Package: CaDENCE 1.2.5

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>.

Authors:Alex J. Cannon

CaDENCE_1.2.5.tar.gz
CaDENCE_1.2.5.zip(r-4.7)CaDENCE_1.2.5.zip(r-4.6)CaDENCE_1.2.5.zip(r-4.5)
CaDENCE_1.2.5.tgz(r-4.6-any)CaDENCE_1.2.5.tgz(r-4.5-any)
CaDENCE_1.2.5.tar.gz(r-4.7-any)CaDENCE_1.2.5.tar.gz(r-4.6-any)
CaDENCE_1.2.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CaDENCE/json (API)

# Install 'CaDENCE' in R:
install.packages('CaDENCE', repos = c('https://alexcannon.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • FraserSediment - Sediment and stream discharge data for Fraser River at Hope

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.49 score 31 scripts 281 downloads 32 exports 1 dependencies

Last updated from:7c754e5e40. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK108
source / vignettesOK146
linux-release-x86_64OK100
macos-release-arm64OK167
macos-oldrel-arm64OK126
windows-develOK69
windows-releaseOK117
windows-oldrelOK103
wasm-releaseOK80

Exports:cadence.costcadence.evaluatecadence.fitcadence.initializecadence.predictcadence.reshapedbgammadblnormdbpareto2dbweibulldpareto2dummy.codegam.stylelogisticpbgammapblnormpbpareto2pbweibullppareto2qbgammaqblnormqbpareto2qbweibullqpareto2rbfrbgammarblnormrbpareto2rbweibullrpareto2rpropxval.buffer

Dependencies:pso