Changelog
Source:NEWS.md
squat 0.3.0
CRAN release: 2024-01-10
New features
- Added
S3
specialization of thestats::predict()
function forprcomp_qts
objects. - Added function
qts2aats()
which allows to switch from quaternion to axis-angle representation of rotations. - Added usual operations
+
,-
,*
andinverse_qts()
for quaternion time series using the Eigen library.
Small improvements
- Make sure quaternion geodesic mean is not flipped.
- Fix issues in PCA:
- avoid numerical overflows due to bad choice of
k
ingam()
; - improved documentation;
- Use same number of basis functions in uni- and multivariate decompositions.
- avoid numerical overflows due to bad choice of
- Updated GHA scripts and
README
. - Fix bug related to Rcpp following RcppCore/Rcpp#1287.
squat 0.2.1
CRAN release: 2023-07-09
- Add
use_fence
robustification option; - Adapt to changes in fdacluster;
- Properly compute tangent spaces along mean QTS;
- Update News section of website.
squat 0.2.0
CRAN release: 2023-06-04
Major features:
- Added hierarichal clustering;
- Added DBSCAN clustering;
- Added distance matrix computation.
Minor improvements:
- Adapted code to match new API in fdacluster package.
squat 0.1.0
CRAN release: 2022-12-22
Major statistical features
- A first API proposal with a class
qts
and a classqts_sample
for which a number of methods are properly implemented. - Available statistical methods for QTS samples:
- Added multiple ways of displaying samples of QTS.
- Added two example datasets.
Improvements
- Make all functions applicable to a single QTS also applicable to QTS samples, with appropriate class for the output.
- Enable
as_qts_sample()
to generate a QTS sample of size 1 from a single QTS as input argument. - Rename
change_points
argument to theplot.qts()
function to better reflect its flexibility. - Added subset operator for QTS sample objects.
- Added
append
S3 method for QTS sample objects. - Added
hemispherize()
function to remove any discontinuities in QTS due to quaternion flips. - Any parallelization computation is now handled using the futureverse principles and, in particular, implemented through the use of the furrr package.