### changes from version 1.0.2 to version 1.1.0 (October 10 2013)
- added new functions (see documentation for details and usage)
   + signifd.analysis
   + simulateH0
- updated documentation
   + signifd.analysis
   + simulateH0
   + functions relying on the former

### changes from version 1.0.1 to version 1.0.2 (August 20 2013)
- updated documentation
	+ equations now included
	+ references now follow CRAN reccomendations
	+ references updated
- fixed meandigit.benftest
	+ is onesided test, p-value calculation was wrong
- removed pdf-manual from tarball
	+ waste of space according to U.Ligges

### changes from version 1.0.0  to version 1.0.1 (June 05 2013)
- fixed memory leak associated with NULL-Distribution simulations

### changes from version 0.5  to version 1.0 (May 14 2013)
- changed names of tests to make them more consistent with nameing in base package:
	new name			|	old name
	----------------------------------------------------------------------
	-chisq.benftest			|	chi_square_benford
		argument: first_digits	|		argument: digits
	-ks.benftest			|	K_S_benford
		argument: first_digits	|		argument: digits
	-mdist.benftest			|	Chebyshev_dist_benford
		argument: first_digits	|		argument: digits
	-edist.benftest			|	Euclidean_dist_benford
		argument: first_digits	|		argument: digits
	-usq.benftest			|	Freedman_Watson_Usquare_benford
		argument: first_digits 	|		argument: digits
	-meandigit.benftest		|	J_S_avg_dev_benford
		argument: first_digits	|		argument: digits
	-jpsq.benftest			|	J_stat_squ_benford
		argument: first_digits	|		argument: digits
	-signifd			|	leading_digits
		argument: number	|		argument: digits
	-signifd.seq			|	sequence_leading
		argument: number	|		argument: digits
- removed ability to choose correlation type "kendall" and "spearman" for the jpsq.benftest (old: J_stat_squ_benford)
	(aleviates some confusion as to what the J_P^2 statistic is, usage of Pearson correlation is most powerfull anyways)

- data name is now correctly printed by the S3 object for all tests.

- meandigit.benftest should have been a two sided test. p-value calculation now reflects this.

- simulation of NULL-hypothesis distribution is now acomplished by c code
	faster by a very large factor. different for each test
	Compare the following times on a i5 Pentium laptop for the chi square test
	##old##
	> system.time(compute_chisquare_H0_R(n=1000,pvalsims=10000))
	   user  system elapsed 
	   207.98    0.06  210.23 
	##new##
	> system.time(compute_chisquare_H0(n=1000,pvalsims=10000))
	   user  system elapsed 
	   0.37    0.00    0.39 
