KrigeKit v0.3.2: a parallel Python–Fortran engine for variogram analysis, kriging, and geostatistical simulation

Abstract. Kriging and geostatistical simulation underpin environmental modeling, yet practitioners must choose between aging file-driven Fortran codes such as GSLIB and convenient but slower high-level packages. We present KrigeKit, an open-source (MIT) library pairing an OpenMP-parallel Fortran engine with a dependency-light Python layer. A single engine provides ordinary, simple, and co-kriging, kriging with external drift, sequential Gaussian and indicator simulation, and space-time kriging. Its hallmark is the breadth of advanced capabilities it exposes together — multiplicative covariance structures, gradient constraints, spatially varying anisotropy, and positive-semidefinite co-kriging fitting — seldom available in one package. Performance comes from parallel block estimation, tabular variogram evaluation, and a per-thread Cholesky cache that cuts the per-block cost from O(n3) to O(n2) on repeated neighborhoods. We verify KrigeKit against analytic results and established packages, characterize its parallel scaling, and demonstrate it on three environmental case studies. KrigeKit ships as pre-built wheels on PyPI for Linux, macOS, and Windows.