NEWS

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penaltyLearning 2024.9.3 (2024-10-02)

• change error() to Rf_error() for R-4.5 R_NO_REMAP.

penaltyLearning 2024.1.25 (2024-02-01)

• now ok to have length(models.vars)>1 (was an un-informative error 'length = 2' in coercion to 'logical(1)' in recent versions of R).

penaltyLearning 2023.8.31 (2023-09-06)

• update un-exported fun arg docs to avoid CRAN NOTE.

penaltyLearning 2021.4.21

• Stop with an error for non-finite predictions.

penaltyLearning 2019.12.3

• test/fix modelSelection for non-monotonic sequences of loss values.

penaltyLearning 2019.11.19

• labelError is OK with model columns that are missing.

penaltyLearning 2019.10.10

• stop with an error for IntervalRegressionCV(., unlogged.outputs).
• new args for IntervalRegressionCV including LAPPLY which defaults to future.apply::future_lapply but can be set to base::lapply for debugging.
• new notConverging data set and test.
• smaller crit before restarting with a larger Lipschitz in IntervalRegressionCV.

penaltyLearning 2019.5.16

• non-strict equality in while(crossing point >= previous breakpoint) to avoid zero-length intervals.
• additional tests for modelSelectionFwd.

penaltyLearning 2019.05.15

• Use modelSelectionFwd C algo for modelSelectionC R function.
• Fix featureMatrix/labelError/ROChange argument checks, if(logical vector length bigger than 1) was used and is now being checked in R-3.6.0.

penaltyLearning 2019.05.03

• modelSelectionFwd and modelSelectionQuadratic.

penaltyLearning 2019.04.18

• IntervalRegressionCV: informative reg.type undefined error.

penaltyLearning 2019.02.28

• set last_lambda=0 when popping.

penaltyLearning 2019.02.27

• import rather than Depend data.table

penaltyLearning 2018.10.23

• IntervalRegression* stops with an informative error if there are no upper/lower limits.
• Remove Remotes/Travis deps.
• ROChange now works when there are problems with no thresholds, e.g. the FPR/TPR does not change at all when varying the penalty from

penaltyLearning 2018.09.24

• labelError stops for unrecognized annotations.

penaltyLearning 2018.09.04 (2018-09-10)

• use future.apply::future_lapply.

penaltyLearning 2017.12.08 (2017-12-08)

• remove vignette to pass CRAN check.

penaltyLearning 2017.11.17

• In vignette, remove cghseg since it has memory problems, use Segmentor instead, with trivial 1 segment model when Segmentor fails.
• Remove cghseg from example(modelSelectionC).
• Don't use fullpage in vignette because that causes a NOTE on CRAN mac.

penaltyLearning 2017.07.12

• try to fix vignette by using cghseg:::segmeanCO instead of Segmentor.

penaltyLearning 2017.07.11 (2017-07-11)

• there is some problem with Segmentor3IsBack on windows, which crashes our vignette re-building in CRAN checks on solaris... not sure why but try to fix via adding tryCatch in vignette.
• Add ... passed from IntervalRegressionCV to IntervalRegressionRegularized.

penaltyLearning 2017.06.14 (2017-06-19)

• labelError bugfix and test case for no predicted changes.
• Simplify examples -- avoid running Segmentor since this crashes on new versions of R on windows.

penaltyLearning 2017.05.08 (2017-06-07)

• IntervalRegressionCV uses future instead of foreach.

penaltyLearning 2017.05.05

• corrections encountered while preparing tutorial,
• - theme_no_space() evaluated at runtime rather than theme_no_space which was evaluated at build time.
• - stop with an error if there are models that have the same number of changes -- this prevents problems for changepoint models, but prevents using the code with L1 regularized models (fused lasso).
• - stop with an error in targetIntervals if the errors column is not numeric. And return an errors column (the minimum number of incorrect labels).

penaltyLearning 2017.04.11

• prepare for CRAN submission: - convert to src/*.cpp files and register routines. - NULL variables to avoid CRAN checks about global variables. - vignette. - many more user-friendly error messages. - coefficients of IntervalRegression models are now returned on the original scale.

penaltyLearning 2017.03.24

• IntervalRegression S3 class with plot, print, and predict methods.
• largestContinuousMinimum C implementation.
• more informative error messages when arguments to R functions are not as expected.
• check for bigger/smaller data sets in ROChange and labelError.
• check for errors in C code and return with non-zero status.

penaltyLearning 2017.01.31

• labelError works when there are more models than labels, and gives an informative error when there are no corresponding models for a given label.

penaltyLearning 2017.01.21

• tests for peak model and for IntervalRegression functions.

penaltyLearning 2017.01.20

• IntervalRegression* functions.

penaltyLearning 2017.01.17

• labelError, targetIntervals, ROChange.

penaltyLearning 2017.01.13

• C solver for linear time modelSelection algorithm, interface via modelSelectionC function.
• modelSelectionR function with original quadratic time algorithm in R code.
• modelSelection which takes a data.frame as input instead of vectors, and uses modelSelectionC.

penaltyLearning 2017.01.12

• First version.