Nonlinear Nonparametric Statistics
Co-Lower Partial Moment (Lower Left Quadrant 4)
Co-Upper Partial Moment (Upper Right Quadrant 1)
Divergent-Lower Partial Moment (Lower Right Quadrant 3)
Divergent-Upper Partial Moment (Upper Left Quadrant 2)
Partial Derivative dy/d_[wrt]
Partial Derivative dy/dx
Lower Partial Moment RATIO
Lower Partial Moment
LPM VaR
NNS ANOVA
NNS ARMA Optimizer
NNS ARMA
NNS Boost
NNS Causation
NNS CDF
NNS Co-Partial Moments Higher Dimension Dependence
NNS Dependence
NNS Numerical Differentiation
NNS Distance
NNS FSD Test
NNS FSD Test uni-directional
NNS gravity
NNS Monte Carlo Sampling
NNS meboot
NNS mode
NNS moments
NNS Normalization
NNS Nowcast
NNS Partition Map
NNS: Nonlinear Nonparametric Statistics
NNS Regression
NNS rescale
NNS SD-based Clustering
NNS SD Efficient Set
NNS Seasonality Test
NNS SSD Test
NNS SSD Test uni-directional
NNS Stack
NNS Term Matrix
NNS TSD Test
NNS TSD Test uni-directional
NNS VAR
Partial Moment Matrix
Upper Partial Moment RATIO
Upper Partial Moment
UPM VaR
NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences. NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).