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

R: Weighted averaging (WA) regression and calibration
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt

WA: Weighted averaging (WA) regression and calibration

GITHUB

nsj3/rioja: Analysis of Quaternary Science Data

R: Weighted averaging (WA) regression and calibration
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt

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)

acp: Anlisis de componentes principales

GITHUB

jcms2665/ACP:

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

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)

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: Autoregressive Conditional Poisson

CRAN

acp: Autoregressive Conditional Poisson

Package: acp
Title: Autoregressive Conditional Poisson
Version: 2.1

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

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: Adaptive conformal prediction method

CRAN

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

Description
Compute prediction intervals and other information by
applying the adaptive conformal prediction (ACP) method

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB

tmcd82070/SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

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: Weighted averaging transfer functions

CRAN

analogue: Analogue and Weighted Averaging Methods for Palaeoecology

and classicial
deshrinking are supported.
Usage

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB

semmons1/TEST-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: SpatialPolygonsDataFrame for the state of Washington, USA

CRAN

SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

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)