It provides the log-likelihood, gradient and observed information matrix for penalized or unpenalized maximum likelihood optimization, when fitting univariate models with continuous response.
RR
(Package: SemiParBIVProbit) :
Causal risk ratio of a binary or continuous endogenous variable
RR can be used to calculate the causal risk ratio of a binary or continuous endogenous predictor/treatment, with corresponding interval obtained using posterior simulation.
SemiParTRIVProbit can be used to fit trivariate binary models where the linear predictors of the two main equations can be flexibly specified using parametric and regression spline components.
It provides the log-likelihood, gradient and observed information matrix for penalized or unpenalized maximum likelihood optimization, when continuous margins are employed.
This function produces a map with geographic regions defined by polygons. It is essentially the same function as polys.plot() in mgcv but with added arguments zlim and rev.col and a wider set of choices for scheme.
It provides the log-likelihood, gradient and observed information matrix for penalized or unpenalized maximum likelihood optimization, when one response is binary and the other continuous. Possible bivariate distributions are bivariate normal, Clayton, rotated Clayton (90 degrees), survival Clayton, rotated Clayton (270 degrees), Joe, rotated Joe (90 degrees), survival Joe, rotated Joe (270 degrees), Gumbel, rotated Gumbel (90 degrees), survival Gumbel, rotated Gumbel (270 degrees), Frank, FGM, AMH.
providing 
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