is.Qident function to iteratively explore the
attribute hierarchy, along with its corresponding S3 methods.att.hierarchy function to test the
identifiability of the Q-matrix, along with its corresponding S3
methods.sim.data function can randomly generate data
with hierarchical structures.CDM function can perform parameter estimation
that incorporates hierarchical structures.get.beta, get.PVAF,
get.R2 and get.priority functions can perform
parameter estimation that incorporates hierarchical structures.validation function will be computed in
parallel when the method is set to “Wlad”, “beta”, or
“MLR-B”.MLR-B accepts alpha.level.fit.fit class, which provides comprehensive
S3 methods.etract.etract is compatible with the
etract function in the GDINA package.i = 1 for the Hull
plot.get.beta, get.priority, get.PVAF,
and get.R2.summary method has been added for the
CDM, simData, and validation
classes.plot method has been added for the
CDM and simData classes.updata method has been added for the
CDM, simData, and validation
classes.Wald.Beta,
Priority, PVAF, and R2 to be
consistent with the original Q matrix.method = 'beta'.eps = 'logit'.CDM, simData and validation
classes defined in the package to offer better interaction for
users.validation function has been
changed when not using the iterative process.method = 'Wald'.method = 'beta'.GDI, Hull, and beta.DESCRIPTION field.beta (β)
method, has been added.Wald now includes the SSA search
method and the item.level iteration level.GDI predicted by
logistic regression (Najera et al., 2019).iter.level = 'item'.validation function.sim.Q function.get.Rmatrix for calculating
the restriction matrix.get.priority for calculating
the priority of attribute.Wald.test for
calculating the Wald test.SSA search for the Hull
method.plot.Hull for the
Hull plot.Wald method with the following
updates: 1. If the search method is stepwise or
forward, it will call the Qval function from
the GDINA package. 2. If the search method is
PAA, the search will follow the PAA. 3. The
information matrix used is the full information matrix, implemented by
the internal function inverse_crossprod from the
GDINA package.getQRR,
getVRR, getTPR, getTNR,
getUSR, and getOSR to zQRR,
zVRR, zTPR, zTNR,
zUSR, and zOSR, respectively.validation function.validation function.validation function.
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