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autosem

Specification search for SEM using heuristic optimization

awesome-machine-learning-interpretability

A curated list of awesome machine learning interpretability resources.

awesome-r

A curated list of awesome R packages, frameworks and software.

bayestestr

:ghost: Utilities for analyzing Bayesian models and posterior distributions

beautiful-visualization-with-python

python数据可视化之美

beautiful-visualization-with-r

《R语言数据可视化之美》配套代码

bioimage

Image manipulation for biological research

broomextra

Helpers for regression analyses using `broom` & `easystats` packages 📈 🔍

candisc

Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

causality

Examine The Causal Structure of Data

circus

:circus_tent: Contains a variety of fitted models to help the systematic testing of other packages

colorplaner

📊ggplot2 Extension to Visualize Two Variables Per Color Aesthetic through Color Space Projection

correlation

:link: Methods for Correlation Analysis

diagrammer

Graph and network visualization using tabular data in R.

doing-meta-analysis-in-r

All R codes for the guide "Doing Meta-Analysis in R"

easystats

:milky_way: The R easystats-project

ecological-sems-in-lavaan

Recreating SEM analyses from papers using the R lavaan package

effectsize

:dragon: Compute and work with indices of effect size and standardized parameters

enmsdm

Faster, better, smarter ecological niche modeling and species distribution modeling

esquisse

RStudio add-in to make plots with ggplot2

export

R package for streamlined export of graphs and data tables.

factoextra

Extract and Visualize the Results of Multivariate Data Analyses

fastqcr

fastqcr: Quality Control of Sequencing Data

fdagstat

An R package that implements the methods of geostatistics for functional data

forecasting

Time Series Forecasting Best Practices & Examples

forestanalysisinr

frk

Fixed Rank Kriging with big data

fssgam

Code for full subsets model fitting using GA(M)M

geometry

R geometry package

geomla

Machine Learning algorithms for spatial and spatiotemporal data