# What's New in Version 7.0

## .NET Core and .NET Standard support

- Support for .NET Core 1.1 and 2.1.
- Support for .NET Standard 1.3 and 2.0.
- Support for .NET Framework 3.5, 4.0, 4.72 and later.
- All packages are available on the Nuget Gallery.

## Linear algebra

### Major enhancements

- Broadcasting vectors in matrix operations.
- Enable Conditional Numerical Reproducibility option for native libraries.
- Upgraded native libraries to Intel® Math Kernel Library version 2019 Update 0.
- Upgraded managed linear algebra library to LAPACK 3.7.0.
- Improved range and accuracy of matrix exponential.
- Vector Map methods that include index as delegate argument.

### New matrix decompositions

- Generalized Eigenvalue Decomposition.
- Generalized Singular Value Decomposition (GSVD).
- Sparse singular value decomposition.
- RQ decomposition, QL decomposition, and LQ decomposition.
- Access to ‘thin’ version of the orthogonal factor Q in a QR decomposition.
- Compute factors of symmetric and Hermitian indefinite decomposition.

### Performance improvements

- Improve performance for level 2 managed sparse BLAS.
- Improve performance for various vector operations.
- The threshold for parallel execution of vector maps can now be configured.

## Mathematics

### General improvements

- The generic Operations<T> class has been optimized to eliminate nearly all overhead for the most frequently used operations on the most common argument types.
`ParallelOptions`

is now exposed for all algorithms to enable cancellation and other scenarios.- Combinatorial iterators to enumerate all combinations, permutations, and Cartesian products of sets of items.
- New overloads for numerical integration methods that take Interval objects to specify bounds.
- Inverse hyperbolic functions for decimal and quad precision numbers.

### Optimization

- The
`NonlinearProgram`

class has a new constructor that accepts variable names. - Symbolic constraints that are linear in the variables are now recognized as such.
- The Nonlinear Program solver can now recover when it encounters an infeasible subproblem.
- Up to 30% improvement in the performance of the Linear Program solver
- Limited Memory BFGS Optimizer.
- LeastSquaresOptimizer base class for nonlinear least squares algorithms.
- Trust Region Reflexive algorithm for nonlinear least squares.
- Trust Region Reflexive algorithm option in nonlinear curve fitting.
- Improved documentation for nonlinear least squares algorithms.

### Special functions

- Jacobi elliptic functions.
- Zeros of Bessel and Airy functions.
- The performance and accuracy of Bessel functions of the first and second kind has been improved.
- Polygamma function.
- Modified Bessel functions of real order.
- “Partial application” functions for incomplete and regularized Gamma and Beta functions.
- Zernike polynomials.

## Statistics and data analysis

### Data access library

- Data Access Library providing a unified API for reading and writing data frames, matrices, and vectors.
- Reading and writing R’s .rda/.rdata and .rds files.
- JSON serialization.
- Other supported formats include: delimited text (CSV, TSV…), fixed-width text, Matrix Market, Matlab®, stata®

### Statistical models

- Use R-style model formulas to specify statistical models.
- Partial Least Squares (PLS) models.
- Linear Discriminant Analysis.
- Kernel Density Estimation.
- Binomial Generalized Linear Model can now be used with count data.
- Two-way ANOVA: support for Type I, Type II, and Type III sums of squares.
- New ConditionalVariances property on GARCH models.
- The performance of ARIMA model fitting has been improved.
- Nicer Summarize for statistical models.

### Hypothesis tests

- Augmented Dickey-Fuller test.
- Cramer-von Mises Goodness-of-fit test.
- Tests for outliers: Grubbs’ test, Generalized ESD test.

### Data analysis

- New aggregators: Range, Mode, CountUnique.
- Improved support for custom aggregators based on accumulators.
- R-style variations of quantiles.
- LOESS and LOWESS smoothing.
- More categorical encodings: Backward difference, Forward difference, Helmert, reverse Helmert, orthogonal polynomial encoding.
- Non-central chi-square, non-central F, non-central beta, and non-central t distributions.
- Anderson-Darling distribution is now public.

Want to go further back? See what was new in version 6.0.