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>.

Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets. It is described and tested in the accompanying paper by 
    Ahlmann-Eltze and Yau (2018) <doi:10.1109/DSAA.2018.00068>.

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.