brms: Mo’ models!

• GAM
• Distributional response (e.g. model the variance as well as the mean)
• Gaussian Processes
• ZIP
• Multivariate
• Missing data imputation from a Bayesian approach
• Measurement error

# additional distributions
model = brm(y ~ x + z, family = skew_normal,
                                student,
                                shifted_lognormal,
                                weibull,
                                frechet,
                                gen_extreme_value,
                                exgaussian,
                                wiener,
                                Beta,
                                von_mises,
                                asym_laplace,
                                hurdle_poisson,_negbinomial,_gamma,_lognormal,
                                zero_inflated_poisson,_negbinomial,_beta,_binomial; zero_one_inflated_beta,
                                categorical,
                                ordinal: cumulative, sratio, cratio, acat)

# model the variance as well as the mean
fit1 <- brm(bf(y ~ x + z, sigma ~ x), 
            data = dat1, 
            family = gaussian)

# missing values
bform <- 
  bf(bmi | mi() ~ age * mi(chl)) +
  bf(chl | mi() ~ age) + set_rescor(FALSE)

fit <- brm(bform, data = nhanes)

# non-linear model with known form
nlform <- bf(cum ~ ult * (1 - exp(-(dev / theta)^omega)),
             ult ~ 1 + (1 | AY), 
             omega ~ 1, 
             theta ~ 1, 
             nl = TRUE)

# measurement error
fit1 <- brm(y ~ me(x1, sdx) + me(x2, sdx), 
            data = dat, 
            save_mevars = TRUE)