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...unction for multivariate mixed-data copula and copula density functions (`npcopula') that exploits Li and Racine (2013) Deprecated: With these new methods we have deprecated the use of `cdf=TRUE' for density and conditional bandwidth objects (i.e. those generated with `npudensbw' and `npcdensbw') and decoupled this functionality into `npudensbw'/`npudistbw' and `npcdensbw'/`npcdistbw' (previously bandwidths optimal for PDFs were used for CDFs given the lack of methods for mixed-data CDF bandwidth selection). Bug fixes: In addition we have corrected a number of bugs r...

...unction for multivariate mixed-data copula and copula density functions (`npcopula') that exploits Li and Racine (2013) Deprecated: With these new methods we have deprecated the use of `cdf=TRUE' for density and conditional bandwidth objects (i.e. those generated with `npudensbw' and `npcdensbw') and decoupled this functionality into `npudensbw'/`npudistbw' and `npcdensbw'/`npcdistbw' (previously bandwidths optimal for PDFs were used for CDFs given the lack of methods for mixed-data CDF bandwidth selection). Bug fixes: In addition we have corrected a number of bugs r...

...evaluated at each of the 176 points. I have 2 continuous and 3 ordered discrete variables. My sample size is 176. So edata is a 176x(2+3) data frame, while tdat is a 1x(2+3) vector. bw_cx[i,] is a 1x (2+3) vector representing the bandwidths h for each variable, which were calculated using the npcdensbw function. (no this is not a mistake, I deliberately use the conditional one) For this I use the below function in R. kerz <- npudens(bws=(bw_cx[i,]),cykertype="epanechnikov",cxkertype="epanechnikov",oxkertype="liracine",tdat=tdata,edat=dat) In version 2.15.2, th...