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.

Books[edit]

References[edit]

  1. ^ Royal Statistical Society webpage on Working Parties at the Wayback Machine (archived February 21, 2007)
  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) at the Wayback Machine (archived 12 October 2010)
  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; Jorgensen, 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. 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.  edit