Code used for trend analysis, regression, correlation, and maximum covariance analysis (MCA).
============================

Functions
---------

1. Maximum Covariance Analysis (MCA)
   - mca.m: Performs Maximum Covariance Analysis between two spatiotemporal fields
   - Supports area-weighted analysis
   - Returns spatial patterns, time series, and squared covariance fractions

2. Trend Analysis
   - field_trend_sen.m: Applies Sen's slope and Mann-Kendall test to spatial fields
   - trend_sen.m: Implements Sen's slope estimator
   - mann_kendall.m: Performs Mann-Kendall trend test
   
3. Regression and Correlation
   - field_regress.m: Performs linear regression of spatial fields against a time series
   - corr_auto.m: Calculates correlation with consideration of autocorrelation


Dependencies
-----------
- MATLAB Statistics Toolbox
- All functions should be in the same directory or MATLAB path


Usage Examples
-------------
1. Maximum Covariance Analysis:
   [U_p, V_p, pc_U, pc_V, scf, s2_sum] = mca(data1, data2, n_mca, 'aw', lat1, lon1, lat2, lon2);

2. Trend Analysis:
   [b, p] = field_trend_sen(data, p_level);

3. Field Regression:
   [b, p, r] = field_regress(data, index);


Notes
-----
- Input data should be properly preprocessed (detrended, demeaned if necessary)
- For spatial data, dimensions should be organized as (latitude × longitude × time)
- Missing values should be represented as NaN