acp: Autoregressive Conditional Poisson (ACP) Regression

CRAN

acp: Autoregressive Conditional Poisson

R: Autoregressive Conditional Poisson (ACP) Regression
acpR Documentation
Autoregressive Conditional Poisson (ACP

acp: Autoregressive Conditional Poisson (ACP) Regression

GITHUB

mpiktas/acp: Autoregressive Conditional Poisson

R: Autoregressive Conditional Poisson (ACP) Regression
acpR Documentation
Autoregressive Conditional Poisson (ACP

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

acp: Adaptive conformal prediction method

CRAN

conformalForecast: Conformal Prediction Methods for Multistep-Ahead Time Series Forecasting

, initial = 1, window = 50)
# ACP with asymmetric nonconformity scores and rolling calibration sets
acpfc <- acp(fc

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

acp: Principal component analysis

CRAN

amap: Another Multidimensional Analysis Package

component analysis
Usage
acp(x,center=TRUE,reduce=TRUE,wI=rep(1,nrow(x)),wV=rep(1,ncol(x)))

acp: Anlisis de componentes principales

GITHUB

jcms2665/ACP:

Description
An<c3><a1>lisis de componentes principales.
Usage

acp: Optimization using an iterative hill-climbing algorithm

GITHUB

matsukik/mrsat: Multiple Response Speed Accuracy Tradeoff

-climbing algorithm
Description
Box-constrained optimization using an iterative hill-climbing algorithm

acp: Autoregressive Conditional Poisson

CRAN

acp: Autoregressive Conditional Poisson

Package: acp
Title: Autoregressive Conditional Poisson
Version: 2.1

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

acp: Add, Commit, and Push

GITHUB

meerapatelmd/glitter: Send Git commands via the R Console

R: Add, Commit, and Push
acpR Documentation
Add, Commit, and Push

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