parameters

Model bootstrapping

Parameters bootstrapping

Centering (Grand-Mean Centering)

Check suitability of data for clustering

Check suitability of data for Factor Analysis (FA)

Kaiser, Meyer, Olkin (KMO) Measure of Sampling Adequacy (MSA) for Factor Analysis

Check if a distribution is unimodal or multimodal

Bartlett's Test of Sphericity

Confidence Intervals (CI)

Compute cluster analysis and return group indices

Compute a linear discriminant analysis on classified cluster groups

Compare model parameters of multiple models

Convert data to numeric

Conversion between EFA results and CFA structure

Partition data into a test and a training set

Degrees of Freedom (DoF)

Compute group-meaned and de-meaned variables

Describe a distribution

Print tables in different output formats

remove empty string from character

remove NULL elements from lists

help-functions

Safe transformation from factor/character to numeric

Safe transformation from factor/character to numeric

for models with zero-inflation component, return required component of model-summary

Find most common occurence

Flatten a list

Bartlett, Anderson and Lawley Procedures

Bentler and Yuan's Procedure

Cattell-Nelson-Gorsuch CNG Indices

Multiple Regression Procedure

Non Graphical Cattell's Scree Test

Standard Error Scree and Coefficient of Determination Procedures

Recode a variable so its lowest value is beginning with zero

Equivalence test

Factor Analysis (FA)

Sample data set

Order (first, second, ...) formatting

Format the name of the p-value adjustment methods

Parameter names formatting

Get Scores from Principal Component Analysis (PCA)

Model Parameters

Parameters from BayesFactor objects

Parameters from Mixture Models

Parameters from ANOVAs

Parameters from special models

Parameters from PCA/FA

Parameters from Generalized Additive (Mixed) Models

Parameters from (General) Linear Models

Parameters from Hypothesis Testing

Parameters from hypothesis tests

Parameters from Cluster Models (k-means, ...)

Parameters from CFA/SEM models

Parameters from Mixed Models

Parameters from multiply imputed repeated analyses

Parameters from multinomial or cumulative link models

Parameters from Structural Models (PCA, EFA, ...)

Parameters from Meta-Analysis

Parameters from Bayesian Models

Parameters from robust statistical objects in WRS2

Parameters from Zero-Inflated Models

Number of clusters to extract

Number of components/factors to retain in PCA/FA

p-values

p-values for Bayesian Models

p-values for Models with Special Components

p-values for Mixed Models

p-values for Marginal Effects Models

p-values for Models with Zero-Inflation

Between-within approximation for SEs, CIs and p-values

Kenward-Roger approximation for SEs, CIs and p-values

"m-l-1" approximation for SEs, CIs and p-values

Satterthwaite approximation for SEs, CIs and p-values

Wald-test approximation for CIs and p-values

Type of model parameters

Pool Model Parameters

Principal Component Analysis (PCA)

Print model parameters

Sample data set

Summary information from random effects

Dimensionality reduction (DR) / Features Reduction

Objects exported from other packages

Rescale design weights for multilevel analysis

Reshape loadings between wide/long formats

Automated selection of model parameters

Simulated draws from model coefficients

Simulate Model Parameters

Compute Skewness and (Excess) Kurtosis

Quantify the smoothness of a vector

Standard Errors

Robust estimation