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rstap: An R Package for Spatial Temporal Aggregated Predictor Models

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Adam Peterson, Brisa Sanchez

Methodology · Submitted 26 Dec 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language Stan.

how it is stated · asserted 1.00 read the record →

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arXiv ID1812.10208 (version 1)
Pith IDKN722W6F, Pith's permanent record of this paper and its review
CitationAdam Peterson, Brisa Sanchez. "rstap: An R Package for Spatial Temporal Aggregated Predictor Models." arXiv:1812.10208 (2018). https://pith.science/paper/KN722W6F
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,235 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.05 · human participants 0.08 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.04 · mentions code or data availability 0.34.

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arXiv listing abstract v1. Results and the main claim are underlined; .

The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language Stan. A variety of distributions and link functions are supported, allowing users to fit this extension to the generalized linear model with both independent and correlated outcomes.

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language Stan. contribution 0.87 0.80 0.14 asserted 1.00
s1 A variety of distributions and link functions are supported, allowing users to fit this extension to the generalized linear model with both independent and correlated outcomes. method 0.75 0.49 0.10 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.76.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

The rstap package implements Bayesian spatial temporal aggregated predictor models in R using the probabilistic programming language Stan. A variety of distributions and link functions are supported, allowing users to fit this extension to the generalized linear model with both independent and correlated outcomes.

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3 arXiv papers cite this in 4 sentences

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  • To that end, the package utilizes to fit models, drawing samples from the posterior distribution of model parameters obtained from an extension of the static HMC sampler [ref] , the Generalized No-U Turn Sampler (NUTS) [ref] . “Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information.

    A Conceptual Introduction to Hamiltonian Monte Carlo · Parameter Estimation · 0.93

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