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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.
- Several bugs fixed.
LMMsolver 1.0.0
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.