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RCBR (version 0.6.2)

Random Coefficient Binary Response Estimation

Description

Nonparametric maximum likelihood estimation methods for random coefficient binary response models and some related functionality for sequential processing of hyperplane arrangements. See J. Gu and R. Koenker (2020) .

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Version

Install

install.packages('RCBR')

Monthly Downloads

156

Version

0.6.2

License

GPL (>= 2)

Maintainer

Roger Koenker

Last Published

November 8th, 2023

Functions in RCBR (0.6.2)

plot.KW2

Plot a KW2 object
logLik.KW1

log likelihood for KW1 procedure
neighbours

Check Neighbouring Cell Counts
rcbr.fit.KW2

NPMLE fitting for random coefficient binary response model
rcbr.fit

Fitting of Random Coefficient Binary Response Models
witness

Find witness point
rcbr.fit.KW1

NPMLE fitting for the Cosslett random coefficient binary response model
bounds.KW2

Prediction of Bounds on Marginal Effects
NICER

New Incremental Cell Enumeration (in) R
GH

Current Status Linear Regression
Horowitz93

Horowitz (1993) Modal Choice Data
KW.control

Control parameters for NPMLE of bivariate random coefficient binary response
GK.control

Control parameters for Gautier-Kitamura bivariate random coefficient binary response
logLik.GK

log likelihood for Gautier Kitamura procedure
NICERd

New (Accelerated) Incremental Cell Enumeration (in) R
GH.se

Current Status Linear Regression Standard Errors
polycount

Check Cell Count for degenerate hyperplane arrangements
predict.KW2

Prediction of Marginal Effects
prcbr

Profiling estimation methods for RCBR models
predict.GK

Prediction of Marginal Effects
polyzone

Identify crossed polygons from existing cells when adding a new line (works only for dim = 2)
plot.GK

Plot a GK object
rcbr.fit.GK

Gautier and Kitamura (2013) bivariate random coefficient binary response
rcbr

Estimation of Random Coefficient Binary Response Models
KWDual

Dual optimization for Kiefer-Wolfowitz problems