Running 'test_Unique.R' [169s]
  Running 'tinytest.R' [160s]
Running the tests in 'tests/test_Unique.R' failed.
Complete output:
  > library(classInt)
  > set.seed(1)
  > data_censored<-c(rep(0,10), rnorm(100, mean=20,sd=1),rep(26,10))
  > cl2<-classIntervals(data_censored, n=4, style="fixed",dataPrecision=2,fixedBreaks=c(-1,1,19,25,30))
  > 
  > print(cl2, unique=FALSE)
  style: fixed
    one of 166,650 possible partitions of this variable into 4 classes
   [-1,1)  [1,19) [19,25) [25,30] 
       10      11      89      10 
  > print(cl2, unique=TRUE)
  style: fixed
    one of 166,650 possible partitions of this variable into 4 classes
  Class found with one single (possibly repeated) value: changed label
        0  [1,19) [19,25)      26 
       10      11      89      10 
  > 
  > ### example from man page
  > classIntervals(data_censored, n=5, style="fixed", fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30))
  style: fixed
    one of 4,082,925 possible partitions of this variable into 5 classes
   [15.57,25)     [25,50)     [50,75)    [75,100) [100,155.3] 
          110          10           0           0           0 
  Warning message:
  In classIntervals(data_censored, n = 5, style = "fixed", fixedBreaks = c(15.57,  :
    variable range greater than fixedBreaks
  > 
  > print(classIntervals(data_censored, n=5, style="sd"), unique=FALSE)
  style: sd
    one of 79,208,745 possible partitions of this variable into 6 classes
  [-5.126688,0.8860022)  [0.8860022,6.898692)   [6.898692,12.91138) 
                     10                     0                     0 
    [12.91138,18.92407)   [18.92407,24.93676)   [24.93676,30.94945] 
                     10                    90                    10 
  > print(classIntervals(data_censored, n=5, style="sd"), unique=TRUE)
  style: sd
    one of 79,208,745 possible partitions of this variable into 6 classes
  Class found with one single (possibly repeated) value: changed label
                     0 [0.8860022,6.898692)  [6.898692,12.91138) 
                    10                    0                    0 
   [12.91138,18.92407)  [18.92407,24.93676)                   26 
                    10                   90                   10 
  > print(classIntervals(data_censored, n=5, style="equal"),  unique=TRUE)
  style: equal
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
            0  [5.2,10.4) [10.4,15.6) [15.6,20.8)   [20.8,26] 
           10           0           0          81          29 
  > print(classIntervals(data_censored, n=5, style="quantile"),  unique=TRUE)
  style: quantile
    one of 4,082,925 possible partitions of this variable into 5 classes
         [0,19.24129) [19.24129,19.87857) [19.87857,20.39315) [20.39315,21.07048) 
                   24                  24                  24                  24 
        [21.07048,26] 
                   24 
  > set.seed(1)
  > print(classIntervals(data_censored, n=5, style="kmeans"),  unique=TRUE)
  style: kmeans
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.11514) [19.11514,20.31048) [20.31048,24.20081) 
                   10                  12                  43                  45 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="hclust", method="complete"),  unique=TRUE)
  style: hclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.01088) [19.01088,21.00347) [21.00347,24.20081) 
                   10                  11                  74                  15 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="hclust", method="single"),  unique=TRUE)
  style: hclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,18.33574) [18.33574,21.78784) [21.78784,24.20081) 
                   10                   3                  94                   3 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="fisher"),  unique=TRUE)
  style: fisher
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.72123) [19.72123,20.85116) [20.85116,24.20081) 
                   10                  33                  49                  18 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="jenks"),  unique=TRUE)
  style: jenks
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0        (0,19.69582] (19.69582,20.82122] (20.82122,22.40162] 
                   10                  33                  49                  18 
                   26 
                   10 
  > 
  > print(classIntervals(data_censored, n=5, style="fixed", fixedBreaks=c(15.57, 25, 50, 75, 100, 155.30)), unique=TRUE)
  style: fixed
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
   [15.57,25)          26     [50,75)    [75,100) [100,155.3] 
          110          10           0           0           0 
  Warning message:
  In classIntervals(data_censored, n = 5, style = "fixed", fixedBreaks = c(15.57,  :
    variable range greater than fixedBreaks
  > print(classIntervals(data_censored, n=5, style="sd"), unique=TRUE)
  style: sd
    one of 79,208,745 possible partitions of this variable into 6 classes
  Class found with one single (possibly repeated) value: changed label
                     0 [0.8860022,6.898692)  [6.898692,12.91138) 
                    10                    0                    0 
   [12.91138,18.92407)  [18.92407,24.93676)                   26 
                    10                   90                   10 
  > print(classIntervals(data_censored, n=5, style="equal"), unique=TRUE)
  style: equal
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
            0  [5.2,10.4) [10.4,15.6) [15.6,20.8)   [20.8,26] 
           10           0           0          81          29 
  > print(classIntervals(data_censored, n=5, style="quantile"), unique=TRUE)
  style: quantile
    one of 4,082,925 possible partitions of this variable into 5 classes
         [0,19.24129) [19.24129,19.87857) [19.87857,20.39315) [20.39315,21.07048) 
                   24                  24                  24                  24 
        [21.07048,26] 
                   24 
  > set.seed(1)
  > print(classIntervals(data_censored, n=5, style="kmeans"), unique=TRUE)
  style: kmeans
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.11514) [19.11514,20.31048) [20.31048,24.20081) 
                   10                  12                  43                  45 
                   26 
                   10 
  > set.seed(1)
  > print(classIntervals(data_censored, n=5, style="kmeans", intervalClosure="right"), unique=TRUE)
  style: kmeans
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  (8.89265,19.11514] (19.11514,20.31048] (20.31048,24.20081] 
                   10                  12                  43                  45 
                   26 
                   10 
  > set.seed(1)
  > print(classIntervals(data_censored, n=5, style="kmeans", dataPrecision=0), unique=TRUE)
  style: kmeans
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
        0  [9,20) [20,21) [21,25)      26 
       10      12      43      45      10 
  > set.seed(1)
  > print(classIntervals(data_censored, n=5, style="kmeans"), cutlabels=FALSE, unique=TRUE)
  style: kmeans
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  8.89265 - 19.11514 19.11514 - 20.31048 20.31048 - 24.20081 
                   10                  12                  43                  45 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="hclust", method="complete"), unique=TRUE)
  style: hclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.01088) [19.01088,21.00347) [21.00347,24.20081) 
                   10                  11                  74                  15 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="hclust", method="single"), unique=TRUE)
  style: hclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,18.33574) [18.33574,21.78784) [21.78784,24.20081) 
                   10                   3                  94                   3 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="fisher"), unique=TRUE)
  style: fisher
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.72123) [19.72123,20.85116) [20.85116,24.20081) 
                   10                  33                  49                  18 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="jenks"), unique=TRUE)
  style: jenks
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0        (0,19.69582] (19.69582,20.82122] (20.82122,22.40162] 
                   10                  33                  49                  18 
                   26 
                   10 
  > print(classIntervals(data_censored, style="headtails"), unique=TRUE)
  style: headtails
    one of 101 possible partitions of this variable into 2 classes
   [0,18.92407) [18.92407,26] 
             20           100 
  > print(classIntervals(data_censored, style="headtails", thr = 1))
  style: headtails
    one of 166,650 possible partitions of this variable into 4 classes
         [0,18.92407) [18.92407,20.86153) [20.86153,23.03872)       [23.03872,26] 
                   20                  72                  18                  10 
  > print(classIntervals(data_censored, style="headtails", thr = 0))
  style: headtails
    one of 101 possible partitions of this variable into 2 classes
   [0,18.92407) [18.92407,26] 
             20           100 
  > print(classIntervals(data_censored, style="box", iqr_mult = 0))
  style: box
    one of 79,208,745 possible partitions of this variable into 6 classes
         [0,19.38567) [19.38567,19.38567) [19.38567,20.11391) [20.11391,20.77193) 
                   30                   0                  30                  30 
  [20.77193,20.77193)       [20.77193,26] 
                    0                  30 
  > print(classIntervals(data_censored, style="box"))
  style: box
    one of 79,208,745 possible partitions of this variable into 6 classes
         [0,17.30627) [17.30627,19.38567) [19.38567,20.11391) [20.11391,20.77193) 
                   10                  20                  30                  30 
  [20.77193,22.85133)       [22.85133,26] 
                   20                  10 
  > x <- c(0, 0, 0, 1, 2, 50)
  > print(classIntervals(x, n=3, style="fisher"), unique=TRUE)
  style: fisher
    one of 3 possible partitions of this variable into 3 classes
  Class found with one single (possibly repeated) value: changed label
         0 [0.5,26)       50 
         3        2        1 
  > print(classIntervals(x, n=3, style="jenks"), unique=TRUE)
  style: jenks
    one of 3 possible partitions of this variable into 3 classes
  Class found with one single (possibly repeated) value: changed label
      0 (0,2]    50 
      3     2     1 
  > if (getRversion() > "3.5.3") {
  +   suppressWarnings(set.seed(1, sample.kind=c("Rounding")))
  + } else {
  +   set.seed(1)
  + }
  > print(classIntervals(data_censored, n=5, style="bclust", verbose=FALSE),  unique=TRUE)
  style: bclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.01088) [19.01088,21.00347) [21.00347,24.20081) 
                   10                  11                  74                  15 
                   26 
                   10 
  > print(classIntervals(data_censored, n=5, style="bclust", hclust.method="complete", verbose=FALSE), unique=TRUE)
  style: bclust
    one of 4,082,925 possible partitions of this variable into 5 classes
  Class found with one single (possibly repeated) value: changed label
                    0  [8.89265,19.79106) [19.79106,21.28327) [21.28327,24.20081) 
                   10                  34                  57                   9 
                   26 
                   10 
  > 
  > # the log-likelihood returns a valid logLik object.
  > stopifnot(
  +   identical(
  +     round(logLik(classIntervals(rep(1:3, each=10), n=2, style="jenks")), 5),
  +     structure(-14.52876, df = 2, nobs = 30L, class = "logLik")
  +   )
  + )
  > # logLik for exact intervals (a single value is the unique member of an
  > # interval) yields a likelihood of zero.
  > stopifnot(
  +   identical(
  +     suppressWarnings(logLik(classIntervals(rep(1:3, each=10), n=3, style="jenks"))),
  +     structure(0, df = 3, nobs = 30L, class = "logLik")
  +   )
  + )
  > 
  > proc.time()
     user  system elapsed 
     0.37    0.09    0.43 
Running the tests in 'tests/tinytest.R' failed.
Complete output:
  > if ( requireNamespace("tinytest", quietly=TRUE) ){
  +   tinytest::test_package("classInt")
  + }
  
  test_box.R....................    0 tests    
  test_box.R....................    0 tests    
  test_box.R....................    0 tests    style: box
    one of 1,906,884 possible partitions of this variable into 6 classes
  
  test_box.R....................    1 tests [0;32mOK[0m 
  test_box.R....................    2 tests [0;32mOK[0m 
  test_box.R....................    3 tests [0;32mOK[0m [0;34m0.1s[0m
  
  test_largeN.R.................    0 tests    
  test_largeN.R.................    0 tests    
  test_largeN.R.................    0 tests    
  test_largeN.R.................    0 tests    
  test_largeN.R.................    1 tests [0;32mOK[0m [0;36m5ms[0m
  All ok, 4 results (0.1s)
  > 
  > proc.time()
     user  system elapsed 
     0.32    0.12    0.43