Model time series using mixture autoregressive (MAR)
             models.  Implemented are frequentist (EM) and Bayesian
             methods for estimation, prediction and model
             evaluation. See Wong and Li (2002)
             <doi:10.1111/1467-9868.00222>, Boshnakov (2009)
             <doi:10.1016/j.spl.2009.04.009>), and the extensive
             references in the documentation.

Developed for the following tasks. 1- simulating realizations from the canonical, 
              restricted, and unrestricted finite mixture models. 2- Monte Carlo approximation for
              density function of the finite mixture models. 3- Monte Carlo approximation for the 
              observed Fisher information matrix, asymptotic standard error, and the corresponding  
              confidence intervals for parameters of the mixture models sing the method proposed by
              Basford et al. (1997) <https://espace.library.uq.edu.au/view/UQ:57525>.

Mixed effects cumulative and baseline logit link models for the analysis of ordinal or nominal responses, with non-parametric distribution for the random effects.

Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.

Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.