From e925b6ed4b76266f3533604f8fb19e2f3ddcc596 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Sun, 25 Oct 2026 00:00:01 +0800 Subject: [PATCH 1/8] polish readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 35ebcbc..118af50 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ ***P**attern **C**ausality Analysis* -*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses symbolic pattern representations and cross mapping to detect directional interactions and infer causal structure from temporal dynamics and spatial snapshots. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. +*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projections to detect causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. > *Refer to the package documentation for more detailed information.* From 07030fb71d4f14189689e334f8566fd8a8887f70 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Sun, 25 Oct 2026 00:00:01 +0800 Subject: [PATCH 2/8] polish readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 118af50..dfc01ef 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ ***P**attern **C**ausality Analysis* -*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projections to detect causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. +*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projection to detect causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. > *Refer to the package documentation for more detailed information.* From 3c43c9449253a00661bd32e36a8f1e2def8faf27 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Sun, 25 Oct 2026 00:00:01 +0800 Subject: [PATCH 3/8] polish readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index dfc01ef..e6a4f13 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ ***P**attern **C**ausality Analysis* -*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projection to detect causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. +*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projection to unveil causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. > *Refer to the package documentation for more detailed information.* From d475f7adce61012586bdb18c7c47f9556cff1d27 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Sun, 25 Oct 2026 00:00:01 +0800 Subject: [PATCH 4/8] polish readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e6a4f13..2aba6ef 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ ***P**attern **C**ausality Analysis* -*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase state reconstruction and symbolic pattern projection to unveil causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. +*pc* is an R package for pattern-based causality analysis in both time series and spatial cross-sectional data. It uses phase space reconstruction and symbolic pattern projection to unveil causal interactions from temporal dynamics and spatial variations. Built on a high-performance C++ backend with a lightweight R interface, *pc* provides efficient and flexible tools for data-driven causality analysis. > *Refer to the package documentation for more detailed information.* From 151e55ec990cce1052bf8938e78c81095414ddab Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Mon, 26 Oct 2026 00:00:01 +0800 Subject: [PATCH 5/8] polish description --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 15af0b9..ba78934 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,7 +6,7 @@ Authors@R: email = "lyu.geosocial@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0009-0002-6003-3800")) -Description: Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description proposed in Lyu et al. (2026) . +Description: Infer causation from observational data through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . License: GPL-3 Encoding: UTF-8 URL: https://stscl.github.io/pc/, https://github.com/stscl/pc From d47c9b8689f7db822700b6237b6afb8b15bdacf8 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Mon, 26 Oct 2026 00:00:01 +0800 Subject: [PATCH 6/8] polish description --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index ba78934..3322450 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,7 +6,7 @@ Authors@R: email = "lyu.geosocial@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0009-0002-6003-3800")) -Description: Infer causation from observational data through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . +Description: Unveil interactions from observational data through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . License: GPL-3 Encoding: UTF-8 URL: https://stscl.github.io/pc/, https://github.com/stscl/pc From 10dd5c51e534bf6de92179a872838611831bda28 Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Mon, 26 Oct 2026 00:00:01 +0800 Subject: [PATCH 7/8] polish description --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3322450..30f6a59 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,7 +6,7 @@ Authors@R: email = "lyu.geosocial@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0009-0002-6003-3800")) -Description: Unveil interactions from observational data through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . +Description: Unveil interactions in complex systems through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . License: GPL-3 Encoding: UTF-8 URL: https://stscl.github.io/pc/, https://github.com/stscl/pc From 2659e10a4356e0d4f9942e8e4097393df2d375fd Mon Sep 17 00:00:00 2001 From: SpatLyu Date: Mon, 26 Oct 2026 00:00:01 +0800 Subject: [PATCH 8/8] polish description --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 30f6a59..ea463e4 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -6,7 +6,7 @@ Authors@R: email = "lyu.geosocial@gmail.com", role = c("aut", "cre", "cph"), comment = c(ORCID = "0009-0002-6003-3800")) -Description: Unveil interactions in complex systems through pattern causality analysis (PC), with the original algorithm for time series data from Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . +Description: Unveil interactions in complex systems through pattern causality analysis (PC), with the original algorithm for time series data by Stavroglou et al. (2020) , as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) , together with a systematic description detailed in Lyu et al. (2026) . License: GPL-3 Encoding: UTF-8 URL: https://stscl.github.io/pc/, https://github.com/stscl/pc