Sparse Multiple Index Models for Nonparametric Forecasting

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Documentation for package ‘smimodel’ version 0.1.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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'