Introduction to sshist

The sshist package provides a suite of tools for optimal density estimation based on the work of Shimazaki and Shinomoto (2007, 2010). The core idea — minimizing the expected Mean Integrated Squared Error (MISE) between an estimator and the unknown underlying density — is available in four flavors:

Function Estimator Dimension Bandwidth
sshist Histogram 1D Fixed (single bin width)
sshist_2d Histogram 2D Fixed per axis
sskernel Kernel density 1D Fixed global
ssvkernel Kernel density 1D Locally adaptive (Abramson)
sskernel2d Kernel density 2D Fixed global (isotropic)
ssvkernel2d Kernel density 2D Locally adaptive (Abramson)

These methods are particularly useful when data exhibits multimodal structure, because naive rules (Sturges, Freedman–Diaconis) tend to over- or under-smooth.

Note: The package supports OpenMP C++ multithreading and parallel bootstrapping out of the box. For CRAN compliance, it defaults to 1 core. You can globally increase this by setting the option below.

library(sshist)
options(sshist.ncores = 2) # Use 2 cores for vignette building

1. Optimal 1D Histogram: sshist

The sshist function performs an exhaustive search over candidate bin counts to find the optimal bin width that minimizes the MISE cost function.

Complete Parameter List:

Comparison with the standard approach

data(faithful)
x_data <- faithful$eruptions

oldpar <- par(mfrow = c(1, 2))

# Standard R histogram (Sturges rule)
hist(x_data,
     main = "Standard hist()  (Sturges)",
     xlab = "Eruption duration (min)", col = "grey90")

# Shimazaki-Shinomoto optimal histogram
res <- sshist(x_data)
hist(x_data, breaks = res$edges,
     main = paste0("sshist()  (N = ", res$opt_n, " bins)"),
     xlab = "Eruption duration (min)", col = "grey90")


par(oldpar)

sshist automatically detects the bimodal structure that the default rule obscures.

The S3 object

sshist() returns an object of class "sshist". The plot() method draws the optimal histogram with a data rug.

print(res)
#> Shimazaki-Shinomoto Histogram Optimization
#> ------------------------------------------
#> Optimal Bins (N): 20 
#> Bin Width (D):    0.175
plot(res)

2. Optimal 1D Kernel Density: sskernel & ssvkernel

For smooth continuous distributions, sskernel evaluates a single fixed global bandwidth, while ssvkernel computes locally adaptive bandwidths based on Abramson’s square-root scaling law. Both use a fast FFT implementation.

Complete Parameter List:

Fixed Global Bandwidth (sskernel)

Estimates a 1D density using a Gaussian kernel with the globally optimal bandwidth.

res_k <- sskernel(x_data)
print(res_k)
#> Shimazaki-Shinomoto Kernel Density Estimation
#> ----------------------------------------------
#> Optimal Bandwidth: 0.1103 
#> Grid Points:       1000
plot(res_k, xlab = "Eruption duration (min)")

Locally Adaptive Bandwidth (ssvkernel)

Uses a stiffness parameter gamma (optimized via MISE) to control how much the bandwidth can vary locally. A Nadaraya–Watson smoother with the selected window function (WinFunc) stabilizes the bandwidth sequence.

res_sv <- ssvkernel(x_data)
print(res_sv)
#> Shimazaki-Shinomoto Adaptive Kernel Density Estimation
#> -------------------------------------------------------
#> Optimal Stiffness (gamma): 1 
#> Grid Points:               1000 
#> Bandwidth Range:           0.04199282 -- 0.2519655
plot(res_sv, xlab = "Eruption duration (min)")

Estimating Uncertainty with Bootstrap

By setting the nbs parameter, you can instruct the function to perform a multi-core bootstrap resampling. For sskernel, this is a standard non-parametric bootstrap. For ssvkernel, it utilizes a Poisson bootstrap suited for point processes.

When bootstrap intervals are calculated, the S3 plot() method automatically overlays a 90% confidence band (between the 5th and 95th percentiles).

# Run 300 bootstrap iterations using 2 cores
res_boot <- sskernel(x_data, nbs = 300)
print(res_boot)
#> Shimazaki-Shinomoto Kernel Density Estimation
#> ----------------------------------------------
#> Optimal Bandwidth: 0.1103 
#> Grid Points:       1000 
#> Bootstrap CI:      90%  ( 300 samples)

# The plot function automatically detects and renders the confb95 band
plot(res_boot, 
     main = "Optimal KDE with 90% Bootstrap Confidence Band", 
     xlab = "Eruption duration (min)",
     col = "#d6604d", 
     band_col = adjustcolor("#d6604d", alpha.f = 0.2))


res_sv_boot <- ssvkernel(x_data, nbs = 300, WinFunc = "Gauss")

print(res_sv_boot)
#> Shimazaki-Shinomoto Adaptive Kernel Density Estimation
#> -------------------------------------------------------
#> Optimal Stiffness (gamma): 0.9947 
#> Grid Points:               1000 
#> Bandwidth Range:           0.0490827 -- 0.2415164 
#> Bootstrap CI:              90%  ( 300 samples)

# The plot function automatically detects and renders the confb95 band
plot(res_sv_boot, 
     main = "Locally Adaptive KDE with 90% Bootstrap Confidence Band", 
     xlab = "Eruption duration (min)",
     col = "#d6604d", 
     band_col = adjustcolor("#d6604d", alpha.f = 0.2))

Note: The raw density values for every bootstrap iteration are preserved in the yb matrix inside the returned object, giving you full flexibility to calculate custom percentiles if needed.

3. Optimal 2D Density Estimation

When analyzing bivariate relationships, the package offers both grid-based histograms and smooth kernel density estimators.

2D Histogram: sshist_2d

Solves the X and Y bin-count problem simultaneously using the same MISE cost function extended to two dimensions.

Complete Parameter List:

res_2d <- sshist_2d(faithful$waiting, faithful$eruptions)
print(res_2d)
#> Shimazaki-Shinomoto 2D Histogram Optimization
#> ---------------------------------------------
#> Optimal Bins X:   9 
#> Optimal Bins Y:   20 
#> Bin Width X:      5.889 
#> Bin Width Y:      0.175

# plot() renders the optimal 2D histogram as a heatmap
plot(res_2d, xlab = "Waiting time (min)", ylab = "Eruption duration (min)")

2D Kernel Density: sskernel2d and ssvkernel2d

Complete Parameter List:

sskernel2d finds the globally optimal isotropic bandwidth. To handle variables on different scales, the data are internally standardized before optimization; the resulting bandwidth is then rescaled back to each original axis.

df <- faithful   # eruptions: ~2-5 min,  waiting: ~40-90 min

# Fixed global bandwidth (C++ accelerated)
res_2d_fixed <- sskernel2d(df$eruptions, df$waiting, n_grid = 256)
print(res_2d_fixed)
#> Shimazaki-Shinomoto 2D Kernel Density Estimation
#> -------------------------------------------------
#> Optimal Bandwidth X: 0.1947 
#> Optimal Bandwidth Y: 2.319 
#> Grid Size:           256 x 256
plot(res_2d_fixed,
     xlab = "Eruption duration (min)",
     ylab = "Waiting time (min)")

ssvkernel2d wraps sskernel2d as a pilot estimator and applies Abramson’s square-root law to derive a per-point local bandwidth.

# Locally adaptive bandwidth (Abramson's method)
res_2d_adap <- ssvkernel2d(df$eruptions, df$waiting,
                            n_grid = 256, sensitivity = 0.5)
print(res_2d_adap)
#> Shimazaki-Shinomoto Adaptive 2D Kernel Density Estimation
#> ----------------------------------------------------------
#> Pilot Bandwidth X:   0.1947 
#> Pilot Bandwidth Y:   2.319 
#> Grid Size:           256 x 256 
#> Lambda Range:        0.653 -- 3.33   (median 0.921 )
plot(res_2d_adap,
     xlab = "Eruption duration (min)",
     ylab = "Waiting time (min)")

The adaptive estimator dramatically sharpens the two high-density clusters while smoothing the near-empty valley between them — dynamically matching local data density.

References