Package: sdim 0.1.0.9000

Gabriel Cabrera

sdim: Supervised Dimension Reduction for Forecasting

Implements five factor extraction methods for asset pricing and macroeconomic forecasting: principal component analysis (PCA), partial least squares (PLS), scaled PCA (sPCA) of Huang, Jiang, Li, Tong, and Zhou (2022) <doi:10.1287/mnsc.2021.4020>, the reduced-rank approach (RRA) of He, Huang, Li, and Zhou (2023) <doi:10.1287/mnsc.2022.4563>, and Instrumented PCA (IPCA) of Kelly, Pruitt, and Su (2019) <doi:10.1016/j.jfineco.2019.05.001>.

Authors:Gabriel Cabrera [aut, cre]

sdim_0.1.0.9000.tar.gz
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sdim_0.1.0.9000.tgz(r-4.6-x86_64)sdim_0.1.0.9000.tgz(r-4.6-arm64)sdim_0.1.0.9000.tgz(r-4.5-x86_64)sdim_0.1.0.9000.tgz(r-4.5-arm64)
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sdim_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
sdim/json (API)

# Install 'sdim' in R:
install.packages('sdim', repos = c('https://gabbocg.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gabbocg/sdim/issues

Pkgdown/docs site:https://gabbocg.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • grunfeld - Grunfeld (1958) investment dataset
  • he2023_dacheng202 - Dacheng 202-portfolio value-weighted returns from He, Huang, Li, Zhou
  • he2023_factors - Factor proxies from He, Huang, Li, Zhou
  • he2023_ff17vw - Fama-French 17-industry value-weighted portfolios from He, Huang, Li, Zhou
  • he2023_ff30vw - Fama-French 30-industry value-weighted portfolios from He, Huang, Li, Zhou
  • he2023_ff48ew - Fama-French 48-industry equal-weighted portfolios from He, Huang, Li, Zhou
  • he2023_ff48vw - Fama-French 48-industry value-weighted portfolios from He, Huang, Li, Zhou
  • he2023_ff5 - Fama-French 5-factor data from He, Huang, Li, Zhou
  • huang2022_ip - Industrial production growth from Huang, Jiang, Li, Tong, Zhou
  • huang2022_macro - FRED-MD macro predictors from Huang, Jiang, Li, Tong, Zhou

On CRAN:

Conda:

openblascpp

5.20 score 1 stars 10 exports 2 dependencies

Last updated from:2a12ce9f88. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK152
linux-devel-x86_64OK158
source / vignettesOK204
linux-release-arm64OK148
linux-release-x86_64OK145
macos-release-arm64OK136
macos-release-x86_64OK420
macos-oldrel-arm64OK189
macos-oldrel-x86_64OK428
windows-develOK111
windows-releaseOK115
windows-oldrelOK107
wasm-releaseOK129

Exports:estimate_ar_resestimate_ardl_multieval_factorsipca_estoos_standardizepca_estpls_estrra_estselect_ar_lag_sicspca_est

Dependencies:RcppRcppArmadillo

Replicating He et al. (2023)
Setup | Replication | Results | References
Rendered fromhe2023-table3.Rmdusingknitr::rmarkdown

Last update: 2026-05-28
Started: 2026-04-22

Replicating Huang et al. (2022)
Data | Methodology | Out-of-sample loop | Results | Key spca_est() features used | References
Rendered fromhuang2022-table4.Rmdusingknitr::rmarkdown

Last update: 2026-05-28
Started: 2026-04-22

Get started with sdim
Overview | Quick start | PCA, PLS, and RRA | Scaled PCA | IPCA | Prediction | Factor evaluation | Bundled datasets
Rendered fromsdim.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-04-22

IPCA with the Grunfeld dataset
The IPCA model | Data preparation | Fitting IPCA | Validation against the Python ipca package | Multiple factors | References
Rendered fromipca-grunfeld.Rmdusingknitr::rmarkdown

Last update: 2026-05-27
Started: 2026-04-23