data.validator
validate your data and create nice reports straight from R (by Appsilon)
lme4cens
Simple Mixed Effect Models and Censoring (by lenz99)
| data.validator | lme4cens | |
|---|---|---|
| 2 | 1 | |
| 147 | 6 | |
| 0.0% | - | |
| 7.7 | 2.0 | |
| 14 days ago | 5 months ago | |
| HTML | HTML | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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.
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.
data.validator
Posts with mentions or reviews of data.validator.
We have used some of these posts to build our list of alternatives
and similar projects.
- RStats: Data Validation with Reports
-
Thoughts on or experiences with validating data with data.validator or validate
I found the data.validator and validate packages and they seem like a good paths forwards for issues like this.
lme4cens
Posts with mentions or reviews of lme4cens.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Fully crossed Tobit model
I was trying to use the censReg package but I'm unclear on how to specify the crossed effects for participant and task? I found this walkthrough but the package they use apparently no longer works.
What are some alternatives?
When comparing data.validator and lme4cens you can also consider the following projects:
RInno - How to install local shiny apps
intro_stats - Introduction to Statistics: an integrated textbook and workbook using R
tech-diff - Compare different technologies. No BS and all sources linked.
vtreat - vtreat is a data frame processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner. Distributed under choice of GPL-2 or GPL-3 license.
dash - Data Apps & Dashboards for Python. No JavaScript Required.
datasciencecoursera - Data Science Repo and blog for John Hopkins Coursera Courses. Please let me know if you have any questions.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing