dsdp
Density Estimation with Semidefinite Programming
The models of probability density functions are Gaussian or exponential distributions with polynomial correction terms. Using a maximum likelihood method, 'dsdp' computes parameters of Gaussian or exponential distributions together with degrees of polynomials by a grid search, and coefficient of polynomials by a variant of semidefinite programming. It adopts Akaike Information Criterion for model selection. See a vignette for a tutorial and more on our 'Github' repository <https://github.com/tsuchiya-lab/dsdp/>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.1-1 |
rolling linux/jammy R-4.5 | dsdp_0.1.1-1.tar.gz |
761.3 KiB |
0.1.1-1 |
rolling linux/noble R-4.5 | dsdp_0.1.1-1.tar.gz |
760.9 KiB |
0.1.1-1 |
rolling source/ R- | dsdp_0.1.1-1.tar.gz |
635.4 KiB |
0.1.1-1 |
latest linux/jammy R-4.5 | dsdp_0.1.1-1.tar.gz |
761.3 KiB |
0.1.1-1 |
latest linux/noble R-4.5 | dsdp_0.1.1-1.tar.gz |
760.9 KiB |
0.1.1-1 |
latest source/ R- | dsdp_0.1.1-1.tar.gz |
635.4 KiB |
0.1.1-1 |
2026-04-23 source/ R- | dsdp_0.1.1-1.tar.gz |
635.4 KiB |
0.1.1 |
2026-04-09 windows/windows R-4.5 | dsdp_0.1.1.zip |
630.9 KiB |
0.1.1 |
2025-04-20 source/ R- | dsdp_0.1.1.tar.gz |
339.7 KiB |