GLIM (software)

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GLIM (an acronym for Generalized Linear Interactive Modelling) is a statistical software program for fitting generalized linear models (GLMs). It was developed by the Royal Statistical Society's Working Party on Statistical Computing (later renamed the GLIM Working Party), [1] chaired initially by John Nelder. [2] It was first released in 1974 with the last major release, GLIM4, in 1993. [3] GLIM was distributed by the Numerical Algorithms Group (NAG). [4]

GLIM was notable for being the first package capable of fitting a wide range of generalized linear models in a unified framework, and for encouraging an interactive, iterative approach to statistical modelling. [5] GLIM used a command-line interface and allowed users to define their own macros. Many articles in academic journals were written about the use of GLIM. [6] [7] [8] [9] [10] [11] [12] GLIM was reviewed in The American Statistician in 1994, along with other software for fitting generalized linear models. [13]

The GLIMPSE system was later developed to provide a knowledge based front-end for GLIM. [14]

GLIM is no longer actively developed or distributed.

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References

  1. ↑ "Royal Statistical Society webpage on Working Parties". Archived from the original on February 21, 2007. Retrieved 2007-12-18.CS1 maint: bot: original URL status unknown (link)
  2. ↑ Nelder, John (1975). "Announcement by the Working Party on Statistical Computing: GLIM (Generalized Linear Interactive Modelling Program)". Journal of the Royal Statistical Society, Series C . 24 (2): 259–261. JSTOR   2346575.
  3. ↑ Francis, Brian; Mick Green; Clive Payne (1993). The GLIM System: Release 4 Manual. Oxford: Clarendon Press. ISBN   0-19-852231-2.
  4. ↑ "Generalized Linear Interactive Modeling Package (GLIM)". Archived from the original on 12 October 2010. Retrieved 2007-12-18.CS1 maint: bot: original URL status unknown (link)
  5. ↑ Aitkin, Murray; Dorothy Anderson; Brian Francis; John Hinde (1989). Statistical Modelling in GLIM. Oxford: Oxford University Press. ISBN   0-19-852203-7.
  6. ↑ Wacholder, Sholom (1986). "Binomial regression in GLIM: Estimating risk ratios and risk differences". American Journal of Epidemiology . 123 (1): 174–184. PMID   3509965.
  7. ↑ Aitken, Murray; Clayton, David (1980). "The Fitting of Exponential, Weibull and Extreme Value Distributions to Complex Censored Survival Data Using GLIM". Journal of the Royal Statistical Society, Series C . 29 (2): 156–163. JSTOR   2986301.
  8. ↑ Aitkin, Murray (1987). "Modelling Variance Heterogeneity in Normal Regression Using GLIM". Journal of the Royal Statistical Society, Series C . 36 (3). JSTOR   2347792.
  9. ↑ Whitehead, John (1980). "Fitting Cox's Regression Model to Survival Data using GLIM". Journal of the Royal Statistical Society, Series C . 29 (3). JSTOR   2346901.
  10. ↑ Berman, Mark; Turner, Rolf T. (1992). "Approximating Point Process Likelihoods with GLIM". Journal of the Royal Statistical Society, Series C . 41 (1): 31–38. JSTOR   2347614.
  11. ↑ Decarli, A.; La Vecchia, C. (1987). "Age, period and cohort models: review of knowledge and implementation in GLIM". Rev. Stat. App. 20: 397–409.
  12. ↑ Jørgensen, Bent (1984). "The Delta Algorithm and GLIM". International Statistical Review / Revue Internationale de Statistique. 52 (3): 283–300. doi:10.2307/1403047. JSTOR   1403047.
  13. ↑ Hilbe, Joseph (1994). "Review: Generalized Linear Models". The American Statistician . 48 (3): 255–265. arXiv: 1308.2408 . doi:10.2307/2684732. JSTOR   2684732.
  14. ↑ Wolstenholme, D.; Obrien, C.; Nelder, J. (1988). "GLIMPSE: a knowledge-based front end for statistical analysis". Knowledge-Based Systems. 1 (3): 173. doi:10.1016/0950-7051(88)90075-5.
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