Package index • tma

All functions

RS.data: Coded Rescushell Chat Data
RS.data.multimodal: Coded Rescushell multi-modal Data
accumulate(): Accumulate Connections from a Multidimensional Array and Context Model
accumulate_contexts(): accumulate_contexts
accumulate_stanza(): Traditional stanza-window accumulation (rENA model)
accumulate_threads(): accumulate_threads
accumulate_unit(): Ground/response accumulation for one unit (tma model)
accumulate_unit_networks(): Accumulate one unit's networks end to end
accumulate_unit_with_rows(): Ground/response accumulation for one unit — returns unit vector and per-row matrix
adjacency_key(): Adjacency Key
aggregate_row_connections(): Aggregate per-response-row tensor connections into a unit vector
aggregate_self_connections(): Aggregate per-line self-connections into a unit vector
apply_tensor(): Tensor-based multi-modal accumulation for one unit (tma model)
apply_tensor_members(): Apply the TMA window/weight tensor, additionally returning per-response-row window membership.
apply_tensor_old(): Apply windowing and weighting to context data for network accumulation
as.character(<adjacency.key>): Convert Adjacency Key to Character (S3 method)
as.double(<adjacency.key>): Convert Adjacency Key to Double (S3 method)
as.matrix(<ena.matrix>): Matrix without metadata
as.matrix(<network.connections>): Convert Network Connections to Matrix (S3 method)
as.network.connection(): Re-class vector as network.connection
as.qe.code(): Convert a vector to 'qe.code' class
as.qe.data(): Convert an object to 'qe.data' class
as.qe.horizon(): Convert a vector to 'qe.horizon' class
as.qe.metadata(): Convert a vector to 'qe.metadata' class
as.qe.unit(): Convert a vector to 'qe.unit' class
as.undirected.vector(): Extract Upper Triangular Elements
as.unordered(): Convert to Unordered Factor
as.unordered(<default>): Default Method for as.unordered
as.unordered(<ordered.ena.connections>): Unorder Connections in a Matrix
as.unordered(<ordered.row.connections>): Convert Ordered Row Connections to Unordered (S3 method)
`$`(<network.matrix>): Extract Metadata or Columns from Network Matrix (S3 method)
colSums.ena.matrix(): Column Sums for ENA Matrices (S3 method)
connection_matrix(): Core connection matrix for one ground+response pair
context_tensor(): Generate a multidimensional array for window and weight parameters
contexts(): Create Contexts for Units of Analysis
conversation_rules(): Conversation rules
decay(): Internal: Decay function factory (legacy)
door(): Apply the ETM door function to code-pair co-occurrence rows
door_accumulation(): Apply door smoothing to an accumulation object
ema_door(): Apply EMA smoothing to co-occurrence data
finalize_row_connections(): Finalise per-response-row tensor connections (fold + weight model)
finalize_self_connections(): Per-line self-connections (diagonal of each directed row)
find_meta_cols(): Find metadata columns
flat_index(): Compute a column-major linear index into a multi-dimensional array
hoo(): Apply a Subsetting Rule to TMA Contexts (Internal)
is.qe.code(): Check if an object is of class 'qe.code'
is.qe.data(): Check if an object is of class 'qe.data'
is.qe.horizon(): Check if an object is of class 'qe.horizon'
is.qe.metadata(): Check if an object is of class 'qe.metadata'
is.qe.unit(): Check if an object is of class 'qe.unit'
loess_door(): Apply LOESS smoothing to co-occurrence data
lookback_door(): Apply lookback door sliding window pooling to co-occurrence data
names(<network.connections>): Title
namesToAdjacencyKey(): Names to Adjacency Key
print(<network.matrix>): Print Method for Network Matrix (S3 method)
remove_meta_data(): Remove meta columns from a data.table or data.frame
replace_door(): Replace raw co-occurrence columns with door-adjusted values
rolling_window_sum(): Rolling backward window sum of a code matrix
row_connections(): Per-row upper-triangle co-occurrence matrix
rules(): Capture Subsetting Rules as Expressions
simple_window(): Internal: Simple window decay (legacy)
test_mockdata: Sample Data
test_smalldata: Sample Data
tma: Transmodal Analysis (TMA)
tma.conversations(): Find conversations by unit
tma.conversations2(): Conversation membership and per-modality window spans (Data View v2)
tma_curve_distance(): Integrated Euclidean curve distance between two fitted trajectories
tma_curve_distance_lag(): Integrated lagged Euclidean distance between two fitted trajectories
tma_dist_dist_correlation(): Distance-distance Pearson correlation between two coordinate configurations
tma_fit_poly_curve(): Fit polynomial parametric curve to ETM/TMA points normalized to t in [0, 1]
tma_poly_critical_points(): Locate critical points (heading turns, velocity zeros, inflections, curvature peaks)
tma_poly_derivative_profile(): Evaluate analytical derivatives of a fitted polynomial curve
tma_signed_turn_lag(): Discrete turn-lag distance between point sets of two conversants
tma_stability_grid(): Evaluate hyperparameter stability grid for door pooling
tma_sweep_signed_turn_lags(): Discrete turn-lag optimization sweep across signed lags
units(): Set Units of Analysis for a TMA Model
view(): Interactive Conversation Viewer
weight_rows(): Apply the weight model per line to already-finalised rows
windows_weights(): Deprecated Alias for context_tensor