An R Package for Supervised Dimension Reduction

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Documentation for package ‘sdim’ version 0.1.0

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

Help Pages

estimate_ardl_multi	Estimate ARDL(p1, p2) model
estimate_ar_res	Estimate AR(p) model
eval_factors	Evaluate extracted factors against target returns
grunfeld	Grunfeld (1958) investment dataset
he2023_dacheng202	Dacheng 202-portfolio value-weighted returns from He, Huang, Li, Zhou (2023)
he2023_factors	Factor proxies from He, Huang, Li, Zhou (2023)
he2023_ff17vw	Fama-French 17-industry value-weighted portfolios from He, Huang, Li, Zhou (2023)
he2023_ff30vw	Fama-French 30-industry value-weighted portfolios from He, Huang, Li, Zhou (2023)
he2023_ff48ew	Fama-French 48-industry equal-weighted portfolios from He, Huang, Li, Zhou (2023)
he2023_ff48vw	Fama-French 48-industry value-weighted portfolios from He, Huang, Li, Zhou (2023)
he2023_ff5	Fama-French 5-factor data from He, Huang, Li, Zhou (2023)
huang2022_ip	Industrial production growth from Huang, Jiang, Li, Tong, Zhou (2022)
huang2022_macro	FRED-MD macro predictors from Huang, Jiang, Li, Tong, Zhou (2022)
ipca_est	IPCA factor extraction
oos_standardize	Standardize columns to zero mean and unit variance
pca_est	PCA factor extraction
pls_est	PLS factor extraction (Matlab-faithful NIPALS algorithm)
predict.sdim_fit	Project new data onto estimated factor loadings
predict.sdim_spca	Project new data onto estimated sPCA factor loadings
rra_est	Reduced-Rank Approach (RRA) factor extraction
select_ar_lag_sic	Select AR lag order by SIC (BIC)
spca_est	Scaled PCA factor extraction