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

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

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: Extracts the weighted averages of a CCA solution

solution
Description
Extracts the weighted averages of a CCA solution

WA: SpatialPolygonsDataFrame for the state of Washington, USA

of Washington.
Usage
data("WA")

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/2-1-psiform.R:

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

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

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

docs/2-1-econometrics.md:

&sanitize=true" align=middle width=57.32199495pt height=16.438356pt/></p>
2. Given data \(Y_{1}, Y_{2}, ., Y_{n},\) write

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

R/1-2-methods.R:

])])
print("x: (first 6x6)")
print(paste("dim(x) =", dim(object@x)[1], "x", dim(object@x)[2]))

docs/2-1-econometrics.md:

&sanitize=true" align=middle width=57.32199495pt height=16.438356pt/></p>
2. Given data \(Y_{1}, Y_{2}, ., Y_{n},\) write

docs/2-1-econometrics.md:

&sanitize=true" align=middle width=57.32199495pt height=16.438356pt/></p>
2. Given data \(Y_{1}, Y_{2}, ., Y_{n},\) write