PyFixest LLM Skill Reference
Dense, machine-readable reference for LLMs. No prose padding.
Version: matches latest PyFixest release.
Package Import
import pyfixest as pf
Core Estimation Functions
pf.feols() — OLS / WLS / IV with Fixed Effects
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() — Poisson Regression with Fixed Effects
pf.fepois(
fml: str, # "Y ~ X1 + X2 | fe1 + fe2"
data: pd.DataFrame,
vcov: str | dict = None,
ssc: dict = None,
fixef_rm: str = "singleton",
iwls_tol: float = 1e-08,
iwls_maxiter: int = 25,
separation_check: list[str] = None, # ["fe"] to check for separated FE
split: str = None,
fsplit: str = None,
) -> Fepois | FixestMulti
pf.feglm() — GLM (without FE demeaning, WIP)
pf.feglm(
fml: str,
data: pd.DataFrame,
family: str, # "gaussian", "logit", "probit"
vcov: str | dict = None,
separation_check: list[str] = None,
split: str = None,
fsplit: str = None,
) -> Feglm | FixestMulti
pf.quantreg() — Quantile Regression
pf.quantreg(
fml: str,
data: pd.DataFrame,
vcov: str | dict = "nid",
quantile: float | list[float] = 0.5, # Single or list of quantiles
method: str = "fn", # "fn" (Frisch-Newton)
split: str = None,
fsplit: str = None,
) -> Feols | FixestMulti
Basic
"Y ~ X1 + X2" # OLS
"Y ~ X1 + X2 | fe1" # OLS + one FE
"Y ~ X1 + X2 | fe1 + fe2" # OLS + two-way FE
"Y ~ X1 + C(categorical)" # Categorical variable as dummies
"Y ~ X1 + i(factor, ref=0)" # Factor variable for event studies
"Y ~ X1 + i(f1, X2)" # Interaction: factor × continuous
"Y ~ 1 | fe1 | X1 ~ Z1" # IV: depvar ~ exog | fe | endog ~ inst
"Y ~ 1 | X1 ~ Z1 + Z2" # IV without FE
"Y ~ X1:X2" # Interaction only
"Y ~ X1*X2" # X1 + X2 + X1:X2
"Y ~ X1 + I(X1**2)" # Polynomial term
Multiple Estimation Operators
"Y ~ X1 + sw(X2, X3)" # Stepwise: two models, X2 then X3
"Y ~ X1 + sw0(X2, X3)" # Stepwise with empty: three models
"Y ~ X1 + csw(X2, X3)" # Cumulative stepwise: X2, then X2+X3
"Y ~ X1 + csw0(X2, X3)" # Cumulative with empty: three models
"Y + Y2 ~ X1" # Multiple dependent variables
"Y ~ X1 | csw0(fe1, fe2)" # Stepwise fixed effects
Split Sample
pf.feols("Y ~ X1 | fe1", data=data, split="group_var") # Separate by group
pf.feols("Y ~ X1 | fe1", data=data, fsplit="group_var") # Separate + full sample
Post-Estimation Methods (Feols object)
fit.summary() # Print summary
fit.tidy(alpha=0.05) # pd.DataFrame: Estimate, Std. Error, t value, Pr(>|t|), CI
fit.coef() # pd.Series of coefficients
fit.se() # pd.Series of standard errors
fit.tstat() # pd.Series of t-statistics
fit.pvalue() # pd.Series of p-values
fit.confint(alpha=0.05) # pd.DataFrame of confidence intervals
fit.confint(joint=True) # Simultaneous confidence bands (multiplier bootstrap)
Changing Inference
fit.vcov("iid") # IID standard errors
fit.vcov("HC1") # Heteroskedasticity-robust
fit.vcov({"CRV1": "cluster_var"}) # One-way cluster-robust
fit.vcov({"CRV3": "cluster_var"}) # CRV3 cluster-robust
fit.vcov({"CRV1": "c1 + c2"}) # Two-way clustering
Returns self — chainable: fit.vcov("HC1").summary().
Visualization
fit.coefplot() # Coefficient plot
pf.coefplot([fit1, fit2], keep="X1") # Compare models
pf.iplot([fit1, fit2], coord_flip=False) # Event study plot (for i() vars)
pf.qplot(fit_qr) # Quantile regression plot
Prediction
fit.predict() # In-sample predictions
fit.predict(newdata=df_new) # Out-of-sample
fit.predict(type="response") # Response scale (GLMs)
fit.predict(type="link") # Link scale (GLMs)
Inference Methods
# Wild cluster bootstrap
fit.wildboottest(param="X1", reps=999, cluster="clust_var")
# Randomization inference
fit.ritest(resampvar="X1=0", reps=1000, cluster="group_id")
# Causal cluster variance estimator (Abadie et al. 2023)
fit.ccv(treatment="treat_var", pk=0.05, n_splits=2, seed=42)
# Wald test: H0: beta = 0
fit.wald_test(R=np.eye(k))
# Wald test: H0: R @ beta = q
fit.wald_test(R=R_matrix, q=q_vector)
IV Diagnostics (Feiv objects)
fit_iv._model_1st_stage # First stage Feols object
fit_iv._f_stat_1st_stage # First stage F-statistic
fit_iv.IV_Diag() # Run IV diagnostics
fit_iv._eff_F # Effective F-stat (Olea & Pflueger 2013)
Online Learning
fit.update(X_new, y_new) # Sherman-Morrison coefficient update
Reporting Functions
pf.etable() — Regression Tables
pf.etable(
models, # Feols, list[Feols], or FixestMulti
type: str = "gt", # "gt" (Great Tables), "tex" (LaTeX), "md" (markdown), "df" (DataFrame)
signif_code: list = None, # e.g. [0.001, 0.01, 0.05]
coef_fmt: str = "b \n (se)", # Format: b=coef, se=SE, p=pval, t=tstat, ci_l, ci_u
keep: str | list = None, # Regex pattern(s) to keep
drop: str | list = None, # Regex pattern(s) to drop
labels: dict = None, # {"old_name": "New Label"}
felabels: dict = None, # {"fe_var": "FE Label"}
show_fe: bool = True,
show_se_type: bool = True,
notes: str = "",
model_heads: list = None, # Custom column headers
caption: str = None, # Via kwargs
file_name: str = None, # Save to file (.tex, .html)
)
pf.summary() — Print Results
pf.summary(models, digits=3) # models: Feols, list, or FixestMulti
pf.dtable() — Descriptive Statistics
pf.dtable(
df: pd.DataFrame,
vars: list, # Column names
stats: list = None, # ["count", "mean", "std", "min", "max", "median"]
bycol: list[str] = None, # Group columns (shown as separate column groups)
byrow: str = None, # Group variable (shown as row sections)
type: str = "gt", # "gt", "tex", "md", "df"
labels: dict = None,
digits: int = 2,
)
Multiple Testing Corrections
pf.bonferroni(models, param="X1") # Bonferroni adjusted p-values
pf.rwolf(models, param="X1", reps=9999, seed=42) # Romano-Wolf
pf.wyoung(models, param="X1", reps=9999, seed=42) # Westfall-Young
Difference-in-Differences
pf.event_study() — Unified Event Study API
pf.event_study(
data: pd.DataFrame,
yname: str, # Outcome column
idname: str, # Unit ID column
tname: str, # Time column
gname: str, # Group (first treatment period) column
xfml: str = None, # Additional covariates formula
cluster: str = None, # Cluster variable
estimator: str = "twfe", # "twfe" or "did2s"
att: bool = True,
)
pf.did2s() — Gardner's Two-Stage DID
pf.did2s(
data: pd.DataFrame,
yname: str,
first_stage: str, # "~ covariates | fe1 + fe2"
second_stage: str, # "~ i(rel_year, ref=-1.0)"
treatment: str, # Treatment indicator column
cluster: str,
weights: str = None,
)
pf.lpdid() — Local Projections DID
pf.lpdid(
data: pd.DataFrame,
yname: str,
idname: str,
tname: str,
gname: str,
vcov: str | dict = None,
pre_window: int = None,
post_window: int = None,
never_treated: int = 0,
att: bool = True,
xfml: str = None,
)
pf.panelview() — Treatment Visualization
pf.panelview(data, unit="unit_col", time="time_col", treat="treat_col")
Small Sample Correction
pf.ssc(
k_adj: bool = True, # Adjust for number of estimated parameters
k_fixef: str = "nonnested", # "nonnested" or "nested" FE adjustment
G_adj: bool = True, # Adjust for number of clusters
G_df: str = "min", # "min" or "conventional"
)
# Usage:
pf.feols("Y ~ X1 | fe1", data=data, ssc=pf.ssc(k_adj=True))
Data Generators
pf.get_data(N=1000, seed=1234, model="Feols") # Synthetic data: "Feols" or "Fepois"
pf.get_twin_data(N_pairs=500, seed=42) # Twin study data (returns to education)
pf.get_worker_panel(N_workers=500, N_firms=50, N_years=11, seed=42) # Worker-firm panel
Variance-Covariance Options
| vcov | Description |
|---|
"iid" | Spherical errors (homoskedastic, uncorrelated) |
"HC1" | Heteroskedasticity-robust (White) |
"HC2" | HC2 robust |
"HC3" | HC3 robust (jackknife-like) |
{"CRV1": "var"} | One-way cluster-robust |
{"CRV3": "var"} | CRV3 cluster-robust |
{"CRV1": "v1 + v2"} | Two-way clustering |
Default: CRV1 clustered by first FE variable (if FE present), else "iid".
Common Patterns
# Basic OLS with FE and clustering
fit = pf.feols("Y ~ X1 + X2 | fe1 + fe2", data=df, vcov={"CRV1": "fe1"})
# IV regression
fit_iv = pf.feols("Y ~ exog | fe1 | endog ~ instrument", data=df)
# Multiple specifications at once
fits = pf.feols("Y ~ X1 | csw0(fe1, fe2, fe3)", data=df)
fits.etable()
# Poisson with FE
fit_pois = pf.fepois("count ~ X1 + X2 | fe1", data=df)
# Event study
fit_es = pf.feols("Y ~ i(rel_time, ref=-1) | unit + time", data=df)
pf.iplot(fit_es)
# Publication table
pf.etable([fit1, fit2, fit3], type="tex", file_name="table1.tex",
labels={"X1": "Treatment"}, felabels={"fe1": "Unit FE"})
# Adjust SE after estimation
fit.vcov({"CRV1": "cluster"}).summary()
# Compare R fixest syntax → PyFixest
# R: feols(Y ~ X1 | fe1, data, cluster = ~fe1)
# Py: pf.feols("Y ~ X1 | fe1", data=data, vcov={"CRV1": "fe1"})
FixestMulti Methods
When multiple estimation syntax is used, returns FixestMulti:
multi = pf.feols("Y + Y2 ~ X1 | csw0(fe1, fe2)", data=df)
multi.etable() # Table of all models
multi.summary() # Print all summaries
multi.coefplot() # Plot all models
multi.vcov("HC1") # Update all models' inference
multi.fetch_model(0) # Get first Feols object
multi.all_fitted_models["Y~X1"] # Access by formula key
anthropics/skills180k