mohsaqr.r-universe.dev

[mohsaqr] Nestimate 0.7.0

saqr@saqr.me (Mohammed Saqr) — Sat, 13 Jun 2026 10:25:16 GMT

Estimate, compare, and analyze dynamic and psychological networks using a unified interface. Provides transition network analysis estimation (transition, frequency, co-occurrence, attention-weighted) Saqr et al. (2025) , psychological network methods (correlation, partial correlation, 'graphical lasso', 'Ising') Saqr, Beck, and Lopez-Pernas (2024) , and higher-order network methods including higher-order networks, higher-order network embedding, hyper-path anomaly, and multi-order generative model. Supports bootstrap inference, permutation testing, split-half reliability, centrality stability analysis, mixed Markov models, multi-cluster multi-layer networks and clustering.

[mohsaqr] bibnets 0.5.0

saqr@saqr.me (Mohammed Saqr) — Sat, 13 Jun 2026 10:11:18 GMT

Imports, constructs, and exports bibliometric networks from scholarly metadata. Reads 'Scopus', 'Web of Science', 'BibTeX', 'RIS', 'OpenAlex', 'Lens.org', 'Dimensions', and 'Crossref' exports. Goes beyond standard co-networks with attention-weighted networks (lead, last, proximity, circular position weights), position-aware counting (harmonic, arithmetic, geometric, golden-ratio), similarity and dissimilarity normalisations, temporal networks with fixed, sliding, and cumulative windows, disparity-filter backbone extraction, historiograph construction, and local citation scoring. Methods described in López-Pernas, Saqr & Apiola (2023) .

[mohsaqr] snakeplot 0.3.0

saqr@saqr.me (Mohammed Saqr) — Sun, 10 May 2026 21:17:16 GMT

Visualize long timelines, extended sequences and temporally chained survey responses and experience sampling data using intuitive serpentine (snake) plots. Supports distribution bars, tick-mark plots, inter-item correlation arcs, faceted multi-construct panels, and daily time-of-day positioning for ecological momentary assessment data.

[mohsaqr] cooccure 0.1.2

saqr@saqr.me (Mohammed Saqr) — Sun, 10 May 2026 21:16:26 GMT

Constructs co-occurrence networks from several types of input data, such as delimited fields, long/bipartite tables, binary matrices, or wide sequences. Returns tidy edge data frames and supports optional scaling, splitting into several networks, thresholding, and subsetting. Provides eight similarity measures, including Jaccard, cosine, and association strength. Supports export to several network and file formats. Network construction and analysis methods follow Saqr, Lopez-Pernas, Conde, and Hernandez-Garcia (2024, ).