50: pass through constructor

RFORGE
SoilR: Models of Soil Organic Matter Decomposition

R: pass through constructor
DecompOp_method__DecompOpR Documentation
pass

eat.unicorns.50: A student strategy

GITHUB
MartinKies/USLR: Reinforcement Learning with R

R: A student strategy
eat.unicorns.50R Documentation
A student strategy

50-mb.print: Print Results of Model-Based Clustering

CRAN
pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Print Results of Model-Based Clustering
mb.printR Documentation
Print

50-indep.logL: Independent Function for Log Likelihood

CRAN
pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Independent Function for Log Likelihood
Independent logLR Documentation

updatelist-50: Update list

CRAN
reshape: Flexibly Reshape Data

entries
Usage
updatelist(x, y)

50-update.class: Update CLASS.spmd Based on the Final Iteration

GITHUB
snoweye/pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Update CLASS.spmd Based on the Final Iteration
const macros = { "\\R

50-indep.logL: Independent Function for Log Likelihood

GITHUB
snoweye/pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Independent Function for Log Likelihood
const macros = { "\\R": "\\textsf

WA: Weighted averaging (WA) regression and calibration

CRAN
rioja: Analysis of Quaternary Science Data

assemblages using weighted averaging (WA) regression and calibration.
Usage
WA(y, x, mono=FALSE, tolDW = FALSE, use.N2=TRUE

WA: Weighted averaging (WA) regression and calibration

GITHUB
nsj3/rioja: Analysis of Quaternary Science Data

assemblages using weighted averaging (WA) regression and calibration.
Usage
WA(y, x, mono=FALSE, tolDW = FALSE, use.N2=TRUE

50-update.class: Update CLASS.spmd Based on the Final Iteration

CRAN
pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Update CLASS.spmd Based on the Final Iteration
Update Class of EM

50-mb.print: Print Results of Model-Based Clustering

GITHUB
snoweye/pmclust: Parallel Model-Based Clustering using Expectation-Gathering-Maximization Algorithm for Finite Mixture Gaussian Model

R: Print Results of Model-Based Clustering
const macros = { "\\R": "\\textsf

tests/50-bbquad-and-translation.R:

GITHUB
antiphon/Kdirectional: Analysis of anisotropy in point patterns using second order statistics

# Translation weights in a bbquad window
library(devtools)
load_all(".")

bookdown/_book/50-Google_API_reference.md:

GITHUB
companieshouse/DARr: Prototype RAP package for Companies House

coordinates, that represents an area: 1/8000th of a degree by 1/8000th of a degree (about 14m x 14m at the equator) or smaller

docs/50-Google_API_reference.md:

GITHUB
companieshouse/DARr: Prototype RAP package for Companies House

coordinates, that represents an area: 1/8000th of a degree by 1/8000th of a degree (about 14m x 14m at the equator) or smaller

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB
tmcd82070/SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

R/50-ExpressionTool-class.R:

GITHUB
tengfei/cwl.R: R objects for common work flow language representation

slug

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

WA: SpatialPolygonsDataFrame for the state of Washington, USA

GITHUB
semmons1/TEST-SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

WA: SpatialPolygonsDataFrame for the state of Washington, USA

CRAN
SDraw: Spatially Balanced Samples of Spatial Objects

of Washington.
Usage
data("WA")

R/calculate_Q10-50.R:

GITHUB
NCAR/RNWMStat: National Water Model Performance Evaluations Statistics

#Calculate the 'annual mean of the flow exceeded 10% of the time' (Boscarello