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bbreg (version 2.0.2)

Bessel and Beta Regressions via Expectation-Maximization Algorithm for Continuous Bounded Data

Description

Functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) and Barreto-Souza, Mayrink and Simas (2020) .

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Version

Install

install.packages('bbreg')

Monthly Downloads

312

Version

2.0.2

License

GPL-2

Maintainer

Vinicius Mayrink

Last Published

February 14th, 2022

Functions in bbreg (2.0.2)

EMrun_bes

EMrun_bes
D2Q_Obs_Fisher_bes

D2Q_Obs_Fisher_bes
EMrun_bet_dbb

EMrun_bet_dbb
EMrun_bes_dbb

EMrun_bes_dbb
DQ2_Obs_Fisher_bet

DQ2_Obs_Fisher_bet
D2Q_Obs_Fisher_bet

D2Q_Obs_Fisher_bet
Ew1z

Ew1z
BF

Body Fat data set
EMrun_bet

EMrun_bet
DQ2_Obs_Fisher_bes

DQ2_Obs_Fisher_bes
Qf_bes_dbb

Qf_bes_dbb
envelope_bet

envelope_bet
d2mudeta2

d2mudeta2
coef.bbreg

coef.bbreg
fitted.bbreg

fitted.bbreg
gradlam_bet_dbb

gradlam_bet
SA

Stress/Axiety data set
pred_accuracy_bet

pred_accuracy_bet
Qf_bet_dbb

Qf_bet_dbb
gradlam_bes_dbb

gradlam_bes_dbb
Ew2z

Ew2z
WT

Weather Task data set
summary.bbreg

summary.bbreg
predict.bbreg

predict.bbreg
dbessel

dbessel
vcov.bbreg

vcov.bbreg
envelope_bes

envelope_bes
score_residual_bet

score_residual_bet
quantile_residual_bes

quantile_residual_bes
simdata_bes

simdata_bes
print.bbreg

print.bbreg
Qf_bes

Qf_bes
quantile_residual_bet

quantile_residual_bet
Qf_bet

Qf_bet
bbreg

bbreg
gradtheta_bet

gradtheta_bet
gradtheta_bes

gradtheta_bes
score_residual_bes

score_residual_bes
infmat_bet

infmat_bet
dbbtest

dbbtest
d2phideta2

d2phideta2
infmat_bes

infmat_bes
simdata_bet

simdata_bet
plot.bbreg

plot.bbreg
pred_accuracy_bes

pred_accuracy_bes
startvalues

startvalues