Methods for extracting out MCMC behaviour from greta model

relevant greta forum post: https://forum.greta-stats.org/t/how-to-get-info-of-mcmc-behaviour-from-the-result-of-mcmc/292

``` r
x <- iris$Petal.Length
y <- iris$Sepal.Length
library(greta)
#>
#> Attaching package: 'greta'
#> The following objects are masked from 'package:stats':
#>
#> binomial, cov2cor, poisson
#> The following objects are masked from 'package:base':
#>
#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,
#> eigen, forwardsolve, gamma, identity, rowMeans, rowSums, sweep,
#> tapply

int <- normal(0, 5)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✓ Initialising python and checking dependencies ... done!
#>
coef <- normal(0, 3)
sd <- lognormal(0, 3)

mean <- int + coef * x
distribution(y) <- normal(mean, sd)
m <- model(int, coef, sd)

draws <- mcmc(m)
#> running 4 chains simultaneously on up to 8 cores
#>

```
```

#> warmup 0/1000 \| eta: ?s warmup == 50/1000 \| eta: 35s warmup ==== 100/1000 \| eta: 23s warmup ====== 150/1000 \| eta: 18s warmup ======== 200/1000 \| eta: 16s warmup ========== 250/1000 \| eta: 14s warmup =========== 300/1000 \| eta: 12s warmup ============= 350/1000 \| eta: 11s warmup =============== 400/1000 \| eta: 10s warmup ================= 450/1000 \| eta: 9s warmup =================== 500/1000 \| eta: 8s warmup ===================== 550/1000 \| eta: 7s warmup ======================= 600/1000 \| eta: 6s warmup ========================= 650/1000 \| eta: 6s warmup =========================== 700/1000 \| eta: 5s warmup ============================ 750/1000 \| eta: 4s warmup ============================== 800/1000 \| eta: 3s warmup ================================ 850/1000 \| eta: 2s warmup ================================== 900/1000 \| eta: 2s warmup ==================================== 950/1000 \| eta: 1s warmup ====================================== 1000/1000 \| eta: 0s
#> sampling 0/1000 \| eta: ?s sampling == 50/1000 \| eta: 9s sampling ==== 100/1000 \| eta: 10s sampling ====== 150/1000 \| eta: 9s sampling ======== 200/1000 \| eta: 8s sampling ========== 250/1000 \| eta: 8s sampling =========== 300/1000 \| eta: 7s sampling ============= 350/1000 \| eta: 7s sampling =============== 400/1000 \| eta: 6s sampling ================= 450/1000 \| eta: 6s sampling =================== 500/1000 \| eta: 6s sampling ===================== 550/1000 \| eta: 5s sampling ======================= 600/1000 \| eta: 4s sampling ========================= 650/1000 \| eta: 4s sampling =========================== 700/1000 \| eta: 3s sampling ============================ 750/1000 \| eta: 3s sampling ============================== 800/1000 \| eta: 2s sampling ================================ 850/1000 \| eta: 2s sampling ================================== 900/1000 \| eta: 1s sampling ==================================== 950/1000 \| eta: 1s sampling ====================================== 1000/1000 \| eta: 0s

attr(draws, “model_info”)*s**a**m**p**l**e**r**s*\[\[1\]\]numerical_rejections
#> \[1\] 0

greta_info \<- function(x){
attr(x, “model_info”)
}

greta_samplers \<- function(x){
greta_info(x)$samplers
}

draw_info \<- greta_info(draws)

draw_info$samplers
#> $`1`
#> Error in vapply(x, format, ““, big.mark = big.mark, big.interval = big.interval, : values must be length 1,
#> but FUN(X\[\[4\]\]) result is length 3

numerical_rejections \<- function(x){
purrr::map(greta_samplers(x), \~purrr::pluck(.x, “numerical_rejections”))
}

numerical_rejections(draws)
#> $`1`
#> \[1\] 0
```

^{Created on 2021-09-20 by the [reprex package](https://reprex.tidyverse.org) (v2.0.1)}

Session info

```r
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.1.0 (2021-05-18)
#> os macOS Big Sur 10.16
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_AU.UTF-8
#> ctype en_AU.UTF-8
#> tz Australia/Perth
#> date 2021-09-20
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
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#> backports 1.2.1 2020-12-09 [1] CRAN (R 4.1.0)
#> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.1.0)
#> callr 3.7.0 2021-04-20 [1] CRAN (R 4.1.0)
#> cli 3.0.1 2021-07-17 [1] CRAN (R 4.1.0)
#> coda 0.19-4 2020-09-30 [1] CRAN (R 4.1.0)
#> codetools 0.2-18 2020-11-04 [1] CRAN (R 4.1.0)
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#> ellipsis 0.3.2 2021-04-29 [1] CRAN (R 4.1.0)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 4.1.0)
#> fansi 0.5.0 2021-05-25 [1] CRAN (R 4.1.0)
#> fs 1.5.0 2020-07-31 [1] CRAN (R 4.1.0)
#> future 1.21.0 2020-12-10 [1] CRAN (R 4.1.0)
#> globals 0.14.0 2020-11-22 [1] CRAN (R 4.1.0)
#> glue 1.4.2 2020-08-27 [1] CRAN (R 4.1.0)
#> greta * 0.3.1.9012 2021-08-06 [1] local
#> highr 0.9 2021-04-16 [1] CRAN (R 4.1.0)
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#>
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