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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c704f96cbd2c4958c8e230be66448832cc0dcdf7 | f4ea90de095579e845aa7cd8e631583c77755beb | /R/Class-hier-fn.R | 1c1746cf1ab4adbb81d7cabea022a2b6af656327 | [] | no_license | cran/classGraph | c35e320f389f372eba1f93fe2c6ed677934323a5 | 1666954776a5334a49c621fd733e1aadebd953ec | refs/heads/master | 2023-08-31T07:13:05.501094 | 2023-08-21T19:50:02 | 2023-08-21T20:30:50 | 17,695,085 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,202 | r | Class-hier-fn.R | if((Rv <- getRversion()) < "3.2.1") {
lengths <- function(x, use.names = TRUE)
vapply(x, length, 1L, USE.NAMES = use.names)
}## R < 3.2.1
subClasses <- function(Cl, directOnly = TRUE, complete = TRUE, ...)
{
## utility for classTree():
if (isClassDef(Cl)) {
cDef <- Cl
Cl <- cDef@cla... |
d0d7b210a89bff73d1a8b9b23796ae874dc8b0ec | c20629bc224ad88e47849943e99fe8bc6ccb1f17 | /Data processing functions/lost.hrs_function.R | f09fec2bc29b5a05bf73b429ea5754bd7053dfab | [] | no_license | nayefahmad/R-vocab-and-experiments | 71a99e4d3ff0414d1306a5c7cabfd79b49df17f9 | 4384d3d473b0a9d28d86c3d36b0b06a1f91b862e | refs/heads/master | 2022-12-13T01:12:11.134952 | 2020-08-24T04:59:52 | 2020-08-24T04:59:52 | 103,333,690 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 4,458 | r | lost.hrs_function.R |
#*******************************************
# FUNCTION TO FIND LOST HOURS IN BOOKING DATA:
#*******************************************
# rm(list = ls())
# source("2017-12-13_vgh_mdc-explore-booking-data.R")
# TODO: ---------------
# fn defn:
lost.hrs <- function(df) {
# dataframe is booking data split... |
339dc835e6ccac3b9f14b29ddc0ac1d79fe31e35 | a1e2e1e9c4b5eb2b2eb56f64ec6df2f83d65a2ce | /common/general_dataset_stats.R | 97c8c44a9a29a0f18a2c3abf65551d1de3bfad6e | [] | no_license | posfaig/wow | e6b2747d17e7eda6869a01a1ced50b6d0a1cad67 | 1d8b7694886706d6152d058a9465c1c34acbb8ee | refs/heads/master | 2021-01-11T19:43:07.244518 | 2017-01-13T01:27:40 | 2017-01-13T01:27:40 | 69,188,269 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,885 | r | general_dataset_stats.R | ######################################################################
###
### Get the general statistics of the datasets
###
######################################################################
library(dplyr)
compute_dataset <- function(data, pred_date, testset_end_date, ...){
print("compute_dataset")
pred... |
014c9ef0102674ce88f2c8a62a5b9a8ca25ecf3a | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/lmreg/examples/stars2.Rd.R | 69096a8b13316dde0da7b314ecca7d69a2350b96 | [] | 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 | 148 | r | stars2.Rd.R | library(lmreg)
### Name: stars2
### Title: Stars data 2
### Aliases: stars2
### Keywords: datasets
### ** Examples
data(stars2)
head(stars2)
|
c0a6cc76bc0a6c4e87be57d17a53ba66b97fe78b | 561beed458dfcf06de55c8b9145613adf3e3dad6 | /old_bic.r | b28969541f1ef0fefa89eb54035edadbb60807d3 | [] | no_license | deniseduma/respls | e75dafd68ecd0fa5905667b78f2a36f44d8947da | f326cf1a84ab4734a67a156917f8a2752597be68 | refs/heads/master | 2016-09-14T05:36:51.454059 | 2016-05-23T22:30:59 | 2016-05-23T22:30:59 | 59,517,165 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,495 | r | old_bic.r | ## Select best w using BIC
cat("\n"); print("BIC")
betas = list(); prs = list();
idx_bic = 1; min_bic = 1e6; all_zero = 1
for (j in 1:nlam) {
w = ws[ ,j]; idx_w = which(w!=0); nnz_w = length(idx_w)
if (nnz_w==0)
next
all_zero = 0
## OLS re-estimation of w
#y = ys[ ,j]; sX = X[ ,idx_w,drop=FA... |
e22aa30cf69877285ef788722f94005563d17afa | 39b1d1b3f6e933d7fc7c30579f76d5027620b3b0 | /R/parse_crossovers.R | 54ad6cc9aec9ee95478e6b8c7324a19fd014e285 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | susjoh/crimaptools | bb46fdf82a889fd1b7e203fe2872c348f71686f0 | 70a6ec563a8544fb533a0b9a653189501bedd5e2 | refs/heads/master | 2021-01-09T20:53:58.204711 | 2020-10-12T22:23:26 | 2020-10-12T22:23:26 | 58,579,445 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,829 | r | parse_crossovers.R | #' parse_crossovers: Parse crossover information from CriMAP chrompic output
#' @param chrompicfile File with output from chrompic
#' @param familyPedigree data.frame containing columns ANIMAL, FATHER, MOTHER
#' and FAMILY. FAMILY defines family groups for crimap. IDs in ANIMAL can be
#' repeated within and between... |
4c27a21156f045fc1e332f953dfb5aff838aab91 | 0ff7d2d3b7a141ca374a1d73a47289871d5d2fda | /R/get_alphabet_freq.R | 4095237cf3c35dfebfa7bde2e07dd08a12b4d1a3 | [] | no_license | tinyheero/shmutils | a13f44111d8323fef68810f7a0f88aad7c09a2ec | a26d06eeb21dca16f70ddb422604ae60d5ec25f6 | refs/heads/master | 2020-12-25T18:23:16.564227 | 2015-07-16T21:55:32 | 2015-07-16T21:55:32 | 39,107,770 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,286 | r | get_alphabet_freq.R | #' Return the Alphabet Distribution Over a GRanges Object
#'
#' Calculates the number of different bases there are in a particlar GRanges
#' object.
#'
#' @param gr GRanges object. There needs to be a id column in the metadata.
#' So that the number of motifs can be associated with the original
#' Gen... |
73d8bb5931616fee34f7632ccf277db2a833edfd | fdae59c4711c9710bbbb78e98a8dd1a6e7026eeb | /rserv/suncorp_rserve/qa/duplicates_Scenario.R | 61ad63a58b5e72f8b925c14f6709b316d6ff2e17 | [] | no_license | saurabh-singh-17/secgov | ac61459751b0e8cfa6f5ec828a87054819329101 | b19b9a838cd0c43562dc8fa771075ca3f2635905 | refs/heads/master | 2020-12-14T01:01:49.172334 | 2017-01-15T07:58:03 | 2017-01-15T07:58:03 | 55,128,035 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,922 | r | duplicates_Scenario.R | # Written by: Tushar Gupta
# Time : Sep 2014
#=================================================================================
# inputPath<-
# outputPath<-
# customkEY<-
# var_list <-c("Store_Format")
# scenarioName <- 'abcd'
# keepOption <-
#===============================for verify and save button==============... |
3c6258110fa5980decd7860f2dc1960b22d0d220 | 0c1885fa442778f9e027975ef82267c5b6d69075 | /learn_hmmr.R | 202eccfbe1d7c39233b5263664c945a63f579af1 | [] | no_license | blinsimon/HMMR | 489a63344350f8a5d31b796ff796f6b90373f4e9 | 45dc511dc94c0a39cfc86441a02a2d5644eff5cd | refs/heads/master | 2020-04-27T00:52:57.589728 | 2019-03-05T13:07:32 | 2019-03-05T13:07:32 | 173,947,996 | 0 | 0 | null | null | null | null | IBM852 | R | false | false | 14,221 | r | learn_hmmr.R | learn_hmmr<- function(x, y, K, p,type_variance, total_EM_tries, max_iter_EM, threshold, verbose){
# learn_hmmr learn a Regression model with a Hidden Markov Process (HMMR)
# for modeling and segmentation of a time series with regime changes.
# The learning is performed by the EM (Baum-Welch) algorithm.
... |
34454d1159ed4c253064e705fbafbaa46e7af6d3 | 4459eb5432916b4ad6c5c5d911b50c9d2fec1ad5 | /man/table.Correlation.Rd | 2d706b7bb49ee43ebf807ec5bd230c9552f088bb | [] | no_license | braverock/PerformanceAnalytics | 057af55b0a4ddeb4befcc02e36a85f582406b95c | 49a93f1ed6e2e159b63bf346672575f3634ed370 | refs/heads/master | 2023-08-03T10:18:27.115592 | 2023-03-29T09:23:17 | 2023-03-29T09:23:17 | 58,736,268 | 209 | 113 | null | 2023-05-23T17:46:08 | 2016-05-13T12:02:42 | R | UTF-8 | R | false | true | 1,341 | rd | table.Correlation.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/table.Correlation.R
\name{table.Correlation}
\alias{table.Correlation}
\title{calculate correlalations of multicolumn data}
\usage{
table.Correlation(Ra, Rb, ...)
}
\arguments{
\item{Ra}{a vector of returns to test, e.g., the asset to be exam... |
45ecea4c881b9a7f66994ec6fee5f3dae1dfc5a4 | fde0347e6cd6aadf0c5850de45f26fc771e67f0e | /Demos/Rbased/CrashCourse/03_ClimateProcessing.R | ccb56c068b864c8b0e5f34d11f8c00048bf14ef4 | [] | no_license | shichaochen/BotanyENMWorkshop2020 | 4b43292776c6948ae639a4d5f4d5e5d245ed9e16 | 9cb02a7a52bdf5d4a453d6b34c7334f7fd9e3d43 | refs/heads/master | 2022-11-24T11:34:20.051259 | 2020-08-01T17:42:12 | 2020-08-01T17:42:12 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 5,777 | r | 03_ClimateProcessing.R | # ClimateProcesssing.R
## Climate Layer Processing
## Original script by Charlotte Germain-Aubrey.
## Modified and created by ML Gaynor.
# Load Packages
library(maptools)
library(raster)
library(rgdal)
library(sp)
library(maps)
library(mapproj)
# Load bioclim layers
alt_l <- raster("data/climate_processing/Bioclim/... |
ba27ed6ee0531c5ba65a216e8f08eb8363431df7 | 9f9cc8931df4007d7ae3d215b1f39b6658200e79 | /BLP.R | 07cf32bce36001da638d0c09b9869b28d68cb9b9 | [] | no_license | ygh929/GP_Gibbs | e45940deedb296efa5f21531054e04289c8ab464 | 5e7150951b9121e643eba64c9e2b799cb3ea2ee1 | refs/heads/master | 2021-01-20T02:15:34.887554 | 2015-08-29T04:51:42 | 2015-08-29T04:51:42 | 20,091,153 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 469 | r | BLP.R | GetXZ<-function(cDat){
points=cDat$points
pairs=cDat$pairs
m=dim(pairs)[1]
n=dim(points)[1]
X=matrix(0,nrow=m,ncol=n)
Z=matrix(NA,nrow=m,ncol=1)
for (i in 1:m){
X[i,min(pairs[i])]=1
X[i,max(pairs[i])]=-1
Z[i,1]=2*I(pairs[i,1]>pairs[i,2])-1
}
list(X,Z)
}
#set the size of train and validate data set
n1=50
... |
b52f983bad7d19a57cfa1b85bebdc969950f4e8c | 84e92a2e0e3f8fbdb724e7b3d70052a9059d5259 | /test/rgtsvm-svmlight.R | cc05be6ed13620e3ed39e5dc4c48e6ea702335e5 | [] | no_license | minghao2016/Rgtsvm | bc9c3fdd25d5a89962e9c3608357041338d5e819 | b7e7bf043b70c1d21a96406ef4e0847922495ff1 | refs/heads/master | 2021-01-21T11:36:24.676439 | 2016-12-12T20:40:56 | 2016-12-12T20:40:56 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 850 | r | rgtsvm-svmlight.R | library(Rgtsvm);
#wget http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale
# normal matrix
#mat <- as.matrix( load.svmlight("http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale") );
# sparse matrix
#mat <- load.svmlight("http://www.csie.ntu.edu.tw/~cjlin/lib... |
d7bd673b458c7887b4efc77208090cc5b7a73b18 | 6593fa55f4216c069a8aa53d2475838b9a3db1c7 | /munge/descarga_mapa_2.R | 0064cca0d4f72d55f074abb8957e621f982a740e | [] | no_license | ecastillomon/ecoVIZ | d6eef354fd80ddcdf266c507e8a61872b9c2cce3 | 6d30494dd4f878b53bf046aff29ec67ba736451a | refs/heads/master | 2020-08-21T22:00:42.174077 | 2020-07-17T00:42:46 | 2020-07-17T00:42:46 | 216,253,783 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,097 | r | descarga_mapa_2.R | library(ggmap)
# ggmap::register_google(key = "AIzaSyB6BLEl5Bhygcrfy-Q4USZ6dMghajMNax0")
# p=ggmap(get_googlemap(center = c(long_centro,lat_centro),
# zoom = 11, scale = 2, source="stamen",
# maptype ='terrain',
# color = 'color'))
# [-99.13, -99.22,... |
85a5dd00a91555b2917574bf389bb2b9838ccf62 | 29445f5eb91f1c9c083a387c140d302e5e3e5b52 | /tests/testthat/test-stitch.R | eaece6c5a554332d0290f7fe5e06fd47f20f88f0 | [
"MIT"
] | permissive | adam-gruer/victor | d9a896c371e33f49d8bcc0e4c11f4d1fc8bb19d2 | 988ce71dbee593fa5aafbf67a86caf4f36daa0dc | refs/heads/master | 2020-08-20T02:51:55.633455 | 2019-11-02T00:08:47 | 2019-11-02T00:08:47 | 215,977,085 | 29 | 2 | MIT | 2019-10-19T23:57:57 | 2019-10-18T08:26:50 | R | UTF-8 | R | false | false | 371 | r | test-stitch.R | test_that("binding two tile's layer with differing attributes", {
perth <- list(longitude = 115.906105,latitude = -31.842274)
au_west <- spoils(zoom = 4, longitude = perth$longitude, latitude = perth$latitude)
sydney <- list(longitude = 151.131706,latitude = -33.805967)
aus_se <- spoils(zoom = 4, longitude = ... |
d3f59dae291a50f44bb38b988da1d2c230b73e71 | dfea2a089f5373848d4bbfddb785f7956ba9d312 | /man/ThreeComp_Volume_Exponent.Rd | 4cbde6f8ae6f29c445a8af1766826beaaa830802 | [] | no_license | cran/PKconverter | c09d298d74948e54bbbd00a169fb4cce66b427d4 | 23d3cac38c79f9164b9710ebabf579b5d9825556 | refs/heads/master | 2020-04-09T07:46:01.820084 | 2020-02-06T08:00:02 | 2020-02-06T08:00:02 | 160,169,464 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,827 | rd | ThreeComp_Volume_Exponent.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ThreeComp_Volume_Exponent.R
\name{ThreeComp_Volume_Exponent}
\alias{ThreeComp_Volume_Exponent}
\title{Convert pharmacokinetic parameters for three compartment model}
\usage{
ThreeComp_Volume_Exponent(V1,alpha,beta,gamma,k21,k31,
V1.sd=NA,alp... |
652274b79ef8cbcfc3351d42367ab07e14f45c1b | 8e0989c127fa440b1356606c5b1616703d76c06d | /R/gif.R | 032417a17600102d424fd27deb5fad5aef4a83fb | [] | no_license | HenrikBengtsson/R.graphics | 8da678165bd6cfad546faf71b78d768a44b3165e | c92a1761f82806ecf1b761d58a59ab55532aa518 | refs/heads/master | 2021-01-20T11:44:10.433776 | 2014-06-19T04:04:58 | 2014-06-19T04:04:58 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,853 | r | gif.R | #########################################################################/**
# @RdocFunction gif
#
# @title "GIF graphics device"
#
# \description{
# Device driver for GIF images.
# Internally \code{png()} and ImageMagick's convert command is used.
# }
#
# @synopsis
#
# \arguments{
# \item{filename}{filename (@c... |
533b49786a6a747e23765df4c9daff6842b9c343 | 58552d1a2700c5a31c1915b27d347fc59ff06283 | /Insights/tools/All Migration - server - mc.R | c3a1ca5c49cab7cb303d870e1b885d915c44e239 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | Treasury-Analytics-and-Insights/Insights-Tool-April-2018 | 6b56f62df6a87f621abdc14b2e8c6c5b9d833308 | 29944ffc9da5843e6e07613d59c30dc1ceeb5085 | refs/heads/master | 2020-03-15T08:21:44.702584 | 2018-07-19T01:10:35 | 2018-07-19T01:10:35 | 132,049,038 | 1 | 1 | null | null | null | null | UTF-8 | R | false | false | 40,167 | r | All Migration - server - mc.R | #Server MC
mig_all_panel <- reactiveValues(
base_data = df.init_mig_all_base_data,
map = spldf.init_mig_all_map,
last_start_year = vt.init_mig_all_param_year_1,
last_end_year = vt.init_mig_all_param_year_2,
tmp_to_use = 1,
last_agg_level = "Total NZ",
vt.last_flow_dir = 'Inflow',
vb_1_value ... |
a058f88a4f7110e068e7556ce0186b8adde3851f | 38915da347869e164d9485f9bbd394fe56d2fcb0 | /1_1MFScondist_jp/ui_E.R | 0c7008e0eecffc750e814ce622eecd8c1584d83d | [
"MIT"
] | permissive | mephas/mephas_web | 24df65c5bdbf1e65c91523f4bfd120abae03e409 | 197e99828d6b9a6a3c1d11b2fc404c9631103ec0 | refs/heads/master | 2023-07-21T14:29:37.597163 | 2023-07-13T03:32:32 | 2023-07-13T03:32:32 | 161,571,944 | 8 | 3 | null | null | null | null | UTF-8 | R | false | false | 4,546 | r | ui_E.R | #****************************************************************************************************************************************************1.2. Exp distribution
sidebarLayout(
sidebarPanel(
h4(tags$b("Step 1. データソースを選ぶ")),
p("数式ベース、シミュレーションベース、又は、ユーザのデータベース"),
#Select Src
selectInput(
"InputSr... |
0df7e8e54a78248aa59b2eed3e02684cff1ff8f0 | afdac1aa116adcd2cb004a636ebd1cf342b878cc | /man/calculate_conditioned_rdi.Rd | 1faec6b41babb608cd29fd59c30d3425ce729a9d | [] | no_license | aNing1210/Test1 | 6260ae7b5f37d1c3073d146f60ceaf8dedd68533 | fdc476a998bd1647e121f1af7c1fd9c1a6c7380e | refs/heads/master | 2020-09-16T19:20:09.248930 | 2019-12-12T04:25:31 | 2019-12-12T04:25:31 | 223,865,898 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,890 | rd | calculate_conditioned_rdi.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/function.R
\name{calculate_conditioned_rdi}
\alias{calculate_conditioned_rdi}
\title{Calculate conditionally RDI value}
\usage{
calculate_conditioned_rdi(cds_subset, super_graph = NULL, rdi_list,
top_incoming_k = 1, uniformalize = FALSE, lo... |
e222ade27c2e4c6b0014cac1b9e3de5f569cf559 | ee314b07bbbcdff0683cb1083a00f2d94e36ab82 | /man/rftk-package.Rd | ef23dcc0b2ffebd1cb636066fa5e3a294460d436 | [] | no_license | kingweck/rftk | 08a6adf3c304d765af7958b948f76849dc2b0b7c | d92513178afd258c3aa0db0dd471c5d688d30073 | refs/heads/master | 2023-01-22T17:20:32.103051 | 2020-11-30T17:54:06 | 2020-11-30T17:54:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 331 | rd | rftk-package.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rftk.R
\docType{package}
\name{rftk-package}
\alias{rftk}
\alias{rftk-package}
\title{rftk: RF Toolkit}
\description{
R functions for working with RF/wireless data.
}
\author{
\strong{Maintainer}: Aaron Anderson \email{aaron.m.anderson.3@gma... |
79311186b602ac668f3993a0b45f2db961c8874c | 25c4d0cf195c5907f0ebddb12cb1e8aa9cb74b2e | /reporting/Summary_of_Findings.R | 24dbb25d04e2d8a7153800f417143b9b1f1a3770 | [] | no_license | rcatlord/reviewing | 32ad628e9c406c3a8ca655f9e3e40a4ea953e3c1 | 3b9d6ff3ab3af899624a5cbc4d2fa986a131dbd3 | refs/heads/master | 2021-06-16T14:22:01.789570 | 2017-05-10T11:01:56 | 2017-05-10T11:01:56 | 79,554,838 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,028 | r | Summary_of_Findings.R | ## Calculate the corresponding risk for GRADE Summary of Findings tables
# See http://cccrg.cochrane.org/sites/cccrg.cochrane.org/files/public/uploads/Summary%20of%20Findings%20Tables.pdf (p.23)
# Data: Petrosino et al., 2013
# 1. Calculate the Assumed Risk
# i.e. risk in control group = n events / N
p <- 98 / 358
rou... |
a95d3e35f738f396e848466222aba05716cc7472 | f2c79b247620463f045187e335e993d8c82e12e8 | /plot1.R | 6767785017e01e36e9b6bd8b4d9a6942b66e1dfe | [] | no_license | opalh/Exploratory-Data-Analysis-Course-Assignment-1 | d7721111cb4986780188451825167ddf040a0283 | 5ec1de556739f45cd9c91d01525778d7c6b54ad2 | refs/heads/master | 2021-06-22T09:50:54.289684 | 2017-08-22T21:35:40 | 2017-08-22T21:35:40 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 826 | r | plot1.R | library(dplyr)
if(!file.exists("E:/R/Exploratory Data Analysis/1/assiment")){dir.create("E:/R/Exploratory Data Analysis/1/assiment")}
url<-"https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip"
download.file(url,"E:/R/Exploratory Data Analysis/1/assiment/dataset.zip")
unzip("E:/R... |
1111b3fce7b187c8e834e1163daeb0b878af290f | 7919d7f2e4b9bdef8553efa8b4f814f218d85321 | /PenalizeMimicry.R | f5a685d36b4f9c8aa6aba79df9fb94c6a9098d4e | [] | no_license | nklange/Gumbel | 3ed5e1432f243bf3b73c1956ab3313a3aea7cfc0 | 5fd73cef0ee9d64b5349ddd32ad34cacce35bb68 | refs/heads/main | 2023-06-19T20:15:22.359652 | 2021-07-26T08:30:05 | 2021-07-26T08:30:05 | 372,556,096 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 29,611 | r | PenalizeMimicry.R | # based on Wagenmakers et al 2004 - find optimal decision rule between models
# i.e. to what extent need which models be penalized to find the optimal decision rule
# in other words, how mimic-y are models even under the optimal decision rule
library(cowplot)
GetGsquared <- function(LL,observedData){
temp <- c(ob... |
466fc846ae7bef5081c22a1d6721642337d58a9a | 5a1147e95d9e1a88b81c22fe2de9910066045871 | /Classification in R.R | 98e2057c9e44704dc7f6292c5b5583f07dd3efec | [] | no_license | sikrimanas/Statistics | fb7b700afefdfef20ab7d6a203a244eda7a19899 | 9a3b070c1dbdad810a709328e8f8447c107ae0e4 | refs/heads/master | 2021-01-12T03:28:28.606005 | 2017-01-06T15:05:33 | 2017-01-06T15:05:33 | 78,214,121 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,390 | r | Classification in R.R | # Classification in R ###
# Author : Manas Sikri
# Date: 13-Dec-2016
# install and load the required packages in R
require(ISLR)
# check the column names for stock market dataset
names(Smarket)
View(Smarket) # view the data
# check the summary of the dataset
summary(Smarket)
?Smarket # help
# plot the variable pairs ... |
12e3646bd1907e141b044a2b815065007fc318a5 | e93cf6300b21ad0e89fcfd5d2ce05029c3247952 | /astsa/R/Kfilter2.R | d3e79e50bb7d2aed35faf7d4c95d23180620bc2f | [
"GPL-3.0-only"
] | permissive | wilsonify/TimeSeries | 51a1b80afa512aad4512471d8031049699b890f1 | 0d67fc256e6a406c560711d448f2db3f66203a23 | refs/heads/master | 2023-01-08T17:43:00.863159 | 2023-01-07T16:57:55 | 2023-01-07T16:57:55 | 146,666,517 | 0 | 0 | MIT | 2018-09-25T12:50:18 | 2018-08-29T22:38:08 | Jupyter Notebook | UTF-8 | R | false | false | 2,281 | r | Kfilter2.R | Kfilter2 <-
function(num,y,A,mu0,Sigma0,Phi,Ups,Gam,Theta,cQ,cR,S,input){
#
######## Reference Property 6.5 in Section 6.6 ###########
# num is the number of observations
# y is the data matrix (num by q)
# mu0 initial mean is converted to mu1 in code
# Sigma0 initial var is converted to Sigma1 in code
... |
9742ab83b96fc56463d68afe090585f5e6356de1 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/bigsparser/tests/testthat/test-SFBM.R | 226355d4691219d7edf81750199a980498994474 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 2,102 | r | test-SFBM.R | ################################################################################
test_that("can create an SFBM from a dgCMatrix", {
spmat <- Matrix::rsparsematrix(1000, 1000, 0.01)
X <- as_SFBM(spmat)
expect_identical(X$p, spmat@p)
expect_equal(dim(X), spmat@Dim)
expect_equal(X$nval, length(spmat@x)... |
f8cab0dc128ef74fc9b15b1e21e168201335fcc4 | 25bf83bed09a2121375343998cad3e7fd2f6fc8e | /man/hopach2tree.Rd | ca57ebf6e4026f82e722e2cfce8963fe4754d55a | [] | no_license | keinstein/hopach | 200f7bdff5dbc51465264a8e274de437f6ca583b | 9a294568a5153541efd8f78461fcac337448c718 | refs/heads/master | 2021-01-12T11:51:07.155403 | 2016-05-03T21:30:44 | 2016-05-03T21:30:44 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,165 | rd | hopach2tree.Rd | \name{hopach2tree}
\alias{hopach2tree}
\alias{makeTree}
\title{function to write MapleTree files for viewing hopach hierarchical clustering results}
\description{
The MapleTree software (http://mapletree.sourceforge.net/) is an open source, cross-platform, visualization tool to graphically browse results of cluster ... |
31a08f506e13d9a263e0422f028fe83f875c26d8 | a32eb8f6c0402f0d1ce059fb9edad3b75cc39904 | /ratchets/R/team-types.R | 0e002ec900e67274a447258de3e5c5a7b556ca66 | [] | no_license | pedmiston/ratchets | 322591c849aa6429f4946645caccf794cab1b923 | 69973708226633b76b693240ffd52fcfc36c6924 | refs/heads/master | 2020-06-17T15:00:28.551616 | 2017-02-17T15:03:44 | 2017-02-17T15:03:44 | 74,994,330 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,643 | r | team-types.R | #' Assign team types by median splits on time and submissions.
#'
#' See also \code{\link{recode_team_type}}.
#'
#' @import dplyr
#' @export
divide_into_team_types <- function(leaderboards) {
# Augment team type map to merge based on TimeSplit and SubmissionSplit
team_type_map <- recode_team_type() %>%
mutate(... |
5a5741d36f206e87d50e6833e18bcfb4ff9cb8ac | d3653226fdc95dff7392b7c18e70e799d40b35d7 | /man/parseGmt.Rd | cecde97ade48b6cacf13d82916eb1324c12cd7ea | [] | no_license | cran/GiANT | 6645b847883d0433728af457d17c7751f57f914a | c82bd331a5ee768c1d3abe9caf878c11f30c4580 | refs/heads/master | 2021-01-21T21:43:11.150637 | 2020-09-29T03:30:02 | 2020-09-29T03:30:02 | 40,326,102 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,033 | rd | parseGmt.Rd | \name{parse GMT files}
\alias{parseGmt}
\title{
Venn Euler Diagramm
}
\description{
Parses a GMT file as downloadable from MSigDB (presented in Subramanian et al.) and returns a list of gene sets.
}
\usage{
parseGmt(file)
}
\arguments{
\item{file}{
A file name.
}
}
\details{
Parses a GMT file and returns a list of g... |
94fbed515275e5eb48958d3c3f1d84dbab2506e0 | f8393f4a20b0e1195ad41e8c326ab565ef281195 | /Heatmap/MODUSHeatmap/MODUSHeatmap_Whitesheet.R | 1fea9f736e822a5e180e5b21b270ee9256d2f1be | [] | no_license | maxgotts/RubensteinMpalaMaps | bcb3609df7a30a69d5f726abc84ba0da1a7aae10 | b862878d6f3d09118ef4c24906d60d85152d23f8 | refs/heads/master | 2023-07-17T02:38:58.306930 | 2021-09-05T20:10:38 | 2021-09-05T20:10:38 | 381,844,819 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,115 | r | MODUSHeatmap_Whitesheet.R | rm(list=ls())
library(lubridate)
library(dplyr)
## Useful functions
military_to_24 <- function(military) { # military is a number
military_time <- paste0(military)
split_military_time <- strsplit(military_time,"")[[1]]
if (length(split_military_time) == 3) split_military_time <- c("0", split_military_time)
s... |
7fdd8f334fadd84c77395ba5576f9db893263d4d | 6a11db10db1f055e737911611760ff1335b8e554 | /R/locale.R | ea5d5d1e143673aa7cdffa1d273b837ee811a1d7 | [] | no_license | cran/assertive.reflection | 947258eb8406a8dd0804a9d0a4c2ca95b25aa9af | c7b09e7fafddc9a8b6845abca45bbd05fb95e87b | refs/heads/master | 2021-05-04T11:23:00.367866 | 2020-07-31T00:00:14 | 2020-07-31T00:00:14 | 48,076,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,749 | r | locale.R | #' Get or set the system locale
#'
#' Wrappers to \code{Sys.getlocale} and \code{Sys.setlocale} for getting and
#' setting the system locale.
#'
#' @param simplify If \code{TRUE}, the locale settings are returned as a
#' character vector, otherwise a list.
#' @param remove_empty_categories if \code{TRUE}, don't includ... |
85d78bbc0beca5d60040528443a2df5d22365bd0 | 97dd874e245ef075bb2d573d27023ce2b5a1b9c3 | /man/gains_chart.Rd | bd6c4f5865d2764464a1c92de18a9255891f03b9 | [] | no_license | ISS-Analytics/gainslift | af0900ac358700a36dd15270c567576de9e6f605 | 8597fefcedefd71bd30357b1aa090ba28e752fce | refs/heads/master | 2020-05-24T10:47:14.696216 | 2019-06-03T15:32:48 | 2019-06-03T15:32:48 | 176,554,571 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 307 | rd | gains_chart.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/plotSettings.R
\name{gains_chart}
\alias{gains_chart}
\title{plotFunctions
Functions to plot identified charts}
\usage{
gains_chart(data, title, xlabel, ylabel)
}
\description{
plotFunctions
Functions to plot identified charts
}
|
79ecf9989bfffaa7d24e39b2fc89b36be0aeda25 | 227ea96bda4039edae16a8765cf45dd5db21b475 | /202-multivariet-regression.R | 06f7f13b2438ddfc75a1a09062233b7d674b18c3 | [] | no_license | data-stache/covid-tracking | 4764883a0085e075120c39cadb1a5a2023985af5 | 5d3f6e74a5e1356a0afbb758529f2023be803ff0 | refs/heads/main | 2023-04-01T11:23:52.328619 | 2021-04-02T14:39:52 | 2021-04-02T14:39:52 | 303,205,157 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,402 | r | 202-multivariet-regression.R | load("rda/mobility.rda")
load("rda/weather.rda")
load("rda/covid.rda")
# REGRESSION DF
dat <- covid %>%
select(date, state, state_name, new_cases_percap) %>%
inner_join(mobility) %>%
select(1:10) %>%
inner_join(weather_usa) %>%
select(1:11) %>%
group_by(state) %>%
mutate(cases = rollapply(new_cases_perca... |
d318538dc5ac7349156695ced19e4fa500252505 | 04ab1edfb4b08adabbf89eb8011b78c35c6f4e0f | /scripts/08_getAndParseMiscFeatures.R | 23a3719fa24c5f54c51d78d65b5c63c8bec993ec | [] | no_license | alanlorenzetti/tlr_v2 | b4bc9d7c00580dd08015273957e919a35cc9b4df | ea4c3e2701cbd5e6357fac83b4f764e6b7969d30 | refs/heads/master | 2023-05-27T11:57:20.296063 | 2021-06-08T22:17:02 | 2021-06-08T22:17:02 | 290,974,388 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,533 | r | 08_getAndParseMiscFeatures.R | # alorenzetti 202008
# description ####
# this script will get/load
# a few additional misc features
# LSm binding sites
# UTR info
# GC content
# processing starts ####
# getting and parsing LSm binding info
# here I will load the table, remove the asRNAs
# and lookup into genes and 5UTRs
# if LSm binding sites are ... |
2871528610f4ef2a4f92348877b3311f09eb7085 | 04b81a5bafa79294872b828bc9760065963a5f27 | /preprocess_nna/add_labels.R | a7440c1ea989a9e0c92d2624a421884492208d44 | [
"CC0-1.0"
] | permissive | MinCiencia/ECQQ | 34088ab1cb625f01f5f62ea6594e07667fcf46f7 | f93a01ce2dd140d073bd81afb9b4733c1d8a34c3 | refs/heads/main | 2023-08-15T06:46:05.025570 | 2021-10-05T13:23:09 | 2021-10-05T13:23:09 | 413,440,130 | 4 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,177 | r | add_labels.R |
library(tidyverse)
library(ggplot2)
library(readxl)
# obtenemos datos mesa
var_diags <- read_excel("input/nna_resulta.xlsx",
sheet = "base") %>% select(ORDEN,everything()) %>%
janitor::clean_names()
var_diags$organizacion <- tolower(var_diags$organizacion)
sum(is.na(var_diags$organizac... |
5d9d46e49e67a8f35cb7ebb816aa0bcafbdb6a02 | 6d4fe0d45fcb7e7f7bc0238abf09d2b6ff2dc566 | /man/move_all.Rd | d5f6246aa73a479d34608968842184b5579cae8f | [] | no_license | anhnguyendepocen/simachine | a1f584c8d5df145f76bd54a4dc0f9eaff348fca3 | 853aa22fced505dbf09e51b9cef405ddb1b7cb19 | refs/heads/master | 2020-03-29T12:19:53.129901 | 2016-12-07T19:24:14 | 2016-12-07T19:24:14 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 376 | rd | move_all.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/move.R
\name{move_all}
\alias{move_all}
\title{Move all objects from one environment to another}
\usage{
move_all(from, to)
}
\arguments{
\item{from}{environment to pull objects from.}
\item{to}{environment to write objects to.}
}
\descripti... |
e84a223660952c21a1aa5fe2fce2659414a5371a | 4e929f4a92a2533e713b87adb06f773748814126 | /R/RProjects/HITHATStats/R/mh19.R | 452bcf0fabd73d02a0dc45632337582f0a32de41 | [
"LicenseRef-scancode-warranty-disclaimer"
] | no_license | jlthomps/EflowStats | f4fe56f17cb675bcc1d618bc838003c2f2e9f81b | 016c9cb65a2f13a041af3eb87debb4f83793238a | refs/heads/master | 2021-01-01T05:33:44.189671 | 2013-12-04T23:30:21 | 2013-12-04T23:30:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,007 | r | mh19.R | #' Function to return the MH19 hydrologic indicator statistic for a given data frame
#'
#' This function accepts a data frame that contains columns named "discharge" and "year_val" and
#' calculates the skewness in log of annual maximum flows.
#'
#' @param x data frame containing a "discharge" column containing dail... |
25f9b48f7a54c71100d1e3e45de5c9aa4d63fedd | 6b6b90966a1db5f00240ace9425c608900de149c | /amazon_clustering.r | 898dbc0168009e013397feef86aad24b410e9832 | [] | no_license | ambarket/comp597 | e78716284fd27a8d8ad0bc3d8fa3e235c20a6c5b | eddce73de18df6250e1503f18d63b663008cc05f | refs/heads/master | 2021-01-01T20:00:01.395463 | 2015-05-01T08:20:40 | 2015-05-01T08:20:40 | 32,291,850 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,971 | r | amazon_clustering.r | rm(list = ls())
setwd("C:\\Users\\ambar_000\\Desktop\\597\\Amazon dataset")
rm(list = ls())
#amazon <- read.csv("Amazon_Instant_Video_no_text2.csv")
amazon_instant_video <- read.csv("Amazon_Instant_Video.csv")
str(amazon)
table(count.fields("Amazon_Instant_Video_no_text2.csv", sep=",", quote=""))
txt <-readL... |
5a899990bfe1eae94bbcced3cd11eeac2110265a | 6e4cf749acf3c491bd76e95a462aa97aae0b1a7f | /man/merge2nodes.Rd | 99255511c3872dd507b6990cdbcba3c3546bb31e | [] | no_license | cran/gRapfa | 56c44e6cbad56ab6e13700082615477cc167467d | 69adad4efb0cfca0b5b312c4aca1ab33b634b5a1 | refs/heads/master | 2021-01-02T08:52:35.659438 | 2014-04-10T00:00:00 | 2014-04-10T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,883 | rd | merge2nodes.Rd | \name{merge2nodes}
\alias{merge2nodes}
%\alias{MergeNodes}
\title{
Merge two nodes
}
\description{
Calculates various quantities in connection with merging two nodes in a level of a sample tree.
}
\usage{
merge2nodes(NS, mnode, test = TRUE, get.map = FALSE, doMerge = FALSE)
}
\arguments{
\item{NS}{
\... |
204c5c4403ac29e9d09828ca1d4d6839d6201a53 | 745d585395acad1376d84f8ca1284c13f2db70f0 | /tests/testthat/test-replace_column.R | 226987f8a798d7874c79bbf4905f5a9565b8f70a | [] | no_license | pik-piam/quitte | 50e2ddace0b0e2cbfabf8539a0e08efe6bb68a0b | 4f5330695bd3d0e05d70160c1af64f0e436f89ea | refs/heads/master | 2023-08-20T04:15:16.472271 | 2023-08-09T08:14:32 | 2023-08-09T08:14:32 | 206,053,101 | 0 | 8 | null | 2023-08-09T08:14:34 | 2019-09-03T10:39:07 | R | UTF-8 | R | false | false | 4,213 | r | test-replace_column.R | context('replace_column()')
test_that(
'Test replace_column() results',
{
# ---- simple example with matching old and match column names ----
model_data <- data.frame(
model = c('Model1', '2ndModel', 'Model Three'),
region = c('Region 1', 'Region 2', 'Region 1'),
value = 1:3,
stri... |
5556e8a8a1d2ca87c0dcd0ee846d1c88d5cdb5da | aa684167a345fdd921969c0d66e6ec98166990fb | /tests/testthat/test-rogue.R | 163e40030d7b1aa82f885feccc9923def8c72457 | [
"MIT"
] | permissive | ashther/ashr.rogue | ae6b19bf59cbd0688e61bfbd8c2ef61b5957e4b0 | adbde55c9963d95aa61f0b97b750371b1b990ae3 | refs/heads/master | 2020-04-23T01:03:55.521301 | 2019-08-02T08:47:05 | 2019-08-02T08:47:05 | 170,791,022 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,045 | r | test-rogue.R | context("test-Rogue")
test_that("can create a rogue instance", {
expect_silent({rogue <- Rogue$new()})
expect_equal(rogue$proxy, NULL)
expect_equal(rogue$useragent, NULL)
expect_equal(rogue$iter_max, 1)
expect_equal(rogue$is_quite, FALSE)
expect_equal(rogue$is_random, FALSE)
expect_equal(rogue$is_record,... |
90381bd5deb51112ef0fd8f364afa0c90ff663ab | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/StreamMetabolism/examples/window_chron.Rd.R | 5d0a141386978db6e986810b29bd936e92036e70 | [] | 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 | 269 | r | window_chron.Rd.R | library(StreamMetabolism)
### Name: window_chron
### Title: Time Windows of Diurnal Curves
### Aliases: window_chron
### Keywords: chron
### ** Examples
#with real data
data(DOTemp)
d <- window_chron(DOTemp, "8/18/70", "06:00:00", "8/18/70", "20:15:00")
plot(d)
|
dfc30ed8c89fc9753cbbfb37044503eb3a57c3a3 | 8002f1c25aaf74fde2aa4a162a23906083189f52 | /tests/testthat/test-FilterGB.R | 96d78fbe8ab3732166e77d50d09b57c5d21b8607 | [] | no_license | zijianni/scCB2 | 8d4e657cbd8e121559acbc48936384e286cd9b48 | 5ba289850b4785e6fd71b4a2b4217620ad9ffc4f | refs/heads/master | 2023-04-27T09:44:42.589719 | 2023-04-19T04:14:44 | 2023-04-19T04:14:44 | 191,231,709 | 11 | 1 | null | null | null | null | UTF-8 | R | false | false | 578 | r | test-FilterGB.R | data(mbrainSub)
test_that("Input data format", {
expect_error(FilterGB(1), "at least two dimensions")
expect_error(FilterGB("1"), "at least two dimensions")
})
test_that("Gene filtering threshold", {
for(k in sample(0:100,5)){
dat_temp <- FilterGB(mbrainSub,g_threshold = k)
expec... |
5df7e535dce1b153eb5d29968758d6a282938560 | b220e9b4c9db0a1590358b22480ea00beb3fba0f | /R/Region_TwoSampleTTest.R | 6a36f2c32d2b88d4e066011b9d428d1c7720a4de | [] | no_license | catfishy/jagust | 9741a74812c15c4c812b7143404ae1092e76e4d7 | 8877d29b340d6489fbb20542dc5d1188ec512531 | refs/heads/master | 2021-04-19T01:21:36.722907 | 2016-11-09T22:54:44 | 2016-11-09T22:54:44 | 37,677,259 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,485 | r | Region_TwoSampleTTest.R |
input_file = '/usr/local/jagust/ADNI_BL_AV45Neg_Normals.csv'
df = read.csv(input_file)
slope.thresh = 0.004
# get the accumulators
# df = df[complete.cases(df$AV45_NONTP_BigRef_Slope),]
# df$accum = factor(df$AV45_NONTP_BigRef_Slope > slope.thresh)
df = df[complete.cases(df$AV45_NONTP_BigRef_Slope_3pts),]
df$accum = ... |
0db091ca4f18d78c804a7bc356129772052414a5 | b022e68f0139455784d95133deb4cf8f487142ce | /man/traprule.Rd | d8d1644596852b35c15f17624792ea47fd8bcce8 | [] | no_license | cran/REBayes | 1e311610a28f37509d2da28f81385b17e84b4bbf | e26237baf78f2dc4bb776ae29a2ddfce68963435 | refs/heads/master | 2022-05-13T04:13:53.603760 | 2022-03-22T17:20:02 | 2022-03-22T17:20:02 | 17,681,954 | 4 | 2 | null | null | null | null | UTF-8 | R | false | true | 413 | rd | traprule.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/misc.R
\name{traprule}
\alias{traprule}
\title{Integration by Trapezoidal Rule}
\usage{
traprule(x, y)
}
\arguments{
\item{x}{points of evaluation}
\item{y}{function values}
}
\value{
A real number.
}
\description{
Integration by Trapezoidal... |
45132d05f0267c6ccbec4f4c143504d7967737ad | 3aa5f28626782b046730018b9727f3bcd1ce024b | /ch_4__advance_R/33__Lists.R | f83cfdc00296b52223588c0b1fd86c9bbc252103 | [] | no_license | asamadgithub/R__complete | 1c854d56f4f3add69256c011c8871b911a108886 | 3d3899aa77958e305bc8a523ed4afd946822bf15 | refs/heads/master | 2022-11-26T23:19:17.183332 | 2020-08-04T15:36:35 | 2020-08-04T15:36:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,248 | r | 33__Lists.R | # --------------------------------------Import the data and formating date_Time stamps via POSIXct
utl <- read.csv("https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P3-Machine-Utilization.csv")
utl$Utilization <- 1 - utl$Percent.Idle
#?POSIX,?POSIXct,...
utl$POSIX_time <- as.POSIXct(utl$Timestamp, fo... |
c1110ea4ee1b59b259d001d27e85fb1df229a62c | 60fe479cf2eb4cda139dd758af86af30d31f3006 | /Forecasting using Covid19 data.R | 0f23234af35363220bf8a0d4077ee621c6dba7fe | [] | no_license | riteshtripathi2010/Rstudio | 3b1bc14eb59fe3bb5432d024a830a0dfae1ba95c | c27226fa0c5a299956bdf7d11aa797dda169f4c1 | refs/heads/master | 2021-07-16T18:08:56.262023 | 2020-08-02T11:41:22 | 2020-08-02T11:41:22 | 195,048,572 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,972 | r | Forecasting using Covid19 data.R | #Forecasting COVID19 Cases with R, using facebook's prophet library
install.packages("covid19.analytics")
install.packages("ape")
library(covid19.analytics)
library(dplyr)
library(prophet)
library(lubridate)
library(ggplot2)
setwd("/Users/riteshtripathi/Desktop/")
tsc <- read.csv("ts-confirmed.csv", header = TRUE)
#w... |
d055b728010f59c06a1cf4a66c286073253d3870 | fb7e9d43adb368b42dc85124a591f5622fc53c7c | /nba_stat/nba_stat.R | 42252f9207bc2f17832fe207df3edb1023929593 | [] | no_license | expdal3/data-visualisation | ba63a999dc2ec3730033aa032eb1327b86e02167 | ef9cb5fed7f66a8b46d3eb327b4e71f98fb7cb07 | refs/heads/master | 2021-01-13T22:00:31.288451 | 2020-04-01T13:10:07 | 2020-04-01T13:10:07 | 242,508,336 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | r | nba_stat.R | library(dplyr)
library(plyr)
library(data.table)
## read in csv ad text file
dataset = read.csv("C:\\Users\\uC260543\\Work\\DataSciencePractices\\R\\nba-players-stats\\Seasons_Stats.csv",header=T)
dataset <- data.frame(dataset)
colnames(dataset)
#head(dataset,5)
dataset[is.na(dataset)]<-0 #replace NA with 0... |
49ab3ff92aa5849027996440bbdd2835a562eb09 | a70271ce4777831c477b4272021e5bfa8555f47f | /man/restrictions.Rd | a54080f40676811c856df655bd1f490cfcad8b74 | [] | no_license | moquedace/aqp | 9f016e990a6c522e04afb100dcfa1a81debd046e | 73f7f6a64edf727f24258341c94eb591fd783173 | refs/heads/master | 2023-04-08T18:59:15.038547 | 2021-05-04T20:58:41 | 2021-05-04T20:58:41 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 506 | rd | restrictions.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Class-SoilProfileCollection.R
\docType{methods}
\name{restrictions}
\alias{restrictions}
\alias{restrictions,SoilProfileCollection-method}
\title{Retrieve restriction data from SoilProfileCollection}
\arguments{
\item{object}{a SoilProfileCol... |
fe54345772061b093682a43f167d033a07ec9393 | c98eff6792c2f139086ddce955f6e980788cba7e | /code/functions/pres.abs.lag.R | 1b5dc2e19b3598e93ef7f44e9292394c40865055 | [] | no_license | efcaguab/ws-allith-habitat-selection | 82ac41e46e8ce549d39e7c4eb7506a09f887f6f5 | 0fc73a947a450f46ee33bc8bc4858025f457182b | refs/heads/master | 2022-01-23T10:44:53.804290 | 2019-07-10T21:38:24 | 2019-07-10T21:38:24 | 109,056,794 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,927 | r | pres.abs.lag.R | # FUNCTION ----------------------------------------------------------------
# Function to calculate the vectors of presence absence
pres.abs.lag <- function (start.date, end.date, sightings, dates){
# Create a data frame with the detections
sight <- data.frame (id = sightings, date = dates) %>%
filter (date >=... |
947a540355e12d7c4007dc97affec8f25d0388e1 | 3e508d7cd0798f70f70242a837f99123397fc1c4 | /tests/sim/20210909-sim/look-at-results.R | d16546938a20637bbc09bc722ada03a68a5e70eb | [] | no_license | jlivsey/Dvar | f0d142df9866a4232947d114891111097c4c5851 | 824cdcc822829d655b905bfeca54a40b82f16e36 | refs/heads/master | 2023-07-26T19:17:28.531305 | 2023-07-06T13:55:28 | 2023-07-06T13:55:28 | 237,249,747 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 934 | r | look-at-results.R | library(tidyverse, warn.conflicts = FALSE, quietly = TRUE)
simDir <- "~/Github/Dvar/tests/sim/20210909-sim"
nsim <- 1
# ---- Load Results ----
simLevels <- c(0:4, 6:10)
R <- matrix(NA, 7224, length(simLevels))
colnames(R) <- letters[1:length(simLevels)]
for(jj in seq_along(simLevels)){
simNum <- simLevels[jj]
p... |
4bd4055383e8fb732f2666cdd59c987f547248cd | bf825ce5fcbda03c083caf45b549f4ae350e27af | /Projekty/problem sąsiedztwa.R | 951ebbefeaa39ce7ca0710a062053d5cb8d4b82a | [] | no_license | ajandria/AWD | a48cbafe3a6e0eb2b6075bda5cf0060582d79ec1 | 0f7f4ef1dc569d2eecb36e2b9d471f1b0770588e | refs/heads/master | 2023-04-06T15:26:32.827834 | 2020-11-26T15:49:08 | 2020-11-26T15:49:08 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,353 | r | problem sąsiedztwa.R |
# -------------------------------------------------------------------------
#
library(tidyverse)
obliczDystans <- function(df1, df2) {
x1 <- df1$x
y1 <- df1$y
z1 <- df1$z
x2 <- df2$x
y2 <- df2$y
z2 <- df2$z
dystans <- sqrt((x2-x1)^2+(y2-y1)^2+(z2-z1)^2)
return(dystans)
}
set.seed(424242)
# 1. - wy... |
dc0681c9d6f438e25f84e380965e9828ed1cd851 | f9c3eb0584c42c25db3e9f719d03c92d38a607c2 | /app.R | ce0c5e9cae9fcb7fabc234618a72d105a51f5a22 | [
"MIT"
] | permissive | strojank/loan-vs-savings | 264c154e15549a7f751ddc9e93c20f48737c3552 | 24edf54d03eb78b35d8d33d36ae0cd4340a9526a | refs/heads/master | 2020-08-04T23:03:37.518693 | 2019-10-23T06:08:17 | 2019-10-23T06:08:17 | 212,307,226 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,022 | r | app.R | # Shiny app
library(shiny)
library(plotly)
library(tidyverse)
source('pomozneFunkcije.R')
options(scipen = 999)
library(DT)
# Define UI for application that draws a histogram
ui <- fluidPage(
titlePanel("When to take a loan?"),
# Sidebar with inputs.
sidebarLayout(
sidebarPanel(
helpText(
"... |
9b2b013ea4c30bfac34ef26c666f936ca3702371 | 106a8e26bd103fa96745a9e94f3b2ca40dd33d23 | /tests/testthat/test-worm_download.R | e4c2b37c7d841f897134a1b845f2d4afb632b617 | [
"MIT"
] | permissive | rikenbit/WormTensor | babece93b314999d076694058f59444c73b646bc | 3b08a92cf4da2f33221aa1c7a122803dee06177b | refs/heads/master | 2023-04-10T08:09:59.990702 | 2022-10-04T07:25:20 | 2022-10-04T07:25:20 | 470,419,278 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,160 | r | test-worm_download.R | .dist2mat <- function(x) {
as.matrix(x)
}
############ Euclid ############
## dist object
Ds_Euclid <- worm_download("Euclid")$Ds
expect_true(object.size(Ds_Euclid) != 0)
expect_equal(length(Ds_Euclid), 24)
expect_true(all(lapply(Ds_Euclid, is) == "dist"))
## matrix object
Ms_Euclid <- lapply(Ds_Euclid, .dist2mat... |
451dfbb7aae1c7ea3227357e07c54fd91caec39c | 9f57e0ad44b78d809c262fa0ffb659232fdb8d5e | /Art of R Programming Code/Ch2/findud.R | 4203569aa81ec773044cbe79378b199e915463b5 | [] | no_license | abhi8893/Intensive-R | c3439c177776f63705546c6666960fbc020c47e8 | e340ad775bf25d5a17435f8ea18300013195e2c7 | refs/heads/master | 2020-09-22T00:57:36.118504 | 2020-08-31T09:23:57 | 2020-08-31T09:23:57 | 224,994,015 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 376 | r | findud.R | # findud() converts vector v to 1s, 0s, representing an element
# increasing or not, relative to the previous one; output length is 1
# less than input
findud <- function(v) {
vud <- v[-1] - v[-length(v)] _label~findvud1@
return(ifelse(vud > 0,1,-1)) _label~findvud2@
}
udcorr <- function(x,y) {
ud <- lapply(... |
7e8851c91d5c262f02ac127bc0cddc8d5a9e6abf | f4fffe026383f8f681c8b2ef2e7b2ec0f8143688 | /man/brauer_2008.Rd | 02d4082228ee4148ebcf4ae3b8a674873c13d255 | [] | no_license | DavisVaughan/romic | 436da67b077937d1c13af9701d39a00f083e6694 | f3470f5cd42b6ee8322db3f10a1c254766a5dc3e | refs/heads/master | 2023-05-08T05:44:49.460728 | 2021-05-18T13:10:03 | 2021-05-18T13:10:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,486 | rd | brauer_2008.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{brauer_2008}
\alias{brauer_2008}
\alias{brauer_2008_tidy}
\alias{brauer_2008_triple}
\title{Brauer 2008}
\format{
A tibble with 18,000 rows and 8 columns:
\describe{
\item{name}{Common gene name}
\item{BP}{Gene... |
f4013e94761da12a48533e3114038c8585749d93 | 799f724f939763c26c4c94497b8632bad380e8f3 | /tests/testthat/test-tokens_ngrams.R | 833cdf6164089c6d86d660cd019da1c83c71aef2 | [] | no_license | chmue/quanteda | 89133a7196b1617f599e5bba57fe1f6e59b5c579 | aed5cce6778150be790b66c031ac8a40431ec712 | refs/heads/master | 2020-12-01T02:59:48.346832 | 2017-11-22T18:35:37 | 2017-11-22T18:35:37 | 50,363,453 | 2 | 0 | null | 2016-01-25T16:18:50 | 2016-01-25T16:18:50 | null | UTF-8 | R | false | false | 4,036 | r | test-tokens_ngrams.R | library(quanteda)
library(testthat)
context('test ngrams.R')
test_that("test that ngrams produces the results from Guthrie 2006", {
toks <- tokens(c('insurgents killed in ongoing fighting'))
bi_grams <- c('insurgents_killed', 'killed_in', 'in_ongoing',
'ongoing_fighting')
two_skip_bi_gram... |
b2a89df694f8d1df3a0c7b01e0e7fc8d5549753a | 93730e6fee0ae4e3bb028714d6e8a5e335632121 | /man/search_subset_node.Rd | aec08f6a72d9ce66ac774a6b0c740886af2e4fa3 | [] | no_license | jeevanyue/PIVOT | 56ef7c2d2eb30a197b1c0387ae79e73605ab7eae | 46fa6af11f19c320ee338452ccff745aa93a1f6d | refs/heads/master | 2021-01-01T06:30:45.988532 | 2017-07-17T02:51:20 | 2017-07-17T02:51:20 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 352 | rd | search_subset_node.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data_map.R
\name{search_subset_node}
\alias{search_subset_node}
\title{PIVOT data management function}
\usage{
search_subset_node(tbl, node)
}
\description{
This function is for determining which sample subset the filter operation is performe... |
e9b8d5de340a9ffd1ffa3dc68f37ef28642e5d30 | 58f007d9b5414c747998bf0bc655ed2428ac06dc | /autres_exemples/gh.R | f7aeaf21739da5124a778c37b9a1db17dba12ea6 | [] | no_license | GuillaumePressiat/formation_pmsi_R | 9e9cfc76532c2e97ae9e4d64850c0b3d959f3f3a | 78b9db64b79bc8ad179bdd90e60ebcc7db63d335 | refs/heads/master | 2020-03-25T03:00:28.155493 | 2018-09-07T13:19:49 | 2018-09-07T13:19:49 | 143,318,013 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,865 | r | gh.R |
# Faire une base par GH
liste_hop <- c('750100125', '940100035')
library(pmeasyr)
library(dplyr, warn.conflicts = FALSE)
p <- noyau_pmeasyr(
annee = 2018,
mois = 7,
path = '~/Documents/data/mco',
progress = FALSE,
tolower_names = TRUE
)
rsa_gh <- liste_hop %>%
purrr::map(function(i){
p$fine... |
380cc47cc82a1ebe838b4f4f910e74e9dfbd62b2 | 2fa33aeef712fa0a1b8043b40261d218a37cafa2 | /R/pbino.R | 8b61c9a1eea0b48736ef9e7afce03156a8636299 | [] | no_license | cran/bayess | 778e3cd961acecec0ccbf0de66048543af82c98c | 30208f8c4b61bc73e5885875b8134f05a963719c | refs/heads/master | 2022-09-03T00:53:47.483683 | 2022-08-11T09:30:08 | 2022-08-11T09:30:08 | 17,694,647 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 190 | r | pbino.R | pbino=function(nplus)
{
# POSTERIOR PROBABILITIES OF N FOR THE BINOMIAL CAPTURE MODEL UNDER UNIFORM PRIOR (N_MAX=400)
prob=c(rep(0,max(nplus,1)-1),1/(max(nplus,1):400+1))
prob/sum(prob)
}
|
c45f0c3e1ffce75a5583c42a5b3d5c2ee633d671 | 5a1e2c2f2d8ab9668acc3cbfe61b3251ea8e3fc6 | /R/split_dat.R | 81badbba8dc2376855f210930dab93851570fae1 | [] | no_license | helixcn/phylotools | 1fc5162f5dc5d1c9483a25f4ac162c5bf27c695b | 758d338b2d2004c5b94f931802d269251f4fd7a1 | refs/heads/master | 2021-06-08T22:55:57.141365 | 2021-03-24T15:55:30 | 2021-03-24T15:55:30 | 35,886,086 | 10 | 11 | null | null | null | null | UTF-8 | R | false | false | 1,176 | r | split_dat.R | #### author: Jinlong Zhang <jinlongzhang01@gmail.com>
#### institution: Kadoorie Farm and Botanic Garden, Hong Kong
#### package: phylotools
#### URL: http://github.com/helixcn/phylotools
#### date: 26 MAY 2015
#### modified: 8 DEC 2017
### Create fasta files based on the groups specified
split_dat <- function... |
043c9cfc8c94d1978ae1224447e4e7d5d0eaabd4 | c8533654476eeec28d52b2dd47f471009a12c593 | /man/predict.ConsRegArima.Rd | 4bb1162e7bf516fb90c20b19d36042fb478b287e | [] | no_license | puigjos/ConsReg | e64d03eca6b6b37d116682e7cdda10da73decb63 | 50b7df1faaccc888036bed3622c4651e669be672 | refs/heads/master | 2021-05-20T01:42:51.925681 | 2020-04-03T13:29:22 | 2020-04-03T13:29:22 | 252,132,981 | 2 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,357 | rd | predict.ConsRegArima.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/predict_ConsRegArima.R
\name{predict.ConsRegArima}
\alias{predict.ConsRegArima}
\alias{print.predict.ConsRegArima}
\alias{plot.predict.ConsRegArima}
\title{Predict function for ConsRegArima object}
\usage{
\method{predict}{ConsRegArima}(objec... |
059664936fc9ac4daf462b89ae7ffb9db92d09b6 | e189d2945876e7b372d3081f4c3b4195cf443982 | /man/icevision_Flip.Rd | 61b983dd40b7a6cbd34f8f311607fda990763446 | [
"Apache-2.0"
] | permissive | Cdk29/fastai | 1f7a50662ed6204846975395927fce750ff65198 | 974677ad9d63fd4fa642a62583a5ae8b1610947b | refs/heads/master | 2023-04-14T09:00:08.682659 | 2021-04-30T12:18:58 | 2021-04-30T12:18:58 | 324,944,638 | 0 | 1 | Apache-2.0 | 2021-04-21T08:59:47 | 2020-12-28T07:38:23 | null | UTF-8 | R | false | true | 491 | rd | icevision_Flip.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/icevision_albumentations.R
\name{icevision_Flip}
\alias{icevision_Flip}
\title{Flip}
\usage{
icevision_Flip(always_apply = FALSE, p = 0.5)
}
\arguments{
\item{always_apply}{always_apply}
\item{p}{p}
}
\value{
None
}
\description{
Flip the in... |
63e8b8ce55346dbbbbbce18321f8ec59c5f873c4 | ac40ac14de2765386f86ad7a6c4d44293226ce1f | /Machine Learning II/Lab II/ISLR 7-8 Lab.R | 15afe1f70a65a3e4a4ef838cf5e760a4ca09e29d | [] | no_license | King-AK/MSBA | 846974427be025769294a6ed8e3cc65ab55e1e31 | ece0a24e4ed8da11b95317de35287a75280a1a3e | refs/heads/master | 2020-01-23T21:24:17.933797 | 2017-06-06T15:34:13 | 2017-06-06T15:34:13 | 67,090,677 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 7,287 | r | ISLR 7-8 Lab.R | #VJ Davey
#ISLR 7.8 Lab
#non-linear modeling
rm(list = ls())
library(ISLR)
attach(Wage)
#Going to analyze the Wage data in order to illustrate that nonlinear fitting procedures can be easly implemented in R
### 7.8.1 Polynomial Regression and Step Functions
fit = lm(wage~poly(age,4),data=Wage)
summary(fit)
... |
c1fa80208403498f786701b8a9d379dbdb077a5d | 85641490bc20f312161d6ababd651e2503bab20c | /Metabolomics stats course.R | 64d786edc5a409d38e89b33cb05944dffd6bafb1 | [] | no_license | JoeRothwell/MOOCs | 159236fcbf2baf7e0382d73c1e2222a5d262f8bb | 70f8583d376ee779fe25b4427cad36f5b2aafa75 | refs/heads/master | 2023-04-29T22:41:56.455539 | 2023-04-24T06:28:18 | 2023-04-24T06:28:18 | 180,340,799 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 8,065 | r | Metabolomics stats course.R | # Functions 1 -----------------------------------
describe <- function(object, ...) {
UseMethod("describe")
}
describe.default <- function(object, var.name, na.rm=TRUE) {
if (!is.numeric(object)) {
stop(deparse(substitute(object)), " must be numeric")
}
if (missing(var.name)) {
var.nam... |
bf0ec8e2590c30856bd0ea5c6769d999a24ff03d | 21327fc1d5030fc4bb69e6505b66b471290fae5e | /commands.R | f12df4b8259aadf00b92842c5346d677a430a767 | [] | no_license | sportebois/Rbash | 995f6c6aac874cde109c7ae661d76cc49d005421 | de809e643055f4856c5175bb44e71bdb4f4d53f3 | refs/heads/master | 2021-01-10T03:42:28.372645 | 2016-03-14T01:23:53 | 2016-03-14T01:23:53 | 53,816,427 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 290 | r | commands.R | #commands.R
print("sourcing 'commands.R'")
source("defaultTestValue.R")
testVal <- getOption("test", default = "defaultTestInCommands")
print(paste0("test value is ", testVal))
testVal2 <- getOption("test2", default = "defaultTest2InCommands")
print(paste0("test2 value is ", testVal2))
|
2f26cb4c59202ac86d289aadb0a1c2efca0d2bd3 | c3bcb4169d97579d67e14198b7ed827562f7c7bb | /Supervised_Learning/project_1.R | 3fbcc8c83b83cda25cce266725438b14ae39f679 | [] | no_license | xiaomizhou616/Machine-Learning | c11c5cfa582231abd6438ca2c5fe937129a37887 | 66287a275a62305c340933041c981b7661badbe6 | refs/heads/master | 2020-04-11T06:46:37.706765 | 2018-12-13T08:20:40 | 2018-12-13T08:20:40 | 161,591,458 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 32,848 | r | project_1.R | #protein solubility dataset
#preprocessing data
total=read.csv("solubility2.csv")
total[,1]<-NULL
total[,1]<-NULL
total[ ,1][total[ ,1]>100]<-100
total[ ,1]<-total[ ,1]/100
colnames(total)[1] <- "solubility"
total[,3]<-total[,1]
total[,1]<-NULL
x<-na.omit(total)
x[ ,1]<-as.character(x[ ,1])
length(x[ ,1])
library("prot... |
3b5dc22392ec65738fe393c30f4d2a671c0e78fd | b73f1bbdfad964a17c9225f2d3ef41d28f15cc69 | /cachematrix.R | 54a50ee734fbbd0415eb96cb9177ec10e05a45c9 | [] | no_license | ankitsharma222/ProgrammingAssignment2 | b08d80cf018fdcc2c3e29532281b4821c7f65b05 | cd47e3d97c935fdd4aa78c624fc91c4c1cf66cc4 | refs/heads/master | 2021-01-17T17:50:08.324287 | 2015-02-19T20:17:18 | 2015-02-19T20:17:18 | 30,935,524 | 0 | 0 | null | 2015-02-17T20:13:05 | 2015-02-17T20:13:05 | null | UTF-8 | R | false | false | 1,168 | r | cachematrix.R | ## these functions help in caching the inverse of a matrix rather than computing it repeatedly
##to set or get the value of matrix or its inverse
makeCacheMatrix<-function(x=matrix()){
m<-NULL
set<-function(y=matrix()){
x<<-y
m<<-NULL
##to assign new data to x and discard... |
5f98a1ca38df5101d8a801995b76fa9edeb2daad | 1cb7c242633970d93fd874dddd140b4461e35278 | /Rscripts/ensemble/compute_ensemble.R | 8fd322b9cd874019bdfb79b1ba6a0a612f16ffe2 | [
"MIT"
] | permissive | chenxofhit/scRNAseq_clustering_comparison | 5c2e1e79ea200dde38edeaa7dad5057541d2af82 | de91456d411f97a45b871d55490d1e14b09031f5 | refs/heads/master | 2020-03-24T06:05:20.625297 | 2019-08-20T15:23:45 | 2019-08-20T15:23:45 | 142,515,237 | 0 | 0 | MIT | 2018-07-27T02:02:45 | 2018-07-27T02:02:45 | null | UTF-8 | R | false | false | 4,998 | r | compute_ensemble.R | ## -------------------------------------------------------------------------- ##
## Ensemble between methods, per run, all k
## -------------------------------------------------------------------------- ##
args <- (commandArgs(trailingOnly = TRUE))
for (i in 1:length(args)) {
eval(parse(text = args[[i]]))
}
print(cl... |
990dce1a7e47e3340c5517439776c4c3167a0714 | 02210921d469ae6c7daf9e159ea985aab6347517 | /R/app_server.R | 46cea8775fb3f6af3f8c468a04bf649aa2edc3ea | [
"MIT"
] | permissive | frdanconia/shiny_etf_portfolio | e6e94380ac482da53fb1322a1197ca968cad1490 | 580d9d4c1c53f94e7c77a78058c78794f3b4940a | refs/heads/main | 2023-03-03T22:25:16.162724 | 2021-01-23T13:26:32 | 2021-01-23T13:26:32 | 332,216,492 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 301 | r | app_server.R | #' @import shiny
app_server <- function(input, output,session) {
# List the first level callModules here
library(tidyquant) # due to issue: https://github.com/business-science/tidyquant/issues/116
callModule(mod_universe_server, "universe_ui_1")
callModule(mod_finder_server, "finder_ui_1")
}
|
6ecfe9b7168449c56e5e9945f4a64131ec604c4e | 8ab151cc5bfb154cc4ae4b1d97ddd6b2bedc95fa | /man/solar.long.Rd | cc205edf43403c5b7c1edc1524173c37d5038ea2 | [] | no_license | arturochian/MetFns | 5eafd4bc404edbbdefd27223c5b8a99d32cd048d | 5ce9fc52efdac3c2a12aa18282ab71e53aacf115 | refs/heads/master | 2020-04-06T04:20:15.871591 | 2014-09-16T00:00:00 | 2014-09-16T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 939 | rd | solar.long.Rd | \name{solar.long}
\alias{solar.long}
\title{
Calculation of solar longitude
}
\description{
Calculates solar longitude with respect to the equinox of 2000.0 for given year, month, day and time.
}
\usage{
solar.long(year, month, day, time)
}
\arguments{
\item{year}{
numeric vector of length 4 specifying ... |
7ff68bee61ea077074444adfff8499f12195c7a0 | 34382754d6fe101536f2a5755801845849a3e1d3 | /Covid19/ui.R | de7a9d29dee0dd6f5edc7396355a512911c70cea | [] | no_license | nohturfft/covid19_a | 11284c018cb2e742e35bb6eacb0e5af3efe31e91 | 189eb2f9374c9f9cf3448b407220552b17994109 | refs/heads/master | 2021-03-16T17:17:07.652394 | 2020-03-14T11:43:45 | 2020-03-14T11:43:45 | 246,926,050 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,214 | r | ui.R | #
# This is the user-interface definition 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(dygraphs)
# Define UI for application that draws a histogram
shinyUI... |
74b1a91941b836e1131c9a1252add411121b2b80 | a206f33c8cbd90abf2f400f79b233b4e56c89f23 | /clusterizacion/clusterizacion_steepest.R | 3501d9d296e643dbec62d59c4e5f76338a549c85 | [] | no_license | andresrabinovich/algoritmos-geneticos | 60a403860fcad3932e5f18bad23a6ac9312c12f1 | 6b3923981c2f51ed735451f735dd12e1c63a0d75 | refs/heads/master | 2021-01-10T13:46:18.419293 | 2015-07-15T12:53:51 | 2015-07-15T12:53:51 | 36,511,094 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 6,776 | r | clusterizacion_steepest.R | #///////////////
#CONFIGURACIONES
#///////////////
poblacion = 1;
pm = 1; #probabilidad de mutacion
pc = 0.1; #probabilidad de single-point crossover
generaciones = 1000;
corridas = 1;
dim_red = 2; #los puntos en la red no son reales, son solo los lugares alrededor de los cuales se van a armar los clusters
puntos_por_... |
79859c428fb939c528c8a114845ced2d61eeb64a | e58cb0a3ce95401501f0f0441a492529632b41f7 | /submitJobs.R | f4f6aa06672ff707953199a0aefe1b847fd36c5f | [] | no_license | larsgr/GRewdPipeline | ea451c75b5f4d91d4f92a941e3b2f3461566ee98 | 77a7d5b17373a2139d34327942fcec300b62fb40 | refs/heads/master | 2020-12-29T02:19:46.034273 | 2019-01-15T10:24:33 | 2019-01-15T10:24:33 | 30,870,561 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,834 | r | submitJobs.R | #
# NOTE: system("source /etc/profile.d/modules.sh && sbatch jobscript.sh") does not work!
#
# The module command is not available to "jobscript.sh".. why not?
# apparently the job script does inherit the environment where sbatch is executed.
#
# Need to add #!/bin/bash –l at the start of each job script...
submitJob... |
b14f34c23409a8bd39f03d6a41867ddbdae7712a | 0420fa9f0a499af5edc75c7ddbe606a7ea684e6c | /data-raw/deprecated/cairo-header-old.R | d83a469983c17ad9e9faa3ff0ca64a6ca8f7ee90 | [
"MIT"
] | permissive | coolbutuseless/cairocore | ca8a2b82f594b086c7de38149fb3d3a5f150b455 | 5bf9f2f170f3c7ee81e0b5c709946745950115d4 | refs/heads/master | 2022-12-03T18:36:56.909944 | 2020-08-30T04:04:22 | 2020-08-30T04:04:22 | 288,104,420 | 12 | 1 | null | null | null | null | UTF-8 | R | false | false | 21,190 | r | cairo-header-old.R |
cairo_header <- list(
cairo_create <- list(
include = TRUE,
gtkdoc = r"{/**
* cairo_create:
* @target: target surface for the context
*
* Creates a new #cairo_t with all graphics state parameters set to
* default values and with @target as a target surface. The target
* surface should be constructe... |
374d1f963849c37a2ba655db2c50ab19675f63dd | 9bd0a8ccaad50f716a942192aa31bddc1644bfbd | /SableSurface_2022.R | b01e9ea5c86919c1d0d5582549137632c8ed23b6 | [] | no_license | arcticecology/Sable | 4ab0df525ae8238cfd7c451431ac090906d4e26d | 5e6ca0269b2fc59f7a3b5c234ed020aff82c1aff | refs/heads/main | 2023-04-11T03:48:00.876984 | 2022-08-30T11:57:01 | 2022-08-30T11:57:01 | 530,626,321 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,920 | r | SableSurface_2022.R |
#The script to add norway to the conservative model because only RA is available
library(rioja)
library(analogue)
library(vegan)
library(labdsv)
setwd("C:\\Users\\Andrew\\Desktop\\Sable\\R\\surface")
species3<-read.csv(file="dataset_merge.csv", row.names=1) #read the csv file
species1<-species3[, -cbind(... |
956bdb76bff4acf2e5546d3d3465c2f415ac3725 | ac82cd0243a121e3749f7dcfb3f31d2fa33b2a9f | /R_Programming/exploring_linux_evolution.R | 244e28a8d820f877c19f428a0c405ec05975ee14 | [
"MIT"
] | permissive | arpithaupd/Data-Science-Projects | 63aa24db88b85f82d2b3e79510fce77f6b596cbf | 423695c130a92db1a188b3d3a13871f0f76f6f5b | refs/heads/master | 2023-04-29T08:54:42.834503 | 2021-04-21T16:12:23 | 2021-04-21T16:12:23 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,446 | r | exploring_linux_evolution.R | # R script to perform the data analysis and visualization of the Linux evolution
# Tasks:
# 1. Identifying the TOP 10 contributors to Linux repository
# 2. Visualizing the commits over the years to Linux repository
#----------------- Reading the dataset
log = read.csv('git_log.csv', header = TRUE, sep = ",")
head(lo... |
a464ab7bce49653298e3a4b3238148f8b15ee36a | 5cdd72710fe3a519859bac1a40fd68723330b2ef | /man/latlon_to_km.Rd | ea9483dc6e92eddddbfaa3ea2ec01192abdb3a3e | [] | no_license | cran/stormwindmodel | 75b81aa790bfbe30afc16d1c0ebe8bf8251b4b24 | 59a439b1592cef52ea2d03cb192fa70b033322c0 | refs/heads/master | 2021-01-12T01:56:16.406086 | 2020-07-27T06:10:02 | 2020-07-27T06:10:02 | 78,445,768 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,118 | rd | latlon_to_km.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/latlon_helpers.R
\name{latlon_to_km}
\alias{latlon_to_km}
\title{Calculate distance between two locations}
\usage{
latlon_to_km(tclat_1, tclon_1, tclat_2, tclon_2, Rearth = 6378.14)
}
\arguments{
\item{tclat_1}{A numeric vector giving latitud... |
82f4f5ffd021ebb6e6428b2c70dacd759452c508 | c3ed0eea77de3338cc4820ca27dc04384676b29b | /man/rescale_capped.Rd | 9a07036b85ac58b6630e3109368c3e25258f0701 | [] | no_license | jrboyd/seqtsne | f3bd94ee23140dc71373af4f1e7ce9ffe60d702f | 5a67cbe5af281ec42906689d1a9961d8fe9de68d | refs/heads/master | 2022-10-30T18:33:13.005047 | 2022-10-28T20:22:58 | 2022-10-28T20:22:58 | 177,857,441 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,070 | rd | rescale_capped.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/functions_util.R
\name{rescale_capped}
\alias{rescale_capped}
\title{wraps scales::rescale to enforce range of \code{to} on output}
\usage{
rescale_capped(x, to = c(0, 1), from = range(x, na.rm = TRUE, finite = TRUE))
}
\arguments{
\item{x}{c... |
ec26e819680a50353f148d5713b36799de04bf51 | 3de27dccf8fdf1855fdacf6c71b138e1b0c48f0e | /L-13.R | ba53c039298aa506a261a2a99bd588488417127b | [] | no_license | tongramt/Forecasting | 2a909478af578eb5a9c64ae9779362f33320b0ec | f9c1478bcbe0a83befadddf03b64ccbaad9efd28 | refs/heads/main | 2023-01-23T17:27:26.631733 | 2020-11-30T09:46:03 | 2020-11-30T09:46:03 | 311,314,162 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 243 | r | L-13.R | # Title : Lecture 13
# Objective : Write and annotate code for lecture 13
# Created by: ThomasGrant
# Created on: 29/10/2020
require("fma")
tslm.airpass<-tslm(airpass~ season)
tslm.airpass
Arima(dowjones,order=c(0,1,0),include.mean=FALSE)
|
8a871a8738acbf0b92ced79bbc62327c321ff211 | e6a9d96db33e6cd819e4f1dbd60c80b0eb53a615 | /man/fit_agTrend_ssl_mgcv.Rd | c528b310e40ad08314e5a3c11609a5ea11002fe2 | [] | no_license | dsjohnson/agTrendTMB | 42384a16831a7f21b4bfe61ad61075b6555cac8f | 403c55da56e891c8126b912ab8958346d7f1d3c8 | refs/heads/master | 2023-01-10T19:30:07.323254 | 2020-11-10T01:19:36 | 2020-11-10T01:19:36 | 307,476,720 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,472 | rd | fit_agTrend_ssl_mgcv.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fit_agTrend_ssl_mgcv.R
\name{fit_agTrend_ssl_mgcv}
\alias{fit_agTrend_ssl_mgcv}
\title{Site-level fitting function}
\usage{
fit_agTrend_ssl_mgcv(.x, obl.corr = FALSE, debug = FALSE)
}
\arguments{
\item{.x}{Data frame for an individual site. M... |
1483c90d8326c4cc9daf0ad99b2a70f10357dad5 | 0b7e9f17d32d84558b95321d92177b7e4532ffdb | /R/match_and_append.R | 919e0205abb2b8a9c83540c0057c702ce854ed9a | [] | no_license | agualtieri/mcla_cleaneR | 0de5d5a40334e1f2e74b028dd9dff8bb402c97fb | 397225a064b486bc48020f4f0d3286a266c862b0 | refs/heads/master | 2020-05-23T13:52:37.834105 | 2019-09-19T12:03:08 | 2019-09-19T12:03:08 | 186,787,354 | 0 | 6 | null | 2019-07-10T09:25:23 | 2019-05-15T08:50:52 | R | UTF-8 | R | false | false | 689 | r | match_and_append.R |
match_and_append <- function(df, source_df, df_variable_for_match, source_df_variable_for_match, variable_to_append){
#setNames(variable_for_match, "variable")
#setNames(variable_to_append, "variable")
df$appended_var <- source_df$variable_to_append[match(df$variable_for_match, source_df$variable_for... |
3b5787ea7aa266e4797e7427fb628b7c085c2943 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/phonTools/examples/spectralslice.Rd.R | a206ae99f4098cf19998b64f6640c02242aeb335 | [] | 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 | 450 | r | spectralslice.Rd.R | library(phonTools)
### Name: spectralslice
### Title: Spectral Slice
### Aliases: spectralslice slice
### ** Examples
## synthesize schwa-like vowel
vowel = vowelsynth (ffs = c(500,1500,2500,3500,4500))$sound
## compare window lengths
par (mfrow = c(3,1))
spectralslice (vowel[500:550], fs = 10000)
spectralsl... |
753442eb1321d43c3c14163c8a2f94ffb846db08 | ffcc3072f9a5190b068f296a6d22404bee24eeb0 | /man/ocrTesseract.Rd | 474a174e2742b72bd532a55725d0ab337cac500d | [
"Apache-2.0"
] | permissive | greenore/ocR | 213661192761fc5fdc9ab7f376a03663737c6e66 | d47d0b5db1d487f42399be361e11ef98c305fce2 | refs/heads/master | 2021-01-20T12:04:29.617030 | 2014-12-18T14:51:32 | 2014-12-18T14:51:32 | 26,242,115 | 6 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,377 | rd | ocrTesseract.Rd | % Generated by roxygen2 (4.0.2): do not edit by hand
\name{ocrTesseract}
\alias{ocrTesseract}
\title{OCR with tesseract}
\usage{
ocrTesseract(dir_path, image_name, output_base, lang = "eng", psm = 5)
}
\arguments{
\item{dir_path}{Path to the directory where the input and output files are.}
\item{image_name}{The name o... |
33e20493de0140eead90b391909145f0165283df | 37fcfce951487d3ba45d2ba3dbc22b6360939b77 | /man/scale_per_minute.Rd | 84b16e1346a30f652a7bef40237b058b096f5ad7 | [] | no_license | abresler/nbastatR | 576155fb58378c03010590c81806515161c03eb5 | aba9179ef644f263387c1536d6ddd26104d79cf4 | refs/heads/master | 2023-08-08T08:52:05.149224 | 2023-07-19T13:09:21 | 2023-07-19T13:09:21 | 43,844,510 | 307 | 94 | null | 2023-02-01T16:59:22 | 2015-10-07T21:00:59 | R | UTF-8 | R | false | true | 356 | rd | scale_per_minute.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/utils.R
\name{scale_per_minute}
\alias{scale_per_minute}
\title{Summarise data per minute}
\usage{
scale_per_minute(data, scale_columns = NULL)
}
\arguments{
\item{data}{a data frame}
\item{scale_columns}{vector of columns to scale}
}
\descr... |
797c1c10cf0533b58b8e5e79f531d89a51834d09 | a48797beca55474d7b39676389f77f8f1af76875 | /man/graph_to_pcs.Rd | 7b994805ac850e0f349c91fcc0e3a56f07803723 | [] | no_license | uqrmaie1/admixtools | 1efd48d8ad431f4a325a4ac5b160b2eea9411829 | 26759d87349a3b14495a7ef4ef3a593ee4d0e670 | refs/heads/master | 2023-09-04T02:56:48.052802 | 2023-08-21T21:15:27 | 2023-08-21T21:15:27 | 229,330,187 | 62 | 11 | null | 2023-01-23T12:19:57 | 2019-12-20T20:15:32 | R | UTF-8 | R | false | true | 1,076 | rd | graph_to_pcs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/toposearch.R
\name{graph_to_pcs}
\alias{graph_to_pcs}
\title{Simulate PCs under an admixture graph}
\usage{
graph_to_pcs(
graph,
nsnps = 10000,
drift_default = 0.02,
admix_default = 0.5,
leaves_only = TRUE
)
}
\arguments{
\item{grap... |
afe6a88b05f53284f158ff0ed7e6d011aa29dda2 | 40261deb3548e47216204512da1012d927bad4aa | /man/fpl_player_hist.Rd | f9192299d305a838809b0da8c6b121658ee9af12 | [] | no_license | jphelps13/fpl.analytics | 05195c122036e083cfc01dbf5329020ca527e4b8 | 4f21706e42cb083125aec50b252efd151994ff80 | refs/heads/master | 2020-03-25T11:02:51.322784 | 2018-08-14T15:36:20 | 2018-08-14T15:36:20 | 143,717,104 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 631 | rd | fpl_player_hist.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/fpl_player_hist.R
\docType{data}
\name{fpl_player_hist}
\alias{fpl_player_hist}
\title{Player historical FTP data}
\format{An object of class \code{data.table} (inherits from \code{data.frame}) with 1696 rows and 26 columns.}
\source{
\url{ht... |
b76e43bc4035110a71ca5ebd0bbd5d821fac321e | 781aef24c9fae0d1f275f9ac0eae0f7164b79a69 | /R/fitted.sca.R | d8336026a25e95d114f617f5f492ef9d6d142471 | [] | no_license | cran/multiway | e3326bc34a72c7a74037c68b053428102fab5cf2 | f2e6377ccb5207ddc6d7d8910a0f3293f87185c3 | refs/heads/master | 2021-01-22T08:53:07.193287 | 2019-03-13T07:20:03 | 2019-03-13T07:20:03 | 34,022,312 | 1 | 2 | null | null | null | null | UTF-8 | R | false | false | 249 | r | fitted.sca.R | fitted.sca <-
function(object,...){
# Calculates Fitted Values (lists) for fit SCA Models
# Nathaniel E. Helwig (helwig@umn.edu)
# last updated: April 9, 2015
sapply(object$D,function(d,b){tcrossprod(d,b)},b=object$B)
} |
0cda1358539a4ce14d089eeb196010cc2b612e26 | bf7507dfab13cf917d2be97fdd2abee5c21ebf04 | /man/ovarianSubsampling.Rd | bc3935ce645ac5553f76652dbc8ff1fef5988c41 | [] | no_license | cbg-ethz/TiMEx | acac638214cd97e7ec10e09f74854f13aa351c7e | f0d0f0f5fd8c58bb5da9d01ffa49bbe6ffc09c19 | refs/heads/master | 2021-01-15T14:50:31.880579 | 2018-11-12T17:22:15 | 2018-11-12T17:22:15 | 38,927,358 | 4 | 3 | null | 2018-11-12T17:22:16 | 2015-07-11T13:53:33 | R | UTF-8 | R | false | true | 1,468 | rd | ovarianSubsampling.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/documentDatasets.R
\name{ovarianSubsampling}
\alias{ovarianSubsampling}
\title{Stability of mutually exclusive groups in ovarian cancer}
\format{\code{ovarianSubsampling} is a list with as many elements as
subsampling frequencies provided (... |
bd8339cf91e43c371d56badb0a059090c4ac0724 | d2d18a40e768ce045c82639e16bf541dd36c660c | /03_diagnostics/hh_size_distro.R | 817e9a6b87501c1bdd8431d5c15996dd0d001658 | [] | no_license | manzorz/wash_mapping | a607dd7f158217b2628cdf8fc75957abe1a64812 | 821fa49b66847d256adfb9e0b6fdbd7fef15a2d5 | refs/heads/master | 2021-07-09T00:14:19.171006 | 2018-01-31T20:53:32 | 2018-01-31T20:53:32 | 95,460,682 | 0 | 0 | null | 2017-06-26T15:21:39 | 2017-06-26T15:21:38 | null | UTF-8 | R | false | false | 3,562 | r | hh_size_distro.R | library(tidyverse)
library(ggpubr)
library(grid)
library(gridExtra)
setwd('J:/LIMITED_USE/LU_GEOSPATIAL/geo_matched/wash')
# read in extraction
load('points_collapsed_2017_07_31.Rdata')
load('polys_collapsed_2017_07_31.Rdata')
# bind rows to create master dataset
alldat <- bind_rows(pt_collapse, poly_collapse)
allda... |
79f5593b9c6f7f51517e35cf4fce542ae4853fbd | 719500684fceaf0a7a80ce663e9cf07802e10b9a | /R/open_url.r | 7ffcaf5f14198ed79f949220d2c48d9659095628 | [] | no_license | pbreheny/breheny | 964baf0670a9eb4975946eae66772d47d9affd11 | 3e15bb78a769616adb49ea50c800d026f48ef8e7 | refs/heads/master | 2023-08-09T18:34:35.865871 | 2023-08-01T15:38:54 | 2023-08-01T15:38:54 | 82,972,930 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 268 | r | open_url.r | #' Open a url: quietly, in the background, and in a new window
#'
#' @param x URL to open
#'
#' @examples
#' open_url('www.google.com')
#' @export
open_url <- function(x) {
system2('sensible-browser', c('--new-window', x), stdout=NULL, stderr=NULL, wait=FALSE)
}
|
66a501d69257b19a349696e42d9f5e07d78bdbab | 089b39df2c24c06c3ff605d82705ba588decb419 | /scripts/05_mdd_enrichment/01_prep_singlecell_for_ldsc.R | aa93ca1c549006ff02b7ff8ea9a7eab01ab7e737 | [] | no_license | JujiaoKang/2020_PNAS_Depression | 09526a8b22baa3ecb635fdf71f9b485480692e32 | e5bcac0d9a456f433e2d7e374af767f8d6ad2cdf | refs/heads/master | 2023-05-27T09:39:26.669418 | 2021-06-07T12:11:02 | 2021-06-07T12:11:02 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 15,120 | r | 01_prep_singlecell_for_ldsc.R | library(tidyverse)
library(EWCE)
library(MAGMA.Celltyping)
library(biomaRt)
library(org.Hs.eg.db)
library(stringr)
# This script will write text files of
####################
# set up directories
####################
base_dir = '/gpfs/milgram/project/holmes/kma52/mdd_gene_expr'
umi_dir = file.path(base_dir, '/data/... |
d96fc9effa63d8a9e7cb2b1d3c6b8993f5f2ac8f | d46a62735f241be070aa0363ab6870ad2fb46015 | /R/node_ckr.R | df439efa95d7d8adf9f3a01b265af59af2567797 | [
"MIT"
] | permissive | DataUrbanEconGeek/wikipop | 7bda325e50500e0061c17388fcf1c5d4dc110b8e | ba073d08adbd8677dcb4941081cba22fe0124b6a | refs/heads/master | 2020-03-22T02:08:54.771580 | 2018-07-08T04:02:03 | 2018-07-08T04:02:03 | 139,352,274 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 168 | r | node_ckr.R | #' A function to check css node
#' @import rvest
#' @export
node_ckr <- function(wikihtml, path){
class(try(html_nodes(wikihtml, css = path),silent=TRUE))[1]
}
|
7ad8d05617dbfba3afa6dd91ffdbc8a8602fcebf | cceb54a5dd3b3053fe70425a9786d670bc48285c | /R/indpca.R | 9bbe5368227641db1e30e5c8f7a1efb348c08876 | [] | no_license | cran/hierfstat | ee123e045e3592a9ce4181b0f4aa8fd96649da62 | 30663f916505fb83451a49bbe0fa2fbe082632eb | refs/heads/master | 2022-05-12T16:31:17.085452 | 2022-05-05T22:40:02 | 2022-05-05T22:40:02 | 17,696,646 | 2 | 3 | null | null | null | null | UTF-8 | R | false | false | 1,574 | r | indpca.R | ###
#'@export
indpca<-function(dat,ind.labels=NULL,scale=FALSE){
#requires ade4
#given a genotype data set dat
#and individual labels lab
#performs a centered, unscaled PCA on individuals
#if scale=TRUE, PCA carried out on correlation matrix
if (is.genind(dat)) dat<-genind2hierfstat(dat)
indp<-pop.... |
ce565e9508d785190a52b1a3fb4a13c81ebc8afc | 0e8b34d1a0ba4f1d75ff94bfbec54ac1fd81ee35 | /hypothesistests.R | 41108a51555a2842c898e14dc2ac0cc743abd36c | [] | no_license | lolfxo/Data_Analysis | f49bb88c2f053d9baad929b9ab6f54ed4617a690 | f78db3120f9414a9f819f1bc022269d88b473edf | refs/heads/master | 2021-09-07T10:58:22.166168 | 2018-02-21T23:16:07 | 2018-02-21T23:16:07 | 116,081,281 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 681 | r | hypothesistests.R | # Script to create the "hypothesistests" package
# Clear current list
# rm(list=ls())
# Get working directory
getwd()
# Create your own R package
# Step 0: Packages I will need
install.packages("devtools")
library("devtools")
devtools::install_github("klutometis/roxygen")
library(roxygen2)
# Step 1: Create your pa... |
acfa6e9265e67cfc59c4be64e357fc9aa05e85c7 | ba3b0d0347da419c1f9fd07bcc5ddb354ccafd2a | /20190130_equating_multiple_test_items.R | b247f400aa9d5a89f29443bb00fbc26fa6239174 | [] | no_license | takuizum/R | ed88c662b5d399444a5a60cd16155e0f49301a62 | e07689fd1ec0a53c500b0b87731ff565e9b6500d | refs/heads/master | 2020-03-30T12:15:17.624459 | 2019-06-17T16:20:09 | 2019-06-17T16:20:09 | 151,216,645 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,988 | r | 20190130_equating_multiple_test_items.R | library(tidyverse)
library(irtfun2)
# gen simulation data set
th_1 <- rnorm(1000, 0, 1)
th_2 <- rnorm(1000, 0, 1)
th_3 <- rnorm(1000, 0, 1)
nofitems <- 70
true_a <- rlnorm(nofitems, 0, 0.4)
true_b <- rnorm(nofitems, 0, 1)
# LETTERS is built-in constatn, that has the 26 upper-case letters of the Roman... |
252e8e35d2877723ca89fc94d9425b2fb557831c | dc202eab79abffe0ebe0db44a84f71c3886c04c8 | /r/sparkr/man/bluemix2d-class.Rd | 490fc141e2aad22b622bd047869aa3acca1bbf98 | [
"Apache-2.0"
] | permissive | gjebran/ibmos2spark | fa4f5b3d1c2dc690d8de8a3ed76940f5c5801e8c | 677b840289b2d7adbb58d5a4c632a3b52d66435a | refs/heads/master | 2021-01-16T18:22:04.078824 | 2017-08-07T22:11:05 | 2017-08-07T22:11:05 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,443 | rd | bluemix2d-class.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/osconfig.R
\docType{class}
\name{bluemix2d-class}
\alias{bluemix2d}
\alias{bluemix2d-class}
\title{sparkcontext: a SparkContext object.}
\description{
credentials: a dictionary with the following required keys:
auth_url
project_id (o... |
d76689bdfee233f31e925d89b8c30a0d823912e0 | bf9ad245511f9fb28c84ab9603d1a90c85a1f7c2 | /R/utilities.R | aeace9bfa370d22f559f31f962809b1249e84c58 | [] | no_license | dcava/wpp | 20f28a14db5a26dd650431c30419767d68ccdeea | 95dc77839caf5d1b48f1d3d93aa8df05f0c60aee | refs/heads/master | 2022-12-10T01:46:23.392824 | 2020-09-15T06:41:41 | 2020-09-15T06:41:41 | 31,527,929 | 1 | 0 | null | 2020-09-15T05:45:12 | 2015-03-02T07:15:33 | HTML | UTF-8 | R | false | false | 5,467 | r | utilities.R | spm <- function(impresults) {
summary(pool(as.mira(impresults)))
}
plotdf <- function (impresult) {
if (class(impresult)=="coxph") {
plotout <- tidy(impresult)
plotout %<>%
select(
term,
estimate,
conf.low,
conf.high,
std.error = robust.se,
p.value)
... |
60fe96d1eb329055faeddb44e2af453f8ba32c54 | fa19d30ddd68641f7baf190b3c192dbd4ac04911 | /r-programming/assignment-2/cachematrix.R | e6512c3939165691e4fd0f8ec5b0d89e6049697b | [] | no_license | rawat-he/datasciencecoursera | 6bb0b07b9e8a9520252388270571ade8549a4274 | fd3d37f2deab6d61519d7a8a1c8ef57473d5bf86 | refs/heads/master | 2021-05-30T10:33:52.690449 | 2015-07-26T12:52:53 | 2015-07-26T12:52:53 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,988 | r | cachematrix.R | ## makeCacheMatrix - This function creates a special "matrix" object that can cache its inverse.
## set - This function set the value of matrix 'x' for which matrix inverse needs to be cached
# - sets value of matrix cache 'inv' value to NULL
# - This function can also be called directly
# - mat <- ... |
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