tg: tg "to grob" helper function

GITHUB

almartin82/mapvizieR: Visualization and Data Analysis tools for NWEA MAP student data

R: tg "to grob" helper function
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

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

TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

CRAN

TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

Package: TGS
Version: 1.0.1
Title: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

R/TGS-package.R
man/TGS-package.Rd

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

TG: Generalized g-Prior Distribution for Coefficients in BMA

CRAN

BAS: Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling

g-priors on coefficients for BAS, where u = 1/(1+g) has a Gamma distribution
supported on (0, 1].
Usage

tg: Run metamodel

GITHUB

lucabutikofer/LingraNR: R Interface to LINGRA-N Tool

in Qi et al. 2018 for
grassland productivity on temporary (tg()), permanent (pg())
and semi-natural (rough grazing, rg

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

TGS-package: TGS: A package for Rapid Reconstruction of Time-Varying Gene...

CRAN

TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

R: TGS: A package for Rapid Reconstruction of Time-Varying Gene...
TGS-packageR Documentation
TGS: A package

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

TGS-package: TGS: A package for Rapid Reconstruction of Time-Varying Gene...

GITHUB

sap01/TGS: Rapid Reconstruction of Time-Varying Gene Regulatory Networks

R: TGS: A package for Rapid Reconstruction of Time-Varying Gene...
TGS-packageR Documentation
TGS: A package

tg: FUSION output on 2013 Triglycerides GWAS

GITHUB

bogdanlab/RHOGE: Genome-wide genetic correlation between two complex traits using TWAS effect-size estimates

FUSION output on 2013 Triglycerides GWAS
Usage
tg

TG: Thermogravimetry curves

CRAN

ILS: Interlaboratory Study

oxalate samples were tested by thermogravimetric (TG) analysis,
obtaining 105 TG curves that shows the mass loss of oxalate

tg: Summarized ToothGrowth data

GITHUB

wch/gcookbook: Data for "R Graphics Cookbook"

ToothGrowth data
Usage
tg

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() {