GEE(endog, exog, groups[, time, family, ...]) |
Estimation of marginal regression models using Generalized Estimating Equations (GEE). |
GLM(endog, exog[, family, offset, exposure, ...]) |
Generalized Linear Models class |
GLS(endog, exog[, sigma, missing, hasconst]) |
Generalized least squares model with a general covariance structure. |
GLSAR(endog[, exog, rho, missing]) |
A regression model with an AR(p) covariance structure. |
Logit(endog, exog, **kwargs) |
Binary choice logit model |
MNLogit(endog, exog, **kwargs) |
Multinomial logit model |
MixedLM(endog, exog, groups[, exog_re, ...]) |
An object specifying a linear mixed effects model. |
NegativeBinomial(endog, exog[, ...]) |
Negative Binomial Model for count data |
NominalGEE(endog, exog, groups[, time, ...]) |
Estimation of nominal response marginal regression models using Generalized Estimating Equations (GEE). |
OLS(endog[, exog, missing, hasconst]) |
A simple ordinary least squares model. |
OrdinalGEE(endog, exog, groups[, time, ...]) |
Estimation of ordinal response marginal regression models using Generalized Estimating Equations (GEE). |
PHReg(endog, exog[, status, entry, strata, ...]) |
Fit the Cox proportional hazards regression model for right censored data. |
Poisson(endog, exog[, offset, exposure, missing]) |
Poisson model for count data |
Probit(endog, exog, **kwargs) |
Binary choice Probit model |
QuantReg(endog, exog, **kwargs) |
Quantile Regression |
RLM(endog, exog[, M, missing]) |
Robust Linear Models |
WLS(endog, exog[, weights, missing, hasconst]) |
A regression model with diagonal but non-identity covariance structure. |