Fitting recurrent events survival models for
    left-censored data with multiple imputation of the number of previous episodes.
    See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.

Estimates random effect latent measurement models, wherein the loadings, residual variances, intercepts, latent means, and latent variances all vary across groups. The random effect variances of the measurement parameters are then modeled using a hierarchical inclusion model, wherein the inclusion of the variances (i.e., whether it is effectively zero or non-zero) is informed by similar parameters (of the same type, or of the same item). This additional hierarchical structure allows the evidence in favor of partial invariance to accumulate more quickly, and yields more certain decisions about measurement invariance. Martin, Williams, and Rast (2020) <doi:10.31234/osf.io/qbdjt>.

Test for overall association between microbiome composition data 
  and phenotypes via phylogenetic kernels. The phenotype can be univariate 
  continuous or binary (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), 
  survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), 
  multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and 
  structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). 
  The package can also use robust regression (unpublished work) and 
  integrated quantile regression (Wang et al. (2021) <doi:10.1093/bioinformatics/btab668>). 
  In each case, the microbiome community effect is modeled nonparametrically 
  through a kernel function, which can incorporate phylogenetic tree information.

A complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.