MAE                     Point estimate accuracy measures
allpred_index           Constructing index coefficient vectors with all
                        predictors in each index
augment.backward        Augment function for class 'backward'
augment.gaimFit         Augment function for class 'gaimFit'
augment.gamFit          Augment function for class 'gamFit'
augment.lmFit           Augment function for class 'lmFit'
augment.pprFit          Augment function for class 'pprFit'
augment.smimodel        Augment function for class 'smimodel'
augment.smimodelFit     Augment function for class 'smimodelFit'
autoplot.smimodel       Plot estimated smooths from a fitted 'smimodel'
avgCoverage             Calculate interval forecast coverage
avgWidth                Calculate interval forecast width
bb_cvforecast           Single season block bootstrap prediction
                        intervals through time series cross-validation
                        forecasting
blockBootstrap          Futures through single season block
                        bootstrapping
cb_cvforecast           Conformal bootstrap prediction intervals
                        through time series cross-validation
                        forecasting
eliminate               Eliminate a variable and fit a nonparametric
                        additive model
forecast.backward       Forecasting using nonparametric additive models
                        with backward elimination
forecast.gaimFit        Forecasting using GAIMs
forecast.gamFit         Forecasting using GAMs
forecast.pprFit         Forecasting using PPR models
forecast.smimodel       Forecasting using SMI models
greedy.fit              Greedy search for tuning penalty parameters
greedy_smimodel         SMI model estimation through a greedy search
                        for penalty parameters
init_alpha              Initialising index coefficients
inner_update            Updating index coefficients and non-linear
                        functions iteratively
lag_matrix              Function for adding lags of time series
                        variables
loss                    Calculating the loss of the MIP used to
                        estimate a SMI model
make_smimodelFit        Converting a fitted 'gam' object to a
                        'smimodelFit' object
model_backward          Nonparametric Additive Model with Backward
                        Elimination
model_gaim              Groupwise Additive Index Models (GAIM)
model_gam               Generalised Additive Models
model_lm                Linear Regression models
model_ppr               Projection Pursuit Regression (PPR) models
model_smimodel          Sparse Multiple Index (SMI) Models
new_smimodelFit         Constructor function for the class
                        'smimodelFit'
normalise_alpha         Scaling index coefficient vectors to have unit
                        norm
possibleFutures_benchmark
                        Possible future sample paths (multi-step) from
                        residuals of a fitted benchmark model
possibleFutures_smimodel
                        Possible future sample paths (multi-step) from
                        'smimodel' residuals
predict.backward        Obtaining forecasts on a test set from a fitted
                        'backward'
predict.gaimFit         Obtaining forecasts on a test set from a fitted
                        'gaimFit'
predict.gamFit          Obtaining forecasts on a test set from a fitted
                        'gamFit'
predict.lmFit           Obtaining forecasts on a test set from a fitted
                        'lmFit'
predict.pprFit          Obtaining forecasts on a test set from a fitted
                        'pprFit'
predict.smimodel        Obtaining forecasts on a test set from a fitted
                        'smimodel'
predict.smimodelFit     Obtaining forecasts on a test set from a
                        'smimodelFit'
predict_gam             Obtaining recursive forecasts on a test set
                        from a fitted 'mgcv::gam'
prep_newdata            Prepare a data set for recursive forecasting
print.backward          Printing a 'backward' object
print.gaimFit           Printing a 'gaimFit' object
print.pprFit            Printing a 'pprFit' object
print.smimodel          Printing a 'smimodel' object
print.smimodelFit       Printing a 'smimodelFit' object
randomBlock             Randomly sampling a block
remove_lags             Remove actual values from a data set for
                        recursive forecasting
residBootstrap          Generate multiple single season block bootstrap
                        series
residuals.smimodel      Extract residuals from a fitted 'smimodel'
scaling                 Scale data
seasonBootstrap         Single season block bootstrap
smimodel-package        smimodel: Sparse Multiple Index Models for
                        Nonparametric Forecasting
smimodel.fit            SMI model estimation
split_index             Splitting predictors into multiple indices
truncate_vars           Truncating predictors to be in the in-sample
                        range
tune_smimodel           SMI model with a given penalty parameter
                        combination
unscaling               Unscale a fitted 'smimodel'
update_alpha            Updating index coefficients using MIP
update_smimodelFit      Updating a 'smimodelFit'
