This is a major release. Its flagship addition is cpm_fit(), a native
reimplementation of Browne's (1992) circular stochastic process model for the
correlational structure of circumplex scales — filling the gap left by the
archived CircE package, the previous R implementation. Alongside it come four
other new analysis families: latent-variable SSM analysis with ssm_sem(),
repeated-measures (longitudinal) SSM analysis, fit_structure() for
exploratory circumplex-structure tests, axes_reliability() for the
reliability of the circumplex axes (Strack et al., 2013), and
ssm_ci_accuracy(), a diagnostic for whether an ssm_analyze() result's
confidence intervals can be trusted at your sample size and profile
(Zimmermann & Wright, 2017). The
plotting layer has been rebuilt on a real ggplot2 coordinate system.
The displacement-interpretability guardrail in print() and summary()
now uses a scale-free rule: a profile's displacement is certified as
interpretable only when the amplitude confidence interval's lower bound sits
at least 0.35 interval-widths above zero. This replaces the rule introduced
in 1.2.0, which certified whenever the lower bound rounded above zero at the
display precision — a threshold that moved with the print digits and meant
different things on different score metrics, and that (as the new
ssm_ci_accuracy() diagnostic makes visible) certified a genuinely zero
amplitude almost every time. The new rule holds false-certification near the
interval's one-sided error rate regardless of scale or display settings. As a
result, some near-zero-amplitude profiles that were previously certified are
now flagged uninterpretable. The threshold is calibrated for the default 95%
confidence interval.
Displacement and angle confidence-interval endpoints that land exactly on
the 0/360 pole are now reported as 360, never 0, matching how the package
labels that pole everywhere else (LM = 360): ssm_analyze() bootstrap
displacement CIs and cpm_fit() bootstrap angle CIs both use the shared
circular-quantile machinery that now applies this labeling. cpm_fit()'s
reported Angle column likewise labels the pole 360 — a reference scale
with a theory angle of 360 previously printed Angle = 0 with a degenerate
CI of [0, 0], and now prints 360 throughout. An exact-pole endpoint is a
measure-zero floating-point corner for real data, so numeric results are
otherwise unchanged.
The package now requires ggplot2 (>= 4.0.0), and ggforce is no longer a
dependency. The declared R requirement moves to R (>= 4.1) to match the floor
ggplot2 already imposes; no installation that worked before is affected.
ssm_score()'s extra arguments passed through ... must now be named
(e.g. prefix = "IIP_") and must be single strings; an unnamed or
non-scalar argument is now an error rather than being silently ignored
(previously it could yield unlabeled or garbled output columns). Rows whose
profile has undefined displacement now produce a single warning reporting how
many such rows there are, rather than one warning per row.
Count-valued arguments (e.g. boots, reps, ncpus, digits, and the
sample size n) across ssm_analyze(), ssm_ci_accuracy(), cpm_fit(),
cpm_simulate(), and ssm_sem() are now uniformly validated as a single
non-negative whole number. A few of these previously accepted a
length-greater-than-one vector without complaint; such input now raises a
clear error.
ssm_plot_circle() now warns and names any profile it cannot place on the
circle because its displacement is undefined (a flat or zero-amplitude
profile), instead of dropping it from the figure without notice.
New cpm_fit() function estimates Browne's (1992) circular stochastic
process model for the correlational structure of circumplex scales or items,
a native replacement for the archived CircE package. It accepts either raw
data or a correlation matrix, estimates item angles and communality indices
(with four model variants), and reports the usual covariance-structure fit
indices (chi-square, RMSEA with a 90% confidence interval, SRMR, CFI, TLI,
AIC, BIC). The returned circumplex_cpm object has print() and summary()
methods. On the raw-data path, confidence intervals are estimated by a
nonparametric bootstrap by default (resampling rows and refitting the model,
with percentile intervals; angle intervals use the package's circular
quantile machinery, so an interval straddling the 0/360 degree boundary is
reported wrapped, as with displacement intervals). Resamples that are
degenerate or fail the convergence criterion are excluded with a warning and
counted in the output. Only the bootstrap consumes R's random number stream:
call set.seed() immediately before cpm_fit() for reproducible intervals
(point estimates are deterministic). On the correlation-matrix path,
intervals are analytic (Wald) — there is no raw data to resample — and
summary() cautions when the sample size is small enough that these may
mis-cover. A scaling argument selects the covariance-scaling family:
"unit" (the default) fits the correlation structure, while "free" fits
Browne's covariance structure with p free variance scales — the
parameterization CIRCUM and CircE use — so cpm_fit() can reproduce their
published output exactly. Free scaling adds p parameters without changing
the degrees of freedom, and reports the fitted variance ratios in a
VarRatio column (without confidence intervals). With correlation input the
two families' model-test statistics are calibration-indistinguishable
(paired simulation at sample sizes 250–50,000), so the default remains the
recommended family for routine inference; use scaling = "free" when the
goal is reproducing published CIRCUM/CircE output. cpm_fit(scaling = "free") also starts its optimizer from the unit-scaling solution, so the
free family's fit statistic can never exceed the default family's on the same
input beyond numerical tolerance (the free family mathematically nests the
default).
The cpm_fit() estimator has been validated against the published
CIRCUM/CircE literature (Grassi, Luccio, & Di Blas, 2010, reanalyzing
Browne's 1992 vocational-interest example) and against independent
OpenMx and lavaan implementations of the same model (both now in Suggests
as test oracles only). CIRCUM and CircE fit Browne's covariance
parameterization with free variance scalings; cpm_fit(scaling = "free")
fits that same family and reproduces their published estimates, chi-square,
and fit indices to printed precision, while the default correlation-structure
fit differs from them slightly in finite samples (same degrees of freedom,
asymptotically equivalent); see the package's design notes for details. A
large seeded simulation study measured the coverage of both interval methods:
based on its results, summary() now also cautions about analytic intervals
at any sample size below 50,000 when the fitted solution is near a parameter
boundary or weakly identified (Heywood case, removed harmonic, very small
correlation-function weight, ill-conditioning, or competing near-tied
optima — the caution names which), the regime where they measurably
mis-covered. Percentile bootstrap intervals were confirmed as the better
default but are themselves conservative-liberal in spots (notably for
near-boundary correlation-function weights); improving them is planned
follow-up work.
New fit_structure() function evaluates whether a set of scales forms a
circumplex using the exploratory criteria of Acton & Revelle (2004). Four
criteria are computed from the first two unrotated principal-axis factors of
the scales' correlations — the Fisher Test of equal axes, the Gap Test of
equal spacing, and the Variance (VT2) and Rotation tests of interstitiality —
and a fifth, the RANDALL correspondence index (Hubert & Arabie, 1987; Tracey,
1997), tests the hypothesized circular order of the scales with a
randomization test that yields an exact p-value. The factor-analytic
statistics are classified against interpretive cutoffs that were re-derived by
simulation under Acton & Revelle's own generating model for eight (octant)
scales — their published cutoffs were calibrated on far more variables and do
not transfer — and that are keyed to the scoring, since these criteria work
best with a general factor removed. fit_structure() deviation-scores
(ipsatizes) by default for that reason, with a raw opt-out. Missing values
are handled by listwise deletion by default (a listwise argument, matching
ssm_analyze()), so all five tests share one complete-case correlation
matrix — the metric the cutoffs were calibrated on. The returned
circumplex_structure object has print(), summary(), and plot()
methods; interpretations are presented as the heuristic likelihood
classifications they are, never as significance tests.
New axes_reliability() function estimates the reliability (and standard
error of measurement) of the two circumplex axes with the item-level
restricted tau-equivalent CFA of Strack, Jacobs, and Grosse Holtforth (2013).
The model decomposes each item's variance into a general factor, the two
circumplex axes, scale specificity, and item specificity, and reads the axes'
reliability off the isolated axes-variance component with the Spearman-Brown
formula — a confirmatory, item-level complement to fit_structure()'s
exploratory scale-level criteria. The Nunnally-Bernstein axis reliability is
reported alongside for comparison (it overestimates when scale specificity is
large). Items are supplied through a circumplex_instrument or an explicit
angle-and-item map; missing data are handled by listwise deletion; a boundary
fit returns NA reliability rather than a clipped value; and the returned
circumplex_axes_reliability object has print() and summary() methods. A
bundled simulated dataset, simulated_items, is included for the examples.
New cpm_simulate() function draws standardized observations from a fitted
cpm_fit() model's implied correlation matrix, using the model's exact
positive-semidefinite factor representation. It returns a numeric matrix with
one column per scale (in fitted order, named), whose population correlation
matrix is the fitted Phat. Call set.seed() immediately before it for
reproducible draws.
ssm_sem() estimates the
Structural Summary Method profile of one or more external measures against
the latent circumplex content of the scales — the disattenuated analog of
the correlation-based ssm_analyze() — from a fixed-theoretical-angle
measurement model fitted with lavaan (now a runtime Suggests dependency for
this feature family; everything else works without it). Confidence
intervals for all parameters are built in-package by propagating draws of
the model's free parameters (multivariate-normal by default, or a full
lavaan bootstrap via ci_method = "boot") through the profile and SSM
transforms and the same circular-quantile machinery as ssm_analyze() —
never lavaan's delta-method or percentile intervals, which ignore the
angular branch cut. The default draws propagate lavaan's robust
(sandwich) covariance, which the package's coverage validation found
necessary to keep the intervals calibrated when the fixed-angle model
only approximates the data; the default estimator is MLR, so the global
fit indices print() reports are likewise the robust/scaled versions
(circumplex scale scores are typically skewed). Includes ssm_sem_syntax()
(an inspectable lavaan model-syntax generator that works without lavaan
installed) and ssm_sem_parameters() (estimate from a lavaan fit you have
modified or fitted yourself). Results are circumplex_ssm objects, so
ssm_table() and the ssm_plot_* functions work on them unchanged. With a
grouping variable, ssm_sem() fits the measurement model across groups and
gates a latent group contrast on measurement invariance: it tests a
configural-metric-scalar ladder using lavaan's own nested-model test (the
scaled difference test under robust estimators) at the invariance rung
(defaulting to each path's required level) and the invariance_alpha level,
and computes the disattenuated contrast only when the required rung is
retained. When invariance is rejected it reports an honest non-comparison —
the verdict plus each group's separate configural profile — rather than a
contrast that would confound structural difference with measurement
non-invariance. Supplying grouping without measures analyzes the latent
mean path (each group's model-implied latent mean profile). The
observed-score group contrast in ssm_analyze() remains the right tool when
invariance cannot be assumed; it answers its own, different question.New repeated-measures (longitudinal) SSM analyses: ssm_analyze() gains an
occasions argument taking a named list of column blocks, one per
occasion, each selecting the same circumplex scales measured at that
occasion (wide data, one row per person). Every occasion yields its own
profile row, occasions cross with grouping, and contrast = TRUE with
exactly two occasions (single group) estimates the paired within-person
contrast — second listed occasion minus first — through both engines: the
bootstrap resamples persons (preserving within-person dependence
nonparametrically) and the Monte Carlo engine draws the stacked occasion
mean vectors jointly. Cross-occasion column alignment is validated by stem
matching (a reordered occasion block errors instead of silently rotating
displacement). Occasions analyses are listwise-only across waves, with the
dropped-person count messaged and a selection caution documented. Results
from occasions analyses carry a new Occasion column that is present only
for such analyses — downstream code should test for the column by name.
Coverage of the paired contrasts was validated by simulation at nominal
rate across boundary cells (displacement changes near 0 and 180 degrees,
CIs straddling the 0/360 pole, small samples, three occasions); note that
paired contrasts are not unconditionally more efficient than
independent-groups designs (see the new Occasions section in
?ssm_analyze).
New ssm_analyze_long() provides a long-format (one row per person per
occasion) interface to the repeated-measures occasions analysis. It reshapes
the data to the wide layout ssm_analyze() expects and delegates to it, so
the estimation, paired within-person contrasts, and listwise missing-wave
handling are unchanged. Occasion order is taken from the factor levels (or
first appearance) of the occasion column and is never sorted
alphabetically, so a T10/T2 pair keeps its temporal order.
New per-person (intraindividual) SSM scoring: ssm_parameters_id() scores
each person's own circumplex profile through the closed-form SSM transform
and returns a per-person parameter table — one row per person, with an
id argument that first averages a person's rows (e.g., occasions of
intensive longitudinal data) within person before scoring. Degenerate
profiles keep their row with NA parameters (never a silent drop), and an
na_rate column exposes each person's share of missing scale cells. A
summary() method aggregates the table at the group level using circular
statistics for displacement (circular mean and mean resultant length,
never arithmetic means of angles), reporting how many undefined
displacements were excluded. Two documented caveats: the circular mean of
per-person displacements (equal weight per person) is a different quantity
from the displacement of the group mean profile (amplitude-weighted), and
by the triangle inequality the group profile's amplitude is at most the
mean per-person amplitude, strictly smaller when directions disperse.
New Bayesian draws adapter: ssm_draws() converts posterior draws from a
user-fitted Bayesian model (e.g., a brms cosine regression) into SSM
parameter draws and summarizes them with the package's circular-statistics
machinery — circular quantiles for displacement (credible intervals that
straddle 0/360 wrap instead of inverting), posterior medians for the
linear parameters (the amplitude posterior is right-skewed), and the
circular mean for displacement, with the marginal-coherence caveat
documented. Two draw shapes are accepted and never guessed: (e, x, y)
parameter draws (type = "parameters", required because a 3-column
matrix is ambiguous) and profile draws (one column per scale, with
angles). Draws with undefined displacement are excluded from the
displacement summaries only, with an honest warning that says "posterior
draws" and "credible interval". ssm_draws() objects also apply the
package's displacement-certification rule to the amplitude credible
interval: when the interval's lower bound sits under 0.35 interval-widths
above zero, printing notes that the displacement is not interpretable, and
the verdict is stored in $details$certified.
New growth-model support for repeated-measures SSM analysis, documented in a
new vignette ("Growth Models on SSM Parameters"): fit a joint mixed model
to the per-person Cartesian coordinates from ssm_parameters_id() (the
reference recipe uses glmmTMB, now in Suggests; fitting the coordinates with
separate univariate models silently zeroes their cross-covariance and
produces wrong displacement intervals), then convert fixed-effect draws to
amplitude/displacement trajectories with ssm_draws(). The recipe was
validated by simulation: pointwise displacement coverage is nominal in a
pole-crossing design, and the univariate shortcut demonstrably fails coverage
under correlated person effects.
New angle_unwrap() helper unwraps a temporally ordered sequence of
angles onto a continuous branch (350, 10, 30 becomes 350, 370, 390),
supporting the vignette's alternative unwrap-then-model recipe. Inputs
are wrapped to [0, 360) first; an exact 180-degree step ascends (the
package's half-turn convention); NA makes later waves branch-ambiguous
and so propagates onward.
New ssm_ci_accuracy() function assesses, by simulation, whether the
confidence intervals of an ssm_analyze() result would cover the true SSM
parameters at their nominal rate if the population looked like the fitted
estimates, at the observed sample size(s) — the CI-trustworthiness
diagnostic of Zimmermann & Wright (2017), generalized to the user's own
configuration (grouping, contrasts, measures, engine, resample count, and
interval level). The population's scale structure is characterized by a
cpm_fit() model (or, optionally, the observed correlations); each
simulated dataset replays the object's own interval procedure. Coverage is
reported per profile row, parameter, and amplitude condition — a ladder of
populations with the amplitude scaled toward zero, where percentile
amplitude intervals are theoretically weakest — along with one-sided miss
rates, interval widths, the certification rate of the printed
displacement-interpretability guardrail, and displacement coverage
conditional on certification. For a contrast row — a signed difference that
print.circumplex_ssm() never certification-gates — the displacement
verdict and printed coverage are reported unconditionally, matching that
profiles-only stance (its conditional coverage is retained in the object as
a descriptive). Coverage at the as-estimated condition is
classified against Bradley's (1978) liberal robustness band using 95%
Wilson score intervals, and print()/summary() translate the
classifications into a plain-language verdict, including a line reporting
how often the guardrail would certify displacement if the true amplitude
were zero (the scale-free rule holds this near the interval's one-sided
error rate, and a caution is raised only if it materially exceeds that).
summary() also annotates the realism of the simulated population
(structural-model convergence and fit, against conventional RMSEA/SRMR
benchmarks with citations) and, when an amplitude estimate is itself below
half its CI width, notes that the analysis already sits in the near-zero
regime and adds a ladder rung at the certification margin. A plot() method
draws coverage across the amplitude ladder with the Bradley band shaded.
Simulation replicates can be parallelized (parallel/ncpus) with
seed-identical results, and the caller's random-number state is restored
on exit. To support the diagnostic, ssm_analyze() now stores per-group
sufficient statistics (sizes, scale SDs, and correlation matrices) in its
output; objects created by earlier versions can be assessed by re-supplying
the original data via ssm_ci_accuracy(..., data = ), which is checked
for consistency against the stored profiles.
ssm_ci_accuracy() also assesses repeated-measures occasions analyses. Its
plug-in population is a multivariate normal with the observed stacked
cross-occasion covariance, so the within-person dependence across occasions
is carried into the simulation (rather than ignored); it reports CI
trustworthiness per occasion and for the paired within-person contrast. A
flat occasion is refused by name, a rank-deficient stacked covariance is
flagged (the fit-statistic pass rate becomes descriptive), and because the
occasions population is the observed covariance the structure/cpm
arguments are not accepted on that path.
ssm_analyze() gains a method argument offering a Monte Carlo alternative
to the bootstrap (method = "montecarlo"): SSM parameter replicates are
drawn from the asymptotic sampling distribution of the group mean vector or
measure-scale correlation vector (a multivariate normal with empirically
estimated covariance; correlations are drawn jointly across measures on the
Fisher z scale) and propagated through the SSM transformation. It produces
intervals closely matching the bootstrap on large samples while running in a
fraction of the time, but relies on asymptotic normality and requires
listwise-complete data, so the bootstrap remains the default and the
recommended choice for small samples. summary() reports which method
produced the intervals.
ssm_analyze() gains parallel and ncpus arguments (passed to
boot::boot()) to distribute the bootstrap computation across multiple CPU
cores. Because the resample indices are drawn in the main R process before
any work is distributed, results for a given set.seed() are identical
regardless of these settings, so parallelizing never changes your estimates
or confidence intervals.
The Monte Carlo interval engine (ssm_analyze(method = "montecarlo")) is
faster on correlation-based analyses: the influence-function covariance is
built in one vectorized pass and all profile rows are propagated through
the SSM transformation in a single compiled call. Results are unchanged
(byte-identical for a fixed seed).
ssm_score() is now vectorized internally (one compiled call instead of a
row-wise loop), making it much faster on large data sets. Results are
unchanged.
Circumplex figures are now built on a real ggplot2 coordinate system. The new
coord_circumplex() owns the amplitude-to-radius scaling and the
displacement-to-angle transform in one place, so a canvas and its data layers
can no longer disagree about the outer-ring amplitude. It adds a configurable
amplitude center (the rings relabel and the amplitudes remap together) and a
theme-responsive canvas: the rings, spokes, and labels drawn by
ggcircumplex() now restyle through + theme_*(). It always draws an
amplitude ring at amax, so every circumplex canvas closes at its rim and no
point is drawn past the last visible ring; that rim ring is unlabeled unless
amax is itself one of the axis breaks. A non-finite amax or center is
rejected with a message naming the argument. ggcircumplex(),
geom_ssm_point(), geom_ssm_arc(), and ssm_plot_circle() keep their
signatures and correct output. The per-layer amax argument (and
geom_ssm_arc()'s n) are no longer needed and are ignored with a one-time
note.
New ggcircumplex() function builds an empty circumplex plotting canvas
(amplitude rings, displacement spokes, and scale labels) as a ggplot2
object that you can add layers to with +. It accepts a set of scale
angles and labels, or a circumplex_instrument object to derive both
automatically. The package's own ssm_plot_circle() draws on the same canvas.
ggcircumplex() and scale_x_circumplex() label and place circumplex scales
at their exact angles, including non-integer angles (for example, the
22.5-degree spacing of a 16-scale instrument), instead of rounding them to
whole degrees.
New geom_ssm_point() and geom_ssm_arc() layers draw SSM profile points
and their confidence-region arcs directly in circumplex space on a
ggcircumplex() canvas, taking amplitude and displacement as aesthetics and
handling the polar transform (including wrap-around at the 0/360 degree
boundary) internally. These make it possible to build custom circumplex
figures by composing ggplot2 layers.
New scale_x_circumplex() provides an angle-labeled x-axis scale for linear
circumplex plots (such as the score-by-angle curve). It labels axis breaks
with their angle in degrees by default, or with custom labels or a
circumplex_instrument's scale abbreviations, using the same conventions as
ggcircumplex().
The circumplex ggplot2 layers are extensible and ergonomic. The
GeomSsmPoint, GeomSsmArc, and CoordCircumplex ggproto generators are
exported so downstream packages can subclass them. The amplitude (radial) axis
and its labels are drawn in the widest gap between the displacement spokes,
so they no longer overlap a spoke label; coord_circumplex() gains an
r_axis_angle argument to place it manually. The canvas theme is exported as
theme_circumplex(). geom_ssm_point() and geom_ssm_arc() follow the
ggplot2 na.rm convention: with na.rm = FALSE they warn (with the count)
before dropping profiles that cannot be placed, while the default
na.rm = TRUE drops them silently. ssm_plot_circle(repel = TRUE) now gives
a clear error when the suggested ggrepel package is not installed.
The new geom_ssm_path() layer draws a profile's movement across occasions as
a path on the circumplex canvas, so change in amplitude and displacement reads
as motion in circumplex space rather than only as separate parameter panels.
Each segment is curved along the circle by coord_circumplex(). Consecutive
occasions are joined the short way around the 0/360 boundary, so a step from
350 to 10 degrees is drawn as a 20 degree arc across the pole rather than a
340 degree sweep the long way round. Occasions are connected in data order,
with group separating one series from another and an optional order
aesthetic to sort within a series; an optional arrow marks the direction of
time. An occasion with no defined displacement (a flat or zero-amplitude
profile) breaks the path rather than being interpolated through, and the
segment after the gap is still drawn on the correct branch.
ssm_plot_circle() gains a path argument that adds this movement path to
its usual points and confidence wedges, for results from
ssm_analyze(occasions = ) and ssm_analyze_long(). Occasions are connected
in the order they were supplied, never alphabetically.
The new ssm_plot_trajectory() plots how each SSM parameter changes across
occasions, one panel per parameter with its confidence interval as a band, for
results from ssm_analyze(occasions = ) and ssm_analyze_long(). The
displacement panel is drawn on an unwrapped branch, so a profile whose
displacement crosses the 0/360 boundary is shown as one continuous path
instead of jumping a full turn, and each confidence bound is placed on its own
estimate's branch. Occasions appear in the order they were supplied, never
alphabetically. An occasion whose amplitude is too close to zero for its
displacement to be interpretable is marked with a hollow point, and a profile
with no defined displacement leaves a gap rather than a spurious segment.
ssm_plot_trajectory() also accepts a trajectory table: a data frame with
one row per time point, a numeric time column named by the new time
argument, and a_est/a_lci/a_uci and d_est/d_lci/d_uci columns
(optionally the e_*, x_*, and y_* triples and a logical certified
column). This is the shape a model-based workflow assembles by evaluating a
fitted growth model at each time point and passing the draws through
ssm_draws(), and it is plotted on a continuous time axis, so unequally
spaced time points are drawn at their actual spacing. Only the panels the
table can fill are drawn. The displacement unwrap, the interval placement, and
the hollow marking of uninterpretable time points are shared with the
occasions path; when no certified column is supplied, the figure makes no
interpretability claim rather than asserting one.
New plot() method for circumplex_cpm objects draws the estimated item
configuration on the ggcircumplex() canvas: each scale appears at its
estimated angle and at a radius given by its communality, with a wedge
spanning its angle and communality confidence intervals where these are
estimable (scales with an inestimable interval are drawn as a point only and
named).
The amplitude axis labels are now drawn over a translucent backdrop, so they stay readable where a data layer falls behind them. The amplitude axis is drawn on top of the plotted data, which kept the labels visible but not legible: a label crossing a dark marker, an arrowhead, or a dense scatter had too little contrast against it to read. The backdrop is deliberately translucent rather than opaque, so it restores contrast without hiding the data it covers.
New vignette, "Evaluating Circumplex Structure": how to test whether an
instrument fits a circumplex in your sample with cpm_fit() (reading and
benchmarking the fit indices, comparing the constrained model variants,
and the boundary-solution/chi-square cautions from the package's
validation simulations), and how to check whether SSM confidence
intervals can be trusted at your sample size and profile with
ssm_ci_accuracy(). Summarizes Zimmermann & Wright's (2017) simulation
findings as cited context (transcribed from the published article),
reproduces their Study 5 analyses on the bundled jz2017 data, and adds
guidance on when to trust each SSM parameter and on what ipsatizing
octant scores costs an SSM analysis. The diagnostic itself was validated
against the article: configured to transcribed Zimmermann & Wright
simulation conditions, it reproduces their published accuracy
classifications (validation scripts and results are recorded in the
package's development repository).
New vignette, "SEM-Based SSM Analysis," teaching the latent SSM: the disattenuated estimand and how it differs from the observed profile, why amplitude and displacement intervals are built in-package rather than by lavaan, the two group-difference estimands (observed vs. invariance-gated latent) side by side, and the model-conditional assumptions that make the latent parameters interpretable.
New precomputed vignette, "Bayesian SSM Analysis," derives the
cosine-regression mapping (pinning the atan2 argument order with an
executable known-direction check), walks a brms random-intercept example
whose posterior draws ship with the package, and exhibits the Rayleigh-shaped
prior that independent (x, y) priors induce on amplitude (brms is a new
optional Suggests dependency used only by that vignette's frozen
model-fitting chunk).
New vignette, "Advanced Circumplex Visualization," teaches the plotting API:
coord_circumplex() as the owner of the amplitude-to-radius mapping, the
configurable circle center and amplitude-axis placement, restyling the canvas
through theme_circumplex() and ordinary theme() calls, subclassing the
exported GeomSsmPoint/GeomSsmArc objects to build reusable layers, and
plotting a trajectory across occasions.
The reference index now groups the plotting API into "Complete Plots" and
"Building Blocks". The ssm_plot_* functions cross-link to each other, so
ssm_plot_trajectory() is reachable from its siblings' help pages, and the
composable layers (ggcircumplex(), coord_circumplex(), the geom_ssm_*()
layers, scale_x_circumplex(), and theme_circumplex()) likewise cross-link
to each other.
Clarified in the documentation of ssm_parameters(), ssm_score(), and
ssm_analyze() that the reported model fit is a bounded R-squared in
[0, 1] for equally spaced angles (more generally, for any angle set
satisfying first- and second-harmonic balance); for angle sets violating
that balance the closed-form estimator is not a least-squares fit and the
reported fit can fall below 0.
Fixed a bug where a bootstrap resample under pairwise deletion
(listwise = FALSE) could crash ssm_analyze() with mean(): object has no elements when the resample happened to draw only missing values for one
scale. Such a scale now yields an NA mean (matching the correlation path),
and the affected resample is excluded from the confidence intervals as a
degenerate profile, consistent with the existing degeneracy handling.
Fixed a bug where the displacement of a group contrast between two exactly
opposed profiles (a half-turn apart) was reported as -180 degrees instead
of +180, inconsistent with the documented (-180, 180] convention for
contrasts. Such a contrast is now reported as +180.
instruments() now derives its listing from the bundled instrument data
rather than a hardcoded table, so it always reflects the instruments
actually shipped. As part of this, the listed name for the IIP-SC now
reads "Inventory of Interpersonal Problems Short Circumplex" (matching its
stored metadata).ssm_plot_circle(), ssm_plot_curve(),
ssm_plot_contrast()) now warn when given an unrecognized argument (e.g., a
misspelled parameter name) instead of silently ignoring it.ssm_analyze(),
ssm_score(), ipsatize(), score(), norm_standardize(), and
self_standardize() previously errored when given a matrix despite
advertising matrix support; they now coerce it to a data frame internally.ssm_score() now accepts numeric column indexes for scales (e.g.,
scales = 1:8), consistent with its documentation and with ssm_analyze();
it previously required character names.print() or summary()) now adds a note under
any profile whose model fit is inadequate (R-squared < .70; interpret only
elevation) or whose amplitude confidence interval includes zero (the
displacement is not interpretable). The notes apply to profiles only, not to
contrast rows.norm_standardize() now matches each scale to its normative data by angular
position rather than exact numeric equality, so 0 and 360 degrees are treated
as the same angle (previously passing 0 for a scale stored at 360 failed with
a cryptic error). An angle with no matching normative row, or with more than
one, now produces an informative error naming the available angles.NA displacement
and fit with a warning (previously an arbitrary angle and -Inf); a profile
with real variance but zero amplitude returns NA displacement and a fit
of 0. Bootstrap resamples that produce degenerate profiles (e.g., a
resampled measure with zero variance) no longer crash ssm_analyze(); they
are excluded from the confidence intervals with a warning reporting the
count. Genuinely small amplitudes are unaffected — the degeneracy test
operates at machine-noise scale only.NA) value in the grouping variable of
ssm_analyze() crashed with a cryptic error under pairwise deletion
(listwise = FALSE). Such observations are now dropped before analysis with
a message reporting how many were removed, in both deletion modes; if no
observations remain, a clear error is given.is_null_or_char() dropped its n argument). ssm_analyze() now
errors if measures_labels does not match the number of measures (or is
given without measures), ssm_plot_circle()/ssm_plot_curve() now error
if angle_labels does not match the number of angles (previously mismatched
labels could be silently recycled onto the wrong scales), and
ssm_table()/html_render() now require caption to be a single string.ssm_score() silently ignored its angles argument and
always used octants(): custom angle sets of the same length produced
incorrect results without warning, and angle sets of a different length
(e.g., poles() with four scales) errored. Results from ssm_score() with
the default angles = octants() are unaffected. (found in 2026-07 audit)Improve handling of radian distributions crossing the 0/2pi boundary
Add unit tests regarding the above cases
Optimize pairwise correlation C++ code
Fix bug with angular median calculation retaining rejected candidates
Update RcppArmadillo dependency
Fix some deprecated ggplot args
self_standardize() function for standardizing variables using sample means and SDsFix some typos in documentation
Change plot tests to accommodate changes to ggplot2
Nearly all code rewritten/refactored to streamline and reduce dependencies.
Removed support for non-standard evaluation
The contrast argument to ssm_analyze() is now TRUE or FALSE instead of "none", "model", or "test". Model contrasts were removed and TRUE yields test contrasts.
Many arguments renamed (e.g., .data to data, .ssm_object to ssm_object, xy to drop_xy)
Removed ssm_plot() function in favor of ssm_plot_circle(), ssm_plot_curve(), and ssm_plot_contrast().
Renamed standardize() function to norm_standardize()
Added ssm_plot_curve()
Added CAIS and IEI instrument data
Added profile scores, results, and plotting to models with contrasts
Added PANO() function for conveniently creating scale names
All internal and external data are now data frames instead of tibbles
Rewrote all vignettes to use the updated functions, arguments, etc.
Harmonized the results and scores fields in the output of ssm_analyze()
Added many unit tests, increasing the package to 100% code coverage
Added many assertions to check for invalid input arguments
Harmonized the tidying function arguments (e.g., prefix, suffix, append)
Added print methods for degree and radian classes
Replace internal non-standard evaluation with .data references
Minor visual improvements to print and summary methods for ssm_objects
Fix a bug when comparing R versions
Update {vdiffr} tests
Update GitHub Actions
Fixed a bug related to NaN values and dplyr::na_if()
Updated package website using new version of {pkgdown}
Fix testing error on Solaris systems
Update package description paragraph
Add cpp11 plugin for Rcpp
Exclude devel folder from linguist statistics
Add angle_labels argument to ssm_plot() to allow users to customize the angle labels around a circular plot
Add palette argument to ssm_plot() to allow users to customize the color palette (from {RColorBrewer}) of a circular plot
Replaced the font_size argument to ssm_plot() with the legend_font_size and scale_font_size arguments to allow users to customize the font size of different elements of a circular plot
Update ggsave() documentation for future compatibility
Update {Rcpp} code for future compatibility
Added a black border to the points in a circular plot to greater distinguish them visually
Change CI notation from [] to () to play nice with pandoc
Update to {testthat} 3E and add ssm_plot() tests using {vdiffr}
Recompile vignettes with new version of {roxygen2}
Replace TravisCI with GitHub Actions
Update dependency versions and require R >= 3.4.0
Fix issues related to how R 4.0.0 handles S3 methods
Modernize ssm_plot() function to use new tidyr syntax
Update travis CI configuration to be more explicit
Adjust the test of quantile.radian() to account for changes to %% starting in R 3.6.1 Patched
Add the name of the package to the S3 class names (e.g., circumplex_radian instead of radian) to minimize the risk of overlapping classes between packages
Add some supplementary files to the R build ignore list to avoid notes during CRAN check
Add APA-style citations to instrument documentation in addition to DOI links.
Add "Instruments" menu to package website for viewing documentation pages.
Adjust the test of quantile.radian() to account for changes to %% starting in R 4.0.0
iitc provides instrument information for the Inventory of Influence
Tactics Circumplex.Fix CRAN warnings by setting LazyData: true.
Fix CRAN note by replacing relative URLs with absolute URLs.
Nonstandard evaluation is now handled using {{}} notation.
Updated the formatting on this NEWS changelog to match tidyverse style.
Avoid a bug with dplyr 0.8.1 and S3 methods on Linux systems.
Update the web address for Johannes in the README document.
New ssm_parameters() calculates SSM parameters (without
confidence intervals) from a vector of scores.
New ssm_score() calculates SSM parameters by row.
Added support for older versions of R (3.3.x).
Updated the "Introduction to SSM" vignette's figures.
Replaced use of dplyr::funs() as this function is being deprecated.
Fixed a bug in the normative data for ipipipc that prevented standardization.
Fixed a bug caused by changes in how random numbers are generated in R 3.6.x.
Fixed several broken links by running package through new version of usethis.
Fixed warnings related to documentation inherited from other packages.
iis32 now has normative data.
Added open-access (i.e., full item text) to the iis32 and iis64.
iis32 item ordering and scoring now match the author's version.
iis32 response anchors now range from 1 to 6 and match norms.
Changed use of tibble functions to avoid problems when new version releases.
Removed dependency on MASS package (until it is used by exported functions).
Added functions and documentation for numerous circumplex instruments.
Added functions for ipsatizing and scoring item-level data.
Added function for standardizing scale-level data using normative data.
Changed OpenMP flags in Makevars to fix a compile problem on Debian machines.
Fixed a bug related to calculating angular medians in the presence of NAs.
Changed the default to plot profiles with low fit (but with dashed borders).
Import and export functions from rlang tidy evaluation.
Added unit testing of various functions to increase code coverage.
Redesigned package website to be more attractive and clear.
Updated the SSM vignette to use the standardize() function.
ssm_plot() now uses dashed borders to indicate that a profile has low prototypicality/fit.Fixed bug that prevented compilation on Solaris systems.
Fixed bug that prevented CRAN checks on old R versions.
Improved the formatting of vignette source code.