nparLD 2.3.1

• Fixed the ordering of output rows and cell-effect plots for numeric factor levels. Output tables and plots now preserve the canonical factor-level order instead of applying lexicographic ordering to character representations of factor levels.

nparLD 2.3.0

Major redesign

• Added a general formula interface for crossed factorial longitudinal designs.
• Added inference for hypotheses in marginal distribution functions (hypothesis = "H0F") and unweighted relative marginal effects (hypothesis = "H0p").
• Added support for missing observations.
• Added support for dependent replicate measurements via the replicate argument.
• Added subject-level and observation-level cell weighting for relative marginal effects.
• Added multiple contrast procedures and simultaneous confidence intervals.
• Added rank- and pseudo-rank-based inference through a unified interface.
• Added the argument covariance, which optionally includes the estimated covariance matrix in the output.
• Added updated documentation, examples, README, vignette, and example datasets.
• Added the brdu data set for illustrating dependent replicate measurements.
• Added term-specific plots of factor-information estimates and confidence intervals via plot(fit, term = ...).

Interface changes

• Factor ordering is intended to follow ordinary R factor levels before calling nparLD().
• Earlier design-specific ordering arguments are no longer part of the main interface.
• Classical designs such as LD-F1, F1-LD-F1, LD-F2, F1-LD-F2, and F2-LD-F1 are now specified through the formula interface.

nparLD 2.3.2

• Added accessor functions for fitted-model results.
• Revised print and summary methods.
• Improved p-value formatting.
• Added accessors for covariance, contrast matrices, and permutation results.
• Corrected Monte Carlo p-value calculation for paired permutation tests.
• Improved documentation, examples, and plotting reproducibility.

nparLD 2.3.3

• Revised print() and summary() methods for fitted nparLD objects. print() now provides a concise model overview and the ANOVA-type statistic, whereas summary() provides detailed effect estimates and inferential results.
• Multiple contrast, factor-specific, permutation, and other detailed results remain available through summary() and dedicated accessor functions.
• Updated documentation and examples accordingly.