WA: Weighted averaging (WA) regression and calibration

^2 + v2^2.
Function crossval also returns an object of class WA and adds the following named elements:
predicted

WA: Weighted averaging (WA) regression and calibration

^2 + v2^2.
Function crossval also returns an object of class WA and adds the following named elements:
predicted

RAB: real adaboost (Friedman et al

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

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

RAB: real adaboost (Friedman et al

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

RAB: Compute the relative absolute bias of multiple estimators

bias1 <- bias(samp1, pop)
samp2 <- rnorm(5000, 1)
bias2 <- bias(samp2, pop)

WA: SpatialPolygonsDataFrame for the state of Washington, USA

of Washington.
Usage
data("WA")

wa: Weighted averaging transfer functions

## tolerance DW
mod3 <- wa(SumSST ~ ., data = ImbrieKipp, tol.dw = TRUE,
min.tol = 2, small.tol = "mean

WA: SpatialPolygonsDataFrame for the state of Washington, USA

of Washington.
Usage
data("WA")

WA: SpatialPolygonsDataFrame for the state of Washington, USA

of Washington.
Usage
data("WA")

wa: Extracts the weighted averages of a CCA solution

solution
Description
Extracts the weighted averages of a CCA solution

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

transform". Communications in Statistics - Theory and Methods, 41(9): 1543-1556. DOI
Examples
X=stats::rgamma(20,3,6)

wa: Extracts the weighted averages of a CCA solution

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

dbca-wa/turtleviewer: WA Turtle Data Viewer

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

dbca-wa/wastdr: WA Sea Turtle Database 'WAStD' API Wrapper

Type: Package
Package: wastdr
Title: WA Sea Turtle Database 'WAStD' API Wrapper

R/wa-counties-boundaries.R:

(class =  `sf`)
#' @rdname wa-counties-boundaries
#' @export

R/2-1-psiform.R:

<- length(mat_list)
n <- length(y)
p <- numeric(d)

docs/2-1-econometrics.tex.md:

\theta \\
1 - \theta
\end{cases}

R/1-2-0crossfit.R:

#' differences Step 1. Nuisance training. and Step 2. Pseudo-outcome
#' regression", "4.2 Estimating the bounds" and "4.3

docs/2-1-econometrics.tex.md:

\theta \\
1 - \theta
\end{cases}