The R statistical software is powerful for not just statistic, but is now expanding to other fields (See below)

The goal of this site is multiple fold. Continuous updates for latest count of published

Monthly updated graphic showing the number of active R pacakges published in CRAN by year

Categories of R packages

Comprehensive list that will feature all packages including newly published ones too

Certainly! Here's an updated list of 100 categories of R packages without duplicates:
package list for each category is coming soon, you'd love it!

1. Data Manipulation
2. Data Visualization
3. Statistical Modeling
4. Machine Learning
5. Natural Language Processing
6. Image Processing
7. Time Series Analysis
8. Spatial Analysis
9. Network Analysis
10. Text Mining
11. Web Scraping
12. Geospatial Analysis
13. Bioinformatics
14. Genomics
15. Proteomics
16. Metabolomics
17. Transcriptomics
18. Multivariate Analysis
19. Optimization
20. Bayesian Analysis
21. Survival Analysis
22. Econometrics
23. Financial Analysis
24. Social Network Analysis
25. Clustering
26. Classification
27. Regression Analysis
28. Association Rule Mining
29. Anomaly Detection
30. Simulation
31. Experimental Design
32. Hypothesis Testing
33. Data Cleaning
34. Time Series Forecasting
35. Dimensionality Reduction
36. Graph Algorithms
37. Reinforcement Learning
38. Markov Chain Monte Carlo
39. Graphical User Interfaces (GUIs)
40. Data Import/Export
41. Package Development
42. Data Wrangling
43. Statistical Inference
44. Statistical Computing
45. Data Imputation
46. Nonparametric Statistics
47. Predictive Modeling
48. Exploratory Data Analysis
49. Robust Statistics
50. Statistical Graphics
51. Survey Analysis
52. Data Reshaping
53. Social Media Analysis
54. Network Visualization
55. Deep Learning
56. Data Mining
57. Latent Variable Modeling
58. Spatial Statistics
59. Recommender Systems
60. Sampling Techniques
61. Statistical Genetics
62. Time Series Clustering
63. Sentiment Analysis
64. Causal Inference
65. Natural Language Generation
66. Item Response Theory
67. Graph Database Analysis
68. Hidden Markov Models
69. Stochastic Processes
70. Text Classification
71. Structural Equation Modeling
72. Data Fusion
73. Data Compression
74. Statistical Process Control
75. Graph Embedding
76. Functional Data Analysis
77. Optimal Experimental Design
78. Data Privacy
79. Collaborative Filtering
80. Data Augmentation
81. Statistical Power Analysis
82. Longitudinal Data Analysis
83. Graph Clustering
84. Nonlinear Regression
85. Survival Prediction
86. Data Smoothing
87. Data Ethics
88. Graph Theory
89. Data Quality Assessment
90. Spectral Analysis
91. Item Response Analysis
92. Graph Matching
93. Statistical Disclosure Control
94. Graph Partitioning
95. Data Governance
96. Data Integration These categories cover a wide range of applications and analyses that can be performed using R packages.

By RPKG.net by Obi Obianom