Change log file for ks

1.8.1 -Modified p-value calculation for large -ve Z-statistics. 
      -Fixed bug for binned estimation for unconstrained bandwidths for kde() 

1.8.0 -Added density derivative selectors Hpi(,deriv.order=r), Hlscv(,deriv.order=r) for r>0 from J. Chacon.
      -Changed vech(H) terms to vec(H) in AMISE estimators.
      -Changed default binning gridsize for 3-d data from rep(51,3) to rep(31,3).
      -Added verbose option to b/w selectors (in double sum) for tracking progress
      -Changed LSCV, SCV selectors optimisation from Nelder-Mead to BFGS.
      -Changed Fortran linear bining code to C (and fixed bugs in Fortran code). 
      -Added modification to linear binning for boundary points.
      -Removed explicit derivatives in BCV selector optimisation.
 

1.7.4 -Fixed small bug in partitioning method for kde.points.sum().

1.7.3 -Changed partitioning method for dmvnorm.deriv.sum() and kde.points.sum().

1.7.2 -Changed p-value calculation for kde.test().

1.7.1 -Reinstated single partial derivative of mv normal for scalar variance matrix dmvnorm.deriv.scalar.sum()
       for use in AMSE pilot plug-in selectors. 
      -More efficient form of kdde().
  
1.7.0 -Added KDE-based 2-sample test kde.test().
      -Modified output of plotmixt().
      -Added "double.loop" option to kfe() for large samples - increases running time, reduces memory. 
      -Modified dmvnorm.deriv.sum() gto improve memory memory management for large samples.
      -Cleaned up code for plug-in bandwidth selectors and kernel functional estimators.
      -Cleaned up help files. 
      -Disabled kfold b/w selectors. 



1.6.13 -Added flag to automatically compute probability contour levels in kde(). 

1.6.11 -Added own version of filled contours as option disp="filled.contour2" and different 
        colours for disp="slice" contours.

1.6.10 -Added k-fold b/w selectors.

1.6.9  -Added approximate option in contourLevels().
       -Added kdde() kernel density derivative estimators.

1.6.8 Added 1-d LSCV selector hlscv().
 
1.6.7 Corrected ISE for normal mixtures, from J.E. Chacon.

1.6.6 Added MISE, AMISE, ISE functions for normal mixtures derivatives. 
      Changes to internal double sum calculations from J.E. Chacon.

1.6.x -1-d binned KDE fix from M.P. Wand.
      -Streamlined code sharing with feature package (all binning code now
       contained only in ks).
      -Reorganised and renamed internal bandwidth selection functions (mostly
       double sums of normal densities). 



1.5.11 Fixed small bugs in drvkde, vech, Hpi(, pilot="unconstr")
   
1.5.10 Added drvkde (kernel density derivative estimator 1-d) from 
       feature using M.P. Wand's code.

1.5.x  -Added normal mixture (A)MISE-optimal selectors: hamise.mixt, 
        hmise.mixt, Hamise.mixt, Hmise.mxt.
       -Added distribution functions for 1-d KDEs: dkde, pdke, qkde, rkde   
       -Added plug-in selectors for 1-d data (exactly the code for dpik from 
        KernSmooth). For KDE, this is hpi, for KDA, this is 
        hkda(, bw="plugin").
       -Made changes to specifying line colour (col rather than lcol) in 
        plot.kde, plot.kda.kde and partition class colour (partcol) in 
        plot.kda.kde. 
       -Added plot3d() capabilities from rgl to 3-d plot - removing own axes
        drawing functions.
       -New functions to compute pilot functinal estimators hat{psi}_r(g).
        These are exact, and are more efficient than binned estimators for small
        samples (~100), and are available in d > 4.


1.4.x -Vignette illustrating 2-d KDE added      
      -Binned estimation implemented for KDE with diagonal selectors and 
       pilot functional estimation with diagonal selectors.
      -Filled contour plots added as disp=filled option in plot.kde().
      -compare.kda.cv() and compare.cv() modified to improve speed.
      -Hscv.diag() and Hbcv.diag() added for completeness.



1.3.5 Fixed small bug in compare.kda.cv() and compare.kda.diag.cv().

1.3.4 RGL-type plots added for 3-d data. Specification of 3-d contour levels 
      now same order as 2-d contours. 

1.3.x Multivariate (for 3 to 6 dimensions inclusive) bandwidth selectors added
      for Hpi(), Hpi.diag(), Hlscv(), Hlscv.diag() and Hscv(). 
      NB: because Hbcv() and Hbcv.diag()  performed poorly for 2-d, these 
      weren't implemented in higher dimensions.



1.2.x -Package checked by CRAN testers and accepted on the CRAN website. 
       To pass all the necessary checks involved some internal programming
       changes but has not affected the user interface.
      -The child mortality data set unicef is used in the examples.



1.1.x -S3 type objects have been introduced.  The output from kde() are 
       `kde' objects. The output from kda.kde() and pda.pde() are `dade' 
       objects. Corresponding plot functions are called automatically by 
       invoking `plot'.
      -Kernel discriminant analysers are now available. Parametric (linear and 
       quadratic) discriminant analysers are accessed using  `pda'.
      -adapt library is no longer required. This was formerly used on
       the functions for integrated squared error computations ise.mixt()
       and iset.mixt().
