Provides a new method for identification of clusters of genomic
 regions within chromosomes. Primarily, it is used for calling clusters of 
 cis-regulatory elements (COREs). 'CREAM' uses genome-wide maps of genomic regions
 in the tissue or cell type of interest, such as those generated from chromatin-based 
 assays including DNaseI, ATAC or ChIP-Seq. 'CREAM' considers proximity of the elements 
 within chromosomes of a given sample to identify COREs in the following steps:
 1) It identifies window size or the maximum allowed distance between the elements 
 within each CORE, 2) It identifies number of elements which should be clustered 
 as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which 
 does not pass the threshold considered in the approach.

Create Pairwise Comparison Matrices for use in the Analytic Hierarchy Process. The Pairwise Comparison Matrix created will be a logical matrix, which unlike a random comparison matrix, is similar to what a rational decision maker would create on the basis of a preference vector for the alternatives considered.

Setup and retrieve HTTPS and SSH credentials for use with 'git' and 
    other services. For HTTPS remotes the package interfaces the 'git-credential' 
    utility which 'git' uses to store HTTP usernames and passwords. For SSH 
    remotes we provide convenient functions to find or generate appropriate SSH 
    keys. The package both helps the user to setup a local git installation, and
    also provides a back-end for git/ssh client libraries to authenticate with 
    existing user credentials.

A tiny package to generate CRediT author statements (<https://credit.niso.org/>). 
    It provides three functions: create a template, read it back and generate the CRediT author statement in a text file.

Provides a highly efficient R tool suite for Credit Modeling, Analysis and Visualization.Contains infrastructure functionalities such as data exploration and preparation, missing values treatment, outliers treatment, variable derivation, variable selection, dimensionality reduction, grid search for hyper parameters, data mining and visualization, model evaluation, strategy analysis etc. This package is designed to make the development of binary classification models (machine learning based models as well as credit scorecard) simpler and faster. The references including: 1 Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS; 2 Bezdek, James C.FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences (0098-3004),<DOI:10.1016/0098-3004(84)90020-7>.