Package index • parameters

Comprehensive Model Parameters
`compare_parameters()` `compare_models()`	Compare model parameters of multiple models
`dominance_analysis()`	Dominance Analysis
`model_parameters()` `parameters()`	Model Parameters
`pool_parameters()`	Pool Model Parameters
`random_parameters()`	Summary information from random effects
`format(<parameters_model>)` `print(<parameters_model>)` `summary(<parameters_model>)` `print_html(<parameters_model>)` `print_md(<parameters_model>)`	Print model parameters
`sort_parameters()`	Sort parameters by coefficient values
`standardize_parameters()` `standardize_posteriors()`	Parameters standardization
`standardize_info()`	Get Standardization Information
Documentation of Specific Class Objects
`model_parameters(<aov>)`	Parameters from ANOVAs
`model_parameters(<befa>)`	Parameters from Bayesian Exploratory Factor Analysis
`model_parameters(<default>)`	Parameters from (General) Linear Models
`model_parameters(<zcpglm>)`	Parameters from Zero-Inflated Models
`model_parameters(<cgam>)`	Parameters from Generalized Additive (Mixed) Models
`model_parameters(<mlm>)`	Parameters from multinomial or cumulative link models
`model_parameters(<glmmTMB>)`	Parameters from Mixed Models
`model_parameters(<hclust>)`	Parameters from Cluster Models (k-means, ...)
`model_parameters(<mira>)`	Parameters from multiply imputed repeated analyses
`model_parameters(<lavaan>)` `model_parameters(<principal>)`	Parameters from PCA, FA, CFA, SEM
`model_parameters(<data.frame>)` `model_parameters(<brmsfit>)`	Parameters from Bayesian Models
`model_parameters(<BFBayesFactor>)`	Parameters from BayesFactor objects
`model_parameters(<rma>)`	Parameters from Meta-Analysis
`model_parameters(<htest>)` `model_parameters(<coeftest>)`	Parameters from hypothesis tests
`model_parameters(<glht>)`	Parameters from Hypothesis Testing
`model_parameters(<glimML>)`	Parameters from special models
`model_parameters(<t1way>)`	Parameters from robust statistical objects in `WRS2`
`model_parameters(<compare.loo>)`	Bayesian Model Comparison
Standard Errors, Confidence Intervals, Degrees of Freedom and p-values
`standard_error()`	Standard Errors
`ci(<default>)`	Confidence Intervals (CI)
`p_value()`	p-values
`degrees_of_freedom()` `dof()`	Degrees of Freedom (DoF)
Approximation Methods
`ci_kenward()` `dof_kenward()` `p_value_kenward()` `se_kenward()`	Kenward-Roger approximation for SEs, CIs and p-values
`ci_satterthwaite()` `dof_satterthwaite()` `p_value_satterthwaite()` `se_satterthwaite()`	Satterthwaite approximation for SEs, CIs and p-values
`ci_betwithin()` `dof_betwithin()` `p_value_betwithin()`	Between-within approximation for SEs, CIs and p-values
`ci_ml1()` `dof_ml1()` `p_value_ml1()`	"m-l-1" approximation for SEs, CIs and p-values
Effect Existence and Significance
`equivalence_test(<lm>)`	Equivalence test
`p_calibrate()`	Calculate calibrated p-values.
`p_direction(<lm>)`	Probability of Direction (pd)
`p_function()` `consonance_function()` `confidence_curve()` `format(<parameters_p_function>)` `print(<parameters_p_function>)` `print_html(<parameters_p_function>)`	p-value or consonance function
`p_significance(<lm>)`	Practical Significance (ps)
Parameter Sampling
`bootstrap_model()`	Model bootstrapping
`bootstrap_parameters()`	Parameters bootstrapping
`simulate_model()`	Simulated draws from model coefficients
`simulate_parameters()`	Simulate Model Parameters
Feature Reduction
`reduce_parameters()` `reduce_data()`	Dimensionality reduction (DR) / Features Reduction
`select_parameters()`	Automated selection of model parameters
Data Reduction
Cluster Analysis
`cluster_analysis()`	Cluster Analysis
`cluster_centers()`	Find the cluster centers in your data
`cluster_discrimination()`	Compute a linear discriminant analysis on classified cluster groups
`cluster_meta()`	Metaclustering
`cluster_performance()`	Performance of clustering models
`n_clusters()` `n_clusters_elbow()` `n_clusters_gap()` `n_clusters_silhouette()` `n_clusters_dbscan()` `n_clusters_hclust()`	Find number of clusters in your data
`predict(<parameters_clusters>)`	Predict method for parameters_clusters objects
Factors and Principal Components
`convert_efa_to_cfa()` `efa_to_cfa()`	Conversion between EFA results and CFA structure
`factor_analysis()` `principal_components()` `rotated_data()` `print_html(<parameters_efa>)` `predict(<parameters_efa>)` `print(<parameters_efa>)` `sort(<parameters_efa>)` `closest_component()`	Principal Component Analysis (PCA) and Factor Analysis (FA)
`factor_scores()`	Extract factor scores from Factor Analysis (EFA) or Omega
`get_scores()`	Get Scores from Principal Component or Factor Analysis (PCA/FA)
`n_factors()` `n_components()`	Number of components/factors to retain in PCA/FA
`reduce_parameters()` `reduce_data()`	Dimensionality reduction (DR) / Features Reduction
`reshape_loadings()`	Reshape loadings between wide/long formats
Table and Value Formatting
`display(<parameters_model>)`	Print tables in different output formats
`format_order()`	Order (first, second, ...) formatting
`format_parameters()`	Parameter names formatting
`format_p_adjust()`	Format the name of the p-value adjustment methods
`format_df_adjust()`	Format the name of the degrees-of-freedom adjustment methods
`format(<compare_parameters>)` `print(<compare_parameters>)` `print_html(<compare_parameters>)` `print_md(<compare_parameters>)`	Print comparisons of model parameters
`parameters_type()`	Type of model parameters
`reexports` `equivalence_test` `ci` `n_parameters` `p_direction` `p_significance` `standardize_names` `supported_models` `print_html` `print_md` `display` `describe_distribution` `demean` `rescale_weights` `visualisation_recipe` `kurtosis` `skewness`	Objects exported from other packages
Functions exported from other packages
`reexports` `equivalence_test` `ci` `n_parameters` `p_direction` `p_significance` `standardize_names` `supported_models` `print_html` `print_md` `display` `describe_distribution` `demean` `rescale_weights` `visualisation_recipe` `kurtosis` `skewness`	Objects exported from other packages
Global options
`parameters-options`	Global options from the parameters package
Example Data Sets
`qol_cancer`	Sample data set
`fish`	Sample data set

Reference

Comprehensive Model Parameters

Documentation of Specific Class Objects

Standard Errors, Confidence Intervals, Degrees of Freedom and p-values

Approximation Methods

Effect Existence and Significance

Parameter Sampling

Feature Reduction

Data Reduction

Cluster Analysis

Factors and Principal Components

Table and Value Formatting

Functions exported from other packages

Global options

Example Data Sets