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ncvreg
2025-04-03
Rutas de regularización para modelos de regresión penalizados con SCAD y MCP
3 years
Works with Finder
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Github Watches
28
Github Forks
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Regularization paths for MCP and SCAD penalized regression models
ncvreg is an R package for fitting regularization paths for linear
regression, GLM, and Cox regression models using lasso or nonconvex
penalties, in particular the minimax concave penalty (MCP) and smoothly
clipped absolute deviation (SCAD) penalty, with options for additional
L2 penalties (the "elastic net" idea). Utilities for carrying
out cross-validation as well as post-fitting visualization,
summarization, inference, and prediction are also provided.
- To get started using
ncvreg, see the "getting started" vignette - To learn more, follow the links under "Learn more" at the ncvreg website
- For details on the algorithms used by
ncvreg, see the original article: Breheny P and Huang J (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Annals of Applied Statistics, 5: 232–253 - For more about the marginal false discovery rate idea used for post-selection inference, see Breheny P (2019) Marginal false discovery rates for penalized regression models. Biostatistics, 20: 299-314 and Miller R and Breheny P (2023) Feature-specific inference for penalized regression using local false discovery rates. Statistics in Medicine, 42: 1412–1429.
- I also teach a course on high-dimensional data analysis; the lecture notes are publicly available and may be helpful, in particular the lectures on MCP/SCAD and marginal FDR.
Installation
To install the latest release version from CRAN:
install.packages("ncvreg")
To install the latest development version from GitHub:
remotes::install_github("pbreheny/ncvreg")
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Reviews
5
(1)
user_NQzHIWgo
2025-04-17
As a dedicated user of MCP application, I highly recommend ncvreg by pbreheny. This comprehensive tool offers efficient regularization paths for linear and logistic regression models, tailored especially for high-dimensional data. Its integration and functionality within the R environment are seamless, making it an invaluable resource for statistical analysis and research. Highly efficient and user-friendly!