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# Optimization Results
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Container for maximum likelihood and maximum a posteriori estimation results. The CmdStanMLE class provides access to optimized parameter values and optimization iterations when available.
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## Capabilities
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### Parameter Access
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Access optimized parameter estimates in multiple formats.
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```python { .api }
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def optimized_params_np(self):
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    """
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    Get optimized parameters as NumPy array.
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    Returns:
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    np.ndarray: Final parameter estimates
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    """
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def optimized_params_pd(self):
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    """
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    Get optimized parameters as pandas DataFrame.
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    Returns:
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    pd.DataFrame: Parameters with names as index
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    """
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def optimized_params_dict(self):
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    """
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    Get optimized parameters as dictionary.
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    Returns:
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    dict: Mapping from parameter names to values
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    """
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def optimized_iterations_np(self):
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    """
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    Get optimization iterations as NumPy array.
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    Returns:
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    np.ndarray or None: Iteration history if save_iterations=True
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    """
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def optimized_iterations_pd(self):
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    """
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    Get optimization iterations as pandas DataFrame.
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    Returns:
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    pd.DataFrame or None: Iteration history if save_iterations=True
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    """
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```
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### Variable Access
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Access individual Stan variables with automatic type handling.
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```python { .api }
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def stan_variable(self, var, inc_iter=False):
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    """
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    Get value for specific Stan variable.
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    Parameters:
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    - var (str): Variable name
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    - inc_iter (bool): Include optimization iterations if available
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    Returns:
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    float or np.ndarray: Variable value with original Stan dimensions
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    """
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def stan_variables(self, inc_iter=False):
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    """
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    Get all Stan variables as dictionary.
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    - inc_iter (bool): Include optimization iterations if available
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    Returns:
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    dict: Mapping from variable names to values
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    """
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```
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### File Operations
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```python { .api }
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def save_csvfiles(self, dir=None):
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    """
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    Save CSV output files to directory.
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    Parameters:
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    - dir (str or PathLike, optional): Target directory
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    Returns:
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    None
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    """
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```
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## Properties
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```python { .api }
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# Results information
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mle.column_names         # Tuple[str, ...]: Parameter names
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mle.metadata            # InferenceMetadata: Run configuration and timing
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```
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## Usage Examples
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```python
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# Run optimization
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mle = model.optimize(data=data, algorithm="lbfgs", iter=1000)
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# Access results in different formats
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params_dict = mle.optimized_params_dict()
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print(f"MLE estimate for theta: {params_dict['theta']}")
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# Get specific variables
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theta_mle = mle.stan_variable("theta")
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sigma_mle = mle.stan_variable("sigma")
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# Save results
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mle.save_csvfiles(dir="./mle_results")
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```