Updating to version 1.6.0

[pedrovma]

The changes in this new version are:

• Pandas objects (Series or DF) can now be passed as regression variables (y, x, yend, q, regimes), suppressing the need to explicitly provide their respective names in these cases. The names are automatically collected from the pandas objects. This applies to all regression functions.

• Implementation of Flexible WX. Through a list of Booleans in argument slx_vars, the user can now select which x variables are to be lagged. This new feature applies to OLS, TSLS, ML and GM spatial models (both lag and error).

• Kernel weights are now allowed in SLX and SDM models.

• Added Durbin-Wu-Hausman test on endogeneity of variables to TSLS.

• Spatial impacts are now computed for regression models with regimes.

• Added VIF method to OLS results.

• Added compatibility with libpysal.Graph.

[codecov]

Codecov Report

Attention: Patch coverage is 58.57988% with 210 lines in your changes missing coverage. Please review.

Project coverage is 67.6%. Comparing base (326100f) to head (ab7353f).

Files	Patch %	Lines
spreg/user_output.py	60.5%	19 Missing and 11 partials ⚠️
spreg/error_sp.py	46.2%	25 Missing and 3 partials ⚠️
spreg/error_sp_het.py	34.1%	23 Missing and 4 partials ⚠️
spreg/error_sp_hom.py	35.7%	23 Missing and 4 partials ⚠️
spreg/sputils.py	46.9%	16 Missing and 1 partial ⚠️
spreg/panel_utils.py	6.7%	12 Missing and 2 partials ⚠️
spreg/output.py	75.0%	10 Missing and 2 partials ⚠️
spreg/twosls_sp.py	58.6%	9 Missing and 3 partials ⚠️
spreg/ml_lag.py	47.1%	7 Missing and 2 partials ⚠️
spreg/probit.py	53.3%	4 Missing and 3 partials ⚠️
... and 9 more

Additional details and impacted files

@@           Coverage Diff           @@
##            main    #145     +/-   ##
=======================================
+ Coverage   67.5%   67.6%   +0.1%     
=======================================
  Files         41      41             
  Lines       9365    9626    +261     
  Branches    1370    1433     +63     
=======================================
+ Hits        6320    6508    +188     
- Misses      2651    2704     +53     
- Partials     394     414     +20     

Files	Coverage Δ
spreg/diagnostics.py	87.0% <100.0%> (+1.0%)	⬆️
spreg/error_sp_het_regimes.py	82.2% <100.0%> (+0.2%)	⬆️
spreg/error_sp_hom_regimes.py	82.0% <100.0%> (+0.2%)	⬆️
spreg/error_sp_regimes.py	64.4% <100.0%> (+0.4%)	⬆️
spreg/ml_error_regimes.py	83.6% <100.0%> (+0.1%)	⬆️
spreg/panel_fe.py	94.5% <100.0%> (ø)
spreg/sp_panels.py	87.0% <100.0%> (ø)
spreg/sur.py	76.8% <ø> (ø)
spreg/twosls_regimes.py	64.5% <100.0%> (+0.7%)	⬆️
spreg/ml_error.py	88.2% <66.7%> (+1.0%)	⬆️
... and 18 more

[martinfleis]

Just a technical - this should probably be a 1.6.0 given there's new stuff coming, not 1.5.1 indicating a patch release as per semantic versioning.

[knaaptime]

sweet. +1 on the 1.6 instead of 1.5.1

one quick question @pedrovma, is it possible to get impacts as a dataframe in the current API? specifically,

• with model.summary you get a text printout of coefs and impacts (etc),
  • with model.output, you get coefs as a dataframe,
  • but there's no equivalent like model.impacts to get you direct and indirect in pandas form, right?

[pedrovma]

one quick question @pedrovma, is it possible to get impacts as a dataframe in the current API? specifically,

* with  `model.summary` you get a text printout of coefs and impacts (etc),
  
  * with `model.output`, you get coefs as a dataframe,
  * but there's no equivalent like `model.impacts` to get you direct and indirect in pandas form, right?

You are right! What you can get is model.sp_multipliers, a dictionary with the multipliers (direct, indirect, total). There is no attribute with a pandas object containing the spatial impacts. It is something we can consider adding!

[knaaptime]

we might want to add one test that sends a graph through the estimation, just to show that it works, but otherwise, great

[pedrovma]

we might want to add one test that sends a graph through the estimation, just to show that it works, but otherwise, great

There is one here. =)

[knaaptime]

snap :P 🙌