Nonlinear Nonparametric Statistics
Partial Moment Matrix
Upper Partial Moment RATIO
Upper Partial Moment
UPM VaR
LPM VaR
Co-Lower Partial Moment (Lower Left Quadrant 4)
Co-Upper Partial Moment (Upper Right Quadrant 1)
NNS gravity
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
NNS rescale
NNS ANOVA
NNS ARMA Optimizer
NNS ARMA
NNS Boost
NNS Causation
NNS CDF
NNS SD Efficient Set
NNS Co-Partial Moments Higher Dimension Dependence
NNS Dependence
NNS Numerical Differentiation
NNS Distance
NNS FSD Test
NNS FSD Test uni-directional
NNS Monte Carlo Sampling
NNS meboot
NNS mode
NNS moments
NNS Normalization
NNS Nowcast
NNS Partition Map
NNS: Nonlinear Nonparametric Statistics
NNS Regression
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
Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: 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).