forked from imbs-hl/ranger
-
Notifications
You must be signed in to change notification settings - Fork 0
/
NEWS
119 lines (97 loc) · 4.08 KB
/
NEWS
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
##### Version 0.7.2
* Handle sparse data of class Matrix::dgCMatrix
* Add prediction of standard errors to predict()
##### Version 0.7.1
* Allow devtools::install_github() without subdir and on Windows
* Bug fixes
##### Version 0.7.0
* Add randomized splitting (extraTrees)
* Better formula interface: Support interactions terms and faster computation
* Split at mid-point between candidate values
* Improvements in holdoutRF and importance p-value estimation
* Drop unused factor levels in outcome before growing
* Add predict.all for probability and survival prediction
* Bug fixes
##### Version 0.6.0
* Set write.forest=TRUE by default
* Add num.trees option to predict()
* Faster version of getTerminalNodeIDs(), included in predict()
* Handle new factor levels in 'order' mode
* Use unadjusted p-value for 2 categories in maxstat splitting
* Bug fixes
##### Version 0.5.0
* Add Windows multithreading support for new toolchain
* Add splitting by maximally selected rank statistics for survival and regression forests
* Faster method for unordered factor splitting
* Add p-values for variable importance
* Runtime improvement for regression forests on classification data
* Bug fixes
##### Version 0.4.0
* Reduce memory usage of savest forest objects (changed child.nodeIDs interface)
* Add keep.inbag option to track in-bag counts
* Add option sample.fraction for fraction of sampled observations
* Add tree-wise split.select.weights
* Add predict.all option in predict() to get individual predictions for each tree for classification and regression
* Add case-specific random forests
* Add case weights (weighted bootstrapping or subsampling)
* Remove tuning functions, please use mlr or caret
* Catch error of outdated gcc not supporting C++11 completely
* Bug fixes
##### Version 0.3.0
* Allow the user to interrupt computation from R
* Transpose classification.table and rename to confusion.matrix
* Respect R seed for prediction
* Memory improvements for variable importance computation
* Fix bug: Probability prediction for single observations
* Fix bug: Results not identical when using alternative interface
##### Version 0.2.7
* Small fixes for Solaris compiler
##### Version 0.2.6
* Add C-index splitting
* Fix NA SNP handling
##### Version 0.2.5
* Fix matrix and gwaa alternative survival interface
* Version submitted to JSS
##### Version 0.2.4
* Small changes in documentation
##### Version 0.2.3
* Preallocate memory for splitting
##### Version 0.2.2
* Remove recursive splitting
##### Version 0.2.1
* Allow matrix as input data in R version
##### Version 0.2.0
* Fix prediction of classification forests in R
##### Version 0.1.9
* Speedup growing for continuous covariates
* Add memory save option to save memory for very large datasets (but slower)
* Remove memory mode option from R version since no performance gain
##### Version 0.1.8
* Fix problems when using Rcpp <0.11.4
##### Version 0.1.7
* Add option to split on unordered categorical covariates
##### Version 0.1.6
* Optimize memory management for very large survival forests
##### Version 0.1.5
* Set required Rcpp version to 0.11.2
* Fix large $call objects when using BatchJobs
* Add details and example on GenABEL usage to documentation
* Minor changes to documentation
##### Version 0.1.4
* Speedup for survival forests with continuous covariates
* R version: Generate seed from R. It is no longer necessary to set the
seed argument in ranger calls.
##### Version 0.1.3
* Windows support for R version (without multithreading)
##### Version 0.1.2
* Speedup growing of regression and probability prediction forests
* Prediction forests are now handled like regression forests: MSE used for
prediction error and permutation importance
* Fixed name conflict with randomForest package for "importance"
* Fixed a bug: prediction function is now working for probability
prediction forests
* Slot "predictions" for probability forests now contains class probabilities
* importance function is now working even if randomForest package is
loaded after ranger
* Fixed a bug: Split selection weights are now working as expected
* Small changes in documentation