NEWS

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FDboost 1.1-4 (2026-03-24)

Bug fixes

• Suppressed deprecation warnings from refund::pffrSim() in examples to keep checks clean with recent refund versions.
• Stabilized tests/general_tests.R by forcing serial evaluation (mc.cores = 1) to avoid parallel applyFolds() crashes on some check platforms.

FDboost 1.1.0

Miscellaneous

• Anisotropic tensor-product operators b1 %A0% b2 and b1 %Xa0% b2 now also work when lambda is specified for b1 and df is specified for b2 (or vice versa).

New features

• New function clr() to compute the centered-log-ratio transform and its inverse for density-on-scalar regression in Bayes spaces.
• New dataset birthDistribution.
• New vignette illustrating density-on-function regression on the birthDistribution data.
• Function factorize() added for tensor-product factorization of estimated effects or models.

FDboost 0.3.4

Bug fixes

• Fix predict() for bsignal() with newdata and the functional covariate given as a numeric matrix, raised in #17.
• Deprecated argument LINPACK in solve() removed.

FDboost 0.3.3

New features

• It is now possible to specify several time variables as well as factor time variables in the timeformula. This feature is needed for the manifoldboost package.

Miscellaneous

• The function stabsel.FDboost() now uses applyFolds() instead of validateFDboost() to do cross-validation with recomputation of the smooth offset. This is only relevant for models with a functional response. This will change results if the model contains base-learners like bbsc() or bolsc(), as applyFolds() also recomputes the Z-matrix for those base-learners.

Bug fixes

• Adapted functions integrationWeights() and integrationWeightsLeft() for unsorted time variables.
• Changed code in predict.FDboost() such that interaction effects of two functional covariates like bsignal() %X% bsignal() can be predicted with new data.
• Adapt FDboost to R 4.0.1 by explicitly using the first entry of dots$aggregate (i.e., dots$aggregate[1] != "sum") in predict.FDboost() so that it also works with the default, where aggregate is a vector of length 3 and later only the first argument is used via match.arg().

FDboost 0.3.2

Bug fixes

• Deprecated argument corrected in cvrisk() removed.

FDboost 0.3.1

Bug fixes

• cvrisk() has by default adequate folds for a noncyclic fitted FDboostLSS model, see #14.

Miscellaneous

• Replaced cBind() (which is deprecated) with cbind().

FDboost 0.3.0

User-visible changes

• New function bootstrapCI() to compute bootstrapped coefficients.
• Added the dataset emotion containing EEG and EMG measures under different experimental conditions.
• With scalar response, FDboost() now works with the response as a vector (instead of a 1-row matrix); thus, fitted() and predict() return a vector.

Bug fixes

• update.FDboost() now works with a scalar response.
• FDboost() works with family Binomial(type = "glm"), see #1.
• applyFolds() works for factor response, see #7.
• cvLong() and cvMA() return a matrix for only one resampling fold with B = 1 (proposed by Almond Stoecker).

Miscellaneous

• Adapt FDboost to mboost 2.8-0, which allows for mstop = 0.
• Restructure FDboostLSS() such that it calls mboostLSS_fit() from gamboostLSS 2.0-0.
• In FDboost, set options("mboost_indexmin" = +Inf) to disable internal use of ties in model fitting, as this breaks some methods for models with responses in long format and for models containing bhistx(), see #10.
• Deprecated validateFDboost(), use applyFolds() and bootstrapCI() instead.

FDboost 0.2.0

User-visible changes

• Added function applyFolds() to compute the optimal stopping iteration.

Bug fixes

• Allows for extrapolation in predict() with bbsc().

FDboost 0.1.2

Bug fixes

• Fixed a bug in bolsc(): correctly use the index in bolsc()/bbsc(). Previously, each observation was used only once for computing Z.

User-visible changes

• Added function %Xa0% that computes a row-tensor product of two base-learners where the penalty in one direction is zero.
• Added function reweightData() that computes the data for Bootstrap or cross-validation folds.
• Added function stabsel.FDboost() that refits the smooth offset in each fold.
• Added argument fun to validateFDboost().
• Added update.FDboost() that overwrites update.mboost().

Miscellaneous

• FDboost() works with family = Binomial().

FDboost 0.1.1

Bug fixes

• Fixed oobpred in validateFDboost() for irregular response and resampling at the curve level so that plot.validateFDboost() works for that case.
• Fixed 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().

Miscellaneous

• plot.FDboost() works for more effects, especially for effects like bolsc() %X% bhistx().

FDboost 0.1.0

User-visible changes

• New operator %A0% for Kronecker product of two base-learners with an 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.

Bug fixes

• FDboostLSS() works with families = GammaLSS().

Miscellaneous

• Operator %A% uses weights in the model call. This only works correctly for weights on the level of blg1 and blg2 (same as weights on rows and columns of the response matrix).
• Calls to internal functions of mboost are done using mboost_intern().
• hyper_olsc() is based on hyper_ols() from mboost.

FDboost 0.0.17

User-visible changes

• Changed the operator %Xc% for the row tensor product of two scalar covariates. The design matrix of the interaction effects is constrained such that the interaction is centered around the intercept and around the two main effects of the scalar covariates (experimental!). Use, for example, bols(x1) %Xc% bols(x2).

FDboost 0.0.16

User-visible changes

• Changed the operator %Xc% for row tensor product where the sum-to-zero constraint is applied to the design matrix resulting from the row-tensor product (experimental!). Specifically, an intercept-column is first added, and then the sum-to-zero constraint is applied. Use, for example, bolsc(x1) %Xc% bolsc(x2).
• The functional index s is now used as argsvals in the FPCA conducted within bfpc().

FDboost 0.0.15

User-visible changes

• New operator %A% that implies anisotropic penalties for differently specified df in the two base-learners.

Bug fixes

• No penalty is applied in the direction of ONEx in a smooth intercept specified implicitly by ~1, for example, bols(ONEx, intercept=FALSE, df=1) %A% bbs(time).

Miscellaneous

• Effects containing %A% or %O% are not expanded with the timeformula, allowing for different effects over time in the model.

FDboost 0.0.14

User-visible changes

• Added the function FDboostLSS() to fit GAMLSS models with functional data using R-package gamboostLSS.
• New operator %Xc% for row tensor product where the sum-to-zero constraint is applied to the design matrix resulting from the row-tensor product (experimental!).
• Allowed newdata to be a list in predict.FDboost() when used with signal base-learners.
• Expanded coef.FDboost() so that it works for 3-dimensional tensor products of the form bhistx() %X% bolsc() %X% bolsc() (with David Ruegamer).
• Added a new possibility for scalar-on-function regression: if timeformula=NULL, no Kronecker product with 1 is used, which changes the penalty (otherwise, the direction of 1 would also be penalized).

Miscellaneous

• New dependency on R-package gamboostLSS.
• Removed dependency on R-package MASS.
• Used 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.

FDboost 0.0.13

User-visible changes

• In function FDboost(), the offset is supplied differently. For a scalar offset, use offset = "scalar". The default remains offset = NULL.
• predict.FDboost() has a new argument toFDboost (logical).
• fitted.FDboost() has argument toFDboost explicitly (not only via ...).
• New base-learner bhistx(), especially suited for effects used with %X%, e.g., bhistx() %X% bolsc().
• coef.FDboost() and plot.FDboost() now handle effects like bhistx() %X% bolsc().
• For predict.FDboost() with effects bhistx() and newdata, the latest mboostPatch is necessary.

Bug fixes

• The check for the necessity of a smooth offset works for missing values in a regular response (spotted by Tore Erdmann).

FDboost 0.0.12

• Internal experimental version.

FDboost 0.0.11

User-visible changes

• integrationWeights() now gives equal weights for regular grids.
• New base-learner bfpc() for a functional covariate where both the functional covariate and the coefficient are expanded using fPCA (experimental feature!). Only works for regularly observed functional covariate.

Bug fixes

• coef.FDboost() only works for bhist() if the time variable is the same in the timeformula and in bhist().
• predict.FDboost() now checks that only type = "link" can be predicted for newdata.

FDboost 0.0.10

User-visible changes

• Changed the default difference penalties to first-order difference (differences = 1), improving identifiability.
• New method cvrisk.FDboost() that uses (by default) sampling on the levels of curves, which is important for functional responses.
• Reorganized documentation of cvrisk() and validateFDboost().
• In bhist(), an effect can be standardized.

Miscellaneous

• Added a CITATION file.
• Uses mboost 2.4-2, which exports all important functions.

Bug fixes

• main argument is always passed in plot.FDboost().
• bhist() and bconcurrent() now work for equal time and s.
• predict.FDboost() works with tensor-product base-learners like bl1 %X% bl2.