Torsten - library of C++ functions that support applications of Stan in Pharmacometrics

  •        266

This library provides Stan language functions that calculate amounts in each compartment, given an event schedule and an ODE system. We are working with Stan development team to create a system to add and share Stan packages. In the mean time, the current repo contains forked version of Stan with Torsten. The latest version of Torsten (v0.87) is compatible with Stan v2.19.1. Torsten is agnostic to which Stan interface you use. Here we provide command line and R interfaces.

https://github.com/metrumresearchgroup/Torsten

Tags
Implementation
License
Platform

   




Related Projects

rstan - RStan, the R interface to Stan

  •    R

RStan is the R interface to Stan. RStan's source code repository is hosted here on GitHub. Stan's source repository is defined as a submodule. See how to work with stan submodule in rstan repo.

Automatic Translation from OPENMP to MPI

  •    

We intend to develop a tool that can automatically convert programs written in OpenMP sharedmemory parallel applications to MPI for execution in distributed memory systems.This will make it convenient to code in OpenMP and deploy the application to distributed system under MPI.

BDA_py_demos - Bayesian Data Analysis demos for Python

  •    Jupyter

to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan

  •    R

The brms package provides an interface to fit Bayesian generalized (non-)linear multivariate multilevel models using Stan, which is a C++ package for performing full Bayesian inference (see http://mc-stan.org/). The formula syntax is very similar to that of the package lme4 to provide a familiar and simple interface for performing regression analyses. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include non-linear and smooth terms, auto-correlation structures, censored data, missing value imputation, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Multivariate models (i.e. models with multiple response variables) can be fitted, as well. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with posterior predictive checks, leave-one-out cross-validation, and Bayes factors. As a simple example, we use poisson regression to model the seizure counts in epileptic patients to investigate whether the treatment (represented by variable Trt) can reduce the seizure counts and whether the effect of the treatment varies with the baseline number of seizures a person had before treatment (variable log_Base4_c). As we have multiple observations per person, a group-level intercept is incorporated to account for the resulting dependency in the data.

pystan - PyStan, the Python interface to Stan

  •    Python

PyStan provides a Python interface to Stan, a package for Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. For more information on Stan and its modeling language, see the Stan User's Guide and Reference Manual at http://mc-stan.org/.


stan - Stan development repository (home page is linked below)

  •    R

There are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line. There are separate repositories in the stan-dev GitHub organization for the interfaces, higher-level libraries and lower-level libraries.

Hybrid OpenMP MPI Benchmark

  •    

HOMB is a simple benchmark based on a parallel iterative Laplace solver aimed at comparing the performance of MPI, OpenMP, and hybrid codes on SMP and multi-core based machines.

Shared Genomics Project MPI Codebase

  •    

The Shared Genomics project has developed parallelised statistical applications (MPI/OpenMP) which can analyse large genomic data-sets containing thousands of Single Nucleotide Polymorphisms (SNP). The code is based on the popular PLINK SNP-analysis program.

Parallel Dwarfs

  •    CSharp

The Parallel Dwarfs project is a suite of 13 kernels (as VS projects in C++/C#/F#) parallelized using various technologies such as MPI, OpenMP, TPL, MPI.Net, etc. It also has a driver to run them, collect traces, and visualize the results using Vampir, Jumpshot, Xperf and Excel

atomspace - The OpenCog hypergraph database, query system and rule engine

  •    C++

The OpenCog AtomSpace is a knowledge representation (KR) database and the associated query/reasoning engine to fetch and manipulate that data, and perform reasoning on it. Data is represented in the form of graphs, and more generally, as hypergraphs; thus the AtomSpace is a kind of graph database, the query engine is a general graph re-writing system, and the rule-engine is a generalized rule-driven inferencing system. The vertices and edges of a graph, known as "Atoms", are used to represent not only "data", but also "procedures"; thus, many graphs are executable programs as well as data structures. The AtomSpace is a platform for building Artificial General Intelligence (AGI) systems. It provides the central knowledge representation component for OpenCog. As such, it is a fairly mature component, on which a lot of other systems are built, and which depend on it for stable, correct operation in a day-to-day production environment.

PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

  •    Python

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.

Bios8366 - Advanced Statistical Computing at Vanderbilt University's Department of Biostatistics

  •    Jupyter

Course covers numerical optimization, statistical machine learning, Markov Chain Monte Carlo (MCMC), variational inference (VI) algorithms, data augmentation algorithms with applications for model fitting and techniques for dealing with missing data. Prerequisites: Bios 6341 (Fundamentals of Probability), Bios 6342 (Contemporary Statistical Inference), or permission of instructor. Students must be familiar with basic probability, have some formal programming experience, and be comfortable using the Git version control system.

ODE for Java

  •    Java

ODE for Java is a binding between C dynamics library ODE (Open Dynamics Engine, http://ode.org) and Java. Separate packages provide among other things a Java3D integration and thus a scenegraph representation of selected ODE objects.

FeatherCNN - FeatherCNN is a high performance inference engine for convolutional neural networks.

  •    C++

FeatherCNN, developed by Tencent TEG AI Platform, is a high-performance lightweight CNN inference library. FeatherCNN is currently targeting at ARM CPUs, and is capable to extend to other devices in the future. Highly Performant FeatherCNN delivers state-of-the-art inference computing performance on a wide range of devices, including mobile phones (iOS/Android), embedded devices (Linux) as well as ARM-based servers (Linux).

Anakin - High performance Cross-platform Inference-engine, you could run Anakin on x86-cpu,arm, nv-gpu, amd-gpu,bitmain and cambricon devices

  •    C++

Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineers and is a large-scale application of industrial products.

flownet2-pytorch - Pytorch implementation of FlowNet 2

  •    Python

Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. Multiple GPU training is supported, and the code provides examples for training or inference on MPI-Sintel clean and final datasets. The same commands can be used for training or inference with other datasets. See below for more detail.

DALI - A library containing both highly optimized building blocks and an execution engine for data pre-processing in deep learning applications

  •    C++

Today’s deep learning applications include complex, multi-stage pre-processing data pipelines that include compute-intensive steps mainly carried out on the CPU. For instance, steps such as load data from disk, decode, crop, random resize, color and spatial augmentations and format conversions are carried out on the CPUs, limiting the performance and scalability of training and inference tasks. In addition, the deep learning frameworks today have multiple data pre-processing implementations, resulting in challenges such as portability of training and inference workflows and code maintainability. NVIDIA Data Loading Library (DALI) is a collection of highly optimized building blocks and an execution engine to accelerate input data pre-processing for deep learning applications. DALI provides both performance and flexibility of accelerating different data pipelines, as a single library, that can be easily integrated into different deep learning training and inference applications.

gpu-rest-engine - A REST API for Caffe using Docker and Go

  •    C++

This repository shows how to implement a REST server for low-latency image classification (inference) using NVIDIA GPUs. This is an initial demonstration of the GRE (GPU REST Engine) software that will allow you to build your own accelerated microservices. This repository is a demo, it is not intended to be a generic solution that can accept any trained model. Code customization will be required for your use cases.

Anti Inference Hub

  •    Java

Anti Inference Hub is the first dynamic query processing engine that defends against the Inference Problem in Multilevel Databases by integrating smoothly with common DBMSs (Oracle, PostgreSQL, and MySQL), and monitoring queries submitted by users. Please post your questions to Anti Inference Hub mailing list at: https://lists.sourceforge.net/lists/listinfo/aih-list.

UACluster2

  •    

UACluster2 is set of manuals and tools to create and manage high performance computing cluster based on Microsoft Hyper-V virtual machines. It needs Microsoft HPC Server 2008 (Microsoft HPC Server 2008 R2) as a basis of cluster creation.