Recent Releases of mlfit

mlfit - v0.6.1

What's Changed

fix(ml_replicate): Getting a replication algorithm now guarantee to w… by @asiripanich in https://github.com/mlfit/mlfit/pull/81

Full Changelog: https://github.com/mlfit/mlfit/compare/v0.6.0.9001...v0.6.1

- R
Published by asiripanich about 3 years ago

mlfit - mlfit 0.6.0 (2023-02-01)

mlfit 0.6.0 (2023-02-01)

Soft deprecated the prior_weights argument. The name of a weight column in the reference sample to be used as prior weights should be specified using the prior_weight argument in special_field_names().
the group_controls and individual_controls arguments of ml_problem() now have NULL as their default value.
Fixed ml_problem() not allowing single-level control when the geo_hierarchy argument is not NULL. (#78, @walkerke)
It is now possible to use prior weights in a geo_hierarchy ml_problem(). Simply specify the name of the weight column in your reference sample using the prior_weight argument in special_field_names(). (#78, @walkerke)

Full Changelog: https://github.com/mlfit/mlfit/compare/v0.5.3.9005...v0.6.0

- R
Published by asiripanich about 3 years ago

mlfit -

- R
Published by asiripanich over 4 years ago

mlfit -

- R
Published by asiripanich over 4 years ago

mlfit - v0.2

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 ::

- R
Published by krlmlr about 10 years ago

mlfit - v0.1-4

new functions compute_margins and margins_to_df for validation
don't alter column names of controls if they are of type data.table (explicitly convert to data.frame)

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Published by krlmlr almost 11 years ago

mlfit - v0.1-3

proper handling of corner cases (reference sample with one row)

- R
Published by krlmlr almost 11 years ago

mlfit - v0.1-2

proper handling of corner cases (grand total controls and dummy controls with only one category)
explicit error message if reference sample is not sorted

- R
Published by krlmlr almost 11 years ago

mlfit - v0.1-1

support specification of prior weights in construction of fitting problems

- R
Published by krlmlr almost 11 years ago

mlfit - v0.1

new functions fitting_problem, is.fitting_problem, special_field_names
all fitting functions now expect an object of class fitting_problem (as returned by the fitting_problem and import_IPAF_problem functions); former calls like ml_fit(ref_sample, controls, field_names) now need to be written as ml_fit(fitting_problem(...))

- R
Published by krlmlr almost 11 years ago

mlfit - v0.0-14

use grake package instead of laeken
new argument ginv to ml_fit_dss, passed down to calibWeights

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Published by krlmlr almost 11 years ago

mlfit - v0.0-13

fix example for ml_fit_dss

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Published by krlmlr almost 11 years ago

mlfit - v0.0-12

new function ml_fit_dss with an implementation very close to the paper by Deville et al. (1993); implementation in the laeken package
normalize weights to get rid of precision problems
allow partly uncontrolled attributes and controls without observations in the reference sample (with a warning, #24)
better error reporting for non-factor controls and existence of group ID column
improve warning and progress messages

- R
Published by krlmlr over 11 years ago

mlfit - v0.0-11

return correct weights -- regression introduced in v0.0.9
rewrite transformation of weights using sparse matrices and a home-grown Moore-Penrose inverse for our (very special) transformation matrix (#17)
warn on missing observations for nonzero controls (#20)
ml_fit_entropy_o also returns flat weights
allow arbitrary order in control total tables (#19)
remove observations that correspond to zero-valued control totals, with warning; don't warn if no corresponding observations need to be removed (#16)

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Published by krlmlr over 11 years ago

mlfit - v0.0-10

support multiple controls at individual or group level, also detect conflicting control totals
support fitting one-dimensional problems (where only group-level controls are given)

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Published by krlmlr over 11 years ago

mlfit - v0.0.9

new function flatten_ml_fit_problem: transform representation as returned by import_IPAF_result into a matrix, a control vector and a weights vector
function ml_fit_entropy_o: use BB::dfsane instead of BB::BBsolve for solving the optimization problem; rename argument BBsolve_args to dfsane_args
function ml_fit: new parameter verbose
aggregate identical household types, implement prior weights (so far only internally)

- R
Published by krlmlr over 11 years ago

mlfit - v0.0.8

Add example for ml_fit (#11)
allow additional arguments for the algorithms; ml_fit_entropy_o now accepts a named list BBsolve_args that contains additional arguments to BB::BBsolve
Faster internal data preparation for ml_fit_entropy_o

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Published by krlmlr over 11 years ago

mlfit - v0.0.7

Fix dependency issues (#13, #14)
Add example for ml_fit_entropy_o (#11)
Print more helpful error message if control totals and reference sample categories do not overlap (#11)

v0.0.6 (2014-02-09)

import_IPAF_results now returns a class of type IPAF_results
New functions ml_ipf and ml_ipf_entropy_o, implementation does not yet return the same weights as the Python code
Convert control columns to factors

v0.0.5 (2014-02-07)

Fix importing configuration files with more than one control of any type and with comments in the control definition
New parameter config_name to import, defaults to config.xml

v0.0.4 (2013-12-06)

Parameter all_weights to import that allows importing also intermediate weights. The output format of import has changed, the weights for each algorithm are now always a list of weight vectors, even in the default case all_weights == FALSE (#5).

v0.0.3 (2013-11-28)

Import results of old Python code (#1).

v0.0.2 (2013-11-26)

Initial setup

- R
Published by krlmlr over 11 years ago