# PMM3: Linear Regression for Symmetric Platykurtic Errors

#### Serhii Zabolotnii

#### 2026-05-29

## The Problem: When OLS and PMM2 Are Not Enough

Ordinary Least Squares (OLS) is optimal for Gaussian errors. PMM2 improves upon OLS when errors are asymmetric (skewed). But what about errors that are symmetric yet non-Gaussian?

Many real-world processes produce errors with lighter tails than the normal distribution (platykurtic errors, \(\gamma_4 < 0\)). In these cases:

- OLS is suboptimal — it does not exploit the known platykurtic structure
- PMM2 provides no improvement — it relies on the third moment (skewness), which is zero for symmetric distributions

### Where Platykurtic Errors Arise

- Bounded measurement errors: sensors with a fixed operating range
- Rounding errors: uniformly distributed within the rounding interval
- Truncated normal processes: censored at fixed bounds
- Beta-distributed disturbances: bounded economic indicators, proportions
- Quality control: manufacturing tolerances with hard limits

## The Solution: PMM3 (Polynomial Maximization Method, S=3)

PMM3 constructs a cubic stochastic polynomial using the fourth and sixth central moments. The key theoretical quantities:

- Excess kurtosis: \(\gamma_4 = m_4 / m_2^2 - 3\)
- Sixth-order coefficient: \(\gamma_6 = m_6 / m_2^3 - 15 m_4/m_2^2 + 30\)
- Efficiency factor: \(g_3 = 1 - \gamma_4^2 / (6 + 9\gamma_4 + \gamma_6)\)

The factor \(g_3\) is the ratio \(\text{Var}(\hat{\beta}_{\text{PMM3}}) / \text{Var}(\hat{\beta}_{\text{OLS}})\). When \(g_3 < 1\), PMM3 achieves lower variance than OLS.

### The Newton-Raphson Algorithm

PMM3 iteratively refines OLS estimates using the score equation:

\[ Z_{1\nu} = \varepsilon_\nu (\kappa - \varepsilon_\nu^2), \quad \kappa = m_4 / m_2 \]

with Jacobian \(J = X^\top \text{diag}(3\varepsilon^2 - \kappa) X\). The solver includes step-size limiting and a divergence guard for numerical stability.

Reference: Based on the theoretical framework of the Polynomial Maximization Method (Zabolotnii et al., 2018).

## Getting Started

    library(EstemPMM)
    set.seed(42)

## Example 1: Simple Regression with Uniform Errors

Uniform errors are the canonical platykurtic case: \(\gamma_4 \approx -1.2\), symmetric, bounded.

### Data Generation

    n <- 300

    # Generate predictor
    x <- rnorm(n, mean = 5, sd = 2)

    # True parameters
    beta_0 <- 10
    beta_1 <- 2.5

    # Generate uniform errors (platykurtic, gamma4 ≈ -1.2)
    errors <- runif(n, -sqrt(3), sqrt(3))  # variance = 1

    # Response variable
    y <- beta_0 + beta_1 * x + errors
    dat <- data.frame(y = y, x = x)

### Visualize Error Distribution

    fit_ols_temp <- lm(y ~ x, data = dat)
    res_ols <- residuals(fit_ols_temp)

    par(mfrow = c(1, 2))
    hist(res_ols, breaks = 30, main = "Residual Distribution (Uniform Errors)",
         xlab = "Residuals", col = "lightblue", border = "white")
    qqnorm(res_ols, main = "Q-Q Plot")
    qqline(res_ols, col = "red", lwd = 2)

    par(mfrow = c(1, 1))

    cat("Skewness (gamma3):", round(pmm_skewness(res_ols), 3), "\n")
    #> Skewness (gamma3): 0.026
    cat("Excess kurtosis (gamma4):", round(pmm_kurtosis(res_ols), 3), "\n")
    #> Excess kurtosis (gamma4): -1.183

The Q-Q plot shows lighter tails than normal (S-shaped curve with compressed ends). The skewness is near zero but kurtosis is clearly negative.

### Automatic Method Selection

    recommendation <- pmm_dispatch(res_ols)
    #> PMM method dispatch
    #>   n = 300 | gamma3 = +0.026 | gamma4 = -1.183
    #>   g2(PMM2) = 0.9992 | g3(PMM3) = 0.3083
    #>   Selected: PMM3 (expected variance reduction: 69.2%)
    #>   Reason: |gamma3| = 0.026 < 0.3 (symmetric) and gamma4 = -1.183 < -0.7 (platykurtic). PMM3 expected to reduce variance by 69.2%.

### Fit PMM3

    fit_pmm3 <- lm_pmm3(y ~ x, data = dat)
    fit_ols  <- lm(y ~ x, data = dat)

    # Display PMM3 summary
    summary(fit_pmm3)
    #> PMM3 estimation results (S = 3, symmetric errors)
    #> Call:
    #> lm_pmm3(formula = y ~ x, data = dat)
    #>
    #> Coefficients:
    #> (Intercept)           x
    #>    9.916678    2.518456
    #>
    #> Central moments of initial residuals:
    #>   m2 = 0.9555941
    #>   m4 = 1.658808
    #>   m6 = 3.424153
    #>
    #> Theoretical characteristics of PMM3 (S = 3):
    #>   gamma4 = -1.183442
    #>   gamma6 = 6.675667
    #>   g3     = 0.3082707  (expected ratio Var[PMM3]/Var[OLS])
    #>   kappa  = 1.231908
    #>
    #> Algorithm information:
    #>   Convergence status: TRUE
    #>   Iterations: 3

### Compare with OLS

    comparison <- data.frame(
      Parameter = c("Intercept", "Slope"),
      True  = c(beta_0, beta_1),
      OLS   = coef(fit_ols),
      PMM3  = coef(fit_pmm3),
      Diff  = coef(fit_pmm3) - coef(fit_ols)
    )
    print(comparison, row.names = FALSE)
    #>  Parameter True      OLS     PMM3        Diff
    #>  Intercept 10.0 9.804314 9.916678  0.11236314
    #>      Slope  2.5 2.537942 2.518456 -0.01948666

    cat("\nResidual sum of squares:\n")
    #>
    #> Residual sum of squares:
    cat("  OLS:  ", sum(residuals(fit_ols)^2), "\n")
    #>   OLS:   286.6782
    cat("  PMM3: ", sum(residuals(fit_pmm3)^2), "\n")
    #>   PMM3:  287.1956

    cat("\nTheoretical variance ratio g3 =",
        fit_pmm3@g_coefficient, "\n")
    #>
    #> Theoretical variance ratio g3 = 0.3082707
    cat("Expected variance reduction:",
        round((1 - fit_pmm3@g_coefficient) * 100, 1), "%\n")
    #> Expected variance reduction: 69.2 %

## Example 2: Three-Way Comparison (OLS vs PMM2 vs PMM3)

For symmetric platykurtic errors, PMM3 should outperform both OLS and PMM2. PMM2 should offer no improvement since \(\gamma_3 \approx 0\).

    # Fit all three methods
    fit_ols  <- lm(y ~ x, data = dat)
    fit_pmm2 <- lm_pmm2(y ~ x, data = dat)
    fit_pmm3 <- lm_pmm3(y ~ x, data = dat)

    comparison3 <- data.frame(
      Method = c("OLS", "PMM2", "PMM3"),
      Intercept = c(coef(fit_ols)[1], coef(fit_pmm2)[1], coef(fit_pmm3)[1]),
      Slope     = c(coef(fit_ols)[2], coef(fit_pmm2)[2], coef(fit_pmm3)[2])
    )
    print(comparison3, row.names = FALSE, digits = 5)
    #>  Method Intercept  Slope
    #>     OLS    9.8043 2.5379
    #>    PMM2    9.8058 2.5376
    #>    PMM3    9.9167 2.5185

    cat("\nTrue values: Intercept =", beta_0, ", Slope =", beta_1, "\n")
    #>
    #> True values: Intercept = 10 , Slope = 2.5
    cat("\nEfficiency factors:\n")
    #>
    #> Efficiency factors:
    vf2 <- pmm2_variance_factor(fit_pmm2@m2, fit_pmm2@m3, fit_pmm2@m4)
    cat("  PMM2 g2 =", vf2$g2,
        " (improvement:", round((1 - vf2$g2) * 100, 1), "%)\n")
    #>   PMM2 g2 =  (improvement:  %)
    cat("  PMM3 g3 =", fit_pmm3@g_coefficient,
        " (improvement:", round((1 - fit_pmm3@g_coefficient) * 100, 1), "%)\n")
    #>   PMM3 g3 = 0.3082707  (improvement: 69.2 %)

With symmetric platykurtic errors, PMM2’s \(g_2 \approx 1\) (no gain), while PMM3’s \(g_3 \ll 1\) (substantial gain).

## Example 3: Real Data Diagnostic — Auto MPG Horsepower

The `auto_mpg` dataset contains fuel consumption data for 398 vehicles. The regression of MPG vs Horsepower (quadratic model) is a useful practical diagnostic case because it is nearly symmetric but has positive, not negative, excess kurtosis. The current PMM3 implementation targets symmetric platykurtic residuals, so `pmm_dispatch()` should reject PMM3 for this example.

    data(auto_mpg)

    # Remove missing horsepower values
    auto_complete <- na.omit(auto_mpg[, c("mpg", "horsepower")])

    # Visualize the relationship (clearly nonlinear)
    plot(auto_complete$horsepower, auto_complete$mpg,
         main = "MPG vs Horsepower",
         xlab = "Horsepower", ylab = "Miles per Gallon",
         col = "steelblue", pch = 16, cex = 0.8)

### Fit Quadratic Model

    # Quadratic OLS
    fit_auto_ols <- lm(mpg ~ horsepower + I(horsepower^2), data = auto_complete)

    # Check residual properties
    res_auto <- residuals(fit_auto_ols)
    cat("Residual diagnostics:\n")
    #> Residual diagnostics:
    cat("  Skewness (gamma3):", round(pmm_skewness(res_auto), 3), "\n")
    #>   Skewness (gamma3): 0.218
    cat("  Excess kurtosis (gamma4):", round(pmm_kurtosis(res_auto), 3), "\n")
    #>   Excess kurtosis (gamma4): 1.299
    sym_test <- test_symmetry(res_auto)
    cat("  Symmetric:", sym_test$is_symmetric, "\n")
    #>   Symmetric: TRUE

    # Dispatch recommendation
    dispatch_auto <- pmm_dispatch(res_auto)
    #> PMM method dispatch
    #>   n = 392 | gamma3 = +0.218 | gamma4 = +1.299
    #>   g2(PMM2) = 0.9856 | g3(PMM3) = 0.8951
    #>   Selected: OLS (expected variance reduction: 0.0%)
    #>   Reason: gamma3 = 0.218, gamma4 = 1.299: near-Gaussian residuals. No PMM advantage expected. Use OLS.

### Dispatcher Recommendation

    # Dispatcher-selected workflow: keep OLS, because gamma4 is positive.
    cat("Dispatcher method:", dispatch_auto$method, "\n")
    #> Dispatcher method: OLS
    cat("Reason:", dispatch_auto$reasoning, "\n")
    #> Reason: gamma3 = 0.218, gamma4 = 1.299: near-Gaussian residuals. No PMM advantage expected. Use OLS.

This example is intentionally included as a guardrail: near-symmetry alone is not sufficient for PMM3. PMM3 is appropriate when the residuals are both near-symmetric and platykurtic (negative excess kurtosis).

### Visualize the OLS Fit

    hp_seq <- seq(min(auto_complete$horsepower),
                  max(auto_complete$horsepower), length.out = 200)
    newdata <- data.frame(horsepower = hp_seq)

    pred_ols  <- predict(fit_auto_ols, newdata = newdata)

    plot(auto_complete$horsepower, auto_complete$mpg,
         main = "MPG vs Horsepower: OLS Quadratic Fit",
         xlab = "Horsepower", ylab = "Miles per Gallon",
         col = "gray70", pch = 16, cex = 0.7)
    lines(hp_seq, pred_ols, col = "blue", lwd = 2)
    legend("topright", legend = "OLS", col = "blue", lty = 1, lwd = 2)

## Example 4: Real Data — Auto MPG (Linear, PMM2 Case)

For contrast, the regression of MPG vs Acceleration produces asymmetric residuals (\(\gamma_3 \approx 0.5\)), where PMM2 is appropriate instead.

    # Linear model: MPG vs Acceleration
    fit_accel_ols <- lm(mpg ~ acceleration, data = auto_mpg)
    res_accel <- residuals(fit_accel_ols)

    cat("Acceleration residual diagnostics:\n")
    #> Acceleration residual diagnostics:
    cat("  Skewness (gamma3):", round(pmm_skewness(res_accel), 3), "\n")
    #>   Skewness (gamma3): 0.497
    cat("  Excess kurtosis (gamma4):", round(pmm_kurtosis(res_accel), 3), "\n")
    #>   Excess kurtosis (gamma4): -0.33

    # Dispatch
    dispatch_accel <- pmm_dispatch(res_accel)
    #> PMM method dispatch
    #>   n = 398 | gamma3 = +0.497 | gamma4 = -0.330
    #>   g2(PMM2) = 0.8519 | g3(PMM3) = 0.9779
    #>   Selected: PMM2 (expected variance reduction: 14.8%)
    #>   Reason: |gamma3| = 0.497 > 0.3 and g2 = 0.852 < 0.95: moderate asymmetry, PMM2 worthwhile (14.8% reduction).

    # Compare
    fit_accel_pmm2 <- lm_pmm2(mpg ~ acceleration, data = auto_mpg, na.action = na.omit)
    vf2_accel <- pmm2_variance_factor(fit_accel_pmm2@m2, fit_accel_pmm2@m3, fit_accel_pmm2@m4)
    cat("\nPMM2 g2 =", vf2_accel$g2,
        " (improvement:", round((1 - vf2_accel$g2) * 100, 1), "%)\n")
    #>
    #> PMM2 g2 =  (improvement:  %)

This demonstrates how `pmm_dispatch()` guides users to the correct method: PMM3 for symmetric platykurtic residuals, PMM2 for asymmetric residuals.

## Example 5: Multiple Regression

PMM3 extends naturally to multiple predictors.

    n <- 300
    x1 <- rnorm(n)
    x2 <- runif(n, -2, 2)
    x3 <- rnorm(n, 3, 1)

    # True parameters
    beta <- c(5, 1.5, -0.8, 2.0)

    # Beta-symmetric errors (platykurtic, gamma4 ≈ -0.86)
    errors_beta <- (rbeta(n, 3, 3) - 0.5) * sqrt(12 * 3)

    y_multi <- beta[1] + beta[2]*x1 + beta[3]*x2 + beta[4]*x3 + errors_beta
    dat_multi <- data.frame(y = y_multi, x1 = x1, x2 = x2, x3 = x3)

    # Fit
    fit_multi_ols  <- lm(y ~ x1 + x2 + x3, data = dat_multi)
    fit_multi_pmm3 <- lm_pmm3(y ~ x1 + x2 + x3, data = dat_multi)

    comparison_multi <- data.frame(
      Parameter = c("Intercept", "x1", "x2", "x3"),
      True  = beta,
      OLS   = coef(fit_multi_ols),
      PMM3  = coef(fit_multi_pmm3),
      Diff  = coef(fit_multi_pmm3) - coef(fit_multi_ols)
    )
    print(comparison_multi, row.names = FALSE, digits = 4)
    #>  Parameter True     OLS    PMM3      Diff
    #>  Intercept  5.0  4.9943  4.9668 -0.027433
    #>         x1  1.5  1.6223  1.6114 -0.010928
    #>         x2 -0.8 -0.7644 -0.7735 -0.009073
    #>         x3  2.0  1.9923  1.9992  0.006979

    cat("\ng3 =", fit_multi_pmm3@g_coefficient,
        " (improvement:", round((1 - fit_multi_pmm3@g_coefficient) * 100, 1), "%)\n")
    #>
    #> g3 = 0.9000364  (improvement: 10 %)

## Example 6: No-Intercept Model

PMM3 supports models without an intercept term.

    # Model through the origin
    y_noint <- 3 * x1 + errors_beta[1:n]
    dat_noint <- data.frame(y = y_noint, x1 = x1)

    fit_noint <- lm_pmm3(y ~ x1 - 1, data = dat_noint)
    cat("True slope: 3\n")
    #> True slope: 3
    cat("PMM3 estimate:", coef(fit_noint), "\n")
    #> PMM3 estimate: 3.11516
    cat("Converged:", fit_noint@convergence, "\n")
    #> Converged: TRUE

## Interpreting the PMM3 Summary

The `summary()` method reports several PMM3-specific quantities:

    summary(fit_pmm3)
    #> PMM3 estimation results (S = 3, symmetric errors)
    #> Call:
    #> lm_pmm3(formula = y ~ x, data = dat)
    #>
    #> Coefficients:
    #> (Intercept)           x
    #>    9.916678    2.518456
    #>
    #> Central moments of initial residuals:
    #>   m2 = 0.9555941
    #>   m4 = 1.658808
    #>   m6 = 3.424153
    #>
    #> Theoretical characteristics of PMM3 (S = 3):
    #>   gamma4 = -1.183442
    #>   gamma6 = 6.675667
    #>   g3     = 0.3082707  (expected ratio Var[PMM3]/Var[OLS])
    #>   kappa  = 1.231908
    #>
    #> Algorithm information:
    #>   Convergence status: TRUE
    #>   Iterations: 3

Quantity | Meaning
--- | ---
m2, m4, m6 | Central moments of the initial (OLS) residuals
gamma4 | Excess kurtosis: negative = platykurtic, zero = Gaussian
gamma6 | Sixth-order cumulant coefficient
g3 | Theoretical efficiency factor: \(\text{Var}(\hat\beta_{\text{PMM3}}) / \text{Var}(\hat\beta_{\text{OLS}})\)
kappa | Moment ratio \(m_4/m_2\) used in the NR score equations
Convergence | Whether the Newton-Raphson algorithm converged
Iterations | Number of NR iterations performed

Rule of thumb: If \(g_3 < 0.90\), PMM3 provides a meaningful improvement over OLS (\(> 10\%\) variance reduction).

## Adaptive Mode

By default, PMM3 computes \(\kappa\) from OLS residuals and holds it fixed throughout the Newton-Raphson iterations. In adaptive mode, \(\kappa\) is re-estimated from the current residuals at each iteration:

    fit_fixed    <- lm_pmm3(y ~ x, data = dat, adaptive = FALSE)
    fit_adaptive <- lm_pmm3(y ~ x, data = dat, adaptive = TRUE)

    comparison_adapt <- data.frame(
      Mode       = c("Fixed kappa", "Adaptive kappa"),
      Intercept  = c(coef(fit_fixed)[1], coef(fit_adaptive)[1]),
      Slope      = c(coef(fit_fixed)[2], coef(fit_adaptive)[2]),
      Iterations = c(fit_fixed@iterations, fit_adaptive@iterations)
    )
    print(comparison_adapt, row.names = FALSE, digits = 5)
    #>            Mode Intercept  Slope Iterations
    #>     Fixed kappa    9.9167 2.5185          3
    #>  Adaptive kappa    9.9177 2.5183          4

Adaptive mode can improve estimates when the error distribution departs significantly from what the initial OLS residuals suggest, at the cost of additional iterations.

## Diagnostic Plots

PMM3 provides four diagnostic plots:

    plot(fit_pmm3)

1. Residuals vs Fitted — check for non-linearity and heteroscedasticity
2. Q-Q Plot — platykurtic residuals show lighter tails (compressed ends)
3. Scale-Location — check homoscedasticity
4. Residual Histogram — platykurtic distributions appear flatter than a bell curve, often with “chopped” tails

## Utility Functions

### Automatic Method Selection

    # Symmetric platykurtic errors → PMM3
    pmm_dispatch(runif(500, -1, 1))
    #> PMM method dispatch
    #>   n = 500 | gamma3 = -0.026 | gamma4 = -1.272
    #>   g2(PMM2) = 0.9990 | g3(PMM3) = 0.2438
    #>   Selected: PMM3 (expected variance reduction: 75.6%)
    #>   Reason: |gamma3| = 0.026 < 0.3 (symmetric) and gamma4 = -1.272 < -0.7 (platykurtic). PMM3 expected to reduce variance by 75.6%.

    # Asymmetric errors → PMM2
    pmm_dispatch(rexp(500) - 1)
    #> PMM method dispatch
    #>   n = 500 | gamma3 = +2.351 | gamma4 = +8.946
    #>   g2(PMM2) = 0.4953 | g3(PMM3) = 0.7901
    #>   Selected: PMM2 (expected variance reduction: 50.5%)
    #>   Reason: |gamma3| = 2.351 > 1.0: strong asymmetry detected. PMM2 expected to reduce variance by 50.5%.

    # Gaussian errors → OLS
    pmm_dispatch(rnorm(500))
    #> PMM method dispatch
    #>   n = 500 | gamma3 = -0.001 | gamma4 = +0.007
    #>   g2(PMM2) = 1.0000 | g3(PMM3) = 1.0000
    #>   Selected: OLS (expected variance reduction: 0.0%)
    #>   Reason: gamma3 = -0.001, gamma4 = 0.007: near-Gaussian residuals. No PMM advantage expected. Use OLS.

### Testing Symmetry

    test_symmetry(residuals(fit_ols))
    #> $gamma3
    #> [1] 0.02563207
    #>
    #> $is_symmetric
    #> [1] TRUE
    #>
    #> $message
    #> [1] "|gamma3| = 0.026 <= 0.3: residuals are symmetric. PMM3 may be appropriate."

### Computing Moments

    mom <- compute_moments_pmm3(residuals(fit_ols))
    cat("m2 =", mom$m2, "\n")
    #> m2 = 0.9555941
    cat("m4 =", mom$m4, "\n")
    #> m4 = 1.658808
    cat("m6 =", mom$m6, "\n")
    #> m6 = 3.424153
    cat("gamma4 =", mom$gamma4, "\n")
    #> gamma4 = -1.183442
    cat("kappa =", mom$kappa, "\n")
    #> kappa = 1.231908
    cat("g3 =", mom$g3, "\n")
    #> g3 = 0.3082707

### Sixth-Order Cumulant

    pmm_gamma6(residuals(fit_ols))
    #> [1] 6.675667

### Variance Factor

    pmm3_variance_factor(mom$m2, mom$m4, mom$m6)
    #> $gamma4
    #> [1] -1.183442
    #>
    #> $gamma6
    #> [1] 6.675667
    #>
    #> $g3
    #> [1] 0.3082707

## When to Use PMM3

Use PMM3 when:

- Residuals are symmetric (\(|\gamma_3| \leq 0.3\))
- Residuals are platykurtic (\(\gamma_4 < -0.7\))
- Error distributions are bounded (uniform, beta-symmetric, truncated normal)
- Sample size is moderate to large (\(n \geq 100\), ideally \(n \geq 200\))

Use PMM2 instead when:

- Residuals are skewed (\(|\gamma_3| > 0.3\))

Stick with OLS when:

- Residuals are approximately Gaussian (\(|\gamma_4| < 0.7\), \(|\gamma_3| < 0.3\))
- Very small samples (\(n < 50\))

### Decision Workflow

1. Fit OLS as baseline
2. Check residuals: `pmm_skewness()`, `pmm_kurtosis()`, `test_symmetry()`
3. Use `pmm_dispatch()` for automatic recommendation
4. Refit with the recommended method
5. Compare coefficients and efficiency factors

## Efficiency Summary (Monte Carlo Evidence)

Based on Monte Carlo simulations (R = 1000 replications):

Error Distribution | \(\gamma_4\) | \(g_3\) | Variance Reduction
--- | --- | --- | ---
Uniform | \(-1.20\) | \(0.64\) | 36%
Beta(2,2) symmetric | \(-0.86\) | \(0.78\) | 22%
Beta(3,3) symmetric | \(-0.57\) | \(0.88\) | 12%
Truncated Normal (\(|x| \leq 2\)) | \(-0.47\) | \(0.91\) | 9%
Gaussian (control) | \(0.00\) | \(1.00\) | 0%

Key insight: The more platykurtic the errors (\(\gamma_4\) further from 0), the larger the efficiency gain from PMM3.

## Next Steps

- PMM3 Time Series: See `vignette("pmm3_time_series")` for AR, MA, ARMA, ARIMA models with PMM3
- PMM2 Introduction: See `vignette("pmm2_introduction")` for asymmetric error estimation
- PMM2 Time Series: See `vignette("pmm2_time_series")` for PMM2 time series models
- Bootstrap Inference: See `vignette("bootstrap_inference")`

## References

1. Zabolotnii S., Warsza Z.L., Tkachenko O. (2018). Polynomial Estimation of Linear Regression Parameters for the Asymmetric PDF of Errors. Springer AISC, vol 743. doi:10.1007/978-3-319-77179-3_75

2. Package functions: `?lm_pmm3`, `?pmm_dispatch`, `?compute_moments_pmm3`, `?test_symmetry`, `?pmm_gamma6`

3. GitHub: https://github.com/SZabolotnii/EstemPMM