Structure mining from 'XGBoost' and 'LightGBM' models.
Key functionalities of this package cover: visualisation of tree-based ensembles models,
identification of interactions, measuring of variable importance,
measuring of interaction importance, explanation of single prediction
with break down plots (based on 'xgboostExplainer' and 'iBreakDown' packages).
To download the 'LightGBM' use the following link: <https://github.com/Microsoft/LightGBM>.
'EIX' is a part of the 'DrWhy.AI' universe.

c(
person("Szymon", "Maksymiuk", email = "sz.maksymiuk@gmail.com", role = c("aut", "cre")),
person("Ewelina", "Karbowiak", email = "ewelina.karbowiak12@gmail.com", role = c("aut")),
person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut","ths"))
)
MASS, ggplot2, data.table, purrr, xgboost, DALEX, ggrepel,
ggiraphExtra, iBreakDown, tidyr, scales
Szymon Maksymiuk [aut, cre],
Ewelina Karbowiak [aut],
Przemyslaw Biecek [aut, ths]

MCMC Procedures for Estimating Enzyme Kinetics Constants
Boseung Choi, Grzegorz A. Rempala, Jae Kyoung Kim
Functions for estimating catalytic constant and Michaelis-Menten constant
for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs
sampler based on the Bayesian framework. Additionally, a function to create
plot to identify the goodness-of-fit is included.

Hyukpyo Hong, Boseung Choi, Jae Kyoung Kim
Functions for estimating catalytic constant and Michaelis-Menten constant
for enzyme kinetics model using Metropolis-Hasting algorithm within Gibbs
sampler based on the Bayesian framework.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Edmunds Cers <edmunds.cers@gmail.com>, Janis Valeinis <valeinis@lu.lv>

c(
person("Janis", "Valeinis", role = "aut", email = "janis.valeinis@lu.lv", comment = c(ORCID = "0000-0003-0989-0444")),
person("Edmunds", "Cers", role = "aut", email = "edmunds.cers@gmail.com"),
person("Janis", "Gredzens", role = "cre", email = "janis.gredzens@lu.lv"))
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.
Janis Valeinis [aut] (<https://orcid.org/0000-0003-0989-0444>),
Edmunds Cers [aut],
Janis Gredzens [cre]