878e50336efdd42a02a5b5dabd8fdd24b6ba | [] | no_license | Rajat-181/Tennis-Analytics-in-R | e2da720f7598075fb78090466c69fe92138276e4 | c7763a7eb9275ffd8c735704cf4d5c036ffe1411 | refs/heads/master | 2020-04-01T07:36:57.044899 | 2018-10-16T18:39:42 | 2018-10-16T18:39:42 | 152,996,275 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,374 | r | makePlots.R | length(unique(data$Player_ID.x))
playerdt <- function(playername,datestart1,datestart2){
playername <- "Federer, Roger"
datestart1 <- as.POSIXct("2016-01-08")
datestart2 <- as.POSIXct("2018-01-08")
subdata = data[(data$Scheduled > datestart1 & data$Scheduled < datestart2),]
player_matches <- sub... |
e38759fdca8b55da5e2a1446515839f43a1f4693 | 5c861208fb29b256e6d47ec79c41d6fea7d47310 | /man/set_zenodo_certificate.Rd | efbf3bdf5ae3d1cfd94056a5b495b0cc4a5b207a | [
"MIT"
] | permissive | codecheckers/codecheck | 9f2acf527f5aa3e75619f53aca59873e4ce1ba36 | b0c6c07cc7d24313809de110c3a04925215ea027 | refs/heads/master | 2023-05-22T16:24:38.127552 | 2022-10-11T15:10:08 | 2022-10-11T15:10:08 | 256,862,293 | 6 | 1 | MIT | 2020-12-08T20:32:26 | 2020-04-18T22:06:52 | R | UTF-8 | R | false | true | 734 | rd | set_zenodo_certificate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codecheck.R
\name{set_zenodo_certificate}
\alias{set_zenodo_certificate}
\title{Upload the CODECHECK certificate to Zenodo.}
\usage{
set_zenodo_certificate(zen, record, certificate)
}
\arguments{
\item{zen}{- Object from zen4R to interact wit... |
1c8dbd7c94b9537b9c51c3e7a927cc92e26a039c | 8d81ecafe5095bd5b180d5e1c9d871c66b6a8f76 | /Rscripts/0112_Rscript_two.R | 1f566052cf2a84085412d241c21b4709b4ef0a2f | [] | no_license | jimrothstein/try_things_here | 0a3447b5578db293685fb71c7368f1460d057106 | d2da5ce3698dd21c2fe1a96c30f52cefe6f87de4 | refs/heads/main | 2023-08-28T08:55:43.314378 | 2023-08-25T23:47:23 | 2023-08-25T23:47:23 | 98,355,239 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 337 | r | 0112_Rscript_two.R | #!/usr/bin/env Rscript
# at commnad line
#
# =================================
# SELF-CONTAINED
# USAGE: ./095_R_script_execute.R
#
# OTHER CLI:
# - Run R # loads whole thing
# - Rscript -e "8*8" # returns answer only
#
# =================================
print("hello")
args <- commandArgs(trailingOnly ... |
2749dd84dceb8b426f9e0c7330b384ee8f0d4c46 | 694286ae6914bc02acbaf1ac983f7ba77ad775f8 | /R/make_contrasts.R | 099aacabf570ce8b99fff03791debc4571c11c7e | [] | no_license | bcjaeger/graft-loss | 8c3629fd6a15bec96b3454f8255ff3f33eb6b702 | 058aae6ec33b8d7e041f60dbcc0ea77685bafa24 | refs/heads/master | 2023-04-24T10:17:41.838232 | 2021-05-12T18:08:44 | 2021-05-12T18:08:44 | 330,760,753 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 998 | r | make_contrasts.R | ##' .. content for \description{} (no empty lines) ..
##'
##' .. content for \details{} ..
##'
##' @title
##' @param linpred
make_contrasts <- function(linpred) {
rslt_contrast <- tibble(contrast = character(),
estimate = double(),
lower = double(),
... |
1f07f8fd200bbdc30642fe325e633aa6cffc51d5 | e6237c044b69c29f1c01e2cb7bb435f58a30eeec | /R/formula_rdbe.R | d4a6d72e7663f32ae2541271394b6db8b1bfcdef | [] | no_license | LiChenPU/Formula_manipulation | 7dbc5c53110b6363991dc58df209f4dd8712bf90 | 79dc992d2de9038035574e9a04e794586b6c452d | refs/heads/master | 2022-01-23T11:03:52.003455 | 2022-01-12T07:44:25 | 2022-01-12T07:44:25 | 177,156,070 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 2,403 | r | formula_rdbe.R |
#' formula_rdbe
#'
#' @param formula e.g. "C2H4O1"
#' @param elem_table a table records unsaturation
#'
#' @return the ring and double bond number
#' @export
#'
#' @examples formula_rdbe(formula = "C2H4O1", elem_table = lc8::elem_table)
formula_rdbe = function(formula = "C2H4O1", elem_table = lc8::elem_table){
rdb... |
95e48929a49ad846f53b0d7cced3954f5296f6c3 | 7067be1932d71266ddaa9a4ecab50e2f26c99539 | /Submission/Statistical_and_Spatial/Nairobi_Analysis.R | 146399a1a8efb21cb1e0ad21eb4314427a00ab23 | [] | no_license | CoryWilliamsGIS/Dissertation | d7d793bdde884a2396b4deff8803ca3a9e6c5e5a | f182d2eedfd63654dc7ff7fe8b605f1278c0bfb6 | refs/heads/master | 2020-03-28T13:55:21.798959 | 2018-09-12T11:44:33 | 2018-09-12T11:44:33 | 148,441,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 125,284 | r | Nairobi_Analysis.R | #Global Variations in OpenStreetMap
#Cory Williams
#Greater Manchester Ward Analysis
#Load relevant libraries
library(caret)
library(pls)
library(rgdal)
library(dplyr)
library(ggplot2)
library(ggmap)
library(RColorBrewer)
library(readxl)
library(corrplot)
library(Hmisc)
library(relaimpo)
library(re... |
294459558604bbd341fd57d550327a6a98d26d79 | 2baa8f641eb762c36fb50db7a812998531b96687 | /run_analysis.R | 1c42a8d7987510a7f57dfa3d51193f7577af82bf | [] | no_license | HariharanJayashankar/Getting-and-Cleaning-Data---Programming-Assignment | 5da3588cbd95bebd47f69e9911488376183fbc25 | 4dc999be96774925fc2ef7594a81723ae43f4ac5 | refs/heads/master | 2021-08-31T18:22:05.122430 | 2017-12-22T10:26:03 | 2017-12-22T10:26:03 | 115,102,820 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,793 | r | run_analysis.R | library(dplyr)
#downloading and unzipping the file
if(!file.exists("Data.zip")){
download.file("https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip", destfile = "Data.zip")
}
if(!file.exists("UCI HAR Dataset")){
unzip("Data.zip")
}
#Importing data
labels ... |
0e1c039ac3db92b838953c0eba0664dd6730df8e | f4105cb1aad7f9110478aa4253a748ee6b585c38 | /R/Sept2014_ReportFigures.R | eb7871aef586ac510bebf8d3c6b0f2b333ffaa8b | [] | no_license | kmanlove/SheepBehavior | da8611fa81e2a5abfffca7bcf9ec5db696a7bcf2 | bc54d918212393a9e5d6b0e27364381eef8d3d2e | refs/heads/master | 2021-01-01T20:40:49.984218 | 2015-05-13T16:16:45 | 2015-05-13T16:16:45 | 31,034,897 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,286 | r | Sept2014_ReportFigures.R | #---------------------------------------------------------------------#
#-- code for preliminary analysis of bhs contact patterns from 2014 --#
#---------------------------------------------------------------------#
bb <- read.csv("./data/CleanBlackButteFollows_090214.csv", header = T)
aso <- read.csv("./data/CleanAso... |
0e22345ff7e78aa2eac8efa04e18dd2c78e5e853 | 125a18c7eba0ca722425fadbfd5e7c1e1692ae86 | /man/data_ynorm.Rd | 378bcce99856f21aa8821905bb0dff668eba7443 | [] | no_license | cran/bbemkr | 8a41414f3161d48028bfbaf4480c894d9357e6bf | 376a966dfd17a52129d4cbaaa092d1684477674a | refs/heads/master | 2020-04-17T07:51:10.950435 | 2014-04-05T00:00:00 | 2014-04-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 470 | rd | data_ynorm.Rd | \name{data_ynorm}
\alias{data_ynorm}
\docType{data}
\title{
Simulated response variable
}
\description{
The response variable is simulated from the functional form of
\eqn{data_ynorm = sin(2*pi*x1) + 4*(1-x2)*(1+x2) + 2*x3/(1+0.8*x3*x3) + rnorm(1,0,0.9)}, where \code{x1}, \code{x2} and \code{x3} are
simulated from a un... |
9c3bd80043f317dac8a195a2cc27580a2f37bb62 | c7a7e02bfe49d5195cda8cf973b09c24e3094b15 | /GH_250m_crop_Cocoa.R | c436c08909030d598e45ef47d7e9c52a854e9b14 | [] | no_license | iSDAgri/AlexVerlinden | 836af00bae78d90da2e3fb52c442ac5eb8f983d3 | 867fbbf57c06a9a4b0347e2df398fa127c76315a | refs/heads/master | 2021-05-04T13:07:43.265314 | 2017-05-07T13:35:24 | 2017-05-07T13:35:24 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,012 | r | GH_250m_crop_Cocoa.R | # Script for crop distribution models GH using ensemble regressions
# basis for cropland mask is 13000 point survey for Ghana conducted by AfSIS for Africa
# grids are from Africasoils.net
# field data are collected by GhaSIS 2016
#script in development to test crop distribution model with glmnet based on presence/ab... |
0dc1092f74f1291ea7c0adba5370a53dde33e894 | 109734b597c2d760725a1a050174a5d11b3c1a9b | /man/stratrand.Rd | 366f7df8f33714dc272a53608c9be3194389c786 | [] | no_license | rubak/spatstat | c293e16b17cfeba3e1a24cd971b313c47ad89906 | 93e54a8fd8276c9a17123466638c271a8690d12c | refs/heads/master | 2020-12-07T00:54:32.178710 | 2020-11-06T22:51:20 | 2020-11-06T22:51:20 | 44,497,738 | 2 | 0 | null | 2020-11-06T22:51:21 | 2015-10-18T21:40:26 | R | UTF-8 | R | false | false | 2,073 | rd | stratrand.Rd | \name{stratrand}
\alias{stratrand}
\title{Stratified random point pattern}
\description{
Generates a \dQuote{stratified random} pattern of points in a window,
by dividing the window into rectangular tiles and placing
\code{k} random points in each tile.
}
\usage{
stratrand(window, nx, ny, k = 1)
}
\arguments{
... |
3aa09a229c5eb12cfe533e0c52264418992750a0 | 5f074caca95f3046218031636b62036dded7f56b | /src/inverted-index.R | 63873bf22d1f614680f32cc7f74edbc9077c0870 | [
"MIT"
] | permissive | xuleisanshi/rhadoop-examples | c1471c53966c446746c390494760f1c05640572c | 7bc197e84bd04147a409fb4b1f287634b7fd3862 | refs/heads/master | 2021-05-27T19:43:44.666320 | 2014-03-13T11:32:15 | 2014-03-13T11:32:15 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 575 | r | inverted-index.R | ## Load and initialize libraries
library(rhdfs)
hdfs.init()
library(rmr2)
# Define the wordcount application
invertedIndex = function(input, output = NULL, pattern = '[[:punct:][:space:]]+') {
mapper <- function(., lines) {
keyval(tolower(unlist(strsplit(x = lines, split = pattern))), Sys.getenv("map_input_file"... |
73625b890905a8a77060fbfee9319d47450e27aa | 62971ba2128f643d37b85452c293a967c9820b31 | /plot-violin/violinplot_snr.R | c90a7cb960ca176da4b91d658eb6e658b1b48467 | [
"MIT"
] | permissive | comp-music-lab/lullaby-analysis | dd1f1d1d01d18797054d60abdafb85c451b3264c | 2361eab4b047b7dc2269b42917ab0d8f463af0eb | refs/heads/main | 2023-06-30T11:38:57.255144 | 2021-08-02T00:46:20 | 2021-08-02T00:46:20 | 389,928,027 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,585 | r | violinplot_snr.R | # Clear variables
rm(list = ls())
# load libraries
library(ggplot2)
library(Hmisc)
# get data
sr_table <- read.csv("../data/IPL_snr.csv", header = TRUE, sep = ",", quote = "")
# confidence intervals for medians
source("./med_confint_e.R")
# fig : subjective rate violinplots
ylab <- expression(paste("SNR (dB) by Gau... |
79abe8182b33bc477b4c5a8a92a8bf6b6c9189db | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/spectacles/examples/ids.Rd.R | 57650d22520a61bf9ab094bc53311ef805d7e729 | [] | 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 | 549 | r | ids.Rd.R | library(spectacles)
### Name: ids
### Title: Retrieves or sets the ids of a 'Spectra*' object.
### Aliases: ids ids<- ids,Spectra-method ids<-,Spectra-method
### ids<-,SpectraDataFrame-method ids<-,Spectra-method
### ids<-,SpectraDataFrame-method
### ** Examples
# Loading example data
data(oz)
spectra(oz) <- s... |
f625634adbe295f2d88d99c4e78ac4a264aeb614 | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_Makkink/AFL_ET0_Makkink/ET0_Makkink_valgrind_files/1615848808-test.R | 3baa0a8dd0638a1f2897508e70f58672bbf995c6 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 712 | r | 1615848808-test.R | testlist <- list(Rs = numeric(0), atmp = numeric(0), relh = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), temp = c(8.5728629954997e-312, 2.05810269315294e-312, 2.51947000254159e+93, 2.51947000254151e+93, -1.03644984280403e+156, 3.55262942202735e-157, 2.73593267390447e+... |
1da5969d6306e55ea2ed2857f7c23116dbf87860 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/alphahull/examples/dw_track.Rd.R | 048219e3775c1719826f32be610600276a8e89cf | [] | 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 | 1,391 | r | dw_track.Rd.R | library(alphahull)
### Name: dw_track
### Title: RBM-sausage calculation of tracking data
### Aliases: dw_track
### Keywords: nonparametric
### ** Examples
## Not run:
##D library(move)
##D library(ggmap)
##D # Data from Movebank
##D # Study Name: Dunn Ranch Bison Tracking Project
##D # Principal Investigator: Ste... |
34f0c89390a485222daddbb37ca2eb863b76fe6b | fdf8f6f14a9ea320629c338890f96a907087c03f | /utils/format_data.R | 8ffe4c5702654f90214c2bb1b5c6849d14eccef1 | [] | no_license | sds-dubois/mltk | cb393a9c7640bb2f0bcb965ea2e54cfc4be90767 | 20bb38a8ffdc2a345e593a58812ce98bf8fe3afe | refs/heads/master | 2021-01-17T12:33:42.111887 | 2016-03-31T23:48:56 | 2016-03-31T23:48:56 | 52,417,018 | 1 | 1 | null | 2016-03-15T01:19:31 | 2016-02-24T05:32:02 | Java | UTF-8 | R | false | false | 3,076 | r | format_data.R | # Author: Sebastien Dubois
# Libraries ---------------------------------------------------------------
library(readr)
library(dplyr)
library(stringr)
# Load --------------------------------------------------------------------
dir <- "sutter/"
# healogics/
# sutter/
val_name <- "val-12-4-15"
# "test-2013-only-4-3-... |
38df17af7dad8a93a94479e57b76f2beada05c5a | 3b2e52985e8337c9440d696c643459356d105bc6 | /code/PostToPlotly.R | d913b65484d0d81c92ad4d17cbbaaad09d736fb2 | [] | no_license | jasonboyer/W4150 | 204949d0032dbbf18af86557f8a81f4add04cb96 | 5e314694436c25903ec606d056847ae058aba584 | refs/heads/master | 2020-06-19T01:08:39.407020 | 2016-12-13T11:52:54 | 2016-12-13T11:52:54 | 74,928,580 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 681 | r | PostToPlotly.R | # PostToPlotly.R - Code to post plots to plot.ly for
# public web viewing and interaction
#
# Add the following lines in .Rprofile to enable uploading:
#
# Sys.setenv("plotly_username"="your_plotly_username")
# Sys.setenv("plotly_api_key"="your_api_key")
#
# See https://plot.ly/ggplot2/getting-started/
#
library(plot... |
37bf39db301509c0a26473ef884562c0eaab0928 | 3b3cfcc6673aec9aeda77884a976ca34095b8690 | /Ex20.R | 51c8db899c5b173f11fee0f46582823a110be720 | [] | no_license | MrTorstein/STK4900 | 2f593cd55e7590e537dc8447f942a23da1f35c07 | de4e69e86ebda08c44890f169cc1ab218a239e0d | refs/heads/main | 2023-07-20T02:15:40.442590 | 2021-08-24T20:51:25 | 2021-08-24T20:51:25 | 399,600,319 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,208 | r | Ex20.R | #Exercise 20:
# In this exercise we will study data from an experiment where one wanted to assess the toxicity of the substance rotenone.
# Groups of about 50 insects were exposed to various doses of rotenone, and the number of insects that died at each dose level was recorded.
# The data are available at the course... |
d4a75745f540d495823e741f1d60cb4fbed098a0 | 930051798caf4bf826ce6a966b4ba16b28f1d30a | /R/signalEarly.R | 5600a648ede02602705401ff90849c9768cb7af3 | [] | no_license | jcheng5/future | e5891014fd63f52b240e45e31b1b5b6f66506eca | f4c170a52fe2333d5b91c0336a74a20056e8de42 | refs/heads/master | 2021-06-28T16:19:40.335834 | 2017-09-09T15:12:13 | 2017-09-09T15:12:13 | 103,759,888 | 3 | 0 | null | 2017-09-16T14:51:13 | 2017-09-16T14:51:13 | null | UTF-8 | R | false | false | 1,239 | r | signalEarly.R | signalEarly <- function(future, collect = TRUE, ...) {
## Future is not yet launched
if (future$state == "created") return(future)
earlySignal <- future$earlySignal
## Don't signal early?
if (!earlySignal) return(future)
debug <- getOption("future.debug", FALSE)
if (debug) mdebug("signalEarly(): Retrie... |
5c227186107c74bc4efcbcaf640eef3e0d701fbb | 12a9bea8cfff9e5dcd44651102a5e0adf477164a | /R/dollar.R | 53040c0ada1a783c4dd9ad335541588f07446cc6 | [] | no_license | duncantl/RLLVMCompile | 2b98a04f1f7e71f973a281b40457f5730e38f284 | 7fad5bd394a6f74ace0f6053a5d08e4f15cf3a1f | refs/heads/master | 2021-01-19T01:42:02.316459 | 2017-03-07T00:49:31 | 2017-03-07T00:49:31 | 3,894,344 | 32 | 3 | null | 2015-03-03T13:27:54 | 2012-04-01T18:04:28 | R | UTF-8 | R | false | false | 1,794 | r | dollar.R |
compile.dollar = `compile.$` =
function(call, env, ir, ..., .targetType = NULL)
{
elName = as.character(call[[3]])
obj = call[[2]]
val = compile(obj, env, ir)
ty = valType = getType(val)
pointerToStruct = isPointerType(ty)
if(pointerToStruct)
valType = getElementTyp... |
d23e9f9e69289b32e8cc174ab753b3168ea572e9 | ee25547fd3549440d9da5ccd61fa349084562421 | /man/summary.ctmm.Rd | f3bb66bf517a040758a893340553974eb180ad06 | [] | no_license | ctmm-initiative/ctmm | bd63d800261e2ee4d9695473a043fa54157567d2 | d50c79c1d2a4f72b5cf1d82f2ff4f33e051f8015 | refs/heads/master | 2023-08-09T14:05:57.214843 | 2023-07-26T09:37:10 | 2023-07-26T09:37:10 | 81,976,079 | 38 | 8 | null | 2019-07-09T13:34:55 | 2017-02-14T18:26:27 | R | UTF-8 | R | false | false | 4,357 | rd | summary.ctmm.Rd | \name{summary.ctmm}
\alias{summary.ctmm}
\encoding{UTF-8}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Summarize a continuous-time movement model}
\description{ This function returns a list of biologically interesting parameters in human readable format, as derived from a continuous-time moveme... |
ae4a19250451d6edfec06e082b8b4c7dd42da1cb | de2f15629bdcf7860da6336745253b088484aa7d | /R/app.R | a9ee77dfa5b3995694829c8192694d6552707ca0 | [
"Apache-2.0"
] | permissive | MarkMc1089/snapenium | 27dcefeacb720ac472ebe55c390a5b3003e0ea8d | 075b1cdab59233b0fddc806a51ba07e7f3ea62c6 | refs/heads/master | 2023-09-05T03:24:36.994548 | 2021-11-12T08:07:33 | 2021-11-12T08:07:33 | 427,276,317 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 637 | r | app.R | #' Open browser to URL
#'
#' @param url URL to open
#'
#' @return RemoteDriver client
#' @export
#'
#' @examples
#' driver <- open_browser("http://google.co.uk")
#' close_browser(driver)
Open_browser <- function(url = "") {
rD <- RSelenium::rsDriver(port = netstat::free_port(), browser = "chrome", verbose = FALSE)
... |
19659f92364135906f3c65789e4a5dd3d4d93866 | 13dbbbced8d21bf74dacb3d0ac32751a84daf6b1 | /Scripts/Current Magic Scripts/MUX_PLSR_biplots.R | fdace6c1941071e64bceb867bd1777e7037e6b12 | [
"CC0-1.0"
] | permissive | hammondnw/MUX | a5b66a937de06b06c886303278aa4cb54957022f | a1bfb96de11cecc67b1a9461e2ab297247fb3d45 | refs/heads/main | 2023-04-08T00:02:58.215416 | 2023-02-22T18:18:01 | 2023-02-22T18:18:01 | 567,000,683 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,514 | r | MUX_PLSR_biplots.R | #### Script for making bi-plots for PLSR calibration data ####
### Author: Nick Hammond
### Last Edited: 09/06/2022
# Set wd, load packages
library(lubridate)
library(tidyverse)
library(magrittr)
require(transformr)
library(stringr)
library(readxl)
library(pls)
library(scales)
library(ggpubr)
library(patchwork) # To ... |
8954833972206ccc4c8a7fa987677a45fccdd9bd | 085c1f0d348b6be6eef1917e74cfde2247853d84 | /tests/testthat/test_S3methods.R | f2597a863f99ff8e19f88404ae19e372375303a2 | [] | no_license | saviviro/gmvarkit | 657691acd9577c5aacefe01778bb53d8746613c2 | ad17dd159d0dfa816dcdf9f3ff8be8d633e3b0ef | refs/heads/master | 2023-07-11T19:48:43.851308 | 2023-06-26T08:18:28 | 2023-06-26T08:18:28 | 193,906,969 | 6 | 1 | null | null | null | null | UTF-8 | R | false | false | 8,244 | r | test_S3methods.R | context("S3 Methods")
library(gmvarkit)
# NOTE that some elements of these tests use random elements obtained from simulation algorithms
## A(M)(p)_(p)(M)(d)
# p=1, M=1, d=2, parametrization="mean"
phi10_112 <- c(0.75, 0.8)
A11_112 <- matrix(c(0.29, 0.02, -0.14, 0.9), nrow=2, byrow=FALSE)
Omega1_112 <- matrix(c(0.60... |
bb78286a1b21b40726aa214ee8318fc381fd17dc | c5b41136c0d7f9803bbba09023ef56ea42d835fc | /3_2.R | 00bdacb37a0d9b5586d51ee4f3424dbe14f4f416 | [] | no_license | jaimetbeili/Probability | dadc4f6d4624147d661e1232c35e94e3f16b38ba | 716982bdc804eae8828c91a0d241927f9d19bc66 | refs/heads/main | 2023-06-24T09:45:44.707542 | 2021-07-26T03:59:11 | 2021-07-26T03:59:11 | 386,747,914 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,556 | r | 3_2.R | #PROBABILIDAD CONTINUA (NO DISCRETA)
library(tidyverse)
library(dslabs)
data(heights)
x <- heights %>% filter(sex=="Male") %>% pull(height)
x
F <- function(a) mean(x <= a)
1 - F(70.5) # probability of male taller than 70 inches
F(70)-F(50)
1 - pnorm(70.5, mean(x), sd(x))
# plot distribution of exact heights in... |
2773493f7cd85f9e76587c2d5551c21ff6aaff8d | 1381f990ef0cabf26ed25f481467ba9454d1eaf3 | /mustache.R | 380214d3c321721c75dd696bdb447b2b798f6e5b | [] | no_license | jimsforks/misc | baad41003332bdb74e1c8c2571035c5d4ac6fa3a | cc71517f23e86e0f699fa48dc25005996e63dbd4 | refs/heads/master | 2022-04-12T10:41:29.302523 | 2020-04-03T13:15:25 | 2020-04-03T13:15:25 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 707 | r | mustache.R | library(rlang)
eval_tidy_quo <- function(expr, data = NULL, env = caller_env()) {
eval_tidy(enquo(expr), data = data, env = env)
}
b <- 5
a <- sym("b")
# This is not tidy evaluation, a is unquoted eagerly?
eval_tidy({{a}})
eval_tidy(a)
eval_tidy({{a}}, data = list(b = 3))
eval_tidy(a, data = list(b = 3))
# Tidy e... |
ae117ed569b4c89d096a48017c03377d011d3f0a | 34ff74b689a1ec845f7c042f836869dbe0fb3efb | /math.R | 60f1c97da6bd4f2343be12f55745941d362a0c0e | [] | no_license | olk/examples_R | 1d454c3c050b7cf885d91666aeb6b07082b919fe | 9abdf814e84875ec6bab789bc28d5e29469d7795 | refs/heads/master | 2020-03-08T16:16:48.943453 | 2018-08-09T17:37:02 | 2018-08-16T16:53:36 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,439 | r | math.R | # prod()
eo <- function(p) {
np <- 1 - p # compute for idenpendent events `not happen` propability
total <- 0.0 # initialize
for (i in 1:length(p)) {
total <- total + p[i] + prod(np[-i]) # `prod()` producti of the elements of a vector
}
return(total)
}
# cumulative sums and products
x <- c(... |
8cdc2b42028348f014ce4eaadfdb634bb5edee58 | 918732e0125a50ad6c85b2bb8eac70d8706f3bb0 | /old_files/results_figures_20yr_timestd_bytrt_N01.R | 0e5740ae9972400518c6b28669d6480b4ea2b2f3 | [] | no_license | klapierre/community_difference_synthesis | 8373379a0bdd80eef74fcb2fbafda50def9bbf98 | c510047549d3a57ee6e3720ef710aefa94f8671d | refs/heads/master | 2020-03-09T20:43:37.382314 | 2019-07-31T21:55:36 | 2019-07-31T21:55:36 | 128,992,032 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 158,677 | r | results_figures_20yr_timestd_bytrt_N01.R | ################################################################################
## results_figures_20yr_N01.R: Compiles Bayesian output and makes figures for the primary analysis of richness and compositonal differences between treatment and control plots for datasets cut off at 20 years.
##
## Author: Kimberly La P... |
49b38788640789e2d39c995943e9b512aa8d62ce | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/FRESA.CAD/R/bootstrapVarElimination.Bin.R | ef85104e2322178b65500b5b5d94fc77ca8d2fe6 | [] | 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 | 7,106 | r | bootstrapVarElimination.Bin.R | bootstrapVarElimination_Bin <- function (object,pvalue=0.05,Outcome="Class",data,startOffset=0, type = c("LOGIT", "LM","COX"),selectionType=c("zIDI","zNRI"),loops=64,print=TRUE,plots=TRUE)
{
seltype <- match.arg(selectionType)
pvalue <- as.vector(pvalue);
boot.var.IDISelection <- function (object,pvalue=0.05,Out... |
2eac21f0c4322d43e9d35dc8c03cf5758379149d | ed4ff8cb04c1f9e05ea8fee202acbd68a1b389c3 | /R/forcequotes-package.R | ec39da252588606ca5d8f31916294df18ffd8450 | [] | no_license | hrbrmstr/forcequotes | bc4d4d1bbbf94491e12abe647e0b412af9e342af | 9bcc7afc4b6c3c4a6ed5a34ae0ecbbc06c54c092 | refs/heads/master | 2020-04-16T08:39:38.933814 | 2019-01-12T21:02:52 | 2019-01-12T21:02:52 | 165,432,513 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 835 | r | forcequotes-package.R | #' Return Random Star Wars Quotes
#'
#' \if{html}{
#' \figure{force-quotes.png}{options: align="right" alt="Figure: force-quotes.png"}
#' }
#'
#' \if{latex}{
#' \figure{force-quotes.png}{options: width=10cm}
#' }
#'
#' Now you can use the R 'Force' to get random quotes from your favorite
#' space opera. This is a thin ... |
6b3b56100fb62822cc98ec7cfc5899f27e9b5994 | 7b8bfc26427028fe08ce34cf460649f7c9496258 | /RSHINY_Boston_Property.R | bfe165cd40b07870de7ed1248717f800399d094a | [] | no_license | shivinigam/Boston-Property-Analysis | bc6dcaabe8e82b0ccb233f4f56639da81a673bfb | d0c2e246a10eeff0720c744eeab809760632601b | refs/heads/master | 2020-12-28T04:21:41.802948 | 2020-02-04T10:35:06 | 2020-02-04T10:35:06 | 238,180,496 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,893 | r | RSHINY_Boston_Property.R | library(shiny)
library(tidyverse)
library(ggplot2)
library(dplyr)
library(plotly)
options(scipen=8)
#datacleaning
getwd()
setwd("C:/Users/HP/Documents/")
getwd()
#data_clean <- read.csv("property.csv")
data_clean <- read.csv(file.choose(), header = TRUE)
View(data_clean)
subset1<- subset(data_clean, ... |
05ae8917bc77ba225b3e62e501fbea601a1b50f2 | d538c8d0eeee04b0b0d8c41ada8aed76771d2e47 | /Week 3/Week 3/RTest-4.R | 3d09c33520679ba516722655bcb8dc5cce83817c | [] | no_license | zeelat7/StatistiscalAnalysisPart1 | 1cdb90a83f3845465f7cf06a765fd392a9671945 | 53ddd71b2945b2b578df3485e484403edfaf35c9 | refs/heads/master | 2020-04-24T05:10:35.536941 | 2019-02-20T18:36:50 | 2019-02-20T18:36:50 | 171,727,916 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 403 | r | RTest-4.R | #R Test Number 4
#set up matrix
lin_equat <- matrix(c(1,-1,1,1,1,-1,1,1,1),nrow = 3, byrow = TRUE)
lin_equat
#right side of the equal sign
answers <- matrix(c(1,1,3), nrow = 3, byrow = TRUE)
answers
#use solve to solve the equation
solution <- solve(lin_equat, answers)
solution
#assign values to x y and z
x = solut... |
e706168b61be8587d61b22342ed91c1b08930ac8 | 8b7209ca270699166ea918784205d97b820138cb | /rpart_demo.R | 992982d19789767326606c7542f156c1d547ed7c | [] | no_license | horver/big-data-hf | bc9db81654111bf286eabb216115a321122f7416 | ea0141e3d03fe6678470c8dab61720c40e5e662b | refs/heads/master | 2021-08-23T07:12:56.865544 | 2017-12-04T02:53:24 | 2017-12-04T02:53:24 | 109,955,383 | 0 | 0 | null | null | null | null | ISO-8859-2 | R | false | false | 2,280 | r | rpart_demo.R | library("rpart")
library("corrgram")
data<-read.csv("E:/BME-MSc-2.felev/bigdata/Dropbox/data/globalterrorismdb_0617dist.csv", header = T, sep = ";", skipNul = T)
# korrelációs diagram
vars2 <- c("iyear","imonth","iday","extended","country","region", "specificity","vicinity","multiple","success","suicide",
... |
9d02b538424493c6952cc27968f213fd5649163a | a74b1a6a3f69fb6461b1d5fd616fd91adbc276c1 | /scripts/channel-messages-scraper.R | 2eeb557e5d4273abe44ef2fc490bdbe04ca27bd1 | [] | no_license | EndenDragon/INFO201-Group-Project | df84eab8f2e2759c1a6be972e631a37eaba0f259 | 343433d8d6316170adfff1318e6cec829c98059e | refs/heads/master | 2020-03-15T01:56:33.006394 | 2018-05-31T00:44:47 | 2018-05-31T00:44:47 | 131,905,817 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,102 | r | channel-messages-scraper.R | # This file scrapes the channel messages and saves it to the data folder
library(httr)
library(jsonlite)
library(dplyr)
source("./scripts/api-keys.R")
# All the messages fetched
channel_messages <- data.frame()
# Channel ID to scrape
# 366123119464939534 - #rules
# 369356771804053515 - #announcements
# 362689877751... |
e5b4dc7cb4c8f3e20170664293e2c0c36db03dbe | 6cfede497caf67b5a1e4745b56b029e5ccce128f | /Unfiled/SR14Forecast/datasourceid35/script/hhinc_by_category_plots.R | 2ca05b8f184ff19fe34b3084a03c86ebd6196111 | [] | no_license | SANDAG/QA | 3bce623e269c745cd7c60933be8d81bab14a0e27 | 37edb55a7e79f205d44b67eb18e6474689268477 | refs/heads/master | 2023-08-19T10:47:05.145384 | 2023-08-17T15:57:01 | 2023-08-17T15:57:01 | 138,326,659 | 6 | 3 | null | 2023-02-24T22:07:34 | 2018-06-22T16:48:58 | Jupyter Notebook | UTF-8 | R | false | false | 10,543 | r | hhinc_by_category_plots.R | #hhinc by category plots
datasource_id=35
pkgTest <- function(pkg){
new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
if (length(new.pkg))
install.packages(new.pkg, dep = TRUE)
sapply(pkg, require, character.only = TRUE)
}
packages <- c("data.table", "ggplot2", "scales", "sqldf", "rstudioa... |
c7ad03774af28df8874bae0702e3e39e13509569 | c9e0c41b6e838d5d91c81cd1800e513ec53cd5ab | /man/gtkRcAddWidgetClassStyle.Rd | 1c8fca5f8a0e9357d83feb99985b13d2022ff402 | [] | no_license | cran/RGtk2.10 | 3eb71086e637163c34e372c7c742922b079209e3 | 75aacd92d4b2db7d0942a3a6bc62105163b35c5e | refs/heads/master | 2021-01-22T23:26:26.975959 | 2007-05-05T00:00:00 | 2007-05-05T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 726 | rd | gtkRcAddWidgetClassStyle.Rd | \alias{gtkRcAddWidgetClassStyle}
\name{gtkRcAddWidgetClassStyle}
\title{gtkRcAddWidgetClassStyle}
\description{
Adds a \code{\link{GtkRcStyle}} that will be looked up by a match against
the widget's class pathname. This is equivalent to a:
\code{
widget_class PATTERN style STYLE
}
statement in a RC file.
\strong{WARNIN... |
9339913a96be1c2351a530de9efe4c028c0d640e | 814f85cb23505aaee9c4680573b44574a493f1e7 | /motives.R | e7b56ec8b68e63a0c614702167a4888dadc57210 | [] | no_license | shubh24/HomicideOffender | 61f45eb064f5324914d20e2d3373fca43d08b09a | adafb4c2e0cf7de4c80d35dd17e73317e93b1f7f | refs/heads/master | 2021-01-12T07:17:19.008296 | 2016-12-22T11:47:42 | 2016-12-22T11:47:42 | 76,934,684 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,017 | r | motives.R | library(ggplot2)
df = read.csv("Serial Killers Data.csv", stringsAsFactors = TRUE)
m_df = df[,c("Code", "Type", "MethodDescription", "DateDeath")]
m_df$DateDeath = as.character(m_df$DateDeath)
m_df$DateDeath[nchar(m_df$DateDeath) == 8] = substr(m_df$DateDeath[nchar(m_df$DateDeath) == 8], 5, 8)
m_df$DateDeath[nchar(m_... |
b4b6bacb9e711191d9d5e9effc1239fdcb177f9f | e125e0841d363410954ddac03d447841202a3c01 | /R/shapley.R | 0489bce55eeb2704e24d57d0753c98f40473d423 | [
"MIT"
] | permissive | laurencelin/SHAPforxgboost | d464fc414336cb7316afd3044c4103290528191a | 10eff9b439f1d6b5fb6abf3027da44fe4e3b106c | refs/heads/master | 2023-04-19T05:10:07.823583 | 2021-05-10T20:40:35 | 2021-05-10T20:40:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,739 | r | shapley.R | # return matrix of shap score and mean ranked score list
shap.score.rank <- function(xgb_model = xgb_mod, shap_approx = TRUE,
X_train = mydata$train_mm){
require(xgboost)
require(data.table)
shap_contrib <- predict(xgb_model, X_train,
predcontrib = TRUE, appr... |
ddacf275532495507031cf43522c158b5707f1da | 0329677920e29e68778c623b05bf4ca69e528c41 | /Part 1.2 - test/simple linear regression/Transformation/NORMALITY TRANSFORMATION.R | 52bf72a4e9045ed2a1d56e58d9354738c91a67ab | [] | no_license | celestialized/Machine-Learning | b2075b83139f66bc31c02c64cfe27dfbf19e4ab6 | df30af31f04d03d9796974daf82373436fb6460e | refs/heads/master | 2021-09-21T16:55:51.432572 | 2018-08-29T20:33:13 | 2018-08-29T20:33:13 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 68 | r | NORMALITY TRANSFORMATION.R | # NORMALITY TRANSFORMATION
# if normality assumption does not hold
|
98275c89c7683626b7fc09339287862f72e52c23 | 644a786c1838f3d109cbaa1cf60ba68d54e87703 | /Blog 16- Dataviz guidelines/guidelines.R | f7e5a1180364b87edbc0a3c4b9e04cf87659cb44 | [] | no_license | j450h1/Blog-post | 95a9872ea1099d733562fea7a273849ea63bfcbc | 3a3368e8fb0ba7e58e9fd732eb3bca784fcf9ce4 | refs/heads/master | 2023-06-28T23:28:14.541786 | 2021-07-23T06:27:31 | 2021-07-23T06:27:31 | 387,708,069 | 0 | 0 | null | 2021-07-20T07:23:04 | 2021-07-20T07:23:03 | null | UTF-8 | R | false | false | 5,350 | r | guidelines.R | library(ggplot2)
library(tidyverse)
library(patchwork)
library(scales)
library(magrittr)
# read data file
age <- read.csv("life-expectancy.csv")
# rename columns
age <- age %>% rename(Country = Entity)
# countries of G8 summit
G8 <- c("Canada","France","Germany","Italy",
"Japan","Russia","United Kingdom",... |
e1fff2848d4e1fd37e8c594a006decf8f7af288a | 6a21a808a668533db92472b1e1adbe59dd37517e | /R/dev/rawMatlab.R | e095bca2c416b77ba812af9ff105efaadfb850a9 | [] | no_license | mdsumner/mdsutils | 7198548e9059750a026a102409b8c88e3b39e7ea | f162246b5944050853ecb991e545eae7e3b833d2 | refs/heads/master | 2021-01-19T05:57:32.736249 | 2018-01-22T21:08:27 | 2018-01-22T21:08:27 | 11,871,571 | 2 | 1 | null | 2018-01-22T21:08:28 | 2013-08-04T00:34:16 | R | UTF-8 | R | false | false | 46,593 | r | rawMatlab.R | library(R.matlab)
readMat.default <-
structure(function (con, maxLength = NULL, fixNames = TRUE, verbose = FALSE,
sparseMatrixClass = c("Matrix", "SparseM", "matrix"), ...)
{
this <- list()
nbrOfBytesRead <- 0
detectedEndian <- "little"
ASCII <- c("", "\001", "\002", "\003", "\004", "\005", "\006",... |
e3ebaed008791e23fa8c2b71ec6ad370bb983f02 | 6e707cd7044ecd3bebf0a5013b224e48ef2dc819 | /results_processing/adhoc_plots.R | 7b46f1f182708f0f824825cedfec2e2d817192d1 | [] | no_license | abhivij/bloodbased-pancancer-diagnosis | 6836e308ae382a56fd4bb45811acd1d5934f2b99 | c538549d0be03b909c595d32f9b367beba3116b1 | refs/heads/master | 2023-06-26T23:35:30.244214 | 2023-06-15T12:23:31 | 2023-06-15T12:23:31 | 283,672,783 | 3 | 4 | null | 2022-07-03T14:50:17 | 2020-07-30T04:52:40 | R | UTF-8 | R | false | false | 6,233 | r | adhoc_plots.R | setwd("~/UNSW/VafaeeLab/bloodbased-pancancer-diagnosis/results_processing/")
library(tidyverse)
library(viridis)
library(ComplexHeatmap)
source("metadata.R")
source("../utils/utils.R")
setwd("~/UNSW/VafaeeLab/bloodbased-pancancer-diagnosis/results/results_breastcanceronly/")
data_info <- read.table('data_info.csv', s... |
402d895274ab9c4a9a4f2189dffde6015715c237 | dfc09c7ef198fee792872212b0d557c202988c43 | /hSap/gffView.R | aff13406a8bac7ec9e7d666699ef96f03e4884fd | [] | no_license | jamidifilippo/mscResearch | 79e6b141e387c8bfb170a160acb905d9ca678af2 | 1127823e51452e2064465357ce7888aac0184cca | refs/heads/master | 2020-06-05T04:53:09.307999 | 2019-09-04T09:33:51 | 2019-09-04T09:33:51 | 192,319,786 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 323 | r | gffView.R | #!/usr/bin/env Rscript
args = commandArgs(TRUE)
if(length(args)!=1){
stop("incorrect number of args")
}
library(rtracklayer)
library(Biostrings)
x <- function(file=args[1]){
gff <- readGFF(file)
print(head(gff))
print(nrow(gff))
print(colnames(gff))
print(levels(gff$type))
print(tail(unique(gff$seqid)))
}
x... |
77f1df57fe294eeef99a8ea11f39739f84db1c81 | 05a54772dc8837743fb69c75147f26cf0ca4031d | /man/JSconsole-package.Rd | 394b068a3b291eee0ebf18b1d3cb45d7346b3e06 | [] | no_license | stla/JSconsole | 871532102a48b9f0face672407a497ffb5181f87 | 966d708d2b59ebdb97d64227e5090baf54af6d89 | refs/heads/master | 2022-12-27T11:55:19.265904 | 2020-10-08T06:46:02 | 2020-10-08T06:46:02 | 298,496,056 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 463 | rd | JSconsole-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/JSconsole-package.R
\docType{package}
\name{JSconsole-package}
\alias{JSconsole-package}
\title{JSconsole addin}
\description{
This package provides a RStudio addin to send some JavaScript code in the
V8 console. To run the addin, open a Java... |
10055f9afdaccbbc2093e062c07bae2ee0328c18 | 70ff2dd600c75b5f14a76b759cf2bfebab8defd2 | /Modelo.R | 402a779f229ce41921e434e25df18f8f6fd5354d | [] | no_license | andremenezees/ReinforcementLearningTicTacToe | 8c8a6ba78106814e96af9935e2abc9b9feb4ce27 | 8a4efe28b33ce43b2df32a27652257dcb1992627 | refs/heads/master | 2022-12-08T06:40:58.381761 | 2020-08-29T00:58:45 | 2020-08-29T00:58:45 | 291,164,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 336 | r | Modelo.R | install.packages("ReinforcementLearning")
library("ReinforcementLearning")
control <- list(alpha = 0.2, gamma = 0.4, epsilon = 0.1)
modelo <- ReinforcementLearning(tictactoe, s = "State", a = "Action",
r="Reward", s_new = "NextState",
iter = 2, co... |
77b2158e0a0a69fe8a8bd0b7ba66f986bd534151 | d3410af0856f5ed552896a2bcd51548e5dd312eb | /man/charitable.Rd | fb55b11d27a169733173116c74bc361095a8a14a | [
"CC0-1.0",
"LicenseRef-scancode-unknown-license-reference"
] | permissive | lnsongxf/experimentdatar | 4f0810c49d29656a2757771eb843480a8bda7867 | f71a9d072aabadf4da95e71baa757842a2d295c9 | refs/heads/master | 2021-01-01T09:48:14.429333 | 2019-02-11T12:15:59 | 2019-02-11T12:15:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 518 | rd | charitable.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ExperimentData.R
\docType{data}
\name{charitable}
\alias{charitable}
\title{charitable data}
\format{A tibble with variables:
\describe{
\item{TBA}{TBA}
}}
\source{
\url{https://github.com/gsbDBI/ExperimentData/tree/master/Charitable}
}
\usag... |
4500f30aa086c6b8c81926af71dea06221059241 | 393d5197702ff1c73873efe408c4b608bd6cdd7d | /UTEFA industry breakout group survey.R | e118e28709507ceeedbcca366b7ccc1b7c48a8e2 | [] | no_license | rhungc/UTEFA- | 5d0a030d3b9705e843f8d5e3625689bfbf87d524 | c2dc92315fc6d6f03d3b1c8c56b788e360878e34 | refs/heads/main | 2023-01-01T09:39:48.713895 | 2020-10-21T03:17:45 | 2020-10-21T03:17:45 | 305,886,030 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,225 | r | UTEFA industry breakout group survey.R | library(tidyverse)
library(ggplot2)
signup_rank <- read.csv("Industry Rank .csv")
## A table of ppl's first choice
head(signup_rank)
choice1 <- signup_rank %>% select(Response.., Industry.choice.1) %>%
group_by(Industry.choice.1) %>%
arrange(Industry.choice.1) %>%
as_tibble() %>%
rename(Industry = Industr... |
f751adbfe7785c6118b44e3520c5f4456c29c9ba | 0128be0f0a6ac91173df430de36aa7903dea7071 | /R/GBS_QC.R | 9cc63334568a2b58b8e573e8aec36e0f169bf33e | [
"MIT"
] | permissive | solgenomics/sgn | f564f0da7fc35a9c127e644d948adc32a9d7bdf9 | db87e84fc65803cbb4d3fa0c2e46279521650f92 | refs/heads/master | 2023-08-31T00:17:49.476691 | 2023-08-30T13:49:46 | 2023-08-30T13:49:46 | 644,423 | 53 | 31 | MIT | 2023-09-14T17:11:21 | 2010-05-03T13:50:53 | PLpgSQL | UTF-8 | R | false | false | 3,629 | r | GBS_QC.R | #to use
#R --slave --args output_test00.txt qc_output.txt < ~code/code_R/GBS_QC.R
myarg <- commandArgs()
cat(myarg,"\n");
m=length(myarg)
cat(m,"\n");
f_in<-myarg[4:4]
f_out<-myarg[5:5]
#f_plot<-myarg[6:6]
#f_output<-myarg[7:7]
#cat(f_index,"\n")
#cat(f_acc,"\n")
#cat(f_plot,"\n")
#cat(f_output,"\n")
#f_acc="WEMA_... |
1471ef9eef177c6d2d45ee5b272fb21e45860988 | d0893f6e1b7ee85dd0ad8973fa7b0176707f68a9 | /R/numberofclusters.R | 07e0e93ee840eaa6facb429271c3b3c2619675eb | [] | no_license | Displayr/flipCluster | 0a75b0a66663f46dc6353402914a0a2777175cdf | d6457f5771459cae8af2c2d79fb6c17c0913d4e2 | refs/heads/master | 2023-06-15T07:35:29.514490 | 2023-06-13T09:35:24 | 2023-06-13T09:35:24 | 69,005,123 | 2 | 2 | null | 2023-04-20T11:14:49 | 2016-09-23T08:26:03 | R | UTF-8 | R | false | false | 2,232 | r | numberofclusters.R | #
# selectkMeans <- function(x, min.n.clusters = 2,max.n.clusters = 8, init.centers = NULL, n.starts = 100, iter.max = 100, ignore.error = FALSE,n.tests = 1000, assumption = "sample",save = TRUE)
# {x <- as.matrix(x)
# result <- statistics <- matrix(NA,nrow = max.n.clusters - min.n.clusters + 1, ncol = 15,
# dimn... |
c4796f9aa729aaa8a02b75ac93764acef1b74907 | 7285409708ec8be4057a2cd535aef5a96eb84c8c | /254a82f9402ff85ce4e4afe97abb2c72930ed01f/types.R | 8e351416eee3e4b2d58adcdca2cf0f8edadc77ad | [] | no_license | mengxingwu/OCEAN5098 | c7e9196cb09720f559df6c0fedbc44b8bdb23c6f | 86ca087114a59d1497f528c1818dabbcf5700133 | refs/heads/main | 2023-08-11T04:46:33.209493 | 2021-10-16T06:36:53 | 2021-10-16T06:36:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,561 | r | types.R | x <- c(1.0, -3.4, 2, 140.1) # numeric and double
typeof(x)
mode(x)
x <- 4
typeof(x)
x <- 4L
typeof(x)
x <- c("bubul", "magpie", "spoonbill", "barbet")
typeof(x)
x <- 3
y <- 5.3
x + y
x <- "3"
y <- "5.3"
# not run: x+ y
###########################################################
# Error in x + y: non-numeric ar... |
5e9320695fa7dff3e9199260018b1a3d114f2654 | ec65e719d4363226bf16072986de93932ac88357 | /code/R.R | 3de10e692a82dd4995f1082a15b21426abd79720 | [
"MIT"
] | permissive | bigsk05/FibonacciSequence | 9fad3ea9ab3be36f7b2a85d85f2da1d38493509c | cb01cae8f577cc997749f0986449fc6da618e13f | refs/heads/master | 2023-06-19T22:21:26.932892 | 2021-07-18T06:46:53 | 2021-07-18T06:46:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 364 | r | R.R | #!/usr/bin/env Rscript
calc <- function(n){
if (n <= 1){
return(n)
}else{
return(calc(n - 1) + calc(n - 2))
}
}
main <- function(){
ts=proc.time()
for ( i in seq(from = 0, to = 29, by = 1)){
calc(i)
#print(calc(i))
}
tf=proc.time()
print... |
2582e58865ecfd5b82cb62bd2b04fc7f930ace30 | 1ad58f3b10a5b5f2566605bbdd77ed1670c40963 | /cachematrix.R | 515e50a96c4cefd7bffcfe81fb6316a40f056264 | [] | no_license | magusverma/ProgrammingAssignment2 | 6ac7e212af83abf78c0515bac54762f6062b3c6a | 541e1f6206626cc8facc008c6e3d33c619c371c6 | refs/heads/master | 2020-12-25T16:02:32.629596 | 2014-06-22T20:32:51 | 2014-06-22T20:32:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,459 | r | cachematrix.R | ## Code Written by Magus Verma for R Course of Coursera , Week 2 Programming Assignment
## This defines the special "matrix" object needed for assignment
# Start with something like x <- makeCacheMatrix()
makeCacheMatrix <- function(x = matrix()) {
# inverse_of_x holds the inverse of the matrix x computed using set... |
59370800183b796deb116845a2b525b7a8a50073 | e2ac77e6bb5fc9e2169f1b4f827d3108550078d1 | /Educational/R/Coursera/Getting and Cleaning Data/Week 1/quiz1.R | 58c314c6fd1a6beeba57a24ba38af8cb7129f9fa | [] | no_license | Rajmirukula/programming | c2ab4dbe5ee6ca78be6661800eb6e334c71501ee | 2801e8c641b101494ee8586ca28f50116dffb261 | refs/heads/master | 2020-07-03T16:42:06.104576 | 2016-10-02T23:12:14 | 2016-10-02T23:12:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,338 | r | quiz1.R | # Ethan Petuchowski
# 6/2/14
#########
# CSV #
#########
# Download the 2006 microdata survey about housing for the state of Idaho
housingUrl <- "https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv"
setwd("/Users/ethan/code/non_apple/programming/Educational/R/Coursera/Getting and Cleaning Data/Week 1... |
bf80839ca0f59a109f8bfd350d3577eedaa85a60 | fc757ea51c4861201a7ec95c1f7ae7c6dcbac35b | /R/mobforest.output.R | d32fb309ebb14e261f97225cba6162785e379d78 | [] | no_license | RTIInternational/mobForest | 0550d7be8b66d71c2e1a9d57fc5ebf51f11ebb67 | 26530e3ff46c91ef9e25744d9ea65c36a59f1356 | refs/heads/master | 2022-10-07T22:51:06.785414 | 2019-07-31T20:21:26 | 2019-07-31T20:21:26 | 116,028,774 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,873 | r | mobforest.output.R | #' Model-based random forest object
#'
#' Random Forest Output object that stores all the results including
#' predictions, variable importance matrix, model, family of error
#' distributions, and observed responses.
#'
#' @param oob_predictions Predictions on out-of-bag data.
#' @param general_predictions Predictions ... |
daf57740529a41ddad090035d211b4a17abc8078 | 7b102f9c8f2e3f9240090d1d67af50333a2ba98d | /gbd_2017/nonfatal_code/ckd/age_sex_split/age_sex_split.R | fd5f6975bfedf671667f328d8c9fc9e1a099a076 | [] | no_license | Nermin-Ghith/ihme-modeling | 9c8ec56b249cb0c417361102724fef1e6e0bcebd | 746ea5fb76a9c049c37a8c15aa089c041a90a6d5 | refs/heads/main | 2023-04-13T00:26:55.363986 | 2020-10-28T19:51:51 | 2020-10-28T19:51:51 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,451 | r | age_sex_split.R | #######################################################################################
### Date: 11/7/2016
### Project: GBD Nonfatal Estimation
#######################################################################################
###################
### Setting up ####
###################
rm(list=ls())
i... |
2cf6863f895b044ec0f80b098bbf7340340f6d68 | 3df381fdd831150bec7d97a705d069e1ba892e13 | /R/validate_arguments.R | 76bf00b99f57a716ae28fa505b1aef3d341a135b | [
"MIT"
] | permissive | king8w/ecocomDP | 8e4f451ac69356c6bfe4e3c405c0a02a7ccab6a0 | 7adf6af8070d35a9123138fc35e2b9a6c4893125 | refs/heads/master | 2023-03-20T05:57:39.115850 | 2021-03-13T00:26:59 | 2021-03-13T00:26:59 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,965 | r | validate_arguments.R | #' Validate arguments of ecocomDP functions
#'
#' @description
#' Validate input arguments to ecocomDP functions.
#'
#' @param fun.name
#' (character) Name of function from which \code{validate_arguments()} is
#' called.
#' @param fun.args
#' (named list) Arguments passed to calling function and formatt... |
1ddc729a03da90e4ecf75f4583ec4f9f27291e2d | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/powerMediation/examples/ssMediation.VSMc.logistic.Rd.R | b237ae011f19c4ab8b47cd8e6405aa2ff6e75e4d | [] | 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 | 485 | r | ssMediation.VSMc.logistic.Rd.R | library(powerMediation)
### Name: ssMediation.VSMc.logistic
### Title: Sample size for testing mediation effect in logistic regression
### based on Vittinghoff, Sen and McCulloch's (2009) method
### Aliases: ssMediation.VSMc.logistic
### Keywords: test
### ** Examples
# example in section 4 (page 545) of Vittin... |
e5b55c500ef630ba1c99d292ff98ac2cd347227e | b94bde90fdb3e38483293d906c0b5f0669af647e | /simsem/man/tagHeaders.Rd | 69a2c2cd9d0009241ec2ff31fffd3f180208cc76 | [] | no_license | pairach/simsem | c2da13f31af4b8ed986647320090bbd9edc0c400 | 8194f63851ed0c0dbd447726988b0a58619ec43a | refs/heads/master | 2020-12-25T01:50:53.664082 | 2012-05-29T21:38:06 | 2012-05-29T21:38:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,207 | rd | tagHeaders.Rd | \name{tagHeaders}
\alias{tagHeaders}
\alias{tagHeaders-methods}
\alias{tagHeaders,ANY-method}
\alias{tagHeaders,VirtualRSet-method}
\title{
Tag names to each element
}
\description{
This element of a vector will be tagged by the names of the vector with the position of the element. This element of a matrix w... |
b9f50ec0b073175455c5ebb6f8aaaf91363932b5 | 1a98fadbdbc7805c39c51534814664dc34717342 | /man/qlogout.Rd | a1225aa8a4192d520e94cdc6d06874f80a0edd19 | [] | no_license | jtuomist/quiltr | 6e613380f63962e4a6be7548adb71ba8b0906234 | 11a9342280e5f482a8d53dea9f8278385fb39831 | refs/heads/master | 2020-05-20T05:42:57.690081 | 2019-05-07T14:13:37 | 2019-05-07T14:13:37 | 185,413,347 | 0 | 0 | null | 2019-05-07T13:59:33 | 2019-05-07T13:59:33 | null | UTF-8 | R | false | true | 267 | rd | qlogout.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/auth.R
\name{qlogout}
\alias{qlogout}
\title{Log out of Quilt}
\usage{
qlogout()
}
\value{
Deletes your saved auth token
}
\description{
Log out of Quilt
}
\examples{
\dontrun{qlogout()}
}
|
a237f3e45df28444ccac774973807a326419d722 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/sirt/examples/IRT.mle.Rd.R | 4e9f15da79eaffec8db404b521855d89c922dfa9 | [] | 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 | 3,480 | r | IRT.mle.Rd.R | library(sirt)
### Name: IRT.mle
### Title: Person Parameter Estimation
### Aliases: IRT.mle
### Keywords: Person parameters
### ** Examples
## Not run:
##D #############################################################################
##D # EXAMPLE 1: Generalized partial credit model
##D ###########################... |
2f859b73a3d959f67870612ca065dbfc6ef5a732 | 29f8f3ee59c366ea408633d183614bc39b49b26d | /Duke_DGNN/[DGNN] incidence_rti_africa.R | 7a32eec249479c2cacaae9a8076ffe38dfd46c24 | [] | no_license | souzajvp/analytical_codes | 92db345dc75f128c2f25fb7b28f0891139ffea98 | dcc49662253ba1dbd4f54b8c4caea40232632783 | refs/heads/master | 2023-05-23T06:06:12.058469 | 2021-06-07T18:11:00 | 2021-06-07T18:11:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,329 | r | [DGNN] incidence_rti_africa.R | ###################################################
#TEMPLATE_FOR _META_ANALYSIS_OF_DIAGNOSTIC_ACCURACY#
#this script follows a combination of guidelines proposed by Doebler and Holling, according to (http://cran.r-project.org/web/packages/mada/vignettes/mada.pdf)#
#
#
##################################################... |
4285f326d1f48704fa0fd43dddb209a191708b60 | 364d3c7f7b87095baadbbacefb69a0a7c107c87c | /man/set_hparams_xgbTree.Rd | c03376586dcf03dfd11f750523ab25f4934ea701 | [
"MIT",
"CC-BY-4.0"
] | permissive | SchlossLab/mikropml | 32c9d0cd7351d667b2fc7522eabdcfb73e28d699 | 3dcc9bc0c49e0e65714fd9a1e0045a749ada76e8 | refs/heads/main | 2023-06-11T15:23:19.409104 | 2023-04-15T17:02:49 | 2023-04-15T17:02:49 | 226,981,416 | 41 | 12 | NOASSERTION | 2023-08-21T15:44:37 | 2019-12-09T22:37:38 | R | UTF-8 | R | false | true | 535 | rd | set_hparams_xgbTree.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hyperparameters.R
\name{set_hparams_xgbTree}
\alias{set_hparams_xgbTree}
\title{Set hyperparameters for SVM with radial kernel}
\usage{
set_hparams_xgbTree(n_samples)
}
\arguments{
\item{n_samples}{number of samples in the dataset}
}
\value{
... |
f496080f6ef40276137c18a5d1a28e86711abf7a | 034104842a843a5f412b880968d2801edcc266dd | /text_processing.R | 22e1c0c5815854c94e440a4c35c94fd8360b8fa8 | [] | no_license | RedTent/KLADBLOK | 8d84b9108023772b483a6caf493b1607392d9839 | 23fc2dcc23f7e2e19e73b5b3353a86ce7a169687 | refs/heads/master | 2021-09-11T04:01:24.782558 | 2021-08-27T15:03:56 | 2021-08-27T15:03:56 | 138,591,621 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,009 | r | text_processing.R | library(readr)
library(stringr)
library(dplyr)
library(wordcloud2)
stopwoorden <- c("de", "in", "het", "en", "van", "een", "te", "op", "voor", "aan", ">", "dat", "dit", "niet",
"als", "die", "naar", "er", "wat", "hun", "of", "zo", "zodat", "tot", "door", "�", "bij" , "is",
"met", "om... |
534ff6694e655e7680f906797710481c953b21f3 | 1d669fa585876ecc72322eda3cc75fd5ecebb1a6 | /R/se.R | 2b77675f304596ac49c9405e63352bfe07a4c299 | [] | no_license | afilazzola/LearnCommAnalysis | 06e01e088f166f6823855e36d07f888201a35c95 | 3eeea18a74ebcba07ba79c2891f1930e155f5da1 | refs/heads/master | 2020-04-04T14:24:04.083140 | 2018-11-06T03:34:36 | 2018-11-06T03:34:36 | 155,997,895 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 60 | r | se.R | ## Standard error
se <- function(x) {sd(x)/sqrt(length(x))}
|
ce2e8b33880e121ee23165c680561e884121f9f4 | 337e914fb4383becb3694d1bb0b453b6a1b01dd2 | /Shiny_server/server.R | 84e619cc2ae6a8bd7ff64a1f7c0379bd38f98bee | [] | no_license | cguillamet/Shiny-App-Cancer | 0c14db5f55a449b08b498c2fda01809396680133 | 121351f23c572713ddb950396232a7095b9686a7 | refs/heads/master | 2023-04-17T20:39:39.080814 | 2021-04-26T15:45:38 | 2021-04-26T15:45:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,043 | r | server.R | library(shiny)
library(ggplot2)
library(dplyr)
library(shinydashboard)
datos <- read.csv("base2015_2020.csv")
datos$año <- substr(datos$FECDIAG, start = 1, stop = 4)
datos <- filter(datos, año %in% c("2015", "2016", "2017", "2018", "2019"))
datos2 <- datos %>%
group_by(TOP..cat.) %>%
tally() %>%
mutate(po... |
73875fe78e7b57de052d7a1af6e3bb34646e1522 | ae50d81889d88e0510bd4d076c6a559848cf112a | /Repositories/Dropbox/Dropbox_Cache_Example/server.R | bf74a6aaff47da2ba386bc5d20db139beb19c604 | [
"MIT"
] | permissive | ToonTalk/Live-Data_Scripts-and-Templates | a0f784a0320cc9a8dac5e030b51f44aa72cec32c | 4855d56f7b6b2be212ff2f7df3c867788e22e225 | refs/heads/gh-pages | 2021-01-15T12:30:57.498318 | 2016-08-05T14:54:57 | 2016-08-05T14:54:57 | 65,015,940 | 0 | 0 | null | 2016-08-05T12:12:43 | 2016-08-05T12:12:42 | null | UTF-8 | R | false | false | 6,045 | r | server.R | library(xlsx)
library(shiny)
library(rdrop2)
library(lubridate)
library(digest)
library(plyr)
token <- readRDS("droptoken.rds")
original_file_name <- "pms_data.csv"
unique_name_fn <-
function() {
sprintf("%s_%s.csv", digest::digest(paste0(as.integer(Sys.time(
)), runif(1))), "user_downloaded")
}
sort_loc... |
496da59acdc3a28028e308fc1f73d928707f07e2 | de4d8e9a2d968c2f90e17e4aaa384705175525f7 | /server.R | 578e12db358d25a6c61c8209f2be35e5de9b4142 | [] | no_license | Marika3/ShinyVolcano | ee8fc282640ae5ad6330bdcbda79b0029f01896f | ef67bcbdd0ac24285ccabac5d75bdb78199cc9a2 | refs/heads/master | 2021-01-18T19:05:00.537308 | 2014-06-19T09:05:41 | 2014-06-19T09:05:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 534 | r | server.R | library(shiny)
shinyServer(
function(input, output) {
output$ThreeDee <- renderPlot({
localHeight <- input$sliderHeightScale
localTheta <- input$sliderTheta
localPhi <- input$sliderPhi
localShade <- input$sliderShade
z <- (localHeight + 1) * volcano
x <- 10 * (1:nrow(z))
... |
0a38c71e267a4395a003354d5fc18927823abbd6 | 2daeca90cc3b7c681059feecc8196dfe04a9e793 | /R_scripts/plotold.R | 8c6b5325a1613eacf21bb281d6bb8c05e17b9a08 | [] | no_license | kosticlab/athlete | ccadf6e164bc5e8102b153c92f03c1cf3c2c1341 | 8b14036231bc517891a4261235766b42805dcfaf | refs/heads/master | 2021-03-27T14:44:46.189366 | 2018-05-23T18:39:48 | 2018-05-23T18:39:48 | 93,183,051 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,751 | r | plotold.R | library(pheatmap)
library(RColorBrewer)
library(viridis)
quantile_breaks <- function(xs, n = 10) {
breaks <- quantile(xs, probs = seq(0, 1, length.out = n))
breaks[!duplicated(breaks)]
}
matdf <- read.csv(file=paste(paste("/Users/jacobluber/Desktop/athlete/",commandArgs(trailingOnly = TRUE),sep=""),"_ra.csv",sep="... |
0d9b2d9c1678ae368b8d57f400c705dc696e26e5 | b3c39d9bc7cdd82f225cc1707c69c55513519a1d | /man/KRV.Rd | 3a525294aa783ab0e168a80b6f774c54b6679b5a | [] | no_license | teyden/MiRKC | d81e02a0e2b349635faea46102b5ee69fbafe740 | 7de32668537ff68d7cbebadafb8d70a338525de9 | refs/heads/master | 2020-12-27T09:21:48.191058 | 2020-06-21T08:22:56 | 2020-06-21T08:22:56 | 237,850,361 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,325 | rd | KRV.Rd | \name{KRV}
\alias{KRV}
\title{
Kernel RV Coefficient Test
}
\description{
kernel RV coefficient test to evaluate the overall association between microbiome composition and high-dimensional or structured phenotype.
}
\usage{
KRV(kernel.otu, y = NULL, X = NULL, kernel.y)
}
\arguments{
\item{kernel.otu}{... |
b7f06577cbc6bb5b67766bd44b5be04311c9d156 | 4592565db17d3d5a4bfa8fc820d7516beb4fa115 | /demo/seminr-primer-chap5.R | 009629e3d614af385380dc4f4ab327c85755ffbf | [] | no_license | sem-in-r/seminr | 1b370286c58f4e658a02fb5df21fabe585fcfb4a | ae2524aae5f4f0bda3eb87faf80378af5baccea1 | refs/heads/master | 2023-04-04T00:29:48.969724 | 2022-06-30T17:02:07 | 2022-06-30T17:02:07 | 70,557,585 | 49 | 16 | null | 2022-10-13T15:22:28 | 2016-10-11T04:58:23 | R | UTF-8 | R | false | false | 6,375 | r | seminr-primer-chap5.R | ### Accompanying Code for:
## Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R - A Workbook (2021)
## Hair, J.F. (Jr), Hult, T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., and Ray, S.
## Chapter 5: Evaluation of formative measurement models
# Load the SEMinR library
library(seminr)
# Load the cor... |
a10659f1bbfb37cf9f2f6098423404b97da5234e | 9db51d5978be195fc2a4abdd6eafb7a7a3bc7cfa | /03.gene-processing/conclude-p-value-skat.R | f4c9885ac07950e9f3e86e0bb813ee35b186344f | [] | no_license | numvarn/SNPsR | a9f6bf299a4a2d1efef02a81d1d92dea1c9db226 | 27e8fb2800f0b0c6ed1bd5b020add1bf3c4a03e6 | refs/heads/master | 2020-04-12T09:43:34.282004 | 2017-06-02T08:21:55 | 2017-06-02T08:21:55 | 62,023,474 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,255 | r | conclude-p-value-skat.R | # Config values
setwd("~/ResearchCode/SNPsR")
gene_grouped_file <- "result/grouping-gene/10.GroupingComplete.csv"
conclude_data <- read.csv(gene_grouped_file)
outfile_conclusion <- "result/skat/conclusion-p-value/conclusion-skat-3000replicated.csv"
# Floder that stroe skat results
skat_result_path <- "result/skat/0.... |
00e6aa9ede6dc9642b3a4f72fbae9083ad8216d5 | 39068b86a43d69300bd9e91c7650dfb0d28403eb | /evalsims/getEmpiricalP3_BH.R | 391625534c4c47948315736cad9f46f2c8a783f6 | [] | no_license | DrK-Lo/MINOTAUReval | c19009747b1e4ef3b30bd68da8fdcfc6476ca413 | 2626fca5300e2b1e53c12e2aa4e0d74cbeb2cb24 | refs/heads/master | 2021-01-23T14:03:59.330344 | 2016-09-29T13:33:03 | 2016-09-29T13:33:03 | 56,006,040 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 819 | r | getEmpiricalP3_BH.R | returnEmpP <- function(AllObservedValues, NonCodingValues){
getEmpP <- function(Obs, sort.Null){
#Obs is a single observed value
#sort.Null is a list of the null values in ascending order
if(is.na(Obs)){
return(NA)
}else{
options(warn=-1)
out = max(which(sort.Null<=Obs)... |
8144508294b0228628848c03da6eeb1e49828c53 | 22c4136caee081ec1a5bb68dd204b9f765858a2e | /R/clipHullsToLand.R | 06180157b971ce8c2658b5bf55d952fcacf99fc2 | [
"MIT"
] | permissive | JCur96/sfe | 03aab8c5911c5e024276f934f2e6195794571df0 | 5d2e5a869f325194d764e5ef3f8a478b8b5f4cf9 | refs/heads/master | 2021-07-06T14:06:03.102348 | 2020-09-02T14:19:18 | 2020-09-02T14:19:18 | 193,727,104 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,119 | r | clipHullsToLand.R | #' Clips convex hulls to landmasses
#'
#' \code{clipHullsToLand} returns the clipped convex hulls by species for a
#' single object of class sf (a data frame).
#'
#' These hulls are clipped to landmasses only.
#' To generate hulls and clip them to landmasses with a single function see
#' \link[sfe]{makeLandClipp... |
315a572e66f2e70f1cabe60cf9576084b2f4ed6d | a0f6077dbe42b6329e78e9b252f5b51d3ec0c479 | /holtwinters.R | 25d8331c7950c1fcb10c0d77a5f319a5f48b0a24 | [] | no_license | prithvi1029/hierarchichal-time-series | a7492aceddefbe583f866c7a6209eabc614c552e | e741158830279603ee37e04166eaf5362155a273 | refs/heads/master | 2020-09-04T08:02:05.672777 | 2019-11-05T10:07:52 | 2019-11-05T10:07:52 | 219,690,829 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,469 | r | holtwinters.R | library(xts)
library(forecast)
library(MLmetrics)
# Create the dates object as an index for your xts object
train_dates <- seq(as.Date("2014-01-19"), length = 154, by = 7)
#train_sales <- xts(train_sales, order.by = train_dates)
test_dates <- seq(as.Date("2017-01-01"), length = 22, by = 7)
#test_sales <- xts(v... |
69f95b07b8493eb3dde7666cf61aa1add68740c6 | 2bec5a52ce1fb3266e72f8fbeb5226b025584a16 | /eseis/man/signal_filter.Rd | acbf563ab1bd1faedbf9b375840b67d9132a0ab0 | [] | 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 | true | 2,708 | rd | signal_filter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/signal_filter.R
\name{signal_filter}
\alias{signal_filter}
\title{Filter a seismic signal in the time or frequency domain}
\usage{
signal_filter(
data,
f,
fft = FALSE,
dt,
type,
shape = "butter",
order = 2,
p = 0
)
}
\argument... |
79dca9edaf22573cf9444d8781062518fb9a9717 | 6700a5a2525b1d5eeaa47913f2d859c386ed2f81 | /man/dmcFitSubject.Rd | 1f27ac18af3b242e22e3227b3dfaf1cce6846e3b | [] | no_license | amanirad/DMCfun | fbba2d0183b0d1c94e0e60bc6e11f0562a1baf29 | e717cda849df4234df56837b92a64be1ebe19aec | refs/heads/master | 2023-07-30T14:35:52.483186 | 2021-09-20T10:36:37 | 2021-09-20T10:36:37 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 4,741 | rd | dmcFitSubject.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dmcFit.R
\name{dmcFitSubject}
\alias{dmcFitSubject}
\title{dmcFitSubject: Fit DMC to individual participant data using optim (Nelder-Mead)}
\usage{
dmcFitSubject(
resOb,
nTrl = 1e+05,
startVals = list(),
minVals = list(),
maxVals = ... |
d7c5257502915b92d0f1c8c39c303cdc0ce7164f | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/vegan/examples/bioenv.Rd.R | 0ac9a7c0f5fe13d795830f18b084b34e20f8780e | [] | 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 | 511 | r | bioenv.Rd.R | library(vegan)
### Name: bioenv
### Title: Best Subset of Environmental Variables with Maximum (Rank)
### Correlation with Community Dissimilarities
### Aliases: bioenv bioenv.default bioenv.formula summary.bioenv bioenvdist
### Keywords: multivariate
### ** Examples
# The method is very slow for large number of ... |
a0f11ed0d5bcb14bf3b11504cf626ffb7d122725 | 3768f2217015f96978395d6ab0353509f7b6fd10 | /analysis/02_Run_MAP_Fit.R | ba2078258e568bf95d7978a7ae0772487cdfcb1f | [] | no_license | Dpananos/PKBayes | 4cbb0ca20dc6073755a5ab6fb0d3633614ea200c | 628b145ac3ae8355cb26ff86068aba2bb80d3fa2 | refs/heads/master | 2020-09-21T19:43:28.614187 | 2020-08-16T22:35:34 | 2020-08-16T22:35:34 | 224,901,887 | 4 | 0 | null | 2020-03-26T17:48:50 | 2019-11-29T18:08:30 | Jupyter Notebook | UTF-8 | R | false | false | 2,189 | r | 02_Run_MAP_Fit.R | library(bayesplot)
library(here)
library(rstan)
suppressPackageStartupMessages(library(tidyverse))
library(tidybayes)
options(mc.cores = parallel::detectCores())
rstan_options(auto_write = TRUE)
`%notin%` <- Negate(`%in%`)
# --- Load in data and model ---
# Load Simulated data. Only want 100 subjects
d = here('dat... |
1e6fa4f913c7c7994257a3d23fee918a6928d2fa | 60b0a066ab9fc0ac131650ab55e2bcf4343a578b | /man/callSubtypes.Rd | 64e830a1aff623e360935598455ecf43e70aabf2 | [
"Apache-2.0"
] | permissive | sky-xian/ImmuneSubtypeClassifier | a3141ea19cb4c0daf4da4e00554a1d2d61dc4e12 | 30e6215c390bf12761d17cbc9647ac4527e319ba | refs/heads/master | 2020-07-15T20:09:20.669888 | 2019-08-15T00:34:45 | 2019-08-15T00:34:45 | 205,640,363 | 0 | 1 | NOASSERTION | 2019-09-01T06:51:03 | 2019-09-01T06:51:03 | null | UTF-8 | R | false | true | 535 | rd | callSubtypes.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/callSubtypes.R
\name{callSubtypes}
\alias{callSubtypes}
\title{callSubtypes
Make subtype calls for each sample}
\usage{
callSubtypes(mods, X)
}
\arguments{
\item{mods}{xgboost model list}
\item{X}{gene expression matrix, genes in rows, sampl... |
50ca902346ce8bbf7eed972c9a8bc136bb2f5406 | 31df5e6e37bee75ba4b22a972cfa57a632ba2d23 | /_test/test_trec.R | a77a24021058e5bcad8de632083ceaf6786bea65 | [] | no_license | Sun-lab/asSeq | 47fa0b451530114ce2e0cdcd40e64afbf21335d7 | 3dcfb91d603213057c570a98d532c6cfc5618929 | refs/heads/master | 2022-09-28T13:23:00.427278 | 2022-09-11T16:33:06 | 2022-09-11T16:33:06 | 134,029,698 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 3,067 | r | test_trec.R |
# -------------------------------------------------------------------------
# read in data
# -------------------------------------------------------------------------
setwd("~/research/eQTL_seq/result/YRI_Joint_Permute/")
eD = read.table("TReCASE_Permute_YRI_expression_chr2_data_TReC.txt",
sep="\t")
X = ... |
d75ccd4048c90faaf82ab98855d33d5c9a4dcf3e | ef2a9d30cbd541fd282e57f1b28694dce4492320 | /5133126김상학.R | 546bfeb148a83bbfb74f8f62d432f12de2d15fd9 | [] | no_license | DaeguDude/bigdata | 34f64500ce8dc7cf150b2205e0d5a70a0b0de519 | fb2a4aabbaacc4de688d5a1d134ebdd9510ab68e | refs/heads/master | 2020-09-05T00:48:48.307526 | 2019-12-16T04:19:35 | 2019-12-16T04:19:35 | 219,937,809 | 0 | 0 | null | null | null | null | UHC | R | false | false | 2,858 | r | 5133126김상학.R | ##1. 타임시리즈 데이터를 생성하는 함수
timeseries_data<-function(rho,nsmp,dtb, #rho는 rho,nsmp는 표본 수,dtb는 분포
rnorm_mean=0,rnorm_std=1, #정규분포 옵션(평균,표준편차)
runif_min=-1,runif_max=1, #균등분포 옵션(최소,최대)
rcauchy_loc=0,rcauchy_scale=1, #코시분포 옵션(위치모수,척도모수)
... |
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