Package: RWNN 0.4

RWNN: Random Weight Neural Networks

Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.

Authors:Søren B. Vilsen [aut, cre]

RWNN_0.4.tar.gz
RWNN_0.4.zip(r-4.5)RWNN_0.4.zip(r-4.4)RWNN_0.4.zip(r-4.3)
RWNN_0.4.tgz(r-4.4-x86_64)RWNN_0.4.tgz(r-4.4-arm64)RWNN_0.4.tgz(r-4.3-x86_64)RWNN_0.4.tgz(r-4.3-arm64)
RWNN_0.4.tar.gz(r-4.5-noble)RWNN_0.4.tar.gz(r-4.4-noble)
RWNN_0.4.tgz(r-4.4-emscripten)RWNN_0.4.tgz(r-4.3-emscripten)
RWNN.pdf |RWNN.html
RWNN/json (API)

# Install 'RWNN' in R:
install.packages('RWNN', repos = c('https://svilsen.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/svilsen/rwnn/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

3.00 score 85 downloads 9 exports 5 dependencies

Last updated 3 months agofrom:ace4027904. Checks:OK: 3 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 03 2024
R-4.5-win-x86_64OKNov 03 2024
R-4.5-linux-x86_64OKNov 03 2024
R-4.4-win-x86_64NOTENov 03 2024
R-4.4-mac-x86_64NOTENov 03 2024
R-4.4-mac-aarch64NOTENov 03 2024
R-4.3-win-x86_64NOTENov 03 2024
R-4.3-mac-x86_64NOTENov 03 2024
R-4.3-mac-aarch64NOTENov 03 2024

Exports:ae_rwnnbag_rwnnboost_rwnnclassifycontrol_rwnned_rwnnreduce_networkrwnnstack_rwnn

Dependencies:quadprograndtoolboxRcppRcppArmadillorngWELL