Package: ANN2
Type: Package
Title: Artificial Neural Networks for Anomaly Detection
Version: 2.4.0
Date: 2025-10-09
Authors@R: person(given = "Bart",
                  family = "Lammers",
                  role = c("aut", "cre"),
                  email = "bart.f.lammers@gmail.com")
Maintainer: Bart Lammers <bart.f.lammers@gmail.com>
Description: Training of neural networks for classification and
        regression tasks using mini-batch gradient descent. Special
        features include a function for training autoencoders, which
        can be used to detect anomalies, and some related plotting
        functions. Multiple activation functions are supported,
        including tanh, relu, step and ramp. For the use of the step
        and ramp activation functions in detecting anomalies using
        autoencoders, see Hawkins et al. (2002)
        <doi:10.1007/3-540-46145-0_17>. Furthermore, several loss
        functions are supported, including robust ones such as Huber
        and pseudo-Huber loss, as well as L1 and L2 regularization. The
        possible options for optimization algorithms are RMSprop, Adam
        and SGD with momentum. The package contains a vectorized C++
        implementation that facilitates fast training through
        mini-batch learning.
License: GPL (>= 3) | file LICENSE
URL: https://github.com/bflammers/ANN2
Encoding: UTF-8
SystemRequirements: C++17
Imports: Rcpp (>= 1.1.0), reshape2 (>= 1.4.4), ggplot2 (>= 4.0.0),
        viridisLite (>= 0.4.2), methods, rlang
LinkingTo: Rcpp, RcppArmadillo, testthat
Suggests: testthat
RoxygenNote: 7.3.3
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://bflammers.r-universe.dev
Date/Publication: 2025-10-09 19:38:54 UTC
RemoteUrl: https://github.com/bflammers/ann2
RemoteRef: HEAD
RemoteSha: 5c4d018c29078c2b6aaf1476e4ca6c4ffb8dc1f3
NeedsCompilation: yes
Packaged: 2026-06-11 08:12:04 UTC; root
Author: Bart Lammers [aut, cre]