Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.

Providing the kubernetes-like class 'ManagedCloudProvider' as a child class of 
  the 'CloudProvider' class in the 'DockerParallel' package. The class is able to
  manage the cloud instance made by the non-kubernetes cloud service.
  For creating a provider for the non-kubernetes cloud service, the developer
  needs to define a reference class inherited from 'ManagedCloudProvider' and
  define the method for the generics runDockerWorkerContainers(),
  getDockerWorkerStatus() and killDockerWorkerContainers(). For more information, please see
  the vignette in this package and <https://CRAN.R-project.org/package=DockerParallel>.

This will allow easier management of a CRAN-style repository on
    local networks (i.e. not on CRAN). This might be necessary where hosted
    packages contain intellectual property owned by a corporation.

High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.

Several functions can be used to analyze neuroimaging data using multivariate methods based on the 'msma' package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained.

Provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal.

Estimates membership for the Mandelbrot set.

Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.