WA: Weighted averaging (WA) regression and calibration
R: Weighted averaging (WA) regression and calibration
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt
R: Weighted averaging (WA) regression and calibration
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt
R: Weighted averaging (WA) regression and calibration
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt
with drug and dose
Description
Data from Ko et al. (2002)
Package: WA
Type: Package
Title: While-Alive Loss Rate for Recurrent Event in the Presence of
of Washington.
Usage
data("WA")
(either 1 or 2) for each data point in X. Uses Data and Cat to train the classifier.
Usage
KOS(TestData = NULL, TrainData
and classicial
deshrinking are supported.
Usage
of Washington.
Usage
data("WA")
of Washington.
Usage
data("WA")
solution
Description
Extracts the weighted averages of a CCA solution
, i.e. a bootstrap procedure is implemented to perform the test, see crit.values.
Usage
WA(data, k_estimator)
of a CCA solution
Description
Extracts the weighted averages of a CCA solution
Package: turtleviewer
Title: WA Turtle Data Viewer
Version: 0.2.0.20200102
Type: Package
Package: wastdr
Title: WA Sea Turtle Database 'WAStD' API Wrapper
Package: KO
Type: Package
Title: Kevin O'Brien's Personal Package
Package: StockPredictoR
Type: Package
Title: Predicts Stock Price Movement
(class = `sf`)
#' @rdname wa-counties-boundaries
#' @export
(markdown = TRUE)
RoxygenNote: 7.1.1
URL: https://github.com/Heejoo-Ko/GSEpractice2
to sig pos KO
clusters <- cell_meta %>%
split(.$louvain) %>%
-wa/etlTurtleNesting
BugReports: https://github.com/dbca-wa/etlTurtleNesting/issues
License: MIT + file LICENSE
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