## Existing features
- regression methods
- polynomial
- GLMs: logistic, (quasi-)Poisson, Gamma, exponential
- optimization methods
- numerical differentiation, partial derivatives, automatic differentiation (currently with autodiff crate)
- optimizers
- Adam, Levenberg-Marquardt, SGD with (Nesterov) momentum
- numerical integration of functions
- trapezoid, Romberg, 5-point Gauss-Legendre quadrature
- basic statistical distributions
- continuous
- (Multivariate) Normal, Beta, Gamma, Chi Squared, Student's T, Uniform, Exponential, Pareto
- discrete
- Bernoulli, Binomial, Poisson, Discrete Uniform
- sampling, PDFs/PMFs
- analytic means and variances
- mathematical and statistical functions
- gamma, digamma, beta
- logistic, logit, (general) boxcox transform, softmax
- binomial coefficients
- statistical methods
- (sample) covariance, mean, variance, min, max
- time series models
- autoregressive models
- related functions
- autocorrelation, autocovariance, differencing
- validation methods
- resampling
- bootstrap, jackknife
- linear algebra: both BLAS/LAPACK and Rust implementations
- vector and matrix structs
- overloaded arithmetic operations for combinations of {matrix, vector, scalar} with automatic broadcasting a la numpy
- general utilities
- dot product, (blocked) matrix multiplication, matrix inversion, Toeplitz matrix, Vandermonde matrix, (infinity) norm, linear solve, transpose, design matrix
- vector-vector, scalar-vector, vector-scalar operations with loop unrolling
- decompositions and solvers
- LU, Cholesky
- signal processing
- convolutions
- filters
- Savitzky-Golay (LOESS)
## Planned features
- distributions: CDFs, fitting to data
- more time series models (SARIMA, exponential smoothing models, trend decomposition)
- non-linear optimizers (BFGS)
- ODE integrators (leapfrog, RK4)
- clustering algorithms (k-means/EM, DBSCAN)
- more regression models (mixed models, GP, penalized models, splines)
- prediction trees (CART, random forests, gradient boosted trees)
- order statistics (quantiles)
- statistical tests (t-test, ANOVA, Kolmogorov-Smirnov, Anderson-Darling)
- data preprocessing (outlier detection, standardization, dimensionality reduction (PCA))
- more linear algebra decompositions (QR, SVD)
- samplers? rejection, RWM, HMC, NUTS, (dynamic) nested sampling