Author: Rutger M. van den Bor and Willem M. van der Wal
Maintainer: Rutger M. van den Bor <r.m.vandenbor@umcutrecht.nl>
Description: Displays state-event charts, for graphical presentation of longitudinal data.

Description: To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.

Title: Computing Statistically-Equivalent Path Models
Description: Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix.
This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools.
The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone.
The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.
Imports: combinat, Matrix, Rdpack, stats

Title: Similarity-First Search Seriation Algorithm
person("Utz-Uwe", "Haus", role = c("aut", "cre", "cph"),
Description: An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most \eqn{n} sweeps for a matrix of size \eqn{n}). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property.