NEWS.md

In biglasso: Extending Lasso Model Fitting to Big Data

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 ([e-mail])
• 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.

biglasso documentation built on May 29, 2024, 1:50 a.m.