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Fix typos in README
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README.md

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The [TensorCrossInterpolation module](https://github.com/tensor4all/TensorCrossInterpolation.jl) implements the *tensor cross interpolation* algorithm for efficient interpolation of multi-index tensors and multivariate functions.
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This algorithm is used in the *quantics tensor cross interpolation* (QTCI) method for exponentially efficient interpolation of functions with scale separation. QTCI is implemented in the [QuanticsTCI.jl](https://github.com/tensor4all/quanticstci.jl) module.
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This algorithm is used in the *quantics tensor cross interpolation* (QTCI) method for exponentially efficient interpolation of functions with scale separation. QTCI is implemented in the [QuanticsTCI.jl](https://github.com/tensor4all/QuanticsTCI.jl) module.
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## Installation
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This module has been registered in the General registry. It can be installed by typing the following in a Julia REPL:
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```julia
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using Pkg; Pkg.add("TensorCrossInterpolation.jl")
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using Pkg; Pkg.add("TensorCrossInterpolation")
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```
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## Usage
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- The return type of `f` (`Float64` in this case) must be stated explicitly in the call to `crossinterpolate2`.
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The resulting `TensorCI2` object can be further manipulated, see [user manual](https://tensor4all.github.io/TensorCrossInterpolation.jl/dev).
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To evaluate the TCI interpolation, simply call your `TensorCI1` object like you would call the original function:
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To evaluate the TCI interpolation, simply call your `TensorCI2` object like you would call the original function:
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```julia
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originalvalue = f([1, 2, 3, 4, 5, 6, 7, 8])
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interpolatedvalue = tci([1, 2, 3, 4, 5, 6, 7, 8])

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