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Changelog

Source: NEWS.md

latents 0.9.23

  • New plot_enumeration() draws the information criteria of an enumeration grid with its arguments listed on its own, so an editor completes them; it is the view plot() draws for an enumeration.
  • plot() and plot_enumeration() take criterion = "all" for every information criterion in the grid. Pairs that coincide in a single-level grid are drawn once.

latents 0.9.22

  • Numeric categorical models now support plot(what = "profiles"), with statistic = "mean", "median", or "mode" on the original category scale. These summaries use fitted probabilities, support FIML and membership covariates, and do not compute standard errors. Mixed-model profile plots continue to show continuous indicators.

latents 0.9.21

  • The version submitted to CRAN; the same code as 0.9.20.

latents 0.9.20

  • Tests fit each model from a single start unless the test is about starts, which halves the time the tests take on CRAN. No package code changed.

latents 0.9.19

  • Every help page has an example: the plot() methods for model comparisons, family and transition enumerations, pooled imputations, cross-level and general transition fits, and predict() for growth mixtures, which shows the refusal and where the trajectories are.
  • The transition-enumeration example in ?get_results.latents_transition_enumeration fits the first 50 students, so it runs in a few seconds on Windows as well (it took 12 s there).
  • The remaining tests that compare latents with another implementation move to tests/equivalence/: the depmixS4 and LMest fits, exact path sums, dense likelihoods, enumerated EM steps and independent Hessians, together with their reference helpers and fixtures. The installed tests no longer contain any such comparison.

latents 0.9.18

  • The growth mixture guide moves from the vignettes to the package website (same address), so the package ships three vignettes: lpa, lca and lta. Its model fits made it most of the vignette build time.

latents 0.9.17

latents 0.9.16

  • Every help page now has an example that runs in the automatic checks. The growth mixture pages (get_results(), plot(), simulate()) and compare_models() open with a fit on the first 60 students of growth_scores, which runs in about a second; the full-data examples stay in \donttest{}. The growth_scores, growth_schools and student_esm pages open with a summary of the data. The compare_models() example no longer fits a random intercept that course_engagement cannot support.
  • The package ships four vignettes: lpa, lca, lta and growth-mixture. The workflow guides (workflow-lpa, workflow-lca) and the guides to evaluation, covariates, mixture regression, latent class growth analysis and group-class models move to articles on the package website, with unchanged addresses. This keeps the installed package small and the checks quick.
  • Tests that compare latents with other implementations (numDeriv, mclust, MASS, glm(), Latent GOLD and Mplus results, reference implementations, the numbers in the workflow guides) move to tests/equivalence/, which is not part of the package and runs in its own CI job. numDeriv is no longer a suggested package. Slow tests that refit many models are skipped on CRAN; every model family keeps tests that run there.
  • The README’s enumeration example names its covariance structure (model = "VVI"), as candidate_fit() needs once several structures are crossed, and its text matches the regenerated output.

latents 0.9.15

  • Vignettes and worked examples save and restore the user’s display options (digits and width) after use, including the scripts installed in inst/doc.

  • Corrects regression and growth inference: robust covariance checks the number and rank of independent units, multilevel growth BIC uses clusters, enumerated models retain the appropriate inference, and boundary parameters no longer erase unrelated delta-method standard errors.

  • Stabilizes Poisson and negative-binomial regression densities, NB dispersion derivatives near the Poisson limit, and growth residual calculations for outcomes with large locations. Single-class binary regressions are accepted; nonfinite outcomes and evaluated predictors are refused explicitly.

  • Trajectory tables include offsets and report response-scale standard errors. Alternate time columns work in tables and plots; random-effect spread holds other predictors fixed, and observed means respect sampling weights.

  • Withholds regression bootstrap likelihood-ratio p-values when refits fail validation. Model comparisons also check trials, weights and cluster layout.

  • Handles unassigned regression classes, weighted transition class counts and matrix-valued prediction inputs correctly. Noise cases retain their mass in profile percentages and appear as class zero in sequence tables and plots.

latents 0.9.14

  • Declares MASS and withr, used by the tests, in Suggests (the 0.9.13 check warned about them as undeclared).

latents 0.9.13

One engine

  • Every estimator now runs on one shared kernel (see validation/ENGINE_DESIGN.md): one EM driver (with SQUAREM for the profile models), one start runner, one quasi-Newton finish, one measurement block (Gaussian, categorical, ordinal and count indicators) shared by the profile, covariate, cross-level and both transition engines, one forward-backward pass for both transition engines, shared latent structures for the regression and growth models, and shared inference, alignment and simulation services. Results are unchanged: 251 recorded fits of every engine and option reproduce bit for bit (equivalence/engine-golden/), except where a defect below was fixed.

New models

  • Multilevel growth mixture models: mixture_regression(cluster = ) nests the persons (id) in clusters (schools, clinics) with n_group_classes group classes of clusters that shift how probable each trajectory class is, and group_membership covariates of the group class. The clusters are the independent units of the likelihood, standard errors and BIC. New tables "group_classes" and "clusters"; enumerate_regressions() compares the number of group classes; simulate() draws the two levels. New dataset growth_schools; the growth vignette has a section on it.
  • Negative-binomial regression mixtures: mixture_regression(family = "negative_binomial"), NB2 with a dispersion per class or shared (variance = "equal"). One class reproduces MASS::glm.nb(). A dispersion at the Poisson limit warns (latents_boundary) and the other standard errors are conditional on it.
  • Ordinal regression mixtures: mixture_regression(family = "ordinal"), a proportional-odds (cumulative logit) regression within each class with class-specific ordered thresholds in place of the intercept. The outcome is an ordered factor, a factor or whole-number categories. Thresholds are rows "threshold:<lower>|<upper>" of the coefficient table; class means are expected category scores; predict(type = "probabilities") gives the category probabilities and simulate() draws categories in the outcome’s own type. One class reproduces MASS::polr(). Mixtures that one outcome per class assignment cannot identify (two categories, or no predictors) are refused with latents_not_identified.

Documentation

  • New vignette “Latent class growth analysis: classes of trajectories” (vignette("trajectory-classes")): trajectory classes without random effects, every plot view, curved trajectories, pass/fail and ordered-band trajectories, distal outcomes, and when to prefer the growth mixture model. The growth mixture vignette now focuses on random effects and the multilevel model, with its plots.
  • Coefficient forest plots of trajectory fits label every estimate with the same number of decimals per panel (two, or three for small values).
  • The recovery table of a regression mixture prints in the report layout.

Bug fixes

  • Growth mixture models gain a start from the persons’ own least-squares trajectories. Every previous start came from the model without random effects, which can split persons by their level; the growth fit then stopped at a lower maximum (by 9 to 11 log-likelihood units on test data where classes differ in slope).
  • A fit with a noise component: summary() and get_results(x, "all") no longer fail; "classification_errors" and "bch_weights" (which were wrong for noise units) refuse with latents_unsupported_noise. sensitivity() no longer reports NA agreement for noise fits.
  • parameter_inference() of a general lta() fit now uses the caller’s step in its table, not only in the attached covariance.
  • get_tna() and get_group_tna() on a general lta() fit refuse with latents_unsupported_tna instead of a bare “no applicable method”.
  • An additive-family information matrix that is negative definite is refused with latents_singular_information instead of an unclassed Cholesky error.
  • A group covariate that varies within a group, and all starts failing in the covariate and homogeneous transition engines, now raise classed conditions (latents_bad_data, latents_all_starts_failed).
  • Two-level regression summaries label a class logit’s intercept within a group class “Intercept, group class 1” rather than “Intercept x group_class_1”.

latents 0.9.12

Growth mixture models

  • mixture_regression() gains random, random_covariance and random_diagonal: random effects within classes, so persons scatter around their class trajectory (the Gaussian growth mixture model). The likelihood is exact; estimation is EM with the random effects as missing data, finished by quasi-Newton with analytic scores. Validated against an independent dense likelihood, numerical derivatives and lcmm::hlme() (six configurations; the same likelihood at hlme’s estimates and at least its maximum).
  • Variable names may replace one-sided formulas: random = "wave", random = "intercept", common = "x", membership = "age".
  • A degenerate random-effect covariance (a variance at zero, or perfectly correlated random effects) warns (latents_random_boundary) and reports no interval for it.
  • New compare_models(): fits to the same data side by side, with BIC differences and Schwarz weights; plot() draws the comparison.
  • Trajectory models (id with class_level = "group", with or without random) share new ggplot2 views: class trajectories with confidence bands, the spread of persons around them and observed class means; individual trajectories; a coefficient forest; predicted random effects; and a classification structure plot.
  • Result tables of these fits print in a report layout (readable labels, intervals as [low, high], APA p-values, fit statistics as a card) while remaining plain numeric data frames.
  • Growth fits work with enumerate_regressions(), including its bootstrap likelihood-ratio test, have a simulate() method, and support distal outcomes in three_step() (person-level, bias-corrected); three_step() results print in the report layout. predict() and r3step() refuse growth fits with classed conditions.
  • New dataset growth_scores and vignette “Growth mixture models”.

latents 0.9.11

  • Transition fits no longer report false non-convergence. The quasi-Newton finish counted only L-BFGS-B’s code 0 as converged, but its line search can stop at the maximum (code 52, “ABNORMAL_TERMINATION_IN_LNSRCH”) when rounding hides any further ascent, which is platform-dependent. It now restarts once from where it stopped and accepts the fit when the log likelihood cannot be raised by more than tol. In 120 negative-binomial test datasets 5 were flagged unconverged at points with scores of about 1e-6 that a restart improved by less than 1e-12; none are now. This, not the iteration cap, is what failed the 0.9.8-0.9.10 CI checks on macOS and R 4.1 (the 0.9.10 test change is reverted).

latents 0.9.10

  • Test-only fix: a negative-binomial transition test required convergence within max_iter = 5, which held on some platforms only (macOS CI and R 4.1 needed more iterations, and the package correctly warned). The test now allows 500 iterations; its checks of dispersion floors, monotone likelihood and stationary scores are unchanged.

latents 0.9.9

Transition models: full-covariance simulation and bootstrap inference

  • Transition fits with full covariance structures (EEE, VVV and the other non-diagonal structures, homogeneous or extended) can now be simulated, so bootstrap_lrt() compares them. Each profile’s rows are drawn through the Cholesky factor of its covariance; diagonal fits keep their random stream.
  • parameter_inference(method = "bootstrap", data = ) works on transition fits. Persons are resampled with replacement and refitted; each replicate’s profiles are matched to the original’s on the measurement and its group classes on what the matched profiles make of them, with initial logits rebased when the reference profile moves. Standard errors are the replicates’ standard deviation and intervals their percentiles. This gives inference for extended models whose Wald inference is unavailable (full covariance structures) and supports sampling weights.
  • Fixed a 0.9.8 regression: transition fits refused boundary = "fix", which get_results(fit, "responses") and summary() request, so categorical transition response tables lost their standard errors. Transition inference holds nothing at a bound, so "fix" now agrees with "error"; a fit with an active bound is still refused with latents_boundary_fit.

latents 0.9.8

Model review fixes

  • Ordinal/count indicators now work through lca(), multilca() and enumerate_lca(), as documented. Bootstrap and imputation label alignment move their measurement parameters together, restore the ordinal reference profile, and match profiles using the extra indicators.
  • Negative-binomial bootstrap comparisons preserve the fitted count model and refuse incompatible indicator specifications. Supplied fitting data are checked against all measured indicators and model-specific sufficient statistics before inference or comparison.
  • Poisson log densities and negative-binomial dispersion derivatives use numerically stable calculations. Invalid extra-indicator column shapes and nonfinite ordinal values are refused. Single-level covariate inference accepts the original data without demanding an internal identifier.
  • Second-order occasion transitions omit coefficient coordinates that never apply. Stayer tables report identity transitions; second-order inference includes the second-order coefficient block. Count boundaries retain their constraints during transition optimization and refuse Wald inference.
  • Extended transition coef() now carries complete measurement covariance coordinates for all fourteen structures, with likelihood-preserving decoding. The existing restrictions on extended-model Wald inference remain explicit.
  • Transition fits with only categorical, ordinal or count indicators return all result tables successfully, including an empty continuous-profile table. Transition data validation checks original category labels and occasion values.
  • One-profile NB fits with varying dispersion retain the correct profile label and report their dispersion standard error.
  • Group-class bootstrap comparisons verify group sizes and scatter and refuse equivalent one-class family specifications. Weighted restricted cross-level fitting avoids collisions with indicator names used for internal weights.
  • Sampling weights normalize safely at extreme common scales. Regression mixture class counts, shares and composition respect sampling weights; invalid integer controls and seeds are refused before conversion.
  • Confidence levels and numerical differentiation controls receive explicit validation on the reviewed model surfaces.

Follow-up review fixes

  • Negative-binomial fits near the Poisson limit no longer stop short of the maximum. The dispersion objective is convex there in log dispersion, and the old Newton step could stall while still reporting convergence (fitted dispersion 4.2e-7 against a profile maximum of 1.4e-5, with singular Wald inference). The M-step now uses modified Newton with reflected eigenvalues.
  • bootstrap_lrt() refuses a full-covariance transition null up front with latents_unsupported_inference, rather than failing every replicate and returning p_value = NA with a generic warning.
  • parameter_inference() on transition fits refuses method = "bootstrap" and boundary = "fix" with latents_unsupported_inference instead of silently returning the Wald table. On general lta() fits, adjust now produces a p_adjusted column.
  • bootstrap_lrt() accepts the caller’s own data frame for single-level fits.
  • Under sampling weights, the "classification" table’s estimated_n and estimated_proportion (and so the odds of correct classification and diagnostics()’ smallest class) use weighted posterior totals. The same applies to restricted cross-level composition and group_classes counts.

latents 0.9.7

Ordinal and count indicators everywhere; negative-binomial counts

  • Membership covariates (profile_covariates, group_covariates) and lta() (every extension, invariant or occasion-specific measurement) now take ordinal and count indicators, with Wald inference, weights and simulation (bootstrap_lrt() for transition fits).
  • count_model = "negative_binomial" models counts more variable than a Poisson within a profile (NB2: variance mu + alpha mu^2), with count_dispersion = "varying" (per profile) or "equal" (shared). get_results(fit, "count_means") adds the dispersion and its standard error. A dispersion at zero is the Poisson limit: the fit warns (latents_boundary) and Wald inference is refused there.
  • Checked against Latent GOLD 6.1 (poisson overdispersed), class-varying and shared dispersion: log likelihoods agree to 4e-5 and parameters to its printed precision; seven reference cases in all, stored as a test fixture.
  • The Newton line searches of the membership logits, ordinal and count M-steps accept a step that changes the objective by rounding only, instead of halving it up to forty times at the maximum (30x faster on a negative- binomial fit).

latents 0.9.6

Bug fix

  • multilpa() with profile_covariates or group_covariates silently fitted any covariance structure other than EEI, VVI, EEE and VVV as one of those four (for example model = "VEI" gave VVI) and did not record the structure. It now refuses them with latents_unsupported_structure; fit the covariate-free model with the structure and use three_step() or r3step().
  • ?"latents-conditions" documents the condition classes added in 0.9.3–0.9.6.

latents 0.9.5

Ordinal and count indicators

  • multilpa(), lpa(), lca() and multilca() take ordinal and count, naming indicators in vars:
    • ordinal: an adjacent-category logit with category intercepts shared by every profile and one location per profile (Latent GOLD’s default ordinal model): (K - 1) + (C - 1) parameters per indicator instead of C (K - 1) as categorical. Ordered factors or whole numbers.
    • count: Poisson, one mean per profile.
  • New tables: get_results(fit, "ordinal") (category probabilities and locations, with standard errors) and get_results(fit, "count_means").
  • Mixed freely with continuous and categorical indicators, in single- and two-level fits, with missing = "fiml" and weights. Wald (analytic scores) and bootstrap inference, bootstrap_lrt(), predict(), sensitivity() and enumerate_classes() handle them.
  • Checked against Latent GOLD 6.1 on five datasets (mixed, ordinal only, count only, three classes, two-level): log likelihoods agree to 4e-5 and every parameter to 5e-5, the precision Latent GOLD prints (equivalence/latentgold-ordinal/); the results are a test fixture.
  • Not yet available with membership covariates, start, fixed, prior, noise, lta() or the group-class families (latents_unsupported_indicator).

latents 0.9.4

  • The membership M-step of covariate fits (profile_covariates, group_covariates) now solves its weighted multinomial logits by Newton-Raphson with the exact Hessian instead of BFGS. BFGS stalled above its score tolerance on about half of the steps of an ordinary fit, which left fits flagged unconverged (and refused standard errors) on some platforms but not others. Estimates are unchanged; fits converge in fewer EM iterations.
  • Continuous integration passes again: the R 4.1 floor job no longer tries to install mice (its current dependency chain needs R >= 4.4), one lint is fixed, and the checkout action is updated.

latents 0.9.3

Sampling weights

  • multilpa(), lpa(), lca(), multilca(), lta() and mixture_regression() take weights, the name of a column holding one sampling weight per independent unit (each id group, or each row of a single-level fit). The fit maximizes the pseudo log likelihood sum_j w_j log L_j, with the weights scaled to sum to the number of units (Mplus’s convention), so AIC and BIC stay on the sample’s scale.
  • Covered: every covariance structure, categorical indicators, missing = "fiml", membership covariates, every family (additive, dispersion, additive-dispersion, restricted and full cross-level), every extension of lta(), and all three nestings of mixture_regression().
  • Integer weights reproduce the fit to the data with each unit repeated that many times, to 1e-9 or better in the log likelihood, for every engine (tested). A one-profile weighted fit reproduces Mplus 9’s weighted regression (TYPE = COMPLEX, WEIGHT) and closed-form weighted least squares (equivalence/weights/).
  • Standard errors are the sandwich: parameter_inference(), vcov(), confint() and get_results() default to vcov_type = "robust" for a weighted fit and refuse "observed" and "opg" (latents_unsupported_weights). method = "bootstrap" resamples units with their weights.
  • A weighted fit prints its weight column and Kish’s effective sample size; get_results(fit, "model") gains a weights column. Effective counts and proportions are weighted; classification and posterior tables report each unit’s own posterior.
  • Refused with latents_unsupported_weights: bootstrap_lrt() and enumerate_regressions(bootstrap = ) (the parametric bootstrap ignores the design), three_step(), r3step(), prior and noise. Weights that vary within a unit, are negative or missing raise latents_bad_weights.

latents 0.9.2

Choosing and testing transition models

  • enumerate_classes(..., time = ) enumerates latent transition models over n_profiles and n_group_classes (and covariance structures named in model), with every other lta() argument held fixed; read with get_results(), plot() and candidate_fit(grid, n_profiles = , n_group_classes = ).
  • bootstrap_lrt() compares nested transition fits (data required): more profiles or group classes, occasion-varying against homogeneous transitions, added covariates, occasion-specific measurement, second order, a stayer class. The null model is simulated with the data’s groups, occasions, covariates and missing values kept.
  • lta(mover_stayer = TRUE) adds a class of stayers who never change profile (Goodman’s mover-stayer model), with its own initial distribution.
  • The extended transition model now takes missing = "fiml" and covariance structures (model =, covariance_model = "full"); standard errors with diagonal covariances (including FIML).
  • A transition or initial probability estimated at zero (a move never observed, common with occasion-varying transitions on sparse late occasions) is reported as a boundary fit (latents_boundary, standard errors withheld) instead of as non-convergence.

latents 0.9.1

Latent transition analysis: beyond homogeneous transitions

  • lta() gains arguments that relax each default assumption of the model:
    • transition_covariates: covariates (fixed or changing over occasions) shift the transition probabilities through a multinomial logit per origin profile, the log odds of moving to each other profile rather than staying;
    • initial_covariates: covariates shift the starting profile;
    • transitions = "occasion": a separate transition matrix for each move;
    • measurement = "occasion": profile means and variances (or response probabilities) per occasion;
    • order = 2: second-order transitions, the next profile depending on the two previous ones;
    • model =: the fourteen covariance structures ("VEI", "EEV", …) for the homogeneous model, as in multilpa().
  • The extensions return a multilpa_lta fit with tidy tables (get_results(fit, "transition_coefficients"), "transitions" per occasion, "second_order_transitions", "initial_coefficients", "profiles", …), Wald standard errors from analytic scores (observed, robust or OPG), coef(), vcov(), logLik(), nobs(), BIC() and plot(). EM is finished by a quasi-Newton search on the exact likelihood.
  • External agreement, 30 of 30 quantities (equivalence/lta-extensions/): Mplus User’s Guide examples 8.13 and 8.14 and an Mplus model with occasion-specific thresholds (log-likelihoods within Mplus’s printed precision, estimates within 2e-3), depmixS4 covariate transitions (log-likelihood within 1e-7) and LMest time-heterogeneous transitions (within 4e-8). Second-order transitions, which no external program fits, are checked against an exact sum over every profile path.

latents 0.9.0

Experimental: the Houle et al. (2026) multilevel families

  • multilpa(family = ) fits the multilevel latent profile families of Houle, Morin & Harvey (2026) beside the existing profile model (family = "profiles", their dispersion-heterogeneity model):
    • group-class families with no individual profiles, each group carrying a Gaussian intercept per indicator: "additive" (classes differ in means; within-group variance shared), "dispersion" (classes differ in within-group variances) and "additive_dispersion" (both); between_variance = "varying" or "equal". The likelihood is exact (no quadrature); zero between-group variances are reached by a boundary maximization with a Karush-Kuhn-Tucker check; interior fits are finished by Newton steps.
    • cross-level families with individual profiles and group classes from the same indicators, following the published manifest-aggregation specification: "restricted_cross_level" and "full_cross_level". Their likelihood is a working likelihood (group means reuse the ratings), so it compares only cross-level fits of the same data; no standard errors.
  • Tidy tables for every family through get_results() ("parameters", "group_classes", "groups", "intercepts", "recovery", … for the group-class families; "profiles", "composition", … for the cross-level ones), summary(), plot(), coef(), vcov(), confint(), logLik(), nobs() and parameter_inference() (observed, cluster-robust or OPG covariance from analytic group scores).
  • enumerate_classes(family = c("additive", "dispersion", "additive_dispersion"), n_group_classes = ) crosses families, between-variance restrictions and class counts; candidate_fit(grid, n_group_classes = , model = ) picks one. bootstrap_lrt() tests nested group-class fits by parametric bootstrap.
  • latents_weak_class warning with effective_groups in the "group_classes" table: a class supported by fewer than 50 effective groups (small, poorly separated, or both) has intervals that can be miscalibrated. Threshold set by a predeclared rule and evaluated on held-out simulations (flags 100% of a rare, weakly separated condition and 0.8% of well-separated ones). Also flags designs with fewer than about 50 groups per class.
  • Evidence (validation/ADDITIVE_SIMULATION.md, equivalence/latentgold-families/): predeclared simulations with 1000 datasets per condition (additive: 31 of 34 reference-condition parameters meet every gate; dispersion and additive-dispersion: 45 of 46; every miss is the ML small-sample downward bias of a between-group variance, coverage 0.925-0.947). Latent GOLD 6.1 agrees on parameter counts in all eight external cases, and its likelihoods and posteriors converge to latents’ as its quadrature is refined (largest remaining gaps 1.6e-4 and 2.0e-4). Robust standard errors are needed for heavy-tailed ratings.
  • New vignette: vignette("additive").
  • Not yet available for these families: missing data, covariates, and an external Mplus comparison (Mplus is not available here).

latents 0.8.8

plot() returns ggplot objects (breaking)

  • plot() on a fit (multilpa, covariate and transition fits), on an enumeration and on a diagnostics() result now returns ggplot objects instead of drawing with base graphics. Print one to draw it, save it with ggplot2::ggsave(), or restyle it with + ggplot2::theme(). ggplot2 stays in Suggests: a plot without it raises latents_missing_package, and diagnostics() and report() print a message and carry on.
  • what = "all", plot() on a diagnostics result and an enumeration plotted with combine = FALSE return a latents_plots list, named by view, that draws every plot when printed.
  • The styling arguments palette, symbols, linetypes, style and the style constants passed through ... are gone; an unknown argument now raises latents_bad_argument instead of being ignored.
  • Every view shares one profile order (largest first), one profile share (the posterior share every table reports) and Okabe-Ito colours paired with shapes. Bars start at zero, the heatmaps have colour keys, and the case diagnostics are strips of every case with each profile’s mean marked.
  • New views: "parallel" (every case as a line, one panel per profile), "pairs" (scatter-plot matrix with each profile’s 95% covariance ellipse) and, for enumerations, what = "tree" (an icicle of how profiles split as more are added). plot_views() lists them.
  • Model comparison switches from direct labels to a legend beyond five series; failed candidates are marked with a cross on the panel floor.
  • plot(diagnostics(fit)) on a one-profile fit returns the sizes and average posterior views instead of refusing.

latents 0.8.7

Messages

  • The single-level notice from multilpa(), multilca() and enumerate_classes() with id = NULL is now one line (“Fitted a single-level latent profile model.”). The error for a missing id is shorter too.

ggplot2 views (in development)

  • Internal ggplot2 builders for every clustering view: profiles, bars (from zero), heatmap (mixture-standardized, with a colour key), raincloud, sizes, entropy and posteriors (per-case strips), average posterior probability and model comparison. New views: parallel coordinates, a scatter-plot matrix with 95% covariance ellipses and uncertainty-sized points, and an icicle of how profiles split across numbers of profiles (Zappia and Oshlack, 2018), with bands carrying posterior mass and colours following each profile’s lineage.
  • Every view uses one profile share (the posterior share), one display order (largest first) and Okabe-Ito colours paired with shapes.
  • ggplot2 is in Suggests. plot() still draws the base-graphics views; the switch comes in a later release.

latents 0.8.6

Revision review fixes

  • Mixture-regression prediction retains the training membership factor levels, contrasts and transformed terms. Posterior predictions rebuild group IDs for new rows; fitted plots retain grouping and membership columns and accept transformed regression predictors.
  • A group-membership formula with one slope and no intercept is optimized as a slope, rather than mistaken for an intercept-only model.
  • r3step(by_group_class = TRUE) now handles the one-group-class limit and agrees with the pooled regression for both observed and robust covariance.
  • enumerate_lpa() preserves an omitted model, allowing the documented covariance switches and all-categorical inputs through the wrapper.
  • bootstrap_lrt() refuses prior-fitted models whose likelihoods are evaluated at posterior modes. Its fixed-mask missingness assumptions are clarified, and multiple-imputation guidance distinguishes algebraic equivalence from evidence about coverage and imputation-model validity.

latents 0.8.5

Single-level verbs, covariance-structure grids and workflow vignettes

  • New lpa() and lca() fit single-level latent profile and latent class models. They are multilpa(id = NULL) and multilca(id = NULL) under their ordinary names, return the same object, and refuse id and n_group_classes.
  • multilpa() and lpa() take model, the three-letter covariance code ("EEE", "VVI", …), as an alternative to variance_model / covariance_model or volume / shape / orientation; giving both is an error.
  • New enumerate_lpa() and enumerate_lca() compare single-level latent profile and latent class models, as lpa() and lca() fit one: enumerate_lpa(data, vars, n_profiles = 1:6) crosses the counts with the covariance structures, and enumerate_lca(data, vars, n_classes = 1:6) treats every indicator as categorical. enumerate_classes() remains the verb for nested data and needs id.
  • The two-level verbs (multilpa(), multilca(), enumerate_classes()) with id = NULL say which analysis was estimated – a single-level latent profile or latent class analysis – and name the single-level verbs; called without id they are refused with the same pointer.
  • enumerate_classes(model = ) now defaults to "basic": the four structures that combine equal or varying variances with covariances absent or present (EEI, VVI, EEE, VVV). "all" fits the 14 structures; codes can still be named. With one continuous indicator the structures that coincide are fitted once; with only categorical indicators no structure is crossed; a structure set through the other arguments replaces the default, and model = NULL restores the previous grid. Because a grid now holds several structures per class count, candidate_fit() needs model to pick one of them.
  • New data set srl: five self-regulated learning scales for 300 respondents simulated by a large language model.
  • New vignettes vignette("workflow-lpa") and vignette("workflow-lca"): a latent profile and a latent class analysis from the data to a reported model.
  • get_results(fit, "profiles") and get_results(fit, "responses") carry mean_standard_error, variance_standard_error and probability_standard_error without the data being passed; they are NA, with a message, when the information matrix cannot be formed.
  • diagnostics() draws its classification plots by default (plots = TRUE): profile sizes, posterior probabilities, entropy contributions and the average posterior probability matrix.
  • plot(what = "raincloud"): for each profile and indicator, the density, a quartile box and the observations.
  • Wald intervals for probabilities are formed on the logit scale and for variances on the log scale, so they stay inside the parameter’s range; confint() returns exactly the intervals parameter_inference() reports.
  • A probability at or below 1e-6 counts as on its bound for inference: EM approaches zero slowly and stopped at 1e-8, leaving a singular information matrix that boundary = "fix" could not hold.
  • r3step() and three_step() name their adjusted p-value p_adjusted and their classes profile_1, group_class_1, as parameter_inference() does (were p_value_adjusted and class_1).
  • id = NULL now raises its latents_single_level notice as a message, not a warning: a single-level model is a legitimate choice.
  • diagnostics() prints only the individual level for a fit with one group class, and a single-level fit’s assignments carry no group_class column.
  • plot(what = "profiles") draws 95% intervals when the fit has standard errors (intervals = TRUE, profiles dodged apart); plot(what = "responses") draws intervals too.
  • plot(what = "bars") and the profile plot no longer fail on a bound-active or unconverged fit: they draw without whiskers and the subtitle says why.
  • plot(what = "heatmap") on an all-categorical fit draws the response probabilities: one row per class, one column per category, values printed where they fit.
  • Covariate fits draw every measurement and classification view ("bars", "heatmap", "raincloud", "sizes", "avepp" are new for them).
  • The enumeration plot uses display names (AIC, BIC, ICL), draws one BIC panel when the group-level and individual-level values coincide (a single-level grid), labels series only by what distinguishes them, and spaces labels by the text height. Gridlines and zero lines stay inside the panel. Okabe-Ito yellow now comes seventh, after the six colours that read well on the light panel.
  • summary() of an enumeration prints a compact candidates table and names get_results() for the rest.
  • An all-categorical fit prints as a latent class analysis rather than with a residual covariance, and a single-level fit’s header reads “BIC”.
  • A covariate fit with one group class names its membership intercept (Intercept), not group_class_1.
  • Help-page examples use the bundled data sets and verbs, with no $ or bracket indexing; a test keeps it that way.

Bootstrap likelihood-ratio tests with missing data and covariates

  • bootstrap_lrt() now accepts fits made with missing = "fiml": every simulated replicate is given the observed missing cells before it is refitted, so the reference distribution loses the same information as the observed statistic. The pattern is treated as fixed (independent of the profiles).
  • bootstrap_lrt() now accepts membership-covariate fits: the covariates are held at their observed values and memberships are drawn from the fitted logits. A one-group-class null may be compared with an alternative that adds group covariates or slopes by group class; otherwise both models must use the same membership regressions (latents_bad_nesting). A covariate replicate whose likelihood has converged counts as valid even if a membership logit is still drifting (an over-fitted class), since the statistic reads only the maximized likelihood; the replicate table’s new logits_settled column records it.
  • Models with different missing-data handling are now refused as incomparable (latents_incomparable_models).

Standard errors for latent transition models

  • parameter_inference(), vcov() and confint() now work on lta() fits instead of refusing them: observed-information, robust (clustered on sequences) and OPG standard errors for the measurement model, the group-class shares, the initial profile probabilities and the transition probabilities. The scores follow the Fisher identity (expected counts from the forward-backward pass minus the counts the fitted probabilities imply) and match numerical differentiation of the likelihood; the standard errors match a Richardson-extrapolated Hessian to 1e-6 and depmixS4’s numerical ones to within its own finite-difference error. Over 300 simulated panels, 95% intervals covered 0.947-0.960. The latents_no_inference condition is retired.
  • coef() on an lta() fit now uses the names and order of vcov(): under variance_model = "equal" it reports the shared variances once, under covariance_model = "full" the covariance matrices, and the group-class shares come before the initial and transition probabilities.

Multiple imputation for missing covariates

  • New pool_imputations(): fits multilpa() to every completed data set (a list of data frames, or a mids object from mice), aligns each fit’s profile and group-class labels to the first, and pools with Rubin’s rules. The table adds df, within, between, riv and fmi to the columns of parameter_inference(); get_results(x, "imputations") and get_results(x, "fits") show each imputation and the relabelling it needed, and plot() shows the imputations’ estimates beside the pooled interval. This is the route for missing covariates, which missing = "fiml" cannot integrate out. Aligning a covariate fit reparameterizes its membership logits, whose reference category moves with the labels. Pooled values agree with mice::pool.scalar() to machine precision. A failing imputation raises latents_pooling_failed rather than being dropped.

First-stage uncertainty in staged fits

  • parameter_inference(method = "bootstrap") now accepts a fit_staged() result instead of refusing it. Every resample of the groups refits both stages, so the standard errors and percentile intervals carry the measurement’s sampling variability into the group-class estimates; the Wald default still conditions on the measurement. The table reports every parameter, the measurement included. On a simulated two-class design the within-class profile probabilities’ errors were 14% and 32% wider than the conditional ones, and a bootstrap that held the first stage reproduced the conditional Wald errors to within 3%.

Profile-covariate slopes by group class

  • multilpa(profile_covariates = , profile_slopes = "group_class") lets each group class have its own profile-covariate slopes, so a covariate can predict profile membership differently in different kinds of group (a cross-level interaction). The coefficients are reported with terms such as z:group_class_1, and inference, robust and OPG covariances cover them. The default, "shared", is the previous model. With one group class the two are the same model. Naming it without profile_covariates raises latents_bad_argument.

Missing indicators in membership-covariate models

  • multilpa(profile_covariates = , group_covariates = , missing = "fiml") now fits: missing indicators are integrated out of the measurement density exactly as in the covariate-free model, for diagonal and full residual covariance and for categorical and mixed indicators. Previously this combination was refused with latents_bad_argument. Standard errors (observed, robust and OPG) cover it through the Fisher identity: a missing value enters the scores through its conditional mean and covariance given the row’s observed values. Covariates themselves must still be complete; a missing covariate now raises the classed latents_bad_data.

Faster estimation

  • multilpa(acceleration = "squarem"), the new default, accelerates EM with SQUAREM (Varadhan & Roland 2008). A cycle extrapolates along the last two EM steps and finishes with an ordinary EM step, falling back to plain EM whenever the extrapolation would lower the likelihood, so the path stays monotone; convergence is judged over the whole cycle, so it is never looser than plain EM’s. acceleration = "none" reproduces plain EM’s path (use it to follow mclust or Mplus step by step). Prior fits, membership-covariate models always use plain EM. Results can differ slightly from earlier versions: the accelerated fit usually ends closer to the maximum, and on a flat, weakly identified likelihood it may stop at a different point of the ridge.
  • The first start of multilpa() is now Ward’s hierarchical clustering, with each cluster’s own means and spread; the rest remain random k-means. Over 14 structures on 3 datasets it never lowered the best-of-10 likelihood and raised it in 3 of 36 cases, for example on iris VVV from -186.57 to mclust’s -180.19. Fits may therefore land on a better maximum, or on the same maximum with profiles numbered differently.
  • EVE and VVE take a fixed number of warm-started orientation steps per EM iteration (a generalized EM step) instead of solving the orientation to convergence inside every M-step, which took a median of 220 inner steps per iteration. The same maxima are reached 3.5 to 8 times faster, and SQUAREM now applies to them.
  • enumerate_classes(id = NULL) enumerates single-level models, the search mclust::mclustBIC() performs, with one single-level notice for the grid.
  • New predict() method for multilpa() and multilca() fits: the modal profile ("class"), every profile’s probability ("posterior"), or each row’s log density under the fitted mixture ("density") for new rows, prepared as the fit prepared its own (categorical levels, centering, missing data, noise). Groups in the new data are classified as new groups. On the training rows it reproduces the fit’s posteriors, and it matches mclust::estep() at the same parameters.
  • descriptives(by = ) lists strata in a fixed order (a factor’s levels, otherwise sorted) rather than in order of first appearance.
  • The E-step no longer computes a missingness pattern per row for complete data and takes row maxima without a per-row apply(): about 14 times faster per iteration, with identical results.
  • On 5,000 rows, 4 indicators and 3 profiles, 13 of the 14 covariance structures now fit faster than mclust::Mclust() from one start (VVV is the exception), and 13 of 14 reach a higher likelihood.

Mixture regression

  • New mixture_regression() fits finite mixtures of regressions (clusterwise, latent class or regression-mixture models) for "gaussian", "binomial" (0/1, factor or cbind(successes, failures)) and "poisson" (with offset()) outcomes. Classes can belong to each row, to a whole group (class_level = "group", flexmix’s y ~ x | id), or to each row with a second-level group class that shifts the class shares within groups (n_group_classes, Vermunt 2003). Membership covariates at both levels (membership, group_membership), coefficients shared across classes (common), and equal residual variances (variance = "equal").
  • Analytic scores (Fisher’s identity) give observed-information, sandwich (clustered on the independent unit, or on id for a single-level fit) and outer-product standard errors; validated against a numerical Hessian of the likelihood in every nesting. A Monte Carlo study (validation/mixture-regression-recovery.R, 200 replications) found 91.5–97% coverage of nominal 95% intervals for the two-level model.
  • get_results() tables: coefficients (with Benjamini-Hochberg-adjusted slopes and odds/rate ratios), classes, membership, group_classes, fit (AIC, BIC, SABIC, ICL, entropy), assignments, groups, fitted, starts, classification and recovery (against a known classification). predict(), simulate(), plot(), summary(), coef(), vcov(), confint(), logLik() and nobs() methods.
  • New enumerate_regressions() compares class counts in one table, with an optional parametric bootstrap likelihood-ratio test.
  • A binary outcome with one trial per class assignment is refused (latents_not_identified), since that mixture is not identified.
  • Single-level and group-level fits reproduce flexmix’s likelihood at its estimates and its binomial and Poisson estimates (equivalence/test-mixture-regression-flexmix.R).
  • New dataset study_hours and vignette("mixture-regression").

Fixes

  • multilca(id = NULL) now fits one group class by default, as multilpa() does, instead of refusing the default n_group_classes = 2.
  • equivalence/run.R evaluated tests in the retired multilpa package’s namespace when it was still installed; it now uses latents.

mclust parity

  • parameter_inference(), vcov() and confint() give Wald standard errors for all fourteen covariance structures, not only EEI, VVI, EEE and VVV. The ten that constrain the volume, the shape or the orientation across profiles are differentiated in their own free coordinates — log volumes, log shapes with the determinant-one constraint built in, and Cayley-transform orientations (log-Cholesky factors for VEE and EVV) — with exactly as many dimensions as the structure has parameters, and carried to the reported variances and covariances by the delta method. Robust ("robust", "opg") errors work for them too. Validated against an independent numerical Hessian of the observed-data likelihood in a different chart.
  • multilpa(prior = prior_control()) gives maximum a posteriori estimates under the conjugate prior of Fraley and Raftery (2007), mclust’s priorControl(): the same default hyperparameters and the same M-steps as mclust::me(), for the ten structures mclust defines a prior for. log_likelihood, and the criteria built from it, are the unpenalized likelihood at the posterior mode, as in mclust. Wald inference is refused for such a fit; the bootstrap refits with the prior. New condition class latents_unsupported_prior.
  • multilpa(noise = TRUE) adds mclust’s uniform noise component over the data’s hypervolume (hypvol()), for one group class and continuous, complete indicators, with any covariance structure and with or without prior. The noise is profile 0 in get_results(fit, "assignments") (with a posterior_noise column), "posteriors", "profile_probabilities" and the classification diagnostics. The component counts two parameters, as in mclust, so BIC equals mclust’s. New condition class latents_unsupported_noise, raised also by the verbs that do not yet account for a noise component.

New controls

  • parameter_inference() and vcov() give standard errors for a one-step covariate model with categorical or mixed indicators (multilca(profile_covariates = ), multilpa(categorical = , profile_covariates = )). Each category’s probability is reported with a delta-method standard error and no Wald test, as for the covariate-free model.
  • parameter_inference(vcov_type = "opg") inverts the outer product of the group scores (the BHHH estimate). It is the estimator glca reports, and reproduces its standard errors; "observed" remains the default.
  • parameter_inference(boundary = "fix") holds categorical response probabilities that sit on min_probability at that bound and reports the other parameters conditionally on them. The default, "error", refuses as before, now with a message that names the alternative.
  • multilpa(), multilca() and lta() gain select_start. "converged" reports the best converged start whenever one converged, instead of an unconverged start that edges it out by a negligible likelihood.
  • r3step() gains by_group_class. TRUE gives every group class its own profile intercepts, with shared slopes and one latent group class per group, the two-level form of the one-step model. The pooled regression attenuates the slopes when group classes differ in their profile mix.

Changed defaults

  • r3step() now defaults to vcov_type = "robust", standard errors clustered on groups, matching three_step(). Pass vcov_type = "observed" for the previous behaviour.

Bug fixes

  • A covariate fit now stores min_probability, so inference recognises response probabilities on their bound and refuses with latents_boundary_fit instead of a numerically singular information.

latents 0.8.4

  • The website URL in DESCRIPTION ends in a slash, and the README gives the CRAN installation command.
  • The package check is about a third faster: vignettes use fewer random starts, the slowest examples use smaller data, and six exhaustive test files are skipped on CRAN (they run on GitHub Actions).

latents 0.8.3

  • The README opens with the package description, and every vignette and article names its authors.

latents 0.8.2

  • The package description is rewritten.

latents 0.8.1

latents 0.8.0

Package renamed

  • The package is renamed from multilpa to latents, a name that covers latent profile, latent class and latent transition models and the planned extensions. The model functions keep their names (multilpa(), multilca(), lta()), as do the result classes. Condition classes now use the latents_ prefix (for example latents_bad_argument, previously multilpa_bad_argument), multilpa_plot_types() is plot_views(), and the conditions catalogue is ?"latents-conditions". The vignettes are vignette("lpa", package = "latents"), "evaluation", "covariates", "lca" and "lta".

New features

  • multilca() fits a two-level latent class model: multilpa() with every indicator categorical, so the items are named once. Mixed models remain multilpa(categorical = ).