This function model-averages the estimate of a parameter of interest among a set of candidate models, computes the unconditional standard error and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002). This model-averaged estimate is also referred to as a natural average of the estimate by Burnham and Anderson (2002, p. 152).
This function model-averages the estimate of a parameter of interest among a set of candidate models, and computes the unconditional standard error and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002).
This function creates a model selection table based on the deviance information criterion (DIC). The table ranks the models based on the DIC and also provides delta DIC and DIC weights. dictab selects the appropriate function to create the model selection table based on the object class. The current version works with objects of bugs and rjags classes.
This function extracts the standard errors (SE) of the fixed effects of a mixed model fit with coxme, glmer, lmer, and lmekin and adds the appropriate labels.
This function computes the condition number for models of unmarkedFit classes as the ratio of the largest eigenvalue of the Hessian matrix to the smallest eigenvalue of the Hessian matrix.
This function creates a model selection table based on one of the following information criteria: AIC, AICc, QAIC, QAICc. The table ranks the models based on the selected information criteria and also provides delta AIC and Akaike weights. aictab selects the appropriate function to create the model selection table based on the object class. The current version works with lists containing objects of aov, betareg, clm, clmm, clogit, coxme, coxph, fitdist, fitdistr, glm, gls, gnls, hurdle, lavaan, lm, lme, lmekin, maxlikeFit, mer, merMod, multinom, nlme, nls, polr, rlm, survreg, vglm, and zeroinfl classes as well as various models of unmarkedFit classes but does not yet allow mixing of different classes.
This function computes an alternative version of model-averaging parameter estimates that consists in shrinking estimates toward 0 to reduce model selection bias as in Burnham and Anderson (2002, p. 152), Anderson (2008, pp. 130-132) and Lukacs et al. (2010). Specifically, models without the parameter of interest have an estimate and variance of 0. modavgShrink also returns unconditional standard errors and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002).
This function computes Akaike's information criterion (AIC), the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc) from user-supplied input instead of extracting the values automatically from a model object. This function is particularly useful for output imported from other software.
This function model-averages the effect size between two groups defined by a categorical variable based on the entire model set and computes the unconditional standard error and unconditional confidence intervals as described in Buckland et al. (1997) and Burnham and Anderson (2002). This can be particularly useful when dealing with data from an experiment (e.g., ANOVA) and when the focus is to determine the effect of a given factor. This is an information-theoretic alternative to multiple comparisons (e.g., Burnham et al. 2011).
providing 
.