Flexible framework for specifying survival distributions through
    their hazard (failure rate) functions. Define arbitrary time-varying
    hazard functions to model complex failure patterns including bathtub
    curves, proportional hazards with covariates, and other non-standard
    hazard behaviors. Provides automatic computation of survival, CDF, PDF,
    quantiles, and sampling. Implements the likelihood model interface for
    maximum likelihood estimation with right-censored and left-censored
    survival data.

Provides functions to switch the 'BLAS'/'LAPACK' optimized backend
    and change the number of threads without leaving the R session, which needs
    to be linked against the 'FlexiBLAS' wrapper library
    <https://www.mpi-magdeburg.mpg.de/projects/flexiblas>.

Implements a fast, flexible method for simulating continuous
    variables with specified rank correlations using the Iman–Conover
    transformation (Iman & Conover, 1982 <doi:10.1080/03610918208812265>)
    and back-ranking. Includes plotting tools and error-diagnostics.

Exports 'flextable' objects to 'xlsx' files,
    utilizing functionalities provided by 'flextable' and 'openxlsx2'.

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.