The content falls into the following groupings: (i) Data, (ii)
    Factor manipulation functions, (iii) Design functions, (iv) ANOVA functions, (v)
    Matrix functions, (vi) Projector and canonical efficiency functions, and (vii)
    Miscellaneous functions. There is a vignette describing how to use the 
    design functions for randomizing and assessing designs available as a 
    vignette called 'DesignNotes'. The ANOVA functions facilitate the extraction of 
    information when the 'Error' function has been used in the call to 'aov'. 
    The package 'dae' can also be installed from 
  <http://chris.brien.name/rpackages/>.

Contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

Diff, patch and merge for data frames. Document changes in data
    sets and use them to apply patches. Changes to data can be made visible by using
    render_diff(). The 'V8' package is used to wrap the 'daff.js' 'JavaScript' library
    which is included in the package.

Data and miscellanea to support the book "Introduction to Data analysis with R for Forensic Scientists." This book was written by James Curran and published by CRC Press in 2010 (ISBN: 978-1-4200-8826-7).

Provides robustness checks to align estimands with the identification 
    that they require. Given a 'dagitty' object and a model specification, 
    'DAGassist' classifies variables by causal roles, flags problematic controls, 
    and generates a report comparing the original model with minimal and canonical 
    adjustment sets. Exports publication-grade reports in 'LaTeX', 'Word', 'Excel',
    'dotwhisker', or plain text/'markdown'. 'DAGassist' is built on 'dagitty', an 
    'R' package that uses the 'DAGitty' web tool (<https://dagitty.net/>) for 
    creating and analyzing DAGs. Methods draw on Pearl (2009) <doi:10.1017/CBO9780511803161>
    and Textor et al. (2016) <doi:10.1093/ije/dyw341>.

Hidden Markov models (HMMs) are a formal foundation for making probabilistic models of linear sequence. They provide a conceptual toolkit for building complex models just by drawing an intuitive picture. They are at the heart of a diverse range of programs, including genefinding, profile searches, multiple sequence alignment and regulatory site identification. HMMs are the Legos of computational sequence analysis. In graph theory, a tree is an undirected graph in which any two vertices are connected by exactly one path, or equivalently a connected acyclic undirected graph. Tree represents the nodes connected by edges. It is a non-linear data structure. A poly-tree is simply a directed acyclic graph whose underlying undirected graph is a tree. The model proposed in this package is the same as an HMM but where the states are linked via a polytree structure rather than a simple path.