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A simple, straightforward way to estimate density nonparametrically is kernel density estimator, for instance, in R a built-in function density() is for this, with different kernel choices "gaussian", "epanechnikov", "rectangular", "triangular", "biweight", "cosine", and "optcosine". Should you are unhappy with this function and eager for an extention, take a look at the following papers and associated codes:

"Exact Mean Integrated Squared Error of Higher-Order Kernels" Econometric Theory (2005).
"Bandwidth Selection for Nonparametric Distribution Estimation" unpublished working paper (2004).
"Nonparametric Estimation of Smooth Conditional Distributions" unpublished working paper (2004).
"Interval Forecasts and Parameter Uncertainty" Journal of Econometrics (2006).

http://www.ssc.wisc.edu/~bhansen/progs/progs_np.html

One of widely applied non-parametric density estimation methods. Fast and accurate state-of-the-art bivariate kernel density estimator with diagonal bandwidth matrix. The kernel is assumed to be Gaussian. The two bandwidth parameters are chosen optimally without ever using/assuming a parametric model for the data or any "rules of thumb".  Unlike many other procedures, this one is immune to accuracy failures in the estimation of multimodal densities with widely separated modes.

http://www.mathworks.com/matlabcentral/fileexchange/17204