Changelog

Source: NEWS.md

MultiLevelIPF 0.3.7.9000 (2021-03-26)

• Add ml_replicate() for replicating the reference sample of a fitted problem (#38).
• Fix the error when a level is missing from the reference sample (#32, @asiripanich).
• Fix test on Windows.
• Avoid converting sparse matrix to full matrix.
• If the controls contain values of zero for existing observations in the reference sample, the removal of these observations now works in all cases (#30).

MultiLevelIPF 0.3-7 (2018-01-22)

• Add overview in package documentation.
• Add examples to all functions.
• Explicitly document return values to ml_fit().

MultiLevelIPF 0.3-6 (2017-03-28)

• Set default maximum number of iterations for HIPF to 2000.
• Convert documentation to Markdown.

MultiLevelIPF 0.3-5 (2016-09-03)

• Add “Driven by” and “Related work” sections to the README.

MultiLevelIPF 0.3-4 (2016-08-03)

• Use a sparse matrix for the flattened reference sample.

MultiLevelIPF 0.3-3 (2016-06-06)

• Status messages with verbose = TRUE are prepended with a time stamp.
• Fail if NA group ID found.

MultiLevelIPF 0.3-2 (2016-04-25)

• Reorganized and renamed internal datasets.

MultiLevelIPF 0.3-1 (2016-04-17)

• Fitting result contains iterations and tol members (#28).
• Fixed model matrix of “separate” type if only grand totals are given.
• ml_fit() gains tol argument, which determines the success of a fitting operation.
• ml_fit objects have new members success, rel_residuals, and flat_weighted_values (#28).
• HIPF and IPU stop iterating if tolerance is reached.
• IPU and HIPF abort iteration when the weights do not change measurably between two iterations (#27).

MultiLevelIPF 0.3 (2016-04-14)

• Features
  • New algoritms: HIPF (#2) and IPU.
• Interface
  • New as.flat_ml_fit_problem() is used to coerce input for the ml_fit_ functions.
  • format() and print() methods for classes fitting_problem, flat_ml_fit_problem and ml_fit.
  • Flattened reference sample now contains observations in rows, and controls in columns (#26).
  • flatten_ml_fit_problem() gains new model_matrix_type argument that allows selecting an alternative model matrix building method where all cross-classifications are allocated to a column, regardless of overlaps. Flattened problems store the type of model matrix used, it is also shown with the format() and print() methods.
• Improvements
  • Reference sample doesn’t need to be ordered by group ID anymore.
  • Remove individualsPerGroup special variable.
  • Allow problems with individual-only controls.
  • Check for correspondence of levels between sample and controls.
  • Check for NA values in controls.
• Technical changes
  • Use grake package again for calibration, because the alternatives are worse: sampling uses a too low tolerance, survey forcibly loads MASS, and laeken could work but is unrelated (which is the reason grake has been started in the first place).
  • Duplicate rows are kept in the reference sample.
  • Rename control_totals to target_values.
  • New toy_example() allows easier access to bundled examples, load with readRDS().
  • Move legacy format (IPAF) and related functions to data-raw directory.
  • Use factors internally.
• Performance
  • Use dplyr functions instead of aggregate().
• Tests
  • Specific test for households with the same signature.
• Documentation
  • Enhance example.
  • Include flat example problem (group size = 1 for all groups).
• Cleanup
  • Cleanup and split of flatten_ml_fit_problem().

Version 0.2 (2016-01-30)

• New functions compute_margins() and margins_to_df() for validation
• Support specification of prior weights in construction of fitting problems
• Use survey::grake() instead of grake::calibWeights().
• Adapt to change of undocumented behavior in base R.
• Don’t alter column names of controls if they are of type data.table (explicitly convert to data.frame)
• Proper handling of corner cases (reference sample with one row, and grand total controls and dummy controls with only one category)
• Allow character variables (in addition to factors) as control variables
• Explicit error message if reference sample is not sorted
• If name of count column in controls is not specified, it is determined automatically (with a message in verbose mode)
• Expansion of weights loads Matrix package if necessary
• Clarify documentation
• Straighten out imports, use importFrom instead of ::