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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 (wwaa0208@gmail.com)
• 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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.