Calculate replicate weights and summary statistics
Calculate Standard Error of Intraclass Correlation
Generate replicates of a dataset using Balanced Repeated Replication
Assignment of test items to blocks
Generation of random correlation matrix
Generation of covariance matrices
Assignment of item blocks to test booklets
Calculate ñ
Calculate variance within classes
Calculate variance between classes
Checks if provided parameters are ignored
Simulate item responses from an item response model
Calculate the total variance
Randomly generate a matrix of factor loadings
Check if an error condition is satisfied
Check class of n or N
Assignment of test booklets to test takers
lsasim: A package for simulating large scale assessment data
Print messages about clusters
PISA 2012 mathematics item - item block indicator matrix
Check if List is Valid
Generate an ANOVA table for LSASIM clusters
Generation of item parameters from uniform distributions
PISA 2012 mathematics item block - test booklet indicator matrix
Convert Vector to Expanded List
Generate cluster sample
Defines vector as range
Generate analytical covariance matrix
Attribute Labels in Hierarchical Structure
Customize Summary
Print the ANOVA table
Generate latent regression covariance matrix
Generation of random cumulative proportions
Generate n_X and n_W for clusters
Item parameter estimates for 2012 PISA mathematics assessment
Generation of ordinal and continuous variables
Recalculate final weights
Generate cluster samples with individual questionnaires
Generates cat_prop for questionnaire_gen
Dataset summary statistics
Trim sample
Sample from range
Generate cluster samples with lowest-level questionnaires
Transform regular vector into selection vector
Correlation matrix from the PISA 2012 background questionnaire
Prints welcome message on package load
validate_questionnaire_gen
Wrapper-functions for check_condition
questionnaire_gen_polychoric
Generation of ordinal and continuous variables
Draw Cluster Structure
Setup full YXW covariance matrix
Weight responses
Randomly generate the quantity of background variables
Intraclass correlation
Generate replicates of a dataset using Jackknife
Analytical point-biserial conversion
Label respondents
Marginal proportions from the PISA 2012 background questionnaire
Generation of ordinal and continuous variables
Pluralize words
Sampling variance of the mean for replications
Generate data from a Zero-truncated Poisson
Generation of item response data using a rotated block design
Whitelist message
Sample from population structure
Split variables in cat_prop
Summarizes clusters
Generate regression coefficients