TO DO
------

o  Generalizing  'wgt.himedian':  We'd want a C API on which R builds.

   There are pure R implementations:
    - 'weighted.median()' in limma
    	 and I have generalized it ---> inst/ex-funs.R
    - more general code (different 'tie' strategies; weighted *quantile*s)
      in /u/maechler/R/MM/STATISTICS/robust/weighted-median.R
    - The 'Hmisc' package has wtd.quantile()

o covOGK():
  The argument name 'weight.fn' is pretty ugly and the default function
  name 'hard.rejection()' is just awful (we need a globally available
  function as 'role model'.

  - Consider making 'hard rejection'  the default though!
  -   "        "    n.iter = 1 or = 2 the default.

  - Could allow 'n.iter = 0' to simply compute Cov()_{ij} = rcov(X_i, X_j)

o scaleTau2():  Also do a cheap finite-sample correction [MM] !
		[done partly; but undocumented, since bound to change]

--- rrcov etc ---

o rrcov.control() __ NEEDS  name change ! ______
   probably use  mcd.control() and lts.control()

   or forget about *control() completely?
   since there are only a few in each ??????/

o tolellipse() --> renamed to tolEllipsePlot()
     - maybe use  cluster::ellipsoidPoints()
     - allow other percentiles than just  97.5%
     - maybe *return* something

o plot(mcd. ) [ R/covPlot.R ] : should show the call

o ltsReg():  has  undocumented '$resid'
	     in addition to '$residuals' and '$raw.residuals';
	     drop it or document it !

--- glmrob --

o glmrob(*, weights.on.x = "robCov")  currently uses  MASS::cov.rob(), i.e.
  "MVE" and Andreas had a comment that "mcd" is worse.
  But still, I (MM) strongly believe we should use  covMcd() instead.
  HOWEVER: Need something better when 'X' has (binary!) factors!
  "hat" +- works, but needs more work

o Maybe  we should consider  'family = gaussian'  {analogy to glm()}
  as a possible setting, and just divert to  lmrob() in that case

o psi.*(....,  rho = FALSE/TRUE)   functions from Andreas
  should be replaced by using the new  psi_func  objects
  --> ./R/psi-funs-AR.R  &  ./man/pkg-internal.Rd

o predict.glmrob() and probably also residuals.glmrob()
  should allow a 'type = *' specification like the *.glm() methods.

o glmrob() needs tests in ./tests/   [maybe take from man/glmrob.Rd]
	   take those from Martin's old 'robGLM1' package (need more!)
o --> first test already shows that Martin's tests for "huberC == Inf"
      were *not* yet moved from robGLM1 to glmrob()...

o also, ni = 0 does not work quite as it should ( ./tests/binom-ni-small.R )

o obj $ df ...  maybe should be defined -- for "glm" methods to be
  applicable

o summary.glmrob() should be better documented;
  we should decide if the current return value is fine.

o Eva's code (and MM's) also computed & returned the "asymptotic efficiency"!

o anova.glmrob(): More modularization, allowing to provide own 'test' function.
  Test if Huber's C are different. Need theory to compare different C's and
  same model (which includes classical vs robust).

o drop1() would be nice


--- nlrob ---

o nlrob() needs tests in ./tests/ -- you can take some from man/nlrob.Rd

o summary.nlrob() is currently a "no-op" -- printing it should summarize
		  robustness weights!

------

o Add data sets from the MMY-book  -- try to use the same variable names as
  in the book [for that, need to wait for the book; to look at outputs or
  S-plus scripts].

--- lmrob --- --- --- --- ---

o more tests in	tests/

o fully implement and test the multivariate case (y = matrix with > 1 col.)

o src/lmrob.c :

   Things to fix:

   - does many many vector and vector-matrix things itself
     instead of using BLAS and LAPACK
   - does median() , MAD() instead of using R's  sort() routines
