logKDE review
A focused, open-source R package for estimating densities from strictly positive data.
Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
For R users estimating a density from strictly positive observations, logKDE applies kernel density estimation to log-transformed data. It is an open-source R package, not a standalone statistical application, and its scope is narrower than general-purpose statistical software. The core choices are a direct estimator, logdensity, and logdensity_fft, which uses the fast Fourier transform. Rcpp-backed computation supports work with large data, while plotting behavior is compatible with R's plot and density-style output objects.
Its methods are the main reason to consider it. Kernel options include Gaussian, Epanechnikov, Laplace, logistic, triangular and uniform; bandwidth selection includes logcv and logg. A vignette provides mathematical descriptions and examples, so the package's documentation is aimed at users who want to understand the estimation approach as well as call its functions. It is distributed through CRAN and can also be installed from GitHub. This makes it a fit for R workflows rather than a desktop interface for analysts seeking point-and-click statistical tools.
The trade-off is focus: logKDE addresses density estimation for positive-domain data, rather than statistical testing, regression, or broader analysis needs. It is available as open-source software, with documentation and community support channels. Choose it when log-transformed kernel density estimation and its kernel or bandwidth choices match the task; users who need a wider set of statistical methods or a standalone application should look elsewhere.
logKDE pros and cons
- Where it wins
- Offers direct and FFT-based log-density estimators.
- Includes six kernel choices and logcv and logg bandwidth methods.
- Rcpp-backed computation and output compatible with plot and density styles.
- Where it doesn't
- Specialized to kernel density estimation for strictly positive data.
- Requires working in R; it is not a standalone statistical application.
- Does not provide statistical tests or regression models.
logKDE fact sheet, pricing and score →
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