Transmodal Analysis (TMA)
accumulate_contexts
fast accumulate networks
accumulate_threads
Accumulate Connections from a Multidimensional Array and Context Model
Adjacency Key
Apply windowing and weighting to context data for network accumulation...
Convert Adjacency Key to Character (S3 method)
Convert Adjacency Key to Double (S3 method)
Matrix without metadata
Convert Network Connections to Matrix (S3 method)
Re-class vector as network.connection
Convert a vector to 'qe.code' class
Convert an object to 'qe.data' class
Convert a vector to 'qe.horizon' class
Convert a vector to 'qe.metadata' class
Convert a vector to 'qe.unit' class
Extract Upper Triangular Elements
Default Method for as.unordered
Unorder Connections in a Matrix
Convert Ordered Row Connections to Unordered (S3 method)
Convert to Unordered Factor
Special attribute names for context columns
Extract Metadata or Columns from Network Matrix (S3 method)
n choose 2
Column Sums for ENA Matrices (S3 method)
Generate a multidimensional array for window and weight parameters
Create Contexts for Units of Analysis
Conversation rules
Internal: Decay function factory (legacy)
Find metadata columns
Apply a Subsetting Rule to TMA Contexts (Internal)
Check if an object is of class 'qe.code'
Check if an object is of class 'qe.data'
Check if an object is of class 'qe.horizon'
Check if an object is of class 'qe.metadata'
Check if an object is of class 'qe.unit'
Title
Names to Adjacency Key
Print Method for Network Matrix (S3 method)
Remove meta columns from a data.table or data.frame
Capture Subsetting Rules as Expressions
Internal: Simple window decay (legacy)
Find conversations by unit
TMA for ENA
Set Units of Analysis for a TMA Model
Interactive Conversation Viewer
Deprecated Alias for context_tensor
A robust computational framework for analyzing complex multimodal data. Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" <doi:10.18608/jla.2025.8423>.