MultiFractal Detrended Fluctuation Analysis (MFDFA)
Applies the MultiFractal Detrended Fluctuation Analysis (MFDFA) to time series. The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information.

Bayesian Estimation of the Additive Main Effects and Multiplicative Interaction Model (bayesammi)
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).

Quantitative Exploration of Elastic Net Families for Generalized Linear Models (eNetXplorer)
Provides a quantitative toolkit to explore elastic net families and to uncover correlates contributing to prediction under a cross-validation framework. Fits linear, binomial (logistic) and multinomial models. Candia J and Tsang JS (2018), (application note under review).

Missing not at Random Imputation Models for Multiple Imputation by Chained Equation (miceMNAR)
Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the ‘mice’ package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman’s model also named sample selection model as described in Galimard et al (2016) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model.

Coefficients of Interrater Reliability – Generalized for Randomly Incomplete Datasets (irrNA)
Provides coefficients of interrater reliability, that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and the Coefficient of Concordance according to Kendall & Babington Smith (1939) <doi:10.1214/aoms/1177732186>.