| Name | Version | Summary | date |
| localprojections |
0.1.4 |
This module implements the local projections models for single entity time series and panel / longitudinal data, as well as threshold versions. |
2024-04-01 07:02:50 |
| model-confidence-set |
0.1.1 |
model-confidence-set provides a Python implementation of the Model Confidence Set (MCS) procedure (Hansen, Lunde, and Nason, 2011), a statistical method for comparing and selecting models based on their performance. |
2024-03-12 10:11:58 |
| statsframe |
0.0.3 |
Customizable data and model summaries in Python. |
2024-01-31 16:25:06 |
| pydatasummary |
0.0.2 |
Customizable data and model summaries in Python. |
2024-01-24 19:38:42 |
| getfactormodels |
0.0.4 |
Retrieve data for various multifactor asset pricing models. |
2023-12-23 20:21:10 |
| paneltime-mp |
0.0.2 |
Multiprocessing interface |
2023-12-10 17:08:38 |
| differences |
0.2.0 |
difference-in-differences estimation and inference in Python |
2023-12-10 01:21:41 |
| paneltime |
1.2.46 |
An efficient integrated panel and GARCH estimator |
2023-12-01 11:58:07 |
| qtregpy |
0.1.2 |
A Python package to implement the Quantile Transformation Regression. |
2023-11-26 15:56:20 |
| dame-flame |
0.61 |
Causal Inference Covariate Matching |
2023-08-21 21:34:36 |
| estimagic |
0.4.6 |
Tools to solve difficult numerical optimization problems. |
2023-06-05 15:45:06 |
| DyGyS |
0.0.5 |
DyGyS is a package for Maximum Entropy regression models with gravity specification for undirected and directed network data. Moreover, it can solve them, generate the graph ensemble, compute several network statistics, calculate model selection measures such as AIC and BIC and quantify their reproduction accuracy in topological and weighted properties. |
2023-01-21 10:59:38 |