The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

multid 1.0.0

• 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• ml_dadas and sem_dadas deprecated (superceded by ddsc_ml and ddsc_sem)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

multid 0.9.0

• Renamed variance_test output in ddsc_sem
• Added ddsc_ml -function for deconstructing difference score correlation with multi-level modeling

multid 0.8.0

• 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

multid 0.7.1

• 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

multid 0.7.0

• 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

multid 0.6.0

• 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

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

multid 0.3.0

• 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).

multid 0.2.0

• 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

multid 0.1.0

• First submission of multid package

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.