Data Validation Infrastructure (validate)
Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity.
Continuous Time Structural Equation Modelling (ctsem)
An easily accessible continuous (and discrete) time dynamic modelling package for panel and time series data, reliant upon the OpenMx. package (http://openmx.psyc.virginia.edu) for computation. Most dynamic modelling approaches to longitudinal data rely on the assumption that time intervals between observations are consistent. When this assumption is adhered to, the data gathering process is necessarily limited to a specific schedule, and when broken, the resulting parameter estimates may be biased and reduced in power. Continuous time models are conceptually similar to vector autoregressive models (thus also the latent change models popularised in a structural equation modelling context), however by explicitly including the length of time between observations, continuous time models are freed from the assumption that measurement intervals are consistent. This allows: data to be gathered irregularly; the elimination of noise and bias due to varying measurement intervals; parsimonious structures for complex dynamics. The application of such a model in this SEM framework allows full-information maximum-likelihood estimates for both N = 1 and N > 1 cases, multiple measured indicators per latent process, and the flexibility to incorporate additional elements, including individual heterogeneity in the latent process and manifest intercepts, and time dependent and independent exogenous covariates. Furthermore, due to the SEM implementation we are able to estimate a random effects model where the impact of time dependent and time independent predictors can be assessed simultaneously, but without the classic problems of random effects models assuming no covariance between unit level effects and predictors.
Conover-Iman Test of Multiple Comparisons Using Rank Sums (conover.test)
Computes the Conover-Iman test (1979) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. conover.test makes k(k-1)/2 multiple pairwise comparisons based on Conover-Iman t-test-statistic of the rank differences. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Conover-Iman test may be understood as a test for median difference. conover.test accounts for tied ranks. The Conover-Iman test is strictly valid if and only if the corresponding Kruskal-Wallis null hypothesis is rejected.