NEWS | R Documentation |

add function applyFolds() to compute the optimal stopping iteration

allow for extrapolation in predict() with bbsc()

bugfix in bolsc(): correctly use index in bolsc() / bbsc(), before: for the computation of Z each observation was used only once

add function the penalty in one direction is zero

add function reweightData() that computes the data for Bootstrap or cross-falidation folds

add function stabsel.FDboost() that refits the smooth offset in each fold

add argument 'fun' to validateFDboost()

add update.FDboost() that overwrites update.mboost()

FDboost() works with family = Binomial()

fix oobpred in validateFDboost() for irregular response and resampling on the level of curves and thus plot.validateFDboost() works for that case

fix scope of formula in FDboost(): now the formula given to mboost() within FDboost() uses the variables in the environment of the formula specified in FDboost()

plot.FDboost() works for more effects, especially for effects like bolsc()

new operator anisotropic penalty for the special case where lambda1 or lambda2 is zero

the base-learner bbsc() can be used with center = TRUE, derived by Almond Stoecker

in FDboostLSS() a list of one-sided formulas can be specified for timeformula

FDboostLSS works with families = GammaLSS()

operator of blg1 and blg2 (which is the same as weights on rows and columns of the response matrix)

call to internal functions of mboost is done using mboost_intern()

hyper_olsc() is based on hyper_ols() of mboost

changed the operator The design matrix of the interaction effects is constrained such that the interaction is centred around the intercept and around the two main effects of the scalar covariates (experimental!); use e.g. as bols(x1)

changed the operator the design matrix resulting from the row-tensor product (experimental!), such that first a, intercept-column is added to the design-matrix and then the sum-to-zero constraint is applied, use e.g. as bolsc(x1)

use the functional index s as argsvals in the FPCA conducted within bfpc()

new operator

do not penalize in direction of ONEx in smooth intercept specified implicitly by ~1, as bols(ONEx, intercept=FALSE, df=1)

do not expand an effect that contains effects in the model

add the function FDboostLSS() to fit GAMLSS models with functional data using R-package gamboostLSS

new operator the design matrix resulting from the row-tensor product (experimental!)

allow newdata to be a list in predict.FDboost() in combination with signal base-learners

expand coef.FDboost() such that it works for 3-dimensional tensor products of with bhistx() the form bhistx() %X% bolsc() %X% bolsc() (with David Ruegamer)

add a new possibility for scalar-on-function regression: for timeformula=NULL, no Kronecker-product with 1 is used, which changes the penalty as otherwise in the direction of 1 is penalized as well.

new dependency on R-package gamboostLSS

remove dependency on R-package MASS

use the argument 'prediction' in the internal computation of the base-learners (work in progress)

throw an error if 'timeLab' of the hmatrix-object in bhistx() is not equal to the time-variable in 'timeformula'.

in function FDboost() the offset is supplied differently, for a scalar offset, use offset = "scalar", the default is still the same offset=NULL

predict.FDboost() has new argument toFDboost (logical)

fitted.FDboost() has argument toFDboost explicitly and not only in ...

new base-learner bhistx() especially suited for effects with

coef.FDboost() and plot.FDboost() suited for effects like bhistx

for predict.FDboost() with effects bhistx() and newdata the latest mboostPatch is necessary

check for necessity of smooth offset works for missings in regular response (spotted by Tore Erdmann)

Internal experimental version.

integrationWeights() gives equal weights for regular grids

new base-learner bfpc() for a functional covariate where functional covariate and the coeffcient are both expanded using fPCA (experimental feature!); only works for regularly observed functional covariate.

the function coef.FDboost() only works for bhist() if the time variable is the same in the timeformula and in bhist()

predict.FDboost() has a check that for newdata only type="link" can be predicted

change the default in difference-penalties to first order difference penalty differences=1, as then the effects are better identifiable

new method cvrisk.FDboost() that uses per default sampling on the levels of curves, which is important for functional response

reorganize documentation of cvrisk() and validateFDboost()

in bhist(): effect can be standardized

add a CITATION file

use mboost 2.4-2 as it exports all important functions

main argument is always passed in plot.FDboost()

bhist() and bconcurrent() work for equal time and s

predict.FDboost() works with tensor-product base-learners bl1 %X% bl2