Presents an explanatory animation of normal quantile-quantile plots based on a water-filling analogy.  The animation presents a normal QQ plot as the parametric plot of the water levels in vases defined by two distributions.  The distributions decorate the axes in the normal QQ plot and are optionally shown as vases adjacent to the plot.  The package draws QQ plots for several distributions, either as samples or continuous functions.

This function performs QR factorization without pivoting to a real or complex matrix. It is based on Anderson. E. and ten others (1999) "LAPACK Users' Guide". Third Edition. SIAM.

Methods for detecting structural breaks, determining the
    number of breaks, and estimating break locations in linear quantile
    regression, using one or multiple quantiles, based on Qu (2008) and
    Oka and Qu (2011).  Applicable to both time series and repeated
    cross-sectional data. The main function is rq.break().

        References for detailed theoretical and empirical explanations:

        (1) Qu, Z. (2008).  "Testing for Structural Change in Regression
    Quantiles."  Journal of Econometrics, 146(1), 170-184
    <doi:10.1016/j.jeconom.2008.08.006>

        (2) Oka, T., and Qu, Z. (2011).  "Estimating Structural Changes in
    Regression Quantiles."  Journal of Econometrics, 162(2), 248-267
    <doi:10.1016/j.jeconom.2011.01.005>.

Functions are provided that implement the 
    use of the Fieller's formula methodology, for 
    calculating a confidence interval for a ratio of
    (commonly, correlated) means.  See Fieller (1954)
    <doi:10.1111/j.2517-6161.1954.tb00159.x>.  Here,
    the application of primary interest is to studies
    of insect mortality response to increasing doses
    of a fumigant, or, e.g., to time in coolstorage.
    The formula is used to calculate a confidence
    interval for the dose or time required to achieve
    a specified mortality proportion, commonly 0.5 
    or 0.99.  Vignettes demonstrate link functions
    that may be considered, checks on fitted models,
    and alternative choices of error family.  Note
    in particular the betabinomial error family.
    See also Maindonald, Waddell, and Petry (2001) 
    <doi:10.1016/S0925-5214(01)00082-5>.