Explore Probability Distributions for Bivariate Temporal Granularities (gravitas)
Provides tools for systematically exploring large quantities of temporal data across different temporal granularities (deconstructions of time) by visualizing probability distributions. ‘gravitas’ computes circular, aperiodic, single-order-up or multiple-order-up granularities and advises on which combinations of granularities to explore and through which distribution plots.

Bifactor Indices Calculator (BifactorIndicesCalculator)
The calculator computes bifactor indices such as explained common variance (ECV), hierarchical Omega (OmegaH), percentage of uncontaminated correlations (PUC), item explained common variance (I-ECV), and more. This package is an R version of the ‘Excel’ based ‘Bifactor Indices Calculator’ (Dueber, 2017) <doi:10.13023/edp.tool.01> with added convenience features for directly utilizing output from several programs that can fit confirmatory factor analysis or item response models.

Ridge-Type Penalized Estimation of a Potpourri of Models (porridge)
The name of the package is derived from the French, ‘pour’ ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2019) or ii) from non-replicated data by means of the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi.org/10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018, <doi.org/10.1002/bimj.201700102>).

Simulated Maximum Likelihood Estimation of Mixed Logit Models for Large Datasets (mixl)
Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2019) <doi:10.3929/ethz-b-000334289>.

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