Why neural-network imputation? | The architecture: deepimp_arch() | Imputing mixed-type data with impNNet() | Compositional data and rounded zeros with impNNetCoDa() | Choosing a backend | Reproducibility | A note on multiple completions | Reference
Package: deepImp 1.1.0
deepImp: Imputation with Deep Learning Methods
Imputation of mixed-type and compositional data with neural networks. The architecture (number and size of hidden layers, dropout, activation, optimiser) is user-configurable. See Templ (2021) <doi:10.1007/978-3-030-71175-7>.
Authors:
deepImp_1.1.0.tar.gz
deepImp_1.1.0.zip(r-4.7-any)deepImp_1.1.0.zip(r-4.6-any)deepImp_1.1.0.zip(r-4.5-any)
deepImp_1.1.0.tgz(r-4.6-any)deepImp_1.1.0.tgz(r-4.5-any)
deepImp_1.1.0.tar.gz(r-4.7-any)deepImp_1.1.0.tar.gz(r-4.6-any)
deepImp_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
deepImp/json (API)
| # Install 'deepImp' in R: |
| install.packages('deepImp', repos = c('https://matthias-da.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/matthias-da/deepimp/issues
Datasets:
- beer - Beer ageing volatile-compound data
Last updated from:cf96541d53. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 199 | ||
| source / vignettes | OK | 381 | ||
| linux-release | OK | 210 | ||
| macos-release | OK | 156 | ||
| macos-oldrel | OK | 141 | ||
| windows-devel | OK | 143 | ||
| windows-release | OK | 178 | ||
| windows-oldrel | OK | 136 | ||
| wasm-release | OK | 145 |
Exports:deepimp_archdeepimp_arch_smallgetImputedguessTypeimpNNetimpNNetCoDanew_deepimp
Dependencies:ashbitbit64bitopsbootcallrclasscliclustercolorspacecorocpp11crayoncvToolsdata.tableDEoptimRdescdeSolvedplyre1071farverfdafdsFNNfsgenericsggplot2gluegtablehdrcdehmsisobandjsonlitekernlabKernSmoothkslabelinglaekenlatticelifecyclelmtestlocfitluzmagrittrMASSMatrixmclustmgcvmulticoolmvtnormNADAnlmennetotelpcaPPperrypillarpkgconfigplsplyrpracmaprettyunitsprocessxprogressproxypspurrrR6rainbowrangerRColorBrewerRcppRcppArmadilloRcppEigenRCurlreshape2rlangrobCompositionsrobustbaserobustHDrrcovrsvdS7safetensorsscalesspsparsepcastringistringrsurvivaltibbletidyrtidyselecttorchtruncnormutf8vcdvctrsVIMviridisLitewithrxgboostzCompositionszeallotzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Beer ageing volatile-compound data | beer |
| Architecture configuration for neural-network imputation | deepimp_arch deepimp_arch_small |
| Extract imputed data from a deepimp object | getImputed |
| Guess the measurement type of each variable | guessType |
| Neural-network imputation for mixed-type data | impNNet |
| Neural-network imputation of rounded zeros in compositional data | impNNetCoDa |
| Construct a deepimp object | new_deepimp |