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**NNS (v0.5.5)**

now on CRAN has an updated partial derivative routine **. This function estimates true average partial derivatives, as well as ceteris paribus conditions for points of interest.**

`dy.d_()`

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 **, and for both regressors**

`x_2 = 0.5`

**and**

`x_1`

**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",]

[[1]]

[1] 0.2454744

[[2]]

[1] 0.2439307

The analytical solution for both regressors at

**is 0.25.**

`x_1 = x_2 = 0.5`

The referenced paper gives many more examples, comparing

**to kernel regression gradients and OLS coefficients.**

`dy.d_()`

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