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Changelog

Source: NEWS.md

VaSSTra 0.4.0

  • plot() on states gains type = "variables": grouped boxplots of the clustering indicators split by state, one panel per time point, so each indicator’s separation of the states is visible at every point rather than only on average. by_time = FALSE pools the time points into a single panel, the boxplot counterpart of the profile plot.
  • Added state_descriptives(): the numeric companion to that plot, a tidy per-state, per-indicator table of count, mean, standard deviation, median, and range. scale = "original" reports the raw indicators and by_time = TRUE breaks the summary out per time point.
  • plot() on an evaluate() result now colours the state row with the palette pinned on the fit (state_colors), so it matches the other state plots; the trajectory row keeps its distinct palette.

VaSSTra 0.3.0

  • standardize gains a "subject" option that z-scores each indicator within each subject, so states reflect deviations from a person’s own baseline rather than absolute level. The default is unchanged ("time"), and "global" and "none" are still available.
  • Added transition_table(): the transition counts behind the flow and network views as a tidy from/to/count/probability data frame, aggregated over time by default or broken out per consecutive time pair with by_time = TRUE. group restricts a trajectory fit to one group.
  • Added three state-clustering methods, all from the already-imported cluster package: "clara" (sampling-based PAM for larger data), "fanny" (fuzzy clustering, hardened to its nearest partition), and "diana" (divisive hierarchical clustering). They are available through state_method in vasstra(), method in step1_states(), and state_choices(), and are selected by average silhouette like the other non-LPA methods.
  • plot() on an evaluate() result now draws the trajectory groups in a palette distinct from the states, so the state and trajectory evaluation rows are no longer coloured identically (which read as a correspondence between unrelated groupings).
  • Added a state_colors argument to vasstra(), step1_states(), and step2_sequences() that stores a state palette on the fit, so every plot reuses it without repeating colors. Accepts a named vector (matched by state, and preserved through state_order and set_labels()) or one colour per state in state order; an explicit colors on a plot still wins.
  • The state profile heatmap now draws the first state as the top row, so it reads in the same order as the profile bars, state sizes, and legends.
  • Fixed the state-sizes plot, whose bar heights were taken in cluster (discovery) order while the labels followed the display order, mislabelling the bars when state_order reordered the states.
  • Added a state_order argument to vasstra(), step1_states(), and step2_sequences() that fixes the order the states appear in every plot – distribution stacking, sequence and transition legends, transition and flow nodes, and the combined grid – independent of the order the clusters were discovered in. Because Nestimate sorts states alphabetically, the sequence views recode the states internally to force the requested order and draw a matching ordered legend. Colours follow the state order; pass colors for a fixed state-to-colour mapping.
  • plot() on trajectories now accepts a vector of views in type, for example plot(fit, type = c("transition", "index", "distribution")). Several views (or the new "transition" network) lay out a grid with one row per trajectory and one column per requested view, reproducing the familiar per-cluster VaSSTra figure in a single call, with one shared state legend along the bottom (legend = "none" omits it). A single sequence view keeps the existing faceted whole-cohort figure, so plot(fit$trajectories) is unchanged.
  • Added flow_plot(): alluvial and individual flow views of state movement between consecutive time points, rendered by the suggested cograph package. type = "alluvial" draws aggregated bands whose width is the number of subjects making each move; type = "individual" draws one line per subject, bundled automatically so large cohorts stay legible. The state palette, state order, and time labels are taken from the fit, so a flow plot is directly comparable with the sequence heatmap. color_by selects the state that colours a flow, and group restricts a trajectory plot to one group. Unlike the base-graphics plot() methods, flow_plot() returns a ggplot object.
  • Added transition_plot(): the state transition network, with states as nodes, transitions as directed edges, and node size encoding a centrality. The network is built by Nestimate::build_tna() (or build_ftna() for weights = "count") and the centralities come from Nestimate::net_centrality(), so they match tna::centralities(); cograph draws it. size selects the measure ("InStrength" by default), loops controls whether self-transitions count toward node size (FALSE by default, so a persistent state is not large merely for retaining its own members), size_range sets the node diameters, and group restricts the network to one trajectory. The call draws the network and returns the tidy state-by-centrality table it drew.
  • transition_plot(sequences = TRUE) draws the state sequences beside the transition network on one device — the conventional pairing of raw data and movement summary. TRUE uses an index plot; "heatmap" and "distribution" select the left panel. Both panels share the state palette, so a state has the same colour in each.
  • Bug fix: colors passed to plot() on sequences or trajectories was applied to states in alphabetical order rather than by state, so every state could be drawn in another state’s colour. Nestimate assigns state_colors positionally to alphabetically sorted states and ignores factor levels; colours are now re-aligned by state name. Only user-supplied colors were affected — the default palette was never mismapped.
  • Added vignette("flow-plots"), which uses the flow views to show that the engagement cohort’s flat state distribution is a balanced exchange rather than an absence of change, and that movement between the extreme states almost always passes through the middle one.
  • Added examples to all eight plot() methods, as.data.frame.vasstra(), and launch_app(); every exported topic is now documented with a runnable example.
  • Corrected the state_choices() and trajectory_choices() plot documentation, which still described the pre-redesign star and cross markers instead of the ring and hollow-point markers actually drawn.

VaSSTra 0.2.0

  • The sequence heatmap is now the default plot for complete sequence sets (plot(sequences) / plot(fit, which = "sequences")): it keeps every aligned sequence visible at full resolution. Titles are now type-specific; "distribution" and "index" remain one argument away, and grouped trajectory plots keep "index" as their default.

  • Added launch_app(): an interactive Shiny application (suggested shiny + DT) covering the complete workflow — data upload or the built-in engagement data, role mapping with detection pre-fill, automated or explicit counts with an in-app decisions log, state / sequence / trajectory plots, evaluation panels, tidy fit-index tables, in-app group renaming through set_labels(), and tidy CSV exports at every analysis unit.

  • Added fit_indices(): tidy fit statistics for the selected clustering (one row) or all compared candidates (compare = TRUE). LPA reports log-likelihood, AIC, BIC, SABIC, CAIC, AWE, CLC, KIC, ICL (all on the conventional lower-is-better scale), normalized entropy, and minimum and maximum average posterior class probabilities; hard methods report their own objectives; silhouette and group sizes are always included and inapplicable columns are dropped.

  • The extended information criteria and posterior-probability summaries are also recorded in state_choices() candidate tables and step-1 diagnostics.

  • Added set_labels(): rename fitted states and trajectories in place — full vectors or partial named renames such as c("State 1" = "Disengaged") — propagated through every derived table, sequence, and recorded positive/negative state, without refitting or changing any value.

  • Latent profile analysis is now the default state method (state_method = "lpa", mclust "EEI" — tidyLPA model 1, the model used in the VaSSTra chapter). k-means, PAM, and hierarchical clustering remain available; mclust moved from Suggests to Imports.

  • Automatic count selection applies a 5 percent minimum group share, so spuriously small states or trajectories are never auto-selected; explicit state_choices()/trajectory_choices() comparisons keep their permissive defaults.

  • Labels imply the count: with n_states/n_trajectories left on "auto", supplying three labels fits three groups.

  • n_states and n_trajectories also accept a candidate vector (for example 2:4) to compare exactly those counts and fit the recommended one.

  • fit_state_choice()/fit_trajectory_choice() fit the recommended candidate when called with only the choices object, and select candidates by n_states/method/lpa_model (or n_trajectories/dissimilarity/method) instead of candidate_id.

  • Redesigned line plots: state profiles use solid weighted lines with direct labels at the line ends (no legend, no symbol rotation); choice curves use solid palette lines with ring markers for metric optima, hollow points for ineligible candidates, and a subtitle for the metric direction.

  • vasstra(data) now runs with no other arguments: subject, time, and indicator roles are resolved from explicit arguments, attached role metadata, or common column names, and the numbers of states and trajectories default to "auto". Every automated decision is reported with a message and recorded in diagnostics$selection.

  • step1_states(), step2_sequences(), and step3_trajectories() gain the same automation, so each step also runs alone with minimal arguments; step2_sequences() detects a single categorical state column in plain data frames.

  • Added the evaluate() verb for states, trajectories, and complete fits: one tidy row per compared cluster count with best and fitted markers, plus a per-cluster quality table with mean silhouette widths.

  • Added evaluation plots (plot(evaluate(fit))): selection curve, per-cluster silhouette widths, and group sizes in one layout.

  • Added state plot types "bars" (grouped indicator means) and "all" (profile + bars + heatmap + sizes overview).

  • Unified all base-graphics plots on one colorblind-safe palette and a lighter shared style; sequence plots remain delegated to Nestimate.

  • Automatic selection surfaces the underlying error when every candidate fails (for example missing indicator values with missing = "error").

  • vasstra() now errors only when both variables and state are supplied; when neither is given, indicator columns are resolved automatically.

VaSSTra 0.1.0

  • Added a four-step, pipe-friendly VaSSTra workflow.
  • Added a complete one-call vasstra() interface.
  • Added tidy S3 results and summaries.
  • Added the ready-to-load engagement chapter data with clear raw and course-standardized indicator names plus attached VaSSTra data roles.
  • Added metadata-aware one-call workflows and tidy complete-fit tables at the subject, observation, state-profile, and trajectory units.
  • Added state_choices() and fit_state_choice() for explicit comparison and fitting of k-means, PAM, hierarchical, and optional mclust LPA states.
  • Added trajectory_choices() and fit_trajectory_choice() for tidy Nestimate comparisons across sequence distances, clustering methods, and trajectory counts.
  • Added lightweight state profile, heatmap, size, and metric-aware choice plots; sequence-based visualizations remain delegated to Nestimate.
  • Added sequence clustering and all sequence plots through Nestimate.
  • Added a base-R state-profile plot; no TraMineR dependency is required.