Fitting and testing probabilistic choice models,
especially the Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952;
Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972),
and preference tree (Pretree) models (Tversky & Sattath, 1979).

Fitting and testing multiattribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky &
Sattath, 1979).

Fitting and testing multi-attribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley &
Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky &
Sattath, 1979).

Fitting and testing multi-attribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley
& Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky
& Sattath, 1979).

Entropy reweighting to create balanced samples
Package implements entropy balancing, a data preprocessing
procedure that allows users to reweight a dataset such that the
covariate distributions in the reweighted data satisfy a set of
user specified moment conditions. This can be useful to create
balanced samples in observational studies with a binary
treatment where the control group data can be reweighted to
match the covariate moments in the treatment group. Entropy
balancing can also be used to reweight a survey sample to known
characteristics from a target population. This package is
currently in alpha phase (feedback is appreciated).

Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to  match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population. This package is currently in alpha phase (feedback is appreciated).