Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error.  Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

A unified interface for simplifying cloud storage interactions, 
    including uploading, downloading, reading, and writing files, with functions
    for both 'Google Drive' (<https://www.google.com/drive/>) and 'Amazon S3' 
    (<https://aws.amazon.com/s3/>).

Interface to the Google Cloud Machine Learning Platform
  <https://cloud.google.com/ml-engine>, which provides cloud tools for training machine
  learning models.

The 'CloudOS' client library for R makes it easy to interact with 
    CloudOS in the R environment for analysis.

Access Cloudstor via their WebDAV API. This package can read, write, and navigate Cloudstor from R.

Provides means of plots for comparing utilization data of compute systems.

Computes the uniform rate of profit, the vector of price of 
    production and the vector of labor values; and also compute measures of deviation 
    between relative prices of production and relative values.  
    <https://scholarworks.umass.edu/econ_workingpaper/347/>. You provide the  
    input-output data and 'clptheory' does the calculations for you.

A new methodology for linear regression with both curve response 
    and curve regressors, which is described in Cho, Goude, Brossat and Yao 
    (2013) <doi:10.1080/01621459.2012.722900> and (2015) 
    <doi:10.1007/978-3-319-18732-7_3>. The key idea behind this methodology is 
    dimension reduction based on a singular value decomposition in a Hilbert 
    space, which reduces the curve regression problem to several scalar linear 
    regression problems.