Package index • idiographic

All functions

argument_coverage(): Argument-by-argument validation coverage
as.data.frame(<glasso_path_result>): Tidy a graphical-lasso path
as.data.frame(<glasso_result>): Tidy a graphical-lasso fit
as.data.frame(<idiographic_fit>) as.data.frame(<idiostats_fit>): Convert a consolidated fit to its prediction table
as_netobject(): Coerce to a netobject
as_netobject(<gvar_result>): Coerce a gvar_result to plottable netobjects
as_netobject(<net_gimme>): Plottable netobject(s) from a GIMME fit
as_netobject(<net_mlvar>): Plottable netobjects from an mlVAR fit
as_netobject(<var_bayes_result>): Coerce a var_bayes_result to plottable netobjects
best_model(): Best overall model row
clan(): Who is in the extreme groups
coefs(): Tidy coefficients from a fitted mlvar model
compare(): Compare idiographic fits
compare_idiographic(): Compare idiographic estimators on one dataset
contextual(): Compare the within-person and between-person effect of each predictor
correlate_persons(): Correlate variables within each person
describe_persons(): Describe each person's series
diagnostics(): Model diagnostics
edges(): Tidy edge table for any idiographic result
effects(<idiostats_effects>): Tidy treatment effects
equivalence(): Report method-equivalence evidence
equivalence_table(): Package-wide equivalence and validation ledger
esm_srl: Momentary self-regulated-learning experience-sampling data
estimate_stability(): Estimate edge stability by block resampling (experimental)
estimator_info(): Inspect a registered estimator
extract_edges(): Tidy edge table from a network object
find_subgroups(): Discover subgroups of people
fit_effects(): Estimate treatment effects and their heterogeneity
fit_gimme(): GIMME: Group Iterative Multiple Model Estimation
fit_glm(): Fit pooled, subgroup and person-specific generalized linear models
fit_graphical_var(): Graphical VAR Estimation
fit_graphical_var_each(): Fit a graphical VAR for every subject
fit_heterogeneity(): Study how something varies across people
fit_idiographic(): Fit an idiographic model through the unified interface
fit_lm(): Fit pooled, subgroup and person-specific linear models
fit_idiographic_ml() fit_individualized_ml() fit_ml(): Fit person-specific machine-learning models
fit_ml_panel(): Fit machine learning with the consolidated scoped result contract
fit_mlvar(): Build a Multilevel Vector Autoregression (mlVAR) network
fit_mlvar_bayes(): Build a Bayesian multilevel VAR network (Mplus DSEM-targeted)
fit_mlvar_mplus(): Build an Mplus-backed multilevel VAR network
fit_rolling(): Rolling-origin validation for ordered repeated measures
fit_rolling_graphical_var(): Estimate rolling-window graphical VAR networks
fit_rolling_var(): Estimate rolling-window ordinary VAR networks
fit_subgroups(): Fit subgroup-specific models
fit_usem(): Build a user-specified unified SEM network
fit_var(): Build an ordinary least-squares VAR network
fit_var_bayes(): Build a Bayesian VAR(1) network (unregularized, Mplus-targeted)
fit_var_each(): Fit an ordinary least-squares VAR for every subject
fit_within_between(): Fit a within-between (hybrid) model
get_estimator(): Get a registered estimator function
glasso_fit(): Fit a graphical lasso at a fixed penalty
glasso_kkt(): Certify a graphical-lasso solution from its optimality conditions
glasso_path(): Fit a graphical lasso over a path of penalties
groups(): Tidy subgroup assignments
heterogeneity(): Tidy heterogeneity results
idiographic idiographic-package: idiographic: Person-Specific Statistics and Dynamic Networks
importance(): Feature importance
individuals(): Focus an idiographic fit on all individual models
learners(): How well each learner detects heterogeneity
list_estimators(): Registered idiographic estimators
matrices(): Print model matrices for idiographic results
metrics(): Tidy model metrics
models(): List every model the package knows about
nodes(): Tidy per-node strength table for any idiographic result
overall(): Focus an idiographic fit on overall metric rows
people(): Focus an idiographic fit on selected people
person(): Focus an idiographic fit on one person
plot_components(): Plot within-person against between-person effects
plot_diagnostics(): Plot model diagnostics
plot_effects(): Plot sorted treatment-effect groups
plot_gimme(): Faithful GIMME network plot (the gimme-package convention, via cograph)
plot(<var_result>) plot(<gvar_result>) plot(<var_bayes_result>) plot(<net_mlvar>) plot(<net_usem>) plot(<net_gimme>) plot(<var_list>) plot(<gvar_list>) plot(<rolling_var_result>) plot(<rolling_gvar_result>) plot(<stability_result>): Plot an idiographic network result
plot_importance(): Plot feature importance
plot_metrics(): Plot model metrics
plot_predictions(): Plot held-out prediction trajectories
plot_subgroups(): Plot subgroups
plot_subjects(): Plot per-person performance
plot_tuning(): Plot tuning results
plot_variance(): Plot the within/between split of variance
pool_coefs(): Pool person-specific coefficients, separating real spread from noise
pooled(): Focus an idiographic fit on pooled models
predict(<idioml_result>): Predict from an idiographic ML result
predictions(): Tidy held-out predictions
preprocess(): Preprocess and audit idiographic time-series data
preprocess_panel(): Prepare repeated-measures data for idiographic modelling
print(<forecast_result>): Print method for forecast validation results
print(<glasso_path_result>): Print a graphical-lasso path
print(<glasso_result>): Print a graphical-lasso fit
print(<gvar_list>): Print a list of per-subject graphical VARs
print(<gvar_result>): Print Method for gvar_result
print(<idioml_result>): Print method for idiographic ML fits
print(<model_comparison>): Print method for model comparisons
print(<net_gimme>): Print Method for net_gimme
print(<net_mlvar>): Print method for net_mlvar
print(<net_mlvar_bayes>): Print method for net_mlvar_bayes
print(<net_usem>): Print method for uSEM fits
print(<preprocess_result>): Print method for preprocessing results
print(<rolling_gvar_result>): Print method for rolling graphical VAR results
print(<rolling_var_result>): Print method for rolling VAR results
print(<stability_result>): Print method for stability results
print(<var_bayes_result>): Print method for var_bayes_result
print(<var_list>): Print a list of per-subject ordinary VARs
print(<var_result>): Print method for ordinary VAR fits
register_estimator(): Register an idiographic estimator or workflow
remove_estimator(): Remove a registered estimator
shrink_coefs(): Shrink each person's coefficient towards the pooled effect
srl: Self-regulated learning intensive longitudinal data (Chapter 20)
subgroups(): Focus an idiographic fit on subgroup models
summary(<gvar_result>): Summary Method for gvar_result
summary(<idiographic_fit>) summary(<idiostats_fit>): Summarise a consolidated fit
summary(<idioml_result>): Summary method for idiographic ML fits
summary(<net_gimme>): Summary Method for net_gimme
summary(<net_mlvar>): Summary method for net_mlvar
summary(<net_usem>): Summary method for uSEM fits
summary(<preprocess_result>): Summary method for preprocessing results
summary(<var_bayes_result>): Summary method for var_bayes_result
summary(<var_result>): Summary method for ordinary VAR fits
test_subgroups(): Test whether subgroups exist at all
tuning(): Tuning results
validate_forecast(): Validate one-step forecasts from idiographic VAR models (experimental)
variance_components(): Split repeated-measures variance into within- and between-person parts