Started on NEWS

NEWS.md

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-# shapr (development version) 
+# shapr 1.0.0
 
-* Release a Python wrapper (`shaprpyr`, [#325](https://github.com/NorskRegnesentral/shapr/pull/325)) for explaining predictions from Python models (from Python) utilizing almost all functionality of `shapr`. The wrapper moves back and forth back and forth between Python and R, doing the prediction in Python, and almost everything else in R. This simplifies maintenance of `shaprpy` significantly. The wrapper is available [here](https://github.com/NorskRegnesentral/shapr/tree/master/python).
+* (Just some notes so far)
+* Adaptive estimatio/convergence detection
+* Verbosity
 * Complete restructuring motivated by introducing the Python wrapper. The restructuring splits the explanation tasks into smaller pieces, which was necessary to allow the Python wrapper to move back and forth between R and Python.
 * As part of the restructuring, we also did a number of design changes, resulting in a series of breaking changes described below.
 
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 ### New features
 
+* Adatpive sampling of Shapley value subsets
+* Release a Python wrapper (`shaprpyr`, [#325](https://github.com/NorskRegnesentral/shapr/pull/325)) for explaining predictions from Python models (from Python) utilizing almost all functionality of `shapr`. The wrapper moves back and forth back and forth between Python and R, doing the prediction in Python, and almost everything else in R. This simplifies maintenance of `shaprpy` significantly. The wrapper is available [here](https://github.com/NorskRegnesentral/shapr/tree/master/python).
 * Introduce batch computation of conditional expectations ([#244](https://github.com/NorskRegnesentral/shapr/issues/244)). 
 This essentially compute $v(S)$ for a portion of the $S$-subsets at a time, to reduce the amount of data needed to be held in memory. 
 The user can control the number of batches herself, but we set a reasonable value by default ([#327](https://github.com/NorskRegnesentral/shapr/pull/327)).