A Python module to perform exploratory & confirmatory factor analyses.
Full Changelog: https://github.com/EducationalTestingService/factor_analyzer/compare/v0.5.0...v0.5.1
scipy.minimize
changes by @ikeuchi-screen in https://github.com/EducationalTestingService/factor_analyzer/pull/124
nose
to nose2
by @desilinguist in https://github.com/EducationalTestingService/factor_analyzer/pull/127
Full Changelog: https://github.com/EducationalTestingService/factor_analyzer/compare/v0.4.1...v0.5.0
This is a minor release with a few new features, improvements, and bugfixes.
IMPORTANT: We no longer support Python 3.7 or older.
new_order
variable by @jbiggsets in https://github.com/EducationalTestingService/factor_analyzer/pull/98
psi
and use correct phi_
. by @desilinguist in https://github.com/EducationalTestingService/factor_analyzer/pull/113
Full Changelog: https://github.com/EducationalTestingService/factor_analyzer/compare/v0.4.0...v0.4.1
This is a minor release with a few new features, improvements, and bugfixes.
IMPORTANT: Although factor_analyzer
can work on Python < 3.7, we do not support these older versions.
Full Changelog: https://github.com/EducationalTestingService/factor_analyzer/compare/v0.3.2...v0.4.0
This is a minor release of factor_analyzer
. It includes the following bug fixes.
oblimin
is calculated when using gamma
.ddof
.NaN
s.This is a hotfix release, which includes two primary updates:
ConfirmatoryFactorAnalyzer
class's fit()
method now returns self
.sklearn
, numpy
, and scipy
.This is a major release which includes a number of improvements, primarily aimed at providing more functionality for factor_analyzer
, and making it compatible with scikit-learn
.
The factor_analyzer
package now includes a confirmatory_factor_analyzer
module, which allows enables to fit a CFA model by specifying the target factor loading matrix. This is not as full-featured as some CFA functions that may be available in other packages (such as R's sea
or lavaan
libraries), but it provides basic functionality to perform CFA. Some of the major limitations include (1) users cannot specify a target variance-covariance matrix for the factor loadings, and (2) users cannot specify other identification constraints. These are features that we may add in a future release.
All major factor_analyzer
classes are not fully compatible with scikit-learn
. This includes the Rotator
, FactorAnalyzer
, and ConfirmatoryFactorAnalyzer
classes. These classes now inherit from scikit-learn
's BaseEstimator
class and implement fit()
and transform()
methods. Users can now use objects from these classes in sklearn
pipelines.
Along with the ConfirmatoryFactorAnalyzer
class, factor_analyzer
provides a ModelSpecification
class (and an associated ModelSpecificationParser
class) to encapsulate the model specification for CFA. This primarily involves the specification of a target factor loading matrix.
The transform()
methods have been modified slightly to rely on the mean / standard deviation from the original data set when generating factor scores.
The ConfirmatoryFactorAnalyzer
class also provides standard error estimates.
Various new utility functions have been added.