No packages match

gpumetropolis - GPU-Portable Vendor-Agnostic Metropolis-Hastings Sampler

A generic random-walk Metropolis-Hastings sampler for Markov chain Monte Carlo. The user declares a model by writing its log-likelihood and log-prior as one-sided formulas; the package compiles them to a stack-machine bytecode that a single portable kernel interprets, so the same model runs on the CPU and on the GPU from one source. The sampler advances many independent chains in one batched pass. A distributional equivalence harness based on the two-sample Kolmogorov-Smirnov test and the split R-hat statistic of Gelman and Rubin (1992) <doi:10.1214/ss/1177011136> checks the GPU output against the CPU output. Dispatching the kernel to a GPU helps when many chains are run or when the log-density is expensive to evaluate over a large data set, and does not help for small models run with few chains.

Last updated

rustcargo

3.18 score

harness - Curated Agentic Harnesses for R Professional Roles

A bootstrapper that launches a command-line coding agent of the user's choice in a terminal tab pre-configured for a professional R role. Each role is described by a curated harness: a subset of community skills, a system prompt, a folder layout, and quality gates. The package does not run an agent loop and does not call a language model; it discovers the chosen coder binary, generates its configuration, links the curated skills, and opens the terminal. Code written by the agent is run manually by the user, by design, so that every generated script passes through a human audit gate before execution.

Last updated

agenticclaudecoding-agentllmreproducible-researchrstudio

2.00 score 1 scripts

harness - Curated Agentic Harnesses for R Professional Roles

A bootstrapper that launches a command-line coding agent of the user's choice in a terminal tab pre-configured for a professional R role. Each role is described by a curated harness: a subset of community skills, a system prompt, a folder layout, and quality gates. The package does not run an agent loop and does not call a language model; it discovers the chosen coder binary, generates its configuration, links the curated skills, and opens the terminal. Code written by the agent is run manually by the user, by design, so that every generated script passes through a human audit gate before execution.

Last updated

1.70 score 1 scripts