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94d0a51617450f53f1eab683a082a70b11a0f7f5 | 03eb83ea48a164f07095afd746cfc79217fe069d | /WEEK 2 Assigment/code.R | a07c7fcc47d8275443ed7b971720f3722221c989 | [] | no_license | Luis1494/Data-Science-Capstone | e158512f21330d53178181c29ba087ca628e580b | a9995966a02760582886ef14ce98048c0c2d4a2b | refs/heads/master | 2022-12-07T01:29:59.323457 | 2020-08-17T00:46:55 | 2020-08-17T00:46:55 | 288,049,424 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,329 | r | code.R | ## Load data
setwd("D:/1-1. R studio/Lecture10. Data science capstone/week2/final/en_US")
blogs<-readLines("en_US.blogs.txt",warn=FALSE,encoding="UTF-8")
news<-readLines("en_US.news.txt",warn=FALSE,encoding="UTF-8")
twitter<-readLines("en_US.twitter.txt",warn=FALSE,encoding="UTF-8")
## Summarize data
size_blogs<-file... |
88f958d4c7b34d657e970bd97b3e9f278d3c885b | 5bb2c8ca2457acd0c22775175a2722c3857a8a16 | /R/datasets.R | 20a5b1c9c1d42fd1e69d475725889067e12c53a0 | [] | no_license | IQSS/Zelig | d65dc2a72329e472df3ca255c503b2e1df737d79 | 4774793b54b61b30cc6cfc94a7548879a78700b2 | refs/heads/master | 2023-02-07T10:39:43.638288 | 2023-01-25T20:41:12 | 2023-01-25T20:41:12 | 14,958,190 | 115 | 52 | null | 2023-01-25T20:41:13 | 2013-12-05T15:57:10 | R | UTF-8 | R | false | false | 362 | r | datasets.R | #' Cigarette Consumption Panel Data
#'
#' @docType data
#' @source From Christian Kleiber and Achim Zeileis (2008). Applied
#' Econometrics with R. New York: Springer-Verlag. ISBN 978-0-387-77316-2. URL
#' <https://CRAN.R-project.org/package=AER>
#' @keywords datasets
#' @md
#' @format A data set with 96 observations a... |
9634f8e72d39a2b9c768d85bb690646d8e41c633 | 25298b75d8e54e34261ce7816c9ed95774566dbc | /man/weighted.median.boot.se.Rd | 1199f28a92fbf51a63b70178a164e25413d758ac | [] | no_license | BroadbentJim/MendelianRandomization | 7946787c662beee9c5f7d69189f655c1b4b2425d | 100d624bae0c5ac296887493c46b0b64ed656d8f | refs/heads/master | 2022-12-07T02:10:17.287876 | 2020-09-03T11:30:24 | 2020-09-03T11:30:24 | 289,373,305 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,466 | rd | weighted.median.boot.se.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mr_median-methods.R
\name{weighted.median.boot.se}
\alias{weighted.median.boot.se}
\title{Weighted median standard error function}
\usage{
weighted.median.boot.se(Bx, By, Bxse, Byse, weights, iter, seed)
}
\arguments{
\item{Bx}{A numeric vect... |
e924340d4de2d1a758ef662ede89b56142d67dd0 | 9c4ddae677019ee2e808444a3e02298b675ec2a7 | /Pool_meta_sensitivity_v6.r | d1cd29b12d52f1348dd970dbb6b19cd5151a7d46 | [] | no_license | sean-harrison-bristol/MR_interactions | c736d2755afafff32eb2a9f20b965223a76f7e2d | 8074f35c6a5658a5934e51272ec84ec0d721e5db | refs/heads/master | 2020-05-29T09:02:09.969128 | 2019-05-28T14:51:37 | 2019-05-28T14:51:37 | 189,048,431 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 14,306 | r | Pool_meta_sensitivity_v6.r | #script_name: Pool_meta_sensitivity_v6.r
#project: 4-way decomp: paper 1
#script author: Teri North
#script purpose: pool estimates across simulation repeats by
# -taking the mean betahat & SE of betahats (to generate MC 95% CI for betahat)
# -take the mean SE and the SD of betahat... |
4e36dd909249ac03479b9e84bb013bf80b63d094 | 02e094167c8ad54218fa47aaa1c49ff40fdbf7b5 | /Layout.R | 08310a1cb4b620090436148e06f4a06d88c55912 | [] | no_license | teerapong588/Optimiation_project | 910be94bad123bd48aee41904ad1a68266e4d147 | 27272594d4c40775c8629dd0477d36f762b66331 | refs/heads/master | 2020-05-03T13:52:50.047320 | 2019-04-18T05:13:08 | 2019-04-18T05:13:08 | 178,663,276 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,140 | r | Layout.R | source("bs_tabs.R", local = TRUE)
source("mv_td_tabs.R", local = TRUE)
source("mv_rb_kde_tabs.R", local = TRUE)
source("mv_rb_spe_tabs.R", local = TRUE)
source("mv_rb_covmcd_tabs.R", local = TRUE)
source("mv_rb_sre_tabs.R", local = TRUE)
source("mcv_tabs.R", local = TRUE)
Header <- dashboardHeader(title = "Portfolio O... |
c3026bff270dedcd53b0c56f31017df9bf5264c5 | 62d8ea7d6bc9104f3f42178abc5df9e8e4acfb84 | /Clase_1.R | 6aaa0fec087cdc1c2ffac9d7f1bd042dc5c55a97 | [] | no_license | TrinyEG/curso_R | e43096132b6eb2e593b00b37eb5d035e1aede853 | f4b39428445663c6647f1445266eefc0e6c7e0e1 | refs/heads/main | 2023-08-31T02:59:03.477125 | 2021-06-17T17:36:23 | 2021-06-17T17:36:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,344 | r | Clase_1.R | # Vectores de dos dimensions
c() # Este es el signo que indica un vector
c(2,3)*c(4,5) #multiplicacion
c(2,3)+c(4,5) #Suma
c(2,3)-c(4,5) #Resta
c(2,3)/c(4,5) #Division
# Vectores de dimensiones diferentes
c(2,3,4,6,1)*c(4,5)
## Asignación de objetos
# Pueden usar el signo = o el signo compuesto <- para hacer asignaci... |
4d0d59522a89cf7fd58df063304c65c26631c4c8 | 1c1b46425349d21577d020f96e82990de60205b7 | /run_analysis.R | eb88fd75b935b4d0daa52fc9d1be907a79652bf1 | [] | no_license | Lchiffon/getting-and-cleaning-data | 865479f7676a0a6c9c68412bddb728092c688a3a | cd4e1800dcfe63681c3cf7cea3207989842fad51 | refs/heads/master | 2021-01-17T11:55:03.539546 | 2014-04-24T01:36:28 | 2014-04-24T01:36:28 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,486 | r | run_analysis.R | setwd("C:/Users/Administrator/Desktop/data/UCI HAR Dataset")
## set the working Directory
data_test<-read.table("./test/x_test.txt")
data_train<-read.table("./train/x_train.txt")
data=rbind(data_train,data_test)
## combine the test set and the train set
mean_x<-rowMeans(data)
std_x<-apply(data,1,funct... |
82ecb6620b010913c7cbc39413f6d639f495bfd4 | 29585dff702209dd446c0ab52ceea046c58e384e | /BMS/R/c.bma.R | af5551566a86d5a88204fc5ea030492d6b31731e | [] | 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 | 199 | r | c.bma.R | c.bma <-
function (..., recursive = FALSE)
{
if (!missing(recursive))
warning("note that argument recursive has no meaning and is retained for compatibility")
combine_chains(...)
}
|
4a1b66c70f748aaf2f42230ea336daaa7f95df22 | d1d6630c1952b1a9d481e35ec3c8ffc8af4aa2c7 | /man/preprocess_SCD.Rd | 5b8219f14641760dcdf02a9aaf736321c1073b63 | [] | no_license | cran/scdhlm | 379a2e83a57df15274f92ce87abd1d49204b493e | 09db652d54a71995870149f9f9e16e3ac6bc7c9b | refs/heads/master | 2023-03-16T04:41:16.745426 | 2023-03-12T09:30:02 | 2023-03-12T09:30:02 | 69,391,400 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,871 | rd | preprocess_SCD.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/preprocess-function.R
\name{preprocess_SCD}
\alias{preprocess_SCD}
\title{Clean Single Case Design Data}
\usage{
preprocess_SCD(
design,
case,
phase,
session,
outcome,
cluster = NULL,
series = NULL,
center = 0,
... |
ca5644676ffb6b9dfce12f011855b324b7fabe73 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Schaum'S_Outline_Series_-_Theory_And_Problems_Of_Statistics_by_Murray_R._Spiegel/CH14/EX14.14.33/Ex14_14_33.R | c19ec9a557e670912d41acdb5c17e11fe03d69cf | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 504 | r | Ex14_14_33.R | #PAGE=318
n=24
r=0.75
m=0.05
a=0.6
z1=1.1513*log((1+r)/(1-r),10)
z1=round(z1,digits = 3)
u=1.1513*log((1+a)/(1-a),10)
u=round(u,digits = 4)
s=1/sqrt(n-3)
s=round(s,digits = 4)
z=(z1-u)/s
z=round(z,digits = 2)
z
a=0.05
x1=1-a
x=qt(x1,df=1/0)
x=round(x,digits = 2)
x
if(x>z) k<-TRUE
k
b=0.... |
63d5dd0a1b698a999b7c6f41b0161b16603761de | 91294be1f45be0ebe4e588866decab350e7e59a7 | /CrabStats/GapAnalysis.R | 0f3f3958ad763770cad13184e0d726a6df901b38 | [] | no_license | Zheng261/CrabitatResearch | 6530f5bbc9df8b6406addcbbf48ed7b798c025fd | 769c00061088638a9b8d581311eb4e0db7b79ff6 | refs/heads/master | 2021-06-24T02:27:57.075776 | 2019-05-25T10:52:57 | 2019-05-25T10:52:57 | 140,462,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,400 | r | GapAnalysis.R | #for (crab in unique(crabs201X$CrabNum)) {
#thisCrab = crabs201X[which(crabs201X$CrabNum == crab),]
#timeVec = thisCrab$Date[2]
#dateVec = as.POSIXct(paste(thisCrab$Date,thisCrab$Time),format="%m/%d/%y %H:%M:%S")
#dateVec2 = c(dateVec[1],dateVec[-length(dateVec)])
#plot(dateVec,thisCrab$Latitude)
#plot(dateVec,thisCra... |
4c618d081934715efa02a5922bdb13fb481a35b5 | bd8c894931368fa85ec44590f3f0b6d0dd21c5ac | /R/POTW_compliance_functions.R | 0f76951ec1ad6eab4b78c5c7dcf3ae5323348c75 | [] | no_license | SCCWRP/POTW_Compliance | b7953f60edbe3faf3903edb2462bb7043937dcd0 | cfdce8820171f7361361a46b8f1dcc9e3d58c835 | refs/heads/master | 2020-04-14T18:45:37.938887 | 2019-12-19T00:09:18 | 2019-12-19T00:09:18 | 164,031,980 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 47,558 | r | POTW_compliance_functions.R | # POTW_compliance_functions.R
# Functions used in the code "app.R" in the program "POTW_compliance_v202_start.R"
#
# List of functions:
#
# settings.list.create
# settings.change.params
# SCB_map
# grep_start
# grep.start.mult
# calc.param.sel
# plot.profiles
# plot.one.profile
# plot.ref.profile
# plot.prof.graph
... |
1d93678df8a488790318eff63521f15151c40a75 | 1ea5000a33609aa567ae78a734afaf6ddafb7cf1 | /cachematrix.R | 547936203a5c457b06c8230afef59e64793d6b08 | [] | no_license | GabeZeta/ProgrammingAssignment2 | e722244cbc6bb5be884bfbf791b20a8b178a047b | a0c50a9e0e53217f4853723a391099d297b77296 | refs/heads/master | 2020-12-29T00:42:28.017064 | 2015-01-25T23:45:12 | 2015-01-25T23:45:12 | 29,831,462 | 0 | 0 | null | 2015-01-25T21:14:10 | 2015-01-25T21:14:09 | null | UTF-8 | R | false | false | 2,258 | r | cachematrix.R | # Matrix inversion is usually a costly computation and there may be some
# benefit to caching the inverse of a matrix rather than compute it repeatedly.
# The following two functions are used to cache the inverse of a matrix.
# The function makeCacheMatrix() creates a special "matrix" object that
# can cache its inve... |
3339bb5d78dfbd704559b7dbe7b737e9b34ac262 | a37c2fff0d0efd25be5daaaac630bfe20f11cb20 | /R/3_parse_data.R | 5874a0dd033afb69036d4a45c4be5dc173f1c1a2 | [] | no_license | mackerman44/champ_Q4s | b156ee1f4290534e2d9ef164f6dffcb9667b37a0 | e68df34379b4cde8302659ec0f72a0330a5ba89c | refs/heads/master | 2021-02-13T10:24:12.104652 | 2020-04-01T16:52:07 | 2020-04-01T16:52:07 | 244,687,846 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,332 | r | 3_parse_data.R | # parse data by watershed, channel unit type, and/or tier 1, and remove any dataset too small (min_samp_size) for comparisons or plotting
parse_data = function(data = spc_ls_hab_df, spc, ls, min_samp_size = 20) {
#------------------------------
# parse data by watershed
wtr = unique(spc_ls_hab_df$Watershed)
fo... |
071e2692cf1a65d01efaec7d3c5f2d344f34cb84 | e71d5e89bf3460f647b320e044d8772112139913 | /server.R | d4cff81ef4e593f6d9d760ea1c482122b9234634 | [] | no_license | JorgeSauma/WineTester | 74977f99c5cb46261db1e455fb7e1c215051ea74 | f11ab9bb4c1b6f78477a4b027c112ee1f9534615 | refs/heads/master | 2021-05-01T23:18:00.935375 | 2018-02-09T17:28:47 | 2018-02-09T17:28:47 | 120,932,417 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,722 | r | server.R | library(shiny)
library(caret)
library(tidyr)
library(randomForest)
value=-1
#wine_data<-read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv")
wine_data<-read.csv("winequality-red.csv")
colnames(wine_data) <- "Wine_colnames"
Tidy_wine_data <- separate(wine_data, Wine_co... |
eb82896736e87b1bb869aa8a69a3896e908494fb | 4dc8d0a645b02b4de44dfa2b1188d4fc32eff151 | /Assignment/A1. Credit Rating/CreditRating.R | 874da979d1b943e4284315f328f48314617e2fd4 | [] | no_license | Hitali-Shah/SDM | 000e92acd2755388bb75979dff09f34e14fefb0e | 9b9ede7828fba94d917f25129e259235d9b9e8cc | refs/heads/master | 2023-08-08T01:03:02.019536 | 2021-09-16T14:32:41 | 2021-09-16T14:32:41 | 398,692,801 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 532 | r | CreditRating.R | library(rio)
library(stargazer)
credit_score = import("CreditRating.xlsx")
colnames(credit_score) = tolower(make.names(colnames(credit_score)))
as.factor(credit_score$student)
as.factor(credit_score$married)
as.factor(credit_score$ethnicity)
as.factor(credit_score$gender)
score1.out = lm(rating~limit+cards+student+bala... |
59ca57c1fed3ad323d9d2dbf0e81085743df670a | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/3504_0/rinput.R | 48adc92cf9a6d2b1c1fdad50796202a0359fe014 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("3504_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="3504_0_unrooted.txt") |
588d0557d8fdb1b383b36537adec7aad6a5e417a | da04803dd0714434a1e0d458616fd9ecfdecbcce | /R/plot-abn.R | 6f4b13245af6fdf3eee547cb5fd693aa085ebe2e | [] | no_license | cran/abn | b232e17d29eba356f5b1df5d50c27e17de860422 | e393f625a9de98adb351ac007b77c87d430cb7bf | refs/heads/master | 2023-05-25T02:14:43.027190 | 2023-05-22T12:50:24 | 2023-05-22T12:50:24 | 17,694,223 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,880 | r | plot-abn.R | ## plot-abn.R --- Author : Gilles Kratzer Last Modified on: 06/12/2016 Last Modified on: 10/03/2017 Last modification: 19.05.2017 Node color list Last mod: 13.06.2017 Arc direction Last mod:
## 18/07/2017
## major rewrite rf 2021-04
# for final submission elimiate and # `print()` lines
plotabn <- function(...) {
... |
f4c31f11d5e165b83d97f375403bf40d41ba0f88 | 478f7c571fa3f63a3a15b904d71457c8a86b51c5 | /code/074_Modeling_lda.R | 586de5d4d948c44ff9d6f7ba160155deb5d80dff | [] | no_license | amacaluso/Statistical_Learning | d5270eb4b8cbdfed9edc7bd85618abb5bb9d70aa | 52d3d797d9e76f3634cf317547c744ef208b2615 | refs/heads/master | 2020-03-09T08:10:38.620706 | 2019-07-31T20:37:23 | 2019-07-31T20:37:23 | 128,682,966 | 4 | 1 | null | 2019-07-29T15:40:33 | 2018-04-08T21:21:52 | HTML | UTF-8 | R | false | false | 11,344 | r | 074_Modeling_lda.R | ### ***** IMPORT ***** ###
##########################
source( 'code/Utils.R')
#SEED = 12344321
source( 'code/020_Pre_processing.R') # REQUIRE SEED
### ***** SAVING FOLDER ***** ###
folder = "results/MODELING/CLASSIFICATION"
dir.create( folder )
##################################
# DISCRIMINANT ANALYSIS
## Linear... |
6db647bbce3b00bedcbe635dd607d2b9c5ceb772 | a0ceb8a810553581850def0d17638c3fd7003895 | /scripts/rstudioserver_analysis/WKM_and_BM_together/find_TSS_topics_winsizes_for_heatmap_MouseBM.R | c534fd0c67f5f9fc6ddac907bc93783c755eb39f | [] | no_license | jakeyeung/sortchicAllScripts | 9e624762ca07c40d23e16dbd793ef9569c962473 | ecf27415e4e92680488b6f228c813467617e7ee5 | refs/heads/master | 2023-04-15T22:48:52.272410 | 2022-10-24T10:45:24 | 2022-10-24T10:45:24 | 556,698,796 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,162 | r | find_TSS_topics_winsizes_for_heatmap_MouseBM.R | # Jake Yeung
# Date of Creation: 2020-06-17
# File: ~/projects/scchic/scripts/rstudioserver_analysis/WKM_and_BM_together/find_TSS_topics_winsizes_for_heatmap_MouseBM.R
# description
rm(list=ls())
library(hash)
library(ggrastr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(data.table)
library(Matrix)
librar... |
47a5d6614965555589cd3b98f790e83956349211 | 5a4ac3a10eb6ea4e5dc6b0588ce3fa03bf3c175e | /Day014/day14.R | f5d1dea91fba62866f16e9bcf773cf3d36752252 | [] | no_license | woons/project_woons | 9bda2dcf1afebe4c3daf9c20a15605dec9ddbae3 | 3958979aa22ddba7434289792b1544be3f884d95 | refs/heads/master | 2021-03-16T08:40:40.350667 | 2018-05-04T05:18:45 | 2018-05-04T05:18:45 | 90,750,693 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,370 | r | day14.R | ##############################
# Day14 _ Regular Expression
##############################
library(stringr)
library(rebus)
x <- c("cat", "coat", "scotland", "tic toc")
# Match two characters, where the second is a "t"
str_view(x, pattern = ANY_CHAR %R% "t")
# Match a "t" followed by any character
str_view(x, pattern... |
0474b0a050af388aa44328107f86fa57f8282ab2 | 77c3d4443e4ec9f25ef4c6f2c9bbb6d8d608f007 | /man/cone_logo_text.Rd | 959cf9a88c0218d988b3611d11faae9e8647bf98 | [
"MIT"
] | permissive | phildwalker/TeamBrand | 9c576407ad64783c39bcc4181ff1301c3deabf09 | 2b338e884b10874b2baa37c40acc2d137f0e84ec | refs/heads/main | 2023-03-27T21:43:18.635726 | 2021-03-18T17:57:24 | 2021-03-18T17:57:24 | 348,366,738 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 437 | rd | cone_logo_text.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cone_logo_text.R
\name{cone_logo_text}
\alias{cone_logo_text}
\title{Generate Cone Health Stylized Text Logo}
\usage{
cone_logo_text(background = "#F9F9F9")
}
\arguments{
\item{background}{Background color of the logo text. Defaults to
\code{... |
f64f5cc07878b8be7e1e822e3706fa8b3887bcc0 | a57550b1724f3f926526dcfbce86b6fc76e6feb3 | /R/stanmodels.R | e37c6f554ec9ef5596599746ccd746ec735e0867 | [] | no_license | mhandreae/rstanarm | 874cdb4266d8cad1832cb29c31e3ab2bea39573b | e13a2db260930af139b2ae5a58f539194342e73e | refs/heads/master | 2020-05-29T11:34:36.151733 | 2015-09-22T17:16:38 | 2015-09-22T17:16:38 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,619 | r | stanmodels.R | # This file is part of rstanarm.
# Copyright 2013 Stan Development Team
# rstanarm is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
#... |
c4ab77b2ab0c0c8a0815076f2be55d72776a0f43 | fe1fb5584e8461c3cd8332514ea51cd8b6df991c | /Analysis of Financial Data with R 4.r | aeaead6bcaff1ded8d3f8249a7d874fcdb8903fb | [] | no_license | Allisterh/R-project---Econometrics-Theory-and-Applications | 370535f138b61522e865a9a6ebceb9c1d93e1dbf | e94794e12301274ac72d61caa1c205b303a00996 | refs/heads/master | 2023-02-25T15:43:27.028094 | 2021-02-03T19:55:39 | 2021-02-03T19:55:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 34,109 | r | Analysis of Financial Data with R 4.r | > getwd()
[1] "C:/Users/Alexa~Chutian/Documents"
> setwd('C:/#Baruch/Econometrics/Financial Data')
> da = read.table("Data2.txt", header=T)
> head(da)
date intc sp
1 19730131 0.010050 -0.017111
2 19730228 -0.139303 -0.037490
3 19730330 0.069364 -0.001433
4 19730430 0.086486 -0.040800
5 19730531 -0... |
5ea97dd5eac69c44feff2b6bca030c3c6ec98c08 | 09f649e97f4274903bec4f8466d456234c0de222 | /test/mlr_codes/mlr_ksvm_undersampling_LOO_0.2.R | b3eb4d24c7aca35eeebb42c426b4b74b9e3deb5e | [] | no_license | VeronicaFung/DComboNet | d33ddb2303dc827f79a90acf9e5320328e178723 | 545417d7d4181df455b2d119ee767f9921114db9 | refs/heads/master | 2023-06-05T15:36:50.203124 | 2021-06-18T09:23:47 | 2021-06-18T09:23:47 | 256,992,605 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,345 | r | mlr_ksvm_undersampling_LOO_0.2.R | setwd('/picb/bigdata/project/FengFYM/mlr_models/')
options(stringsAsFactors = F)
# install.packages('mlr')
# install.packages('mlr3')
library(mlr)
library(mlrMBO)
set.seed(123)
source('scripts/learner_tuning_v2/elasticnet_learner.R')
source('scripts/learner_tuning_v2/glm_learner.R')
source('scripts/learner_tuning_v2/k... |
0295309e7de5490385468cc68d0656f9b220c146 | 8cb3d409c80826aea5823fef0f96b9158a372bd5 | /Training.R | 1a7403050f7271013ab5719f14b2cdae10081908 | [] | no_license | vahtykov/r-vvp | fea6cf7756d3bb258caa4e768ca84433e350e807 | 617a705f53fffaa70ea9107cecc78a4710b7e6cd | refs/heads/master | 2023-04-13T04:16:01.917107 | 2021-04-25T19:14:49 | 2021-04-25T19:14:49 | 350,828,663 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,487 | r | Training.R | list.dirs("D:/RData/DIPLOM")
library("dplyr")
library("caret")
library("AER")
library("ggplot2")
library("sandwich")
library("ivpack")
h <- read.csv("D:/RData/DIPLOM/dataFull.csv", header=TRUE, sep=";")
glimpse(h)
OP1 <- lm(VVP ~ SG4Z + X4BR, data = h)
OP2 <- lm(VVP ~ X4BRZ + SNZP, data = h)
in_train <- createDataPa... |
f88ad7ccc91aaffba9b3b82bb69c3531e6bf76d6 | c1748fa8115e11b8a09f1891ecc327994dfc90d9 | /InequalityShiny/server.R | f1634bddf52c98a111d58ed00a0a55e2225ba555 | [] | no_license | codrin-kruijne/Developing-Data-Products-Course-Project | 8af188b28fd4942198e9da3514d45c21aea9958c | 014bdaad922fd82f54853ff5e50ff4e9ba2a1d62 | refs/heads/master | 2020-03-11T09:26:33.632952 | 2018-04-17T20:22:33 | 2018-04-17T20:22:33 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,490 | r | server.R | #
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(ggplot2)
library(plotly)
# Define server logic required to draw a histogram
shiny... |
5c25ac8c3246e2f8983e4ebadf958213733e15f0 | f6ce51c36418153e08d4fb2a843da95e5b0b9031 | /lab7/R/ridgereg_coef.R | ba50303ff52ddcfdf183c7cd4e3ce5971c5f11a2 | [] | no_license | ClaraSchartner/lab7 | 9ecb1378eeca9765c205d4fca78a340af7830d09 | 8050c59e2ee596438bdfb910f17a0ae032c2bb57 | refs/heads/master | 2021-01-10T18:20:04.778799 | 2015-10-19T12:44:58 | 2015-10-19T12:44:58 | 43,873,866 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 323 | r | ridgereg_coef.R | #'Coefficient
#'
#'\code{coef} extract model coefficients from objects of class \code{"ridgereg"}.
#'
#'@param x an object of class.
#'@param ... further arguments passed to or from other methods.
#'
#'@return coefficients extracted from the model object.
#'
coef.ridgereg <- function(x, ...){
return(x$coefficients... |
829e9948166f52d4b17c4f499b09e59fbee1e191 | 69feddab3de98770afbc27ab90563f983eccdd5f | /Assignment2.R | a110f7eae16658d4c9b2267075b4b91c6d3f186c | [] | no_license | DahamLee/Marketing-Analytics | 11cf4e193a421100a22fd56cb73c6a9d49fccbb5 | 410d1da07d65d1b9b1a1bff1c7b5bfbd7a3f8361 | refs/heads/master | 2021-04-12T09:27:44.699544 | 2018-05-05T00:58:32 | 2018-05-05T00:58:32 | 126,731,947 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,945 | r | Assignment2.R | rm(list=ls())
library("Matrix")
library("lme4")
library("MCMCpack")
cc.data = read.csv("/Users/daham/Desktop/Marketing Analysis/assignment2/CreditCard_SOW_Data.csv", header=T)
cc.data$ConsumerID = as.factor(cc.data$ConsumerID)
cc.data$logIncome = log(cc.data$Income)
cc.data$logSowRatio = log(cc.data$WalletShare/(1-cc... |
7032b616598156f3e6ad1570659386d5125942bd | 816247c509847002300485ff792778d607a7c119 | /R/sim_ipc.R | 478ebc880537a2d01f588ba79c796920fd9d3c17 | [] | no_license | mgaldino/line4PPPsim | 93d93c593e22583e4d00df5371d4b93c9850c2cd | a026c6d5f83ffc25712eb3b6f11d13a7f530c98e | refs/heads/master | 2020-04-02T02:29:55.803353 | 2019-02-11T15:28:31 | 2019-02-11T15:28:31 | 153,912,413 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 816 | r | sim_ipc.R | #' @title Simulates fiscal impact of line 4 subway PPP
#'
#' @description This package allows the user to run Monte Carlo simulation to assess the fiscal impact of lline 4 PPP in São Paulo.
#'
#' @param ipc_0 A number
#' @param ipc_realizado A vector
#'
#' @return A vector of inflation for 33 years
#'
#' @examples sim... |
d374804342ab35577d0ef8ed72a6588acbfd2d75 | 3fb9a252b8ff2ce0611b78a41859eaf5aa075f52 | /man/Compensation.Rd | 68eeb720239a9655a2416578a30308c88def98ed | [] | no_license | DillonHammill/CytoExploreRData | ad23a2e80034b4d722edf697d5849053ee3adb20 | 488edf083092247ad547172906efe6f8c2aa8700 | refs/heads/master | 2022-07-22T14:40:50.036551 | 2020-08-27T01:19:51 | 2020-08-27T01:19:51 | 158,751,860 | 0 | 0 | null | 2019-10-11T08:49:28 | 2018-11-22T21:31:30 | HTML | UTF-8 | R | false | true | 964 | rd | Compensation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CytoExploreRData.R
\docType{data}
\name{Compensation}
\alias{Compensation}
\title{CytoExploreR Compensation Data Set.}
\format{
A \code{flowSet} containing the 7 compensation controls.
}
\source{
Compensation controls used for an in vitro OT-... |
41e8b216f0845c6290bcc5a7df66ae3a8f177f3f | df33361a1d939a735c33f79514f6dfe6ded15ea9 | /agecount.R | cdbc9fd2e6dbd2357849c7e5e6be3b5892fc6e0b | [] | no_license | kgracekennedy/BaltimoreHomicides | ffdf7b958dd20bdc19610738a4feb4f7a055b018 | 31bfe9cb9b69fb283c31886c16fce15b94413082 | refs/heads/master | 2016-08-06T18:51:57.886538 | 2014-12-17T20:33:35 | 2014-12-17T20:33:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 546 | r | agecount.R | agecount <- function(age = NULL) {
## Check that "age" is non-NULL; else throw error
## Read "homicides.txt" data file
homicides=readLines("homicides.txt")
## Extract ages of victims; ignore records where no age is given
#male. nono years, Age
r <- regexec("(Age:|male,) +(.*?) years", homicides)
ages=regmat... |
1f0743909716aa65d8c5bea1b972a35d41ed39c9 | 8457643a6fc09b349cc6ff2bf3573dfce9f3b589 | /cachematrix.R | ba0ddbf8e1700a5ceaa233e56ac8e8c79e796cf5 | [] | no_license | nursharmini/ProgrammingAssignment2 | ee8d52f84905d7faf19f2d36713e25c5109d6428 | cfb9dcaba034dd9bb2f13c418fa686a4d953bf98 | refs/heads/master | 2021-01-09T09:00:59.934083 | 2015-07-13T06:54:33 | 2015-07-13T06:54:33 | 38,992,607 | 0 | 0 | null | 2015-07-13T05:19:05 | 2015-07-13T05:19:05 | null | UTF-8 | R | false | false | 1,785 | r | cachematrix.R | ## These functions calculate the inverse of a matrix and saves it
## into cache. When the user attempts to calculate the matrix inverse,
## the previous value is returned instead of compute it repeatedly.
#The makeCacheMatrix function, creates a special "matrix", which is really a list containing a function to
# 1. s... |
6aa716aea0057ff60e390fcb51c2f8478f711443 | 638dc9da4b99cdce4c32a29c7c672ef0c5863141 | /man/CST_EnsClustering.Rd | a7ca4a9c2d86dd839e6f4316ac0082b274f1c6fa | [] | no_license | rpkgs/CSTools | 887f2d6a31a55b9130b2b2f26880493af584ce1e | ab20bc268756ef30668157cebf56246102c94dcd | refs/heads/master | 2023-09-01T15:38:40.114800 | 2021-10-05T06:20:21 | 2021-10-05T06:20:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 6,709 | rd | CST_EnsClustering.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CST_EnsClustering.R
\name{CST_EnsClustering}
\alias{CST_EnsClustering}
\title{Ensemble clustering}
\usage{
CST_EnsClustering(
exp,
time_moment = "mean",
numclus = NULL,
lon_lim = NULL,
lat_lim = NULL,
variance_explained = 80,
nu... |
227b109b0dce5d45fe3bd05e6a731802b93e851e | b0255d4e54415b6fb1519b8fc0e4d1ca6717b080 | /man/vec2symmat.Rd | 4950f8d6fa6441e51d569ac67af8f850b5a621ef | [] | no_license | mrdwab/SOfun | a94b37d9c052ed32f1f53372a164d854537fcb4a | e41fa6220871b68be928dfe57866992181dc4e1d | refs/heads/master | 2021-01-17T10:22:12.384534 | 2020-06-19T22:10:29 | 2020-06-19T22:10:29 | 16,669,874 | 30 | 3 | null | null | null | null | UTF-8 | R | false | true | 785 | rd | vec2symmat.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vec2symmat.R
\name{vec2symmat}
\alias{vec2symmat}
\title{Creates a Symmetric Matrix from a Vector}
\usage{
vec2symmat(invec, diag = 1, byrow = TRUE)
}
\arguments{
\item{invec}{The input vector}
\item{diag}{The value for the diagonal}
\item{... |
2e16497aa48ec8e4857def24e63b4c56c844e167 | f817d4d29c02c8aba4ad52f0a0de03f1bf3ade8f | /R/endpoint.R | 2ef9e7a7ac7d71f3b63304fe6c6b69649a9a227c | [
"MIT"
] | permissive | be-marc/vetiver | 463d397814169ba7237a9b2e46eac2a13025254e | f1e67302f4775997d4c54c1253904b1ccbca63f6 | refs/heads/main | 2023-08-26T06:59:30.023856 | 2021-11-02T23:43:37 | 2021-11-02T23:43:37 | 423,813,743 | 0 | 0 | NOASSERTION | 2021-11-02T11:16:53 | 2021-11-02T11:16:52 | null | UTF-8 | R | false | false | 1,833 | r | endpoint.R | #' Post new data to a deployed model API endpoint and return predictions
#'
#' @param object A model API endpoint object created with [vetiver_endpoint()].
#' @param new_data New data for making predictions, such as a data frame.
#' @param ... Extra arguments passed to [httr::POST()]
#'
#' @return A tibble of model pre... |
82587366a43bda7f432a9d578cdf0b3a56e92271 | 1dedfa2451f5bdf76dc6ac9f6f2e972865381935 | /tests/testthat/test-density_standard.R | a6ca92d56c5215b3729c42185a6043918d0b09b4 | [
"MIT"
] | permissive | nhejazi/haldensify | 95ef67f709e46554085371ffd4b5ade68baf06a4 | e2cfa991e2ba528bdbf64fd2a24850e22577668a | refs/heads/master | 2022-10-07T09:51:03.658309 | 2022-09-26T18:07:59 | 2022-09-26T18:07:59 | 165,715,134 | 15 | 6 | NOASSERTION | 2022-08-24T14:03:36 | 2019-01-14T18:43:32 | R | UTF-8 | R | false | false | 3,414 | r | test-density_standard.R | library(data.table)
set.seed(76924)
# simulate data: W ~ Rademacher and A|W ~ N(mu = \pm 1, sd = 0.5)
n_train <- 100
w <- rbinom(n_train, 1, 0.5)
w[w == 0] <- -1
a <- rnorm(n_train, 2 * w, 0.5)
# learn relationship A|W using HAL-based density estimation procedure
haldensify_fit <- haldensify(
A = a, W = w,
n_bins... |
078da30f258fc36403b2950687853338643af1fe | e2f3ace7d5476cc8042514b3f93e466098aaf641 | /man/exprToPlotmathExpr.Rd | a7d66c69be51aef334ddc958b8e197bb246cbc8c | [] | no_license | erp12/rgp | 1527a5901fb6cb570e9461487fadb89a9bd66dd9 | 4f6e7a03585f75a139d232b8b817527d15c74d47 | refs/heads/master | 2020-12-31T02:22:38.126098 | 2016-08-22T21:42:32 | 2016-08-22T21:42:32 | 66,305,730 | 0 | 0 | null | 2016-08-22T20:30:13 | 2016-08-22T20:30:13 | null | UTF-8 | R | false | false | 553 | rd | exprToPlotmathExpr.Rd | % Generated by roxygen2 (4.0.1): do not edit by hand
\name{exprToPlotmathExpr}
\alias{exprToPlotmathExpr}
\title{Convert any expression to an expression that is plottable by plotmath}
\usage{
exprToPlotmathExpr(expr)
}
\arguments{
\item{expr}{The GP-generated expression to convert.}
}
\value{
An expression plottable by... |
f582da9477a6dcbbbae8a88e42a2e2f882e5b140 | 6622b0950dc4e57a3826b678054b04765ad22740 | /man/sfactorDdp.Rd | 6c90f4a4d74ca40c90d465a08188c899444a6cc4 | [] | no_license | RafaelSdeSouza/nuclear | f2d6e187298268968ea165b5a793b85c657019dc | 86823e86b4ca0bb12083c60b033e89e583eb6301 | refs/heads/master | 2022-02-13T00:32:51.725874 | 2019-08-09T14:48:14 | 2019-08-09T14:48:14 | 104,823,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 742 | rd | sfactorDdp.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sfactorDdp.R
\name{sfactorDdp}
\alias{sfactorDdp}
\title{Estimate Astrophysical S-factor}
\format{\describe{
\item{x}{
The function has two arguments: ecm, a.scale }
}}
\usage{
sfactorDdp(ecm = ecm, a.scale = a.scale)
}
\arguments{
\item{ec... |
46fc4b7896bbd2f578acc3ce98a692b150db1d85 | e955508b9901acb0eab7b32ca4d0429344a23087 | /function.R | 37a70f8f8e1be262d8ce8ac46fdd6d359546b366 | [] | no_license | joncgoodwin/sb | fa574f014063c06937bf44eb9c58f5e14f4f79c1 | bd3fc66f2c72cb1a9d2deab6b8532f080f81920e | refs/heads/master | 2021-01-18T23:50:47.373453 | 2016-07-14T01:28:04 | 2016-07-14T01:28:04 | 55,804,305 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,518 | r | function.R |
write_sleeping_beauty <- function(x,y) { #x=filename,
#y=citation threshold
#libraries
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales) #for log axis in plotgraph
library(stringr)
... |
a8b10b2c7f3210c785769de7a9dbb1822e278d3d | 03b686d96bc53751f3323cf9eb50bb4884db816b | /Source/ESS_explore.R | 59200807bb2ef3e67beb75037a4d9999abe7244d | [] | no_license | callum-lawson/Annuals | 728028ffeb28488aac457898561afbe04747054f | b55d2037f4f038ef7979c918bc352297a0fc48aa | refs/heads/master | 2021-11-11T09:36:11.838807 | 2018-03-14T13:55:15 | 2018-03-14T13:55:15 | 82,462,159 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,405 | r | ESS_explore.R | ### Develop explanations for patterns in ESS germination results ###
source("Source/invasion_functions_iterative.R")
# Empirical simulations ---------------------------------------------------
outlist <- evolve(
nr=1000,nt=10100,nb=100,
zam=zamo+mam*zsdo,wam=wamo+mam*wsdo,
zsd=zsdo*msd,wsd=wsdo*msd,rho=0.82,
... |
7b94c44976089e84898ad1a529efda2312a02dd8 | 400b426c3e3b56b34c38c71d473df223089906ab | /R/util.R | 0c3720d6ec6cdd6c187fcf6ef33f7913b5dabafa | [] | no_license | poissonconsulting/poiscon | fcea48c3e994ff86dfd7cc521aba1842ebb24ce3 | 97007c1f318cfebb21905b8f42e74486984a1970 | refs/heads/master | 2021-06-11T18:47:30.563459 | 2021-02-12T22:56:24 | 2021-02-12T22:56:24 | 12,257,120 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 539 | r | util.R | #' Remove Dots Colnames
#'
#' Goes through all the data.frame objects in
#' the current environment and removes any dots
#' from the colnames
#' @export
remove_dots_colnames_data_frames <- function () {
for(obj in ls(envir = parent.frame())) {
expr <- parse(text = paste0(
"if(is.data.frame(", obj, ")) {", ... |
1e78accf1999f4317dc405ed362fd9cb0346de6a | 86e99dfcbc67dd4e5a86c8f7e575f7af4b60fd36 | /man/getCurves.Rd | 440539436115a72e90b5332fd3b519f0aaaf78f5 | [] | no_license | thejimymchai/slingshot | 4c55e8cfbd3bcb64e660d466e4d65a2aed55a457 | b1d9722247196d1b76a2b6bd65c0d9dfb630ba16 | refs/heads/master | 2023-02-24T08:32:38.851469 | 2020-11-17T16:34:07 | 2020-11-17T16:34:07 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 7,398 | rd | getCurves.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/AllGenerics.R, R/getCurves.R
\name{getCurves}
\alias{getCurves}
\alias{getCurves,SlingshotDataSet-method}
\alias{getCurves,SingleCellExperiment-method}
\title{Construct Smooth Lineage Curves}
\usage{
getCurves(sds, ...)
\S4method{getCurves}{... |
2f933c64718af0c19cc018109015b28563cf3273 | c348e148840c1260985291d1adb8f7860fb6037f | /12/ex5-1-3_boxplot.R | 0ab7131fd2659adef48b24427e1d19213c338e3a | [] | no_license | harute931507/R_practice | 8d478ad884bb8cd15c35b941499bc4f7c8c09dfe | aa882783d915a58048e7fbb061b3b7df87ec1f3e | refs/heads/master | 2020-03-31T03:45:56.599245 | 2018-10-06T19:59:58 | 2018-10-06T19:59:58 | 151,876,825 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 692 | r | ex5-1-3_boxplot.R | ?boxplot
example(boxplot)
boxplot(iris[1:4], cex.axis=0.7)
boxplot(iris[1:4], cex.axis=0.7, notch=TRUE)
boxplot(iris[1:4], cex.axis=0.7, col=heat.colors(4))
boxplot(Sepal.Length~Species, data=iris, col="gold",
ylim=c(1,8), xlim=c(0.25, 3.5), outline=F,
boxwex=0.35, at=c(1:3)-0.2)
boxplot(Sepal.Width~S... |
1464bf6704a8c5f2cf4ae6e9f37281f36e70bc10 | 328d9398e187c5fa9e6fd6c50c5a1173d5829499 | /R/zzz.R | ffa832aa067a3068ce7154f40ead592b19f1e0a3 | [
"CC-BY-4.0"
] | permissive | PascalCrepey/HospiCoV | 5915e03026871c97c828b1025ba1a2c010381108 | 9a36c370f8bd384a9d83e35847aa20eb95fc88f5 | refs/heads/master | 2021-03-30T02:11:29.487767 | 2020-04-09T13:01:30 | 2020-04-09T13:01:30 | 248,004,955 | 8 | 2 | null | null | null | null | UTF-8 | R | false | false | 146 | r | zzz.R | utils::globalVariables(c("Time",
"Scenario",
"..extraCols",
"AgeGroup"
)) |
e23ddca2f14782a1839350a3ed991d23525217e0 | afdcee7512dad0231bcf6f0ab92a95e82d7d0f70 | /figure3.R | f8e1a77c89b98ad4eff502d35ad17e9f1385d93e | [] | no_license | harrispopgen/gagp_mut_evol | 4383cce285cdadbbd603a4101fb20c1e2acc179a | 9ab6369a23a4dab10cdb490eb9c855df7111b03d | refs/heads/master | 2022-12-16T13:56:05.952725 | 2020-09-24T16:59:50 | 2020-09-24T16:59:50 | 210,941,133 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 17,818 | r | figure3.R | # Analyze mutational signatures per individual (lots of PCAs)
# Set working directory here
# setwd("~/Documents/Harris_project_directory")
options(stringsAsFactors = F)
library(ggplot2)
library(ggridges)
library(plyr)
library(RSvgDevice)
library(ggfortify)
library(scales)
library(grid)
library(gridExtra)
library(cowp... |
fad7d1d18b2d7e9ad06c9b6d4af75c8d68419193 | bb173b7f6d00e1e7dbd368fef30dbed8837c21a1 | /man/pnud_uf.Rd | 426e5dfd5711634707f76af924a63ce6439ea52e | [
"MIT"
] | permissive | abjur/abjData | 2eb67b4196472bca0df0d78a54481d1185ec8948 | afa83b359917b6e974fbe7281f340a66b2a86cfe | refs/heads/master | 2023-04-30T18:48:02.359007 | 2023-01-12T22:41:37 | 2023-01-12T22:41:37 | 77,081,132 | 19 | 4 | NOASSERTION | 2020-12-08T19:16:05 | 2016-12-21T19:42:23 | R | UTF-8 | R | false | true | 557 | rd | pnud_uf.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils-data.R
\docType{data}
\name{pnud_uf}
\alias{pnud_uf}
\title{UNDP data by Federative Units}
\format{
a data frame with 81 rows e 235 columns.
\describe{
\item{ano}{for more information, check \code{\link{pnud_siglas}}}
}
}
\source{
\u... |
55d0a5ecd9113348a95f564df5730db5201ab501 | 3f08010675b3f874336656abbf2b0ac77939649d | /src/server/rserve/server.r | 76d1179474e90a109cf701bf40ac650a4ce32bbb | [] | no_license | ithailevi/L8K | f277b594cd0aa9c040d051378627e16166846c62 | a005c96cc0f1d8f688c19768bccb62e12cb4152b | refs/heads/master | 2016-09-06T16:58:07.822282 | 2013-06-27T09:29:45 | 2013-06-27T09:30:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 145 | r | server.r | # item choosing function
item_choosing = function(items_number)
{
items = as.integer(runif(as.numeric(items_number),1,99));
return(items);
}
|
58708784e24c395d1c8a9ddcb808cc5a0297bce5 | cfcc1f8ff8d8b134c8bc52a64c0218772d12a604 | /Regression.R | 87511d7522f410e7a2c15a8dca3b423b1d0c50a0 | [] | no_license | nikhilbakodiya/R-programming | 55e48a84a1f15c714c63803fa4913b54ce20fed3 | 777b8162839a03dc7528bd6a96b893709e9b46dd | refs/heads/master | 2020-11-28T11:47:55.152187 | 2019-12-23T18:47:18 | 2019-12-23T18:47:18 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,282 | r | Regression.R | #Regression (Linear)
height=c(121,134,145,146,132,189,178,174)
weight=c(56,78,57,69,59,64,65,66)
relation=lm(weight~height)
relation #y=a+bx, i.e. weight=57.71+0.042*height
newheight=data.frame(height=192)#here height=192 in the above equation
weight=predict(relation,newheight) # x=57.71+0.042*192
weight
#M... |
68d8a636cfb519d469a363fe4c5c7dcc080c52d0 | 5fd22a88b5a1ccc9dc74e8405986cc913b3543b2 | /Basics of R and Data Types/R Matrices/Matrix_Selection_and_Indexing.R | f4a055915a54de29a9183b12beb302ea595d2be1 | [] | no_license | cyork95/R-for-Data-Science-and-Machine-Learning | 99c46714abbf8b13582c6f2b5574a4a4ee0b0d74 | 044d805586dfcd6bce60fbc218f192634713aa27 | refs/heads/master | 2021-01-04T05:29:43.358293 | 2020-02-15T17:37:45 | 2020-02-15T17:37:45 | 240,408,014 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 218 | r | Matrix_Selection_and_Indexing.R | matrix.exp <- matrix(1:50, byrow=TRUE, nrow = 5)
first.row <- matrix.exp[1,]
first.col <- matrix.exp[,1]
first.three.rows <- matrix.exp[1:3,]
first.three <- matrix.exp[1:3, 1:3]
specific.place <- matrix.exp[2:3, 5:6] |
83688fb791d9b52fe5f694811b16ad130456dc2c | f32dbf645fa99d7348210951818da2275f9c3602 | /R/mtapspec.R | 69456b8838515bacd4817309f7855b80a26c1c2f | [] | no_license | cran/RSEIS | 68f9b760cde47cb5dc40f52c71f302cf43c56286 | 877a512c8d450ab381de51bbb405da4507e19227 | refs/heads/master | 2023-08-25T02:13:28.165769 | 2023-08-19T12:32:32 | 2023-08-19T14:30:39 | 17,713,884 | 2 | 4 | null | null | null | null | UTF-8 | R | false | false | 1,615 | r | mtapspec.R | `mtapspec` <-
function(a, dt, klen=length(a), MTP=NULL)
{
##### multi-taper spectrum analysis
#### Mspec = mtapspec(a$y,a$dt, klen=4096, MTP=list(kind=2,nwin=5, npi=3,inorm=0) )
#####
if(missing(MTP))
{
kind=2;
nwin=5;
npi=3;
inorm=1;
}
else
{
kind=MTP$ki... |
c5be2946bd152c910ac30ae420b933a32e0a10a9 | 8ebb7a4fc2583ad1bb04253b338c95f04be498ef | /man/swTFreeze.Rd | 2e3ca4eaf84496f0905da98a8d3769d44665ca12 | [] | no_license | landsat/oce | 8c9c3e27b9981e04c7cf1138a0aa4de8d2fc86b9 | f6e0e6b43084568cd2c931593709a35ca246aa10 | refs/heads/master | 2020-12-31T07:33:10.029198 | 2014-08-10T10:05:43 | 2014-08-10T10:05:43 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 788 | rd | swTFreeze.Rd | \name{swTFreeze}
\alias{swTFreeze}
\title{Seawater freezing temperature}
\description{Compute freezing temperature of seawater.}
\usage{swTFreeze(salinity, pressure=NULL)}
\arguments{
\item{salinity}{either salinity [PSU] or a \code{ctd} object from which
salinity will be inferred.}
\item{pressure}{seawater ... |
1605b76011ae66701fe602b6fd6c831947df1e4e | 627bbc07d4557dfe5b49ba88b9bd253a6b47068e | /R/zzz.R | c4f1c3f6c79864d7f619217fd28f06275a7e609b | [] | no_license | cran/coxrobust | e54c5a53e4a16dbf51a30f8ff6154320b593eadb | bd29efa9d2c3c990d3b2daddcad04ac1c25d4bd0 | refs/heads/master | 2022-05-17T04:41:27.999639 | 2022-04-06T13:02:33 | 2022-04-06T13:02:33 | 17,671,544 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 92 | r | zzz.R | # .onUnload <- function(libpath) {
#
# library.dynam.unload("coxrobust", libpath)
#
# }
|
a8c6bae9251905d6d08ad6a1554ec0e28b4f712a | 350f369998282044eeff0794540189c89ad8710c | /R/qle-package.R | def71029785e0ad0e6277fad37272a8c84c1d4d6 | [] | no_license | cran/qle | 26b2edf6e372d4a966aa85754ba4c88377036290 | 857a96cfcf8dbbf116c944c23924f6cedb37abd8 | refs/heads/master | 2021-09-24T10:39:46.030022 | 2018-10-08T11:00:03 | 2018-10-08T11:00:03 | 110,973,979 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,754 | r | qle-package.R | # Copyright (C) 2017 Markus Baaske. All Rights Reserved.
# This code is published under the GPL (>=3).
#
# File: qle-package.R
# Date: 27/10/2017
# Author: Markus Baaske
#
# General description of the package and data sets
#' Simulation-Based Quasi-Likelihood Estimation
#'
#' We provide a method for parameter ... |
421cd83d928008d49be90c3d8efccd065b9c0268 | 10b908437ccb5123218ee56191cd4bf42c6051df | /Geo_again/Astral_tree/1.Relabel_gene_trees_uniqueTaxid.R | 8f723b9bdc73137f502a9e2f384ab193a1fb4358 | [] | no_license | AlexanderEsin/Scripts | da258f76c572b50da270c66fde3b81fdb514e561 | b246b0074cd00f20e5a3bc31b309a73c676ff92b | refs/heads/master | 2021-01-12T15:10:47.063659 | 2019-03-10T15:09:38 | 2019-03-10T15:09:38 | 69,351,736 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,751 | r | 1.Relabel_gene_trees_uniqueTaxid.R | #!/usr/local/bin/Rscript
library(ape)
library(RSQLite)
library(stringr)
direct <- "/Users/aesin/Desktop/Geo_again/Group_fastTree/"
in_tree_dir <- file.path(direct, "Final_trees")
out_tree_dir <- file.path(direct, "Final_trees_relab")
no_dup_tree_dir <- file.path(direct, "Final_trees_noDup")
database_path <- "/Use... |
e4b7911b9581cdba9163ba0f746d47983e18f46a | 846eb90003c329750ca6078a7d4941cd87e578cc | /Section 2/Section2.4/Video24.R | 4918920382decc9a286680b216b9c05a961496fd | [] | no_license | PacktPublishing/Learning-Data-Analysis-with-R-Video- | 62685d9a9f9116184afb0791e243f6f8443bbf82 | 151713640dcdc4887f8e867064f73745749c49fa | refs/heads/master | 2021-06-27T06:03:25.744205 | 2021-01-19T13:09:03 | 2021-01-19T13:09:03 | 187,592,737 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,165 | r | Video24.R | #Volume 1
#Section 2
#Video 4
#Author: Dr. Fabio Veronesi
#Load the required packages
library(sp)
library(raster)
#For this video we are going to use the data.frame we created
#in video 1.1
#Setting the working directory
setwd("E:/OneDrive/Packt - Data Analysis/Data")
#Set the URL with the CSV Fil... |
7784c6f5c6c8fed1618ea6f6918ea7de5ae629b0 | de92c076034b4ccf601aea725f226b427db3bbef | /codigos_en_r/arbol_con_c50.R | fbd4d7fdaebb28518bcac45c22defe59c5a224e0 | [
"Apache-2.0"
] | permissive | armandovl/estadistica_multivariante_r | 7b29d935c8ddace14133e3b8cc983c2727c934ec | 9eec99d819d21b358ccc1a9c01ab8af084160c8c | refs/heads/main | 2023-03-04T18:50:49.680927 | 2021-02-11T04:04:22 | 2021-02-11T04:04:22 | 335,741,063 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,503 | r | arbol_con_c50.R |
#..................Traer e inspeccionar los datos...................
Datos1<-read.csv("bases_de_datos/atitanic.csv") #traemos el dataframe
#..Traer los datos desde github
miURL="https://raw.githubusercontent.com/armandovl/estadistica_multivariante_r/main/bases_de_datos/atitanic.csv"
Datos1<-read.csv(url(miURL))
he... |
1f6a1d05ac1b1339ffd26209d49e89bbaf9a2cdc | f8eb55c15aec611480ede47d4e15e5a6e472b4fa | /analysis/0037_bond_returns.R | e225b5ba419ed298676b943bb1910047f7e607ea | [] | no_license | nmaggiulli/of-dollars-and-data | a4fa71d6a21ce5dc346f7558179080b8e459aaca | ae2501dfc0b72d292314c179c83d18d6d4a66ec3 | refs/heads/master | 2023-08-17T03:39:03.133003 | 2023-08-11T02:08:32 | 2023-08-11T02:08:32 | 77,659,168 | 397 | 32 | null | null | null | null | UTF-8 | R | false | false | 5,468 | r | 0037_bond_returns.R | cat("\014") # Clear your console
rm(list = ls()) #clear your environment
########################## Load in header file ######################## #
setwd("~/git/of_dollars_and_data")
source(file.path(paste0(getwd(),"/header.R")))
########################## Load in Libraries ########################## #
library(ggplot... |
50837709eea39b53940d0d7843bd7ce32d2ba8f3 | 669fb662d125367d271b57070613801f6750178d | /R/AllGenerics.R | 28967614215db4f41e033fa662ecd5e3d5ede3c7 | [] | no_license | petterbrodin/flowWorkspace | be72910302bcc1af2a94a6fd7f20f41bc874e851 | f18e57a16c8389b66e999b45f80d62a29e6439e0 | refs/heads/master | 2021-01-17T21:33:01.093721 | 2012-09-26T22:32:44 | 2012-09-26T22:32:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,000 | r | AllGenerics.R | setGeneric("openWorkspace", function(file){
standardGeneric("openWorkspace");
})
setGeneric("closeWorkspace",function(workspace){
standardGeneric("closeWorkspace")
})
setGeneric("parseWorkspace",function(obj,...){
standardGeneric("parseWorkspace")
})
setGeneric("getNodes",function(x,...){
standardGeneric("getNodes"... |
6da8fe99c4dd34ea5c4bb323eb223fae4146c33e | 78bcb722fda2bad52e146e4bb6aeb14a29bf7d77 | /man/fixNamesForEMU.Rd | 20d5c7d0545cd95e6f897d801e52228a92331107 | [] | no_license | richardbeare/ultRa | 94b3b2d04afaa049017900fc2d91379a63fbc0c5 | 4ff4d3d5929fa58b3906548733837498f97294b9 | refs/heads/master | 2020-12-24T16:07:04.399327 | 2018-04-30T03:55:39 | 2018-04-30T03:55:39 | 28,159,290 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 672 | rd | fixNamesForEMU.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/exportSSFF.R
\name{fixNamesForEMU}
\alias{fixNamesForEMU}
\title{Create modified names for AAA files. Removes punctuation that offends tcltk}
\usage{
fixNamesForEMU(files)
}
\arguments{
\item{files}{the output of list.files}
}
\value{
list of... |
57eecc267ced51f699f5cbf17156cfb79952886e | 3ba50ff12a4bdfebdf136aa0a637b6fbfd2827a3 | /DSR Lab/3/3a.R | c9439d183b67c0297a2833eba7625e6c220f1dfb | [] | no_license | smaransrao/DSRlab | fb96b9c5f1694e49d4920f4a00bc99f221adc5ee | 5d20584ba68668db4481e7674f5a76380e4eeae4 | refs/heads/master | 2021-07-10T20:26:04.929164 | 2020-12-06T10:03:32 | 2020-12-06T10:03:32 | 221,524,322 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 297 | r | 3a.R | v1 <- c(10, 1, 37, 5, 12)
v2 <- c(8, 3, 9, 6, 4)
v3 <- c(18, 9, 12, 4, 6)
v4 <- c(8, 27, 6, 32, 23)
v5 <- c(12, 13, 16, 9, 10)
x <- rbind(v1, v2, v3, v4, v5)
m <- matrix(x, nrow = 5, ncol = 5)
m
names <- c('Thistle', 'Vipers', 'Golden Rain', 'Yellow', 'Blackberry')
l <- list(x, names)
l |
404666c668ca2f019a54e706007d6176692c4c1e | 5bcc79d20267e222255f7133ccdfd589158fa5d7 | /R/nullParaEst.R | 03c1bac5b35ad37734af8739bc29485df33d2de1 | [
"MIT"
] | permissive | zhonghualiu/DACT | d32fed4428892cbd9cf9e81ad8b0f2c6ee3ce02b | fd518e727a3ea0e1e7294754807a6c8cdc7185df | refs/heads/master | 2023-02-20T04:29:23.521419 | 2023-02-06T02:35:07 | 2023-02-06T02:35:07 | 296,997,733 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 974 | r | nullParaEst.R | nullParaEst<-function (x,gamma=0.1)
{
# x is a vector of z-values
# gamma is a parameter, default is 0.1
# output the estimated mean and standard deviation
n = length(x)
t = c(1:1000)/200
gan = n^(-gamma)
that = 0
shat = 0
uhat = 0
epshat = 0
phiplus = rep(1,1000)
phiminus = rep(1,1000)
dphi... |
88a0551b2c77d37d2aeabc68d6fb9737d16adeef | 05b71bc93cd7b6f41ee19a1d6ded9a34bbaeeea2 | /R/Modelling/0_data_management/sentiment_scaler.R | 671a45e41ba15c80a1b0c003f7bb97eb07876432 | [] | no_license | Nicholas-Autio-Mitchell/master_thesis | 697b0972bc6e56a1a7146da1e524e5904f79344c | 326d7c2b30f2eed6f2a4e82edbb090bfa1c495bf | refs/heads/master | 2023-07-07T02:22:14.353564 | 2023-06-26T09:20:16 | 2023-06-26T09:20:16 | 69,510,760 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,236 | r | sentiment_scaler.R |
###################### ====================================================== ######################
###################### Scale all sentiment results, maintaining dispersion ######################
###################### ====================================================== ######################
#' All sentimen... |
e94745582b2478cbaadddb7faf212dfa2512d1fc | c745a74ab42c02097d0132ab07702f76c2807924 | /R代码/数据预处理/排序.R | 4838955947aa6cda814652ccc41eeae84d38b344 | [] | no_license | Kaleid-fy/R-language- | 13f7b8ced2f88b5a64f3762786c2cd11f84649ee | 0e31f5775c994858972c2a639e1ca48af8335f60 | refs/heads/master | 2020-03-25T00:09:51.012230 | 2018-08-16T15:25:24 | 2018-08-16T15:25:24 | 143,172,027 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 714 | r | 排序.R | ### 排序
# R中涉及排序的基本函数有order、sort和rank三个。
# order函数返回的是排序数据所在向量中的索引，
# rank函数返回该值处于第几位（在统计学上称为秩），
# sort函数则返回的是按次排好的数据。
(x <- c(19,84,64,2))
order(x)
rank(x)
sort(x)
# 下面再看一个例子，来更加深入了解order的用法
d <- data.frame(x=c(19,84,64,2,2),
y=c(20,13,5,40,21))
d
# 按x的升序排序，如果x一样，则按y的升序排序
d[order(d$x,d$y),... |
9503a2c0c7235bee52b6a4dc38fb7b16eb57d721 | 4b10c2e443fcbec746cb8f5db8aedf0a0933a439 | /man/TreeWalkerDiscrete.Rd | c75b815378c61f3c5e427f3b337931c30bb06098 | [] | no_license | laurasoul/dispeRse | 81968d976ce9477f45584f62c9a7baa87bb42273 | 0f1316bc963fa8cea3ed3da0f7bb585e8acd7079 | refs/heads/master | 2021-06-05T09:02:45.991357 | 2021-05-24T21:15:14 | 2021-05-24T21:15:14 | 33,941,723 | 5 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,391 | rd | TreeWalkerDiscrete.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TreeWalkerDiscrete.R
\name{TreeWalkerDiscrete}
\alias{TreeWalkerDiscrete}
\title{Generate random birth-death tree with associated coordinates}
\usage{
TreeWalkerDiscrete(
b = 0.1,
d = 0.05,
steps = 50,
slon = 0,
slat = 0,
stepleng... |
6c2bbc649f291f777d38097fd421c6c830f74643 | 27edde77c68ce3cfd1149ea659d56658f5d83bec | /temp.R | 46a046042411213ae7417d1be8674edd50ab9fc0 | [] | no_license | brophyj/book_v1 | b8307f4067200f4a61fa5e910956693dd39f2166 | b5367d8c0e0cfcd5151c6b8469b00a6b6cb8b87a | refs/heads/main | 2023-03-24T06:18:53.954211 | 2021-03-03T02:42:10 | 2021-03-03T02:42:10 | 343,909,995 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,294 | r | temp.R | library (BayesFactor)
data(sleep)
## Compute difference scores
diffScores = sleep$extra[1:10] - sleep$extra[11:20]
## Traditional two-tailed t test
t.test(diffScores)
dead <- c(938,1238)
alive <- c(18760, 26340)
prop.test(dead,alive)
bf = proportionBF(y = 15, N = 25, p = .5)
bf
mat <- matrix(c(50,48,21,41), nrow=... |
a97d05ac53b0aec679ed2b7797141f7f7b52bbfc | ef84851bd06ab41faa62190f6c8464809605cbb9 | /functions/plot.novel.comms.R | 173ff0619d3f087662d139743f47c2089dbc7cba | [] | no_license | TimothyStaples/novelty-cenozoic-microplankton | f306c22161c7fdaf840c1662f67178a91c92748a | 0a062c18a6e661d1d0a4af750186a9e42448470a | refs/heads/master | 2022-12-23T11:58:01.238855 | 2020-09-15T23:36:37 | 2020-09-15T23:36:37 | 288,867,070 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,984 | r | plot.novel.comms.R | plot.novel.comm <- function(site.sp.mat, alpha, metric, site, axis.label){
return.data <- identify.novel.gam(site.sp.mat, alpha, metric, site=site, plot=FALSE,
plot.data=TRUE)
min.p <- return.data[[3]]
seq.p <- return.data[[2]]
save.data <- return.data
return.data<-return.data[[1]]... |
3109cbf5d39896a4c741e1230ddb77e0c0e1d8c5 | 1bd01254e9226ec9777a91b29df09ec70b4824e6 | /scripts/F_ESC_Binomial_Beta_Hurdle_GAM.R | 711e338e081df8da939381c95448bc48f7d620b5 | [] | no_license | RafaelSdeSouza/Beta_regression | b75148071e37faa4a73ed896f8058ca129b4c61e | 66c8a5dad19464edba2fa8390bf7e0e729e15c5f | refs/heads/master | 2020-05-22T00:03:37.197545 | 2019-03-22T01:51:32 | 2019-03-22T01:51:32 | 37,638,437 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 10,639 | r | F_ESC_Binomial_Beta_Hurdle_GAM.R | rm(list=ls(all=TRUE))
library(mgcv);library(ggplot2);library(reshape2);library(ggthemes);library(MASS);library(hexbin);library(scales)
# Read data
Data=read.csv("..//data/FiBY.csv")
Data=subset(Data,redshift < 25)
##
## Log modulus transformation
L_M <-function(x){sign(x)*log10(abs(x) + 1)}
## fEsc is the variable ... |
4beaf170279bf31e3ec1997d920640dc6987531a | 36d73bd4ec51b24f9aa427003d41ace725c23a14 | /man/scNMT.Rd | b987f78b23768b08ff872cf38e5da15b33a30267 | [] | no_license | drisso/SingleCellMultiModal | f613c4f7b7470f27ee25445160ecd798bdd5f89c | 2685521119f5b162809da3f5f73dab01cb08a1de | refs/heads/master | 2022-11-07T04:59:38.832585 | 2020-06-23T13:33:40 | 2020-06-23T13:33:40 | 279,685,426 | 1 | 0 | null | 2020-07-14T20:22:47 | 2020-07-14T20:22:46 | null | UTF-8 | R | false | true | 2,523 | rd | scNMT.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/scNMT.R
\name{scNMT}
\alias{scNMT}
\title{Single-cell Nucleosome, Methylation and Transcription sequencing}
\source{
\url{http://ftp.ebi.ac.uk/pub/databases/scnmt_gastrulation/}
}
\usage{
scNMT(
dataType = "mouse_gastrulation",
modes = "*... |
9f439679ec331d415da9ecfe60e28c2bb1c07c40 | 87092bd3c5d1e8c864502f851085ec80bda39705 | /PAMR.r | f9948057218d1199cf276a54f5d41ec5c66a6bd3 | [] | no_license | ngokchaoho/robust-median-mean-reversion | 0c66c964883ecf2ec9f4736f1e267a07dd8feeee | 5cf3fa4e28f1dbd36217441b71254cf7456ed8c0 | refs/heads/master | 2020-04-10T10:40:56.094305 | 2018-11-20T05:57:48 | 2018-11-20T05:57:48 | 160,973,104 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,772 | r | PAMR.r |
pamr_run <- function(fid, data, tc)
{
data_matrix=data
t = nrow(data_matrix)
m = ncol(data_matrix)
cum_ret = 1
daily_ret = NULL
cumpro_ret = NULL
e = 0.5
tc = 0
SumReturn = 1
day_weight = t(as.matrix(rep(1/m,times = m)))
day_weight_o = t(as.matrix(rep(0,times = m)))
daily_portfoli... |
89358038aa9d1c0680038f894e6e65efc5e88614 | 1cbad6b517ea7555ccab4123e510f9f1050cfc9c | /naomi/R/utils.R | d81ff1df1e41f2c192933ebb104eb758db7f1661 | [
"MIT"
] | permissive | jeffeaton/naomi-model-paper | 483139c7052b717f52a4f35a81821e5d2b8a297e | c7207417f79da3e7be2bcbb265a798520623e0ef | refs/heads/master | 2023-06-15T19:14:32.322162 | 2021-07-16T21:12:21 | 2021-07-16T21:12:21 | 360,933,843 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,235 | r | utils.R | naomi_write_csv <- function(...) {
write.csv(..., row.names = FALSE, na = "")
}
naomi_read_csv <- function(file, ..., col_types = readr::cols()) {
as.data.frame(csv_reader(file, TRUE)(file, ..., col_types = col_types))
}
readr_read_csv <- function(file, ..., col_types = readr::cols()) {
csv_reader(file, TRUE)(f... |
0c3ffdb9ef72a45a5f4a065e45ce49d415811424 | 3dcc2b4999a6325d98c7537851b10cd44fe589a7 | /Week2_RandomWalks.R | c1d64a8a8df02b6a2419d75b422d97c49c7a4593 | [] | no_license | robjohnnoble/MathProcFin_R_code | 93d271ff4770a84cc6e582ffcd0124d299a04317 | 50fb16cec4b4762fd0ead77c45956251104a3e35 | refs/heads/main | 2023-02-25T01:08:34.196940 | 2021-02-01T13:07:04 | 2021-02-01T13:07:04 | 331,593,620 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,588 | r | Week2_RandomWalks.R | # variance of random walk:
var_rw <- function(p, n, x, y) n * p * (1 - p) * (x - y)^2
# expectation of random walk:
exp_rw <- function(n, p, x, y) n * (p * x + (1 - p) * y)
# PMF of random walk:
rw_pmf <- function(p, x, y, w_n, n) {
k <- (w_n - n * y) / (x - y)
if(k != round(k)) return(NA)
if(choose(n, k) == 0) r... |
df8ad0e60e8cc54b56be6639803af1f651f6f280 | 8516a1b12744c52a8775250fea9be7f2bf535f4b | /shiny_form_table/ui.R | 171dccc300720096a3b66aea0885a94a161be421 | [] | no_license | Ronlee12355/ShinyTrials | 5696482712d87fce6349bfbb5f8fc3915b32154c | c00691d7a1cdbdd0f608bbfe2c09dbe3878fe590 | refs/heads/master | 2021-03-22T22:29:47.263599 | 2020-04-12T06:04:46 | 2020-04-12T06:04:46 | 247,402,563 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,190 | r | ui.R | library(shiny)
shinyUI(fluidPage(
# header title
titlePanel('First try of shiny app, only for form elements'),
br(),
shinythemes::themeSelector(),
wellPanel(
dateInput('date', 'Date Choose: ', startview = 'month', language = 'zh-CN'),
sliderInput('num', 'Choose a number: ', min = 0, max = 100, value ... |
bb6a68c1d421ab701bd4a66b1d4d03bb5654b3b7 | a7c370386ab2e6534985275107323a128b0e16fe | /man/define.versions.Rd | b4e645c7c5b9e9aa87670469632e6723e37d73f8 | [
"MIT"
] | permissive | sarkar-deep96/climateR | 05f4a7c62f24266f03ea1a70d7ade01ebdba54cf | 93332328c1bf6f875dc2e0d184f0fdf597501852 | refs/heads/master | 2023-07-03T08:12:00.709572 | 2021-08-03T14:54:05 | 2021-08-03T14:54:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 856 | rd | define.versions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utility_define_versions.R
\name{define.versions}
\alias{define.versions}
\title{Define climateR versions}
\usage{
define.versions(
dates,
first.date = "1950-01-01",
sep.date = "2006-01-01",
scenario,
future.call,
historic.call,
... |
0e3e04354bb43118eb02cab10849f43c2d4fff7b | 79c2ddfa41d2a18da3ac243d600e01944cafb175 | /cachematrix.R | 3209cb13b90697e439aebbf6c874fa37ed69dbd5 | [] | no_license | carojasq/ProgrammingAssignment2 | 946811dd1d836795092789af6fe3f873bdc70f7c | 0de34e180292b3eb90395d41bbd717b561e26493 | refs/heads/master | 2020-05-01T01:16:03.889898 | 2014-12-21T01:30:02 | 2014-12-21T01:30:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 872 | r | cachematrix.R | # This function build a special matrix to help with inverse matrix caching
makeCacheMatrix <- function(x=matrix()) {
inverse_matrix <- NULL
set <- function(y) {
x <<- y
m <<- NULL
}
get <- function() x
setinverse <-function(inverse) inverse_matrix <<- inverse
getinverse <- function() inverse_matrix
... |
e36c4a5abccfddef80ad6c3167263a8a00f3c8c2 | ea524efd69aaa01a698112d4eb3ee4bf0db35988 | /man/TeamcityReporter.Rd | f2b2b79de6243360dc0c6bd14fde65844205f782 | [
"MIT"
] | permissive | r-lib/testthat | 92f317432e9e8097a5e5c21455f67563c923765f | 29018e067f87b07805e55178f387d2a04ff8311f | refs/heads/main | 2023-08-31T02:50:55.045661 | 2023-08-08T12:17:23 | 2023-08-08T12:17:23 | 295,311 | 452 | 217 | NOASSERTION | 2023-08-29T10:51:30 | 2009-09-02T12:51:44 | R | UTF-8 | R | false | true | 890 | rd | TeamcityReporter.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/reporter-teamcity.R
\name{TeamcityReporter}
\alias{TeamcityReporter}
\title{Test reporter: Teamcity format.}
\description{
This reporter will output results in the Teamcity message format.
For more information about Teamcity messages, see
htt... |
7fd73428d8407e4d087ea4981907262c003d4703 | a33b1a6c61f80539343be9ac6aec5412f30cdc12 | /20170620geologyGeometry/libraryC/orientationsUsingC.R | 8e936fcecf5e0467fa5bdb0b867695a216065654 | [
"Apache-2.0",
"MIT"
] | permissive | nicolasmroberts/nicolasmroberts.github.io | 9a143c93859f2b3f133ade1acf54fb1ba1c966d3 | f6e8a5a02eea031fb68c926d6d922846eeb71781 | refs/heads/master | 2022-09-08T22:03:26.646877 | 2022-07-27T20:50:50 | 2022-07-27T20:50:50 | 117,170,161 | 0 | 1 | MIT | 2018-03-04T23:16:00 | 2018-01-12T00:23:20 | HTML | UTF-8 | R | false | false | 8,791 | r | orientationsUsingC.R |
# Copyright 2016 Joshua R. Davis
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing... |
aa51a2afa717fb9635012ee7e8bb4e75c44ef3f1 | 67c401741bb8e2518c66977b3293d6901259c2fc | /_archive/_archive/random_forests/rf_code_dmacs.R | 15e89287afc3b3de0d4019f4064825e945d52397 | [
"MIT"
] | permissive | andrewcistola/healthy-neighborhoods | 64e37462d39270a02b915c6a56a4abf9f9413136 | 08bd0cd9dcb81b083a003943cd6679ca12237a1e | refs/heads/master | 2023-01-28T23:15:12.850704 | 2020-11-18T15:29:32 | 2020-11-18T15:29:32 | 192,378,489 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,206 | r | rf_code_dmacs.R | ## Code Prep
setwd("C:/Users/drewc/Documents/GitHub/healthy_neighborhoods")
library(dplyr)
library(randomForest)
library(MASS)
library(reshape)
rf = read.csv("rf/rf_data_dmacs.csv")
## Random Forest
rf$Tract <- NULL
rf = rf %>% mutate_if(is.factor, as.numeric)
of <- randomForest(
formula = Di... |
4a9cc42c50382edf3f963e2a4c36e19f6af698ca | 0c1b525e3c773211a1158ed6ec71cd80c9a5caa3 | /library/timetk/function/transform/normalize_vec.R | f35d4eeb3eaedb882339db1628d6204aea8d7529 | [] | no_license | jimyanau/r-timeseries | c0f6d55d6be43a2f066a3f948e23378da2cb70d2 | 04e3375bc5cb2fe200f6b259907ccdf6424871d7 | refs/heads/master | 2023-07-12T23:21:05.971574 | 2021-08-14T23:20:53 | 2021-08-14T23:20:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,376 | r | normalize_vec.R | # ***************************************************************************************
# Library : timetk
# Function : normalize_vec
# Created on: 2021/8/14
# URL : https://business-science.github.io/timetk/reference/normalize_vec.html
# **********************************************************************... |
9f8635c1676f9aa9fd6facfca69d6b31e6d68f3b | 956033e3826cfdcefdf81725c1343e94d9c12a2c | /R/ACQRS_sub.R | 66b3fb6ae444dfe9f5521565c3a600184d3c2e46 | [] | no_license | Allisterh/emssm | c38505a401c612289622b1607bfab1718f6bd752 | 2ae000e49c48a00e9dfa06f36bcb27b085b7fa2e | refs/heads/master | 2023-08-14T04:28:40.562688 | 2021-10-04T14:25:16 | 2021-10-04T14:25:16 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,687 | r | ACQRS_sub.R | #'
#' Estimate the state space model using subspace algorithm
#'
#' Subspace algorithm for the estimation of model
#' \deqn{x_{t+1} = Ax_{t} + w_{t}}
#' \deqn{y_{t} = Cx_{t} + v_{t}}
#' where \eqn{w_{t}} and \eqn{v_{t}} have zero mean and covariance matrices
#' \deqn{Var(w_{t})=Q, Var(v_{t})=R, Cov(w_{t},v_{t})=S}
#'
#... |
b5573a63cfb7c27b2b23d27b11a6d34c1c61a963 | b6d80916052926fff06f988a6840335dec6cc997 | /skyline_external_tool/AvG_skylineexternaltool/AvantGardeDIA_Help_GitHubRepo.R | a71eafb8ddab19466fd3b9dc7fd4dd6d55ff9db4 | [
"BSD-3-Clause"
] | permissive | SebVaca/Avant_garde | 043df8165272b0becf823bd4d13338fc25a55652 | 2c2fc25789b2bef8524a867d97158d043244297c | refs/heads/master | 2021-06-07T18:51:36.910796 | 2021-05-07T18:07:59 | 2021-05-07T18:07:59 | 167,582,571 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 52 | r | AvantGardeDIA_Help_GitHubRepo.R | browseURL("https://github.com/SebVaca/Avant_garde")
|
6d6a3e1e42f5ed52709cdfd389511558eae7dfc8 | b68ba79cfb162536c78644772b08440e6d32fd79 | /plot3.R | dd8fdfde4c2a6488bb6c4643a8427f4904f9abb8 | [] | no_license | farabi1038/ExData_Plotting1 | 6d6201a5c9f40bed7cf07b459865f0889d33c500 | 9297e58083e77130d741777e28929736bcca5a78 | refs/heads/master | 2021-01-20T02:28:06.442521 | 2017-04-26T01:14:32 | 2017-04-26T01:14:32 | 89,408,803 | 0 | 0 | null | 2017-04-25T21:33:05 | 2017-04-25T21:33:05 | null | UTF-8 | R | false | false | 722 | r | plot3.R | File <- "/Users/FARABI/Desktop/fg.txt"
data <- read.table(File, header=TRUE, sep=";", stringsAsFactors=FALSE, dec=".")
subSet <- data[data$Date %in% c("1/2/2007","2/2/2007") ,]
#str(subSetData)
datetime <- strptime(paste(subSet$Date, subSet$Time, sep=" "), "%d/%m/%Y %H:%M:%S")
g <- as.numeric(subSet$Global_active_pow... |
f993acb3af92cd4eb1bc35f35597ed06b911b67a | 29585dff702209dd446c0ab52ceea046c58e384e | /DiceOptim/R/goldsteinprice.R | 8b637fe628761eac4245f8a3fe0d437a0c6ef43b | [] | 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 | 692 | r | goldsteinprice.R | goldsteinprice <- function(x)
{
# Goldstein and Price test function (standardized version)
# --------------------------------------------------------
# Dimension: n = 2
# Number of local minima: 4
# The global minimum:
# x* = c(0.5, 0.25), f(x*) = -3.129172
# The local minima:
# x*,2 = c(0.35, 0.4), f(x*,2) ... |
b8b76e0ba914aebc01161f17a4e50591b09550af | 62f1743aae6487e3e53b8f55c7e6fbf07d9abaa1 | /plot1.R | 51f484a15ed8faf2cfebeeb394eeeec38b656f76 | [] | no_license | cgerstner/ExData_Plotting1 | 082d44142ccdbca65dcc63e6b0d30138e2895f68 | 4fd5fc1794cbef0ec68023c68e8c9bc54181ae1a | refs/heads/master | 2020-03-14T04:19:12.852952 | 2018-04-28T22:05:59 | 2018-04-28T22:05:59 | 131,439,518 | 0 | 0 | null | 2018-04-28T19:49:39 | 2018-04-28T19:49:39 | null | UTF-8 | R | false | false | 654 | r | plot1.R | zipFile <- "household_power_consumption.zip"
dataFile <- "household_power_consumption.txt"
url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
if (!file.exists(zipFile)) {
download.file(url, zipFile)
}
if (!file.exists(dataFile)) {
unzip(zipFile)
}
power <- read.csv(dataF... |
b48e170b372b073f525ccde5706e587bf9c814a7 | 9992af6db68a9d3a92844b83cf992210da05cc32 | /CINormalizada.R | a3d3168c57ab5380329a02ab1f04625e2df340f3 | [] | no_license | cristinacambronero/CovarianzaR | bcaa06f763ef13220e1090995f2e989663ebf19a | e899f3234ca19ccff410d838e6ecff81d17bf5bf | refs/heads/master | 2020-04-13T12:53:38.498218 | 2015-07-26T09:42:44 | 2015-07-26T09:42:44 | 39,720,639 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,031 | r | CINormalizada.R | datos3<-CI21548Empresas
install.packages("quantmod")
library(quantmod)
***********************************************************
/* ELIMINAMOS LAS EMPRESAS QUE TENGAN MAS DE 200 NA */
***********************************************************
indices<-c(0)
for(i in 2:length(CI21548Empresas)){
w<-c(... |
6beb400b462144879072d20c1222d9cb2baebc3b | d2c8e45888d5be7f4a6cbcc516b11827a2f16911 | /man/calcul_p.Rd | 975aa5fd32988fda944556337cacc4614a871d82 | [] | no_license | genostats/tail.modeling | 73652274649cc73f47466809c6eb285ccf41236d | 8248c2a3eb4416e3d48b094301f1c9a8cc58b1a5 | refs/heads/master | 2020-03-14T11:59:40.699431 | 2018-06-04T10:43:05 | 2018-06-04T10:43:05 | 131,601,540 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,382 | rd | calcul_p.Rd | \name{calcul_p}
\alias{calcul_p}
\title{Estimation of the p-value with Pareto's function or Box-Cox function used on distribution's tail.
}
\description{ Estimates the p-value of a given data set zsim with the test statistics Zobs thanks to Pareto's method or Box-Cox method with their different estimated parameters... |
115df55f6bab76f382e58e648248bf10bb6bf1f9 | 0ca11666bce33a12e0e33a972d53438d0dc3674c | /tests/testthat.R | ee23c4ed3eb69db311d9c7748698f779bb08e0b8 | [
"MIT",
"LicenseRef-scancode-warranty-disclaimer"
] | permissive | alixlahuec/syntaxr | 8305ac297632eda8c42325f0363c517f1581d47d | 252646cb70546f5f949bebec84482dff9f442cfa | refs/heads/master | 2022-11-26T20:50:47.540924 | 2020-08-04T21:33:20 | 2020-08-04T21:33:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 58 | r | testthat.R | library(testthat)
library(syntaxr)
test_check("syntaxr")
|
ce3430497322d961e9e86dfd821c4a899e9d6fe0 | 0db9b9ad4b00a908d9ddba1f157d2d3bba0331c4 | /tests/testthat/test-as_point.R | fc070cb4185b561e77b0b9a919f3ddf1ec125224 | [
"MIT"
] | permissive | elipousson/sfext | c4a19222cc2022579187fe164c27c78470a685bb | bbb274f8b7fe7cc19121796abd93cd939279e30a | refs/heads/main | 2023-08-18T15:29:28.943329 | 2023-07-19T20:16:09 | 2023-07-19T20:16:09 | 507,698,197 | 16 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,192 | r | test-as_point.R | test_that("as_point works", {
# Check numeric inputs
expect_true(is_point(as_point(c(0, 1))))
expect_true(is_point(as_points(c(0, 1), c(1, 0))))
expect_true(is_multipoint(as_points(c(0, 1), c(1, 0), to = "MULTIPOINT")))
# Check crs parameter
expect_true(is.na(sf::st_crs(as_points(c(0, 1), c(1, 0), to = "MUL... |
401c690d6ecfef66595e3e1b89031eec6c356126 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rdrobust/examples/rdbwselect.Rd.R | 24e2afb7543d339273493173256c5674712896e0 | [] | 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 | 293 | r | rdbwselect.Rd.R | library(rdrobust)
### Name: rdbwselect
### Title: Bandwidth Selection Procedures for Local Polynomial Regression
### Discontinuity Estimators
### Aliases: rdbwselect print.rdbwselect summary.rdbwselect
### ** Examples
x<-runif(1000,-1,1)
y<-5+3*x+2*(x>=0)+rnorm(1000)
rdbwselect(y,x)
|
3f8f30fa47d9fab743a2b5b5a5f060e3af6cfa3e | 2d34708b03cdf802018f17d0ba150df6772b6897 | /googlesourcerepov1.auto/man/projects.repos.testIamPermissions.Rd | 0d6c4a6f4fc8d2f53691712cb0ed43cdf95a1fa6 | [
"MIT"
] | permissive | GVersteeg/autoGoogleAPI | 8b3dda19fae2f012e11b3a18a330a4d0da474921 | f4850822230ef2f5552c9a5f42e397d9ae027a18 | refs/heads/master | 2020-09-28T20:20:58.023495 | 2017-03-05T19:50:39 | 2017-03-05T19:50:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,257 | rd | projects.repos.testIamPermissions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/sourcerepo_functions.R
\name{projects.repos.testIamPermissions}
\alias{projects.repos.testIamPermissions}
\title{Returns permissions that a caller has on the specified resource.If the resource does not exist, this will return an empty set ofp... |
f61e384e3842a882c1f9f2df5b1ae482e70af5ea | 9132996d08213cdf27c8f6d444e3f5b2cfdcfc85 | /R/add_cplex_solver.R | 8da1b8f72e7b2c0c940db4af7664a5e14eceebde | [] | no_license | prioritizr/prioritizr | 152013e81c1ae4af60d6e326e2e849fb066d80ba | e9212a5fdfc90895a3638a12960e9ef8fba58cab | refs/heads/main | 2023-08-08T19:17:55.037205 | 2023-08-08T01:42:42 | 2023-08-08T01:42:42 | 80,953,648 | 119 | 30 | null | 2023-08-22T01:51:19 | 2017-02-04T22:45:17 | R | UTF-8 | R | false | false | 10,638 | r | add_cplex_solver.R | #' @include Solver-class.R
NULL
#' Add a *CPLEX* solver
#'
#' Specify that the
#' [*IBM CPLEX*](https://www.ibm.com/products/ilog-cplex-optimization-studio/cplex-optimizer) software
#' should be used to solve a conservation planning problem (IBM 2017) .
#' This function can also be used to customize the behavior of th... |
82e295558df237523c2db0c32a828733fe8083d4 | 2c4dbf42a157b8691ad66da48a34c98e92407d18 | /R/12-create-data-subsets.R | 315930d62b7b575126edf6c972a8deff352365ae | [] | no_license | timkiely/spatially-conscious-ml-model | 05f829b8efb181fe4f0f1454427589a3443f0d1a | 3a81a9ce61a48dd8d34aca427370968f9580c2bd | refs/heads/master | 2021-10-10T12:19:08.269686 | 2019-01-10T16:39:12 | 2019-01-10T16:39:12 | 95,896,422 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,620 | r | 12-create-data-subsets.R |
source("R/helper/load-packages.R")
source("R/helper/source-files.R")
data_path <- "C:/Users/tkiely/Dropbox/MSPA/Thesis/Analysis/full-data"
## all base data:
base_data <- read_rds(paste0(data_path,"/","p05_pluto_with_sales.rds"))
# 1) ----------------------------------------------------------------------
# step ... |
bb73020ea02aafb2e54723cea72d624d8cddcde3 | 1239b241f22041185e473772c97be748982fd005 | /tests/tests.R | e3010bee1cd2b354f7cc19ed57038f0bc6a2c185 | [] | no_license | djvanderlaan/lvec | 7e5e3b030b477e8edb439662541b37e8e7b5e6de | fec0f36d32cfbef2905f105a22917b4098c4ae85 | refs/heads/master | 2022-11-10T00:55:40.310109 | 2022-10-22T14:07:09 | 2022-10-22T14:07:09 | 72,359,688 | 8 | 1 | null | null | null | null | UTF-8 | R | false | false | 53 | r | tests.R | library(lvec)
library(testthat)
test_check("lvec")
|
074e4170d7e98bd082abc12a5dea362aa3b2f45f | 4f6723c128a8cf6f41d146e71c59e5cf4323f6c3 | /R/qp1qc_solver.R | e932b4f832a3e4bc7822119bf6fdfe810bfe078e | [] | no_license | aaronjfisher/qp1qc | 6587440fdec6915d484d4121af59f0952fd2ed48 | d414cc5cd0f0ba805ceb5a9c5776523b67a4f5ea | refs/heads/master | 2020-09-03T01:55:45.967140 | 2020-08-19T14:02:44 | 2020-08-19T14:02:44 | 219,356,240 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 22,481 | r | qp1qc_solver.R | # To do:
# !! rather than requiring positive definiteness, also accept diagonal matrices?
# !! = important note to keep track of, or note for future changes.
#' binary search with arbitrary resolution
#'
#' The \code{\link{binsearch}} function in the \code{gtools} package searches only over integers. This is a wrapp... |
e467c547a043570513c32e7b35e93bf267c06017 | e61d4e17b5683e6c5c79588588aa302f24b03863 | /xrp_data_vis.R | 5ae858cf1963b8fb9cab3f7b5e4059661b3d6758 | [] | no_license | Joseph-C-Fritch/web_scrape_project | 89466e585b3e10dab0be11e1d2d7c803945d1962 | 0d56f349421d8f564a4ade6ce15c4bda7be11407 | refs/heads/master | 2020-04-22T02:05:24.170732 | 2019-02-12T19:06:26 | 2019-02-12T19:06:26 | 170,035,903 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,803 | r | xrp_data_vis.R |
library(dplyr)
library(ggplot2)
library(wordcloud2)
library(tm)
library(knitr)
library(Hmisc)
library(corrplot)
df3 <- readr::read_csv("./df3_wsentiment.csv")
df4 <- readr::read_csv("./df4.csv")
df5 = left_join(df3,df4, by = 'week')
df30 = df5%>%
mutate(daily_percent_change = (daily_price_change/open)*100)%>%
se... |
6c185a9237c3d59e1f974897248706cb3fa2393e | dc054313b0da31cb82de6b8bafa2999379e4ed5a | /cachematrix.R | 862c938a31cd52a294f1f35c0e8d43227a892a80 | [] | no_license | anfide/ProgrammingAssignment2 | 75baa1edc3170179a1fff7c2f9fd8cafc5fde585 | 8477ec8b25e6280a1b270225250a1092d0fcec61 | refs/heads/master | 2021-01-15T14:41:57.082954 | 2014-11-27T09:43:57 | 2014-11-27T09:43:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | cachematrix.R | ## Maintain a "matrix vector" that holds a matrix and its inverse.
# ( NOTE: It could be built by a simple vector with two elements (matrix, inverse)
# but I think there would be one main disadvantage:
# the update of the inverse value would be complete responsibility of the
# matrix user
#
## Create a special "ma... |
353880e228f0e134f7de1bc36c37f20f3ca90117 | e97358ae5d7dcdf22f2ae865f11101de7fcccebb | /R/plot size at age v time.R | e76cd24185feab410c5133229f0d06c9682b7d5a | [] | no_license | tessington/biochronology | 2a5cc0041e0f9cf0d0d9725264025ba614a99220 | ca0dee5b41860eca36f45250402b8080ff53c99c | refs/heads/master | 2023-05-06T12:04:21.783805 | 2021-05-25T00:23:16 | 2021-05-25T00:23:16 | 258,607,344 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,258 | r | plot size at age v time.R | require(rstan)
require(KernSmooth)
require(viridis)
####### Plotting Function #####
interp.fun <- function(win, df) {
df$h.at.age <- df$Lstart / df$wstart
# find the right row
index <- which(df$wstart>=win)[1]
if (is.na(index)) h.predict <- df$h.at.age[nrow(df)]
if (!is.na(index)) h.predict <- approx(x = df... |
e25c15f52100451b4f9b37c6dea956437acefa10 | 60632022e8d582f96869911de94a3cc87a4ec464 | /R/merge_data_sources.R | 59623e331e933656255d6a8b19508799162ee1ca | [] | no_license | guillecarc/COVID19-global-forecasting | 508726a9cb81646b5f877458dbc547c15e1bf667 | 4f33a44416e60012f28eb2bf2c5e1fc5fa24b1ff | refs/heads/master | 2022-04-22T18:45:18.326789 | 2020-04-20T06:27:33 | 2020-04-20T06:52:46 | 254,617,115 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,062 | r | merge_data_sources.R | get_data <- function(train_test_path = "./data/train_test/week4/",
UNPop_path = "./data/World Population Prospects 2019 - UN/",
KaggleTemp_path = "./data/Climate change earth surface temperature data - Kaggle/GlobalLandTemperaturesByCountry.csv",
AppleMob_p... |
7b1069bafe857b39fc98e13387607a7ce7d39c07 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/qdap/examples/end_inc.Rd.R | 64a439274dc2532c6cd8a31a62d3543e51722710 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 410 | r | end_inc.Rd.R | library(qdap)
### Name: end_inc
### Title: Test for Incomplete Sentences
### Aliases: end_inc
### Keywords: incomplete
### ** Examples
## Not run:
##D dat <- sentSplit(DATA, "state", stem.col = FALSE)
##D dat$state[c(2, 5)] <- paste(strip(dat$state[c(2, 5)]), "|")
##D end_inc(dat, "state")
##D end_inc(dat, "state"... |
3c172f2be56d835de2cb312601a6d267ca949775 | 2e627e0abf7f01c48fddc9f7aaf46183574541df | /PBStools/man/getName.Rd | f34f8f2729491054ea278f4a9034d6ca0347436b | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | pbs-software/pbs-tools | 30b245fd4d3fb20d67ba243bc6614dc38bc03af7 | 2110992d3b760a2995aa7ce0c36fcf938a3d2f4e | refs/heads/master | 2023-07-20T04:24:53.315152 | 2023-07-06T17:33:01 | 2023-07-06T17:33:01 | 37,491,664 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,431 | rd | getName.Rd | \name{getName}
\alias{getName}
\title{Get String Names from Literals or Named Objects}
\description{
Get string names from user supplied input. If the name supplied
exists as an object in the parent frame, the object will be assessed
for its potential as a source of names.
}
\usage{
getName(fnam)
}
\ar... |
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