Title: The Analysis of Variance with EMS
Description: The analysis of variance table including the expected mean squares (EMS) for various types of experimental design is provided. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.

Description: Provides the analysis of variance table including the expected mean squares (EMS) for various types of experimental design. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.

Title: Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Maintainer: Who to complain to <uwe.menzel@math.uu.se>
Description: The package provides functions to carry out a
Goodness-of-fit test for discrete multivariate data. It is
tested if a given observation is likely to have occured under
the assumption of an ab-initio model. A p-value can be
calculated using different distance measures between observed
and expected frequencies. A Monte Carlo method is provided to
make the package capable of solving high-dimensional problems.

tested if a given observation is likely to have occurred under

Description: Goodness-of-fit tests for discrete multivariate data. It is
the assumption of an ab-initio model. Monte Carlo methods are provided to

Title: Estimation of Missing Values for a Data Matrix
Description: Estimation of missing values in a matrix by a k-th nearest neighboors algorithm

Packaged: Mon Feb 23 10:48:00 2009; hornik

Packaged: Wed Feb  8 12:58:22 2006; hornik

Title: Entropy Minimization over Variable Clusters (EMVC)
Author: H. Robert Frost and Jason H. Moore
Description: Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters.