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

total: Total count

CRAN

surveytable: Streamlining Complex Survey Estimation and Reliability Assessment in R

R: Total count
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

total: Total sequences

GITHUB

ahopki14/tcrSeqR: Tools for analyzing data from Adaptive's ImmunoSeq platform

R: Total sequences
totalR Documentation
Total sequences

total: Total sequences

GITHUB

ahopki14/immunoSeqR: Tools for analyzing data from Adaptive's ImmunoSeq platform

R: Total sequences
totalR Documentation
Total sequences

total: Total Moose Workflow

GITHUB

psolymos/moosecounter: Adaptive Moose Surveys

R: Total Moose Workflow
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

total: Total present value

CRAN

dynamicpv: Evaluates Present Values and Health Economic Models with Dynamic Pricing and Uptake

R: Total present value
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

ko: Example nonhuman demand data with drug and dose

CRAN

beezdemand: Behavioral Economic Easy Demand

with drug and dose
Description
Data from Ko et al. (2002)

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

totals: Total sum of amounts

CRAN

compositions: Compositional Data Analysis

R: Total sum of amounts
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

total: Total number of bytes allocated

GITHUB

HenrikBengtsson/profmem: Simple Memory Profiling for R

R: Total number of bytes allocated
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

total: Total number of bytes allocated

CRAN

profmem: Simple Memory Profiling for R

R: Total number of bytes allocated
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

Total: Total Effect Matrix Over a Specific Time Interval

CRAN

cTMed: Continuous-Time Mediation

R: Total Effect Matrix Over a Specific Time Interval
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt

totals: Returns the total number of reads that map to an ORF and the...

GITHUB

celalp/ribofootprintR: Fast data extraction and visualization from .bam files for ribosome footprint profiling experiments

R: Returns the total number of reads that map to an ORF and the...
totalsR Documentation
Returns the total number

totals: Extract time-totals from TRIM output

CRAN

rtrim: Trends and Indices for Monitoring Data

R: Extract time-totals from TRIM output
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

total: Total number of cells in a categorical Raster* object

GITHUB

simonmoulds/r_lulcc: Land Use Change Modelling in R

R: Total number of cells in a categorical Raster* object
totalR Documentation
Total number of cells in a categorical

total: Total number of cells in a categorical Raster* object

CRAN

lulcc: Land Use Change Modelling in R

R: Total number of cells in a categorical Raster* object
totalR Documentation
Total number of cells in a categorical

ampel-leipzig/ameld: Data and Model of End-Stage Liver Disease used in the AMPEL Project

GITHUB

ampel-leipzig/ameld: Data and Model of End-Stage Liver Disease used in the AMPEL Project

Package: ameld
Title: Data and Model of End-Stage Liver Disease used in the AMPEL Project
Version: 0.0.32

data-raw/ampel-eldd.R
data-raw/ampel-eldr.R

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB

tmcd82070/SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

KOS: Function which generates feature weights, discriminant

CRAN

biClassify: Binary Classification Using Extensions of Discriminant Analysis

(either 1 or 2) for each data point in X. Uses Data and Cat to train the classifier.
Usage
KOS(TestData = NULL, TrainData