Achim Zeileis takes over maintenance from Hannah Frick.
Updated example reference output for CRAN checks.
mRm package which got removed
from CRAN.RNGversion("3.5.0") for reproducibility of previous results due
to the changes in sample() for R version 3.6.0.lattice package in the vignettes in preparation to changes
in the flexmix package.effect() and allEffects() methods when effects
package is loaded.mptmix() for MPT mixture models, also known as
latent-class MPT models. The design follows that of raschmix() and
btmix(). Internally, the flexmix driver FLXMCmpt() is called to fit the
finite mixture model. The function has not yet been fully tested and
may change in future versions.btmix().The manuscript "Rasch Mixture Models for DIF Detection: A Comparison
of Old and New Score Specifications." has now been published in
Educational and Psychological Measurement, 75(2), 208-234.
doi:10.1177/0013164414536183.
A preprint version is included as vignette("scores", package = "psychomix").
Improved functionality for raschmix() to allow for item response
data in the itemresp class.
Improved axis labeling in plot() method for raschmix objects.
In the plot() method nchar(..., type = "width") is now used to determine
the default abbreviation.
If suggested packages are needed internally, these are only called
with :: semantics and not require()d anymore.
Adapted raschmix() to work with both the old psychotools version
0.2-0 and the new 0.3-0.
Updated the "scores" vignette which is now also accepted for publication
in Educational and Psychological Measurement.
Improved functionality for raschmix() to allow for differences
between components in terms of identified parameters.
Improved function raschmix() to leverage new functionality from
the flexmix package: Parameter estimates from the previous M-step
can now be used for initialization.
Function raschmix() can now model the score distribution to be
equal across all components (for both a "saturated" and a "meanvar"
specification of the score model).
raschmix() functionality in the
package, accompanying the manuscript "Flexible Rasch Mixture Models
with Package psychomix" by Frick, Strobl, Leisch, and Zeileis,
published in the Journal of Statistical Software 48(7). See
citation("psychomix") for details.For increased numerical stability the default minprior control
parameter in raschmix() is now 0.05 (as in flexmix) and not 0
(as in the previous psychomix version).
Revised vignette("raschmix", package = "psychomix"). Specifically,
there is a discussion of how the FLXMCrasch() can be used directly
with flexmix() or stepFlexmix() from the flexmix package.
Improved function simRaschmix() to allow for a flexible specification
of the data generating process.
Added an effectsplot() function that leverages the effects package
for visualizing the effects of the concomitant variables (if any) in
the mixture model. This has not yet been fully tested and may change
in future versions.
Added a new function btmix() for Bradley-Terry mixture models. The
design follows that of raschmix() rather closely. Based on btReg.fit()
from package psychotools, there is a flexmix driver called FLXMCbtreg().
The btmix() function is a convenience interface calling stepFlexmix()
with the FLXMCbtreg() driver. This has not yet been fully tested and
may change in future versions.
psychomix package for fitting
psychometric mixture models based on flexmix infrastructure. At the
moment only Rasch mixture models are implemented in various flavors:
with/without concomitant variables, different parametrizations
of the score distribution (saturated vs. mean/variance specification).
See vignette("raschmix", package = "psychomix") for details.