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Changelog

Source: NEWS.md

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

CRAN release: 2024-04-16

  • 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

CRAN release: 2023-11-27

  • 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

CRAN release: 2023-04-14

  • 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

CRAN release: 2022-12-15

  • 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

CRAN release: 2022-08-19

  • 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

CRAN release: 2022-04-21

  • First and second order derivatives are now calculated correctly.
  • Several small bugs fixed.
  • Updated tests to pass checks on macM1.

LMMsolver 1.0.1

CRAN release: 2022-03-28

  • 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

CRAN release: 2021-11-02

  • Initial CRAN version