Main.Rmd

mmc Multi-Membership Covariates
#### Description 
Specify covarariates that vary over different levels of multi-membership grouping factors thus requiring
special treatment. This function is almost solely useful, when called in combination with
mm. Outside of multi-membership terms it will behave very much like cbind.

#### Usage
<pre><code>
mmc(...)
</code></pre>
#### Arguments

... One or more terms containing covariates corresponding to the grouping levels
specified in mm.
#### Value
A matrix with covariates as columns.
See Also
mm

#### Examples 
```{r}
## Not run:
# simulate some data
dat <- data.frame(
y = rnorm(100), x1 = rnorm(100), x2 = rnorm(100),
g1 = sample(1:10, 100, TRUE), g2 = sample(1:10, 100, TRUE)
)
# multi-membership model with level specific covariatevalues
dat$xc <- (dat$x1 + dat$x2) / 2
fit <- brm(y ~ xc + (1 + mmc(x1, x2) | mm(g1, g2)), data = dat)
summary(fit)
mo 109
## End(Not run)
```

MultiNormal The Multivariate Normal Distribution
#### Description 
Density function and random generation for the multivariate normal distribution with mean vector
mu and covariance matrix Sigma.

#### Usage
<pre><code>
dmulti_normal(x, mu, Sigma, log = FALSE, check = FALSE)
rmulti_normal(n, mu, Sigma, check = FALSE)
</code></pre>
#### Arguments

* ``x``: Vector or matrix of quantiles. If x is a matrix, each row is taken to be a quantile.
mu Mean vector with length equal to the number of dimensions.
Sigma Covariance matrix.
log Logical; If TRUE,values are returned on the log scale.
check Logical; Indicates whether several input checks should be performed. Defaults
to FALSE to improve efficiency.
n Number of samples to draw from the distribution.
#### Details
See the Stan user’s manual http://mc-stan.org/documentation/ for #### Details on the parameterization



nsamples.brmsfit Number of Posterior Samples
#### Description 
Extract the number of posterior samples stored in a fitted Bayesian model.

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
nsamples(x, subset = NULL, incl_warmup = FALSE, ...)
nsamples(x, ...)
</code></pre>
#### Arguments

x An R object
subset An optional integer vector defining a subset of samples to be considered.
incl_warmup A flag indicating whether to also count warmup / burn-in samples.
* `` ... ``: Further arguments passed to or from other methods.
#### Details
Currently there are methods for brmsfit objects.

134 print.brmsprior
print.brmsfit Print a summary for a fitted model represented by a brmsfit object
#### Description 
Print a summary for a fitted model represented by a brmsfit object

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
print(x, digits = 2, ...)
</code></pre>
#### Arguments

x An object of class brmsfit
digits The number of significant digits for printing out the summary; defaults to 2. The
effective sample size is always rounded to integers.
* ``...``: Additional arguments
 that would be passed to method summary of brmsfit.
Author(s)
Paul-Christian Buerkner <paul.buerkner@gmail.com>
print.brmsprior Print method for brmsprior objects
#### Description 
Print method for brmsprior objects

#### Usage
<pre><code>
## S3 method for class 'brmsprior'
print(x, show_df, ...)
</code></pre>
#### Arguments

x An object of class brmsprior.
show_df Logical; Print priors as a single data.frame (TRUE) or as a sequence of sampling
statements (FALSE)?
... Currently ignored.


stancode.brmsfit Extract Stan model code
#### Description 
Extract Stan model code

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
stancode(object, version = TRUE, ...)
stancode(object, ...)
</code></pre>
#### Arguments

object An object of class brmsfit
version Logical; indicates if the first line containing the brms version number should be
included. Defaults to TRUE.
... Currently ignored
#### Value
Stan model code for further processing.
standata.brmsfit 151
standata.brmsfit Extract Data passed to Stan
#### Description 
Extract all data that was used by Stan to fit the model

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
standata(object, newdata = NULL, re_formula = NULL,
incl_autocor = TRUE, new_objects = list(), internal = FALSE,
control = list(), ...)
standata(object, ...)
</code></pre>
#### Arguments

object An object of class brmsfit.
newdata An optional data.frame for which to evaluate predictions. If NULL (default), the
original data of the model is used.
re_formula formula containing group-level effects to be considered in the prediction. If
NULL (default), include all group-level effects; if NA, include no group-level effects.
incl_autocor A flag indicating if ARMA autocorrelation parameters should be included in the
predictions. Defaults to TRUE. Setting it to FALSE will not affect other correlation
structures such as cor_bsts, or cor_fixed.
new_objects A named list of objects containing new data, which cannot be passed via argument
newdata. Currently, only required for objects passed to cor_sar and
cor_fixed.
internal Logical, indicates if the data should be prepared for internal use in other postprocessing
methods.
control A named list currently for internal 
usage only.
... More Arguments passed to make_standata.
#### Value
A named list containing the data originally passed to Stan.

summary.brmsfit Create a summary of a fitted model represented by a brmsfit object
#### Description 
Create a summary of a fitted model represented by a brmsfit object

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
summary(object, priors = FALSE, prob = 0.95,
mc_se = FALSE, use_cache = TRUE, ...)
</code></pre>
#### Arguments

object An object of class brmsfit
priors Logical; Indicating if priors should be included in the summary. Default is
FALSE.
prob Avalue between 0 and 1 indicating the desired probability to be covered by the
uncertainty intervals. The default is 0.95.
mc_se Logical; Indicating if the uncertainty caused by the MCMC sampling should be
shown in the summary. Defaults to FALSE.
use_cache Logical; Indicating if summary results should be cached for future use by rstan.
Defaults to TRUE. For models fitted with earlier versions of brms, it may be necessary
to set use_cache to FALSE in order to get the summary method working
correctly.
... Other potential </code></pre>
#### Arguments

Author(s)
Paul-Christian Buerkner <paul.buerkner@gmail.com>
156 theme_black
theme_black Black Theme for ggplot2 Graphics
#### Description 
A black theme for ggplot graphics inspired by a blog post of Jon Lefcheck (https://jonlefcheck.
net/2013/03/11/black-theme-for-ggplot2-2/).

#### Usage
<pre><code>
theme_black(base_size = 12, base_family = "")
</code></pre>
#### Arguments

base_size base font size
base_family base font family
#### Details
When using theme_black in plots powered by the bayesplot package such as pp_check or stanplot,
I recommend using the "viridisC" color scheme (see examples).
#### Value
A theme object used in ggplot2 graphics.

#### Examples 
```{r}
## Not run:
# change default ggplot theme
theme_set(theme_black())
# change default bayesplot color scheme
bayesplot::color_scheme_set("viridisC")
# fit a simple model
fit <- brm(count ~ log_Age_c + log_Base4_c * Trt + (1|patient),
data = epilepsy, family = poisson(), chains = 2)
summary(fit)
# create various plots
plot(marginal_effects(fit), ask = FALSE)
pp_check(fit)
stanplot(fit, type = "hex", pars = c("b_Intercept", "b_Trt1"))
## End(Not run)
```

theme_default 157
theme_default Default bayesplot Theme for ggplot2 Graphics
#### Description 
This theme is imported from the bayesplot package. See theme_default for a complete documentation.
</code></pre>
#### Arguments

base_size base font size
base_family base font family
#### Value
A theme object used in ggplot2 graphics.

vcov.brmsfit 161
vcov.brmsfit Covariance and Correlation Matrix of Population-Level Effects
#### Description 
Get a point estimate of the covariance or correlation matrix of population-level parameters

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
vcov(object, correlation = FALSE, pars = NULL, ...)
</code></pre>
#### Arguments

object An object of class brmsfit.
correlation Logical; if FALSE (the default), compute the covariance matrix, if TRUE, compute
the correlation matrix.
pars Optional names of coefficients to extract. By default, all coefficients are extracted.
... Currently ignored.
#### Details
Estimates are obtained by calculating the maximum likelihood covariances (correlations) of the
posterior samples.
#### Value
covariance or correlation matrix of population-level parameters

#### Examples 
```{r}
## Not run:
fit <- brm(count ~ log_Age_c + log_Base4_c * Trt_c + (1+Trt_c|visit),
data = epilepsy, family = gaussian(), chains = 2)
vcov(fit)
## End(Not run)
```


waic.brmsfit Compute the WAIC
#### Description 
Compute the widely applicable information criterion (WAIC) based on the posterior likelihood
using the loo package. for more details see waic

#### Usage
<pre><code>
## S3 method for class 'brmsfit'
waic(x, ..., compare = TRUE, resp = NULL,
pointwise = FALSE, model_names = NULL)
waic.brmsfit 163
</code></pre>
#### Arguments

x A fitted model object.
... More fitted model objects or further arguments passed to the underlying postprocessing
functions.
compare A flag indicating if the information criteria of the models should be compared
to each other via compare_ic.
* `` resp``: Optional names of response variables. If specified, predictions are performed
only for the specified response variables.
pointwise A flag indicating whether to compute the full log-likelihood matrix at once or
separately for each observation. The latter approach is usually considerably
slower but requires much less working memory. Accordingly, if one runs into
memory issues, pointwise = TRUE is the way to go.
model_names If NULL (the default) will use model names derived from deparsing the call. Otherwise
will use the passed values as model names.
#### Details
When comparing models fitted to the same data, the smaller the WAIC, the better the fit. For
brmsfit objects, WAIC is an alias of waic. Use method add_ic to store information criteria in the
fitted model object for later 
#### Usage
<pre><code>.
#### Value
If just one object is provided, an object of class ic. If multiple objects are provided, an object of
class iclist.
Author(s)
Paul-Christian Buerkner <paul.buerkner@gmail.com>
References
Vehtari, A., Gelman, A., & Gabry J. (2016). Practical Bayesian model evaluation using leaveone-
out cross-validation and WAIC. In Statistics and Computing, doi:10.1007/s11222-016-9696-4.
arXiv preprint arXiv:1507.04544.
Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for
Bayesian models. Statistics and Computing, 24, 997-1016.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable
information criterion in singular learning theory. The Journal of Machine Learning Research, 11,
3571-3594.

#### Examples 
```{r}
## Not run:
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
data = inhaler, family = "gaussian")
164 Wiener
waic(fit1)
# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
data = inhaler, family = "gaussian")
# compare both models
waic(fit1, fit2)
## End(Not run)
```



Index
Topic datasets
epilepsy, 56
inhaler, 74
kidney, 82
acat (brmsfamily), 21
add_ic, 8, 42, 89, 163
add_ic<- (add_ic), 8
add_loo (add_ic), 8
add_waic (add_ic), 8
addition-terms, 6
as.array.brmsfit
(posterior_samples.brmsfit),
121
as.data.frame, 121
as.data.frame.brmsfit
(posterior_samples.brmsfit),
121
as.matrix.brmsfit, 120
as.matrix.brmsfit
(posterior_samples.brmsfit),
121
as.mcmc (as.mcmc.brmsfit), 9
as.mcmc.brmsfit, 9
asym_laplace (brmsfamily), 21
AsymLaplace, 10
autocor, 11
bayes_factor, 5, 14, 125
bayes_factor (bayes_factor.brmsfit), 11
bayes_factor.brmsfit, 11
bayes_R2, 26
bayes_R2 (bayes_R2.brmsfit), 12
bayes_R2.brmsfit, 12
bayesplot, 5, 128
bernoulli (brmsfamily), 21
Beta (brmsfamily), 21
bf (brmsformula), 26
bf-helpers (brmsformula-helpers), 34
binomial, 25
bridge_sampler, 11, 12, 26, 125
bridge_sampler
(bridge_sampler.brmsfit), 14
bridge_sampler.brmsfit, 14
bridge_sampler.stanfit, 14
bridgesampling:bayes_factor, 12
bridgesampling:bridge_sampler, 14
bridgesampling:post_prob, 125
brm, 5–7, 15, 25–27, 39, 51, 70, 106, 117, 146,
147, 157
brm_multiple, 29, 37
brms, 19, 25, 26
brms (brms-package), 5
brms-package, 5
brmsfamily, 6, 16, 18, 19, 21, 25, 26, 32, 38,
53, 54, 65, 94, 96, 105, 116
brmsfit, 6, 19
brmsfit (brmsfit-class), 25
brmsfit-class, 25
brmsformula, 5–7, 16, 18, 19, 24–26, 26, 34,
36, 38, 52, 65, 67, 68, 94, 96, 103,
104, 106, 107, 109, 113, 116, 117,
142, 144, 157
brmsformula-helpers, 34
brmshypothesis, 36, 73
brmsprior, 26
brmsprior (set_prior), 143
brmsprior-class (set_prior), 143
cat, 18, 95
categorical (brmsfamily), 21
cbind, 108
coef.brmsfit, 40, 72
combine_models, 39, 41
compare, 42
compare_ic, 41, 80, 88, 163
control_params
(control_params.brmsfit), 42
control_params.brmsfit, 42
cor_ar, 43, 45, 47
166
INDEX 167
cor_arma, 44, 44, 46, 47, 51
cor_arma-class (cor_arma), 44
cor_arr, 45, 45, 47
cor_brms, 16, 26, 38, 46, 65, 94, 96, 116
cor_brms-class (cor_brms), 46
cor_bsts, 47, 47, 59, 151, 159
cor_bsts-class (cor_bsts), 47
cor_car, 47, 48
cor_errorsar (cor_sar), 51
cor_fixed, 47, 49, 59, 60, 151, 159
cor_icar (cor_car), 48
cor_lagsar (cor_sar), 51
cor_ma, 45, 47, 50
cor_sar, 47, 51, 60, 151
cov_fixed (cor_fixed), 49
cratio (brmsfamily), 21
cs, 52
cse (cs), 52
cumulative (brmsfamily), 21
custom_family, 21, 53
customfamily, 25
customfamily (custom_family), 53
dasym_laplace (AsymLaplace), 10
density, 55
density_ratio, 55
dexgaussian (ExGaussian), 57
dfrechet (Frechet), 63
dgen_extreme_#### Value (GenExtreme#### Value), 64
dhurdle_gamma (Hurdle), 71
dhurdle_lognormal (Hurdle), 71
dhurdle_negbinomial (Hurdle), 71
dhurdle_poisson (Hurdle), 71
diagnostic-quantities
(log_posterior.brmsfit), 87
dinv_gaussian (InvGaussian), 75
dmulti_normal (MultiNormal), 111
dmulti_student_t (MultiStudentT), 112
dshifted_lnorm (Shifted_Lognormal), 148
dskew_normal (SkewNormal), 149
dstudent_t (StudentT), 154
dvon_mises (VonMises), 162
dwiener (Wiener), 164
dzero_inflated_beta (ZeroInflated), 165
dzero_inflated_binomial (ZeroInflated),
165
dzero_inflated_negbinomial
(ZeroInflated), 165
dzero_inflated_poisson (ZeroInflated),
165
E_loo, 91
environment, 54
epilepsy, 56
ExGaussian, 57
exgaussian (brmsfamily), 21
exponential (brmsfamily), 21
expose_functions
(expose_functions.brmsfit), 58
expose_functions.brmsfit, 58
expose_stan_functions, 58
expp1, 58
extract_draws, 61, 86, 129, 132, 140
extract_draws (extract_draws.brmsfit),
59
extract_draws.brmsfit, 59
family, 21, 25
fitted, 13, 54, 91, 92, 99, 130
fitted.brmsfit, 60
fixef (fixef.brmsfit), 62
fixef.brmsfit, 40, 62
formula, 16, 38, 65, 94, 96, 116
Frechet, 63
frechet (brmsfamily), 21
future, 17
gam, 28
gamm, 16, 28, 38, 96, 146
Gamma, 25
gen_extreme_#### Value (brmsfamily), 21
GenExtreme#### Value, 64
geom_contour, 99
geom_errorbar, 99
geom_jitter, 99
geom_point, 99
geom_raster, 99
geom_rug, 99
geom_smooth, 99
geometric (brmsfamily), 21
get_prior, 16, 18, 38, 65, 94, 96, 147
ggplot, 100, 152
ggtheme, 37, 100, 118
gp, 28, 66, 146
gr, 68
gtable, 118
horseshoe, 69, 145
168 INDEX
Hurdle, 71
hurdle_gamma (brmsfamily), 21
hurdle_lognormal (brmsfamily), 21
hurdle_negbinomial (brmsfamily), 21
hurdle_poisson (brmsfamily), 21
hypothesis, 16, 36–38, 95, 96
hypothesis (hypothesis.brmsfit), 72
hypothesis.brmsfit, 72
inhaler, 74
inv_logit_scaled, 76
InvGaussian, 75
is.brmsfit, 76
is.brmsfit_multiple, 77
is.brmsformula, 77
is.brmsprior, 77
is.brmsterms, 78
is.cor_arma (is.cor_brms), 78
is.cor_brms, 78
is.cor_bsts (is.cor_brms), 78
is.cor_car (is.cor_brms), 78
is.cor_fixed (is.cor_brms), 78
is.cor_sar (is.cor_brms), 78
is.mvbrmsformula, 79
is.mvbrmsterms, 79
kfold, 139
kfold (kfold.brmsfit), 80
kfold-helpers, 81
kfold.brmsfit, 80
kidney, 82
lasso, 83, 145
launch_shinystan, 84, 152
launch_shinystan
(launch_shinystan.brmsfit), 84
launch_shinystan.brmsfit, 84
lf (brmsformula-helpers), 34
log_lik, 54, 91, 92
log_lik (log_lik.brmsfit), 86
log_lik.brmsfit, 86
log_posterior (log_posterior.brmsfit),
87
log_posterior.brmsfit, 87
logit_scaled, 85
logLik.brmsfit (log_lik.brmsfit), 86
logm1, 85
lognormal (brmsfamily), 21
LOO (loo.brmsfit), 88
loo, 5, 26, 41, 42, 81, 86, 88, 138, 139
loo (loo.brmsfit), 88
LOO.brmsfit (loo.brmsfit), 88
loo.brmsfit, 88
loo::kfold_split_balanced, 81
loo::kfold_split_stratified, 81
loo::loo_model_weights, 90
loo_linpred (loo_predict.brmsfit), 91
loo_model_weights, 110, 119, 126
loo_model_weights
(loo_model_weights.brmsfit), 90
loo_model_weights.brmsfit, 90
loo_predict (loo_predict.brmsfit), 91
loo_predict.brmsfit, 91
loo_predictive_interval
(loo_predict.brmsfit), 91
loo_R2 (loo_R2.brmsfit), 92
loo_R2.brmsfit, 92
make_conditions, 93, 98, 142
make_stancode, 5, 94
make_standata, 5, 95, 151
marginal_effects, 5, 93, 102, 142
marginal_effects
(marginal_effects.brmsfit), 97
marginal_effects.brmsfit, 97
marginal_smooths
(marginal_smooths.brmsfit), 101
marginal_smooths.brmsfit, 101
MCMC, 117
mcmc_combo, 118
mcmc_pairs, 115
me, 35, 103
mgcv::s, 142
mgcv::t2, 142
mi, 29, 104
mixture, 32, 105
mm, 28, 107, 108
mmc, 107, 108
mo, 109
model_weights, 119, 126, 127
model_weights (model_weights.brmsfit),
110
model_weights.brmsfit, 110
mono (mo), 109
monotonic (mo), 109
MultiNormal, 111
MultiStudentT, 112
mvbf, 32
INDEX 169
mvbf (mvbrmsformula), 112
mvbrmsformula, 16, 33, 36, 38, 65, 94, 96,
112, 116, 117
neff_ratio (log_posterior.brmsfit), 87
negbinomial (brmsfamily), 21
ngrps (ngrps.brmsfit), 113
ngrps.brmsfit, 113
nlf (brmsformula-helpers), 34
nsamples (nsamples.brmsfit), 114
nsamples.brmsfit, 114
nuts_params (log_posterior.brmsfit), 87
pairs, 114
pairs.brmsfit, 114
par.names (parnames), 115
pareto_k_ids, 88, 138
parnames, 72, 115
parse_bf, 78, 79, 116
pasym_laplace (AsymLaplace), 10
pexgaussian (ExGaussian), 57
pfrechet (Frechet), 63
pgen_extreme_#### Value (GenExtreme#### Value), 64
phurdle_gamma (Hurdle), 71
phurdle_lognormal (Hurdle), 71
phurdle_negbinomial (Hurdle), 71
phurdle_poisson (Hurdle), 71
pinv_gaussian (InvGaussian), 75
plan, 18
plot.brmsfit, 117
plot.brmshypothesis (brmshypothesis), 36
plot.brmsMarginalEffects
(marginal_effects.brmsfit), 97
post_prob, 12, 14, 110, 119, 126
post_prob (post_prob.brmsfit), 124
post_prob.brmsfit, 124
posterior.samples
(posterior_samples.brmsfit),
121
posterior_average, 127
posterior_average
(posterior_average.brmsfit),
118
posterior_average.brmsfit, 118
posterior_interval
(posterior_interval.brmsfit),
120
posterior_interval.brmsfit, 120
posterior_linpred, 61
posterior_linpred (fitted.brmsfit), 60
posterior_predict, 133
posterior_predict (predict.brmsfit), 130
posterior_samples
(posterior_samples.brmsfit),
121
posterior_samples.brmsfit, 121
posterior_summary
(posterior_summary.brmsfit),
122
posterior_summary.brmsfit, 122
posterior_table, 123
pp_average, 119
pp_average (pp_average.brmsfit), 125
pp_average.brmsfit, 125
pp_check, 5
pp_check (pp_check.brmsfit), 127
pp_check.brmsfit, 127
pp_mixture (pp_mixture.brmsfit), 128
pp_mixture.brmsfit, 128
PPC, 127, 128
predict, 54, 60, 91, 99
predict.brmsfit, 128, 130
predictive_error (residuals.brmsfit),
139
predictive_interval
(predictive_interval.brmsfit),
133
predictive_interval.brmsfit, 133
print.brmsfit, 134
print.brmshypothesis (brmshypothesis),
36
print.brmsprior, 134
print.brmssummary (print.brmsfit), 134
print.default, 37
prior (set_prior), 143
prior_ (set_prior), 143
prior_samples (prior_samples.brmsfit),
135
prior_samples.brmsfit, 135
prior_string (set_prior), 143
prior_summary (prior_summary.brmsfit),
136
prior_summary.brmsfit, 136
pshifted_lnorm (Shifted_Lognormal), 148
psis, 91
pskew_normal (SkewNormal), 149
pstudent_t (StudentT), 154
170 INDEX
pvon_mises (VonMises), 162
pzero_inflated_beta (ZeroInflated), 165
pzero_inflated_binomial (ZeroInflated),
165
pzero_inflated_negbinomial
(ZeroInflated), 165
pzero_inflated_poisson (ZeroInflated),
165
qasym_laplace (AsymLaplace), 10
qfrechet (Frechet), 63
qshifted_lnorm (Shifted_Lognormal), 148
qskew_normal (SkewNormal), 149
qstudent_t (StudentT), 154
ranef (ranef.brmsfit), 137
ranef.brmsfit, 40, 72, 137
rasym_laplace (AsymLaplace), 10
reloo, 81, 88, 138
residuals.brmsfit, 139
resp_cat (addition-terms), 6
resp_cens (addition-terms), 6
resp_dec (addition-terms), 6
resp_mi (addition-terms), 6
resp_se (addition-terms), 6
resp_trials (addition-terms), 6
resp_trunc (addition-terms), 6
resp_weights (addition-terms), 6
restructure, 141
rexgaussian (ExGaussian), 57
rfrechet (Frechet), 63
rgen_extreme_#### Value (GenExtreme#### Value), 64
rhat (log_posterior.brmsfit), 87
rinv_gaussian (InvGaussian), 75
rmulti_normal (MultiNormal), 111
rmulti_student_t (MultiStudentT), 112
rows2labels, 93, 141
rshifted_lnorm (Shifted_Lognormal), 148
rskew_normal (SkewNormal), 149
rstudent_t (StudentT), 154
runApp, 84
rvon_mises (VonMises), 162
rwiener (Wiener), 164
s, 28, 142
sampling, 18
saveRDS, 8, 18, 39
scale_colour_gradient, 100
scale_colour_grey, 100
set.seed, 72, 119, 126
set_mecor (brmsformula-helpers), 34
set_nl (brmsformula-helpers), 34
set_prior, 16, 18, 30, 31, 38, 65, 70, 84, 94,
96, 106, 143
set_rescor (brmsformula-helpers), 34
Shifted_Lognormal, 148
shifted_lognormal (brmsfamily), 21
skew_normal (brmsfamily), 21
SkewNormal, 149
sratio (brmsfamily), 21
Stan, 5
stan, 17, 19, 43
stan_model, 18
stancode (stancode.brmsfit), 150
stancode.brmsfit, 150
standata (standata.brmsfit), 151
standata.brmsfit, 151
stanfit, 26
stanplot, 5
stanplot (stanplot.brmsfit), 152
stanplot.brmsfit, 152
stanvar, 16, 38, 53, 54, 95, 96, 153
stanvars, 26
stanvars (stanvar), 153
student (brmsfamily), 21
StudentT, 154
summary, 5
summary.brmsfit, 155
t2, 28
t2 (s), 142
TDist, 155
theme, 37, 100, 118
theme_black, 156
theme_default, 37, 100, 118, 157, 157
update, 17
update.brmsfit, 138, 157
update.formula, 157
update_adterms, 158
validate_newdata, 60, 159
VarCorr, 160
VarCorr (VarCorr.brmsfit), 160
VarCorr.brmsfit, 160
vb, 18
vcov.brmsfit, 161
Vectorize, 58
INDEX 171
von_mises (brmsfamily), 21
VonMises, 162
WAIC, 41, 42
WAIC (waic.brmsfit), 162
waic, 5, 26, 86, 162
waic (waic.brmsfit), 162
WAIC.brmsfit (waic.brmsfit), 162
waic.brmsfit, 162
weibull (brmsfamily), 21
Wiener, 164
wiener (brmsfamily), 21
wienerdist, 164
zero_inflated_beta (brmsfamily), 21
zero_inflated_binomial (brmsfamily), 21
zero_inflated_negbinomial (brmsfamily),
21
zero_inflated_poisson (brmsfamily), 21
zero_one_inflated_beta (brmsfamily), 21
ZeroInflated, 165