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docs/_quarto.yml

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@@ -196,45 +196,46 @@ quartodoc:
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- title: Metrics
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desc: ""
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package: modelskill.metrics
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contents:
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- r2
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- rmse
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- root_mean_squared_error
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- c_rmse
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- c_root_mean_squared_error
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- urmse
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# - unbiased_root_mean_squared_error
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- c_urmse
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- c_unbiased_root_mean_squared_error
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- bias
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- c_bias
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- mae
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- mean_absolute_error
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- c_mae
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- c_mean_absolute_error
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- mape
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- cc
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- corrcoef
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- rho
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- spearmanr
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- ev
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- explained_variance
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- nse
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- nash_sutcliffe_efficiency
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- kge
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- kling_gupta_efficiency
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- max_error
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- c_max_error
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- mef
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- model_efficiency_factor
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- si
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- scatter_index
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- pr
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- peak_ratio
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- willmott
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- hit_ratio
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- lin_slope
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- name: metrics
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members:
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- r2
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- rmse
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- root_mean_squared_error
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- c_rmse
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- c_root_mean_squared_error
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- urmse
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# - unbiased_root_mean_squared_error
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- c_urmse
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- c_unbiased_root_mean_squared_error
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- bias
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- c_bias
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- mae
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- mean_absolute_error
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- c_mae
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- c_mean_absolute_error
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- mape
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- cc
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- corrcoef
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- rho
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- spearmanr
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- ev
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- explained_variance
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- nse
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- nash_sutcliffe_efficiency
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- kge
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- kling_gupta_efficiency
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- max_error
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- c_max_error
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- mef
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- model_efficiency_factor
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- si
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- scatter_index
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- pr
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- peak_ratio
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- willmott
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- hit_ratio
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- lin_slope
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- title: Core
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desc: ""

docs/api/index.qmd

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Obtain a comparer object in one of the following ways:
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- From matched data with [from_matched()](`modelskill.from_matched`)
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- After defining [observation](observation/index.qmd)s and [model result](model/index.qmd)s using the [match()](`modelskill.match`) function.
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- After defining [observation](obs.qmd)s and [model result](model.qmd)s using the [match()](`modelskill.match`) function.
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- From a config file with [from_config()](`modelskill.from_config`)
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Do analysis and plotting with the returned [Comparer](`modelskill.Comparer`) (a single observation) or [ComparerCollection](`modelskill.ComparerCollection`) (multiple observations):

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