Replace logical vectors with integer index vectors for lib and pred in simplex and s-mapping forecasting sources (#632).
Prevent duplicate generic registrations and simplify method definitions (#627).
Unify parameter descriptions to lowercase (#570).
Introduce confidence interval ribbon support in plot method for gccm results (#550).
Refine randomization strategy in spatial causality test (#643).
Symbolization C++ functions now compute medians from lib subset only (#599).
Users must now use detrend instead of trend.rm (#559).
Enable column parameter in simplex() and smap() and rename columns parameter to column in multiview() (#565).
gccm results where legend labels did not correctly match the corresponding line colors (#552).Enact fnn R API support for false nearest neighbours method (#512).
Integrate R API and vignette for geographical cross mapping cardinality (#455).
Provide R-level API and vignette for spatial causality test (#403).
Enable custom legend texts and colors in plot method for gccm results (#535).
Reduce computational load in vignettes (#476).
Document overall structure and usage of spEDM in a dedicated vignette (#415).
Create SSR vignette for spatial cross-sectional data state-space reconstruction (#412).
Include references for algorithms in spEDM (#367).
Use non-NA spatial units for lib/pred by default (#499).
Refine internal case data (#348).
Patch memory error caused by mismatch between C++ (0-based) and R (1-based) indexing (#480).
Fix error from non-matrix input in grid-type handling due to R matrix slicing (#474).
Enable parallel.level parameter to specify parallel granularity in gccm R API (#310).
Implement the multiview function for multiview embedding forecasting (MVE) method (#221).
Integrate lib parameter in gccm R API for library units selection (#278).
Set the default k to E+2 in the gccm R API (#261).
Eliminate redundant computations at the source C++ code level (#233).
Add trend.rm option in the R API for embedded, simplex, and smap methods to align with gccm behavior (#191).
Refactor indexing of lag values and embedding vector generation for spatial lattice (#186,#184) and grid data (#183,#181).
Default plotting method places the legend in the top-left corner of the plot now (#325).
Refine simplex & smap output on the R side (#263).
embedded, simplex, smap when input data contains only one attribute column (#246).sdsfun package (#159).tau parameter in the C++ source code and update the R side API (#154).Implement the smap function to enable the selection of the optimal theta parameter (#128).
Add simplex function to support selecting the optimal embedding dimension for variables (#98).
Provide an R-level API for generating embeddings (#97).
Now bidirectional mapping in the gccm result uses a full join structure when organized on the R side (#118).
Support for calculating unidirectional mappings in the gccm function (#117).
Relax gccm C++ source code libsizes minimum value constraint of E+2 (#109).
Provide a complete GCCM workflow for spatial lattice and grid data in the gccm vignette (#100).
Support testing causal links in GCCM with different E and k for cause and effect variables (#96).
Add thread settings for gccm (#94).
Add S-maps cross-prediction support to gccm (#81).
Resolve r crash caused by invalid E #90 and k #89 parameter settings in gccm.
Fix incorrect Pearson correlation calculation in C++ code when input contains NA (#83).
Encapsulate the gccm function using the S4 class (#72).
Add options for tau, k, and progressbar in gccm (#69).
Add print and plot s3 methods for gccm result (#64).
gccm function returns empty results when input grid data contains NA values (#61).GCCM method for spatial lattice and grid data using modern C++.RetroSearch is an open source project built by @garambo | Open a GitHub Issue
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