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Changelog

tma 0.3.12

Released with qe-tma 0.1.11 (tag python/v0.1.11), tma-wasm 0.1.6 (wasm/v0.1.6) and libtma 0.3.12 (conan/v0.3.12). New vignette and qe-tma notebook on accumulation results (connections, misses, self-connections); tma-wasm’s README lists all 15 exported functions and its examples run in the browser on qe-libs.org; libtma’s Conan docs gain a main page.

  • TMA.jl 0.1.6 (released on its own, tag julia/v0.1.6) gets the high-level accumulation of R tma and qe-tma: TMA.accumulate(data, units, conversations, codes; tensor, time_col, ordered, binary, weight_by, misses, self_connections, split_by) returns a TMAAccumulation (connection / self / miss counts, the per-line tables, meta, unit labels, connection names, adjacency key), with context_tensor, contexts (split_by, per-context time axis), rules and conversation_rules. Any Tables.jl table is accepted. Results equal R tma’s: the R reference values of qe-tma’s tests and the interleaved- conversation fixture (unsplit and split by conversation), 62 tests. accumulate is called as TMA.accumulate (Base has one). TMA.jl now depends on LibQE (>= 0.2.1, for the connection layout, as qe-tma uses qe-lib) and Tables.

  • qe-tma’s accumulate() takes hoo_rules= (keyword only), as contexts() does: horizon-of-observation rules, so a unit’s context can be other than its conversation (R tma’s contexts(hoo_rules =)). conversations is now optional; give it, hoo_rules, or both (ORed). Equal to R tma on the getting-started vignette, ordered and unordered.

  • qe-tma ships R’s test_mockdata as tma.datasets.load_test_mockdata().

  • qe-tma has a getting-started walkthrough, python/docs/getting-started.ipynb, the Python port of the R vignette, published with the API docs at https://qe-libs.org/py/docs/qe-tma/getting-started.html (dev builds: py/dev/docs/qe-tma/). The README quick start now runs as written, on the bundled data.

tma 0.3.11

0.3.10 (libtma 0.3.10, qe-tma 0.1.8 / 0.1.9, tma-wasm 0.1.4, TMA.jl 0.1.4) was tagged but never published; its changes ship here.

  • Data: the email addresses in RS.data (2 messages) and RS.data.multimodal

    1. are replaced by [EMAIL], as in rENA’s RS.data (whose text column RS.data’s now matches); nothing else in either dataset changes. The test_reddit and test_reddit2 datasets, which held real Reddit usernames and posts and were not used by any test or vignette, are removed.

  • qe-tma’s accumulate() matches R’s on data where conversations interleave or a unit is in several conversations:

    • contexts() / accumulate() gain split_by (= R’s contexts(split_rules = <columns>)): each unit’s context is split by those columns, each split accumulated on its own (windows never cross one) and summed per unit. rENA’s accumulation splits by the conversation; with split_by=<conversation>, qe-tma equals the stanza window exactly.
    • Without time_col, a row’s time is its position within its context (= R’s context row id), not its row number in the whole data, so windows count the lines a unit can see. Results were wrong whenever other conversations’ rows came in between. Tests: R tma reference (python/tests/fixtures/contexts-split.R), unsplit and split, binary / product / ordered.

  • accumulate_unit() and accumulate_unit_with_rows() with ordered = FALSE (the default) counted every connection twice: they built the undirected connection matrix and then folded it as if it were directed. They now accumulate directed and fold once, as accumulate_unit_networks() and tma’s original accumulate_network() do, so unordered output is the folded ordered output (A then B = 2 gave A & B = 4; now 2). Binary results were unaffected; weighted / non-binary counts halve. accumulate_unit_with_rows()’s row_networks are now directed in both modes. Same fix in qe-tma, tma-wasm and TMA.jl.

  • qe-tma (Python) accumulates only, as R’s tma does; modeling is qe-ena’s. Breaking: TMA().fit() is replaced by tma.accumulate(), which returns a TMAAccumulation with R’s fields – connection_counts_, self_connection_counts_, miss_counts_, self_miss_counts_, the per-line row_* tables, meta (one row per unit), unit_labels_, codes_, connection_names_, adjacency_key_, ordered_ – and gains self_connections (unordered A & A columns, as R). The normalization, SVD and node positions that TMA.fit() ran are gone: model the accumulation with qe-ena (ena.ENA().fit(accum)), as rENA models tma’s in R. qe-tma no longer depends on qe-ena (it imported it for node positions since the libqe split); it requires qe-lib >= 0.2.1.

tma 0.3.9

  • accumulate(self_connections = TRUE) (unordered networks; default FALSE) also puts each code’s self-connection into connection.counts and the per-line table – and with misses, each self-miss into miss.counts – so a model built on them includes self-connections. Columns run A & A, A & B, B & B, A & C, … (the upper triangle with the diagonal), as the adjacency key says (adjacency_key(codes, diagonal = TRUE)). Off by default, so existing results and column layouts are unchanged; ordered networks always carry the diagonal. self.connection.counts is kept separately either way.
  • libtma finalize_row_connections(), aggregate_row_connections() and accumulate_unit_networks() take diagonal (default false; in tma-wasm an optional trailing argument), using libqe’s diagonal fold (n_connections(), fold_directed_network(v, diagonal)).
  • Requires libqe >= 0.2.1 (DESCRIPTION, the libtma Conan recipe and scripts/sync-headers.sh).

tma 0.3.8

  • accumulate() now keeps missed co-occurrences by default (misses = TRUE): every result carries miss.counts, model$row.miss.counts and the self-miss tables. Pass misses = FALSE to skip them, e.g. for many codes or long data (the per-line miss table has one column per directed pair when ordered). qe-tma TMA.fit() follows (misses=True). Existing fields are unchanged.
  • libtma accumulate_unit_networks() returns one unit’s finished networks in one call – fold (unordered), per-line weight model, unit sum and the self-connection diagonal, for hits and misses – and is exported in R, qe-tma, tma-wasm and TMA.jl. accumulate() and TMA.fit() use it instead of folding and aggregating in R / Python, which makes accumulate() faster (about 40% on 15k lines and 12 codes with misses).
  • libtma weight_rows() applies the weight model per line to rows that are already folded (unordered) or directed (ordered) – the weight step of finalize_row_connections() on its own – so callers of accumulate_unit_networks() that need per-line weighted rows (rena-wasm’s rowConnectionCounts) get them from libtma. Exported in R, qe-tma, tma-wasm and TMA.jl.
  • accumulate() keeps self-connections: self.connection.counts (one column per code) and model$row.self.connection.counts (raw, per line). A code’s self-connection on a line is its ground count times its response count – the ordered network’s diagonal, which unordered networks drop when they fold to the upper triangle – weighted per line by weight_by and summed per unit, for ordered and unordered models alike. With misses = TRUE, also self.miss.counts / model$row.self.miss.counts (the code in the ground, absent from the response). Not used in the model yet.
  • libtma finalize_self_connections() / aggregate_self_connections() compute them from apply_tensor_unit’s per-line rows, exported in R, qe-tma, tma-wasm and TMA.jl. qe-tma TMA.fit() stores self_networks_ (and self_miss_networks_ with misses=True).

tma 0.3.7

  • accumulate(misses = TRUE) also accumulates missed co-occurrences: for each response line, the window-weighted ground count of code i when code j is absent from the response, with no response self-term. Misses go through the same steps as connection counts: directed for ordered models, folded to the upper triangle for unordered ones, weight_by applied per line. Results land in miss.counts and model$row.miss.counts. Backed by libtma’s miss_matrix() and apply_tensor_unit(..., return_misses = true); apply_tensor() gains misses = FALSE.
  • Misses in every binding: qe-tma apply_tensor_unit(..., misses=False) and TMA.fit(misses=False) (→ miss_networks_); tma-wasm accumulate_tensor_unit_misses() (same arguments as accumulate_tensor_unit); TMA.jl accumulate_tensor_unit(...; misses=false). Results gain miss_counts / row_miss_counts only when misses are asked for, so existing callers see no change.
  • qe-tma TMA.fit() fixes:
    • connection_names_ match R’s adjacency_key(): ordered labels were transposed (position j*n+i is ground i -> response j, labelled “i & j”), and unordered labels come from libqe’s connection_names (“A & B”, was “A&B”).
    • Units with a single visible line are no longer skipped: as in R, the response self-term connects their co-occurring codes. (A single-row selection is read-only under pandas copy-on-write, so the code matrix is now copied before it reaches the extension.)
  • Works with libqe 0.2.0, which drops its copy of the accumulation code (tma no longer uses it). qe-tma 0.1.4 allows qe-lib<0.3; tma-wasm allows @qe-libs/libqe-wasm 0.2 (its libqe-wasm parity tests skip the functions 0.2 removed).
  • CI installs qe-ena from qe-libs.org instead of building rENA’s develop.

tma 0.3.6

  • tma is a compiled package again. The accumulation C++ (stanza windows, context tensors, row connections, weight models) moved out of libqe into tma as the header-only layer libtma (inst/include/libtma/, wrapped in src/libtma_rcpp.cpp), so libqe can return to generic numerics. Results are unchanged: the code and its tests moved verbatim. tma now exports the accumulation functions libqe did (apply_tensor, apply_tensor_members, accumulate_stanza, rolling_window_sum, row_connections, aggregate_row_connections, finalize_row_connections, …). Installing from source needs a C++ toolchain (Rtools on Windows); requires libqe >= 0.1.9.
  • The Python package pyTMA (import pytma, never published) is now qe-tma (import tma), with libtma compiled in (tma.libtma) instead of calling libqe’s copy. Node positions come from qe-ena and generic numerics from qe-lib (it imported the old pylibqe name, so it no longer installed).
  • New npm package @qe-libs/tma-wasm (wasm/): libtma compiled to WebAssembly, replacing libqe-wasm’s accumulation functions (same signatures).
  • New Julia package TMA.jl (julia/, using TMA) replaces LibQE.jl’s accumulation functions, adding the weight models (aggregate_row_connections, finalize_row_connections). libtma is also published as a Conan package (conan/).

tma 0.3.5

  • accumulate() now looks each sender, receiver and mode value up in the tensor’s own levels (the sorted levels context_tensor() built from the whole dataset). It previously re-encoded character-valued columns with as.factor() inside each unit’s context, so a context containing only some levels mapped them to the wrong tensor slice – for example, a context with only “Second” used “First”’s weight and window. Numeric columns were used directly as positions, so values other than 1..k (such as 0/1) pointed outside the tensor and could crash R. Models whose contexts contain every level, and models built with the default tensor, are unchanged.
  • Values that are not levels of the tensor now raise a clear error.

tma 0.3.4

  • accumulate() gains weight_by, the weight model applied to each line’s connection counts before they are summed per unit (= rENA’s weight.by): "binary", "product" (raw counts), "sqrt" or "log1p" (alias "log"). For unordered networks the weight is applied after each line is folded to the upper triangle; for ordered networks, to each directed cell. It defaults from binary ("binary" when TRUE, "product" when FALSE), so existing calls produce identical results; "binary" on ordered networks keeps the raw directed counts, as before. model$row.connection.counts still holds the raw, unweighted line counts, and weight_by is recorded in _function.params.
  • The per-unit aggregation now uses libqe’s shared aggregate_row_connections() kernel; the R package requires libqe 0.1.5 or newer.
  • pytma 0.1.2: TMA.fit() gains the same weight_by argument and requires pylibqe 0.1.5 or newer.
  • contexts() now warns when a conversation rule compares a column to UNIT with == (e.g. GroupName == UNIT$GroupName) and a unit has more than one value for that column. UNIT$<col> holds every value across the unit’s rows, so == recycles and silently selects the wrong rows; use %in% instead.

tma 0.3.3

  • Added door smoothing helpers for lookback, EMA, and LOESS workflows.
  • Added door_accumulation() as a convenience wrapper for the standard ETM door() plus replace_door() workflow.
  • Added trajectory helpers for polynomial curve fitting, derivative profiles, curve distances, lagged distances, signed turn-lag analysis, and stability grids.
  • Updated the R package dependency to require libqe 0.1.2 or newer for the shared door and trajectory kernels.

tma 0.3.2

  • improved performance of apply_tensor

tma 0.3.1

CRAN release: 2025-09-19

  • added exports: ATTR_NAMES, adjacency_key
  • added class as and is S3 methods for: qe.metadata, qe.data, qe.code, qe.unit, qe.horizon
  • moved jsonlite to Suggests, only required in less often used tma::view()

tma 0.3.0

CRAN release: 2025-08-25

  • context_tensor function for controlling weights and windows across modalities
  • deprecated prior implementations: windows_weights, accum_multidim, decay, simple_window

tma 0.2.0

  • Wide variety of updates, including the abiltiy to supply a windows and weighting matrix used for granular control of the accumulation across modalities (see `?windows_weights)

tma 0.1.0

  • Beta version - never submitted to CRAN