Changes in version 1.0.2.9000 - Development version - When model is supplied to ddsc_ml, level-2 dataset and related descriptives are calculated with model weights if supplied Changes in version 1.0.2 (2025-09-23) - Data download in multivariate_sex_differences -vignette now fails gracefully - Included an exemplary dataset (example_big5) for multivariate_sex_differences -vignette Changes in version 1.0.1 (2025-09-04) - Added coef_text_size to plot_ddsc - Added x_scale and y_scale for plot_ddsc - Fixed problem in checking for multiple observation data in ml_ddsc - Defined sign difference in quantile correlation coefficient (qcc) resulting as rho_tau = zero, not NaN as previously - Updated description and roxygenNote version to DESCRIPTION - Fixed several typos and minor problems (e.g, location of deprecated_functions.R) - Fixed a problem related to "no visible binding for global variable" in D_regularized_out dplyr-pipes Changes in version 1.0.0 (2024-02-15) - Added a possibility to run ddsc_ml with just two observations per upper-level unit - Added a possibility to obtain bootstrap estimates and percentile confidence intervals for non-scaled parameter estimates in ddsc_ml results-table - Added confidence intervals for ddsc_ml results table - Added a possibility to bootstrap in ddsc_sem - ml_dadas and sem_dadas deprecated (superceded by ddsc_ml and ddsc_sem) - Added plot_ddsc function for directly plotting ddsc_sem results - Removed ml_dadas and sem_dadas from README examples. Replaced with ddsc_ml and ddsc_sem Changes in version 0.9.0 (2024-01-16) - Renamed variance_test output in ddsc_sem - Added ddsc_ml -function for deconstructing difference score correlation with multi-level modeling Changes in version 0.8.0 (2023-05-17) - Fixed a typo in D_regularized manual - Added difference between two dependent correlations -function (diff_two_dep_cors) which enables simultaneous estimation and testing of Cohen's q under variable dependency - Added possibility to use manually constructed regularization and estimation datasets, supplied as a list of two dataframes to "data"-argument in D_regularized - Improved the output of diff_two_dep_cors and included an argument for missing data ("ML") - Improved clarity in multivariate sex difference vignette - Added value_correlation -function for testing and quantifying how ipsatizing values influences associations with other variables - Added ddsc_sem -function for deconstructing difference score correlation with structural equation modeling Changes in version 0.7.1 (2022-12-21) - Added na.rm to qcc bootstrap summary over tau-values - Added main and interaction effects, and comparison of their absolute magnitudes to ml_dadas and sem_dadas outputs - Added moderator/intercept difference estimates for dadas-functions - Added abs_coef_diff_test in sem_dadas and ml_dadas to enable tests for slope difference that is not against null but a different numeric value - Added an estimate of scaled difference of slopes for ml_dadas and a derived estimate of component correlation - Fixed URLs and output in README Changes in version 0.7.0 (2022-08-18) - Switched from sample to dplyr::sample_n for bootstrap example in the multivariate sex difference vignette - Minor changes to style and text in the multivariate sex difference vignette - Additional descriptive statistics to reliability_dms output - Allow vpc_at for models with no intercept-slope covariation (conditional level-2 variances are same for all requested level-1 values) - Added function qcc for quantile correlation coefficient - Updated README Changes in version 0.6.0 (2022-06-08) - Bug fixed in D_regularized_out - Addend.data argument added to all D_regularized -functions. In _fold -functions, test-partition of the data is appended, else the entire data frame is added. - Include ICC2 (group-mean reliability) for vpc_at. Enables calculation of sub-group mean-level reliabilities, in case the "at" is a group - Include reliability_dms that calculates difference score reliability coefficient for data that is difference score between two mean values across some upper-level units (e.g., sex differences across countries) - Vignette on estimation of multivariate sex differences with multid added Changes in version 0.5.0 (2022-05-02) - 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 Changes in version 0.4.0 (2022-02-22) - 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 Changes in version 0.3.0 (2022-01-10) - 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). Changes in version 0.2.0 (2021-10-22) - 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 Changes in version 0.1.0 (2021-09-20) - First submission of multid package