c(
person("Iñaki", "Ucar", email="iucar@fedoraproject.org",
role=c("aut", "cph", "cre"), comment=c(ORCID="0000-0001-6403-5550")),
person("Martin", "Koehler", email="koehlerm@mpi-magdeburg.mpg.de",
role=c("aut", "cph"), comment=c(ORCID="0000-0003-2338-9904")))
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>.

Flexible Latent Trait Metrics using the Filtered Monotonic
Polynomial Item Response Model
person("Leah", "Feuerstahler", email = "feuer024@umn.edu",
role = c("aut","cre"))
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 (2016) <http://hdl.handle.net/11299/182267>.

person("Leah", "Feuerstahler", email = "lfeuerstahler@fordham.edu",
role = c("aut","cre"))
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>.

FlexMix implements a general framework for finite
mixtures of regression models using the EM algorithm.
FlexMix provides the E-step and all data handling, 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.

R (>= 1.9.0), graphics, methods, stats, stats4

lattice, modeltools (>= 0.2-16), multcomp
FlexMix implements a general framework for finite mixtures
of regression models using the EM algorithm. FlexMix provides
the E-step and all data handling, 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.