Awesome-shiny-apps-for-statistics

🌟 A curated list of Awesome Shiny Apps for Statistics (ASAS)🌟
https://github.com/huyingjie/Awesome-shiny-apps-for-statistics

Last synced: 4 days ago
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<a id="Common-plots"></a>Common Plots
- Boxplots & Histograms
- List of R graphs
<a id="Common-Statistic"></a>Common Statistic
- Stability of Mean & Median
- Correlation
- When does a significant p-value indicate a true effect?
- Hack p-value
- the Vovk-Sellke maximum p-ratio - the maximum diagnosticity of a two-sided p-value.
- Stability of Mean & Median
- When does a significant p-value indicate a true effect?
<a id="Common-Distribution"></a>Common Distribution
<a id="Random-Samples"></a>Random Samples
<a id="Two-groups-or-multiple-groups-comparison"></a>Two groups or multiple groups comparison
<a id="Hypothesis-Testing"></a>Hypothesis Testing
- Bootstrap resampling - Demonstrate hypothesis testing using bootstrap resampling.
- Power - Demonstrate the relationship of statistical power, effect size, and false positives
- Calculate power - Calculat the power of a statistical hypothesis test for a two-sided symmetrical test and show how statistical power is related to the p-value and the significance level.
- Trade Off - Visualize the trade off between type I and type II errors in a Null Hypothesis Significance Test (NHST).
<a id="Linear-Regression"></a> Linear Regression
- Simple linear regression
- Sum of Square in simple linear regression - Explore how sums of squares are calculated in simple linear regressions.
- Fit a simple linear regression model
- Diagnostics for simple linear regression
- Uncertainty
- Influence analysis - Demonstrates the leverage and influence of an adjustable point/outliers
- Graphs for linear regression with high orders
- Multicollinearity
- Model selection - Choose models between simple regression, additive regression, and interactive models.
- Residual error models
- Meta analysis
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
- Residual error models
<a id="Nonlinear-models-for-continous-variables"></a>Nonlinear Models for Continous Variables
- K-means Clustering
  - Estimate K
  - Iris dataset
<a id="Categorical-Models"></a>Categorical Models
- K-means Clustering
  - Relationship between test accuracy, precision, sensitivity, and specificity
<a id="Survival-Model"></a>Survival Model
- K-means Clustering
<a id="Bayesian-Analysis"></a>Bayesian Analysis
- K-means Clustering
  - Bayes factors
  - Robustness analysis for Bayes factors: Two sample t test
  - Bayesian Inference
  - Posterior distribution - judgement-project-and-bayes/) - Calculate posterior distribution based on different priors
  - Binomial & Normal Distribution
<a id="Longitudinal-Analysis"></a>Longitudinal Analysis
- K-means Clustering
  - Longitudinal data exploration
<a id="Test-Analysis"></a>Test Analysis
- K-means Clustering
  - Text analysis of an uploaded .txt file
  - Text analysis of an uploaded .txt file
<a id="Complete-Data-Analysis"></a>Complete Data Analysis
- K-means Clustering
  - Linear regression
<a id="Help-Wanted"></a>Help Wanted
- K-means Clustering
<a id="Resources"></a>Resources
- Awesome R Shiny - A curated list of resources for R Shiny.

Programming Languages

R 1

Categories

<a id="Survival-Model"></a>Survival Model 32 <a id="Help-Wanted"></a>Help Wanted 30 <a id="Linear-Regression"></a> Linear Regression 25 <a id="Common-Statistic"></a>Common Statistic 7 <a id="Two-groups-or-multiple-groups-comparison"></a>Two groups or multiple groups comparison 5 <a id="Bayesian-Analysis"></a>Bayesian Analysis 5 <a id="Hypothesis-Testing"></a>Hypothesis Testing 4 License 4 <a id="Common-Distribution"></a>Common Distribution 3 <a id="Random-Samples"></a>Random Samples 3 <a id="Common-plots"></a>Common Plots 2 <a id="Nonlinear-models-for-continous-variables"></a>Nonlinear Models for Continous Variables 2 <a id="Test-Analysis"></a>Test Analysis 2 <a id="Categorical-Models"></a>Categorical Models 1 <a id="Longitudinal-Analysis"></a>Longitudinal Analysis 1 <a id="Complete-Data-Analysis"></a>Complete Data Analysis 1 <a id="Resources"></a>Resources 1

Sub Categories

K-means Clustering 78

Keywords

shiny 1 rstudio 1 rshiny 1 awesome-list 1 awesome 1

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Awesome

Awesome-shiny-apps-for-statistics

<a id="Common-plots"></a>Common Plots

<a id="Common-Statistic"></a>Common Statistic

<a id="Common-Distribution"></a>Common Distribution

<a id="Random-Samples"></a>Random Samples

<a id="Two-groups-or-multiple-groups-comparison"></a>Two groups or multiple groups comparison

<a id="Hypothesis-Testing"></a>Hypothesis Testing

<a id="Linear-Regression"></a> Linear Regression

<a id="Nonlinear-models-for-continous-variables"></a>Nonlinear Models for Continous Variables

K-means Clustering

<a id="Categorical-Models"></a>Categorical Models

K-means Clustering

<a id="Survival-Model"></a>Survival Model

K-means Clustering

<a id="Bayesian-Analysis"></a>Bayesian Analysis

K-means Clustering

<a id="Longitudinal-Analysis"></a>Longitudinal Analysis

K-means Clustering

<a id="Test-Analysis"></a>Test Analysis

K-means Clustering

<a id="Complete-Data-Analysis"></a>Complete Data Analysis

K-means Clustering

<a id="Help-Wanted"></a>Help Wanted

K-means Clustering

<a id="Resources"></a>Resources