Additive Model for Ordinal Data using Laplace P-Splines
Generation of a recentered B-spline basis matrix in additive models
Internal function extracting design matrices from formulas in the DALS...
Marginal posterior density function for a remapped non-penalized param...
Posterior density function for the non-penalized parameters in an ordg...
Object resulting from the fit of an additive proportional odds model u...
Fit of an additive proportional odds model for ordinal data using Lapl...
Compute the additive terms estimated using an 'ordgam' model
Object resulting from the fit of a proportional odds model using 'ordr...
Fit a proportional odds model for ordinal data
Log-posterior function for a proportional odds model
Compute the penalty matrix associated to a vector containing fixed (no...
Plot the the additive terms in an <ordgam> object with its credible re...
Print an 'ordregr' or an 'ordgam' object.
Specification of smooth terms in formulas in the ordgam function.
Skew-Normal approximation to a density evaluated on a sparse grid
Skew-t approximation to a density evaluated on a sparse grid
Significance test of an additive term
Additive proportional odds model for ordinal data using Laplace P-splines. The combination of Laplace approximations and P-splines enable fast and flexible inference in a Bayesian framework. Specific approximations are proposed to account for the asymmetry in the marginal posterior distributions of non-penalized parameters. For more details, see Lambert and Gressani (2023) <doi:10.1177/1471082X231181173> ; Preprint: <arXiv:2210.01668>).
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