Log Message:
Update to 1.0.5

Upstream changes:
CHANGES IN VGAM VERSION 1.0-5

NEW FEATURES

    o   extlogit() now handles 'deriv = 3'.
    o   Generic function hdeff() implements an analytic solution
        for the following families: borel.tanner(), felix(), lindley().
        For almost all other families, finite-difference approximations
        to derivatives means that first and second derivatives can be
        computed, even with models with 'xij' terms.
    o   Generic function wald.stat() implements Wald tests with SEs evaluated
        at the null values, not at the original MLE, so do not suffer from the
        Hauck-Donner effect.
    o   "vglm" objects have a new "charfun" slot, for the
        characteristic function.
    o   "summary.vglm" and "summary.vlm" objects have new
        "coef4lrt0", "coef4score0", "coef4wald0" \ 
slots, for
        storing the 'Wald table' equivalent of LRTs, score tests and
        modified Wald tests.
        The latter has its SEs computed at the null values with the
        other coefficients obtained by further IRLS iterations, etc.
        Function summaryvglm() has arguments 'lrt0.arg', 'score0.arg',
        'wald0.arg'.
    o   TIC() is new, for the Takeuchi's Information Criterion.
        Thanks to Khedhaouiria Dikra for suggesting this.
    o   mills.ratio() and mills.ratio2() are exported.
    o   New functions: lrt.stat(), score.stat(), wald.stat(),
        which.etas(), which.xij().
    o   cauchy1() and cauchy() handle multiple responses and have
        been modernized a bit.
    o   Tested okay on R 3.4.3.

BUG FIXES and CHANGES

    o   Setting 'deriv.arg' a positive value in plotvgam() when there
        are no s() terms results in a warning.
        Thanks to Barry Goodwin for detecting this.
    o   cens.poisson() can better handle large lambda values, at least for
        left and right censored data (but not for interval-censored data yet).
        Thanks to Eugenie Hunsicker for picking up deficiencies in the code.
    o   In multinomial.Rd, it was stated that setting parallel = TRUE
        did not make the intercepts the same. It does make them the same.
        Thanks to Stuart Coles for picking this up.
    o   binomialff(multiple.responses = TRUE) returned an incorrect deviance.
    o   bilogistic() uses SFS rather than BFGS as its algorithm.
    o   Deprecated: lrp(), normal1() [use uninormal() instead].