Splitting calculate into separate functions

Related to #650 and #481

``` r
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
x <- normal(0,1)
#> ℹ Initialising python and checking dependencies, this may take a moment.
#> ✔ Initialising python and checking dependencies ... done!
#>
m <- model(x)
draws <- mcmc(m, n_samples = 100)
#> running 4 chains simultaneously on up to 8 CPU cores
#>
#> warmup 0/1000 | eta: ?s warmup == 50/1000 | eta: 9s warmup ==== 100/1000 | eta: 5s warmup ====== 150/1000 | eta: 3s warmup ======== 200/1000 | eta: 3s warmup ========== 250/1000 | eta: 2s warmup =========== 300/1000 | eta: 2s warmup ============= 350/1000 | eta: 2s warmup =============== 400/1000 | eta: 1s warmup ================= 450/1000 | eta: 1s warmup =================== 500/1000 | eta: 1s warmup ===================== 550/1000 | eta: 1s warmup ======================= 600/1000 | eta: 1s warmup ========================= 650/1000 | eta: 1s warmup =========================== 700/1000 | eta: 1s warmup ============================ 750/1000 | eta: 0s warmup ============================== 800/1000 | eta: 0s warmup ================================ 850/1000 | eta: 0s warmup ================================== 900/1000 | eta: 0s warmup ==================================== 950/1000 | eta: 0s warmup ====================================== 1000/1000 | eta: 0s
#> sampling 0/100 | eta: ?s sampling =================== 50/100 | eta: 0s sampling ====================================== 100/100 | eta: 0s
```

returns list

``` r
x_sim_100 <- calculate(x, nsim = 100)
class(x_sim_100)
#> [1] "list"
```

returns greta mcmc object

``` r
x_draws_100 <- calculate(x, values = draws)
class(x_draws_100)
#> [1] "greta_mcmc_list" "mcmc.list"
```

returns list

``` r
x_draws_10 <- calculate(x, values = draws, nsim = 10)
class(x_draws_10)
#> [1] "list"
```

wrap this in a div

Long print method

``` r
x_sim_100
#> $x
#> , , 1
#>
#> [,1]
#> [1,] 1.52312785
#> [2,] -1.00269620
#> [3,] 0.40313423
#> [4,] -0.94800782
#> [5,] -0.25068921
#> [6,] 0.84174599
#> [7,] -1.56150653
#> [8,] -0.41610754
#> [9,] 0.68526787
#> [10,] 0.42023974
#> [11,] -1.31009382
#> [12,] -0.35489471
#> [13,] -1.18936972
#> [14,] 0.68199533
#> [15,] 0.45186941
#> [16,] 1.58773765
#> [17,] -1.86227139
#> [18,] -0.03547664
#> [19,] 0.62766241
#> [20,] -1.97065618
#> [21,] 0.61285152
#> [22,] 1.64218773
#> [23,] -0.15694498
#> [24,] -0.49459452
#> [25,] -0.72593791
#> [26,] 0.74292254
#> [27,] -0.50480985
#> [28,] -0.97899782
#> [29,] -0.70640701
#> [30,] -0.24977424
#> [31,] -1.70372321
#> [32,] -0.40009511
#> [33,] -1.34517457
#> [34,] 1.85699065
#> [35,] -0.66506807
#> [36,] -0.36374652
#> [37,] 1.95047840
#> [38,] 0.10029109
#> [39,] 1.24842285
#> [40,] -0.06027093
#> [41,] 0.34515657
#> [42,] 0.98305282
#> [43,] 2.25441043
#> [44,] 0.09696085
#> [45,] 0.57950113
#> [46,] 0.29787786
#> [47,] -0.15942134
#> [48,] 1.10314495
#> [49,] 1.19449140
#> [50,] -0.36098760
#> [51,] 0.38512243
#> [52,] 0.25612444
#> [53,] -0.41563653
#> [54,] -2.53558022
#> [55,] 1.27617795
#> [56,] 1.54413930
#> [57,] 0.26983091
#> [58,] 0.87238930
#> [59,] 0.79376556
#> [60,] -0.96456114
#> [61,] -0.54072274
#> [62,] -0.10013794
#> [63,] -1.08409507
#> [64,] -2.08060337
#> [65,] 0.39987612
#> [66,] 0.39839121
#> [67,] 1.08574927
#> [68,] 0.82842485
#> [69,] 0.58065965
#> [70,] -0.70331045
#> [71,] -2.30318185
#> [72,] 0.39216465
#> [73,] -0.27333411
#> [74,] 1.14286032
#> [75,] 1.08774520
#> [76,] 0.29215587
#> [77,] 0.33740587
#> [78,] 0.07096404
#> [79,] -0.32009247
#> [80,] -1.10246295
#> [81,] 0.26881633
#> [82,] -0.53019049
#> [83,] -0.16051011
#> [84,] 1.50339165
#> [85,] 0.18676239
#> [86,] 0.90854731
#> [87,] 0.61798837
#> [88,] -2.91602067
#> [89,] 0.04881024
#> [90,] -0.88485784
#> [91,] 0.94909488
#> [92,] -0.48886677
#> [93,] 0.26662627
#> [94,] -0.84784174
#> [95,] -0.29357393
#> [96,] 0.63842954
#> [97,] -1.36294811
#> [98,] 1.15532017
#> [99,] -1.50809912
#> [100,] 0.41895462
x_draws_100
#> $`11`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> x
#> [1,] -6.388623e-01
#> [2,] -6.388623e-01
#> [3,] -7.023665e-01
#> [4,] 3.884059e-01
#> [5,] -2.050561e-05
#> [6,] 9.446629e-01
#> [7,] 1.707045e+00
#> [8,] 1.012296e+00
#> [9,] 1.012296e+00
#> [10,] -1.141553e+00
#> [11,] 5.200906e-01
#> [12,] -3.256210e-01
#> [13,] 4.974144e-01
#> [14,] -4.628561e-01
#> [15,] -2.423415e-01
#> [16,] 7.228467e-01
#> [17,] -4.683122e-01
#> [18,] -1.952568e+00
#> [19,] -1.979805e+00
#> [20,] 1.201659e+00
#> [21,] -1.316254e+00
#> [22,] -1.205955e-02
#> [23,] -1.205955e-02
#> [24,] -1.375918e+00
#> [25,] -8.256535e-01
#> [26,] -9.060643e-01
#> [27,] -9.060643e-01
#> [28,] -9.060643e-01
#> [29,] -1.156520e+00
#> [30,] 6.295776e-01
#> [31,] 6.295776e-01
#> [32,] -7.546948e-01
#> [33,] 3.957713e-01
#> [34,] -5.686425e-01
#> [35,] -4.380607e-01
#> [36,] -4.380607e-01
#> [37,] -2.638072e-01
#> [38,] -2.638072e-01
#> [39,] 2.495054e-01
#> [40,] 6.267549e-01
#> [41,] -1.562596e+00
#> [42,] -2.596999e-01
#> [43,] -2.596999e-01
#> [44,] -2.596999e-01
#> [45,] 1.889034e-01
#> [46,] 1.594336e+00
#> [47,] 5.551226e-01
#> [48,] -6.413664e-01
#> [49,] -5.397836e-01
#> [50,] 1.410929e+00
#> [51,] 1.410929e+00
#> [52,] -8.794205e-01
#> [53,] -8.794205e-01
#> [54,] 5.288678e-02
#> [55,] -4.039862e-01
#> [56,] -3.784818e-01
#> [57,] 6.265101e-01
#> [58,] -7.315609e-01
#> [59,] -7.315609e-01
#> [60,] -1.125738e+00
#> [61,] -1.680373e+00
#> [62,] 1.432234e+00
#> [63,] -3.766879e-01
#> [64,] -8.064413e-01
#> [65,] -3.119481e-01
#> [66,] -3.119481e-01
#> [67,] 1.589301e+00
#> [68,] -6.028714e-01
#> [69,] -6.028714e-01
#> [70,] 6.345224e-01
#> [71,] -1.382557e+00
#> [72,] -1.382557e+00
#> [73,] 6.180795e-01
#> [74,] 7.024468e-01
#> [75,] -1.607321e+00
#> [76,] -2.328559e+00
#> [77,] -2.951194e+00
#> [78,] -2.225492e+00
#> [79,] 2.081200e+00
#> [80,] 1.877702e+00
#> [81,] -2.064759e+00
#> [82,] 3.261673e+00
#> [83,] 5.369873e-01
#> [84,] 9.014988e-01
#> [85,] 1.663617e-01
#> [86,] 7.464145e-01
#> [87,] 3.510203e-01
#> [88,] 7.456550e-01
#> [89,] 2.649857e-01
#> [90,] 3.217605e-01
#> [91,] 5.004245e-01
#> [92,] -6.938995e-01
#> [93,] 8.166547e-01
#> [94,] -2.310686e+00
#> [95,] -9.957807e-01
#> [96,] -5.484057e-01
#> [97,] 5.240421e-01
#> [98,] 3.555777e-02
#> [99,] -4.379320e-01
#> [100,] -9.317757e-01
#>
#> $`12`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> x
#> [1,] 0.44215976
#> [2,] -0.32199935
#> [3,] 0.42455788
#> [4,] -0.35403264
#> [5,] 0.68126812
#> [6,] 0.12685397
#> [7,] 0.12685397
#> [8,] 0.30203375
#> [9,] 1.94753427
#> [10,] 1.72152732
#> [11,] 0.70705489
#> [12,] -0.34487236
#> [13,] -0.34487236
#> [14,] 0.94399604
#> [15,] -0.33260635
#> [16,] 0.62127245
#> [17,] -0.16179599
#> [18,] -2.12876531
#> [19,] -0.42302925
#> [20,] 1.09076950
#> [21,] 0.73287267
#> [22,] -1.67809182
#> [23,] 0.92212426
#> [24,] 0.94614661
#> [25,] -0.48396517
#> [26,] -0.48396517
#> [27,] 0.91273562
#> [28,] 0.91273562
#> [29,] 1.29123302
#> [30,] 1.25137832
#> [31,] 0.09196868
#> [32,] 2.07064916
#> [33,] 0.31113302
#> [34,] -0.07228644
#> [35,] -0.07228644
#> [36,] 0.18564709
#> [37,] 1.26531602
#> [38,] 0.61493456
#> [39,] 0.21971595
#> [40,] 0.21971595
#> [41,] 0.26476841
#> [42,] 1.02977184
#> [43,] 1.02977184
#> [44,] -1.49000026
#> [45,] -0.37036454
#> [46,] -0.37036454
#> [47,] -0.12563422
#> [48,] -0.12563422
#> [49,] -0.12563422
#> [50,] -1.60188702
#> [51,] 0.43053359
#> [52,] 0.43053359
#> [53,] -0.31106740
#> [54,] 1.08951031
#> [55,] 0.07187454
#> [56,] 0.07187454
#> [57,] 0.66129290
#> [58,] 1.51270722
#> [59,] 0.46301825
#> [60,] -0.16238428
#> [61,] 1.35517041
#> [62,] 1.16732722
#> [63,] 1.16732722
#> [64,] 0.54079011
#> [65,] -0.06223976
#> [66,] -0.21917799
#> [67,] 0.40497798
#> [68,] 0.01567904
#> [69,] 0.14810969
#> [70,] 0.11277631
#> [71,] -0.86799463
#> [72,] 0.01675056
#> [73,] 1.52133411
#> [74,] -1.95940947
#> [75,] 1.22240001
#> [76,] 0.57106316
#> [77,] 0.87642394
#> [78,] -1.03085786
#> [79,] -0.01898610
#> [80,] 0.27610761
#> [81,] 0.16479903
#> [82,] 0.04122726
#> [83,] -0.46194273
#> [84,] 0.56695526
#> [85,] 1.41174337
#> [86,] 0.32597703
#> [87,] 0.25257463
#> [88,] 0.23969910
#> [89,] 0.56564003
#> [90,] -0.71038251
#> [91,] 0.61440852
#> [92,] -0.62307371
#> [93,] 0.07477879
#> [94,] 0.02185116
#> [95,] 0.51924624
#> [96,] -1.29337320
#> [97,] -0.93998475
#> [98,] -0.96038441
#> [99,] -1.53021799
#> [100,] -1.30556755
#>
#> $`13`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> x
#> [1,] 1.75996430
#> [2,] 2.42946481
#> [3,] -1.06435103
#> [4,] -0.06737791
#> [5,] -0.22557408
#> [6,] -0.22557408
#> [7,] 1.38981633
#> [8,] -0.44552357
#> [9,] -0.44168071
#> [10,] -1.19910784
#> [11,] -1.25790472
#> [12,] 1.48965293
#> [13,] -1.05139850
#> [14,] -1.87324827
#> [15,] 1.34939637
#> [16,] 1.34939637
#> [17,] 1.72240483
#> [18,] 0.50261604
#> [19,] 0.28467646
#> [20,] 0.28467646
#> [21,] -0.90763651
#> [22,] 0.61437627
#> [23,] 0.64451194
#> [24,] 1.64184713
#> [25,] 0.52533768
#> [26,] 0.20492099
#> [27,] 0.76919405
#> [28,] 0.51792372
#> [29,] -1.88113799
#> [30,] 1.80911175
#> [31,] 1.80911175
#> [32,] 0.89698086
#> [33,] 1.29986794
#> [34,] 0.47153880
#> [35,] 0.15922473
#> [36,] -0.94836707
#> [37,] 0.03722949
#> [38,] -1.39310064
#> [39,] -1.97234557
#> [40,] 0.87435206
#> [41,] 1.18300300
#> [42,] 0.40254644
#> [43,] -1.75701292
#> [44,] 0.58634330
#> [45,] 0.82002223
#> [46,] 0.32894747
#> [47,] 3.48267825
#> [48,] 1.19118961
#> [49,] 1.04739736
#> [50,] 0.22908565
#> [51,] 1.09568578
#> [52,] 0.45629978
#> [53,] 2.33148002
#> [54,] -0.06278391
#> [55,] -1.28644832
#> [56,] 0.55140298
#> [57,] 0.73474077
#> [58,] 3.36907935
#> [59,] 0.94823991
#> [60,] -1.53148927
#> [61,] 0.75777520
#> [62,] -1.24007421
#> [63,] -1.19823386
#> [64,] 0.50552347
#> [65,] 0.73819161
#> [66,] 0.88599614
#> [67,] -0.95913155
#> [68,] -1.42524027
#> [69,] -0.40786577
#> [70,] 0.33839302
#> [71,] 0.96064384
#> [72,] 0.21240254
#> [73,] -0.04793679
#> [74,] -0.04793679
#> [75,] 0.37382926
#> [76,] -2.08404080
#> [77,] -1.95569304
#> [78,] -0.65402259
#> [79,] 1.48627031
#> [80,] 0.88003875
#> [81,] -0.60190980
#> [82,] -0.54151329
#> [83,] -0.54151329
#> [84,] 0.17999911
#> [85,] -1.64540905
#> [86,] -1.91484055
#> [87,] -0.67897657
#> [88,] 1.52362930
#> [89,] 2.73145372
#> [90,] -1.22111141
#> [91,] -2.20480092
#> [92,] -1.01324435
#> [93,] -1.54264312
#> [94,] 0.14047952
#> [95,] -1.33327046
#> [96,] -0.15219480
#> [97,] 0.23976085
#> [98,] 0.12449010
#> [99,] 1.81138574
#> [100,] 3.00095079
#>
#> $`14`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> x
#> [1,] -0.30309986
#> [2,] 2.49765345
#> [3,] -0.49821220
#> [4,] 1.86002751
#> [5,] -1.48107614
#> [6,] -1.10006697
#> [7,] -1.10006697
#> [8,] -0.35302013
#> [9,] -0.70398619
#> [10,] 0.33171062
#> [11,] -0.78924744
#> [12,] -1.95042339
#> [13,] 2.51954606
#> [14,] 0.86438068
#> [15,] 0.86438068
#> [16,] -0.71533351
#> [17,] -0.71533351
#> [18,] 0.96191052
#> [19,] -0.84259708
#> [20,] -0.84259708
#> [21,] -1.58285452
#> [22,] -0.16774968
#> [23,] -0.26447961
#> [24,] 0.36511171
#> [25,] -1.69817490
#> [26,] 0.95417777
#> [27,] -0.43114678
#> [28,] -0.32419971
#> [29,] 0.51612298
#> [30,] 0.51612298
#> [31,] 1.72089360
#> [32,] -1.88185952
#> [33,] -0.96263314
#> [34,] 1.76991941
#> [35,] 1.81632038
#> [36,] -1.51200772
#> [37,] 0.20478782
#> [38,] -0.13296011
#> [39,] 0.70257954
#> [40,] 0.56940547
#> [41,] -0.56310440
#> [42,] 0.97816784
#> [43,] 0.64775733
#> [44,] 2.29835663
#> [45,] 1.16014553
#> [46,] -0.11478989
#> [47,] -0.11478989
#> [48,] 0.19660888
#> [49,] 0.33008409
#> [50,] 1.26995041
#> [51,] 1.26995041
#> [52,] -0.08180267
#> [53,] 0.16299655
#> [54,] 0.39831037
#> [55,] -0.84175541
#> [56,] 0.21797079
#> [57,] -0.81375442
#> [58,] 0.21580877
#> [59,] 2.53830973
#> [60,] 2.02889048
#> [61,] 2.02889048
#> [62,] 2.02889048
#> [63,] 2.34261820
#> [64,] 2.77176502
#> [65,] -0.12538447
#> [66,] 1.29992479
#> [67,] -1.42296804
#> [68,] -0.34580257
#> [69,] 0.22819146
#> [70,] -0.07842067
#> [71,] -0.23083762
#> [72,] -0.60774869
#> [73,] -0.53796636
#> [74,] 0.51271865
#> [75,] -0.06894412
#> [76,] -0.40137213
#> [77,] 0.37345885
#> [78,] -1.76314980
#> [79,] 1.61312752
#> [80,] -1.27988980
#> [81,] -0.73556121
#> [82,] -0.35202646
#> [83,] -1.71897977
#> [84,] -0.08274999
#> [85,] -0.75251381
#> [86,] -0.16996834
#> [87,] -0.24857244
#> [88,] 0.64845706
#> [89,] -0.06937383
#> [90,] -1.14508560
#> [91,] -0.93883284
#> [92,] -1.49748508
#> [93,] 1.61401508
#> [94,] -2.75916493
#> [95,] 3.20254421
#> [96,] 0.47277535
#> [97,] -1.82126063
#> [98,] 0.46386943
#> [99,] 0.95234530
#> [100,] -0.26156366
#>
#> attr(,"class")
#> [1] "greta_mcmc_list" "mcmc.list"
#> attr(,"model_info")
#> attr(,"model_info")$raw_draws
#> $`11`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> draws
#> 1 -6.388623e-01
#> 2 -6.388623e-01
#> 3 -7.023665e-01
#> 4 3.884059e-01
#> 5 -2.050561e-05
#> 6 9.446629e-01
#> 7 1.707045e+00
#> 8 1.012296e+00
#> 9 1.012296e+00
#> 10 -1.141553e+00
#> 11 5.200906e-01
#> 12 -3.256210e-01
#> 13 4.974144e-01
#> 14 -4.628561e-01
#> 15 -2.423415e-01
#> 16 7.228467e-01
#> 17 -4.683122e-01
#> 18 -1.952568e+00
#> 19 -1.979805e+00
#> 20 1.201659e+00
#> 21 -1.316254e+00
#> 22 -1.205955e-02
#> 23 -1.205955e-02
#> 24 -1.375918e+00
#> 25 -8.256535e-01
#> 26 -9.060643e-01
#> 27 -9.060643e-01
#> 28 -9.060643e-01
#> 29 -1.156520e+00
#> 30 6.295776e-01
#> 31 6.295776e-01
#> 32 -7.546948e-01
#> 33 3.957713e-01
#> 34 -5.686425e-01
#> 35 -4.380607e-01
#> 36 -4.380607e-01
#> 37 -2.638072e-01
#> 38 -2.638072e-01
#> 39 2.495054e-01
#> 40 6.267549e-01
#> 41 -1.562596e+00
#> 42 -2.596999e-01
#> 43 -2.596999e-01
#> 44 -2.596999e-01
#> 45 1.889034e-01
#> 46 1.594336e+00
#> 47 5.551226e-01
#> 48 -6.413664e-01
#> 49 -5.397836e-01
#> 50 1.410929e+00
#> 51 1.410929e+00
#> 52 -8.794205e-01
#> 53 -8.794205e-01
#> 54 5.288678e-02
#> 55 -4.039862e-01
#> 56 -3.784818e-01
#> 57 6.265101e-01
#> 58 -7.315609e-01
#> 59 -7.315609e-01
#> 60 -1.125738e+00
#> 61 -1.680373e+00
#> 62 1.432234e+00
#> 63 -3.766879e-01
#> 64 -8.064413e-01
#> 65 -3.119481e-01
#> 66 -3.119481e-01
#> 67 1.589301e+00
#> 68 -6.028714e-01
#> 69 -6.028714e-01
#> 70 6.345224e-01
#> 71 -1.382557e+00
#> 72 -1.382557e+00
#> 73 6.180795e-01
#> 74 7.024468e-01
#> 75 -1.607321e+00
#> 76 -2.328559e+00
#> 77 -2.951194e+00
#> 78 -2.225492e+00
#> 79 2.081200e+00
#> 80 1.877702e+00
#> 81 -2.064759e+00
#> 82 3.261673e+00
#> 83 5.369873e-01
#> 84 9.014988e-01
#> 85 1.663617e-01
#> 86 7.464145e-01
#> 87 3.510203e-01
#> 88 7.456550e-01
#> 89 2.649857e-01
#> 90 3.217605e-01
#> 91 5.004245e-01
#> 92 -6.938995e-01
#> 93 8.166547e-01
#> 94 -2.310686e+00
#> 95 -9.957807e-01
#> 96 -5.484057e-01
#> 97 5.240421e-01
#> 98 3.555777e-02
#> 99 -4.379320e-01
#> 100 -9.317757e-01
#>
#> $`12`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> draws
#> 1 0.44215976
#> 2 -0.32199935
#> 3 0.42455788
#> 4 -0.35403264
#> 5 0.68126812
#> 6 0.12685397
#> 7 0.12685397
#> 8 0.30203375
#> 9 1.94753427
#> 10 1.72152732
#> 11 0.70705489
#> 12 -0.34487236
#> 13 -0.34487236
#> 14 0.94399604
#> 15 -0.33260635
#> 16 0.62127245
#> 17 -0.16179599
#> 18 -2.12876531
#> 19 -0.42302925
#> 20 1.09076950
#> 21 0.73287267
#> 22 -1.67809182
#> 23 0.92212426
#> 24 0.94614661
#> 25 -0.48396517
#> 26 -0.48396517
#> 27 0.91273562
#> 28 0.91273562
#> 29 1.29123302
#> 30 1.25137832
#> 31 0.09196868
#> 32 2.07064916
#> 33 0.31113302
#> 34 -0.07228644
#> 35 -0.07228644
#> 36 0.18564709
#> 37 1.26531602
#> 38 0.61493456
#> 39 0.21971595
#> 40 0.21971595
#> 41 0.26476841
#> 42 1.02977184
#> 43 1.02977184
#> 44 -1.49000026
#> 45 -0.37036454
#> 46 -0.37036454
#> 47 -0.12563422
#> 48 -0.12563422
#> 49 -0.12563422
#> 50 -1.60188702
#> 51 0.43053359
#> 52 0.43053359
#> 53 -0.31106740
#> 54 1.08951031
#> 55 0.07187454
#> 56 0.07187454
#> 57 0.66129290
#> 58 1.51270722
#> 59 0.46301825
#> 60 -0.16238428
#> 61 1.35517041
#> 62 1.16732722
#> 63 1.16732722
#> 64 0.54079011
#> 65 -0.06223976
#> 66 -0.21917799
#> 67 0.40497798
#> 68 0.01567904
#> 69 0.14810969
#> 70 0.11277631
#> 71 -0.86799463
#> 72 0.01675056
#> 73 1.52133411
#> 74 -1.95940947
#> 75 1.22240001
#> 76 0.57106316
#> 77 0.87642394
#> 78 -1.03085786
#> 79 -0.01898610
#> 80 0.27610761
#> 81 0.16479903
#> 82 0.04122726
#> 83 -0.46194273
#> 84 0.56695526
#> 85 1.41174337
#> 86 0.32597703
#> 87 0.25257463
#> 88 0.23969910
#> 89 0.56564003
#> 90 -0.71038251
#> 91 0.61440852
#> 92 -0.62307371
#> 93 0.07477879
#> 94 0.02185116
#> 95 0.51924624
#> 96 -1.29337320
#> 97 -0.93998475
#> 98 -0.96038441
#> 99 -1.53021799
#> 100 -1.30556755
#>
#> $`13`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> draws
#> 1 1.75996430
#> 2 2.42946481
#> 3 -1.06435103
#> 4 -0.06737791
#> 5 -0.22557408
#> 6 -0.22557408
#> 7 1.38981633
#> 8 -0.44552357
#> 9 -0.44168071
#> 10 -1.19910784
#> 11 -1.25790472
#> 12 1.48965293
#> 13 -1.05139850
#> 14 -1.87324827
#> 15 1.34939637
#> 16 1.34939637
#> 17 1.72240483
#> 18 0.50261604
#> 19 0.28467646
#> 20 0.28467646
#> 21 -0.90763651
#> 22 0.61437627
#> 23 0.64451194
#> 24 1.64184713
#> 25 0.52533768
#> 26 0.20492099
#> 27 0.76919405
#> 28 0.51792372
#> 29 -1.88113799
#> 30 1.80911175
#> 31 1.80911175
#> 32 0.89698086
#> 33 1.29986794
#> 34 0.47153880
#> 35 0.15922473
#> 36 -0.94836707
#> 37 0.03722949
#> 38 -1.39310064
#> 39 -1.97234557
#> 40 0.87435206
#> 41 1.18300300
#> 42 0.40254644
#> 43 -1.75701292
#> 44 0.58634330
#> 45 0.82002223
#> 46 0.32894747
#> 47 3.48267825
#> 48 1.19118961
#> 49 1.04739736
#> 50 0.22908565
#> 51 1.09568578
#> 52 0.45629978
#> 53 2.33148002
#> 54 -0.06278391
#> 55 -1.28644832
#> 56 0.55140298
#> 57 0.73474077
#> 58 3.36907935
#> 59 0.94823991
#> 60 -1.53148927
#> 61 0.75777520
#> 62 -1.24007421
#> 63 -1.19823386
#> 64 0.50552347
#> 65 0.73819161
#> 66 0.88599614
#> 67 -0.95913155
#> 68 -1.42524027
#> 69 -0.40786577
#> 70 0.33839302
#> 71 0.96064384
#> 72 0.21240254
#> 73 -0.04793679
#> 74 -0.04793679
#> 75 0.37382926
#> 76 -2.08404080
#> 77 -1.95569304
#> 78 -0.65402259
#> 79 1.48627031
#> 80 0.88003875
#> 81 -0.60190980
#> 82 -0.54151329
#> 83 -0.54151329
#> 84 0.17999911
#> 85 -1.64540905
#> 86 -1.91484055
#> 87 -0.67897657
#> 88 1.52362930
#> 89 2.73145372
#> 90 -1.22111141
#> 91 -2.20480092
#> 92 -1.01324435
#> 93 -1.54264312
#> 94 0.14047952
#> 95 -1.33327046
#> 96 -0.15219480
#> 97 0.23976085
#> 98 0.12449010
#> 99 1.81138574
#> 100 3.00095079
#>
#> $`14`
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 100
#> Thinning interval = 1
#> draws
#> 1 -0.30309986
#> 2 2.49765345
#> 3 -0.49821220
#> 4 1.86002751
#> 5 -1.48107614
#> 6 -1.10006697
#> 7 -1.10006697
#> 8 -0.35302013
#> 9 -0.70398619
#> 10 0.33171062
#> 11 -0.78924744
#> 12 -1.95042339
#> 13 2.51954606
#> 14 0.86438068
#> 15 0.86438068
#> 16 -0.71533351
#> 17 -0.71533351
#> 18 0.96191052
#> 19 -0.84259708
#> 20 -0.84259708
#> 21 -1.58285452
#> 22 -0.16774968
#> 23 -0.26447961
#> 24 0.36511171
#> 25 -1.69817490
#> 26 0.95417777
#> 27 -0.43114678
#> 28 -0.32419971
#> 29 0.51612298
#> 30 0.51612298
#> 31 1.72089360
#> 32 -1.88185952
#> 33 -0.96263314
#> 34 1.76991941
#> 35 1.81632038
#> 36 -1.51200772
#> 37 0.20478782
#> 38 -0.13296011
#> 39 0.70257954
#> 40 0.56940547
#> 41 -0.56310440
#> 42 0.97816784
#> 43 0.64775733
#> 44 2.29835663
#> 45 1.16014553
#> 46 -0.11478989
#> 47 -0.11478989
#> 48 0.19660888
#> 49 0.33008409
#> 50 1.26995041
#> 51 1.26995041
#> 52 -0.08180267
#> 53 0.16299655
#> 54 0.39831037
#> 55 -0.84175541
#> 56 0.21797079
#> 57 -0.81375442
#> 58 0.21580877
#> 59 2.53830973
#> 60 2.02889048
#> 61 2.02889048
#> 62 2.02889048
#> 63 2.34261820
#> 64 2.77176502
#> 65 -0.12538447
#> 66 1.29992479
#> 67 -1.42296804
#> 68 -0.34580257
#> 69 0.22819146
#> 70 -0.07842067
#> 71 -0.23083762
#> 72 -0.60774869
#> 73 -0.53796636
#> 74 0.51271865
#> 75 -0.06894412
#> 76 -0.40137213
#> 77 0.37345885
#> 78 -1.76314980
#> 79 1.61312752
#> 80 -1.27988980
#> 81 -0.73556121
#> 82 -0.35202646
#> 83 -1.71897977
#> 84 -0.08274999
#> 85 -0.75251381
#> 86 -0.16996834
#> 87 -0.24857244
#> 88 0.64845706
#> 89 -0.06937383
#> 90 -1.14508560
#> 91 -0.93883284
#> 92 -1.49748508
#> 93 1.61401508
#> 94 -2.75916493
#> 95 3.20254421
#> 96 0.47277535
#> 97 -1.82126063
#> 98 0.46386943
#> 99 0.95234530
#> 100 -0.26156366
#>
#> attr(,"class")
#> [1] "mcmc.list"
#>
#> attr(,"model_info")$samplers
#> attr(,"model_info")$samplers$`1`
#> hmc_sampler object with parameters:
#> Lmin = 5, Lmax = 10, epsilon = 1.696538, diag_sd = 0.9397129
#>
#> attr(,"model_info")$model
#> greta model
x_draws_10
#> $x
#> , , 1
#>
#> [,1]
#> [1,] -0.73556121
#> [2,] -0.22557408
#> [3,] -0.70398619
#> [4,] 3.26167293
#> [5,] 0.73474077
#> [6,] 0.62651006
#> [7,] -0.07228644
#> [8,] -1.12573752
#> [9,] 1.09076950
#> [10,] -0.90606431
```

This flexibility is really nice, but I do feel like this could instead
be at least two different uses of calculate, since we get different
outputs

^{Created on 2024-07-30 with [reprex v2.1.0](https://reprex.tidyverse.org)}

Session info

``` r
sessioninfo::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.4.0 (2024-04-24)
#> os macOS Sonoma 14.5
#> system aarch64, darwin20
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Australia/Hobart
#> date 2024-07-30
#> pandoc 3.2.1 @ /opt/homebrew/bin/ (via rmarkdown)
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date (UTC) lib source
#> abind 1.4-5 2016-07-21 [1] CRAN (R 4.4.0)
#> backports 1.5.0 2024-05-23 [1] CRAN (R 4.4.0)
#> base64enc 0.1-3 2015-07-28 [1] CRAN (R 4.4.0)
#> callr 3.7.6 2024-03-25 [1] CRAN (R 4.4.0)
#> cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.0)
#> coda 0.19-4.1 2024-01-31 [1] CRAN (R 4.4.0)
#> codetools 0.2-20 2024-03-31 [2] CRAN (R 4.4.0)
#> crayon 1.5.3 2024-06-20 [1] CRAN (R 4.4.0)
#> digest 0.6.36 2024-06-23 [1] CRAN (R 4.4.0)
#> evaluate 0.24.0 2024-06-10 [1] CRAN (R 4.4.0)
#> fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
#> fs 1.6.4.9000 2024-06-26 [1] Github (r-lib/fs@714990b)
#> future 1.33.2 2024-03-26 [1] CRAN (R 4.4.0)
#> globals 0.16.3 2024-03-08 [1] CRAN (R 4.4.0)
#> glue 1.7.0 2024-01-09 [1] CRAN (R 4.4.0)
#> greta * 0.4.5.9000 2024-07-30 [1] local
#> hms 1.1.3 2023-03-21 [1] CRAN (R 4.4.0)
#> htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.4.0)
#> jsonlite 1.8.8 2023-12-04 [1] CRAN (R 4.4.0)
#> knitr 1.48 2024-07-07 [1] CRAN (R 4.4.0)
#> lattice 0.22-6 2024-03-20 [2] CRAN (R 4.4.0)
#> lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.4.0)
#> listenv 0.9.1 2024-01-29 [1] CRAN (R 4.4.0)
#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.4.0)
#> Matrix 1.7-0 2024-03-22 [2] CRAN (R 4.4.0)
#> parallelly 1.37.1 2024-02-29 [1] CRAN (R 4.4.0)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.4.0)
#> png 0.1-8 2022-11-29 [1] CRAN (R 4.4.0)
#> prettyunits 1.2.0 2023-09-24 [1] CRAN (R 4.4.0)
#> processx 3.8.4 2024-03-16 [1] CRAN (R 4.4.0)
#> progress 1.2.3 2023-12-06 [1] CRAN (R 4.4.0)
#> ps 1.7.7 2024-07-02 [1] CRAN (R 4.4.0)
#> purrr 1.0.2 2023-08-10 [1] CRAN (R 4.4.0)
#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.4.0)
#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.4.0)
#> R.oo 1.26.0 2024-01-24 [1] CRAN (R 4.4.0)
#> R.utils 2.12.3 2023-11-18 [1] CRAN (R 4.4.0)
#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.4.0)
#> Rcpp 1.0.12 2024-01-09 [1] CRAN (R 4.4.0)
#> reprex 2.1.0 2024-01-11 [1] CRAN (R 4.4.0)
#> reticulate 1.36.1 2024-04-22 [1] CRAN (R 4.4.0)
#> rlang 1.1.4 2024-06-04 [1] CRAN (R 4.4.0)
#> rmarkdown 2.27 2024-05-17 [1] CRAN (R 4.4.0)
#> rstudioapi 0.16.0 2024-03-24 [1] CRAN (R 4.4.0)
#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.4.0)
#> styler 1.10.3 2024-04-07 [1] CRAN (R 4.4.0)
#> tensorflow 2.16.0 2024-04-15 [1] CRAN (R 4.4.0)
#> tfautograph 0.3.2 2021-09-17 [1] CRAN (R 4.4.0)
#> tfruns 1.5.3 2024-04-19 [1] CRAN (R 4.4.0)
#> vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.4.0)
#> whisker 0.4.1 2022-12-05 [1] CRAN (R 4.4.0)
#> withr 3.0.0 2024-01-16 [1] CRAN (R 4.4.0)
#> xfun 0.45 2024-06-16 [1] CRAN (R 4.4.0)
#> yaml 2.3.9 2024-07-05 [1] CRAN (R 4.4.0)
#>
#> [1] /Users/nick/Library/R/arm64/4.4/library
#> [2] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
#>
#> ─ Python configuration ───────────────────────────────────────────────────────
#> python: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/bin/python
#> libpython: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/libpython3.11.dylib
#> pythonhome: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2:/Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2
#> version: 3.11.9 | packaged by conda-forge | (main, Apr 19 2024, 18:34:54) [Clang 16.0.6 ]
#> numpy: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.11/site-packages/numpy
#> numpy_version: 1.26.4
#> tensorflow: /Users/nick/Library/r-miniconda-arm64/envs/greta-env-tf2/lib/python3.11/site-packages/tensorflow
#>
#> NOTE: Python version was forced by use_python() function
#>
#> ──────────────────────────────────────────────────────────────────────────────
```