Continuous glucose monitoring (CGM) systems provide real-time, 
  dynamic glucose information by tracking interstitial glucose values 
  throughout the day. Glycemic variability, also known as glucose variability, 
  is an established risk factor for hypoglycemia (Kovatchev) and 
  has been shown to be a risk factor in diabetes complications. 
  Over 20 metrics of glycemic variability have been identified. 
  Here, we provide functions to calculate glucose summary metrics, 
  glucose variability metrics (as defined in clinical publications), 
  and visualizations to visualize trends in CGM data.
  Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract>
  American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc>
  Kovatchev B (2019) <doi:10.1177/1932296819826111>
  Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3>
  Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017>
  Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.

Find multiple solutions of a nonlinear least squares problem.  Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gauss–Newton method. Optimization and Engineering, 1-31.  Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67.

Fit composite Gaussian process (CGP) models as described in Ba and Joseph (2012) "Composite Gaussian Process Models for Emulating Expensive Functions", Annals of Applied Statistics. The CGP model is capable of approximating complex surfaces that are not second-order stationary.  Important functions in this package are CGP, print.CGP, summary.CGP, predict.CGP and plotCGP.