Topic: tmle Goto Github
Some thing interesting about tmle
Some thing interesting about tmle
tmle,The R package trajmsm is based on the paper Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories: https://doi.org/10.48550/arXiv.2105.12720.
User: awamaeva
tmle,Nonparametric estimators of the average treatment effect with doubly-robust confidence intervals and hypothesis tests
User: benkeser
Home Page: https://benkeser.github.io/drtmle/
tmle,Estimators of cross-validated prediction metrics with improved small sample performance
User: benkeser
Home Page: https://benkeser.github.io/nlpred/
tmle,Tests for trends in vaccine efficacy by genetic distance
User: benkeser
tmle,Targeted Learning for Survival Analysis
User: benkeser
Home Page: https://benkeser.github.io/survtmle/
tmle,Targeted Learning entry in the Atlantic Causal Inference Conference's 2017 competition
User: ck37
tmle,R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.
User: ck37
tmle, SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
User: ck37
tmle,Variable importance through targeted causal inference, with Alan Hubbard
User: ck37
tmle,Implementing TMLE in the Presence of a Continuous Outcome
User: ehsanx
Home Page: https://ehsanx.github.io/TMLE-Continuous-Outcome
tmle,Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.
User: ehsanx
Home Page: https://ehsanx.github.io/TMLEworkshop/
tmle,TMLE with efficiency guarantees for randomized trials with ordinal outcomes
User: idiazst
tmle,R/medltmle: Estimation and Inference for Natural Mediation Effect in Longitudinal Data
User: imalenica
tmle,R/tstmle01: Estimation and Inference for Marginal Causal Effect with Single Binary Time Series
User: imalenica
tmle,Estimation and Inference for Context-Specific Causal Average Treatment Effect and Optimal Individualized Treatment Effect with Single Time Series
User: imalenica
tmle,Tutorials illustrating the use of baseline information to conduct more efficient randomized trials
User: jbetz-jhu
tmle,Collaborative Targeted Maximum Likelihood Estimation
User: jucheng1992
tmle,Transporting intervention effects from one population to another with targeted learning
User: kararudolph
tmle,npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
User: larsvanderlaan
tmle,Semiparametric inference for relative heterogeneous vaccine efficacy between strains in observational case-only studies
User: larsvanderlaan
Home Page: https://arxiv.org/pdf/2303.11462.pdf
tmle,Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"
User: laurabalzer
tmle,Introduction to Double Robust Estimation for Causal Inference
User: laurabalzer
tmle,R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231
User: laurabalzer
tmle,Doubly-Robust and Efficient Estimators for Survival and Ordinal Outcomes in RCTs Without Proportional Hazards or Odds Assumptions :pill:
User: nt-williams
tmle,Epidemiology analysis package
User: pzivich
Home Page: http://zepid.readthedocs.org
tmle,Streamlined Estimation for Static, Dynamic and Stochastic Treatment Regimes in Longitudinal Data
User: romainkp
tmle,A pure Julia implementation of the Targeted Minimum Loss-based Estimation
Organization: targene
Home Page: https://olivierlabayle.github.io/TMLE.jl/stable/
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.