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> Dense, machine-readable reference for LLMs. No prose padding.
Passed 14 of 23 test cases with the skill, and 0 of 23 without it.
14 fixed0 passed either way9 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
The instructions your model reads when the skill loads.
Dense, machine-readable reference for LLMs. No prose padding. Version: matches latest PyFixest release.
import pyfixest as pf
pf.feols(
fml: str, # Formula: "Y ~ X1 + X2 | fe1 + fe2" or IV: "Y ~ exog | fe | endog ~ inst"
data: pd.DataFrame,
vcov: str | dict = None, # "iid", "HC1"-"HC3", {"CRV1": "clust"}, {"CRV3": "clust"}, {"CRV1": "c1+c2"}
weights: str = None, # Column name for weights
ssc: dict = None, # Small sample correction, see pf.ssc()
fixef_rm: str = "singleton", # "none" or "singleton"
drop_intercept: bool = False,
split: str = None, # Column name to split sample by
fsplit: str = None, # Like split but also fits on full sample
weights_type: str = "aweights", # "aweights" or "fweights"
solver: str = "scipy.linalg.solve",
lean: bool = False,
) -> Feols | FixestMulti
Returns Feols for single model, FixestMulti for multiple estimation syntax.
pf.fepois(
fml: str, # "Y ~ X1 + X2 | fe1 + fe2"
data: pd.DataFrame,
test cases passed, vs 0 / 23 without
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