Allows users to create and deploy the workflow with multiple functions
    in Function-as-a-Service (FaaS) cloud computing platforms.
    The 'FaaSr' package makes it simpler for R developers to use FaaS platforms by providing the following functionality:
    1) Parsing and validating a JSON-based payload compliant to 'FaaSr' schema supporting multiple FaaS platforms
    2) Invoking user functions written in R in a Docker container (derived from rocker), using a list generated from
       the parser as argument
    3) Downloading/uploading of files from/to S3 buckets using simple primitives
    4) Logging to files in S3 buckets
    5) Triggering downstream actions supporting multiple FaaS platforms
    6) Generating FaaS-specific API calls to simplify the registering of a user's workflow with a FaaS platform
    Supported FaaS platforms:
    Apache OpenWhisk <https://openwhisk.apache.org/>
    GitHub Actions <https://github.com/features/actions>
    Amazon Web Services (AWS) Lambda <https://aws.amazon.com/lambda/>
    Supported cloud data storage for persistent storage:
    Amazon Web Services (AWS) Simple Storage Service (S3) <https://aws.amazon.com/s3/>.

Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <DOI:10.1093/biomet/asy009> and  
    'Exact adaptive confidence intervals for linear regression coefficients'
    by Hoff and Yu <DOI:10.1214/18-EJS1517>.

Frequentist assisted by Bayes (FAB) p-values and confidence 
 interval construction. See 
 Hoff (2019) <arXiv:1907.12589> 
 "Smaller p-values via indirect information",
 Hoff and Yu (2019) <doi:10.1214/18-EJS1517> 
 "Exact adaptive confidence intervals for linear regression coefficients", and
 Yu and Hoff (2018) <doi:10.1093/biomet/asy009> 
 "Adaptive multigroup confidence intervals with constant coverage".

Implementation of the Factorized Binary Search (FaBiSearch) methodology for the estimation of the number and the location of multiple change points in the network (or clustering) structure of multivariate high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI) data. FaBiSearch uses non-negative matrix factorization (NMF), an unsupervised dimension reduction technique, and a new binary search algorithm to identify multiple change points.  It requires minimal assumptions. Lastly, we provide interactive, 3-dimensional, brain-specific network visualization capability in a flexible, stand-alone function. This function can be conveniently used with any node coordinate atlas, and nodes can be color coded according to community membership, if applicable. The output is an elegantly displayed network laid over a cortical surface, which can be rotated in the 3-dimensional space. The main routines of the package are detect.cps(), for multiple change point detection, est.net(), for estimating a network between stationary multivariate time series, net.3dplot(), for plotting the estimated functional connectivity networks, and opt.rank(), for finding the optimal rank in NMF for a given data set. The functions have been extensively tested on simulated multivariate high-dimensional time series data and fMRI data. For details on the FaBiSearch methodology, please see Ondrus et al. (2021) <arXiv:2103.06347>. For a more detailed explanation and applied examples of the fabisearch package, please see Ondrus and Cribben (2022), preprint.