ParBayesianOptimization VS vip

Compare ParBayesianOptimization vs vip and see what are their differences.

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ParBayesianOptimization vip
1 1
98 186
- 1.1%
0.0 6.5
over 1 year ago 5 months ago
R R
- -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ParBayesianOptimization

Posts with mentions or reviews of ParBayesianOptimization. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning ParBayesianOptimization yet.
Tracking mentions began in Dec 2020.

vip

Posts with mentions or reviews of vip. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning vip yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing ParBayesianOptimization and vip you can also consider the following projects:

miceRanger - miceRanger: Fast Imputation with Random Forests in R

tmle3mopttx - 🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention

aps2020 - Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics

mlr3learners - Recommended learners for mlr3

lmtp - :package: Non-parametric Causal Effects Based on Modified Treatment Policies :crystal_ball:

textfeatures - 👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

ggplot2 - An implementation of the Grammar of Graphics in R

causalglm - Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning