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 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>.

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>.