The np Package - NexTag Supports Open Source Initiatives

npcdens 15npcdensKernel Conditional Density and Distribution Estimates with MixedDatatypesDescriptionUsagenpcdens computes kernel conditional density estimates on p+q-variate evaluation data, given a setof training data (both explanatory and dependent) and a bandwidth specification (a conbandwidthobject or a bandwidth vector, bandwidth type, and kernel type) using the method of Hall, Racine,and Li (2004). Similarly npcdist computes kernel conditional cumulative distribution estimates.The data may be continuous, discrete (unordered and ordered factors), or some combination thereof.npcdens(bws, ...)## S3 method for class 'formula':npcdens(bws, data = NULL, newdata = NULL, ...)## S3 method for class 'call':npcdens(bws, ...)## S3 method for class 'conbandwidth':npcdens(bws,txdat = stop("invoked without training data 'txdat'"),tydat = stop("invoked without training data 'tydat'"),exdat,eydat,gradients = FALSE,...)## Default S3 method:npcdens(bws,txdat = stop("training data 'txdat' missing"),tydat = stop("training data 'tydat' missing"),exdat,eydat,gradients, ...)Argumentsbwsa bandwidth specification. This can be set as a conbandwidth object returnedfrom a previous invocation of npcdensbw, or as a p + q-vector of bandwidths,with each element i up to i = p corresponding to the bandwidth for column i intxdat, and each element i from i = p + 1 to i = p + q corresponding to thebandwidth for column i − p in tydat. If specified as a vector, then additional