| data.form | Convert input data into appropriate format for TANOVA |
| design.matrix | Generate design matrix for two-way factorial analysis |
| F.stat | Compute F-statistics for ANOVA model |
| F.stat.null | Generation of null F-statistics by bootstrap method |
| F.stat.null2 | Generation of null F-statistics by bootstrap method |
| F.stat2 | Compute F-statistics for ANOVA model |
| fdr.table | TANOVA False Discovery Table |
| gene.classifier1 | Classification of genes by time course analysis of variance(TANOVA) |
| gene.classifier2 | Classification of genes by time course analysis of variance(TANOVA) |
| gene.classifier3 | Classification of genes by time course analysis of variance(TANOVA) |
| group.ix | This is an internal function |
| ls.estimate | Least square estimation |
| NANOVA.test | Non-parametric analysis of variance (NANOVA) |
| NANOVA.test2 | Non-parametric analysis of variance (NANOVA) |
| NANOVA.test3 | Non-parametric analysis of variance (NANOVA) |
| prior.SIGMA | Compute the prior of covariance matrix |
| prior.sigma | Compute the prior of covariance matrix |
| proj | projection direction |
| proj.data | Projection of Raw Data |
| proj.dir | projection direction |
| proj.dir2 | projection direction |
| sig.number | The number of significant genes in the FDR table at specified quantiles |
| sigma.hat | Estimation of Covariance Matrix |
| tanova | Classification of genes by time course analysis of variance(TANOVA) |
| TANOVAmanual | Classification of genes by time course analysis of variance(TANOVA) |
| trigammaInverse | Trigamma Inverse Function |
| z.score | Z Score |