NEWS | R Documentation |
WeMix
can now accept conditional weights. See the cWeights
argument in the mix
function.
the mix
function checks weights and writes a message if they may be conditional and cWeights
is set to FALSE
.
Linear model evaluation is more robust and can handle data with non-invertible Z matrixes within a group.
Linear models now use base::qr
more aggressively because of poor performance of the Matrix::qr.coef
function on a sparse QR when the system is singular. This previously resulted in very large variance estimates. This also fixed an invalid 'times' argument
error.
The code in the vignette was not the code used to generate the results and some values were incorrectly entered in the comparison table under the mix
column. These problems were fixed.
Linear models are now solved using an analytical solution based on work by Bates and Pinheiro, (1998). This solution is significantly faster than the previous adaptive quadrature method.
Non-linear models are still evaluated using adaptive quadrature.
WeMix can now fit weighted three-level linear models, see the Weighted Linear Mixed-Effects Model vignette for details. Non-linear models are still evaluated using adaptive quadrature and are limited to two-level models.
Model evaluation is now possible using Wald tests. Wald tests allow users to test both fixed effects and random effects variances.
Supports binomial models
Added ability to perform group and grand mean centering to increase comparability with Hierarchical Linear and Nonlinear Modeling (HLM) software
Although three-level models are not currently supported, in version 2.0.0, changes were made to the way groups handled and to the data structures used for integration over random effects so as to be compatible with the future development of three-level models.
Corrected the warning message for the fast option (using Rcpp)
fast
option in mix
defaults to FALSE
now to prioritize accuracy over speed.