194
Views
0
CrossRef citations to date
0
Altmetric
Article

jmcm: a Python package for analyzing longitudinal data using joint mean-covariance models

ORCID Icon & ORCID Icon
Pages 5446-5461 | Received 22 Mar 2021, Accepted 03 Oct 2021, Published online: 15 Oct 2021

References

  • Anderson, T. W. 1973. Asymptotically efficient estimation of covariance matrices with linear structure. The Annals of Statistics 1 (1):135–41. doi:10.1214/aos/1193342389.
  • Chen, Z., and D. B. Dunson. 2003. Random effects selection in linear mixed models. Biometrics 59 (4):762–69. doi:10.1111/j.0006-341x.2003.00089.x.
  • Diggle, P. J., and A. P. Verbyla. 1998. Nonparametric estimation of covariance structure in longitudinal data. Biometrics 54 (2):401–15. doi:10.2307/3109751.
  • Liang, K.-Y., and S. L. Zeger. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73 (1):13–22. doi:10.1093/biomet/73.1.13.
  • McCulloch, C. E., and J. M. Neuhaus. 2005. Generalized linear mixed models. Encyclopedia of Biostatistics 4. doi:10.1002/9781118445112.stat07540.
  • Nelder, J. A., and R. W. Wedderburn. 1972. Generalized linear models. Journal of the Royal Statistical Society: Series A (General) 135 (3):370–84. doi:10.2307/2344614.
  • Nocedal, J., and S. Wright. 2006. Numerical optimization. 2nd ed. New York: Springer.
  • Pan, J., and G. Mackenzie. 2003. On modelling mean-covariance structures in longitudinal studies. Biometrika 90 (1):239–44. doi:10.1093/biomet/90.1.239.
  • Pan, J., and G. MacKenzie. 2006. Regression models for covariance structures in longitudinal studies. Statistical Modelling 6 (1):43–57. doi:10.1191/1471082X06st105oa.
  • Pan, J., and Y. Pan. 2017a. Generalised Estimating Equations (GEE/WGEE) using ’Armadillo’ and S4. R package version 0.1. https://cran.r-project.org/web/packages/gee4/index.html.
  • Pan, J., and Y. Pan. 2017b. jmcm: An R package for joint mean-covariance modeling of longitudinal data. Journal of Statistical Software 82 (9):1–29. doi:10.18637/jss.v082.i09.
  • Pinheiro, J. C., and D. M. Bates. 1996. Unconstrained parametrizations for variance-covariance matrices. Statistics and Computing 6 (3):289–96. doi:10.1007/BF00140873.
  • Pourahmadi, M. 1999. Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation. Biometrika 86 (3):677–90. doi:10.1093/biomet/86.3.677.
  • Pourahmadi, M. 2000. Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix. Biometrika 87 (2):425–35. doi:10.1093/biomet/87.2.425.
  • Ye, H., and J. Pan. 2006. Modelling of covariance structures in generalised estimating equations for longitudinal data. Biometrika 93 (4):927–41. doi:10.1093/biomet/93.4.927.
  • Zhang, W., C. Leng, and C. Y. Tang. 2015. A joint modelling approach for longitudinal studies. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 77 (1):219–38. doi:10.1111/rssb.12065.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.