Update to-do list.

README.md

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 ## To do list
 
-* Add a section on posterior predictive checks, to comply with the 3 steps of a Bayesian analysis as defined by Gelman.
-* ~~Add a short section on sequential analysis (today prior is yesterday posterior).~~
-* Write a short introduction to `Nimble` and provide both the `Jags` and `Nimble` codes. 
-* More on prior predictive checks.
-* All plots with `ggplot2`. Add short introduction to the `Tidyverse`.
-* ~~Add an example with Poisson GLM(M) example.~~
-* Add a section on models with varying slopes. Can we use the [LKJ prior](https://www.sciencedirect.com/science/article/pii/S0047259X09000876) in `Jags` and `Nimble`?
-* Add a section on population ecology (occupancy models, capture-recapture models). And/or something on hierarchical models, models with hidden variables. 
-* Add a section on [penalized splines](https://www.cambridge.org/core/books/semiparametric-regression/02FC9A9435232CA67532B4D31874412C) (possibly using package `jagam`) and [spatial analyses](https://r-nimble.org/html_manual/cha-spatial.html).
+   * Add a plot with several lines from posterior distribution of regression parameters to a plot of mean response function of a covariate; then get the credible interval on the prediction. 
+   * Besides `Jags` and `Nimble`, there are other software options to fit models in the Bayesian framework that do not need coding. Check out the [CRAN Task View: Bayesian Inference](https://cran.r-project.org/web/views/Bayesian.html).
+   * Mention free Bayesian books: [here](https://www.bookdown.org/home/tags/bayesian/) and Gelman BDA [there](http://www.stat.columbia.edu/~gelman/book/).  
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+* Add a section on posterior predictive checks, to comply with the 3 steps of a Bayesian analysis as defined by Gelman (set up a probabilistic model, inference and model checking; iterate to improve model).
+* Say more on prior predictive checks. 
 * Say something about confidence, credible and HPD intervals.
 * Add another Metropolis example, with adaptation, with the beta-binomial example, and discuss several levels of acceptance. 
 * Add a section on LOO, and discuss complementarity with WAIC.
-* I went for `Jags` and `Nimble` but if not interested, there are other options, much more user-friendly, to fit models in the Bayesian framework with an `R`-like syntax. Check out the [CRAN Task View: Bayesian Inference](https://cran.r-project.org/web/views/Bayesian.html).
-* Mention free Bayesian books [here](https://www.bookdown.org/home/tags/bayesian/).  
-* Add a plot with several lines from posterior distribution of regression parameters to a plot of mean response function of a covariate; then get the credible interval on the prediction. 
+* Add a section on models with varying slopes. Can we use the [LKJ prior](https://www.sciencedirect.com/science/article/pii/S0047259X09000876) in `Jags` and `Nimble`?
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+* Write a short introduction to `Nimble` and provide both the `Jags` and `Nimble` codes. Translating `Jags` code in `Nimble` is [easy](https://r-nimble.org/quick-guide-for-converting-from-jags-or-bugs-to-nimble). For now, check out [training materials](https://github.com/nimble-training) and [examples](https://r-nimble.org/examples).
+* Do all plots with `ggplot2`; add [short introduction to the `Tidyverse`](https://github.com/oliviergimenez/intro_tidyverse).
+* Add a section on population ecology (occupancy models, capture-recapture models). And/or something on hierarchical models, models with hidden variables. Make use of [nimbleEcology](https://cran.r-project.org/web/packages/nimbleEcology/vignettes/Introduction_to_nimbleEcology.html).
+* Add a section on [penalized splines](https://www.cambridge.org/core/books/semiparametric-regression/02FC9A9435232CA67532B4D31874412C) (possibly using package `jagam`) and [spatial analyses](https://r-nimble.org/html_manual/cha-spatial.html).
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+* ~~Add a short section on sequential analysis (today prior is yesterday posterior).~~
+* ~~Add an example with Poisson GLM(M) example.~~