person("Caíque", "de Oliveira de Souza",
Forest data quality is a package containing nine methods of analysis for forest databases,
from databases containing inventory data and growth models, the focus of the analyzes is related to the
quality of the data present in the database with a focus on consistency , punctuality and completeness of data.
Imports: data.table, sqldf, randomcoloR, ggplot2

Imports: ggplot2, sqldf, randomcoloR, plyr, utils, stats

Title: False Discovery Rate Procedure Under Discrete Null Distributions
Author: Xiongzhi Chen and R.W. Doerge <doerge@purdue.edu>
Description: It is known that current false discovery rate (FDR) procedures can be very conservative
when applied to p-values (and test statistics) with discrete (and heterogeneous) null distributions.
This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing.
The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed.
The package implements the method for three types of test statistics and their p-values:
(a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions,
(c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means.
It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.

Title: False Discovery Rate Procedures Under Discrete and Heterogeneous
Author: Xiongzhi Chen and Rebecca W. Doerge <rwdoerge@andrew.cmu.edu>
Description: It is known that current false discovery rate (FDR) procedures can be very conservative when applied to multiple testing in the discrete paradigm where p-values (and test statistics) have discrete and heterogeneous null distributions. This package implements more powerful weighted or adaptive FDR procedures for FDR control and estimation in the discrete paradigm. The package takes in the original data set rather than just the p-values in order to carry out the adjustments for discreteness and heterogeneity of p-value distributions. The package implements methods for two types of test statistics and their p-values: (a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions, or on if two independent binomial distributions have the same probabilities of success.