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| refund-package | Regression with Functional Data |
| af | Construct an FGAM regression term |
| amc | Additive model with constraints |
| ccb.fpc | Corrected confidence bands using functional principal components |
| cd4 | Observed CD4 cell counts |
| checkError | Internal functions for the refund package |
| coef.pffr | Get estimated coefficients from a pffr fit |
| coefboot.pffr | Simple bootstrap CIs for pffr |
| decorrelate | Internal functions for the refund package |
| DTI | Diffusion Tensor Imaging: tract profiles and outcomes |
| DTI2 | Diffusion Tensor Imaging: more fractional anisotropy profiles and outcomes |
| expand.call | Return call with all possible arguments |
| fbps | Sandwich smoother for matrix data |
| ff | Construct a function-on-function regression term |
| ffpc | Construct a PC-based function-on-function regression term |
| ffpcplot | Plot PC-based function-on-function regression terms |
| fgam | Functional Generalized Additive Models |
| first.last_test | Internal functions for the refund package |
| fitted.pffr | Obtain fitted values for a pffr fit |
| fosr | Function-on-scalar regression |
| fosr.perm | Permutation testing for function-on-scalar regression |
| fosr.perm.fit | Permutation testing for function-on-scalar regression |
| fosr.perm.test | Permutation testing for function-on-scalar regression |
| fosr2s | Two-step function-on-scalar regression |
| fpca.face | Functional principal component analysis with fast covariance estimation |
| fpca.sc | Functional principal components analysis by smoothed covariance |
| fpca.ssvd | Smoothed FPCA via iterative penalized rank one SVDs. |
| fpca2s | Functional principal component analysis by a two-stage method |
| fpcr | Functional principal component regression |
| fpcr.setup | Internal functions for the refund package |
| gasoline | Octane numbers and NIR spectra of gasoline |
| getEst | Extract coefficient function estimate from a fitted fgam model |
| getL | Internal functions for the refund package |
| getNPC.DonohoGavish | Internal functions for the refund package |
| getRsq | Internal functions for the refund package |
| getShrtlbls | Internal functions for the refund package |
| getSpandDist | Internal functions for the refund package |
| imwd_test | Internal functions for the refund package |
| irreg2mat | Internal functions for the refund package |
| lf | Construct an FLM regression term |
| lf.vd | Construct a VDFR regression term |
| list2df | Internal functions for the refund package |
| lofocv | Leave-one-function-out cross-validation |
| lpeer | Longitudinal Functional Models with Structured Penalties |
| lpfr | Longitudinal penalized functional regression |
| lw.test | Internal functions for the refund package |
| model.matrix.pffr | Obtain model matrix for a pffr fit |
| Omegas | Internal functions for the refund package |
| osplinepen2d | Internal functions for the refund package |
| parse.predict.pfr | Internal functions for the refund package |
| pcre | pffr-constructor for functional principal component-based functional random intercepts. |
| peer | Functional Models with Structured Penalties |
| PEER.Sim | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function |
| pffr | Penalized function-on-function regression |
| pffrGLS | Penalized function-on-function regression with non-i.i.d. residuals |
| pffrSim | Simulate example data for pffr |
| pfr | Penalized functional regression and Longitudinal penalized functional regression |
| plot.fosr | Default plotting of function-on-scalar regression objects |
| plot.fosr.perm | Permutation testing for function-on-scalar regression |
| plot.fpcr | Default plotting for functional principal component regression output |
| plot.lpeer | Plotting of estimated regression functions obtained through 'lpeer()' |
| plot.peer | Plotting of estimated regression functions obtained through 'peer()' |
| plot.pffr | Plot a pffr fit |
| postprocess.pfr | Internal functions for the refund package |
| predict.fgam | Prediction from a fitted FGAM model |
| Predict.matrix.pcre.random.effect | mgcv-style constructor for prediction of PC-basis functional random effects |
| Predict.matrix.pss.smooth | Internal functions for the refund package |
| predict.pffr | Prediction for penalized function-on-function regression |
| predict.pfr | Prediction for penalized functional regression |
| preprocess.pfr | Internal functions for the refund package |
| print.summary.pffr | Print method for summary of a pffr fit |
| pspline.setting | Internal functions for the refund package |
| pwcv | Pointwise cross-validation for function-on-scalar regression |
| Q | Simulated longitudinal data with functional predictor and scalar response, and structural information associated with predictor function |
| refund | Regression with Functional Data |
| residuals.pffr | Obtain residuals for a pffr fit |
| rlrt.pfr | Likelihood Ratio Test and Restricted Likelihood Ratio Test for inference of functional predictors |
| safeDeparse | Internal functions for the refund package |
| sff | Construct a smooth function-on-function regression term |
| smooth.construct.pcre.smooth.spec | mgcv-style constructor for PC-basis functional random effects |
| smooth.construct.pss.smooth.spec | P-spline constructor with modified 'shrinkage' penalty |
| summary.pffr | Summary for a pffr fit |
| vis.fgam | Visualization of FGAM objects |