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Data to Accompany: Epidemiological Overview of Cannabis- and Substance- Carcinogensis in Europe: A Lagged Causal Inferential Panel Regression Modelling and Marginal Effects Study

NIAID Data Ecosystem2026-03-14 收录

物质致癌流行病学

因果建模

数据链接：

https://data.mendeley.com/datasets/hh4456yxp4 数据链接链接失效反馈

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资源简介：

Data to accompany paper of same name

应用场景：

创建时间：

2023-01-27

因果建模

逻辑回归

Configurational comparative methods (CCMs) and logic regression methods (LRMs) are two families of exploratory methods that employ very different techniques to analyze data generated by causal structu

DataCite Commons2023-05-24 更新70

Dammann et al_Biol Lett_longevity.R from Longevity of a solitary mole-rat species and its implications for the assumed link between sociality and longevity in African mole-rats (Bathyergidae)

寿命研究

因果建模

R code for modelling causal effects

DataCite Commons2022-11-02 更新40

Source csv Data to Accompany: Geospatiotemporal and Causal Inferential Epidemiological Survey and Exploration of Cannabinoid- and Substance- Related Carcinogenesis in USA 2003-2017.

物质致癌流行病学

地理时空因果推断

csv data files to accompany R script for paper of same title.

NIAID Data Ecosystem20

Face processing M/EEG data for Dynamic Causal Modelling

脑功能成像

因果建模

This dataset consists of BIDS-formatted fMRI data from Wakeman & Henson (2015) that have been processed for DCM analysis. Raw fMRI data from 15 subjects (subject 10 excluded due to fewer runs) hav

DataCite Commons2022-10-14 更新50

Results of unvariate and multivariate analysis using binary logistic regression and marginal structural model.

因果建模

观察性数据分析

Results of unvariate and multivariate analysis using binary logistic regression and marginal structural model.

NIAID Data Ecosystem40