Imports: grid, stats, utils, MASS, XML, stringr, ggplot2, magrittr,
Packaged: 2019-03-11 16:16:24 UTC; Justin Wilkins

Authors@R: c(person("Justin", "Wilkins", email = "justin.wilkins@occams.com", role = c("aut", "cre"), comment=c(ORCID="0000-0002-7099-9396")),
person("Bill", "Denney", email="wdenney@humanpredictions.com", role="aut", comment=c(ORCID="0000-0002-5759-428X")),
Author: Justin Wilkins [aut, cre] (<https://orcid.org/0000-0002-7099-9396>),

Imports: ggplot2, ggrepel, gridExtra, chron

Imports: ggplot2, ggrepel, gridExtra, chron, xml2, dplyr (>= 0.8.5)
Depends: MASS, stringr, magrittr, data.tree, DiagrammeR

Imports: ggplot2, ggrepel, gridExtra, chron, xml2, dplyr (>= 0.8.5),

Title: Parameter Tables for PMx Analyses
Description: Generate nicely formatted HTML tables to display estimation results for

Title: Parametric Network Enrichment Analysis
Description: Collection of functions for Parametric Network Enrichment Analysis.

Description: Includes functions and examples for Parametric Network Enrichment Analysis.

Description: Includes functions and examples to compute Parametric Network Enrichment Analysis.

Title: Prime Numbers and Integer Factorization
Description: An R package for prime number generation, primality testing and integer factorization, currently, using simple methods.

write and display bitmap images stored in the PNG format. It

Description: This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.

Title: Probit-Normal Marginalized Transition Random Effects Models
Author: Ozgur Asar <oasar@metu.edu.tr>, Ozlem Ilk <oilk@metu.edu.tr>
Description: An R package for Probit-Normal Marginalized Transition
Packaged: 2012-09-10 07:11:59 UTC; Administrator

Description: Probabilistic neural networks guess a category for a given
individual defined by a vector of numerical values.
Collate: 'create.R' 'holdout.R' 'kernel.R' 'learn.R' 'optim.R'

Description: The program pnn implements the algorithm proposed by
Specht (1990).  It is written in the R statistical language. It
solves a common problem in automatic learning. Knowing a set of
observations described by a vector of quantitative variables,
we classify them in a given number of groups. Then, the
algorithm is trained with this datasets and should guess
afterwards the group of any new observation. This neural
network has the main advantage to begin generalization
instantaneously even with a small set of known observations. It
is delivered with four functions (learn, smooth, perf and
guess) and a dataset. The functions are documented with
Collate: 'create.R' 'holdout.R' 'kernel.R' 'learn.R' 'pnn-package.r'