Allows maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates. 
            Methods are described in Angelo Mazza, Antonio Punzo, Salvatore Ingrassia (2018) <doi:10.18637/jss.v086.i02>.

Format for converting an R Markdown document to a grid
    oriented dashboard. The dashboard flexibly adapts the size of it's
    components to the containing web page.

Provides tools to work with the Flexible Dirichlet
    distribution. The main features are an E-M algorithm for computing the maximum
    likelihood estimate of the parameter vector and a function based on conditional
    bootstrap to estimate its asymptotic variance-covariance matrix. It contains
    also functions to plot graphs, to generate random observations and to handle
    compositional data.

Provides tools for flexible non-linear least squares model fitting using general-purpose optimization techniques. The package supports a variety of optimization algorithms, including those provided by the 'optimx' package, making it suitable for handling complex non-linear models. Features include parallel processing support via the 'future' and 'foreach' packages, comprehensive model diagnostics, and visualization capabilities. Implements methods described in Nash and Varadhan (2011, <doi:10.18637/jss.v043.i09>).

Standard generalized additive models assume a response function, 
    which induces an assumption on the shape of the distribution of the 
    response. However, miss-specifying the response function results in biased 
    estimates. Therefore in Spiegel et al. (2017) 
    <doi:10.1007/s11222-017-9799-6> we propose to estimate the response function
    jointly with the covariate effects. This package provides the underlying 
    functions to estimate these generalized additive models with flexible 
    response functions. The estimation is based on an iterative algorithm. In 
    the outer loop the response function is estimated, while in the inner loop 
    the covariate effects are determined. For the response function a strictly
    monotone P-spline is used while the covariate effects are estimated based on
    a modified Fisher-Scoring algorithm. Overall the estimation relies on the 
    'mgcv'-package.