Learn R Programming

⚠️There's a newer version (2.0.3) of this package.Take me there.

lavaSearch2 (version 2.0.2)

Tools for Model Specification in the Latent Variable Framework

Description

Tools for model specification in the latent variable framework (add-on to the 'lava' package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.

Copy Link

Version

Install

install.packages('lavaSearch2')

Monthly Downloads

1,743

Version

2.0.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/bozenne/lavaSearch2

Maintainer

Brice Ozenne

Last Published

January 23rd, 2024

Functions in lavaSearch2 (2.0.2)

Add a New Link Between Two Variables in a LVM

Adjust the p.values Using the Quantiles of the Max Statistic

Form all Unique Combinations Between two Vectors

convFormulaCharacter

formula character conversion

Create Rownames for a Contrast Matrix

.dinformation2-internal

Compute the First Derivative of the Expected Information Matrix

Combine formula

autoplot.intDensTri

2D-display of the Domain Used to Compute the Integral

Extract Data From a Latent Variable Model

Extract one Step From the Sequential Procedure

Create Contrast matrix

calcType1postSelection

Compute the Type 1 Error After Selection [EXPERIMENTAL]

Residual Variance-Covariance Matrix With Small Sample Correction.

autoplot_calibrateType1

Graphical Display of the Bias or Type 1 Error

Effects Through Pathways With Small Sample Correction

Normalize var1 and var2

Integrate a Gaussian/Student Density over a Triangle

Find all New Links Between Variables

Test Linear Hypotheses With Small Sample Correction

General Linear Hypothesis Testing With Small Sample Correction

Confidence Intervals With Small Sample Correction

defineCategoricalLink

Identify Categorical Links in LVM

Extract the Coefficient by Type

Create a Mesh for the Integration

selectRegressor

Regressor of a Formula.

gaussian_weight

Estimate LVM With Weights

Degree of Freedom for the Chi-Square Test

hessian2-internal

Compute the Hessian Matrix From the Conditional Moments

Set a Link to a Value

Identify the Endogenous Variables

Display the i.i.d. Decomposition

Jackknife iid Decomposition from Model Object

Power of a Matrix

Extract the Links that Have Been Found by the modelsearch2.

Extract the Type of Each Coefficient

Satterthwaite Correction and Small Sample Correction

Extract the Model that Has Been Retains by the modelsearch2.

Tools for Model Specification in the Latent Variable Framework

Pre-computation for the Score

Data-driven Extension of a Latent Variable Model

Depreciated Method For Small Sample Correction

Symmetrize a Matrix

transformSummaryTable

Apply Transformation to Summary Table

Simulate Repeated Measurements over time

summary.calibrateType1

Display the Type 1 Error Rate

Response Variable of a Formula

information2-internal

Compute the Expected Information Matrix From the Conditional Moments

evalInParentEnv

Find Object in the Parent Environments

Hessian With Small Sample Correction.

Inverse the Information Matrix

Variance-Covariance With Small Sample Correction

Leverage With Small Sample Correction.

Outcome of Linear Hypothesis Testing

tryWithWarnings

Run an Expression and Catch Warnings and Errors

Expected Information With Small Sample Correction.

Influence Function With Small Sample Correction.

score2-internal

Compute the Corrected Score.

Score With Small Sample Correction

Convert Variable Names to Dummy Variables Names.

Compute Key Quantities of a Latent Variable Model

Find the Number of Steps Performed During the Sequential Testing

Effective Sample Size.

Residuals With Small Sample Correction.

summary.modelsearch2

summary Method for modelsearch2 Objects

Latent Variable Model Summary After Small Sample Correction

autplot-modelsearch2

Display the Value of a Coefficient across the Steps.

Simplify a lvm object

Model Coefficients With Small Sample Correction

Simulation Study Assessing Bias and Type 1 Error

Check that Validity of the Dataset