Latent Variable Models
Calculate confidence limits for parameters
Conformal prediction
Add non-linear constraints to latent variable model
Create contrast matrix
Generic method for extracting correlation coefficients of model object
Add covariance structure to Latent Variable Model
Split data into folds
Adds curly brackets to plot
Returns device-coordinates and plot-region
Calculate diagnostic tests for 2x2 table
Check d-separation criterion
Identify candidates of equivalent models
Estimate parameters and influence function.
Estimation of functional of parameters
Estimation of parameters in a Latent Variable Model (lvm)
Add an observed event time outcome to a latent variable model.
Create a Data Frame from All Combinations of Factors
fplot
Read Mplus output
Read SAS output
Add variable to (model) object
Backdoor criterion
Label elements of object
Define constant risk difference or relative risk association for binar...
Combine matrices to block diagonal structure
Calculate bootstrap estimates of a lvm object
Generic bootstrap method
Apply a Function to a Data Frame Split by Factors
Generic cancel method
Extract children or parent elements of object
Identify points on plot
Closed testing procedure
Generate a transparent RGB color
Add color-bar to plot
Report estimates across different models
Finds the unique commutation matrix
Statistical tests
Composite Likelihood for probit latent variable models
Add Confidence limits bar to plot
Extract model summaries and GOF statistics for model object
Extract graph
Finds elements in vector or column-names in data.frame/matrix
Extract i.i.d. decomposition (influence function) from model object
Extract i.i.d. decomposition from model object
Organize several image calls (for visualizing categorical data)
Fix mean parameters in 'lvm'-object
For internal use
Define intervention
Plot/estimate surface
Define labels of graph
Estimation and simulation of latent variable models
Set global options for lava
Initialize new latent variable model
Create random missing data
Two-stage (non-linear) measurement error
Missing value generator
Estimate mixture latent variable model.
Extract model
Model searching
Estimate probabilities in contingency table
Estimate mixture latent variable model
Convert to/from NA
Newton-Raphson method
Concatenation operator
Matching operator (x not in y) oposed to the %in%-operator (x in y)
Define variables as ordinal
Univariate cumulative link regression models
Generic method for finding indeces of model parameters
Calculate partial correlations
Extract pathways in model graph
Polychoric correlation
Dose response calculation for binomial regression models
Convert pdf to raster format
Plot method for 'estimate' objects
Plot path diagram
Plot method for simulation 'sim' objects
Plot regression lines
Prediction in structural equation models
Predict function for latent variable models
Generic print method
Define range constraints of parameters
Appending Surv objects
Add regression association to latent variable model
Create/extract 'reverse'-diagonal matrix or off-diagonal elements
Remove variables from (model) object.
Performs a rotation in the plane
Calculate simultaneous confidence limits by Scheffe's method
Monte Carlo simulation
Simulate model
Spaghetti plot
Stack estimating equations
Extract subset of latent variable model
Summary method for 'sim' objects
Time-dependent parameters
Converts strings to formula
Trace operator
Trim string of (leading/trailing/all) white spaces
Two-stage estimator (non-linear SEM)
Two-stage estimator
Cross-validated two-stage estimator
Extract variable names from latent variable model
vec operator
Wait for user input (keyboard or mouse)
Weighted K-means
Wrap vector
Regression model for binomial data with unkown group of immortals
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.