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.

Implements controlled interrupted time series (CITS) analysis for evaluating interventions in comparative time-series data. 
    The package provides tools for preparing panel time-series datasets, fitting models using generalized least squares (GLS) with optional autoregressive–moving-average (ARMA) error structures, 
    and computing fitted values and robust standard errors using cluster-robust variance estimators (CR2). 
    Visualization functions enable clear presentation of estimated effects and counterfactual trajectories following interventions. 
    Background on methods for causal inference in interrupted time series can be found in Linden and Adams (2011) <doi:10.1111/j.1365-2753.2010.01504.x> 
    and Lopez Bernal, Cummins, and Gasparrini (2018) <doi:10.1093/ije/dyy135>.

Implementation of the Contextual Importance and Utility 
    (CIU) concepts for Explainable AI (XAI). A description of CIU
    can be found in e.g. Främling (2020) <doi:10.1007/978-3-030-51924-7_4>.

Computes a confidence interval for a specified linear combination of the 
    regression parameters in a linear regression model with iid normal errors
    with known variance when there is uncertain prior information that a distinct
    specified linear combination of the regression parameters takes a given 
    value.  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 has the following three practical applications. 
    Firstly, if the error variance has been accurately estimated from previous 
    data then it may be treated as being effectively known. Secondly, for 
    sufficiently large (dimension of the response vector) minus (dimension of 
    regression parameter vector), greater than or equal to 30 (say),
    if we replace the assumed known value of the error variance by its usual 
    estimator in the formula for the confidence interval then the resulting 
    interval has, to a very good approximation, the same coverage probability 
    and expected length properties as when the error variance is known. Thirdly,
    some more complicated models can be approximated by the linear regression 
    model with error variance known when certain unknown parameters are replaced 
    by estimates. This confidence interval is described in 
    Mainzer, R. and Kabaila, P. 
    (2019) <doi:10.32614/RJ-2019-026>, and is a member of the family of 
    confidence intervals proposed by Kabaila, P. and Giri, K. (2009) 
    <doi:10.1016/j.jspi.2009.03.018>.