GJRM0.2-6.7 package

Generalised Joint Regression Modelling

adjCov

Adjustment for the covariance matrix from a fitted gjrm model

adjCovSD

Adjustment for the covariance matrix from a gjrm model fitted to compl...

ATE

Average Treatment Effect of a binary or continuous treatment variable

BCDF

Internal Function

bcont

Internal Function

bdiscrcont

Internal Function

bdiscrdiscr

Internal Function

bprobgHs

Internal Function

bprobgHsCont

Internal Function

bprobgHsContSS

Internal Function

bprobgHsContUniv

Internal Function

bprobgHsDiscr1

Internal Function

bprobgHsDiscr1SS

Internal Function

bprobgHsPO

Internal Function

bprobgHsSS

Internal Function

cond.mv

Conditional Mean/Variance from a Copula Model

conv.check

Some convergence diagnostics

copgHs

Internal Function

copula.prob

Copula probabilities (joint and conditional) from a fitted simultaneou...

CopulaCLM

Internal fitting function

copulaSampleSel

Internal fitting function

cv.inform

Cross validation for informative censoring univariate survival models

distrHs

Internal Function

Dpens

Differentiable penalties

eta.tr

Internal Function

g.tri

Internal Function

gamlss

Generalised Additive Models for Location, Scale and Shape and Beyond

gamlssObject

Fitted gamlssObject object

ggmtrust

ggmtrust for penalised network

GJRM-package

Generalised Joint Regression Modelling

gjrm

Generalised Joint Regression Models with Binary/Continuous/Discrete/Su...

gjrmObject

Fitted gjrm object

gt.bpm

Gradient test

H.tri

Internal Function

haz.surv

Post-estimation calculation of hazard, cumulative hazard and survival ...

k.tau

Kendall's tau a fitted joint model

llpsi

Internal Function

LM.bpm

Lagrange Multiplier Test (Score Test)

lmc

Linear Model Fitting with Constraints

logLik.SemiParBIV

Extract the log likelihood for a fitted copula model

marg.mv

Marginal Mean/Variance

mb

Nonparametric (worst-case and IV) Manski's bounds

numgh

Internal Function

OR

Causal odds ratio of a binary/continuous treatment variable

PE

Partial effect from a binary bivariate model

pen

Internal Function

plot.SemiParBIV

Plotting function

polys.map

Geographic map with regions defined as polygons

polys.setup

Set up geographic polygons

pred.gp

Function to predict quantiles from GP and DGP distributions

predict.CopulaCLM

Prediction function

predict.SemiParBIV

Prediction function

prev

Estimated overall prevalence from a sample selection model

print.ATE

Print an ATE object

print.cond.mv

Print a cond.mv object

print.copulaSampleSel

Print a copulaSampleSel object

print.gamlss

Print a gamlss object

print.gjrm

Print a gjrm object

print.marg.mv

Print a marg.mv object

print.mb

Print an mb object

print.OR

Print an OR object

print.PE

Print an PE object

print.prev

Print an prev object

print.RR

Print an RR object

print.SATE

Print an SATE object

print.SemiParBIV

Print a SemiParBIV object

print.SemiParROY

Print a SemiParROY object

print.SemiParTRIV

Print a SemiParTRIV object

probm

Internal Function

regH

Internal Function

res.check

Diagnostic plots for discrete/continuous response margins

resp.check

Diagnostic plot for a variable

rMVN

Multivariate Normal Variates

rob.const

Bootstrap procedure to help select the robust constant in a GAMLSS

rob.int

Tool for tuning bounds of integral in robust models

RR

Causal risk ratio of a binary/continuous treatment variable

S.m

Internal Function

SATE

Survival Average Treatment Effects of a binary treatment variable

SemiParBIV.fit.post

Internal Function

SemiParBIV.fit

Internal Function

SemiParBIV

Internal fitting function

SemiParROY

Internal fitting function

SemiParTRIV

Internal fitting function

summary.copulaSampleSel

copulaSampleSel summary

summary.gamlss

gamlss summary

summary.gjrm

gjrm summary

summary.SemiParBIV

SemiParBIV summary

summary.SemiParROY

SemiParROY summary

summary.SemiParTRIV

SemiParTRIV summary

TRIapprox

Internal Function

triprobgHs

Internal Function

VuongClarke

Vuong and Clarke tests

working.comp

Internal Function

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.