Application of the filtered monotonic polynomial (FMP) item response
    model to flexibly fit item response models. The package includes tools that
    allow the item response model to be build on any monotonic transformation of
    the latent trait metric, as described by Feuerstahler (2019) <doi:10.1007/s11336-018-9642-9>.

A general framework for finite mixtures of regression
  models using the EM algorithm is implemented. The E-step and all
  data handling are provided, while the M-step can be supplied by the
  user to easily define new models. Existing drivers implement
  mixtures of standard linear models, generalized linear models and
  model-based clustering.

A general estimation framework for multi-state Markov processes with flexible specification of the transition intensities.
    The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate
    effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are 
    no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number
    of states.

Provides color palettes designed to be reminiscent of text on paper.
    The color schemes were taken from <https://stephango.com/flexoki>.
    Includes discrete, continuous, and binned scales that are not necessarily 
    color-blind friendly. Simple scale and theme functions are available for use 
    with 'ggplot2'.

Extends the capabilities for flexible partitioning and model-based clustering
       available in the packages 'flexclust' and 'flexmix' to handle ordinal and
       mixed-with-ordinal data types via new distance, centroid and driver functions that
       make various assumptions regarding ordinality. Using them within the flex-scheme
       allows for easy comparisons across methods.

Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.