Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2022) <doi:10.1016/j.csda.2022.107556>.

Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including
    Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>,
    Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and
    Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and 
    any other extended models involving multiple latent class variables.

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

Perform a Bayesian estimation of the exploratory 
    Sparse Latent Class Model for Binary Data 
    described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) 
    <doi:10.1007/s11336-019-09693-2>.

The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.

In the recent past, measurement of coverage has been mainly through
    two-stage cluster sampled surveys either as part of a nutrition assessment
    or through a specific coverage survey known as Centric Systematic Area
    Sampling (CSAS). However, such methods are resource intensive and often only
    used for final programme evaluation meaning results arrive too late for
    programme adaptation. SLEAC, which stands for Simplified Lot Quality
    Assurance Sampling Evaluation of Access and Coverage, is a low resource
    method designed specifically to address this limitation and is used
    regularly for monitoring, planning and importantly, timely improvement to
    programme quality, both for agency and Ministry of Health (MoH) led
    programmes. SLEAC is designed to complement the Semi-quantitative Evaluation
    of Access and Coverage (SQUEAC) method. This package provides functions for
    use in conducting a SLEAC assessment.