aws: AWS downloader

GITHUB

RajLabMSSM/downloadR: echoverse module: Single- and multi-threaded downloading functions

R: AWS downloader
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

aws: AWS for local constant models on a grid

GITHUB

neuroconductor-releases/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
awsR Documentation
AWS for local constant models on a grid

aws: AWS for local constant models on a grid

GITHUB

WIAS-BERLIN/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

aws: AWS for local constant models on a grid

CRAN

aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

aws: AWS for local constant models on a grid

GITHUB

neuroconductor/aws: Adaptive Weights Smoothing

R: AWS for local constant models on a grid
awsR Documentation
AWS for local constant models on a grid

awes: awes - Adaptively Weighted Ensembles via Stacking

GITHUB

reichlab/adaptively-weighted-ensemble: Adaptively Weighted Ensembles via Stacking

R: awes - Adaptively Weighted Ensembles via Stacking
awesR Documentation
awes - Adaptively Weighted Ensembles via

experiments/20180703_按等级分开和同比计算设计.Rmd:

GITHUB

ahorawzy/Mreport: Monthly Report of traffic condition

title: "20180703_按等级分开和同比计算设计"
author: "wzy"
date: "2018年7月3日"

aws-class: Class '"aws"'

GITHUB

neuroconductor/aws: Adaptive Weights Smoothing

R: Class '"aws"'
aws-classR Documentation
Class "aws"

aws: Adaptive Weights Smoothing

CRAN

aws: Adaptive Weights Smoothing

Package: aws
Version: 2.5-6
Date: 2024-09-29

inst/doc/aws-Example.pdf
inst/doc/aws-Example.R

aws-class: Class '"aws"'

GITHUB

neuroconductor-releases/aws: Adaptive Weights Smoothing

R: Class '"aws"'
aws-classR Documentation
Class "aws"

aws-class: Class '"aws"'

CRAN

aws: Adaptive Weights Smoothing

R: Class '"aws"'
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

aws-class: Class '"aws"'

GITHUB

WIAS-BERLIN/aws: Adaptive Weights Smoothing

R: Class '"aws"'
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

mrpt_calc_cumSum_Period: 计算累计数

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算累计数
mrpt_calc_cumSum_PeriodR Documentation
计算累计数

mrpt_calc_cumSum_item: 计算累计数

GITHUB

takewiki/jlrdspkg: package for jlrds app

R: 计算累计数
mrpt_calc_cumSum_itemR Documentation
计算累计数

leeper/r-aws: A basic wrapper to the AWS Java

GITHUB

leeper/r-aws: A basic wrapper to the AWS Java

Package: r-AWS
Type: Package
Title: A basic wrapper to the AWS Java

inst/aws-java-sdk/currentVersion.txt
inst/aws-java-sdk/lib/aws-java-sdk-1.1.0-javadoc.jar

task/20182920_货运计算1.Rmd:

GITHUB

ahorawzy/Mreport: Monthly Report of traffic condition

x2$湖北省 <- NULL
```{r}
write.csv(x2,file = "D:\\交大云同步\\实习\\19_货运分析\\计算结果\\三市月平均日总行驶量.csv")

AW: Collection of Free Induction Decay of Urine Spectra

CRAN

musicNMR: Conversion of Nuclear Magnetic Resonance Spectra in Audio Files

) from eight urine samples. All urine were collected from the donor "AW". The urine are divided in two group; in each group

aw: Calculate Akaike weights for model averaging

RFORGE

mkin: Kinetic Evaluation of Chemical Degradation Data

as specified
by Burnham and Anderson (2004).
Usage

aw: Calculate Akaike weights for model averaging

CRAN

mkin: Kinetic Evaluation of Chemical Degradation Data

as specified
by Burnham and Anderson (2004).
Usage

aw: Get the atomic weight of an element

CRAN

georefdatar: Geosciences Reference Datasets

Get the atomic weight of an element
Usage
aw(sym, dataSource = "IUPAC")