Bias Reduction in Generalized Linear Models
Bias reduction for adjacent category logit models for ordinal response...
Defunct Functions in package brglm2
brglm2: Bias Reduction in Generalized Linear Models
Auxiliary function for glm() fitting using the brglmFit()method.
Fitting function for glm() for reduced-bias estimation and inference
Bias reduction for multinomial response models using the Poisson trick...
Bias reduction for negative binomial regression models
Extract model coefficients from "brglmFit" objects
Extract estimates from "brglmFit_expo" objects
Extract model coefficients from "brnb" objects
Method for computing confidence intervals for one or more regression p...
Method for computing confidence intervals for one or more regression p...
Method for computing Wald confidence intervals for one or more regress...
Estimate the exponential of parameters of generalized linear models us...
A "link-glm" object for misclassified responses in binomial regressi...
Ordinal superiority scores of Agresti and Kateri (2017)
Predict method for bracl fits
Predict method for brmultinom fits
Residuals for multinomial logistic regression and adjacent category lo...
Method for simulating a data set from "brmultinom" and "bracl"obje...
Simulate Responses
summary() method for brglmFit objects
summary() method for "brnb" objects
Return the variance-covariance matrix for the regression parameters in...
Extract model variance-covariance matrix from "brnb" objects
Estimation and inference from generalized linear models based on various methods for bias reduction and maximum penalized likelihood with powers of the Jeffreys prior as penalty. The 'brglmFit' fitting method can achieve reduction of estimation bias by solving either the mean bias-reducing adjusted score equations in Firth (1993) <doi:10.1093/biomet/80.1.27> and Kosmidis and Firth (2009) <doi:10.1093/biomet/asp055>, or the median bias-reduction adjusted score equations in Kenne et al. (2017) <doi:10.1093/biomet/asx046>, or through the direct subtraction of an estimate of the bias of the maximum likelihood estimator from the maximum likelihood estimates as in Cordeiro and McCullagh (1991) <https://www.jstor.org/stable/2345592>. See Kosmidis et al (2020) <doi:10.1007/s11222-019-09860-6> for more details. Estimation in all cases takes place via a quasi Fisher scoring algorithm, and S3 methods for the construction of of confidence intervals for the reduced-bias estimates are provided. In the special case of generalized linear models for binomial and multinomial responses (both ordinal and nominal), the adjusted score approaches to mean and media bias reduction have been found to return estimates with improved frequentist properties, that are also always finite, even in cases where the maximum likelihood estimates are infinite (e.g. complete and quasi-complete separation; see Kosmidis and Firth, 2020 <doi:10.1093/biomet/asaa052>, for a proof for mean bias reduction in logistic regression).