---
title: "Estimating trial-level effects"
output: rmarkdown::html_vignette
bibliography: citations.bib
csl: apa.csl
link-citations: true
vignette: >
  %\VignetteIndexEntry{Estimating trial-level effects}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- categorical.Rmd is generated from _categorical.Rmd.src Please edit that file -->




## Introduction
By default, the `hmetad` package uses aggregated data (i.e., counts of the  number of trials with the same stimulus, type 1 response, and type 2 response.) This is because data aggregation makes model fitting and simulation much more efficient. But sometimes researchers will be interested in trial-level effects.

One common example would be what is often called "crossed random effects". For example, in a design where all participants make responses to the same set of items, a researcher might want to estimate both participant-level and item-level effects on their model parameters.

We can simulate data from such a design like so:

``` r
library(tidyverse)
library(tidybayes)
library(hmetad)

## average model parameters
K <- 3 ## number of confidence levels
mu_log_M <- -0.5
mu_dprime <- 1.5
mu_c <- 0
mu_c2_0 <- rep(-1, K - 1)
mu_c2_1 <- rep(-1, K - 1)

## participant-level standard deviations
sd_log_M_participant <- 0.25
sd_dprime_participant <- 0.5
sd_c_participant <- 0.33
sd_c2_0_participant <- cov_matrix(rep(0.25, K - 1), diag(K - 1))
sd_c2_1_participant <- cov_matrix(rep(0.25, K - 1), diag(K - 1))

## item-level standard deviations
sd_log_M_item <- 0.1
sd_dprime_item <- 0.5
sd_c_item <- 0.75
sd_c2_0_item <- cov_matrix(rep(0.1, K - 1), diag(K - 1))
sd_c2_1_item <- cov_matrix(rep(0.1, K - 1), diag(K - 1))


## simulate data
d <- expand_grid(
  participant = 1:50,
  item = 1:25
) |>
  ## simulate participant-level differences
  group_by(participant) |>
  mutate(
    z_log_M_participant = rnorm(1, sd = sd_log_M_participant),
    z_dprime_participant = rnorm(1, sd = sd_dprime_participant),
    z_c_participant = rnorm(1, sd = sd_c_participant),
    z_c2_0_participant = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_0_participant)),
    z_c2_1_participant = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_1_participant))
  ) |>
  ## simulate item-level differences
  group_by(item) |>
  mutate(
    z_log_M_item = rnorm(1, sd = sd_log_M_item),
    z_dprime_item = rnorm(1, sd = sd_dprime_item),
    z_c_item = rnorm(1, sd = sd_c_item),
    z_c2_0_item = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_0_item)),
    z_c2_1_item = list(rmulti_normal(1, mu = rep(0, K - 1), Sigma = sd_c2_1_item))
  ) |>
  ungroup() |>
  ## compute model parameters
  mutate(
    log_M = mu_log_M + z_log_M_participant + z_log_M_item,
    dprime = mu_dprime + z_dprime_participant + z_dprime_item,
    c = mu_c + z_c_participant + z_c_item,
    c2_0_diff = map2(
      z_c2_0_participant, z_c2_0_item,
      ~ exp(mu_c2_0 + .x + .y)
    ),
    c2_1_diff = map2(
      z_c2_1_participant, z_c2_1_item,
      ~ exp(mu_c2_1 + .x + .y)
    )
  ) |>
  ## simulate two trials per participant/item (stimulus = 0 and stimulus = 1)
  mutate(trial = pmap(list(dprime, c, log_M, c2_0_diff, c2_1_diff), sim_metad, N_trials = 2)) |>
  select(participant, item, trial) |>
  unnest(trial)
```


```
#> # A tibble: 2,500 × 16
#>    participant  item trial stimulus response correct confidence dprime       c meta_dprime     M meta_c2_0
#>          <int> <int> <int>    <int>    <int>   <int>      <int>  <dbl>   <dbl>       <dbl> <dbl> <list>   
#>  1           1     1     1        0        1       0          3  0.725 -0.843        0.614 0.847 <dbl [2]>
#>  2           1     1     1        1        1       1          3  0.725 -0.843        0.614 0.847 <dbl [2]>
#>  3           1     2     1        0        0       1          2  1.74  -1.24         1.12  0.642 <dbl [2]>
#>  4           1     2     1        1        1       1          3  1.74  -1.24         1.12  0.642 <dbl [2]>
#>  5           1     3     1        0        0       1          3  0.746  0.614        0.565 0.758 <dbl [2]>
#>  6           1     3     1        1        0       0          2  0.746  0.614        0.565 0.758 <dbl [2]>
#>  7           1     4     1        0        0       1          2  1.50  -0.0110       1.21  0.809 <dbl [2]>
#>  8           1     4     1        1        1       1          3  1.50  -0.0110       1.21  0.809 <dbl [2]>
#>  9           1     5     1        0        0       1          1  1.21  -0.231        1.13  0.931 <dbl [2]>
#> 10           1     5     1        1        1       1          1  1.21  -0.231        1.13  0.931 <dbl [2]>
#> # ℹ 2,490 more rows
#> # ℹ 4 more variables: meta_c2_1 <list>, theta <dbl>, theta_1 <dbl>, theta_2 <dbl>
```

Don't worry about the details of the simulation code- what matters is that we have a data set with repeated measures for participants:


``` r
count(d, participant)
#> # A tibble: 50 × 2
#>    participant     n
#>          <int> <int>
#>  1           1    50
#>  2           2    50
#>  3           3    50
#>  4           4    50
#>  5           5    50
#>  6           6    50
#>  7           7    50
#>  8           8    50
#>  9           9    50
#> 10          10    50
#> # ℹ 40 more rows
```
And repeated measures for items:

``` r
count(d, item)
#> # A tibble: 25 × 2
#>     item     n
#>    <int> <int>
#>  1     1   100
#>  2     2   100
#>  3     3   100
#>  4     4   100
#>  5     5   100
#>  6     6   100
#>  7     7   100
#>  8     8   100
#>  9     9   100
#> 10    10   100
#> # ℹ 15 more rows
```

## Standard model with data aggregation
If we would like, we can use the `fit_metad` function on this data with participant-level and item-level effects. However, if we aggregate the data ourselves, we can see that the aggregation doesn't really help us here:


``` r
aggregate_metad(d, participant, item)
#> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> # A tibble: 1,250 × 5
#>    participant  item   N_0   N_1 N[,"N_0_1"] [,"N_0_2"] [,"N_0_3"] [,"N_0_4"] [,"N_0_5"] [,"N_0_6"] [,"N_1_1"]
#>          <int> <int> <int> <int>       <int>      <int>      <int>      <int>      <int>      <int>      <int>
#>  1           1     1     1     1           0          0          0          0          0          1          0
#>  2           1     2     1     1           0          1          0          0          0          0          0
#>  3           1     3     1     1           1          0          0          0          0          0          0
#>  4           1     4     1     1           0          1          0          0          0          0          0
#>  5           1     5     1     1           0          0          1          0          0          0          0
#>  6           1     6     1     1           1          0          0          0          0          0          0
#>  7           1     7     1     1           0          0          0          0          1          0          0
#>  8           1     8     1     1           1          0          0          0          0          0          0
#>  9           1     9     1     1           1          0          0          0          0          0          0
#> 10           1    10     1     1           1          0          0          0          0          0          0
#> # ℹ 1,240 more rows
#> # ℹ 1 more variable: N[8:12] <int>
```

As you can see, the aggregated data set has 1250 rows (with two observations per row), which is not much smaller than the trial-level data that we started with! So, in this case, it will probably be easier *not* to aggregate our data. Nevertheless, there is nothing stopping us from fitting the model like normal:^[Note that in practice, fitting hierarchical models will usually require setting informed priors and adjusting the Stan sampler settings.]

``` r
# Priors are chosen arbitrarily for this example.
# Please choose your own wisely!
priors <- prior(normal(0, .25), class = Intercept) +
  set_prior(
    "normal(0, .25)",
    class = "Intercept",
    dpar = c("dprime", "c")
  ) +
  set_prior("normal(-0.5, .1)", class = "Intercept", dpar = metac2_parameters(K = 3)) +
  prior(normal(0, 1), class = sd) +
  set_prior("normal(0, 1)", class = "sd", dpar = c("dprime", "c", metac2_parameters(K = 3)))

m.multinomial <- fit_metad(
  bf(
    N ~ 1 + (1 | participant) + (1 | item),
    dprime + c +
      metac2zero1diff + metac2zero2diff +
      metac2one1diff + metac2one2diff ~
      1 + (1 | participant) + (1 | item)
  ),
  data = d, init = 0, cores = 4, prior = priors
)
#> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> Compiling Stan program...
#> Start sampling
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
```


```
#>  Family: metad__3__normal__absolute__multinomial 
#>   Links: mu = log; dprime = identity; c = identity; metac2zero1diff = log; metac2zero2diff = log; metac2one1diff = log; metac2one2diff = log 
#> Formula: N ~ 1 + (1 | participant) + (1 | item) 
#>          dprime ~ 1 + (1 | participant) + (1 | item)
#>          c ~ 1 + (1 | participant) + (1 | item)
#>          metac2zero1diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2zero2diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2one1diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2one2diff ~ 1 + (1 | participant) + (1 | item)
#>    Data: data.aggregated (Number of observations: 1250) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Multilevel Hyperparameters:
#> ~item (Number of levels: 25) 
#>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)                     0.14      0.10     0.01     0.37 1.00     1074     1538
#> sd(dprime_Intercept)              0.57      0.14     0.35     0.88 1.00      757     1379
#> sd(c_Intercept)                   0.76      0.12     0.57     1.03 1.00      614     1061
#> sd(metac2zero1diff_Intercept)     0.07      0.05     0.00     0.20 1.00     1876     1525
#> sd(metac2zero2diff_Intercept)     0.13      0.09     0.01     0.32 1.00      927     1399
#> sd(metac2one1diff_Intercept)      0.11      0.08     0.00     0.31 1.00     1085     1964
#> sd(metac2one2diff_Intercept)      0.18      0.10     0.01     0.41 1.00      921     1546
#> 
#> ~participant (Number of levels: 50) 
#>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)                     0.12      0.09     0.01     0.32 1.00     1247     1931
#> sd(dprime_Intercept)              0.44      0.08     0.29     0.62 1.00     1200     2111
#> sd(c_Intercept)                   0.35      0.05     0.27     0.45 1.00     1329     2091
#> sd(metac2zero1diff_Intercept)     0.19      0.10     0.02     0.38 1.01      717      955
#> sd(metac2zero2diff_Intercept)     0.30      0.10     0.11     0.49 1.01     1012     1093
#> sd(metac2one1diff_Intercept)      0.20      0.10     0.02     0.41 1.01      815     1241
#> sd(metac2one2diff_Intercept)      0.36      0.10     0.15     0.56 1.00      887      716
#> 
#> Regression Coefficients:
#>                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept                    -0.23      0.09    -0.41    -0.07 1.00     3386     2946
#> dprime_Intercept              1.10      0.15     0.77     1.36 1.00      930     1255
#> c_Intercept                   0.15      0.14    -0.12     0.42 1.02      275      496
#> metac2zero1diff_Intercept    -0.83      0.06    -0.93    -0.72 1.00     3284     2931
#> metac2zero2diff_Intercept    -0.79      0.06    -0.91    -0.67 1.00     2643     2965
#> metac2one1diff_Intercept     -0.84      0.06    -0.95    -0.71 1.00     3121     2733
#> metac2one2diff_Intercept     -0.79      0.07    -0.92    -0.65 1.00     2296     2603
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
```


## Data preparation
Fitting the trial-level model does not require data aggregation, however it still requires a small amount of data preparation. To fit the model, we will need two things:

  * a column with the stimulus per trial (`0` or `1`), and
  * a column containing the joint type 1/type 2 responses per trial (between `1` and `2*K`).

Our data already has a `stimulus` column but separate columns for the two responses. So, we can add in a joint response column now:

``` r
d <- d |>
  mutate(joint_response = joint_response(response, confidence, K)) |>
  relocate(joint_response, .after = "stimulus")
```


```
#> # A tibble: 2,500 × 17
#>    participant  item trial stimulus joint_response response correct confidence dprime       c meta_dprime     M
#>          <int> <int> <int>    <int>          <int>    <int>   <int>      <int>  <dbl>   <dbl>       <dbl> <dbl>
#>  1           1     1     1        0              6        1       0          3  0.725 -0.843        0.614 0.847
#>  2           1     1     1        1              6        1       1          3  0.725 -0.843        0.614 0.847
#>  3           1     2     1        0              2        0       1          2  1.74  -1.24         1.12  0.642
#>  4           1     2     1        1              6        1       1          3  1.74  -1.24         1.12  0.642
#>  5           1     3     1        0              1        0       1          3  0.746  0.614        0.565 0.758
#>  6           1     3     1        1              2        0       0          2  0.746  0.614        0.565 0.758
#>  7           1     4     1        0              2        0       1          2  1.50  -0.0110       1.21  0.809
#>  8           1     4     1        1              6        1       1          3  1.50  -0.0110       1.21  0.809
#>  9           1     5     1        0              3        0       1          1  1.21  -0.231        1.13  0.931
#> 10           1     5     1        1              4        1       1          1  1.21  -0.231        1.13  0.931
#> # ℹ 2,490 more rows
#> # ℹ 5 more variables: meta_c2_0 <list>, meta_c2_1 <list>, theta <dbl>, theta_1 <dbl>, theta_2 <dbl>
```


## Model fitting
Now that we have our data, we can fit the trial-level model using `joint_response` as our response variable, `stimulus` as an extra variable passed to `brms`, and the argument `categorical=TRUE` to tell `fit_metad` not to aggregate the data:

``` r
m.categorical <- fit_metad(
  bf(
    joint_response | vint(stimulus) ~ 1 + (1 | participant) + (1 | item),
    dprime + c +
      metac2zero1diff + metac2zero2diff +
      metac2one1diff + metac2one2diff ~
      1 + (1 | participant) + (1 | item)
  ),
  data = d, categorical = TRUE, init = 0, cores = 4, prior = priors
)
#> `hmetad` has inferred that there are K=3 confidence levels in the data. If this is incorrect, please set this manually using the argument `K=<K>`
#> Compiling Stan program...
#> Start sampling
```


```
#>  Family: metad__3__normal__absolute__categorical 
#>   Links: mu = log; dprime = identity; c = identity; metac2zero1diff = log; metac2zero2diff = log; metac2one1diff = log; metac2one2diff = log 
#> Formula: joint_response | vint(stimulus) ~ 1 + (1 | participant) + (1 | item) 
#>          dprime ~ 1 + (1 | participant) + (1 | item)
#>          c ~ 1 + (1 | participant) + (1 | item)
#>          metac2zero1diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2zero2diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2one1diff ~ 1 + (1 | participant) + (1 | item)
#>          metac2one2diff ~ 1 + (1 | participant) + (1 | item)
#>    Data: data.aggregated (Number of observations: 2500) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Multilevel Hyperparameters:
#> ~item (Number of levels: 25) 
#>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)                     0.14      0.10     0.01     0.37 1.00     1467     1998
#> sd(dprime_Intercept)              0.57      0.14     0.36     0.89 1.00      854     1603
#> sd(c_Intercept)                   0.76      0.12     0.57     1.02 1.00      792     1543
#> sd(metac2zero1diff_Intercept)     0.07      0.05     0.00     0.19 1.00     2130     2015
#> sd(metac2zero2diff_Intercept)     0.13      0.09     0.01     0.33 1.00     1264     1853
#> sd(metac2one1diff_Intercept)      0.12      0.09     0.00     0.31 1.00     1053     1268
#> sd(metac2one2diff_Intercept)      0.18      0.10     0.02     0.41 1.00      927     1443
#> 
#> ~participant (Number of levels: 50) 
#>                               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)                     0.12      0.09     0.00     0.31 1.00     1295     1610
#> sd(dprime_Intercept)              0.44      0.08     0.29     0.62 1.00     1513     2242
#> sd(c_Intercept)                   0.35      0.05     0.27     0.45 1.00     1330     2340
#> sd(metac2zero1diff_Intercept)     0.18      0.10     0.01     0.38 1.00      739     1234
#> sd(metac2zero2diff_Intercept)     0.30      0.10     0.07     0.51 1.01      698      502
#> sd(metac2one1diff_Intercept)      0.21      0.10     0.02     0.41 1.00      702      973
#> sd(metac2one2diff_Intercept)      0.36      0.10     0.17     0.56 1.01      954      837
#> 
#> Regression Coefficients:
#>                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept                    -0.23      0.09    -0.42    -0.06 1.00     3799     2616
#> dprime_Intercept              1.09      0.15     0.78     1.35 1.00      851     1703
#> c_Intercept                   0.14      0.13    -0.12     0.40 1.01      501     1045
#> metac2zero1diff_Intercept    -0.83      0.05    -0.93    -0.72 1.00     4309     2970
#> metac2zero2diff_Intercept    -0.79      0.06    -0.91    -0.66 1.00     2891     2937
#> metac2one1diff_Intercept     -0.83      0.06    -0.95    -0.71 1.00     2537     2565
#> metac2one2diff_Intercept     -0.79      0.07    -0.92    -0.65 1.00     2479     2625
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).
```

As you can see, aside from the way that the data is formatted, this model is exactly the same as the multinomial model above.





## Extracting model estimates
Obtaining posterior estimates over model parameters, predictions, and other estimates is very similar to the multinomial model (for details, see `vignette("hmetad")`). So, here we will focus on the type 1 ROC curves, this time using `roc1_rvars` instead of `roc1_draws` for increased efficiency.

To get the posterior estimates, we need to specify a dataset to make predictions for, as well as a random effects formula to use in model predictions. For example, to estimate a ROC averaging over participants and items, we can use an empty data set with `re_formula=NA`:

``` r
roc1_rvars(m.multinomial, tibble(.row = 1), re_formula = NA)
#> # A tibble: 5 × 6
#> # Groups:   .row, joint_response, response, confidence [5]
#>    .row joint_response response confidence           p_fa         p_hit
#>   <int>          <int>    <int>      <dbl>     <rvar[1d]>    <rvar[1d]>
#> 1     1              1        0          3  0.600 ± 0.058  0.89 ± 0.031
#> 2     1              2        0          2  0.413 ± 0.058  0.79 ± 0.047
#> 3     1              3        0          1  0.244 ± 0.048  0.65 ± 0.059
#> 4     1              4        1          1  0.136 ± 0.034  0.47 ± 0.062
#> 5     1              5        1          2  0.063 ± 0.020  0.29 ± 0.054
```

The process is exactly the same for the categorical model:

``` r
roc1_rvars(m.categorical, tibble(.row = 1), re_formula = NA)
#> # A tibble: 5 × 6
#> # Groups:   .row, joint_response, response, confidence [5]
#>    .row joint_response response confidence           p_fa         p_hit
#>   <int>          <int>    <int>      <dbl>     <rvar[1d]>    <rvar[1d]>
#> 1     1              1        0          3  0.605 ± 0.057  0.89 ± 0.028
#> 2     1              2        0          2  0.418 ± 0.057  0.79 ± 0.043
#> 3     1              3        0          1  0.248 ± 0.047  0.66 ± 0.055
#> 4     1              4        1          1  0.138 ± 0.033  0.47 ± 0.058
#> 5     1              5        1          2  0.064 ± 0.019  0.30 ± 0.051
```

Next, to get participant-level ROCs (averaging over items), we can use a dataset with one row per participant and only the participant-level random effects:

``` r
roc1_rvars(m.categorical, distinct(d, participant), re_formula = ~ (1 | participant))
#> # A tibble: 250 × 7
#> # Groups:   .row, participant, joint_response, response, confidence [250]
#>     .row participant joint_response response confidence          p_fa         p_hit
#>    <int>       <int>          <int>    <int>      <dbl>    <rvar[1d]>    <rvar[1d]>
#>  1     1           1              1        0          3  0.54 ± 0.103  0.93 ± 0.038
#>  2     2           2              1        0          3  0.75 ± 0.077  0.91 ± 0.042
#>  3     3           3              1        0          3  0.84 ± 0.066  0.96 ± 0.024
#>  4     4           4              1        0          3  0.45 ± 0.099  0.78 ± 0.073
#>  5     5           5              1        0          3  0.63 ± 0.093  0.93 ± 0.035
#>  6     6           6              1        0          3  0.44 ± 0.099  0.91 ± 0.043
#>  7     7           7              1        0          3  0.60 ± 0.092  0.88 ± 0.051
#>  8     8           8              1        0          3  0.40 ± 0.095  0.67 ± 0.088
#>  9     9           9              1        0          3  0.72 ± 0.083  0.88 ± 0.052
#> 10    10          10              1        0          3  0.50 ± 0.099  0.68 ± 0.087
#> # ℹ 240 more rows
```


We can use a similar process to get item-level ROCs (averaging over participants):

``` r
roc1_rvars(m.categorical, distinct(d, item), re_formula = ~ (1 | item))
#> # A tibble: 125 × 7
#> # Groups:   .row, item, joint_response, response, confidence [125]
#>     .row  item joint_response response confidence          p_fa          p_hit
#>    <int> <int>          <int>    <int>      <dbl>    <rvar[1d]>     <rvar[1d]>
#>  1     1     1              1        0          3  0.92 ± 0.026  0.98 ± 0.0115
#>  2     2     2              1        0          3  0.93 ± 0.023  1.00 ± 0.0023
#>  3     3     3              1        0          3  0.59 ± 0.064  0.74 ± 0.0521
#>  4     4     4              1        0          3  0.50 ± 0.066  0.91 ± 0.0292
#>  5     5     5              1        0          3  0.71 ± 0.055  0.93 ± 0.0252
#>  6     6     6              1        0          3  0.50 ± 0.067  0.92 ± 0.0249
#>  7     7     7              1        0          3  0.47 ± 0.067  0.84 ± 0.0413
#>  8     8     8              1        0          3  0.43 ± 0.064  0.89 ± 0.0324
#>  9     9     9              1        0          3  0.56 ± 0.065  0.88 ± 0.0334
#> 10    10    10              1        0          3  0.41 ± 0.065  0.87 ± 0.0349
#> # ℹ 115 more rows
```


## Other benefits
Aside from representing the data in a more convenient format, the trial-level model should be more useful for things like model comparison using the `loo` package, multivariate models, and mediation models. These features should mostly work out of the box but they are still under active development, so stay tuned!