R: Sparse Multiple Index Models for Nonparametric Forecasting

# Sparse Multiple Index Models for Nonparametric Forecasting

## Documentation for package ‘smimodel’ version 0.1.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

smimodel-package | smimodel: Sparse Multiple Index Models for Nonparametric Forecasting
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
MAE | Point estimate accuracy measures
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
MSE | Point estimate accuracy measures
new_smimodelFit | Constructor function for the class 'smimodelFit'
normalise_alpha | Scaling index coefficient vectors to have unit norm
point_measures | Point estimate accuracy measures
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 | 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'