multid 0.5.0

Added option to obtain scaled estimates in ml_dadas. Scaling is done for both difference score components and the difference scores, based on random intercept SDs and random slope SD, respectively, in a reduced model without the predictor and the interaction between predictor and moderator
Added option to test for random effect covariation with likelihood ratio test in ml_dadas from a reduced model without the predictor and the interaction between predictor and moderator
Added option to include covariates in sem_dadas
Added variance test with sem in sem_dadas
Added variance test via parametric bootstrap in ml_dadas
Added cvv_manual -function for calculation of coefficients of variance variation from manually inputted sample sizes and variances of multiple variables

multid 0.4.0

Replaced two-sided tests in sem_dadas for absolute parameters with one-sided tests
Added three variants of coefficient of variance variation in cvv -function (CVV=coefficient of variance variation, SVH=standardized variance heterogeneity, and VR=variance ration between the largest and the smallest variance)
Added vpc_at -function for calculation of intercept variances and variance partition coefficients (VPCs) at selected values of level-1 predictors in two-level models

Added sem_dadas and ml_dadas functions for predicting algebraic difference scores in structural equation (sem_dadas) and multilevel model (ml_dadas). DADAS acronym follows from the joint hypothesis test of Difference between Absolute Differences and Absolute Sums between (regression coefficients on difference score components).

Fixed bug: renaming output in D_regularized_fold functions
Fixed joining data frames by fold.var in D_regularized_fold functions
Added statistical inference to d_pooled_sd
Added probabilities of correct classification option to D_regularized_out and D_regularized_fold_out
Added area under the receiver operating characteristics to D_regularized_out and D_regularized_fold_out
Added probability classification tables to D_regularized_out and D_regularized_fold_out
Added more examples to README file