Package: mlfit 0.6.1.9008
Amarin Siripanich
mlfit: Iterative Proportional Fitting Algorithms for Nested Structures
The Iterative Proportional Fitting (IPF) algorithm operates on count data. This package offers implementations for several algorithms that extend this to nested structures: 'parent' and 'child' items for both of which constraints can be provided. The fitting algorithms include Iterative Proportional Updating <https://trid.trb.org/view/881554>, Hierarchical IPF <doi:10.3929/ethz-a-006620748>, Entropy Optimization <https://trid.trb.org/view/881144>, and Generalized Raking <doi:10.2307/2290793>. Additionally, a number of replication methods is also provided such as 'Truncate, replicate, sample' <doi:10.1016/j.compenvurbsys.2013.03.004>.
Authors:
mlfit_0.6.1.9008.tar.gz
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mlfit_0.6.1.9008.tgz(r-4.4-any)mlfit_0.6.1.9008.tgz(r-4.3-any)
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mlfit.pdf |mlfit.html✨
mlfit/json (API)
| # Install 'mlfit' in R: |
| install.packages('mlfit', repos = c('https://mlfit.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlfit/mlfit/issues
Last updated 23 hours agofrom:d42edaa9c7. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 22 2024 |
| R-4.5-win | OK | Nov 22 2024 |
| R-4.5-linux | OK | Nov 22 2024 |
| R-4.4-win | OK | Nov 22 2024 |
| R-4.4-mac | OK | Nov 22 2024 |
| R-4.3-win | OK | Nov 22 2024 |
| R-4.3-mac | OK | Nov 22 2024 |
Exports:as_flat_ml_fit_problemcompute_marginsdssfitting_problemflatten_ml_fit_problemgginvis_ml_fitis_ml_problemmargin_to_dfml_fitml_fit_dssml_fit_entropy_oml_fit_hipfml_fit_ipuml_problemml_replicatespecial_field_namestoy_example
Dependencies:BBcachemclicodetoolscpp11crayondplyrfansifastmapforcatsgenericsgluehmskimisclatticelifecyclelobstrloggingmagrittrMatrixmemoisepillarpkgconfigplyrprettyunitspryrquadprogR6Rcpprlangstringistringrtibbletidyselectutf8vctrswithrwrswoR
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| mlfit: Iterative Proportional Fitting Algorithms for Nested Structures | mlfit-package mlfit |
| Compute margins for a weighting of a multi-level fitting problem | compute_margins margin_to_df |
| Calibrate sample weights | dss |
| Return a flattened representation of a multi-level fitting problem instance | as_flat_ml_fit_problem flatten_ml_fit_problem |
| Generalized Inverse of a Matrix using a custom tolerance or SVD implementation | gginv |
| Estimate weights for a fitting problem | format.ml_fit is_ml_fit ml_fit ml_fit_dss ml_fit_entropy_o ml_fit_hipf ml_fit_ipu print.ml_fit |
| Create an instance of a fitting problem | format.ml_problem is_ml_problem ml_problem print.ml_problem special_field_names |
| Replicate records in a reference sample based on its fitted weights | ml_replicate |
| Access to toy examples bundled in this package | toy_example |
