Functions to append confidence intervals, prediction intervals,
    and other quantities of interest to data frames. All appended quantities
    are for the response variable, after conditioning on the model and covariates.
    This package has a data frame first syntax that allows for easy piping.
    Currently supported models include (log-) linear, (log-) linear mixed,
    generalized linear models, generalized linear mixed models, and
    accelerated failure time models.

A tool to easily run and visualise supervised and unsupervised state of the art customer segmentation. 
    It is built like a pipeline covering the 3 main steps in a segmentation project: pre-processing, modelling, and plotting.
    Users can either run the pipeline as a whole, or choose to run any one of the three individual steps.
    It is equipped with a supervised option (tree optimisation) and an unsupervised option (k-clustering) as default models.

Implementation of the Contextual Importance and Utility 
    (CIU) concepts for Explainable AI (XAI). A recent description of CIU
    can be found in e.g. Främling (2020) <arXiv:2009.13996>.

Computes a confidence interval for a specified linear combination of 
    the regression parameters in a linear regression model with iid normal 
    errors with unknown variance when there is uncertain prior information 
    that a distinct specified linear combination of the regression 
    parameters takes a specified number. This confidence interval, found by 
    numerical nonlinear constrained optimization, has the required minimum coverage 
    and utilizes this uncertain prior information through desirable 
    expected length properties. This confidence interval is proposed by 
    Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Implementation of the categorical instrumental variable (CIV) 
    estimator proposed by Wiemann (2023) <arXiv:2311.17021>. CIV allows for optimal instrumental 
    variable estimation in settings with relatively few observations per 
    category. To obtain valid inference in these challenging settings, CIV
    leverages a regularization assumption that implies existence of a latent 
    categorical variable with fixed finite support achieving the same first 
    stage fit as the observed instrument.