Skip to content

Latest commit

 

History

History
108 lines (86 loc) · 4.04 KB

NEWS.md

File metadata and controls

108 lines (86 loc) · 4.04 KB
Raw

biglasso 1.6.0

  • New: functions biglasso_fit() and biglasso_path(), which allow users to turn off standardization and intercept

biglasso 1.5.2

  • Update coercion for compatibility with Matrix 1.5
  • Now using GitHub Actions instead of Travis for CI

biglasso 1.5.1

  • Internal Cpp changes: initialize Xty, remove unused cutoff variable (#48)
  • Eliminate CV test against ncvreg (the two packages no longer use the same approach (#47)

biglasso 1.5.0

  • Update headers to maintain compatibility with new version of Rcpp (#40)

biglasso 1.4-1

  • changed R package maintainer to Chuyi Wang ([email protected])
  • fixed bugs
  • Add 'auc', 'class' options to cv.biglasso eval.metric
  • predict.cv now predicts standard error over CV folds by default; set 'grouped' argument to FALSE for old behaviour.
  • predict.cv.biglasso accepts 'lambda.min', 'lambda.1se' argument, similar to predict.cv.glmnet()

biglasso 1.4-0

  • adaptive screening methods were implemented and set as default when applicable
  • added sparse Cox regression
  • removed uncompetitive screening methods and combined naming of screening methods
  • version 1.4-0 for CRAN submission

biglasso 1.3-7

  • update email to personal email
  • coef(cvfit) returns only nonzero cells, as a labelled vector
  • set HSR rules as default
  • option for non-standardization

biglasso 1.3-6

  • optimized the code for computing the slores rule.
  • added Slores screening without active cycling (-NAC) for logistic regression, research usage only.
  • corrected BEDPP for elastic net.
  • fixed a bug related to "exporting SSR-BEDPP".

biglasso 1.3-5

  • redocumented using Roxygen2.
  • registered native routines for faster and more stable performance.

biglasso 1.3-4

  • fixed a bug related to dfmax option. (thanks you Florian Privé!)

biglasso 1.3-3

  • fixed bugs related to KKT checking for elastic net. (thanks you Florian Privé!)
  • added references for screening rules and the technical paper of biglasso package.

biglasso 1.3-2

  • added screening methods without active cycling (-NAC) for comparison, research usage only.
  • fixed a bug related to numeric comparison in Dome test.

biglasso 1.3-1

  • fixed bug in SSR-Slores related to numeric equality comparison.

biglasso 1.3-0

  • version 1.3-0 for CRAN submission.

biglasso 1.2-6

  • added a newly proposed screening rule, SSR-Slores, for lasso-penalized logistic regression.
  • added SSR-BEDPP for elastic-net-penalized linear regression.

biglasso 1.2-5

  • updated README.md with benchmarking results.
  • added tutorial (vignette).

biglasso 1.2-4

  • added gaussian.cpp: solve lasso without screening, for research only.
  • added tests.

biglasso 1.2-3

  • changed convergence criteria of logistic regression to be the same as that in glmnet.
  • optimized source code; preparing for CRAN submission.
  • fixed memory leaks occurred on Windows.

biglasso 1.2-2

  • added internal data set: the colon cancer data.

biglasso 1.2-1

  • Implemented another new screening rule (SSR-BEDPP), also combining hybrid strong rule with a safe rule (BEDPP).
  • implemented EDPP rule with active set cycling strategy for linear regression.
  • changed convergence criteria to be the same as that in glmnet.

biglasso 1.1-2

  • fixed bugs occurred when some features have identical values for different observations. These features are internally removed from model fitting.

biglasso 1.1-1

  • Three sparse screening rules (SSR, EDPP, SSR-Dome) were implemented. Our new proposed HSR-Dome combines HSR and Dome test for feature screening, leading to even better performance as compared to 'glmnet'.
  • OpenMP parallel computing was added to speedup single model fitting.
  • Both exact Newton and majorization-minimization (MM) algorithm for logistic regression were implemented. The latter could be faster, especially in data-larger-than-RAM cases.
  • Source code were rewritten in pure cpp.
  • Sparse matrix representation was added using Armadillo library.

biglasso 1.0-1

  • package ready for CRAN submission.