Brian High
4/06/2018
Phage is a DEOHS server running RStudio Server as a web application.
https://phage.deohs.washington.edu/
Provides the latest QMRA methodologies to determine infection risk cause by either accidental microbial infections or deliberate infections caused by terrorism
- Reviews the latest methodologies to quantify at every step of the microbial exposure pathways, from the first release of a pathogen to the actual human infection
- Provides techniques on how to gather information, on how each microorganism moves through the environment, how to determine their survival rates on various media, and how people are exposed to the microorganism
- Explains how QMRA can be used as a tool to measure the impact of interventions and identify the best policies and practices to protect public health and safety
- Includes new information on genetic methods
- Techniques use to develop risk models for drinking water, groundwater, recreational water, food and pathogens in the indoor environment
Quantitative microbial risk assessment, Charles N. Haas Joan B. Rose; Charles P. Gerba. 2014 Hoboken, New Jersey: Wiley.
- Provides a detailed coverage of the key steps in MRA and how it can be used to improve food safety
- A standard work on one of the most important aspects of food safety management
Microbiological Risk Assessment in Food Processing, Martyn Brown; Michael Stringer. 2002 Boca Raton, Fla. : CRC Press ; Cambridge, England : Woodhead Pub.
- EnvStats is a comprehensive R software library for environmental scientists, engineers, and regulators, based on the open-source statistics and graphics package R.
- This book shows how to use EnvStats to easily produce powerful graphical and statistical analyses of environmental data.
- Includes extensive examples using data sets from regulatory guidance documents.
EnvStats, An R Package for Environmental Statistics, Steven P. Millard. 2013 New York: Springer.
- An introduction to the use of R in biostatistics
- Structured to demonstrate the use of R syntax
- Small and easy-to-follow confidence-building examples
- For students of informatics, medicine, biology, biostatistics and bioinformatics
Introduction to data analysis and graphical presentation in biostatistics with R, MacFarland, Thomas W. 2014 Cham : Springer.
- Written to provide resource materials for statistics classes
- Ordered along the lines of many popular statistics books
- Contains R script programs to demonstrate important topics and concepts
- Contains T/F quizzes to test basic knowledge of the topics covered
- Contains numerous exercises with answers or solutions
Understanding Statistics Using R, Schumacker, R. E., & Tomek, S. 2013 New York: Springer.
- Introduces fundamental data science methodologies using the R programming language
- Covers concepts through real-world datasets and case studies
- Examines cutting edge topics in both research and commercial applications
Beginning Data Science with R, Manas A. Pathak. 2014 Cham: Springer International Publishing.
- Example-based introduction to the statistical computing environment
- Does not assume any previous familiarity with R or other software packages
- R functions are presented in the context of interesting applications with real data
- Probabilistic simulation with the Monte Carlo Method
R by Example, Jim Albert; Maria L. Rizzo. 2012 New York : Springer.
Code samples at instructor's website.
Udemy course on Monte Carlo Simulation with R based on R by Example
The book covers:
- Basic random generation algorithms
- Monte Carlo techniques for integration and optimization
- Convergence diagnoses
- Markov chain Monte Carlo methods
Introducing Monte Carlo Methods with R, Christian P. Robert; George Casella. 2010 New York : Springer.
- Master data management in R
- Perform hypothesis tests using both parametric and nonparametric methods
- Understand how to perform statistical modeling using linear methods
- Model nonlinear relationships in data with kernel density methods
- Use matrix operations to improve coding productivity
- Utilize the observed data to model unobserved variables
- Deal with missing data using multiple imputations
- Simplify high-dimensional data
- Use Monte Carlo simulation in one and two dimensions with the mc2d package
Mastering Scientific Computing with R, Gerrard P.; Johnson R. M. 2015 Packt Publishing.
See Chapter 8. Simulations for details on the use of the mc2d package and several examples.
You can access this eBook without cost if you have a Seattle Public Library card.
- An accessible introduction to the world of multivariate statistics in the life sciences
- Provides a complete description of the general data analysis paradigm
- Specific topics from the area of chemometrics are included in a special section
- R code is provided for all the examples in the book
- Scripts, functions and data are available in a separate, publicly available R package
Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences, Ron Wehrens. 2011 Heidelberg ; New York : Springer.
- This book shows you how to extend the power of Stata through the use of R.
- It introduces R using Stata terminology with which you are already familiar.
- It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches.
- When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses.
R for Stata users Robert A. Muenchen, Joseph M. Hilbe. 2010 New York : Springer.