CaDENCE
Conditional Density Estimation Network Construction and Evaluation
Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.2.5 |
rolling linux/jammy R-4.5 | CaDENCE_1.2.5.tar.gz |
153.0 KiB |
1.2.5 |
rolling linux/noble R-4.5 | CaDENCE_1.2.5.tar.gz |
152.8 KiB |
1.2.5 |
rolling source/ R- | CaDENCE_1.2.5.tar.gz |
68.5 KiB |
1.2.5 |
latest linux/jammy R-4.5 | CaDENCE_1.2.5.tar.gz |
153.0 KiB |
1.2.5 |
latest linux/noble R-4.5 | CaDENCE_1.2.5.tar.gz |
152.8 KiB |
1.2.5 |
latest source/ R- | CaDENCE_1.2.5.tar.gz |
68.5 KiB |
1.2.5 |
2026-04-26 source/ R- | CaDENCE_1.2.5.tar.gz |
68.5 KiB |
1.2.5 |
2026-04-23 source/ R- | CaDENCE_1.2.5.tar.gz |
68.5 KiB |
1.2.5 |
2026-04-09 windows/windows R-4.5 | CaDENCE_1.2.5.zip |
156.0 KiB |
1.2.5 |
2025-04-20 source/ R- | CaDENCE_1.2.5.tar.gz |
68.5 KiB |