Note: In this file, LiblineaR refers to this R package, while 'LIBLINEAR' refers to the original C/C++ library.

2.10-8 : 2017/02/13
	1. Upgraded to 'LIBLINEAR' version 2.10, including cross-validation procedure to find parameter C.
		- two extra arguments in the LiblineaR function.
	2. Argument bias from LiblineaR function can now be given as a numeric, allowing to tune the value of the additional bias feature. This change is backwards-compatible with the previous use of booleans.

1.94-2: 2015/01/30
	1. Cleaned DESCRIPTION

	2. library(SparseM) replaced by 'require' or 'requireNamespace' as appropriate.

1.94-1: 2015/01/30
	1. Upgraded to 'LIBLINEAR' version 1.94, including support vector *regression*
		- argument labels of function LiblineaR was renamed as target. The old naming is still accepted with a warning
		- argument type of function LiblineaR may take extra values (11,12,13)
		- extra argument svr_eps of function LiblineaR for the tolerance of regression loss
		- if class labels are -1 and 1, ensure that positive decision values represent the class 1
		- enriched examples, with cases of regression

	2. Fix CITATION file format to satisfy CRAN requirements

	3. Cleaning and reformatting of man pages

	4. Fixing a bug in class labels ordering 
	
1.80-11: 2014/09/18
	1. Refactored C code in order to facilitate upgrades of 'LIBLINEAR'.
	
	2. Fix memory leak in predictLinear (the buffer x wasn't freed)
	
1.80-10: 2014/09/14
	1. Shortened examples runtime.
	
	2. Uniformized notations in DESCRIPTION file for LiblineaR (this package) and 'LIBLINEAR' (the C/C++ library wrapped by this package). 

1.80-9: 2014/09/12
	1. Replaced all expressions: 

		rand()%(a-b); 
	by 

		GetRNGstate();
		(int) (unif_rand()*(a - b))%(a - b);
		PutRNGstate();

	in linear.cpp, where a and b might take different values.

1.80-8: 2014/09/11

	1. Corrected a bug in memory allocation for sparse matrices:
		src/trainLinear.c

		These changes are to be credited to Christian Wolf.

	2. Added a PACKAGE argument to speed up call to .C(...):
		R/LiblineaR.R
		R/predict.R

1.80-7: 2013/06/24

	1. Modification of the following files in order to support sparse matrices:
		R/LiblineaR.R
		R/predict.R
		src/predictLinear.c 
		src/trainLinear.c

		These changes are mainly to be credited to Kai-Hsiang Hsu, from the Department of Computer Science of the National Taiwan University.

	2. Addition of examples to reflect the use of sparse matrices in the following file:
		man/LiblineaR.Rd

1.80-6: 2013/03/26
	
	1. Corrected a memory mapping bug in predictLinear.c

1.80-5: 2013/03/25

	1. Suppress printing to stdout in linear.cpp, tron.cpp
	
	2. Suppress the use of exit(1); in predictLinear.c

	3. Correct a bug when retrieving weights from C to R for multi-class models
 
1.80-4: 2011/04/21

	1. Correct bugs in linear.cpp (update of solve_l1r_l2_svc and solve_l1r_lr)

1.80-3: 2011/04/20
	
	1. Add:

	extern "C" 

	before each function below :

	//
	// Interface function
	//
	
	in linear.cpp

	2. Suppress useless functions:

	- save_model
	- load_model
	
	in:
	
	linear.cpp
	linear.h	

	3. change all "fprintf" into Rprintf in :
	linear.cpp
	trainLinear.c
	predictLinear.c

	4. change:

	#define Malloc(type,n) (type *)malloc((n)*sizeof(type)) 

	into 

	#define Malloc(type,n) (type *)Calloc(n,type)

	in:
	trainLinear.c
	linear.cpp

	5. replace all the malloc((n)*sizeof(type)) by Calloc(n,type) in predictLinear.c

	5. replace all the realloc(*p,size) by Realloc(*p,n,type) in linear.cpp

	7. replace all free() by Free() in:

	trainLinear.c
	predictLinear.c
	linear.cpp

	8. Add
	
	#include <R.h>
	#include <Rinternals.h>
	#include <R_ext/Rdynload.h>
	
	in linear.cpp

1.80-2: 2011/04/12

	1. Incorporate changes from 'LIBLINEAR' versions 1.51 to 1.80:
		- Use set_print_string_function to set the print function
		- Add free_model_content and free_and_destroy_model functions (avoid memory problem if users declare a model variable)
		- Add check_probability_model (consistent with libsvm)
		- A new solver: coordinate descent for dual logistic regression
		- New optimization method for l1-regularized logistic regression
		- linear.cpp: 
			* Use 1-norm stopping condition for l1-regularized solvers 
			* newton_iter < l/10 replaced by newton_iter <= l/10 in l2r_lr_dual (for l < 10)
		
	2. predict.LiblineaR function can return additional information:
		- probabilities (only for logistic regression models)
		- decision values