Fast distributional regression models for univariate and bivariate responses using Template Model Builder. The current implementation focuses on Gaussian, Student-t, and skew-normal location-scale mod
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資料R · PythonGitHub @itchyshin上線中營運者未確認
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一分鐘完成驗證A fast TMB-based distributional regression package with brms-like syntax, focused on univariate and bivariate location–scale–shape models, where not only μ and σ but also shape, zero inflation, random-effect variance, and residual correlation ρ can be modelled by predictors.
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取自儲存庫 README
正在載入完整 README。 以下為已儲存的摘錄。
Warning — experimental software. drmTMB is experimental and should be
used at your own risk. A successful fit, a green diagnostic, or package
availability is not enough on its own: independently check data preparation,
model specification, convergence, and scientific conclusions. See
Capabilities and limits
for the documented workflows and their current evidence; anything beyond
those stated boundaries remains provisional.檢視完整 README ↗目前無法載入完整 README。請透過原文連結閱讀。 以下為已儲存的摘錄。
Warning — experimental software. drmTMB is experimental and should be
used at your own risk. A successful fit, a green diagnostic, or package
availability is not enough on its own: independently check data preparation,
model specification, convergence, and scientific conclusions. See
Capabilities and limits
for the documented workflows and their current evidence; anything beyond
those stated boundaries remains provisional.檢視完整 README ↗