Bayesian statistics
Shorter, online resources:
 The Bayesian flip: Correcting the Prosecutor’s fallacy by Skorupski and Wainer – a very short conceptual introduction to Bayesian methods
 Bayesian Basics– by Michael Clark– an introduction to Bayesian models in R (57 pages)
 https://github.com/vasishth/
BayesLMMTutorial Online materials to support the Sorensen and Vasishth bayesian tutorial paper.(contains a link to the paper) 
Rstan website RStan is the program we use to implement these models in R.

https://github.com/standev/
rstan/wiki/RStanGetting — RStan quickstart guideStarted  http://jakewestfall.org/misc/SorensenEtAl.pdf — tutorial for mixed effects models with STAN
Books:
 Kruschke, John. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014.—very accessible,written from an experimental psychology perspective.
 Gelman, Andrew, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin. Bayesian Data Analysis. CRC Press, 2013.—written from a statistical (and computational) perspective, but has many important discussion about prior selection (especially for nonexperimental data). It is also important to understand what statisticians (and computational folks focused on data analysis algorithms) are concerned with when designing these techniques.
 Gelman, Andrew, and Jennifer Hill. Data analysis using regression and multilevel/hierarchical models.
Forced alignment
 For an overview of why forced aligners are great look at my slides from my talk on them
 To get textgrids, try any one of these forced aligners: p2fa, SPPAS, FAVEalign
 For help setting up the p2fa on a mac, try linguisticmystic
Praat resources
 For acoustic measurements of files for which you have textgrids, try ProsodyPro
 Mietta’s Praat Scripts (Now called Speech CT, the Speech Corpus Toolkit)
 Shigeto Kawahara’s praat resources: http://user.keio.ac.jp/~kawahara/resource.html