Description: Implementation of the DEMI method for the analysis of high-density microarray data.
Imports: R.utils, affxparser, affy, oligo, plyr, methods, devtools

Depends: R (>= 2.15), methods, affy, affxparser, R.utils, utils, oligo

Title: Deming, Thiel-Sen and Passing-Bablock Regression
Description: Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.

Title: Deming, Theil-Sen, Passing-Bablock and Total Least Squares

Title: Providing Demographic Table with the P-Value, Standardized Mean
Description: The Demographic Table in R combines contingency table for categorical variables, mean and standard deviation for continuous variables. t-test, chi-square test and Fisher's exact test calculated the p-value of two groups. The standardized mean difference were performed with 95 % confident interval, and writing table into document file.
Packaged: 2019-01-05 00:14:28 UTC; loanrobinson

Title: Kernel Density Estimation for Demonstration Purposes
Description: Demonstration code showing how (univariate) kernel
density estimates are computed, at least conceptually,
and allowing users to experiment with different kernels,
should they so wish.  NOTE: the density function in the
stats package should be used for computational efficiency.

should they so wish.  The method used follows directly
the definition, but gains efficiency by replacing the
observations by frequencies in a very fine grid covering
the sample range.  A canonical reference is
stats package uses a more sophisticated method based on the
fast Fourier transform and that function should be used if
computational efficiency is a prime consideration.

Title: Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps
Description: Mimics the demo functionality for 'Shiny' apps in a package. Apps stored to the package subdirectory inst/shiny can be called by demoShiny(topic).

Title: Simulating Differential Equations with Data
person("John", "Zobitz", , "zobitz@augsburg.edu", role = c("aut", "cre"),
Description: Designed to support the visualization, numerical computation,
qualitative analysis, model-data fusion, and stochastic simulation for
autonomous systems of differential equations. Euler and Runge-Kutta methods
are implemented, along with tools to visualize the two-dimensional
phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo
parameter estimator can be used for model-data fusion of differential
equations and empirical models. The Euler-Maruyama method is provided for
simulation of stochastic differential equations. The package was originally
written for internal use to support teaching by Zobitz, and refined to
support the text "Exploring modeling with data and differential equations
using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.
Imports: ggplot2, purrr, tidyr, dplyr, formula.tools, GGally, rlang,
Author: John Zobitz [aut, cre] (<https://orcid.org/0000-0002-1830-143X>)