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Showing content from http://cran.rstudio.com/web/packages/Rcpp/../RcppThread/../rEDM/../Rcpp/../LMMsolver/news/news.html below:

NEWS

• 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.

• Binomial response can now also be modelled as fixed = cbind(failure, succes)
• Categorial 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.

• 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)

• 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.

• 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.

• 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.

• 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.

• 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.

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

• 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.

• Initial CRAN version

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