Extension of predict() function to allow for
interactions in fixed and random term.
bug fixed for predict() if pord==1 in
splines model.
improved stability of Harville algorithm to solve mixed model.
LMMsolver 1.0.12
First derivatives for predict using deriv
argument now also implemented for spl2D and
spl3D.
function effDim() added to get data.frame with
effective dimensions.
In the vignette, an example added how the generalized heritability
can be calculated.
Improved code coverage > 95%.
Data sets barley.uniformity.trial and
oats.data added.
All data included in the package that are needed for tests.
LMMsolver 1.0.11
New function mLogLik() for the calculations of the
log-likelihood and first derivatives as function of precision parameters
theta.
A new argument deriv added to predict() to
calculate the first derivatives for spl1D() functions.
Two examples in vignette updated with predictions of derivatives and
corresponding standard errors.
bug fixed for theta argument of
LMMsolve().
LMMsolver 1.0.10
Cyclic B-splines models added for spl1D() and
spl2D() functions.
Third order differences (pord=3) added for
splxD() functions.
New argument type = c("response", "link") for
predict() function.
bug fixed for GLMM models if weights are close to zero.
LMMsolver 1.0.9
Binomial response can now also be modelled as
fixed = cbind(failure, succes)
Categorical response using family = multinomial()
Vignette updated, with separate section for GLMM.
doi-link added for LMMsolver.
argument offset can be defined as numeric or (new) as
column name in data frame.
example added to predict() function.
problem with calculation of standard errors fixed, because of minor
change in spam.
bug fixed related to convergence for GLMM.
LMMsolver 1.0.8
Vignette has been rewritten, with a new introduction section.
The function predict.LMMsolve added.
Extension of gam models, combining different splxD() is
possible now.
Correction of upper bound nominal effective dimension for large data
sets.
new 2D example Sea Surface Temperature added.
Issue with product of two large matrices fixed.
Improved efficiency initialization for large datasets.
Bug in grpTheta argument of LMMsolve()
fixed.
Deviance function changes, with extra argument
relative, giving the relative conditional deviance as
defined in McCullagh and Nelder. The default is
relative=TRUE, for relative=FALSE it returns
-2*logLik(obj)
LMMsolver 1.0.7
Improved efficiency for models where the residual
argument of LMMsolve() is used.
A data.frame trace with convergence sequence for
log-likelihood and effective dimensions, added as extra output returned
by LMMsolve().
Bug in v1.0.6 for GLMM models fixed.
Coefficients for three way interactions with one factor and two
non-factors are now labelled correctly.
Standard errors in function obtainSmoothTrend() for
GLMM models are now calculated.
LMMsolver 1.0.6
A new argument grpTheta for LMMsolve() to
give components in the model the same penalty.
The dependency package sp is replaced by
sf.
A small bug for models with more than 10.000 observations and only a
numeric variable in the random part of the model is fixed.
Weights are now checked for missing values after removing
observations with missing values in response. This prevents spurious
errors when both response and weight are missing.
LMMsolver 1.0.5
Small bugs in assignment of names to fixed model coefficients when
columns were dropped from the model are fixed.
Calculation of standard errors for coefficients, with
coef(obj, se = TRUE).
Implementation of Generalized Linear Mixed Models (GLMM) with
additional argument family in LMMsolve
function.
Variance components and splines can be conditional on a factor. For
variance components, this is implemented in the
cf(var, cond, level) function. For 1D and 2D splines,
additional arguments cond and level are
added.
Several small bugs fixed.
LMMsolver 1.0.4
Improved computation time for calculation of standard errors.
Implementation in C++ and using the ‘sparse inverse’.
Row-wise Kronecker product for spam matrices
implemented in C++. Important for tensor product P-splines with improved
computation time and memory allocation.
LMMsolver 1.0.3
Improved computation time and memory allocation, especially
important for big data with many observations (the number of rows in the
data frame).
Replaced the default model.matrix function by
Matrix::sparse.model.matrix to generate sparse design
matrices.
In function obtainSmoothTrend the standard errors are
only calculated if includeIntercept = TRUE.
Several small bugs fixed.
LMMsolver 1.0.2
First and second order derivatives are now calculated
correctly.
Several small bugs fixed.
Updated tests to pass checks on macM1.
LMMsolver 1.0.1
weights argument in LMMsolve function added
Function obtainSmoothTrend returns in addition to the
predictions the standard errors.
Generalized Additive Model (GAM) added for one-dimensional splines,
i.e. more spl1D() components can be added to the
spline argument of LMMsolve function
Improved efficiency of calculating the sparse inverse using
super-nodes.
Replaced the original P-splines penalty D'D with a
scaled version which is far more stable if there are many knots.