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
NeedsCompilation: yes
Packaged: 2025-10-09 19:05:32 UTC; bartlammers
Author: Bart Lammers [aut, cre]
Repository: CRAN
Date/Publication: 2025-10-09 23:10:06 UTC
Built: R 4.6.0; x86_64-w64-mingw32; 2025-11-02 04:35:23 UTC; windows
Archs: x64
