lme4 tidying with profile CIs now respects
scale = "vcov" (GH #161, @richmaparker)glance method for TMB objectslme4 >= 2.0.0stanreg tidiers should work for models without random
effectsstanreg 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
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