Changes in version 2.0.5 (2023-01-20)                  

  - documentation updated
  - the case of 3 < n < 8 has been removed from ctbi.outlier
  - The companion paper has been published with final DOI and added to
    ctbi

                 Changes in version 2.0.4 (2022-11-18)                  

  - Fixed a minor mistake concerning the way residuals are handled for
    limited range of possible values (ex. [0,+Inf[ instead of
    ]-Inf,+Inf[)
  - The description of the functions has been updated
  - A temporary DOI has been attached until the review of the original
    manuscript is finished

                        Changes in version 2.0.3                        

  - summary.bin has been added to the output of ctbi, and it condenses
    the information about the size of a bin (bin.size), the minimum
    number of points for a bin to be accepted (bin.size.min.accepted)
    and the Stacked Cycle Index (SCI)
  - the mean of mean cycle has been subtracted and added to the
    long.term
  - examples have been updated

                        Changes in version 2.0.2                        

  - summary.outlier has been added to the output of ctbi, and gives
    information about coeff.outlier (coefficients A,B and C), the
    lower/upper outlier threshold, the number of points used, the value
    of m.star.
  - a minor mistake has been fixed for the residuals
  - the outliers are now present in the column residuals to compare them
    to the lower/upper threshold

                        Changes in version 2.0.1                        

  - ctbi.outliers has been changed with ctbi.outlier. This function now
    only takes an input vector and can handle any univariate datasets
    (no necessarily based on residuals).
  - The Logbox has been updated.
    g_A(m.star)=0.2294exp(2.9416m.star-0.0512m.star^2-0.0684m.star^3)
    and
    g_B(m.star)=1.0585+15.6960m.star-17.3618m.star^2+28.3511m.star^3-11.4726m.star^4
    will respectively calculate the A and B coefficients with m.star =
    (q(0.875)-q(0.625))/(q(0.75)-q(0.25))-0.6165 for right-skewed
    distributions, bounded by [0,2]. The C coefficients is fixed to 50
    (Pearson Family).
  - A "gaussian" method for Logbox has been added, with coeff.outlier =
    c(0.08,2,33)
  - for n < 9, the MAD will be used instead the boxplot rule, with a
    multiplying factor of 10 (instead of usually 3) to limit the number
    of false positive to 0.5%.

                        Changes in version 2.0.0                        

  - The LogBox method has changed from alpha = 1+klog(n) to alpha =
    Alog(n)+B+C/n
  - k.outliers has been replaced by coeffs.outlier = c(A,B,C).
  - SCI.min = Inf has been replaced by SCI.min = NA when no values are
    imputed

                 Changes in version 1.0.1 (2022-03-09)                  

  - F or T have been replaced with FALSE or TRUE
  - Example 2 has been added to the help of ctbi
  - Value has been added to the help of ctbi.plot and hidd.seq

                        Changes in version 1.0.0                        

  - ORCID of Francois Ritter has been updated.
  - n.bin.min <- floor(bin.size*(1-bin.max.f.NA)) has been replaced with
    n.bin.min <- ceiling(bin.size*(1-bin.max.f.NA)). This means that for
    bin.max.f.NA = 0.2 (bins with at least 80% of data are accepted) and
    bin.size = 12 (e.g., 12 months of data per bin), a bin with 9 months
    of data will be rejected.
  - n.bin.min (minimum number of data points for a bin to be accepted)
    has been added as an output of ctbi in list.main.
  - The starting and ending boundaries of the long.term interpolation
    were treated as NA values. This has been removed.
  - The numeric class of data0[,y] has been forced to avoid problems
    with integers.
  - Fixed a minor problem with the error message concerning k.outliers
  - Fixed a plotting issue with the y range.