We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.

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.

A collection of methods for smoothing numerical data, commencing
    with a port of the Matlab gaussian window smoothing function. In addition,
    several functions typically used in smoothing of financial data are included.

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.