WA: Weighted averaging (WA) regression and calibration

CRAN

rioja: Analysis of Quaternary Science Data

method that produces scatter plots of predicted vs observed measurements for the training set.
Value
Function WA returns

WA: Weighted averaging (WA) regression and calibration

GITHUB

nsj3/rioja: Analysis of Quaternary Science Data

method that produces scatter plots of predicted vs observed measurements for the training set.
Value
Function WA returns

RAB: real adaboost (Friedman et al

GITHUB

lgatto/MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

... a demonstration version
Usage
RAB(formula, data, maxiter=200, maxdepth=1)

RAB: Compute the relative absolute bias of multiple estimators

CRAN

SimDesign: Structure for Organizing Monte Carlo Simulation Designs

estimators.
Usage
RAB(x, percent = FALSE, unname = FALSE)

RAB: real adaboost (Friedman et al

BIOC

MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers

... a demonstration version
Usage
RAB(formula, data, maxiter=200, maxdepth=1)

WA: While-Alive Loss Rate for Recurrent Event in the Presence of Death

CRAN

WA: While-Alive Loss Rate for Recurrent Event in the Presence of Death

Package: WA
Type: Package
Title: While-Alive Loss Rate for Recurrent Event in the Presence of

avery-kruger/kitchen: Convolutional Kitchen Sinks in R

GITHUB

avery-kruger/kitchen: Convolutional Kitchen Sinks in R

Package: kitchen
Type: Package
Title: Convolutional Kitchen Sinks in R

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB

tmcd82070/SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

wa: Weighted averaging transfer functions

CRAN

analogue: Analogue and Weighted Averaging Methods for Palaeoecology

mod <- wa(SumSST ~., data = ImbrieKipp)
## extract the fitted values
fitted(mod)

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB

semmons1/TEST-SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

WA: SpatialPolygonsDataFrame for the state of Washington, USA

CRAN

SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

wa: Extracts the weighted averages of a CCA solution

GITHUB

villardon/MultBiplotR: Multivariate Analysis Using Biplots in R

solution
Description
Extracts the weighted averages of a CCA solution

WA: statistic of the Watson goodness-of-fit test for the gamma

CRAN

gofgamma: Goodness-of-Fit Tests for the Gamma Distribution

, i.e. a bootstrap procedure is implemented to perform the test, see crit.values.
Usage
WA(data, k_estimator)

wa: Extracts the weighted averages of a CCA solution

CRAN

MultBiplotR: Multivariate Analysis Using Biplots in R

of a CCA solution
Description
Extracts the weighted averages of a CCA solution

dbca-wa/turtleviewer: WA Turtle Data Viewer

GITHUB

dbca-wa/turtleviewer: WA Turtle Data Viewer

Package: turtleviewer
Title: WA Turtle Data Viewer
Version: 0.2.0.20200102

customize: Plot customization

GITHUB

billy34/SCGLR: Supervised Component Generalized Linear Regression

(plot.SCGLR=list(predictors=TRUE, pred.arrows=FALSE))
plot(genus.scglr)
# setting custom style

CUSTOMERS: Customer data

GITHUB

shawngiese/classic.models: Classic Models from OpenText Analytics Designer

##use with the other classic.models data sets. For example make an inner join with:
## newtable <- merge(CUSTOMERS,ORDERS)

Customer: Customer Functions

GITHUB

charliebone/shopifyr: An R Interface to the Shopify API

R: Customer Functions
CustomerR Documentation
Customer Functions

Customer: Customer Functions

CRAN

shopifyr: An R Interface to the Shopify API

R: Customer Functions
CustomerR Documentation
Customer Functions

customize: Plot customization

CRAN

SCGLR: Supervised Component Generalized Linear Regression

(plot.SCGLR=list(predictors=TRUE, pred.arrows=FALSE))
plot(genus.scglr)
# setting custom style