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Changelog

Source: NEWS.md

VetResearchLMM 2.0.0

Changes visible to users

  • The data sets ex121, ex124, ex125, ex127, ex31, ex32 and ex33, the help topics Examp1.3.2 to Examp3.3, and emmeans_mixed_model() and report_mixed_model() keep their names, columns and arguments.
  • emmeans_mixed_model() and report_mixed_model() are S3 generics: their .default methods behave as in version 1.1.0 (models of lme4 and nlme, through emmeans and report), and new methods accept results of vlm_fit().
  • The help topics Examp* now show the SAS program of the book next to the same analysis with the functions of the package, which reproduces the numbers the book prints; lme4 remains as an optional comparison.
  • The four vignettes of version 1.1.0 are replaced by six (getting started, theory, the examples of Chapters 1-2 and of Chapter 3, validation, application), each also as PDF.

Corrections

  • ex33: the 11th measurement of BO322 is on day 25 (not a second day 23), that of ND66 on day 25 (not 26), and the identifier "BO326 " lost its trailing space, as in Appendix A of the book. With the corrected data the variance components of Example 3.3 are the printed 5.478 and 4.965 (5.522 and 4.943 before), and the first-order autoregressive model gives the printed rho = 0.621.

New

  • vlm_fit(): an engine for linear mixed models that gives the results of SAS PROC MIXED: REML, ML or Type 3 (moment) estimation by average-information Newton steps with bounds; random variance components, random coefficients (vlm_random(), VC or UN) and residual structures (vlm_repeated(): CS, AR(1), SP(POW), UN); the solution for the fixed effects in the overparameterised coding; Type 3 and Type 1 tests; containment, residual, between-within, Satterthwaite and Kenward-Roger degrees of freedom; standard errors of the covariance parameters; AIC, AICC, BIC, the SAS 6 criteria of the book and the null-model likelihood ratio test.
  • vlm_lsmeans(): least-squares means at chosen covariate values, with Tukey-Kramer, Bonferroni and Sidak comparisons.
  • vlm_estimate() and vlm_contrast(): ESTIMATE and CONTRAST statements with coefficients given per effect as in SAS, estimability checks, and the narrow inference space through random-effect coefficients.
  • vlm_blup(): best linear unbiased predictions with standard errors of prediction.
  • vlm_ems(): the analysis of variance of PROC GLM with a RANDOM statement: expected mean squares, synthesised error terms with Satterthwaite degrees of freedom and Type 3 estimates of the variance components.
  • vlm_compare(): information criteria and likelihood ratio tests of several fits.
  • vlm_interpret(), vlm_cite(), vlm_notation(), vlm_reference(), plot() and autoplot() methods, and vlm_app(), a shiny application in which every example of the book loads with one click.
  • Data sets ex122 (Example 1.2.2), ex123 (Example 1.2.3) and ex126 (Example 1.2.6) from Appendix A, and the help topic Examp2.3.2 for the models of Section 2.3.2.
  • 116 numbers printed in the book are recomputed by the tests (inst/extdata/reference.csv); 115 agree and the other is printed truncated. The engine is also checked against lme4, lmerTest, pbkrtest, nlme and emmeans.

Package

  • Imports data.table, ggplot2 and scales; the packages used for comparisons are suggested.

VetResearchLMM 1.1.0

CRAN release: 2026-05-05