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'.

  - 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
	     Default for 'ask' should be smarter: depend on
	     prod(par("mfrow")) < #{plots} (which depends on 'classic' and p=2)

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. [ -> R/glmrobMqle.R ]
  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 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 glmrob() needs a bit more tests in ./tests/
           [also consider those 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()...
   (in other words:  glmrob() should work

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  --> e.g. for predict(<glmrob>, interval="..") !

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  FIXME -- no longer need the "put everything on the right" hack before calling nls()
   --> cleaner code also when robustness weights become 0.

o  residuals( nlrob(), type = "...") should provide types
   	      "as in the litterature"

------

o Add data sets from the MMY-book -- mostly done {do we have *all* ?}


--- 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

----

o Alternative version of covOGK() for correlation-only
  using's Huber's correlation formula which ensures [-1,1] range
  --> ~/R/MM/Pkg-ex/robustbase/robcorgroesser1.R
  and ~/R/MM/STATISTICS/robust/pairwise-new.R

o package 'riv' (author @ epfl.ch!) has 'slc()'  ~=  cov.S(.)  -- in pure R code
  doesn't Valentin have a version too?
  otherwise: test this, ask author for "donation" to robustbase

o adjOutlyingness() :
    typo-bug is corrected; and I have made it more pretty.
    Still a bit problematic when denominator = 0
    Currently leave away all the c/0 = Inf and 0/0 = NaN values.

    MM: Maybe, it's the fact that the   coef = 1.5  should really depend on
        the sample size  n   and will be too large for small n (??)
  --> should ask Mia and maybe Guy Brys

