numDeriv, compiler, splines, Rcpp (>= 0.9.2), RcppArmadillo
(>= 0.3.6)
lava.tobit (>= 0.4-7), prodlim, testthat, ucminf
'biprobit.R' 'biprobit.strata.R' 'pmvn.R' 'claytonakes.R'
'coef.biprobit.R' 'cumh.R' 'Dbvn.R' 'fastapprox.R' 'ipw.R'
'logLik.biprobit.R' 'npc.R' 'onload.R' 'plot.bptwin.R'
'plotcr.R' 'print.biprobit.R' 'print.summary.biprobit.R'
'score.biprobit.R' 'sim.bptwin.R' 'sim.clayton.oakes.R'
'summary.biprobit.R' 'summary.bptwin.R' 'uniprobit.R' 'utils.R'
'vcov.biprobit.R' 'bicomprisk.R' 'event.R' 'procformula.R'
'twinlm.R' 'twinlm2.R' 'twinsim.R' 'methodstwinlm.R'
'sim-nordic-twin.R' 'twostage.R' 'aalenfrailty.R'
'surv.boxarea.R' 'casewise.R' 'cor.R' 'fastpattern.R'
'fastreshape.R' 'binomial.twostage.R' 'clusterindex-reshape.R'
'bptwin.R' 'prop-odds.R' 'phreg.R' 'lifetable.R'
'mets-package.R' 'bptwin2.R'

Implementation of various statistical models for multivariate
event history data. Including multivariate cumulative incidence models, and
bivariate random effects probit models (Liability models). Also contains
two-stage binomial modelling that can do pairwise odds-ratio dependence
modelling based marginal logistic regression models. This is an alternative
to the alternating logistic regression approach (ALR).
'biprobit.R' 'biprobit.strata.R' 'pmvn.R' 'claytonakes.R'
'coef.biprobit.R' 'cumh.R' 'Dbvn.R' 'fastapprox.R' 'ipw.R'
'logLik.biprobit.R' 'npc.R' 'onload.R' 'plot.bptwin.R'
'plotcr.R' 'print.biprobit.R' 'print.summary.biprobit.R'
'score.biprobit.R' 'sim.bptwin.R' 'sim.clayton.oakes.R'
'summary.biprobit.R' 'summary.bptwin.R' 'uniprobit.R' 'utils.R'
'vcov.biprobit.R' 'bicomprisk.R' 'event.R' 'procformula.R'
'twinlm.R' 'twinlm2.R' 'twinsim.R' 'methodstwinlm.R'
'sim-nordic-twin.R' 'twostage.R' 'aalenfrailty.R'
'surv.boxarea.R' 'casewise.R' 'cor.R' 'fastpattern.R'
'fastreshape.R' 'binomial.twostage.R' 'clusterindex-reshape.R'
'bptwin.R' 'prop-odds.R' 'phreg.R' 'lifetable.R'
'mets-package.R' 'bptwin2.R' 'normal0.R'

numDeriv, compiler, Rcpp, splines, survival

lava.tobit (>= 0.4-7), prodlim, testthat (>= 0.11), ucminf