version 0.22.3

Add GenericGradientBooster for regression and classification. See: https://thierrymoudiki.github.io/blog/2024/10/06/python/r/genericboosting and examples/genboost*

version 0.18.2

Gaussian weights in LSBoostRegressor and LSBoostClassifier randomized hidden layer

version 0.17.0

add ElasticNetRegressor solver to LSBoostRegressor and LSBoostClassifier

version 0.16.0

add clustering to LSBoostRegressor, LSBoostClassifier, and AdaOpt
add polynomial features to LSBoostRegressor, LSBoostClassifier

version 0.12.3

add prediction intervals to LSBoostRegressor (split conformal prediction, split conformal prediction with Kernel Density Estimation, and split conformal prediction bootstrap) see examples/lsboost_regressor_pi.py for examples
do not rescale columns with zero variance in LSBoostRegressor and LSBoostClassifier
faster ridge regression for LSBoostRegressor and LSBoostClassifier

version 0.9.0

dowload data from R-universe

version 0.8.11

install numpy before setup
stop using np.int
update Makefile
update examples
no more refs to openmp (for now)
update and align with R version
submit conda version

version 0.8.8

Avoid division by 0 in scaling

version 0.8.7

Fix row_sample in LSBoostRegressor

version 0.3.0

include cython parallel processing Pt.1
include manhattan distance

version 0.2.0

adjust setup.py

version 0.1.0

Initial version