Change log file for ks

1.8.5 
-Fully unconstrained pilot selectors pilot="dunconstr" for Hscv(), 
 Hpi() for density derivative estimation. 
-Unconstrained Hlscv() selector for density derivative estimation.

1.8.4
-Reinstated psi.ns code (more efficient than eta.kfe.y) and SAMSE
 pilot estimators Hpi(, pilot="samse").
-Edited help manual.

1.8.3 
-Added computationally efficient density derivative b/w selectors
 Hpi(deriv.order=), Hscv(deriv.order=), and their diagonal counterparts
 Hpi.diag(), Hlscv.diag().  
-Added computationally efficient kernel functional estimators in
 eta.kfe.y() used in kde.test().
-New pilot selectors for density derivatives.   
-Added abs.cont capability to plot(, disp="filled.contour").
-Removed explicit expressions in psins() for d>2, replaced by
 eta.kfe() evaluations.
-Removed psins() and Theta6() evaluations in gsamse and gamse.scv. 
-Removed kfold arguments.  
 
1.8.2 
-Fixed bug in kde.points.sum() to avoid allocating large matrices for
 unbalanced sample sizes for x and eval.points.   
-Fixed bug in dmvnorm.deriv.sum() which had excluded last partition
 class for double.loop=FALSE.
-Added binned options to kde.test().
-Fixed bug for exact estimation in kfe(). 
-Added plotting colours as function of z-value in plot.kde(,
 disp="persp").   
-Added decoupled calculcation for Hlscv().
-Added optim.fun option to select optimiser function in Hpi, Hpi.diag,
 Hlscv, Hlscv.diag, Hscv, Hscv.diag().  
 
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.E. 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. 
-Changed 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().