Version 1.0.4

Changes

• Updated citation, since the manuscript was published in Machine Learning:
  Zwanenburg, A. and Löck, S. (2026) Location and scale-invariant power transformations for transforming data to normality,
  Machine Learning, 115(3). Available at: https://doi.org/10.1007/s10994-026-06994-3.

Version 1.0.3

Fixes

• Fixed an error that occurred when assessing if a transformation should be
  rejected, but the empirical central normality test p-value could not be
  computed.

Version 1.0.2

Changes

• ecn.test was renamed to ecn_test. It now additionally takes tau, n,
  and kappa as arguments (see function documentation).

• cn.test was deprecated. Use ecn_test with kappa = 1.0 instead.

• When checking if required packages are installed, power.transform now caches
  results. This prevents unnecessary look-up.

Version 1.0.1

Changes

• The empirical central normality test is now dependent on the number of samples.
  The test can now be called using ecn.test. cn.test is a stricter central
  normality test whose test statistics are determined from strictly normal
  distributions, instead of normal distributions with up to 10% outliers.

• The robust location- and shift-invariant transformations now use weights
  optimised for achieving central normality.

v1.0.0

This is the first public release version of power.transform 🎉