References of a result or of the package

vlm_cite() gives the references of the methods behind a result of the package, or every reference of the package, as text or BibTeX. citation("VetResearchLMM") cites the package and the book.

Usage

vlm_cite(x = NULL, style = c("text", "bibtex"))

# Default S3 method
vlm_cite(x = NULL, style = c("text", "bibtex"))

Arguments

x

a result of a function of the package, or NULL (default) for every reference.

style

"text" (default) or "bibtex".

Value

An object of class bibentry (printed in the chosen style).

See also

Other VetResearchLMM utilities: autoplot.vlm_result(), vlm_app(), vlm_interpret(), vlm_notation(), vlm_reference(), vlm_save(), vlm_theme()

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

vlm_cite(
    x     = NULL
  , style = "text"
  )
#> Duchateau, L., Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with Applications in Veterinary Research. International Livestock Research Institute, Nairobi. ISBN 92-9146-038-9.
#> 
#> Patterson, H. D. and Thompson, R. (1971). Recovery of inter-block information when block sizes are unequal. Biometrika, 58(3), 545-554. doi:10.1093/biomet/58.3.545
#> 
#> Harville, D. A. (1977). Maximum likelihood approaches to variance component estimation and to related problems. Journal of the American Statistical Association, 72(358), 320-338. doi:10.1080/01621459.1977.10480998
#> 
#> Gilmour, A. R., Thompson, R. and Cullis, B. R. (1995). Average information REML: an efficient algorithm for variance parameter estimation in linear mixed models. Biometrics, 51(4), 1440-1450. doi:10.2307/2533274
#> 
#> Littell, R. C., Milliken, G. A., Stroup, W. W. and Wolfinger, R. D. (1996). SAS System for Mixed Models. SAS Institute, Cary, NC.
#> 
#> Satterthwaite, F. E. (1946). An approximate distribution of estimates of variance components. Biometrics Bulletin, 2(6), 110-114. doi:10.2307/3002019
#> 
#> Giesbrecht, F. G. and Burns, J. C. (1985). Two-stage analysis based on a mixed model: large-sample asymptotic theory and small-sample simulation results. Biometrics, 41(2), 477-486. doi:10.2307/2530872
#> 
#> Fai, A. H.-T. and Cornelius, P. L. (1996). Approximate F-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments. Journal of Statistical Computation and Simulation, 54(4), 363-378. doi:10.1080/00949659608811740
#> 
#> Kenward, M. G. and Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53(3), 983-997. doi:10.2307/2533558
#> 
#> Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2), 423-447. doi:10.2307/2529430
#> 
#> McLean, R. A., Sanders, W. L. and Stroup, W. W. (1991). A unified approach to mixed linear models. The American Statistician, 45(1), 54-64. doi:10.1080/00031305.1991.10475767
#> 
#> Searle, S. R., Speed, F. M. and Milliken, G. A. (1980). Population marginal means in the linear model: an alternative to least squares means. The American Statistician, 34(4), 216-221. doi:10.1080/00031305.1980.10483031
#> 
#> Kramer, C. Y. (1956). Extension of multiple range tests to group means with unequal numbers of replications. Biometrics, 12(3), 307-310. doi:10.2307/3001469
#> 
#> Searle, S. R., Casella, G. and McCulloch, C. E. (1992). Variance Components. Wiley, New York.
#> 
#> Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/TAC.1974.1100705
#> 
#> Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461-464. doi:10.1214/aos/1176344136