GLIM (software)

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] Two GLIM conferences were held in London (1982) and Lancaster (1985) and the Statistical Modelling Society, with its annual workshops, grew out of them. 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.

  1. ^ "Royal Statistical Society webpage on Working Parties". Archived from the original on February 21, 2007. Retrieved 2007-12-18.{{cite web}}: 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.{{cite web}}: 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.