Algorithm 905: Sheppack: modified Shepard algorithm for interpolation of scattered multivariate data. Scattered data interpolation problems arise in many applications. Shepard’s method for constructing a global interpolant by blending local interpolants using local-support weight functions usually creates reasonable approximations. SHEPPACK is a Fortran 95 package containing five versions of the modified Shepard algorithm: quadratic (Fortran 95 translations of Algorithms 660, 661, and 798), cubic (Fortran 95 translation of Algorithm 791), and linear variations of the original Shepard algorithm. An option to the linear Shepard code is a statistically robust fit, intended to be used when the data is known to contain outliers. SHEPPACK also includes a hybrid robust piecewise linear estimation algorithm RIPPLE (residual initiated polynomial-time piecewise linear estimation) intended for data from piecewise linear functions in arbitrary dimension m. The main goal of SHEPPACK is to provide users with a single consistent package containing most existing polynomial variations of Shepard’s algorithm. The algorithms target data of different dimensions. The linear Shepard algorithm, robust linear Shepard algorithm, and RIPPLE are the only algorithms in the package that are applicable to arbitrary dimensional data.

This software is also peer reviewed by journal TOMS.

References in zbMATH (referenced in 16 articles , 1 standard article )

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  1. Kazem, Saeed; Hatam, A.: Scattered data interpolation: strictly positive definite radial basis/cardinal functions (2021)
  2. Lux, Thomas C. H.; Watson, Layne T.; Chang, Tyler H.; Hong, Yili; Cameron, Kirk: Interpolation of sparse high-dimensional data (2021)
  3. Nouisser, Otheman; Zerroudi, Benaissa: Modified Shepard’s method by six-points local interpolant (2021)
  4. Brás, C. P.; Custódio, A. L.: On the use of polynomial models in multiobjective directional direct search (2020)
  5. Dell’Accio, Francesco; Di Tommaso, Filomena: On the hexagonal Shepard method (2020)
  6. Francomano, Elisa; Paliaga, Marta: A normalized iterative smoothed particle hydrodynamics method (2020)
  7. Cavoretto, Roberto; De Rossi, Alessandra; Dell’Accio, Francesco; Di Tommaso, Filomena: Fast computation of triangular Shepard interpolants (2019)
  8. Esmaeilbeigi, Mohsen; Chatrabgoun, Omid: An efficient method based on RBFs for multilayer data interpolation with application in air pollution data analysis (2019)
  9. Francomano, Elisa; Paliaga, Marta: The smoothed particle hydrodynamics method via residual iteration (2019)
  10. Dell’Accio, F.; Di Tommaso, F.; Nouisser, O.; Zerroudi, B.: Increasing the approximation order of the triangular Shepard method (2018)
  11. Francomano, E.; Paliaga, M.: Highlighting numerical insights of an efficient SPH method (2018)
  12. Cavoretto, Roberto; De Rossi, Alessandra: A trivariate interpolation algorithm using a cube-partition searching procedure (2015)
  13. Cavoretto, Roberto; De Rossi, Alessandra: A meshless interpolation algorithm using a cell-based searching procedure (2014)
  14. Viana, Felipe A. C.; Haftka, Raphael T.; Watson, Layne T.: Efficient global optimization algorithm assisted by multiple surrogate techniques (2013)
  15. Viana, Felipe A. C.; Haftka, Raphael T.; Watson, Layne T.: Sequential sampling for contour estimation with concurrent function evaluations (2012)
  16. Thacker, William I.; Zhang, Jingwei; Watson, Layne T.; Birch, Jeffrey B.; Iyer, Manjula A.; Berry, Michael W.: Algorithm 905: SHEPPACK: modified Shepard algorithm for interpolation of scattered multivariate data (2010)