Package: nnspat
Type: Package
Title: Nearest Neighbor Methods for Spatial Patterns
Version: 0.1.1
Author: Elvan Ceyhan
Maintainer: Elvan Ceyhan <elvanceyhan@gmail.com>
Description: Contains the functions for testing the spatial patterns (of segregation, spatial symmetry,
association, disease clustering, species correspondence and reflexivity) based on nearest neighbor relations,
especially using contingency tables such as
nearest neighbor contingency tables (Ceyhan (2010) <doi:10.1007/s10651-008-0104-x> and
Ceyhan (2017) <doi:10.1016/j.jkss.2016.10.002> and references therein),
nearest neighbor symmetry contingency tables (Ceyhan (2014) <doi:10.1155/2014/698296>),
species correspondence contingency tables and reflexivity contingency tables (Ceyhan (2018) <doi:10.2436/20.8080.02.72>)
for two (or higher) dimensional data. Also contains functions for generating patterns of segregation, association,
uniformity in a multi-class setting (Ceyhan (2014) <doi:10.1007/s00477-013-0824-9>),
and various non-random labeling patterns for disease clustering in two dimensional cases (Ceyhan (2014)
<doi:10.1002/sim.6053>), and for visualization of all these patterns for the two dimensional data.
The tests are usually (asymptotic) normal z-tests and chi-square tests.
License: GPL-2
Encoding: UTF-8
LazyData: TRUE
Imports: MASS, stats, graphics, pcds, Rdpack (>= 0.7)
RdMacros: Rdpack
RoxygenNote: 7.1.2
NeedsCompilation: no
Packaged: 2022-08-29 16:54:49 UTC; ezc0066
Repository: CRAN
Date/Publication: 2022-08-30 07:00:07 UTC

Title: The Number Needed to Treat (NNT) for Survival Endpoint
Description: Estimate the NNT using the proposed method in Yang and Yin's paper (2019)
<doi:10.1371/journal.pone.0223301>, in which the NNT-RMST (number needed to
treat based on the restricted mean survival time) is defined as the RMST (restricted
mean survival time) in the control group divided by the difference in RMSTs
between the treatment and control groups up to a chosen time t.

Title: Supervised Data Transformation by Means of Neural Network Hidden
Authors@R: c(person("Ricardo", "Aler", email = "ricardo.aler@uc3m.es", role = c("aut", "cre")),
person("Jose", "Valls", email = "jvalls@inf.uc3m.es", role = c("aut")))
Description: A supervised transformation of datasets is performed.
The aim is similar to that of Principal Component Analysis (PCA), that is, to carry out data transformation and dimensionality reduction, but in a non-linear supervised way.
This is achieved by first training a 3-layer Multi-Layer Perceptron and then using the activations of the hidden layer as a transformation of the input features.
In fact, it takes advantage of the change of representation provided by the hidden layer of a neural network.
This can be useful as data pre-processing for Machine Learning methods in general, specially for those that do not work well with many irrelevant or redundant features.
Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986) "Learning representations by back-propagating errors" <doi:10.1038/323533a0>.
Imports: nnet, NeuralNetTools, FNN, pracma
Suggests: dplyr, knitr, rmarkdown, ggplot2, ggridges, tidyr, forcats