# Getting Started with NNS: Overview

#### Fred Viole

    # Prereqs (uncomment if needed):
    # install.packages("NNS")

    library(NNS)

# Orientation

Goal. A complete, hands‑on curriculum for Nonlinear Nonparametric Statistics (NNS) using partial moments. Each section blends narrative intuition, precise math, and executable code.

Structure. 1. Foundations — partial moments & variance decomposition 2. Descriptive & distributional tools 3. Dependence & nonlinear association 4. Normalization & Rescaling 5. Hypothesis testing, ANOVA & Stochastic Superiority 6. Regression, boosting, stacking & causality 7. Time series & forecasting 8. Simulation (max‑entropy) & Monte Carlo 9. Portfolio & stochastic dominance

Notation. For a random variable \(X\) and threshold/target \(t\), the population \(n\)‑th partial moments are defined as:

\[ \operatorname{LPM}(n,t,X) = \int_{-\infty}^{t} (t-x)^{n} \, dF_X(x), \qquad \operatorname{UPM}(n,t,X) = \int_{t}^{\infty} (x-t)^{n} \, dF_X(x). \]

The empirical estimators replace \(F_X\) with the empirical CDF \(\hat F_n\) (or, equivalently, use indicator functions):

\[ \widehat{\operatorname{LPM}}_n(t;X) = \frac{1}{n} \sum_{i=1}^n (t-x_i)^n \, \mathbf{1}_{\{x_i \le t\}}, \qquad \widehat{\operatorname{UPM}}_n(t;X) = \frac{1}{n} \sum_{i=1}^n (x_i-t)^n \, \mathbf{1}_{\{x_i > t\}}. \]

These correspond to integrals over the measurable subsets \(\{X \le t\}\) and \(\{X > t\}\) in a \(\sigma\)‑algebra; the empirical sums are discrete analogues of Lebesgue integrals.

# 1. Foundations — Partial Moments & Variance Decomposition

## 1.1 Why partial moments

- Classical variance treats upside and downside symmetrically. Partial moments separate them, allowing asymmetric risk/reward analysis around a chosen target \(t\) (often the mean or a benchmark).
- At \(t=\mu_X\): \[ \operatorname{Var}(X) = \operatorname{UPM}(2,\mu_X,X) + \operatorname{LPM}(2,\mu_X,X)\quad\text{(exact empirical identity)}. \] This is not the same as splitting conditional variances around a threshold; partial moments use a global reference, preserving the between‑group contribution.

## 1.2 Core functions and headers

- `LPM(degree, target, variable)`
- `UPM(degree, target, variable)`

## 1.3 Code: variance decomposition & CDF

    set.seed(42)

    # Normal sample
    y <- rnorm(3000)
    mu <- mean(y)
    L2 <- LPM(2, mu, y); U2 <- UPM(2, mu, y)
    cat(sprintf("LPM2 + UPM2 = %.6f vs var(y)=%.6f\n", (L2+U2)*(length(y) / (length(y) - 1)), var(y)))

    ## LPM2 + UPM2 = 1.011889 vs var(y)=1.011889

    # Empirical CDF via LPM.ratio(0, t, x)
    for (t in c(-1,0,1)) {
      cdf_lpm <- LPM.ratio(0, t, y)
      cat(sprintf("CDF at t=%+.1f : LPM.ratio=%.4f | empirical=%.4f\n", t, cdf_lpm, mean(y<=t)))
    }

    ## CDF at t=-1.0 : LPM.ratio=0.1633 | empirical=0.1633
    ## CDF at t=+0.0 : LPM.ratio=0.5043 | empirical=0.5043
    ## CDF at t=+1.0 : LPM.ratio=0.8480 | empirical=0.8480

    # Asymmetry on a skewed distribution
    z <- rexp(3000)-1; mu_z <- mean(z)
    cat(sprintf("Skewed z: LPM2=%.4f, UPM2=%.4f (expect imbalance)\n", LPM(2,mu_z,z), UPM(2,mu_z,z)))

    ## Skewed z: LPM2=0.2780, UPM2=0.7682 (expect imbalance)

Interpretation. The equality `LPM2 + UPM2 == var(x)` (Bessel adjustment used) holds because deviations are measured against the global mean. `LPM.ratio(0, t, x)` constructs an empirical CDF directly from partial‑moment counts.

# 2. Descriptive & Distributional Tools

## 2.1 Higher moments from partial moments

Define asymmetric analogues of skewness/kurtosis using \(\operatorname{UPM}_3\), \(\operatorname{LPM}_3\) (and degree 4), yielding robust tail diagnostics without parametric assumptions.

Header.

- `NNS.moments(x)`

    M <- NNS.moments(y)
    M

    ## $mean
    ## [1] -0.0114498
    ##
    ## $variance
    ## [1] 1.011552
    ##
    ## $skewness
    ## [1] -0.007412142
    ##
    ## $kurtosis
    ## [1] 0.06723772

## 2.2 Mode estimation (no bin‑or‑bandwidth angst)

Header.

- `NNS.mode(x)`

    set.seed(23)
    multimodal <- c(rnorm(1500,-2,.5), rnorm(1500,2,.5))
    NNS.mode(multimodal,multi = TRUE)

    ## [1] -2.049405  1.987674

## 2.3 CDF tables via LPM ratios

Headers.

- `LPM.ratio(degree = 0, target, variable)` (empirical CDF when `degree=0`)
- `UPM.ratio(degree = 0, target, variable)`
- `LPM.VaR(p, degree, variable)` (quantiles via partial‑moment CDFs)
- `UPM.VaR(p, degree, variable)`

    qgrid <- LPM.VaR(seq(0.05,0.95,.1),0,z) # equivalent to quantile(z,probs = seq(0.05,0.95,by=0.1))
    CDF_tbl <- data.frame(threshold = as.numeric(qgrid), CDF = LPM.ratio(0,qgrid,z))
    CDF_tbl

    ##      threshold  CDF
    ## 1  -0.94052127 0.05
    ## 2  -0.83748109 0.15
    ## 3  -0.71317882 0.25
    ## 4  -0.57443327 0.35
    ## 5  -0.41017671 0.45
    ## 6  -0.20424962 0.55
    ## 7   0.06850182 0.65
    ## 8   0.41462712 0.75
    ## 9   0.94307172 0.85
    ## 10  2.09633977 0.95

# 3. Dependence & Nonlinear Association

## 3.1 Why move beyond Pearson \(r\)

Pearson captures linear monotone relationships. Many structures (U‑shapes, saturation, asymmetric tails) produce near‑zero \(r\) despite strong dependence. Partial‑moment dependence metrics respond to such structure.

Headers.

- `Co.LPM(degree_lpm, x, y, target_x, target_y, degree_y)` / `Co.UPM(...)` (co‑partial moments)
- `PM.matrix(LPM_degree, UPM_degree, target=NULL, variable, pop_adj=TRUE)`
- `NNS.dep(x, y)` (scalar dependence coefficient)
- `NNS.copula(X, target=NULL, continuous=TRUE, plot=FALSE, independence.overlay=FALSE)`

## 3.2 Code: nonlinear dependence

    set.seed(1)
    x <- runif(2000,-1,1)
    y <- x^2 + rnorm(2000, sd=.05)
    cat(sprintf("Pearson r = %.4f\n", cor(x,y)))

    ## Pearson r = 0.0006

    cat(sprintf("NNS.dep  = %.4f\n", NNS.dep(x,y)$Dependence))

    ## NNS.dep  = 0.7097

    X <- data.frame(a=x, b=y, c=x*y + rnorm(2000, sd=.05))
    pm <- PM.matrix(1, 1, target = "means", variable=X, pop_adj=TRUE)
    pm

    ## $cupm
    ##            a          b          c
    ## a 0.17384174 0.05668152 0.10450858
    ## b 0.05668152 0.05566363 0.04414923
    ## c 0.10450858 0.04414923 0.07529373
    ##
    ## $dupm
    ##              a          b            c
    ## a 0.0000000000 0.05675501 0.0005598221
    ## b 0.0143108307 0.00000000 0.0036839026
    ## c 0.0004239566 0.04430691 0.0000000000
    ##
    ## $dlpm
    ##              a           b            c
    ## a 0.0000000000 0.014310831 0.0004239566
    ## b 0.0567550147 0.000000000 0.0443069142
    ## c 0.0005598221 0.003683903 0.0000000000
    ##
    ## $clpm
    ##            a           b           c
    ## a 0.16803827 0.014485430 0.102709867
    ## b 0.01448543 0.037120650 0.003051617
    ## c 0.10270987 0.003051617 0.074865823
    ##
    ## $cov.matrix
    ##              a             b            c
    ## a 0.3418800141  0.0001011068  0.206234664
    ## b 0.0001011068  0.0927842833 -0.000789973
    ## c 0.2062346637 -0.0007899730  0.150159552

    cop <- NNS.copula(X, continuous=TRUE, plot=FALSE)
    cop

    ## [1] 0.5692785

## 3.3 Code: copula

    # Data
    set.seed(123); x = rnorm(100); y = rnorm(100); z = expand.grid(x, y)

    # Plot
    rgl::plot3d(z[,1], z[,2], Co.LPM(0, z[,1], z[,2], z[,1], z[,2]), col = "red")

    # Uniform values
    u_x = LPM.ratio(0, x, x); u_y = LPM.ratio(0, y, y); z = expand.grid(u_x, u_y)

    # Plot
    rgl::plot3d(z[,1], z[,2], Co.LPM(0, z[,1], z[,2], z[,1], z[,2]), col = "blue")

Interpretation. `NNS.dep` remains high for curved relationships; `PM.matrix` collects co‑partial moments across variables; `NNS.copula` summarizes higher‑dimensional dependence using partial‑moment ratios. Copulas are returned and evaluated via `Co.LPM` functions.

# 4. Normalization and Rescaling

NNS provides two main tools for scaling data while preserving rank structure and distributional shape. Both operate via deterministic affine transformations.

## 4.1 Normalization

`NNS.norm()` rescales variables to a common magnitude while preserving distributional structure. The method can be linear (all variables forced to have the same mean) or nonlinear (using dependence weights to produce a more nuanced scaling). In the nonlinear case, the degree of association between variables influences the final normalized values.

Header.

- `NNS.norm(x, linear=TRUE, chart.type = NULL)`

    A <- rnorm(100, mean = 0, sd = 1)
    B <- rnorm(100, mean = 0, sd = 5)
    C <- rnorm(100, mean = 10, sd = 1)
    D <- rnorm(100, mean = 10, sd = 10)

    X <- data.frame(A, B, C, D)

    # Linear scaling
    lin_norm <- NNS.norm(X, linear = TRUE, chart.type=NULL, location=NULL)

Interpretation. `NNS.norm()` brings variables to a common scale without distorting their distributional shape. Linear mode equalizes means; nonlinear mode additionally weights each variable by its dependence with others, so more correlated variables exert greater influence on the final scaling.

## 4.2 Risk‑neutral rescale (pricing context)

`NNS.rescale()` performs one‑dimensional affine transformations.

Header.

- `NNS.rescale(x, a, b, method=c("minmax","riskneutral"), T=NULL, type=c("Terminal","Discounted"))`

    px <- 100 + cumsum(rnorm(260, sd = 1))
    rn <- NNS.rescale(px, a=100, b=0.03, method="riskneutral", T=1, type="Terminal")
    c( target = 100*exp(0.03*1), mean_rn = mean(rn) )

    ##   target  mean_rn
    ## 103.0455 103.0455

Interpretation. `riskneutral` shifts the mean to match \(S_0 e^{rT}\) (Terminal) or \(S_0\) (Discounted), preserving distributional shape.

# 5. Hypothesis Testing, ANOVA & Stochastic Superiority

## 5.1 Concept

Instead of distributional assumptions, compare groups via LPM‑based CDFs. Output is a degree of certainty (not a p‑value) for equality of populations or means.

Header.

- `NNS.ANOVA(control, treatment, means.only=FALSE, medians=FALSE, confidence.interval=.95, tails=c("Both","left","right"), pairwise=FALSE, plot=TRUE, robust=FALSE)`
- `NNS.SS(x, y, ...)`

## 5.2 Code: two‑sample & multi‑group

    ctrl <- rnorm(200, 0, 1)
    trt  <- rnorm(180, 0.35, 1.2)
    NNS.ANOVA(control=ctrl, treatment=trt, means.only=FALSE, plot=FALSE)

    ## $Control
    ## [1] 0.05568255
    ##
    ## $Treatment
    ## [1] 0.2771257
    ##
    ## $Grand_Statistic
    ## [1] 0.1605767
    ##
    ## $Control_CDF
    ## [1] 0.5670595
    ##
    ## $Treatment_CDF
    ## [1] 0.4385169
    ##
    ## $Certainty
    ## [1] 0.6905098
    ##
    ## $Effect_Size_LB
    ##        2.5%
    ## -0.07055716
    ##
    ## $Effect_Size_UB
    ##     97.5%
    ## 0.5317766
    ##
    ## $Confidence_Level
    ## [1] 0.95

    A <- list(g1=rnorm(150,0.0,1.1), g2=rnorm(150,0.2,1.0), g3=rnorm(150,-0.1,0.9))
    NNS.ANOVA(control=A, means.only=TRUE, plot=FALSE)

    ## Certainty
    ## 0.6876008

Math sketch. For each quantile/threshold \(t\), compare CDFs built from `LPM.ratio(0, t, •)` (possibly with one‑sided tails). Aggregate across \(t\) to a certainty score.

## 5.3 Stochastic Superiority

Stochastic superiority asks a different question than equality of means or equality of distributions. Rather than testing whether two samples came from the same population, or whether they share the same mean or median, stochastic superiority measures the probability that a random draw from one distribution exceeds a random draw from another.

For two random variables \(X\) and \(Y\), the stochastic superiority probability is:

\[ P(X > Y) \]

and with ties accounted for, the tie-adjusted stochastic superiority measure is:

\[ P^* = P(X > Y) + \frac{1}{2} P(X = Y) \]

A value of \(P^* = 0.5\) indicates no directional advantage, values above \(0.5\) favor \(X\), and values below \(0.5\) favor \(Y\).

This differs from stochastic dominance. Stochastic superiority is a pairwise exceedance probability, while stochastic dominance requires one distribution to be preferred to another over the entire shared support.

Below is an example comparing two distributions with unequal means.

    set.seed(123)
    x = rnorm(1000, mean = 0, sd = 1)
    y = rnorm(1000, mean = 1, sd = 1)

    NNS.SS(x, y)

    ## $p_gt
    ## [1] 0.233915
    ##
    ## $p_tie
    ## [1] 0
    ##
    ## $p_star
    ## [1] 0.233915

Since \(y\) was generated with a higher mean, the stochastic superiority probability for \(x\) relative to \(y\) should be less than \(0.5\), indicating that a draw from \(x\) is less likely to exceed a draw from \(y\).

We can also obtain confidence intervals for the tie-adjusted superiority probability using maximum entropy bootstrap replicates.

    NNS.SS(x, y, confidence.interval = TRUE, reps = 999, ci = 0.95)[1:5]

    $p_gt
    [1] 0.233915

    $p_tie
    [1] 0

    $p_star
    [1] 0.233915

    $lower
    [1] 0.2105631

    $upper
    [1] 0.2537789

This provides an interpretable effect size for directional comparison between two distributions without requiring identical distributions or equal variances.

For discrete variables, ties may occur with positive probability, and the reported `p_tie` and `p_star` values reflect that adjustment explicitly.

    set.seed(123)
    x = sample(1:5, 100, replace = TRUE)
    y = sample(1:5, 100, replace = TRUE)

    NNS.SS(x, y)

    ## $p_gt
    ## [1] 0.3982
    ##
    ## $p_tie
    ## [1] 0.1992
    ##
    ## $p_star
    ## [1] 0.4978

# 6. Regression, Boosting, Stacking & Causality

## 6.1 Philosophy

`NNS.reg` learns partitioned relationships using partial‑moment weights — linear where appropriate, nonlinear where needed — avoiding fragile global parametric forms.

Headers.

- `NNS.reg(x, y, order=NULL, smooth=TRUE, ncores=1, ...)` → `$Fitted.xy`, `$Point.est`, …
- `NNS.boost(IVs.train, DV.train, IVs.test, epochs, learner.trials, status, balance, type, folds)`
- `NNS.stack(IVs.train, DV.train, IVs.test, type, balance, ncores, folds)`
- `NNS.caus(x, y)` (directional causality score via conditional dependence)

## 6.2 Code: classification via regression + ensembles

    # Example 1: Nonlinear regression
    set.seed(123)
    x_train <- runif(1000, -2, 2)
    y_train <- sin(pi * x_train) + rnorm(1000, sd = 0.2)

    x_test <- seq(-2, 2, length.out = 100)

    NNS.reg(x = x_train, y = y_train, order = NULL, point.est = x_test)

    ## $R2
    ## [1] 0.9276761
    ##
    ## $SE
    ## [1] 0.2015258
    ##
    ## $Prediction.Accuracy
    ## NULL
    ##
    ## $equation
    ## NULL
    ##
    ## $x.star
    ## NULL
    ##
    ## $derivative
    ##   Coefficient X.Lower.Range X.Upper.Range
    ## 2   3.0485215     -1.998139     -1.934371
    ## 3   3.5169373     -1.934371     -1.804387
    ## 4   1.8605016     -1.804387     -1.692769
    ## 5   0.6783073     -1.692769     -1.590916
    ## 6   0.4272848     -1.590916     -1.465816
    ## 7  -0.5144026     -1.465816     -1.376465
    ## ---
    ## 30  -1.5047059      1.218812      1.279774
    ## 31  -1.5723118      1.279774      1.445980
    ## 32   0.1804598      1.445980      1.571629
    ## 33   0.8726461      1.571629      1.689537
    ## 34   3.4198918      1.689537      1.860999
    ## 35   1.5206901      1.860999      1.997618
    ## --- [ 34 rows x 3 cols ]; showing first and last 6. Use as.data.frame(x) for all. ---
    ##
    ## $Point.est
    ##   [1]  0.01571202  0.10442684  0.23470933  0.37680781  0.51890629  0.65039140
    ##   [7]  0.72556318  0.80073496  0.85698998  0.88439634  0.91180269  0.93033285
    ##  [13]  0.94759688  0.96486091  0.95248720  0.93170326  0.87827479  0.79996720
    ##  [19]  0.72165961  0.64335202  0.52449573  0.40285499  0.28121424  0.17901835
    ##  [25]  0.07715655 -0.02470525 -0.15949123 -0.31038828 -0.45880078 -0.54309006
    ##  [31] -0.62737935 -0.71234468 -0.80041101 -0.88847734 -0.96331853 -1.00089554
    ##  [37] -1.03847254 -1.02090671 -0.97905116 -0.93719561 -0.89956805 -0.86273975
    ##  [43] -0.79660165 -0.70265879 -0.60871593 -0.51288396 -0.38856404 -0.25667590
    ##  [49] -0.11829230  0.02443074  0.13442845  0.22276171  0.31109496  0.42473203
    ##  [55]  0.56535922  0.69718932  0.76992246  0.84265560  0.90432326  0.91359751
    ##  [61]  0.92287175  0.93214599  0.94142023  0.92794242  0.90521445  0.88248648
    ##  [67]  0.85975852  0.76020581  0.65704511  0.55388442  0.45072372  0.35051056
    ##  [73]  0.25044944  0.15038831  0.04930377 -0.07695325 -0.20321027 -0.32495818
    ##  [79] -0.44464525 -0.56433232 -0.66432673 -0.72512293 -0.78817431 -0.85170206
    ##  [85] -0.91522981 -0.97875756 -0.99186193 -0.98457062 -0.97727932 -0.95314667
    ##  [91] -0.91788824 -0.88262981 -0.77697785 -0.63880041 -0.50062296 -0.36244551
    ##  [97] -0.25805231 -0.19661029 -0.13516826 -0.07526941
    ##
    ## $pred.int
    ## NULL
    ##
    ## $regression.points
    ##           x           y
    ## 1 -1.998139 -0.01307124
    ## 2 -1.934371  0.18132707
    ## 3 -1.804387  0.63847051
    ## 4 -1.692769  0.84613612
    ## 5 -1.590916  0.91522399
    ## 6 -1.465816  0.96867700
    ## ---
    ## 30 1.279774 -0.73572553
    ## 31 1.445980 -0.99705336
    ## 32 1.571629 -0.97437872
    ## 33 1.689537 -0.87148709
    ## 34 1.860999 -0.28510334
    ## 35 1.997618 -0.07734835
    ## --- [ 35 rows x 2 cols ]; showing first and last 6. Use as.data.frame(x) for all. ---
    ##
    ## $Fitted.xy
    ##            x          y      y.hat  NNS.ID   gradient   residuals
    ## 1 -0.8496899 -0.5752368 -0.4984314 q121122 -2.0861598  0.07680538
    ## 2  1.1532205 -0.6617217 -0.4496971 q221122 -2.9622550  0.21202465
    ## 3 -0.3640923 -0.7048691 -0.8815695 q122122  0.9115004 -0.17670040
    ## 4  1.5320696 -0.8447168 -0.9815176 q222121  0.1804598 -0.13680080
    ## 5  1.7618691 -0.9820881 -0.6241175 q222212  3.4198918  0.35797057
    ## 6 -1.8177740  0.5226887  0.5913898 q111121  3.5169373  0.06870110
    ##   standard.errors
    ## 1       0.1769692
    ## 2       0.1783713
    ## 3       0.1905081
    ## 4       0.2044300
    ## 5       0.2636784
    ## 6       0.2228958
    ## ---
    ##               x          y      y.hat  NNS.ID   gradient    residuals
    ## 995   0.2618615  0.5142993  0.7685458 q211222  1.8001452  0.254246501
    ## 996   1.3184955 -0.7988901 -0.7966085 q221222 -1.5723118  0.002281548
    ## 997   0.5684553  1.1554781  0.9150041 q212122 -0.5625172 -0.240473993
    ## 998  -0.4340050 -0.7748325 -0.9473089 q122121  1.0359249 -0.172476359
    ## 999   0.8383194  0.7041960  0.4257159 q212222 -2.4765129 -0.278480031
    ## 1000 -1.5647037  0.9467853  0.9264240 q111222  0.4272848 -0.020361366
    ##      standard.errors
    ## 995        0.2001082
    ## 996        0.1971693
    ## 997        0.2137362
    ## 998        0.1831159
    ## 999        0.2108312
    ## 1000       0.2078031
    ## --- [ 1000 rows x 7 cols ]; showing first and last 6. Use as.data.frame(x) for all. ---

    # Simple train/test for boosting & stacking
    test.set = 141:150

    boost <- NNS.boost(IVs.train = iris[-test.set, 1:4],
                  DV.train = iris[-test.set, 5],
                  IVs.test = iris[test.set, 1:4],
                  epochs = 10, learner.trials = 10,
                  status = FALSE, balance = TRUE,
                  type = "CLASS", folds = 5)

    mean(boost$results == as.numeric(iris[test.set,5]))
    # [1] 1

    boost$feature.weights; boost$feature.frequency

    stacked <- NNS.stack(IVs.train = iris[-test.set, 1:4],
                         DV.train = iris[-test.set, 5],
                         IVs.test = iris[test.set, 1:4],
                         type = "CLASS", balance = TRUE,
                         ncores = 1, folds = 1)
    mean(stacked$stack == as.numeric(iris[test.set,5]))
    # [1] 1

## 6.3 Code: directional causality

    NNS.caus(mtcars$hp,  mtcars$mpg)  # hp -> mpg

    ## Causation.x.given.y Causation.y.given.x           C(x--->y)
    ##           0.2607148           0.3863580           0.3933374

    NNS.caus(mtcars$mpg, mtcars$hp)   # hp -> mpg

    ## Causation.x.given.y Causation.y.given.x           C(y--->x)
    ##           0.3863580           0.2607148           0.3933374

Interpretation. Examine asymmetry in scores to infer direction. The method conditions partial‑moment dependence on candidate drivers.

# 7. Time Series & Forecasting

Headers.

- `NNS.ARMA`
- `NNS.ARMA.optim`
- `NNS.seas`
- `NNS.VAR`

    # Univariate nonlinear ARMA
    set.seed(42)
    z <- as.numeric(scale(sin(1:480/8) + rnorm(480, sd=.35)))

    # Seasonality detection (prints a summary)
    seasonal_period <- NNS.seas(z, plot = FALSE)
    head(seasonal_period$all.periods)

    ##   Period Coefficient.of.Variation Variable.Coefficient.of.Variation
    ## 1    149                0.5403978                      3.671725e+16
    ## 2    146                0.5704652                      3.671725e+16
    ## 3    193                0.5772260                      3.671725e+16
    ## 4    145                0.6411224                      3.671725e+16
    ## 5    195                0.6510130                      3.671725e+16
    ## 6    194                0.6736148                      3.671725e+16

    # Validate seasonal periods
    NNS.ARMA.optim(z, h = 48, seasonal.factor = seasonal_period$periods, plot = TRUE, ncores = 1)

    ## [1] "CURRNET METHOD: lin"
    ## [1] "COPY LATEST PARAMETERS DIRECTLY FOR NNS.ARMA() IF ERROR:"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.425337970282028"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.314272381262221"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.288043942135049"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.269630388633394"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77, 53 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.264107018738235"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.261855687810987"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61, 47 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.259567580022873"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61, 47, 52 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.259036260976855"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61, 47, 52, 39 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.258819679908979"
    ## [1] "BEST method = 'lin', seasonal.factor = c( 49, 51, 48, 77, 53, 61, 47, 52, 39 )"
    ## [1] "BEST lin OBJECTIVE FUNCTION = 0.258819679908979"
    ## [1] "CURRNET METHOD: nonlin"
    ## [1] "COPY LATEST PARAMETERS DIRECTLY FOR NNS.ARMA() IF ERROR:"
    ## [1] "NNS.ARMA(... method =  'nonlin' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61, 47, 52, 39 ) ...)"
    ## [1] "CURRENT nonlin OBJECTIVE FUNCTION = 2.99141781480761"
    ## [1] "BEST method = 'nonlin' PATH MEMBER = c( 49, 51, 48, 77, 53, 61, 47, 52, 39 )"
    ## [1] "BEST nonlin OBJECTIVE FUNCTION = 2.99141781480761"
    ## [1] "CURRNET METHOD: both"
    ## [1] "COPY LATEST PARAMETERS DIRECTLY FOR NNS.ARMA() IF ERROR:"
    ## [1] "NNS.ARMA(... method =  'both' , seasonal.factor =  c( 49, 51, 48, 77, 53, 61, 47, 52, 39 ) ...)"
    ## [1] "CURRENT both OBJECTIVE FUNCTION = 0.625552444253532"
    ## [1] "BEST method = 'both' PATH MEMBER = c( 49, 51, 48, 77, 53, 61, 47, 52, 39 )"
    ## [1] "BEST both OBJECTIVE FUNCTION = 0.625552444253532"

    ## $periods
    ## [1] 49 51 48 77 53 61 47 52 39
    ##
    ## $weights
    ## NULL
    ##
    ## $obj.fn
    ## [1] 0.2588197
    ##
    ## $method
    ## [1] "lin"
    ##
    ## $shrink
    ## [1] FALSE
    ##
    ## $nns.regress
    ## [1] FALSE
    ##
    ## $bias.shift
    ## [1] -0.009218256
    ##
    ## $errors
    ##  [1]  0.037998635 -0.239228953 -0.316141990 -0.418117619 -0.823606681
    ##  [6] -0.177864620 -0.273284145  0.199097260 -0.269078422  0.335736005
    ## [11]  0.207001239 -0.260052866 -0.374098331 -0.028416380  0.269396784
    ## [16] -0.182028248 -0.529811555  0.857804747 -0.057911905  0.117997928
    ## [21]  0.424976020  0.645090011  0.078429020 -0.213553889 -0.791244537
    ## [26]  0.920545733  0.153751505 -0.081285100  0.619661377  0.496442598
    ## [31]  0.257396953  0.297846774  0.540597384  0.055491180  0.212429542
    ## [36] -0.491954954 -0.289145299  0.660580259  0.052001332  0.279297027
    ## [41] -0.806916112 -0.030591942  0.109210562 -0.135952107  0.045299836
    ## [46]  0.148876853 -0.884313637  0.210474935  0.113092419  0.660054965
    ## [51] -0.435314826 -0.782235750  0.656562239 -0.025353564  0.425236504
    ## [56] -0.846555263 -0.601757846 -0.266020841 -0.513607767  0.451721860
    ## [61] -0.289982748  0.190794363  0.096026418  0.310036015  0.424443802
    ## [66] -0.167961571  1.046312612  0.298616962  0.082761450  0.534065617
    ## [71]  0.261430044  0.122084342 -0.544005536 -0.120869841 -1.135291084
    ## [76]  0.369271897 -0.492958216 -0.892193344  0.043885543  0.398073481
    ## [81]  0.329804904 -0.278855424 -0.409283402 -0.437965243  1.323354381
    ## [86]  0.044144199  0.093216425  0.592139742 -0.484191535  0.662806568
    ## [91] -0.180633968 -0.170869946  0.370987867  0.008514349 -0.090696911
    ## [96] -0.365429200
    ##
    ## $results
    ##  [1] -0.51693959 -0.68128383 -0.78458374 -0.93283301 -1.03903131 -0.87348694
    ##  [7] -0.87884737 -1.01975206 -0.99207304 -0.94874121 -1.01358876 -0.86983927
    ## [13] -0.94505909 -0.79370119 -0.80045643 -0.62238672 -0.49365177 -0.21357396
    ## [19] -0.39782796 -0.33633239 -0.08206682  0.07442965  0.29167524  0.18543598
    ## [25]  0.39554954  0.85664622  0.74166617  0.67340715  0.72779008  0.91300090
    ## [31]  0.87514715  0.88928012  0.70838441  0.94122904  0.84877222  0.68921404
    ## [37]  0.64739105  0.72700981  0.64361948  0.66902228  0.33304850  0.34791061
    ## [43]  0.21002564  0.20985980  0.08722744 -0.03033825 -0.02028085 -0.29414843
    ##
    ## $lower.pred.int
    ##  [1] -1.423175705 -1.587519952 -1.690819861 -1.839069129 -1.945267428
    ##  [6] -1.779723063 -1.785083492 -1.925988180 -1.898309159 -1.854977325
    ## [11] -1.919824884 -1.776075385 -1.851295208 -1.699937305 -1.706692545
    ## [16] -1.528622842 -1.399887888 -1.119810077 -1.304064083 -1.242568507
    ## [21] -0.988302944 -0.831806471 -0.614560884 -0.720800144 -0.510686578
    ## [26] -0.049589898 -0.164569954 -0.232828971 -0.178446036  0.006764783
    ## [31] -0.031088973 -0.016956002 -0.197851710  0.034992917 -0.057463903
    ## [36] -0.217022077 -0.258845072 -0.179226309 -0.262616636 -0.237213835
    ## [41] -0.573187621 -0.558325512 -0.696210481 -0.696376318 -0.819008679
    ## [46] -0.936574370 -0.926516971 -1.200384552
    ##
    ## $upper.pred.int
    ##  [1]  0.38929653  0.22495229  0.12165238 -0.02659689 -0.13279519  0.03274918
    ##  [7]  0.02738875 -0.11351594 -0.08583692 -0.04250509 -0.10735264  0.03639685
    ## [13] -0.03882297  0.11253493  0.10577969  0.28384940  0.41258435  0.69266216
    ## [19]  0.50840816  0.56990373  0.82416930  0.98066577  1.19791136  1.09167210
    ## [25]  1.30178566  1.76288234  1.64790228  1.57964327  1.63402620  1.81923702
    ## [31]  1.78138327  1.79551624  1.61462053  1.84746516  1.75500834  1.59545016
    ## [37]  1.55362717  1.63324593  1.54985560  1.57525840  1.23928462  1.25414673
    ## [43]  1.11626176  1.11609592  0.99346356  0.87589787  0.88595527  0.61208769

Notes. NNS seasonality uses coefficient of variation instead of ACF/PACFs, and NNS ARMA blends multiple seasonal periods into the linear or nonlinear regression forecasts.

# 8. Simulation & Bootstrap & Risk‑Neutral Rescaling

## 8.1 Maximum entropy bootstrap (shape‑preserving)

Header.

- `NNS.meboot(x, reps=999, rho=NULL, type="spearman", drift=TRUE, ...)`

    x_ts <- cumsum(rnorm(350, sd=.7))
    mb <- NNS.meboot(x_ts, reps=5, rho = 1)
    dim(mb["replicates", ]$replicates)

    ## [1] 350   5

## 8.2 Monte Carlo over the full correlation space

Header.

- `NNS.MC(x, reps=30, lower_rho=-1, upper_rho=1, by=.01, exp=1, type="spearman", ...)`

    mc <- NNS.MC(x_ts, reps=5, lower_rho=-1, upper_rho=1, by=.5, exp=1)
    length(mc$ensemble); names(mc$replicates)

    ## [1] 350

    ## [1] "rho = 1"    "rho = 0.5"  "rho = 0"    "rho = -0.5" "rho = -1"

    head(mc$replicates$`rho = 0`)

    ##      Replicate 1 Replicate 2 Replicate 3 Replicate 4 Replicate 5
    ## [1,]   12.080732    6.022924    6.587004    1.808137    8.560541
    ## [2,]    8.511841   12.091955    7.943611    0.870806    6.199911
    ## [3,]    0.123113    6.613393    9.529598    3.201866    5.173936
    ## [4,]    1.768517    4.635251    4.217551    5.348460   14.314581
    ## [5,]   -4.102181    7.055852    7.414148    5.697692   15.412686
    ## [6,]   -0.176066    5.813247    8.752096    5.639663   15.273176

# 9. Portfolio & Stochastic Dominance

Stochastic dominance orders uncertain prospects for broad classes of risk‑averse utilities; partial moments supply practical, nonparametric estimators.

Headers.

- `NNS.FSD.uni(x, y)`
- `NNS.SSD.uni(x, y)`
- `NNS.TSD.uni(x, y)`
- `NNS.SD.cluster(R)`
- `NNS.SD.efficient.set(R)`

    set.seed(42)
    RA <- rnorm(240, 0.005, 0.03)
    RB <- rnorm(240, 0.003, 0.02)
    RC <- rnorm(240, 0.006, 0.04)

    NNS.FSD.uni(RA, RB)

    ## [1] 0

    NNS.SSD.uni(RA, RB)

    ## [1] 0

    NNS.TSD.uni(RA, RB)

    ## [1] 0

    Rmat <- cbind(A=RA, B=RB, C=RC)
    try(NNS.SD.cluster(Rmat, degree = 1))

    ## $Clusters
    ## $Clusters$Cluster_1
    ## [1] "A" "C" "B"

    try(NNS.SD.efficient.set(Rmat, degree = 1))

    ## Checking 1 of 2Checking 2 of 2

    ## [1] "A" "C" "B"

# Appendix A — Measure‑theoretic sketch (why partial moments are rigorous)

Let \((\Omega, \mathcal{F}, \mathbb{P})\) be a probability space, \(X: \Omega\to\mathbb{R}\) measurable. For any fixed \(t\in\mathbb{R}\), the sets \(\{X\le t\}\) and \(\{X>t\}\) are in \(\mathcal{F}\) because they are preimages of Borel sets. The population partial moments are

\[ \operatorname{LPM}(k,t,X) = \int_{-\infty}^{t} (t-x)^k\, dF_X(x), \qquad \operatorname{UPM}(k,t,X) = \int_{t}^{\infty} (x-t)^k\, dF_X(x). \]

The empirical versions correspond to replacing \(F_X\) with the empirical measure \(\mathbb{P}_n\) (or CDF \(\hat F_n\)):

\[ \widehat{\operatorname{LPM}}_k(t;X) = \int_{(-\infty,t]} (t-x)^k\, d\mathbb{P}_n(x), \qquad \widehat{\operatorname{UPM}}_k(t;X) = \int_{(t,\infty)} (x-t)^k\, d\mathbb{P}_n(x). \]

Centering at \(t=\mu_X\) yields the variance decomposition identity in Section 1.

# Appendix B — Quick Reference (Grouped by Topic)

## Overall Theory

- Nonlinear Nonparametric Statistics: Using Partial Moments

## 1. Partial Moments & Ratios

- `LPM(degree, target, variable)` — lower partial moment of order `degree` at `target`.
- `UPM(degree, target, variable)` — upper partial moment of order `degree` at `target`.
- `LPM.ratio(degree, target, variable)`; `UPM.ratio(...)` — normalized shares; `degree=0` gives CDF.
- `LPM.VaR(p, degree, variable)` — partial-moment quantile at probability `p`.
- `Co.LPM(degree_lpm, x, y, target_x, target_y, degree_y)` — co-lower partial moment between two variables.
- `Co.UPM(degree_upm, x, y, target_x, target_y, degree_y)` — co-upper partial moment between two variables.
- `D.LPM(degree, target, variable)` — divergent lower partial moment (away from `target`).
- `D.UPM(degree, target, variable)` — divergent upper partial moment (away from `target`).
- `NNS.CDF(x, target = NULL, points = NULL, plot = TRUE/FALSE)` — CDF from partial moments.
- `NNS.moments(x)` — mean/var/skew/kurtosis via partial moments.

## 2. Descriptive Statistics & Distributions

- `NNS.mode(x, multi = FALSE)` — nonparametric mode(s).
- `PM.matrix(l_degree, u_degree, target, variable, pop_adj)` — co-/divergent partial-moment matrices.
- `NNS.gravity(x, w = NULL)` — partial-moment weighted location (gravity center).

See NNS Vignette: Getting Started with NNS: Partial Moments

## 3. Dependence & Association

- `NNS.dep(x, y)` — nonlinear dependence coefficient.
- `NNS.copula(X, target, continuous, plot, independence.overlay)` — dependence from co-partial moments.

See NNS Vignette: Getting Started with NNS: Correlation and Dependence

## 4. Normalization & Rescaling

- `NNS.norm(x, linear=FALSE)` — normalization retaining target moments.
- `NNS.rescale(x, a, b, method=c("minmax","riskneutral"), T=NULL, type=c("Terminal","Discounted"))` — risk-neutral or min–max rescaling.

See NNS Vignette: Getting Started with NNS: Normalization and Rescaling

## 5. Hypothesis Testing

- `NNS.ANOVA(control, treatment, ...)` — certainty of equality (distributions or means).
- `NNS.SS(x, y, ...)` — stochastic superiority between two variables.

See NNS Vignette: Getting Started with NNS: Comparing Distributions

## 6. Regression, Classification & Causality

- `NNS.part(x, y, ...)` — partition analysis for variable segmentation.
- `NNS.reg(x, y, ...)` — partition-based regression/classification (`$Fitted.xy`, `$Point.est`).
- `NNS.boost(IVs, DV, ...)`, `NNS.stack(IVs, DV, ...)` — ensembles using `NNS.reg` base learners.
- `NNS.caus(x, y)` — directional causality score.

See NNS Vignette: Getting Started with NNS: Clustering and Regression

See NNS Vignette: Getting Started with NNS: Classification

## 7. Differentiation & Slope Measures

- `dy.dx(x, y)` — numerical derivative of `y` with respect to `x` via `NNS.reg`.
- `dy.d_(x, Y, var)` — partial derivative of multivariate `Y` w.r.t. `var`.
- `NNS.diff(x, y)` — derivative via secant projections.

## 8. Time Series & Forecasting

- `NNS.ARMA(...)`, `NNS.ARMA.optim(...)` — nonlinear ARMA modeling.
- `NNS.seas(...)` — detect seasonality.
- `NNS.VAR(...)` — nonlinear VAR modeling.
- `NNS.nowcast(x, h, ...)` — near-term nonlinear forecast.

See NNS Vignette: Getting Started with NNS: Forecasting

## 9. Simulation & Bootstrap

- `NNS.meboot(...)` — maximum entropy bootstrap.
- `NNS.MC(...)` — Monte Carlo over correlation space.

See NNS Vignette: Getting Started with NNS: Sampling and Simulation

## 10. Portfolio Analysis & Stochastic Dominance

- `NNS.FSD.uni(x, y)`, `NNS.SSD.uni(x, y)`, `NNS.TSD.uni(x, y)` — univariate stochastic dominance tests.
- `NNS.SD.cluster(R)`, `NNS.SD.efficient.set(R)` — dominance-based portfolio sets.

For complete references, please see the Vignettes linked above and their specific referenced materials.