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| @@ -1,15 +1,24 @@ | ||
| Package: CryptRndTest | ||
| Type: Package | ||
| Title: Statistical Tests for Cryptographic Randomness | ||
| Version: 1.2.1 | ||
| Date: 2015-12-24 | ||
| Author: Haydar Demirhan | ||
| Version: 1.2.2 | ||
| Date: 2016-02-24 | ||
| Author: Haydar Demirhan | ||
| Maintainer: Haydar Demirhan <haydarde@hacettepe.edu.tr> | ||
| Description: Performs cryptographic randomness tests on a sequence of random integers or bits. Included tests are greatest common divisor, birthday spacings, book stack, adaptive chi-square, topological binary, and three random walk tests. Tests except greatest common divisor and birthday spacings are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests. | ||
| Description: Performs cryptographic randomness tests on a sequence of random | ||
| integers or bits. Included tests are greatest common divisor, birthday spacings, | ||
| book stack, adaptive chi-square, topological binary, and three random walk | ||
| tests. Tests except greatest common divisor and birthday spacings are not | ||
| covered by standard test suites. In addition to the chi-square goodness-of-fit | ||
| test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are | ||
| also generated by some of the cryptographic randomness tests. | ||
| Depends: MissMech, kSamples, sfsmisc, Rmpfr, parallel | ||
| Imports: LambertW, gmp, tseries, methods | ||
| Suggests: R.rsp | ||
| VignetteBuilder: R.rsp | ||
| License: GPL-3 | ||
| RoxygenNote: 5.0.1 | ||
| NeedsCompilation: no | ||
| Packaged: 2016-01-04 14:02:49 UTC; Stat | ||
| Packaged: 2016-02-25 00:43:41 UTC; e27752 | ||
| Repository: CRAN | ||
| Date/Publication: 2016-01-04 15:28:45 | ||
| Date/Publication: 2016-02-25 05:21:43 |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,57 +1,49 @@ | ||
| exportPattern("^[[:alpha:]]+") | ||
| # Generated by roxygen2: do not edit by hand | ||
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| importFrom(sfsmisc,digitsBase) | ||
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| importFrom(tseries,jarque.bera.test) | ||
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| importFrom(kSamples,ad.test) | ||
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| importFrom(MissMech,AndersonDarling) | ||
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| importFrom(LambertW,W) | ||
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| importFrom(gmp,Stirling2) | ||
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| importFrom(Rmpfr,mpfr) | ||
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| importFrom(Rmpfr,mpfrArray) | ||
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| importFrom(parallel,makePSOCKcluster) | ||
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| importFrom(parallel,setDefaultCluster) | ||
| importFrom(parallel,clusterExport) | ||
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| importFrom(parallel,parLapply) | ||
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| importFrom(parallel,stopCluster) | ||
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| importFrom("stats", "chisq.test", "dpois", "ks.test", "pchisq","rnorm", "runif") | ||
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| importFrom("methods", "new") | ||
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| importFrom("utils", "write.table") | ||
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| S3method(adaptive.chi.square,default) | ||
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| S3method(birthday.spacings,default) | ||
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| S3method(book.stack,default) | ||
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| S3method(GCD.test,default) | ||
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| S3method(random.walk.tests,default) | ||
| S3method(topological.binary,default) | ||
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| S3method(GCD.test,main) | ||
| S3method(adaptive.chi.square,default) | ||
| S3method(adaptive.chi.square,main) | ||
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| S3method(birthday.spacings,default) | ||
| S3method(birthday.spacings,main) | ||
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| S3method(book.stack,default) | ||
| S3method(book.stack,main) | ||
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| S3method(GCD.test,main) | ||
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| S3method(print,CryptRndTest) | ||
| S3method(random.walk.tests,default) | ||
| S3method(random.walk.tests,main) | ||
| S3method(topological.binary,default) | ||
| S3method(topological.binary,main) | ||
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| S3method(print,CryptRndTest) | ||
| export(GCD) | ||
| export(GCD.big) | ||
| export(GCD.q) | ||
| export(GCD.test) | ||
| export(Strlng2) | ||
| export(TBT.criticalValue) | ||
| export(adaptive.chi.square) | ||
| export(birthday.spacings) | ||
| export(book.stack) | ||
| export(random.walk.tests) | ||
| export(toBaseTen) | ||
| export(toBaseTwo) | ||
| export(topological.binary) | ||
| importFrom(LambertW,W) | ||
| importFrom(MissMech,AndersonDarling) | ||
| importFrom(Rmpfr,mpfr) | ||
| importFrom(Rmpfr,mpfrArray) | ||
| importFrom(gmp,Stirling2) | ||
| importFrom(kSamples,ad.test) | ||
| importFrom(methods,new) | ||
| importFrom(parallel,clusterExport) | ||
| importFrom(parallel,makePSOCKcluster) | ||
| importFrom(parallel,parLapply) | ||
| importFrom(parallel,setDefaultCluster) | ||
| importFrom(parallel,stopCluster) | ||
| importFrom(sfsmisc,digitsBase) | ||
| importFrom(stats,chisq.test) | ||
| importFrom(stats,dpois) | ||
| importFrom(stats,ks.test) | ||
| importFrom(stats,pchisq) | ||
| importFrom(stats,rnorm) | ||
| importFrom(stats,runif) | ||
| importFrom(tseries,jarque.bera.test) | ||
| importFrom(utils,write.table) |
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| KSADdga=function(e,alfa,n,m,lambda,num.class=10){ | ||
| z=0 | ||
| expected=0 | ||
| p=0 | ||
| p[1:(num.class+1)]=dpois(0:num.class,lambda=lambda) | ||
| p[num.class+1]=p[num.class+1]+sum(dpois((num.class+1):1000,lambda=lambda)) | ||
| N=length(e) | ||
| expected=round(p*N) | ||
| if (sum(expected)!=N){ | ||
| expected[which.max(expected)]=expected[which.max(expected)]-(sum(expected)-N) | ||
| } | ||
| z=rep(0:(length(expected)-1),expected) | ||
| test=kSamples::ad.test(e,z,method="simulated",dist=FALSE,Nsim=1000) | ||
| ADtest=test$ad | ||
| if (min(ADtest[,3])<alfa){ | ||
| sonucAD=0 | ||
| } else{ | ||
| sonucAD=1 | ||
| } | ||
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| KStest=0 | ||
| test2=ks.test(e,z) | ||
| KStest[1]=test2$statistic | ||
| KStest[2]=test2$p.value | ||
| if (KStest[2]<alfa){ | ||
| sonucKS=0 | ||
| } else{ | ||
| sonucKS=1 | ||
| } | ||
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| k=length(expected) | ||
| observed=array(0,k) | ||
| observed[1:length(table(e))]=table(e) | ||
| KKtest=0 | ||
| observed[which(observed==0)]=10^-5 | ||
| expected[which(expected==0)]=10^-5 | ||
| KKtest[1]=sum(((observed-expected)^2)/expected,na.rm=TRUE) #gives test statistic | ||
| KKtest[2]=pchisq(KKtest[1],(k-1),lower.tail=FALSE) #gives p.value of the chi-square test | ||
| if (KKtest[2]<alfa){ | ||
| sonucKK=0 | ||
| } else{ | ||
| sonucKK=1 | ||
| } | ||
| result=list(sonucAD=sonucAD,ADtest=ADtest,sonucKS=sonucKS,KStest=KStest,sonucKK=sonucKK,KKtest=KKtest) | ||
| return(result) | ||
| #' @importFrom stats ks.test | ||
| #' @importFrom stats chisq.test | ||
| #' @importFrom stats dpois | ||
| #' @importFrom stats pchisq | ||
| #' @importFrom stats rnorm | ||
| #' @importFrom stats runif | ||
| #' @importFrom parallel parLapply | ||
| #' @importFrom parallel clusterExport | ||
| #' @importFrom parallel setDefaultCluster | ||
| #' @importFrom parallel stopCluster | ||
| #' @importFrom parallel makePSOCKcluster | ||
| #' @importFrom Rmpfr mpfrArray | ||
| #' @importFrom Rmpfr mpfr | ||
| #' @importFrom gmp Stirling2 | ||
| #' @importFrom LambertW W | ||
| #' @importFrom MissMech AndersonDarling | ||
| #' @importFrom kSamples ad.test | ||
| #' @importFrom tseries jarque.bera.test | ||
| #' @importFrom sfsmisc digitsBase | ||
| #' @importFrom methods new | ||
| #' @importFrom utils write.table | ||
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| #' @S3method adaptive.chi.square default | ||
| #' @S3method birthday.spacings default | ||
| #' @S3method book.stack default | ||
| #' @S3method GCD.test default | ||
| #' @S3method random.walk.tests default | ||
| #' @S3method topological.binary default | ||
| #' @S3method adaptive.chi.square main | ||
| #' @S3method birthday.spacings main | ||
| #' @S3method book.stack main | ||
| #' @S3method GCD.test main | ||
| #' @S3method random.walk.tests main | ||
| #' @S3method topological.binary main | ||
| #' @S3method print CryptRndTest | ||
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| #' @export adaptive.chi.square | ||
| #' @export birthday.spacings | ||
| #' @export book.stack | ||
| #' @export GCD.test | ||
| #' @export random.walk.tests | ||
| #' @export topological.binary | ||
| #' @export GCD | ||
| #' @export GCD.big | ||
| #' @export GCD.q | ||
| #' @export Strlng2 | ||
| #' @export TBT.criticalValue | ||
| #' @export toBaseTen | ||
| #' @export toBaseTwo | ||
|
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| KSADdga=function(e,alfa,n,m,lambda,num.class=10){ | ||
| z=0 | ||
| expected=0 | ||
| p=0 | ||
| p[1:(num.class+1)]=dpois(0:num.class,lambda=lambda) | ||
| p[num.class+1]=p[num.class+1]+sum(dpois((num.class+1):1000,lambda=lambda)) | ||
| N=length(e) | ||
| expected=round(p*N) | ||
| if (sum(expected)!=N){ | ||
| expected[which.max(expected)]=expected[which.max(expected)]-(sum(expected)-N) | ||
| } | ||
| z=rep(0:(length(expected)-1),expected) | ||
| test=kSamples::ad.test(e,z,method="simulated",dist=FALSE,Nsim=1000) | ||
| ADtest=test$ad | ||
| if (min(ADtest[,3])<alfa){ | ||
| sonucAD=0 | ||
| } else{ | ||
| sonucAD=1 | ||
| } | ||
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| KStest=0 | ||
| test2=ks.test(e,z) | ||
| KStest[1]=test2$statistic | ||
| KStest[2]=test2$p.value | ||
| if (KStest[2]<alfa){ | ||
| sonucKS=0 | ||
| } else{ | ||
| sonucKS=1 | ||
| } | ||
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| k=length(expected) | ||
| observed=array(0,k) | ||
| observed[1:length(table(e))]=table(e) | ||
| KKtest=0 | ||
| observed[which(observed==0)]=10^-5 | ||
| expected[which(expected==0)]=10^-5 | ||
| KKtest[1]=sum(((observed-expected)^2)/expected,na.rm=TRUE) #gives test statistic | ||
| KKtest[2]=pchisq(KKtest[1],(k-1),lower.tail=FALSE) #gives p.value of the chi-square test | ||
| if (KKtest[2]<alfa){ | ||
| sonucKK=0 | ||
| } else{ | ||
| sonucKK=1 | ||
| } | ||
| result=list(sonucAD=sonucAD,ADtest=ADtest,sonucKS=sonucKS,KStest=KStest,sonucKK=sonucKK,KKtest=KKtest) | ||
| return(result) | ||
| } |
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