The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Changelog - literanger

0.1.1 - 2024-09-13

Performance improved. R interface and C++ core have been separated.

Changed

• Set Depends to R >= 3.6.0.
• Training speed was increased by ~25% by reducing memory allocations 19e7c475 and inlining access to data. 233063e0.
• Source code underwent a minor re-organisation to separate the R-specific components from the C++ library.

Added

• DataVector class to read, write, and pass data without R.

0.1.0 - 2024-09-03

New feature! literanger can now serialize trained random forests using cereal.

The project has been moved to GitLab: https://gitlab.com/stephematician/literanger.

Changed

• The value-type returned by predict now matches the response type in training ea67c83e
• Bump cpp11 to 0.4.7.

Added

• Functions read_literanger and write_literanger for serialization.

Fixed

• Fixed bug in implementation of always-selected candidates for splitting, e.g. the names_of_always_draw argument 6d31d7f3
• Minor performance tweak 9a3b639a in particular for ‘maxstat’ 37580d9b

0.0.2 - 2023-07-11

Update to pass CRAN’s ASAN check

Changed

• Improve performance of node splitting (d3f6424)

Added

• Add re-entrant log gamma to speed up beta splitting rule (d7f058d)
• Minor fixes to documentation (91b6c6d, 0f62d02)

Fixed

• Fix potential illegal access and incorrect unweighted sampling without replacement (b6df5d9)

0.0.1 - 2023-06-25

First release

A refactoring and adaptation of the ranger package https://github.com/imbs-hl/ranger for random forests. Has faster prediction mode intended for embedding into the multiple imputation algorithm proposed by Doove et al in:

Doove, L. L., Van Buuren, S., & Dusseldorp, E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational statistics & data analysis, 72, 92-104.

Added

• Fit classification and regression trees
• Prediction via most frequent value or mean
• Get predictions as terminal node identifiers in each tree or as a random draw from inbag values in a random tree

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.
Health stats visible at Monitor.