Routines for log-linear models of incomplete contingency tables,
             including some latent class models, via EM and Fisher scoring
             approaches. Allows bootstrapping. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.

Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either Laplace approximation method or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). 
      For details see Niku et al. (2019a) <doi:10.1371/journal.pone.0216129> and 
      Niku et al. (2019b) <doi:10.1111/2041-210X.13303>.

Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.

Functions to calculate predicted values and the difference between
    the two cases with confidence interval for lm() [linear model], glm() [generalized linear model], glm.nb() [negative binomial model],
	polr() [ordinal logistic model], vglm() [generalized ordinal logistic model],	multinom() [multinomial model], tobit() [tobit model],
	svyglm() [survey-weighted generalised linear models] and lmer() [linear multilevel models] using Monte Carlo simulations or bootstrap. Reference: Bennet A. Zelner (2009) <doi:10.1002/smj.783>.

Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.