NEWS
RealVAMS 0.5.0 (2026-09-18)
- Validate data and controls with ordinary R errors. Accept numeric or
integer 0/1 outcomes, preserve missing teacher identifiers, and allow
missing values in unused covariates. Reject unsupported formula offsets.
- Honor verbose=FALSE and the selected binomial link throughout fitting.
REML=TRUE remains the default and has an explicit regression test.
- Correct joint-model VP updates and ML/REML covariance profiling. Return
estimates, pseudo-responses, weights and prediction-error covariances
from the same final working model.
- Respect EM iteration limits, report convergence explicitly, and guard
residual covariance updates against invalid matrices and endless loops.
- Retain fits when optional Hessian differentiation fails and report its
status. Use unrounded values for inference and consistently suppress
ordinary Wald tests and intervals for variance components.
- Support independent-response covariance Hessians and C++ benchmarking
together with VP and school effects without overriding user options.
Apply independence constraints to school covariance components too.
- Retain known teacher histories and last-attended schools when scores
are missing. Support the default fixed-effects model for a single year.
Preserve user predictor columns and numeric teacher-year ordering
beyond nine years.
- Check R/C++ covariance-update agreement during benchmarking; protect
native exception boundaries and qualify registered routine lookups.
- Correct summary residual selection, print return values, and plotting
arguments, year labels and restoration of graphics prompting.
Include school covariance summaries and handle undefined correlations.
- Move package documentation to roxygen source headers, correct data
counts and covariance descriptions, and add testthat regression and
independent numerical-reference tests.
RealVAMS 0.4-3 (2019-04-23)
- Added the joint covariance matrix, C, of the estimated fixed effects and
predicted random effects to the output. See Henderson (1975).
- Paper reference added to manuals and to CITATION file.
RealVAMS 0.4-0 (2017-08-14)
- New arguments added to the RealVAMS function
-- independent.responses defaults to FALSE. If TRUE,
this option will model the responses independently
by fixing the covariances in G at 0 as well as the
covariances in the last row/column of R. The resulting
estimates are the same as those that would be obtained by
modelling the test scores in package GPvam (with REML=FALSE)
and modelling the binary respones in SAS GLIMMIX (RealVAMS
has been validated against these programs).
-- cpp.benchmark defaults to FALSE. If TRUE, this option will
perform the calculations shown in equation (16) of Karl,
Yang, Lohr (2013) using both R and the embedded C++ code
to demonstrate the time savings of using C++. A summary
table is printed at the end.
- The functions in the code of the file vp_cp (the estimation routine)
have been commented to explain their purpose and to reference
which equations from Karl, Yang, Lohr (2013) or Wolfinger and
O'Connell (1993) they represent
RealVAMS 0.3-3 (2017-03-14)
- Add generic summary, plot, and print functions.
- Added detail to documentation for the RealVAMS function
- Reformatted user manual documentation for simulated data
- Renamed some of the elements of the RealVAMS class
RealVAMS 0.3-2 (2015-07-21)
- Minor updates to package structure to match new CRAN requirements.
RealVAMS 0.3-1 (2014-11-01)