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`NNS (v0.5.5)` now on CRAN has an updated partial derivative routine `dy.d_()` . This function estimates true average partial derivatives, as well as ceteris paribus conditions for points of interest.

Example below on the syntax for estimating first derivatives of the function `y = x_1^2 * x_2^2` , for the points `x_1 = 0.5` and `x_2 = 0.5`, and for both regressors `x_1` and `x_2`.

`set.seed(123)`
`x_1 = runif(1000)`
`x_2 = runif(1000)`
```y = x_1 ^ 2 * x_2 ^ 2 dy.d_(cbind(x_1, x_2), y, wrt = 1:2, eval.points = t(c(.5,.5)))["First",] []  0.2454744 []  0.2439307```

The analytical solution for both regressors at `x_1 = x_2 = 0.5` is 0.25.

The referenced paper gives many more examples, comparing `dy.d_()` to kernel regression gradients and OLS coefficients.

For even more `NNS` capabilities, check out the examples at GitHub:
https://github.com/OVVO-Financial/NNS/blob/NNS-Beta-Version/examples/index.md

Reference Paper:
Vinod, Hrishikesh D. and Viole, Fred, Comparing Old and New Partial Derivative Estimates from Nonlinear Nonparametric Regressions
https://ssrn.com/abstract=3681104

Supplemental Materials:
https://ssrn.com/abstract=3681436

Numerical Partial Derivative Estimation – the {NNS} package was first posted on September 3, 2020 at 5:20 pm.