# Getting Started with NNS: Overview

#### Fred Viole

    # Prereqs (uncomment if needed):
    # install.packages("NNS")
    # install.packages(c("data.table","xts","zoo","Rfast"))

    suppressPackageStartupMessages({
      library(NNS)
      library(data.table)
    })
    set.seed(42)

# 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. Hypothesis testing & ANOVA (LPM‑CDF) 5. Regression, boosting, stacking & causality 6. Time series & forecasting 7. Simulation (max‑entropy), Monte Carlo & risk‑neutral rescaling 8. 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)`
- `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)

## 1.3 Code: variance decomposition & CDF

    # 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

    qgrid <- quantile(z, probs = seq(0.05,0.95,by=0.1))
    CDF_tbl <- data.table(threshold = as.numeric(qgrid), CDF = sapply(qgrid, function(q) LPM.ratio(0,q,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, target, x, y)` / `Co.UPM(...)` (co‑partial moments) - `PM.matrix(l_degree, u_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. Hypothesis Testing & ANOVA (LPM‑CDF)

## 4.1 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)`

## 4.2 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] -0.02110331
    ##
    ## $Treatment
    ## [1] 0.4020782
    ##
    ## $Grand_Statistic
    ## [1] 0.1904875
    ##
    ## $Control_CDF
    ## [1] 0.6311761
    ##
    ## $Treatment_CDF
    ## [1] 0.3869042
    ##
    ## $Certainty
    ## [1] 0.3904966
    ##
    ## $Effect_Size_LB
    ##      2.5%
    ## 0.1379073
    ##
    ## $Effect_Size_UB
    ##     97.5%
    ## 0.7182389
    ##
    ## $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
    ## 0.4870367

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. Regression, Boosting, Stacking & Causality

## 5.1 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)

## 5.2 Code: classification via regression + ensembles

    # Example 1: Nonlinear regression
    set.seed(123)
    x_train <- runif(200, -2, 2)
    y_train <- sin(pi * x_train) + rnorm(200, sd = 0.2)

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

    NNS.reg(x = data.frame(x = x_train), y = y_train, order = NULL)

    ## $R2
    ## [1] 0.9311519
    ##
    ## $SE
    ## [1] 0.2026925
    ##
    ## $Prediction.Accuracy
    ## NULL
    ##
    ## $equation
    ## NULL
    ##
    ## $x.star
    ## NULL
    ##
    ## $derivative
    ##     Coefficient X.Lower.Range X.Upper.Range
    ##  1:   1.2434405   -1.99750091   -1.73845327
    ##  2:   1.0742631   -1.73845327   -1.44607946
    ##  3:  -1.7569574   -1.44607946   -1.15089951
    ##  4:  -2.8177481   -1.15089951   -0.92639490
    ##  5:  -2.7344640   -0.92639490   -0.71353077
    ##  6:  -0.9100977   -0.71353077   -0.48670931
    ##  7:   0.9413676   -0.48670931   -0.26368123
    ##  8:   3.1046330   -0.26368123   -0.07056066
    ##  9:   2.3015348   -0.07056066    0.09052852
    ## 10:   3.1725079    0.09052852    0.25161770
    ## 11:   1.3891814    0.25161770    0.45309215
    ## 12:  -1.2585111    0.45309215    0.66464355
    ## 13:  -2.5182434    0.66464355    1.01253792
    ## 14:  -2.9012891    1.01253792    1.23520889
    ## 15:  -0.7677850    1.23520889    1.53007549
    ## 16:   1.8706437    1.53007549    1.63683301
    ## 17:   2.2033668    1.63683301    1.84911541
    ## 18:   2.5081594    1.84911541    1.97707911
    ##
    ## $Point.est
    ## NULL
    ##
    ## $pred.int
    ## NULL
    ##
    ## $regression.points
    ##               x           y
    ##  1: -1.99750091  0.30256920
    ##  2: -1.73845327  0.62467954
    ##  3: -1.44607946  0.93876593
    ##  4: -1.15089951  0.42014733
    ##  5: -0.92639490 -0.21245010
    ##  6: -0.71353077 -0.79451941
    ##  7: -0.48670931 -1.00094909
    ##  8: -0.26368123 -0.79099767
    ##  9: -0.07056066 -0.19142920
    ## 10:  0.09052852  0.17932316
    ## 11:  0.25161770  0.69037987
    ## 12:  0.45309215  0.97026443
    ## 13:  0.66464355  0.70402465
    ## 14:  1.01253792 -0.17205805
    ## 15:  1.23520889 -0.81809091
    ## 16:  1.53007549 -1.04448505
    ## 17:  1.63683301 -0.84477976
    ## 18:  1.84911541 -0.37704378
    ## 19:  1.97707911 -0.05609043
    ##
    ## $Fitted.xy
    ##             x.x          y      y.hat NNS.ID   gradient    residuals
    ##   1: -0.8496899 -0.5969396 -0.4221971 q11222 -2.7344640  0.174742446
    ##   2:  1.1532205 -0.4116052 -0.5802190 q21222 -2.9012891 -0.168613758
    ##   3: -0.3640923 -0.9595645 -0.8855214 q12122  0.9413676  0.074043066
    ##   4:  1.5320696 -1.0644376 -1.0407547 q22122  1.8706437  0.023682815
    ##   5:  1.7618691 -0.8705785 -0.5692793 q22212  2.2033668  0.301299171
    ##  ---
    ## 196: -0.1338692 -0.3949338 -0.3879789 q12221  3.1046330  0.006954855
    ## 197: -0.3726696 -0.5476828 -0.8935958 q12122  0.9413676 -0.345913048
    ## 198:  0.6369213  0.6387223  0.7389134 q21211 -1.2585111  0.100191135
    ## 199: -1.3906135  0.9457286  0.8413147 q11122 -1.7569574 -0.104413995
    ## 200:  0.2914682  1.0429566  0.7457395 q21112  1.3891814 -0.297217109
    ##      standard.errors
    ##   1:       0.2830616
    ##   2:       0.1682392
    ##   3:       0.2096766
    ##   4:       0.1380874
    ##   5:       0.2328510
    ##  ---
    ## 196:       0.1281159
    ## 197:       0.2096766
    ## 198:       0.2474949
    ## 199:       0.2329677
    ## 200:       0.2435048

    # 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 = 1)

    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

## 5.3 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)   # mpg -> hp

    ## 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.

# 6. Time Series & Forecasting

Headers. `NNS.ARMA`, `NNS.ARMA.optim`, `NNS.seas`, `NNS.VAR`

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

    # Seasonality detection (prints a summary)
    NNS.seas(z, plot = FALSE)

    ## $all.periods
    ##      Period Coefficient.of.Variation Variable.Coefficient.of.Variation
    ##   1:     99             5.122054e-01                      7.866136e+16
    ##   2:    147             5.256021e-01                      7.866136e+16
    ##   3:    100             5.598477e-01                      7.866136e+16
    ##   4:    146             5.618687e-01                      7.866136e+16
    ##   5:    199             5.766158e-01                      7.866136e+16
    ##  ---
    ## 235:    235             2.273832e+02                      7.866136e+16
    ## 236:     30             4.402306e+02                      7.866136e+16
    ## 237:     11             4.879713e+02                      7.866136e+16
    ## 238:     46             5.854636e+02                      7.866136e+16
    ## 239:      1             2.621230e+16                      7.866136e+16
    ##
    ## $best.period
    ## Period
    ##     99
    ##
    ## $periods
    ##   [1]  99 147 100 146 199  98  97 200  49 198  48  96 145 196 194 193 195 197
    ##  [19] 144  50 192 149 176  53 191 148  95 156 190 106 150 202 239  52 143 201
    ##  [37] 105 142 189 209 158 238 157 211 160 155 107 212 177 104 210 188 154 208
    ##  [55] 126 163 152 103 207 127 172 117 159 128 206 109 175 224 179  47 170 221
    ##  [73] 227 171  86  85 130  54 222 129 236 178 153 141 237 108 203 181  34 101
    ##  [91] 180 226 161  94  87 213 162  43 115 113 166 102  80  59 204  66  40 135
    ## [109]  61 217  81  90 131  56  44 223  83  60 132  84  74  93  88  45 229 167
    ## [127] 173 133 122 116 151  70 174 187  51 112 219 228 121 136 232 120  37 169
    ## [145]  25 182  22  14 186  82 118  89 184  39  72  67 234  64  18 140  91 216
    ## [163]  57 124 233 168  32  16 134  73  75  92 139  36  41 220 230  58 123  69
    ## [181]  38  76  28 164  26 138 225 125 165 185  68  55  65  35   2 183   6  42
    ## [199]  78 215 137  21   8  20  15  62   7 231  79  13 205  24  17 119  12  31
    ## [217] 214  63  33  27 111   3   9  19  10 218 114   4  23   5 110  71  77  29
    ## [235] 235  30  11  46   1

    # Validate seasonal periods
    NNS.ARMA.optim(z, h=48, seasonal.factor = NNS.seas(z, plot = FALSE)$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( 52 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.449145584053097"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 52, 49 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.364719193840196"
    ## [1] "NNS.ARMA(... method =  'lin' , seasonal.factor =  c( 52, 49, 50 ) ...)"
    ## [1] "CURRENT lin OBJECTIVE FUNCTION = 0.303033712560494"
    ## [1] "BEST method = 'lin', seasonal.factor = c( 52, 49, 50 )"
    ## [1] "BEST lin OBJECTIVE FUNCTION = 0.303033712560494"
    ## [1] "CURRNET METHOD: nonlin"
    ## [1] "COPY LATEST PARAMETERS DIRECTLY FOR NNS.ARMA() IF ERROR:"
    ## [1] "NNS.ARMA(... method =  'nonlin' , seasonal.factor =  c( 52, 49, 50 ) ...)"
    ## [1] "CURRENT nonlin OBJECTIVE FUNCTION = 1.58051085217018"
    ## [1] "BEST method = 'nonlin' PATH MEMBER = c( 52, 49, 50 )"
    ## [1] "BEST nonlin OBJECTIVE FUNCTION = 1.58051085217018"
    ## [1] "CURRNET METHOD: both"
    ## [1] "COPY LATEST PARAMETERS DIRECTLY FOR NNS.ARMA() IF ERROR:"
    ## [1] "NNS.ARMA(... method =  'both' , seasonal.factor =  c( 52, 49, 50 ) ...)"
    ## [1] "CURRENT both OBJECTIVE FUNCTION = 0.447117273188387"
    ## [1] "BEST method = 'both' PATH MEMBER = c( 52, 49, 50 )"
    ## [1] "BEST both OBJECTIVE FUNCTION = 0.447117273188387"

    ## $periods
    ## [1] 52 49 50
    ##
    ## $weights
    ## NULL
    ##
    ## $obj.fn
    ## [1] 0.3030337
    ##
    ## $method
    ## [1] "lin"
    ##
    ## $shrink
    ## [1] FALSE
    ##
    ## $nns.regress
    ## [1] FALSE
    ##
    ## $bias.shift
    ## [1] 0.1079018
    ##
    ## $errors
    ##  [1]  0.06841911 -0.23650978  0.23306891 -0.31474170 -0.16347937  0.56078801
    ##  [7] -0.19340157 -0.54788961 -0.34463351 -0.04971714  0.81131522 -1.04034772
    ## [13] -0.01124973  0.18532001  0.42228850  0.77875534  0.21204992  0.75989291
    ## [19]  0.03648050 -0.12410190  0.78169808 -0.37190642  0.04673305  0.22143951
    ## [25]  0.21784535 -0.36207177  0.06303110  0.27494889  0.61355674 -0.36588877
    ## [31] -0.53670212 -0.59710016 -0.33562214  0.52319489 -0.28558752 -0.06318330
    ## [37]  0.46174079  0.85423779 -0.17957169  0.88745345 -0.22575406 -0.65533631
    ## [43] -0.50769155 -0.18710610 -0.19702948 -0.61676209 -0.64456532 -0.60764796
    ## [49]  0.39155766 -0.99138140 -0.58599672 -0.41332955 -0.35110299 -0.31785231
    ## [55] -0.33368188 -0.79321483 -0.67548303 -0.29994123 -1.40951519 -0.23496159
    ## [61] -0.11326961  0.93761236 -1.12638974  0.56134385 -0.82647659 -0.15698867
    ## [67] -0.66092883 -0.23941287 -0.11793511 -0.13131032  0.23980082  0.11145491
    ## [73] -0.29324462  0.20996125  1.18368703  0.39817389  0.11233666  0.18104853
    ## [79] -0.34704039  1.00778283 -0.12855809 -0.44890273 -0.16127326  0.23878907
    ## [85]  0.08958084 -0.42127816  0.83025782  0.21535622  0.18499525  0.55580864
    ## [91] -0.63033063 -1.18279040  0.01593275 -0.38943895 -0.73303803  0.24461725
    ##
    ## $results
    ##  [1] -0.48668691 -1.12897072 -1.19746701 -1.08366883 -1.17430589 -1.09503747
    ##  [7] -1.27716033 -1.53097703 -1.12872199 -1.25223633 -1.03806114 -1.03996796
    ## [13] -0.86012134 -0.85334076 -1.29205205 -0.41549038 -0.43645317 -0.70613650
    ## [19] -0.12979825 -0.20838439 -0.43674783  0.04939343  0.21128735  0.41848130
    ## [25]  0.58665881  0.65204679  1.11856457  0.81855013  1.33909393  1.17188036
    ## [31]  1.53195554  1.09195702  1.71916462  1.49064664  1.62340003  1.71600851
    ## [37]  1.54806110  1.34095102  1.16533567  1.08567248  0.73267472  0.82949748
    ## [43]  0.65297434  0.09082443  0.55476313  0.53988329 -0.09198186 -0.18380226
    ##
    ## $lower.pred.int
    ##  [1] -1.51339152 -2.15567532 -2.22417162 -2.11037344 -2.20101050 -2.12174208
    ##  [7] -2.30386494 -2.55768164 -2.15542660 -2.27894094 -2.06476575 -2.06667256
    ## [13] -1.88682595 -1.88004537 -2.31875666 -1.44219499 -1.46315778 -1.73284111
    ## [19] -1.15650286 -1.23508900 -1.46345244 -0.97731118 -0.81541725 -0.60822331
    ## [25] -0.44004579 -0.37465782  0.09185996 -0.20815448  0.31238932  0.14517575
    ## [31]  0.50525093  0.06525242  0.69246002  0.46394203  0.59669542  0.68930391
    ## [37]  0.52135649  0.31424642  0.13863106  0.05896787 -0.29402989 -0.19720713
    ## [43] -0.37373026 -0.93588018 -0.47194148 -0.48682132 -1.11868647 -1.21050687
    ##
    ## $upper.pred.int
    ##  [1]  0.54001770 -0.10226611 -0.17076240 -0.05696423 -0.14760128 -0.06833286
    ##  [7] -0.25045572 -0.50427242 -0.10201738 -0.22553172 -0.01135653 -0.01326335
    ## [13]  0.16658327  0.17336385 -0.26534744  0.61121423  0.59025144  0.32056811
    ## [19]  0.89690636  0.81832022  0.58995678  1.07609804  1.23799196  1.44518591
    ## [25]  1.61336342  1.67875140  2.14526918  1.84525474  2.36579853  2.19858497
    ## [31]  2.55866015  2.11866163  2.74586923  2.51735125  2.65010464  2.74271312
    ## [37]  2.57476571  2.36765563  2.19204028  2.11237709  1.75937933  1.85620209
    ## [43]  1.67967895  1.11752903  1.58146774  1.56658789  0.93472275  0.84290235

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.

# 7. Simulation, Bootstrap & Risk‑Neutral Rescaling

## 7.1 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

## 7.2 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$ensemble); head(names(mc$replicates),5)

    ## [1] 350

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

## 7.3 Risk‑neutral rescale (pricing context)

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.

# 8. 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)`

    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] "C" "A" "B"

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

    ## Checking 1 of 2Checking 2 of 2

    ## [1] "C" "A" "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)

## 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, target, x, y)` — co-lower partial moment between two variables.
- `Co.UPM(degree, target, x, 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).
- `NNS.norm(x, method = "moment")` — normalization retaining target moments.

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. Hypothesis Testing

- `NNS.ANOVA(control, treatment, ...)` — certainty of equality (distributions or means).

See NNS Vignette: Getting Started with NNS: Comparing Distributions

## 5. 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

## 6. Differentiation & Slope Measures

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

## 7. 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

## 8. Simulation, Bootstrap & Rescaling

- `NNS.meboot(...)` — maximum entropy bootstrap.
- `NNS.MC(...)` — Monte Carlo over correlation space.
- `NNS.rescale(...)` — risk-neutral or min–max rescaling.

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

## 9. 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.