Empirical likelihood methods for asymptotically efficient
    estimation of models based on conditional or unconditional moment
    restrictions; see Kitamura, Tripathi & Ahn (2004)
    <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013)
    <doi:10.1002/cjs.11183>.
    Kernel-based non-parametric methods for density/regression estimation and
    numerical routines for empirical likelihood maximisation are implemented in
    'Rcpp' for speed.

Estimation of two-state (survival) models and irreversible illness-
    death models with possibly interval-censored, left-truncated and right-censored
    data. Proportional intensities regression models can be specified to allow for
    covariates effects separately for each transition. We use either a parametric
    approach with Weibull baseline intensities or a semi-parametric approach with
    M-splines approximation of baseline intensities in order to obtain smooth
    estimates of the hazard functions. Parameter estimates are obtained by maximum
    likelihood in the parametric approach and by penalized maximum likelihood in the
    semi-parametric approach.

Provides flexible hazard ratio curves allowing non-linear
  relationships between continuous predictors and survival.
  To better understand the effects that each continuous covariate
  has on the outcome, results are expressed in terms of hazard
  ratio curves, taking a specific covariate value as reference.
  Confidence bands for these curves are also derived.

Implementation of the SIC epsilon-telescope method, either
    using single or distributional (multiparameter) regression. Includes classical regression
    with normally distributed errors and robust regression, where the errors are from
    the Laplace distribution. The "smooth generalized normal distribution" is used,
    where the estimation of an additional shape parameter allows the user to move
    smoothly between both types of regression. See O'Neill and Burke (2022)
    "Robust Distributional Regression with Automatic Variable Selection" for more details.
    <doi:10.48550/arXiv.2212.07317>. This package also contains the data analyses from O'Neill and
    Burke (2023). "Variable selection using a smooth information criterion for distributional
    regression models". <doi:10.1007/s11222-023-10204-8>.