Causal Inference: The
Mixtape
Causal
Inference for the Brave and True
Causal Inference in Statistic: A Primer. Pearl et al. amazon
Pearl, J. 2018. The Book of Why. amazon
Angrist and Pischke. 2009. Mostly Harmless Econometrics amazon
Wooldridge, J. 2010. Econometric Analysis of Cross-Section and
Panel Data amazon
James B. Grace. 2006. Structural Equation Modeling and Natural
Systems. Cambridge University Press.
[amazon]
Ken A. Bollen. 1989. Structural Equations with Latent Variables.
Wiley
Press.[amazon]
Rex B. Kline. 2010. Principles and Practice of Structural
Equation Modeling. The Guilford Press.
[amazon]
Bill Shipley. 2000. Cause and Correlation in Biology. Cambridge
University Press.
[amazon]
Rick H. Holyle, ed. 2012. Handbook of Structural Equation Modeling. The Guilford Press. [amazon]
sem
for biology (for this class)
lavaan google
group
semnet
R for Data Science Wickham and
Gromelund. Essential reading.
Getting used
to R, RStudio, and R Markdown 2017. Chester Ismay. The basics.
R Programming
for Data Science. 2016. Roger D. Peng. Provides a more detailed
intro to basic R programming.
Exploratory Data Analysis
with R. 2016. Roger D. Peng. Uses the tidyverse and ggplot2 for data
exploration. Great introduction to these packages and how they can be
made to sing together.
Efficient R
Programming. 2016. Colin Gillespe and Robin Lovelace
Statistical
Inference for Data Science. 2018. Brian Caffo. A wonderful book that
is a companion to his Coursera course, but is open, and full of gret
concepts and R examples.
Regression Models for Data
Science in R. 2018. Brian Caffo. A wonderful primer on regression
models. Incredibly thorough.
Advanced R. 2014. Great
walkthrough of the details and guts of R. From novices to R wizards, you
will learn things you never thought possible (or the actual reasoning
behind that hacky stuff you’ve been doing for years).
Principles of
Econometrics with R 2016. Constantin Colonescu. Yes, it’s
econometrics, but there’s a lot here that’s very generalizable to
biological data analysis in R as well.
A Tour of Time Series Analysis with
R
Fundamentals of Data
Visualization. 2018. Claus Wilke. A wonderful online collection of
best principles and practices for data viz.
Forecasting: Principles and
Practice. 2018. Rob Hyndman and George Athanasaopoules. A great
intro to timeseries and forecasting in R.