How to fit nonparametric mixtures of regression models in a statistical program? I am interested in estimating a nonparametric finite mixture regression model, explained for example here:
https://methodology.psu.edu/media/techreports/09-93.pdf
But I don't know with which program the simulation study (p. 10) or the data set (p. 15) were analyzed.
Does anybody have an idea how to fit a nonparametric mixture regression model for example in R or SAS?  
 A: I can't speak for R but SAS has several approaches to nonparametric mixture regression. The core of them has to be PROC FMM (for finite mixture models). Here is an introduction to this approach that includes a comparison with kernel density estimation:

Finite mixture models provide a flexible framework for analyzing a
  variety of data. Suppose your objective is to describe the
  distribution of a response variable. If the corresponding data are
  multimodal, skewed, heavy-tailed, or exhibit kurtosis, they may not be
  representative of most known distributions. In this case, you often
  use a nonparametric method such as kernel density estimation to
  describe the distribution. A kernel density estimate generates a
  smoothed, numerical approximation to the unknown distribution function
  and estimates the distribution’s percentiles. Although this approach
  is useful, it might not be the most concise way to describe an unknown
  distribution. A finite mixture model provides a parametric alternative
  that describes the unknown distribution in terms of mixtures of known
  distributions. A finite mixture model also enables you to assess the
  probabilities of events or simulate draws from the unknown
  distribution the same way you do when your data are from a known 
  distribution.

This quote is taken from this document: https://support.sas.com/resources/papers/proceedings12/328-2012.pdf
Of course your interest is in nonparametric mixture models but the suggestion of using kernel density estimation for nonparametric mixture modeling opens up a wide range of SAS options. Proc KDE is the core procedure for kernel density estimation. This paper introduces a macro for Smoothing-Spline-Based Functional Mixed Effects Models ... file:///C:/Documents%20and%20Settings/Administrator/My%20Documents/Downloads/v43c01.pdf
This paper describes an adaptation of Proc Mixed for spline smoothing in parametric and nonparametric mixture models... http://www2.sas.com/proceedings/sugi28/268-28.pdf
Here is the abstract:

Mixed models are an extension of regression models that allows for
  incorporation of random effects. The application of mixed-effects
  models to practical data analysis has greatly expanded with consequent
  development of theory and computer software. It also turns out that
  mixed models are closely related to smoothing. Nonparametric
  regression models, especially the general smoothing spline models, are
  well known for their ability to fit an arbitrary mean response
  function. This paper describes the use of the MIXED procedure for
  fitting nonparametric or semi-parametric regression models. Compared
  with such SAS procedures as PROC LOESS and PROC TPSPLINE, the use of
  the PROC MIXED allows the fitting of a wide spectrum of complex
  non-parametric and semiparametric regression models with simultaneous
  modeling of trends and covariance structure. 

Google searches turned up many more papers of an ancillary nature in areas such as semi-parametric survival analysis, spline estimation with TSPLINE, kernel density clustering, and so on, all using SAS procedures.
