Packaged: 2017-11-15 20:55:00 UTC; Bart Lammers

Packaged: 2017-11-16 11:49:32 UTC; Bart Lammers

Packaged: 2017-11-24 11:40:13 UTC; Bart Lammers

Packaged: 2017-11-28 08:34:31 UTC; Bart Lammers

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
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.

LinkingTo: Rcpp, RcppArmadillo, Rcereal, testthat

Packaged: 2020-11-30 22:49:28 UTC; bartlammers

Description: Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.
Imports: ggplot2, MCPAN, multcomp, nparcomp, SimComp

Description: Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with multcomp, SimComp, nparcomp, or MCPAN) or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.

Title: One-Way Analysis of Variance Based on Triangular Fuzzy Numbers
Author: Abbas Parchami (Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran)
Description: Long formulas and complex computations are two disadvantage of the extended ANOVA approach where the observed data are fuzzy rather that crisp; for more details see Parchami et al. (2017) <doi:10.1007/s40747-017-0046-8>. Package 'ANOVA.TFNs' can help to the applied users to overcome these two challenges. The most important functions in package 'ANOVA.TFNs' is FANOVA() which easily draw all membership functions of fuzzy numbers, report observed statistics in fuzzy ANOVA table, calculate the p-value and final result for Fuzzy ANOVA test at the certain significance level.