JBris

fastapi-react-graphql

Example of a simple FastAPI and React app using GraphQL

model-calibration-evaluation

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

multivariate_analysis_examples

Several examples of multivariate techniques implemented in R, Python, and SAS. Multivariate concrete dataset retrieved from https://archive.ics.uci.edu/ml/datasets/Concrete+Slump+Test. Credit to Professor I-Cheng Yeh.

nextflow-graph-machine-learning

A Nextflow pipeline demonstrating how to train graph neural networks for gene regulatory network reconstruction using DREAM5 data.

prefect-surrogate-models

Demonstrating the use of Prefect to orchestrate the creation of machine learning surrogate models as applied to mechanistic crop models.

react-typescript-graphql

A simple search tool to retrieve git repo information from GitHub, GitLab, and Bitbucket. Uses TypeScript and GraphQL for server-side API searches, and React.js for client-side rendering.

stan-cmdstanr-docker

A Docker image to run Stan, cmdstanr, and brms for Bayesian statistical modelling

time_series_anomaly_detection_examples

Several examples of anomaly detection algorithms for time series data.

vue-python-graphql

A simple search tool to retrieve git repo information from GitHub, GitLab, and Bitbucket. Uses aiohttp and Graphene for server-side API searches, and Vue.js for client-side rendering.

calisim

A toolbox for the calibration and evaluation of simulation models.

JBris/dataframe-processor

A simple R library to construct Extract - Transform - Load (ETL) pipelines and merge different datasets together

JBris/dream5_grn_data

Gold standard gene regulatory network data from the DREAM5 challenge.

JBris/gui-app-docker-test

Test to see how to run GUI apps in Docker

JBris/bats-core

Bash Automated Testing System

JBris/conformal

Tools for conformal inference in regression

JBris/Deep-Otolith

JBris/docker-groimp

A Docker container to run GroIMP

JBris/docker-h2o

Docker files for H2O

JBris/dolphin_segmentation

JBris/functional-structural-model-templates

model templates for all common code

JBris/Functional-structural-plant-model-prototype

Developing the next generation functional-structural-plant-model for facilitating flexible configuration

JBris/m4_competition_data

Data for the fourth Makridakis Competition

JBris/machine-learning-r

Some examples of running R in a Docker container with machine learning and MLOps features

JBris/mlflow-docker

Launch an MLFlow server through Docker

JBris/ngboost

Natural Gradient Boosting for Probabilistic Prediction

JBris/stan-docker

A Docker image to run Stan, rstanarm, and brms for Bayesian statistical modelling

JBris/tidymodels-mlflow-targets-docker

Run tidyverse, tidymodels, targets, carrier, and MLFlow within Docker

JBris/XGBoostLSS

An extension of XGBoost to probabilistic forecasting

JBris/buildingenergygeeks

Bringing building energy simulation and statistical learning together

JBris/CASAL2

Integrated Population Dynamics Model (Casal2)

JBris/climate-change-01

JBris/Contour-Stochastic-Gradient-Langevin-Dynamics

An elegant adaptive importance sampling algorithms for simulations of multi-modal distributions (NeurIPS'20)

JBris/deephyper

DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

JBris/DeeplyUncertain-Public

JBris/jax-dag-gflownet

Code for "Bayesian Structure Learning with Generative Flow Networks"

JBris/neuralecology

Code for the paper "Neural hierarchical models of ecological populations"

JBris/swig-cpp-csharp-example

JBris/Transformer-Explainability

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

JBris/URSABench

Codebase for our paper "URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks"

JBris/VGAER

Simple and efficient -- a novel unsupervised community detection with the fusion of modularity and network structure