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
翻译准备中
数据R · PythonGitHub @itchyshin在线运营者未确认
试用过了吗? 点一下就好,无需登录。
个人 GitHub 账号下公开仓库的所有者可以登录并验证所有权,自行管理介绍,无需管理员批准。
登录并生成验证文件,在 30 分钟内将其提交到此仓库的默认分支。其他分支或分叉仓库中的文件无效。然后在下方检查文件,即可无需管理员批准直接管理项目。请保留文件以维持管理权限。
如需登录,将前往登录页面。登录后请回到此处,再次点击所选的验证按钮。
在项目文件夹中运行 /nomorevibe verify,即可验证所有权并自行管理介绍。
一分钟完成验证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.
翻译准备中
取自仓库 README
正在加载完整 README。 以下为已保存的摘录。
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.查看完整 README ↗目前无法加载完整 README。请通过原文链接阅读。 以下为已保存的摘录。
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.查看完整 README ↗