bkamins / PyDataGlobal2020Links
An introduction to DataFrames.jl for pandas users
☆17Updated 2 years ago
Alternatives and similar repositories for PyDataGlobal2020
Users that are interested in PyDataGlobal2020 are comparing it to the libraries listed below
Sorting:
- ☆29Updated 4 years ago
- DEPRECATED IN FAVOR OF TuringLang/Turing-Workshop☆35Updated 4 years ago
- Data and functions to support Julia projects based on the book "Regression and Other Stories" by Andrew Gelman, Jennifer Hill and Aki Veh…☆26Updated last month
- Back end for FixedEffectModels.jl☆20Updated 3 weeks ago
- ☆35Updated 4 years ago
- Inference for partially observed Markov chains☆20Updated 8 years ago
- Taking causal inference to the extreme!☆33Updated last year
- Initial look at directed acyclic graph (DAG) based causal models in regression.☆31Updated last year
- StatististicalRethinking notebook project using Stan and Pluto notebooks.☆18Updated last year
- Linear Regression for Julia☆12Updated 4 years ago
- A Complete Guide to Efficient Transformations of DataFrames☆35Updated last year
- Quantile regression in Julia☆44Updated 2 years ago
- A survival analysis interface for Julia☆31Updated 2 years ago
- ☆29Updated 5 years ago
- Comparing performance and results of mcmc options using Julia☆38Updated 4 years ago
- Synthetic control methods in Julia☆38Updated 2 months ago
- DynamicHMC versions of StatisticalRethinking models☆18Updated 2 years ago
- Julia code for part 2 of the book Causal Inference: What If, by Miguel Hernán and James Robins☆52Updated 3 years ago
- ☆53Updated last month
- Access to World Bank data for Julia☆29Updated 2 years ago
- Analysis of complex surveys☆53Updated last month
- Pluto notebooks accompanying the book Statistics With Julia (https://statisticswithjulia.org).☆82Updated 2 years ago
- Machine learning time series regressions☆13Updated 5 months ago
- Julia package for computing inequality indicators☆12Updated 2 years ago
- ☆53Updated last year
- package for Bayesian and classical estimation and inference based on statistics that are filtered through a trained neural net