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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.
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