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hdnom 5.9.9000

This version is a major refactor of the package, with several technical adjustments to improve the function interface, code structure, and performance. As a result, a few API-breaking changes have been made. Please update your previous code that calls hdnom accordingly. For the detailed changes, check the updated items below.

Improvements

General

  • Renamed exported functions. Most of the exported function have been renamed to be more meaningful and succinct. For example, hdcox.*() are renamed as fit_*(), hdnom.nomogram() is renamed as as_nomogram(), hdnom.validate() is renamed as validate(), and so on.
  • Removed the dependency on rms, by reusing a minimal set of code from rms for nomogram construction and plotting. This results in clearer code structure, better maintainability, and faster package installation/loading speed. Also removed other non-essential package dependencies.
  • The first argument for print functions are now returned invisbily, to make it easier to use them in a pipe.

Model Fitting

  • The components in the model fitting function returns are now unified across model types. For example, the model object can all be accessed by fit$model, and the selected "optimal" hyperparameters can be accessed by fit$lambda. The model type is now stored explicitly as fit$type.

Nomograms

  • as_nomogram (previously hdnom.nomogram()) now accepts the fitted model objects directly instead of the $model component. It now will recognize the model type automatically, thus the previous arguments model.type has been deprecated. so that it is easier to chain the function calls together using magrittr.
  • In as_nomogram, the previous ddist argument is not needed anymore and has been removed. There is also no more need to set a datadist object as a into the global options variable (which was required in the rms user flow).
  • The new nomogram implementation prints and plots the nomogram for the penalized regression models directly. This supersedes the old implementation, which fits an OLS model to regress the linear predictors on the same set of predictors selected by the penalized Cox regression model, aiming to approximate the penalized model. The numerical or visual difference is minimal, though.

Visualizations

  • Add a new ggplot2 theme theme_hdnom() and applies it to most of the validation, calibration, and comparison plots for a consistent, cleaner look across plots within the package.

hdnom 5.0 (2018-05-13)

Improvements

  • Exported the survival curve prediction functions (glmnet.survcurve(), ncvreg.survcurve(), penalized.survcurve()) and Breslow baseline hazard estimator functions (glmnet.basesurv(), ncvreg.basesurv(), penalized.basesurv()).
  • New URL for the documentation website: https://nanx.me/hdnom/.

hdnom 4.9 (2017-09-28)

Improvements

  • Use system font stack instead of Google Fonts in vignettes to avoid pandoc SSL issue.

hdnom 4.8 (2017-03-25)

Improvements

  • Reduced example running time to less than 10s for hdnom.calibrate().

hdnom 4.7 (2017-03-24)

Improvements

  • Better code indentation style.
  • Reduced example running time for fused lasso.
  • Updated gallery images in README.md.
  • HTTPS enabled for the website.

hdnom 4.6 (2017-01-07)

Bug Fixes

  • Fixed issues in parameter tuning and cross-validation procedures for fused lasso models (afc49c9). The user-visible change is that two parameters lambda1 and lambda2 instead of a single "lambda" are now required to fit, validate, and calibrate fused lasso models.

Improvements

  • The argument lambda in hdnom.nomogram is no longer needed and has been deprecated.
  • Allow users to specify eps and max.iter for MCP and SCAD penalty related models. Setting the default values to be 1e-4 and 10000, which is consistent with ncvreg 3.8-0.

hdnom 4.5 (2016-12-24)

Bug Fixes

  • Fixed vanishing axis problem in Kaplan-Meier plot hdnom.kmplot() under ggplot2 2.2.0, which is caused by a previous workaround for a bug introduced in ggplot2 2.1.0.
  • Fixed potential convergence issues for examples under ncvreg >= 3.7-0 new convergence criterion, by increasing max.iter for ncvsurv to a substantially higher value (5e+4).
  • Fixed single lambda support issues in ncvsurv under ncvreg >= 3.7-0.

Improvements

  • Added Windows continuous integration using AppVeyor.
  • New website design for project website: consistent with the web application hdnom.io.

hdnom 4.0 (2016-07-05)

Improvements

  • More concrete examples for several functions.
  • Introduce argument ylim for plot.hdnom.validate(), plot.hdnom.external.validate(), and plot.hdnom.compare.validate() (#4).

hdnom 3.7 (2016-03-25)

Bug Fixes

  • Removed one redundant color from the lancet color palette.

hdnom 3.6 (2016-03-24)

New Features

  • Added 4 new color palettes (JCO, Lancet, NPG, AAAS) for all plots. See the vignette for details.

Bug Fixes

  • Fixed vanishing axis problem in Kaplan-Meier plot due to ggplot2 2.1.0 update.

Improvements

  • New CSS style for the HTML vignette.

hdnom 3.0 (2016-01-03)

New Features

Package

  • New function hdnom.compare.validate() for model comparison by validation
  • New function hdnom.compare.calibrate() for model comparison by calibration
  • New function hdnom.external.validate() for external validation
  • New function hdnom.external.calibrate() for external calibration
  • New predict and print methods for hdcox.model objects
  • New function hdnom.kmplot(): Kaplan-Meier analysis for risk groups using internal/external calibration results
  • New function hdnom.logrank(): Log-rank test for risk groups using internal/external calibration results

Web Application

  • The web application is substantially improved to reflect the new features
  • Record random seeds in the generated reports to improve reproducibility
  • Allow users to download the model objects for further exploration in R

Improvements

  • Improvements on random seed handling for internal validation and calibration
  • The vignette is extended to reflect the new features

Bug Fixes

  • Fixed an error in internal calibration which mistakenly used testing data when should use training data in computation

hdnom 2.1 (2015-10-26)

New Features

Improvements

  • Added exception handling for null models in all hdcox.*() functions. Make examples compatible with ncvreg 3.5-0, which refined CV implementation for survival models and improved computation speed.

hdnom 2.0 (2015-09-15)

New Features

  • Support five more high-dimensional penalized Cox model types:

    • Fused lasso
    • MCP
    • Mnet
    • SCAD
    • Snet

hdnom 1.2 (2015-08-27)

Improvements

  • Reduced example running time for hdnom.validate(), hdnom.calibrate(), hdcox.aenet(), and hdcox.enet() by reducing resampling times.

hdnom 1.1 (2015-08-27)

Bug Fixes

  • Added argument parallel to hdcox.aenet() and hdcox.enet() to enable or disable the use of parallel parameter tuning.

hdnom 1.0 (2015-08-04)

New Features

  • Initial version
  • Nomograms for glmnet models
  • Validation for glmnet models
  • Calibration for glmnet models
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