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.