This outlines a roadmap for basic statistical functionality that Julia needs to offer. It is heavily drawn from the table of contents for MASS.
- Data processing DataFrames.jl
- reshape
- cast
- melt
- plyr
- ddply
- reshape
- Probability distributions Distributions.jl
- Univariate distributions
- Multivariate distributions
- Matrix distributions
- Statistical graphics Gadfly.jl
- Render to native windows by default in REPL
- Add missing functionality found in ggplot2
- Resampling Methods Resampling.jl
- Bootstrap
- Nonparametric
- Parametric
- Cross-validation
- Leave-one out
- k-Fold
- Random resampling
- Bootstrap
- Linear regression GLM.jl
- OLS
- Robust regression QuantileRegression.jl
- ANOVA's
- Generalized linear models GLM.jl
- Nonlinear least squares
- Tree-based methods
- Decision trees DecisionTree.jl
- Random forests
- Hierarchical clustering
- Neural networks
- Perceptrons
- 1-hidden layer
- Deep neural networks
- Convolutional neural networks
- Generalized additive models
- gam
- Random and mixed effect models MixedModels.jl
- lmer
- Clustering Clustering.jl
- k-Means
- dp-Means
- DBSCAN
- Affinity propagation
- Dimensionality reduction DimensionalityReduction.jl
- PCA
- MDS
- ICA
- NMF
- tSNE tSNE.jl
- Factor analysis
- Support vector machines
- Survival analysis
- Cox model
- Time series analysis TimeSeries.jl
- Datetimes Datetime.jl
- AR(p)
- ARIMA
- Spatial statistics
- Kriging
- Optimization JuliaOpt
- Database access
Moved discussion to JuliaStats/Roadmap.jl#1