Title: Semiparametric factor and regression models for symmetric
Description: This package estimates the parameters of a model for
symmetric relational data (e.g., the above-diagonal part of a
square matrix), using a model-based eigenvalue decomposition
and regression. Missing data is accomodated, and a posterior
mean for missing data is calculated under the assumption that
the data are missing at random. The marginal distribution of
the relational data can be arbitrary, and is fit with an
Packaged: Tue Jun 26 15:18:03 2007; hoff

Title: Semiparametric Factor and Regression Models for Symmetric
Description: Estimation of the parameters in a model for
and regression. Missing data is accommodated, and a posterior
ordered probit specification. See Hoff (2007) <arXiv:0711.1146>

Title: Computes confidence intervals for principal components
Description: Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.

Authors@R: c(person(given = "Wayne", family = "Oldford",
person(given = "Erle", family = "Holgersen",
person(given = "Ben", family = "Lafreniere",
person(given = "Tianlu", family = "Zhu",
Description: An eikosogram (ancient Greek for probability picture) divides the unit square
into rectangular regions whose areas, sides, and widths, represent various probabilities
associated with the values of one or more categorical variates.
Rectangle areas are joint probabilities, widths are always marginal (though possibly joint
margins, i.e. marginal joint distributions of two or more variates), and heights of rectangles
Eikosograms embed the rules of probability and are useful for introducing elementary probability
theory, including axioms, marginal, conditional, and joint probabilities, and their
relationships (including Bayes theorem as a completely trivial consequence).
They are markedly superior to Venn diagrams for this purpose, especially in distinguishing
probabilistic independence, mutually exclusive events, coincident events, and associations.
They also are useful for identifying and understanding conditional independence structure.
As data analysis tools, eikosograms display categorical data in a manner similar
to Mosaic plots, especially when only two variates are involved (the only case in which
they are essentially identical, though eikosograms purposely disallow spacing between rectangles).
Unlike Mosaic plots, eikosograms do not alternate axes as each new categorical variate
Instead, only one categorical variate, designated the "response", presents on the vertical axis
and all others, designated the "conditioning" variates, appear on the horizontal.
In this way, conditional probability appears only as height and marginal probabilities as widths.
The eikosogram is therefore much better suited to a response model analysis (e.g. logistic model)
Mosaic plots are better suited to log-linear style modelling as in discrete multivariate analysis.
Of course, eikosograms are also suited to discrete multivariate analysis with each variate in turn
This makes it better suited than Mosaic plots to discrete graphical models based on conditional
independence graphs (i.e. "Bayesian Networks" or "BayesNets").
The eikosogram and its superiority to Venn diagrams in teaching probability is described in
W.H. Cherry and R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/paper.pdf>,
its value in exploring conditional independence structure and relation to graphical and log-linear models
is described in R.W. Oldford (2003) <https://math.uwaterloo.ca/~rwoldfor/papers/eikosograms/independence/paper.pdf>,
and a number of problems, puzzles, and paradoxes that are easily explained with eikosograms are given in
Packaged: 2018-08-16 17:04:14 UTC; rwoldford