Use inline evaluation for results in text (requires Quarto v1.4)

Project.toml

@@ -26,7 +26,6 @@ MixedModelsMakie = "b12ae82c-6730-437f-aff9-d2c38332a376"
 NLopt = "76087f3c-5699-56af-9a33-bf431cd00edd"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 PooledArrays = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
-Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 RCall = "6f49c342-dc21-5d91-9882-a32aef131414"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -55,11 +54,13 @@ Downloads = "1"
 Effects = "1"
 FreqTables = "0.4"
 GLM = "1"
+LinearAlgebra = "1"
 MixedModels = "4,5"
 MixedModelsMakie = "0.3.13"
 NLopt = "1"
 ProgressMeter = "1.7"
 RCall = "0.13"
+Random = "1"
 Scratch = "1"
 StandardizedPredictors = "1"
 StatsAPI = "1"

intro.qmd

@@ -106,10 +106,8 @@ using AlgebraOfGraphics # high-level graphics
 using CairoMakie        # graphics back-end
 using DataFrameMacros   # elegant DataFrame manipulation
 using DataFrames        # DataFrame implementation
-using Markdown          # utilities for generating markdown text
 using MixedModels       # fit and examine mixed-effects models
 using MixedModelsMakie  # graphics for mixed-effects models
-using Printf            # formatted printing
 using ProgressMeter     # progress of optimizer iterations
 using Random            # random number generation
 using StatsBase         # basic statistical summaries
@@ -606,23 +604,9 @@ draw(
 )
 ```
 
-:::{.callout-note collapse="true"}
-
-### Use :compact=true when interpolating numeric values
-
-Find a way to use `:compact=true` when interpolating numeric values into the text
-:::
+The distribution of the estimates of `β₁` is more-or-less a Gaussian (or "normal") shape, with a mean value of `{julia} repr(mean(βdf.value), context=:compact=>true)`  which is close to the estimated `β₁` of `{julia} repr(only(dsm01.β), context=:compact=>true)`.
 
-```{julia}
-#| echo: false
-Markdown.parse(
-  """
-  The distribution of the estimates of `β₁` is more-or-less a Gaussian (or "normal") shape, with a mean value of $(@sprintf("%f", mean(βdf.value)))  which is close to the estimated `β₁` of $(@sprintf("%f", only(dsm01.β))).
-
-  Similarly the standard deviation of the simulated β values, $(std(βdf.value)) is close to the standard error of the parameter, $(only(stderror(dsm01))).
-""",
-)
-```
+Similarly the standard deviation of the simulated β values, `{julia} repr(std(βdf.value), context=:compact=>true)` is close to the standard error of the parameter, `{julia} repr(only(stderror(dsm01)), context=:compact=>true)`.
 
 In other words, the estimator of the fixed-effects parameter in this case behaves as we would expect.
 The estimates are approximately normally distributed centered about the "true" parameter value with a standard deviation given by the standard error of the parameter.
@@ -645,15 +629,7 @@ draw(
 ```
 
 The estimator for the residual standard deviation, $\sigma$, is approximately normally distributed but the estimator for $\sigma_1$, the standard deviation of the `batch` random effects is bimodal (i.e. has two "modes" or local maxima).
-
-```{julia}
-#| echo: false
-Markdown.parse(
-  """
-There is one peak around the "true" value for the simulation, $(only(dsm01.σs.batch)), and another peak at zero.
-""",
-)
-```
+There is one peak around the "true" value for the simulation, `{julia} repr(only(dsm01.σs.batch), context=:compact=>true), and another peak at zero.
 
 The apparent distribution of the estimates of $\sigma_1$ in @fig-dsm01_bs_sigma_density is being distorted by the method of approximating the density.
 A [kernel density estimate](https://en.wikipedia.org/wiki/Kernel_density_estimation) approximates a probability density from a finite sample by blurring or smearing the positions of the sample values according to a *kernel* such as a narrow Gaussian distribution (see the linked article for details).
@@ -748,14 +724,7 @@ dsm01trace = fit(
 DataFrame(dsm01trace.optsum.fitlog)
 ```
 
-```{julia}
-#| echo: false
-Markdown.parse(
-  """
-Here the algorithm converges after 18 function evaluations to a profiled deviance of $(dsm01trace.objective) at θ = $(only(dsm01trace.theta)).
-""",
-)
-```
+Here the algorithm converges after 18 function evaluations to a profiled deviance of `{julia} repr(dsm01trace.objective, context=:compact=>true)` at θ = `{julia} repr(only(dsm01trace.theta), context=:compact=>true)`.
 
 ## Assessing the random effects {#sec-assessRE}