Routines for PLS-based genomic analyses,
        implementing PLS methods for classification with
        microarray data and prediction of transcription factor
        activities from combined ChIP-chip analysis. The >=1.2-1
        versions include two new classification methods for microarray
        data: GSIM and Ridge PLS. The >=1.3 versions includes a
        new classification method combining variable selection and
        compression in logistic regression context: logit-SPLS; and
        an adaptive version of the sparse PLS.

Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.

Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. 
    The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. 
    Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.

Bindings for additional regression models for use with the
    'parsnip' package, including ordinary and spare partial least squares
    models for regression and classification (Rohart et al (2017)
    <doi:10.1371/journal.pcbi.1005752>).

Fit a model with potentially many linear and smooth predictors. Interaction
    effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties
    we use iterative steps alternating between using linear predictors (lasso) and smooth predictors
    (generalised additive model).