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.

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.

Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
Who to complain to <uwe.menzel@math.uu.se>
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.

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 occurred 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.

Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate Data
Goodness-of-fit tests for discrete multivariate data. It is
tested if a given observation is likely to have occurred under
the assumption of an ab-initio model. Monte Carlo methods are provided to
make the package capable of solving high-dimensional problems.