Functions are a bit clearer about some errors when data sets do not agree. In particular, when a PSU/stratum variable is missing and Taylor variance is selected it gives a plain language error.
turning on multiCore now fits the latent regressions with multiple cores too. Previously it would only fit the covariance matrix with multiple cores.
added a nearly singular model check
allow stuDat to have students that are not on the item data without throwing an error.
optimization tries to avoid Newton's method by using the lbfgs package which allows for a condition on the gradient to be set. Newton's method can be very slow for large datasets.
when Newton's method is used, the output is more verbose.
the C++ implementation of the Hessian has been sped up a bit.
Fixed bug in degrees of freedom replication in composite. This causes summary to fail in many cases.
Fixed version number error in this file. 2.1.0 changes had previously been named 2.0.0.
Added degrees of freedom and p-values to mml results
mml should be faster now
Added drawPV functions that draw plausible values from a normal approximation to the posterior distribution. See the drawPVs function help for details.
the object returned by mml now includes an object itemScorePoints that shows, for each item, the expected and actually occupied score points.
If items have invalid score points an error now shows the itemScorePoints table.
The mml function used to use the bobyqa optimizer and now uses a combination of the optim function and then a Newton's method optimizer
The mml function Taylor series covariance calculation for composite results has been updated so that the correlation is calculated for all subscales simultaneously. This results in covariance matrix that is always positive definite. The old method can be used by requesting "Partial Taylor".