lme4
E436334
lme4 is a widely used R package for fitting linear and generalized linear mixed-effects models using efficient numerical optimization methods.
All labels observed (1)
| Label | Occurrences |
|---|---|
| lme4 canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4371842 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: lme4 Context triple: [R, hasPackage, lme4]
-
A.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
-
B.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
C.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
-
D.
Gauss–Markov theorem
The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
-
E.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: lme4 Target entity description: lme4 is a widely used R package for fitting linear and generalized linear mixed-effects models using efficient numerical optimization methods.
-
A.
LIML
LIML is the ICAO airport code for Milan Linate Airport, a major city airport serving Milan, Italy.
-
B.
Frisch–Waugh–Lovell theorem
The Frisch–Waugh–Lovell theorem is a fundamental result in econometrics that shows how the coefficients of a multiple linear regression can be obtained by first partialling out (regressing out) other explanatory variables.
-
C.
“Statistical Confluence Analysis by Means of Complete Regression Systems”
“Statistical Confluence Analysis by Means of Complete Regression Systems” is a foundational econometric work by Ragnar Frisch that develops a systematic regression-based framework for analyzing interdependent economic relationships.
-
D.
Gauss–Markov theorem
The Gauss–Markov theorem is a fundamental result in statistics stating that, under certain conditions, the ordinary least squares estimator is the best linear unbiased estimator (BLUE) of the coefficients in a linear regression model.
-
E.
statistics
Statistics is a Python standard library module that provides functions for calculating mathematical statistics of numeric data, such as means, medians, and variance.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
R package
ⓘ
software package ⓘ |
| availableOn | CRAN ⓘ |
| hasAuthor |
Ben Bolker
ⓘ
Douglas Bates ⓘ Martin Maechler ⓘ Steve Walker ⓘ |
| hasDocumentation | lme4 package vignette ⓘ |
| hasReferencePublication | Journal of Statistical Software article on lme4 ⓘ |
| implements |
generalized linear mixed-effects models
ⓘ
linear mixed-effects models ⓘ |
| license |
GPL-2
ⓘ
GPL-3 ⓘ |
| maintainer | Douglas Bates ⓘ |
| programmingLanguage | R ⓘ |
| providesFunction |
VarCorr
ⓘ
bootMer ⓘ coef ⓘ fixef ⓘ glmer ⓘ glmerControl ⓘ lmer ⓘ lmerControl ⓘ nlmer ⓘ ranef ⓘ simulate ⓘ |
| supports |
Gamma family
ⓘ
Gaussian family ⓘ Poisson family ⓘ binomial family ⓘ crossed random effects ⓘ identity link ⓘ inverse Gaussian family ⓘ log link ⓘ logit link ⓘ maximum likelihood estimation ⓘ mixed-effects models ⓘ nested random effects ⓘ probit link ⓘ random intercept models ⓘ random slope models ⓘ restricted maximum likelihood estimation ⓘ |
| usedFor |
clustered data analysis
ⓘ
hierarchical modeling ⓘ longitudinal data analysis ⓘ multilevel modeling ⓘ |
| usedIn |
biostatistics
ⓘ
ecology ⓘ psychology ⓘ statistics ⓘ |
| uses | numerical optimization methods ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: lme4 Description of subject: lme4 is a widely used R package for fitting linear and generalized linear mixed-effects models using efficient numerical optimization methods.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.