Score simulated PID-5 data | Rank Each Person's Highest Facets | Scale Reliability | Validity Scales for the PID-5 | Normative Scores | Profile Plots | The PID5BF+M | The PID-5 Informant Form | The PID-5 Forensic Faceted Brief Form | The PID-5 Child Forms | Collecting Responses Online with hitop-form

The three engines at a glance | Setup | PCA: components from total variance | EFA: factors from common variance | How close are EFA and PCA loadings? | ESEM: EFA with full model diagnostics | Loading standard errors and confidence intervals | Per-level fit: what it tells you (and what it does not) | The key distinction | Reporting fit with tidy() and autoplot() | Should you care about fit in a bass-ackwards workflow? | How much do the edges differ? | Choosing an engine | Orthogonal or oblique rotation | Varimax and oblimin on the same data | How to decide | Missing data | FIML for continuous PCA/EFA | Which option to use? | Correlation-matrix input | Constraints | suggest_k() with a correlation matrix | Performance with many items (ESEM) | References

Start the server and load the model | Three routes | Request options | A follow-up question | A whole conversation | The raw reply | Errors in a script | Clean up

Install LM Studio | Start the server | Find a model | Download the model | Put the model in memory | Chat with the model | Send several prompts in one call | Clean up | Without the desktop app

Hide the progress messages | Start the server and load the model | The texts | A summary of each text | Labels and ratings as columns | A score from token probabilities | When an input fails | Similarity between texts | All the results | The loaded models | Clean up

Install the command-line tool | Start the daemon | Start the server | Use an API token | Reach a server on another computer | Run a script with the daemon | Stop the daemon

The problem: which solution is right? | Data | Step 1: How many factors? suggest_k() | Step 2: Fit the hierarchy ackwards() | Step 3: Summarize the result | High-level summary | Step 4: Visualize the hierarchy autoplot() | Adjusting the diagram | Step 5: Dig into the factors tidy() | Reading each factor with top_items() | Factor loadings (tidy) | Between-level edges | Variance explained | Step 6: Score observations augment() | Using scores for downstream analysis | Scoring new data (train/test) | Summary: the basic toolkit | Next steps | References

Why Pearson underestimates for ordinal items | The polychoric solution | Automatic detection | Seeing the difference | Correlation matrices | Loadings | Between-level edges | WLSMV for ESEM with ordinal items | Practical guidance: when to use polychoric | References

The problem: depth is where bass-ackwards analyses go wrong | Three verbs, three different questions | Setup | Step 1: Screen the plausible depth range with suggest_k() | Step 2: Gate the depth on replicability with comparability() | Step 3: Fit the hierarchy to the gated depth | Step 4: Find what perpetuates without differentiating, with prune() | How sure are we about these edges? boot_edges() | Step 5: Interpret with the built-in guardrails | Step 6: Validate downstream, out of sample | Common mistakes this workflow prevents | Summary: the workflow in six calls | References

The limitation of adjacent-only edges | Setup | Computing every between-level correlation with pairs = "all" | Reading the skip-level edge table | Reading the strongest edge with care | Pruning: identifying redundant factors | Chains of near-identical factors with prune(x, "redundant") | The pruned-factor diagram | Inspecting structural similarity with prune(x, "artifact") | The near-redundant band | Structural artifact signals | Tuning the thresholds | Practical interpretation | References

Basic Scoring | Appending | Items as Strings | Scale Prefixes | Subscales | Simple Standard Errors (deprecated) | Scale Reliability | Confidence Intervals | What the reference group is | Scoring Only Some Scales | Renaming Item Columns | Method 1: Legacy "Original" Pool Names | Method 2: Matching via Literal Item Text | Labelling Columns

1. Overview | 2. Why a Bayesian SSM? | 3. The cosine model as a linear regression | 4. Fitting the model with brms | 5. From posterior draws to SSM summaries | 6. The induced prior on amplitude | Wrap-up

1. Overview | 2. The question growth modeling answers | 3. From repeated measures to a long table | 4. One joint model, not three separate ones | 5. From fixed effects to $(a(t), d(t))$ with intervals | 6. Certification: when $d(t)$ intervals are not interpretable | 7. A caution about REML intervals at small samples | 8. The unwrap alternative: angle_unwrap() | 9. Caveats and upgrades | 10. The same model in nlme and brms | Wrap-up | References

1. Overview | 2. Two questions about group differences | 3. Invariance-gated latent contrasts | 4. When to trust it: limitations | 5. Relation to the literature | Wrap-up | References

1. Overview | 2. Why a latent SSM? | 3. The measurement model | 4. Estimating a latent profile | 5. Where the confidence intervals come from | 6. What the parameters mean now | Wrap-up | References

1. Overview | 2. The circular canvas | 3. The coordinate system | Moving the center | Moving the amplitude axis | 4. Placing SSM results in the circle | 5. Restyling the canvas | 6. Composing custom layers | 7. The latent circumplex from a CPM fit | Estimated angles as ticks on the rim | Measure points on the same rim | One measure with its amplitude and displacement | The fitted correlation function | A joint confidence ellipse from posterior draws | 8. Trajectories across occasions | The same change as movement on the circle | 9. The angle axis for linear plots | 10. Relationship to the built-in plots | Wrap-up | References

1. Overview | 2. What is axis reliability? | 3. A worked example | 4. Reading the components | 5. Starting from a published correlation matrix | Wrap-up | References

1. Overview | 2. When a fit sits at a boundary | Wrap-up

1. Overview | 2. Two questions to ask before interpreting an SSM analysis | 3. Does the instrument fit a circumplex? | Fitting the circular process model | Reading the fit indices | Comparing model variants | Wrap-up | References

Score simulated PID-5-BF data | Scale Reliability | Normative Scores | Profile Plots

Score simulated PID-5-SF data | Score real PID-5-SF data | Simple Standard Errors (deprecated) | Scale Reliability | Normative Scores | Profile Plots

1. Overview | 2. The correction for the correlation metric | 3. Missing data | 4. Boundary fits | 5. Blockwise instruments | 6. The standard error of measurement | Wrap-up | References

1. Overview | 2. What Zimmermann & Wright (2017) found | 3. Simulating coverage for your own analysis | The full report | 4. When to trust SSM parameters | Wrap-up | References

1. Overview | 2. Generalizing the basic analyses | Generalizing to multiple means | Generalizing to multiple measures | Generalizing to multiple groups and multiple measures | 3. Contrast analyses | Contrasts between groups' means | Contrasts between measures in a group | Contrasts between groups' correlations | 4. Taxonomy of analysis types | 5. Working with SSM tables | Basic customizations of tables | 6. Working with SSM figures | Exporting figures as files | Wrap-up | References

1. Overview | 2. Does the instrument have circumplex structure at all? | The five tests | Where the cutoffs come from | Reading the classifications | How this complements the CPM fit | 3. Ipsatization and what it costs | Wrap-up | References

k is a decision, not an output | The five criteria | PA-PC: parallel analysis on the PC basis | PA-FA: parallel analysis on the FA basis | MAP: minimum average partial | VSS-1 and VSS-2: very simple structure | CD: comparison data | Summary table | Running suggest_k() | The diagnostic plot | Matching the criterion to your engine | The arguments | criteria: which criteria to compute | cor: correlation basis | n_iter: Monte Carlo iterations | seed: reproducibility | k_max: ceiling for the search | When the criteria agree, and when they do not | A worked recommendation for the BFI | References

Setup | Encoding sign and magnitude | sign_by: how direction is shown | magnitude_by: how |r| is shown | Filtering edges | cut_show: minimum |r| to display | Edge colors | color_pos / color_neg: custom direction colors | Monochrome mode | mono = TRUE: a black-and-white convenience wrapper | Correlation labels | show_r / r_digits: annotate edges with r values | Node labels | node_labels: rename individual factors | node_width / node_height: size individual boxes to fit a label | label_template(): generate the scaffold | Item content | show_items: list the items under the deepest-level factors | Structural simplifications | primary_only = TRUE: show only primary-parent edges | order: arrange the deepest level by hand | drop_pruned + show_secondary: the pruned view and its hidden correlations | Level labels | show_level_labels / level_label_size | Arrowheads | show_arrows = FALSE: plain line ends | Edge width | edge_linewidth: uniform vs. |r|-scaled width | Layout orientation | direction = "horizontal": left-to-right layout | Legend | legend = FALSE: suppress all guides | Worked example: publication-ready figure | Saving plots | Diagnostic scree / criteria plot: autoplot.suggest_k() | References

The data | Fitting the 10-level hierarchy | Skip-level structure | The redundancy chase | A chain, worked | Why the direct criterion matters here | The pruned-factor diagram | Fidelity | References

1. Overview | 2. Background and Motivation | Circumplex models, scales, and data | The Structural Summary Method | Understanding the SSM parameters | 3. Example data: jz2017 | 4. Mean-based SSM Analysis | Conducting SSM for a group's mean scores | Visualizing the results with a table and figure | 5. Correlation-based SSM Analysis | Conducting SSM for a group's correlations with an external measure | Wrap-up | References

1. Overview | 2. Loading and Examining Instrument Objects | Previewing the available instruments | Loading a specific instrument | Examining an instrument in-depth | 3. Instrument-related Tidying Functions | Ipsatizing item-level data | Scoring item-level data | Standardizing scale-level data | Wrap-up

Reading a factor with top_items() | Cross-loadings are signal | Showing item wording instead of codes | The sign convention: negative does not mean "low" | Naming in a hierarchy | Borrowing names for the upper levels | Applying your names | Making the names stick with set_factor_labels() | The Forbes letter convention | Starting from a blank slate | References

Basic Scoring | Appending | Items as Strings | Scale Prefixes | Simple Standard Errors (deprecated) | Confidence Intervals | What the reference group is | Scale Reliability | Labelling Columns