ctbi (development version)
ctbi 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
ctbi 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
ctbi 2.0.3 (2022/07/27)
- 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
ctbi 2.0.2 (2022/07/25)
- 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
ctbi 2.0.1 (2022/07/22)
- 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.star2-0.0684m.star3)
and
g_B(m.star)=1.0585+15.6960m.star-17.3618m.star2+28.3511m.star3-11.4726m.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%.
ctbi 2.0.0 (2022/06/10)
- 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- k.outliers has been replaced by coeffs.outlier = c(A,B,C).
- SCI.min = Inf has been replaced by SCI.min = NA when no values are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
ctbi 1.0.1 (2022/03/08)
- 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
ctbi 1.0.0 (2022/03/01)
- 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.