Merge pull request #9 from rkillick/v2.2.2

V2.2.2

.travis.yml

@@ -2,3 +2,8 @@ language: r
 sudo: required
 warnings_are_errors: true
 r_check_revdep: false
+r:
+  - 3.1
+  - oldrel
+  - release
+  - devel

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: changepoint
 Type: Package
 Title: Methods for Changepoint Detection
-Version: 2.2.1
-Date: 2016-02-26
+Version: 2.2.2
+Date: 2016-10-04
 Authors@R: c(person("Rebecca", "Killick",
         role=c("aut","cre"),email="[email protected]"),
 				person("Kaylea", "Haynes", role="aut"),        
@@ -12,8 +12,8 @@ Maintainer: Rebecca Killick <[email protected]>
 BugReports: <[email protected]>
 URL: https://github.com/rkillick/changepoint/
 Description: Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data.  Many popular non-parametric and frequentist methods are included.  The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.
-Depends: R(>= 3.0), methods, stats, zoo(>= 0.9-1)
+Depends: R(>= 3.1), methods, stats, zoo(>= 0.9-1)
 Suggests: testthat
 License: GPL
 LazyLoad: yes
-Packaged: 2016-02-26 12:36:29 UTC; killick
+Packaged: 2016-10-04 12:36:29 UTC; killick

NAMESPACE

@@ -4,6 +4,7 @@ import(zoo)
 import(stats)
 
 export("cpt.mean", "cpt.meanvar", "cpt.var")
+export("BINSEG", "decision", "penalty_decision","PELT","class_input")
 exportClasses(cpt,cpt.reg,cpt.range)
 exportMethods(plot,summary,show,nseg,logLik,likelihood,data.set,data.set.ts,cpts,cpts.full, cpts.ts,cpttype,distribution,test.stat,
 	method,ncpts,ncpts.max,param.est,param, coef,pen.type,pen.value,pen.value.full, minseglen, seg.len,"cpts<-","cpttype<-","data.set<-","distribution<-","test.stat<-","ncpts.max<-","param.est<-","pen.type<-",

NEWS

@@ -1,3 +1,19 @@
+Version 2.2.2
+=============
+* Updated R dependency, thanks to Mateusz Konieczny to identifying the problem (and solution).
+* Updated all functions with minseglen arguments to error when minseglen is set too large for a change to be included in the data, e.g. n=14, minseglen=10.  Thanks to Simon Taylor for spotting this bug.
+* minseglen added to nonparametric Binary Segmentation functions.
+* Initialization of candidate log-likelihood values in C is set to -Inf instead of 0.
+* Fixed bug whereby if a non-MBIC penalty was use with SegNeigh it errored saying "MBIC not implemented for SegNeigh" this was supposed to be displaying for MBIC penalty only.  Thanks to Craig Wenger for spotting this.
+* Fixed bug whereby the AMOC method with Gamma, Exponential or Poisson test statistic was returning the position of the change in a vector of potential changes rather than the actual changepoint location.  This did not affect methods other than AMOC.
+* Changed cpt.meanvar so that if test.stat="Poisson" or test.stat="Exponential" you can have a minseglen=1 (previously minimum was 2).  Thanks to Craig Wenger for the suggestion.
+* Minor changes to PELT and BinSeg functions in preparation for user defined cost functions (coming soon).
+* We now export the BINSEG, PELT, decision, penalty_decision and class_input functions.  This is to allow other packages to make use of these functions (previously these were replicated in other packages).  They are NOT designed for general use, they are for developer use only.
+* added acf generic which returns the acf for each segment.
+* changed logLik to return -2*log-likelihood of the models, previously this was a scaled version.
+* changed the prototype of the cpt class to include a default of "Not Set" for cpttype.
+* change y axis on plot(.,diagnostic=TRUE) to be labelled "Penalty Value", previously it was incorrectly labelled "Difference in Test Statistic".
+
 Version 2.2.1
 =============
 * Fixed a minor bug where additional arguments to the plot function when diagnostic=TRUE were not passed through to the plot.

R/PELT_one_func_minseglen.R

@@ -1,6 +1,6 @@
 PELT = function(sumstat,pen=0, cost_func = "norm.mean", shape = 1, minseglen = 1){
   # function that uses the PELT method to calculate changes in mean where the segments in the data are assumed to be Normal
-  n = length(sumstat)/3 - 1
+  n = length(sumstat[,1]) - 1
   if(n<2){stop('Data must have atleast 2 observations to fit a changepoint model.')}
 
   storage.mode(sumstat) = 'double'
@@ -27,6 +27,6 @@ PELT = function(sumstat,pen=0, cost_func = "norm.mean", shape = 1, minseglen = 1
   if(answer[[6]]>0){
     stop("C code error:",answer[[6]],call.=F)
   }
-  return(list(answer[[10]],sort(answer[[5]][answer[[5]]>0]), answer[[9]], answer[[11]]))
+  return(list(lastchangecpts=answer[[10]],cpts=sort(answer[[5]][answer[[5]]>0]), lastchangelike=answer[[9]], ncpts=answer[[11]]))
 
 }

R/cpt.R

@@ -126,7 +126,10 @@ cpt.var=function(data,penalty="MBIC",pen.value=0,know.mean=FALSE, mu=NA,method="
 cpt.meanvar=function(data,penalty="MBIC",pen.value=0,method="AMOC",Q=5,test.stat="Normal",class=TRUE,param.estimates=TRUE,shape=1,minseglen=2){
   checkData(data)
   if(method=="SegNeigh" & minseglen>2){stop("minseglen not yet implemented for SegNeigh method, use PELT instead.")}
-  if(minseglen<2){minseglen=2;warning('Minimum segment length for a change in mean and variance is 2, automatically changed to be 2.')}
+  if(minseglen<2){
+    if(!(minseglen==1 & (test.stat=="Poisson"|test.stat=="Exponential"))){
+      minseglen=2;warning('Minimum segment length for a change in mean and variance is 2, automatically changed to be 2.')}
+    }
   if(penalty == "CROPS"){
     if(is.numeric(pen.value)){
       if(length(pen.value) == 2){