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

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

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

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

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() {

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

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

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")

aw: Get the atomic weight of an element

GITHUB

abuseki/georefdatar: Geosciences Reference Datasets

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

aw: Average Weight for hourly traffic data

CRAN

vein: Vehicular Emissions Inventories

Description
aw average weight form traffic.
Usage

AWE: Approximate Weight of Evidence Criterion

CRAN

rebmix: Finite Mixture Modeling, Clustering & Classification

(e.textContent, e, { throwOnError: false, macros }); }
return;
AWE-methodsR Documentation

AWE: Approximate Weight of Evidence Criterion

RFORGE

rebmix: Finite Mixture Modeling, Clustering & Classification

(e.textContent, e, { throwOnError: false, macros }); }
return;
AWE-methodsR Documentation