kernopt is an R package that implements Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by (Senga Kiessé and Durrieu 2024). The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.
You can install the development version of kernopt from GitHub with:
# install.packages("pak")
pak::pak("thomasfillon/kernopt")
This is a basic example which shows how to use the kernopt library to compute the discrete optimal kernel values for some parameters:
library(kernopt)
## Compute the discrete optimal kernel values
k_opt <- discrete_optimal(x = 25, z = 1:50, h = 0.9, k = 20)
print(k_opt)
#> [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.01871809 0.01956892
#> [7] 0.02037611 0.02113967 0.02185959 0.02253589 0.02316855 0.02375758
#> [13] 0.02430298 0.02480475 0.02526288 0.02567739 0.02604826 0.02637550
#> [19] 0.02665910 0.02689908 0.02709542 0.02724813 0.02735721 0.02742266
#> [25] 0.02744448 0.02742266 0.02735721 0.02724813 0.02709542 0.02689908
#> [31] 0.02665910 0.02637550 0.02604826 0.02567739 0.02526288 0.02480475
#> [37] 0.02430298 0.02375758 0.02316855 0.02253589 0.02185959 0.02113967
#> [43] 0.02037611 0.01956892 0.01871809 0.00000000 0.00000000 0.00000000
#> [49] 0.00000000 0.00000000
The documentation is available at https://thomasfillon.github.io/kernopt/.
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