stanreg tidier gains exponentiate argument (wish of GH #122)tidy.brmsfit gains optional rhat and ess columns (Alexey Stukalov)lqmm models (David Luke Thiessen)glmmTMB tidying with conf.int=TRUE, random effects in multiple model components, subset of components requested in tidy output (GH #136, Daniel Sjoberg)tidy.brmsfit works better for models with no random/group-level effects (Matthieu Bruneaux)as.data.frame.ranef.lme now processes the optional argument (see ?as.data.frame), so that data.frame(ranef_object) works
stanreg tidier now checks for spurious values in ...
TMB tidierslme tidier gets functionality for information about variance models (use effects = "var_model") (Bill Denney)
support for models with fixed sigma values in lme tidier (Bill Denney)
added tidy and glance methods for allFit objects from the lme4 package
get_methods() function returns a table of all available tidy/glance/augment methods
improved lme tidying for random effects values
brms tidiers no longer use deprecated posterior_samples
glance.lme4 now returns nobs (Cory Brunson)
some tidiers are less permissive about unused arguments passed via ...
TMB tidiers (the TMB package does not return an object of class TMB, so users should run class(fit) <- "TMB" before tidying)term names are no longer “sanitized” in gamlss tidiers (e.g. “(Intercept)” is not converted to “X.Intercept.”)
gamlss glance method returns nobs (GH #113)
Wald confidence intervals for lmerTest models now respect ddf.method
tidy.glmmTMB(.,effects="ran_vals") fixed for stringsAsFactors changes in glmmTMB (GH #103)
tidy.gamlss now works in a wider range of cases (GH #74)
tidy.brmsfit works for models without group effects (GH #100)
dplyr 1.0.0; skip exampleslmer tidier gets ddf.method (applies only to lmerTest fits)
glmmTMB gets exponentiate options
experimental GLMMadaptive tidiers
tibble packagegls tidier gets confint (GH #49)estimate.method in MCMC tidiers goes away; use robust to compute point estimates/uncertainty via median and MAD rather than mean and SEmisc fixes: lme4 tidiers (confint for ran_vals, profile conf intervals fixed), R2jags, gamlss …
ran_vals works for glmmTMB
don’t ignore conf.level in tidy.(merMod|glmmTMB) (GH #30,31: @strengejacke)
levels correct in tidy.brmsfit (GH #36: @strengejacke)
component argument works for random effects in glmmTMB (GH #33: @strengejacke)
brmsfit and rstanarm methods allow conf.method="HPDinterval"tidy.brmsfit gets component column (GH #35: @strengejacke), response column for multi-response models (GH #34: @strengejacke)
component tags are stripped from tidied brmsfit objects
“Intercept” terms in brms fits are re-coded as “(Intercept)” by default, for dotwhisker/cross-model compatibility; for previous behaviour, specify fix.intercept=FALSE
more consistent term names in brmsfit, rstanreg tidiers
improved tidy.MCMCglmm
all methods return tibbles (tbl_df) rather than data frames
the value of the group variable for fixed-effect parameters has changed from "fixed" to NA
brmsfit and rstanarm tidiers are more consistent with other tidiers (e.g. the argument for setting confidence level is conf.level rather than prob)
"ran_vals" extracts conditional modes/BLUPs/varying parameters (deviations from population-level estimates), while "ran_coefs" extracts group-level estimatesimproved nlme tidiers
improved glmmTMB tidiers (can handle some zero-inflation parameters)
lme4 tidiers now optionally take a pre-computed profile argument when using conf.method="profile"
scales="sdcor" [default]) or their variances and covariances (if scales = "varcov")effects = "ran_coefs" for the group-level estimates (previously these effects were extracted with tidy(model, "random")) or effects = "ran_vals" for the conditional modes (deviations of the group-level parameters from the population-level estimates)effects can take a vector of values (those listed above, plus “fixed” for fixed effects). The default value is effects = c(“ran_pars”, “fixed”) which extracts random effect variances/covariances and fixed effect estimates.group specifier (at least for lme4 models); use something like tidyr::unite(term,term,group,sep=".") to collapse the two columns