loxavian.r-universe.dev

[mohsaqr] latents 0.9.37

saqr@saqr.me (Mohammed Saqr) — Sat, 17 Oct 2026 14:55:09 GMT

Provides functions for fitting multilevel latent profile analysis, latent class, and latent transition models for continuous, categorical, and mixed-type variables. Models are estimated by expectation-maximization, handle missing data, and provide standard and robust standard errors. Model selection is supported by information criteria, entropy, classification diagnostics, and a parametric bootstrap likelihood ratio test. Covariates can be related to latent class membership using three-step methods with correction for classification error. Latent transition models estimate transitions between latent profiles across ordered occasions and are compatible with the 'tna' package for network and sequence analysis. The multilevel mixture formulation follows Vermunt (2003) .

[mohsaqr] hypergraphs 0.7.5

saqr@saqr.me (Mohammed Saqr) — Fri, 16 Oct 2026 15:00:42 GMT

Build and analyze higher-order networks, in which a relation can involve more than two nodes or can depend on more than the current node. Covers the three structure families of the higher-order network literature Battiston et al. (2020) , plus a text family that reads a corpus as a hypergraph. Every object is read through one accessor, and every sequence-taking verb reads long, wide, list or model input. Memory networks from categorical sequences: higher-order networks with rule extraction Xu, Wickramarathne, and Chawla (2016) , higher-order network embedding Saebi et al. (2020) , hypergeometric path anomaly detection LaRock et al. (2020) , the multi-order generative model Scholtes (2017) , higher-order centralities Scholtes, Wider, and Garas (2016) , a permutation-based Markov order test, a path-dependence diagnostic, and bootstrap and permutation inference for extracted rules. Simplicial complexes: clique, Vietoris-Rips and pathway complexes, Betti numbers, persistent homology, persistence landscapes, bottleneck and Wasserstein distances, and q-analysis. Hypergraphs: construction from network cliques, sliding windows over sequences, bipartite group membership, or embedding nearest neighbours; structural measures; tensor eigenvector centralities Benson (2019) ; PageRank with edge-dependent vertex weights Chitra and Raphael (2019) ; the weighted normalized hypergraph Laplacian of Zhou, Huang, and Scholkopf (2006) with spectral, symmetric non-negative matrix factorization (NMF), and joint-NMF clustering Hayashi et al. (2020) and transductive label spreading; projections to weighted graphs and s-line graphs; Bernoulli-incidence, stochastic-block, uniform and regular random generators; temporal snapshots; hyperedge betweenness and closeness; Infomap ensembles with adjusted- mutual-information medoid selection; Y/T/O motif profiles and null models by degree-preserving swaps and the configuration model; and hypergraph neural networks in native 'torch': the hypergraph neural network (HGNN), hypergraph attention networks (HyperGAT and its heterogeneous variant), the hypergraph convolutional network (HyperGCN), hypergraph networks with hyperedge neurons (HNHN), AllDeepSets and AllSetTransformer. Text hypergraphs: a corpus becomes a weighted document-word hypergraph in either orientation (bag of words with smoothed term frequency-inverse document frequency (tf-idf), token windows, or embedding nearest neighbours), analysed with tidy verbs for clustering, keyword extraction, few-label classification, and null testing.

[mohsaqr] psychnets 0.5.2

saqr@saqr.me (Mohammed Saqr) — Fri, 16 Oct 2026 15:00:40 GMT

Provides clean-room implementations for estimating psychometric network models, including correlation and partial-correlation networks, Gaussian graphical models with extended Bayesian information criterion (EBIC) regularization, nonparanormal and stepwise selection variants, information-filtering networks (the triangulated maximally filtered graph and the local-global inverse covariance), relative-importance networks, and Ising and mixed graphical models . All methods are implemented from first principles in base R without compiled dependencies and return consistent, tidy outputs. Functions are designed to be transparent and report optimization diagnostics where applicable. For Gaussian graphical models, the graphical lasso stationarity (Karush-Kuhn-Tucker) residual quantifies the deviation of the estimated solution from the optimum of the corresponding convex optimization problem.

[mohsaqr] lagdynamics 0.32

saqr@saqr.me (Mohammed Saqr) — Fri, 16 Oct 2026 15:00:38 GMT

A modern, tidy toolkit for lag sequential analysis and lag transition networks of categorical event and sequence data. It provides an accessible, unified workflow for fitting, inspecting, visualising, and comparing lagged transition patterns, with tidy outputs throughout. Includes confirmatory tools for uncertainty, robustness, and group differences, including bootstrap intervals, analytic certainty, split-half reliability, case-drop stability, permutation tests, and Bayesian group comparisons. Supports long-format event-log import, import from common sequence and state-sequence objects, multi-lag analysis, structural-zero constraints, transition and initial probabilities, plotting of transition structures, and a directed transfer-entropy measure. The lag sequential analysis framework follows Sackett and others (1979) .

[mohsaqr] transitiontrees 0.1.2

saqr@saqr.me (Mohammed Saqr) — Fri, 16 Oct 2026 15:00:36 GMT

Discovers, ranks, and interprets predictive pathways in categorical sequence data. Fits a variable-depth pathway tree (prediction suffix tree; Ron, Singer and Tishby (1996) ) and exposes a pathway-centric application programming interface: most common pathways, most predictively divergent pathways, sharpest pathways, modal-flip diagnostics, and next-state predictions. Multiple pruning criteria (likelihood-ratio G-squared, Kullback-Leibler divergence, and the Akaike and Bayesian information criteria), smoothing for unseen contexts, and sequence sampling are supported. The fit and predictive surface are validated to machine precision against an independent external reference implementation of the model.

[mohsaqr] snakeplot 0.3.1

saqr@saqr.me (Mohammed Saqr) — Fri, 16 Oct 2026 15:00:34 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.

[cran] simulab 0.4.10

saqr@saqr.me (Mohammed Saqr) — Thu, 15 Oct 2026 16:30:02 GMT

Generates a wide variety of data, in different shapes, sample sizes, distributions and parameters. The parameters are either drawn at random or set in advance, and in both cases they are stored with the data, so they are known and their recovery can be tested. Generators cover declared variables with treatment assignment and missing values, correlated and clustered data, latent profile, latent class, factor and item response models, longitudinal and multilevel processes, survival times with competing risks, sequences of states, and static, bipartite, multiplex, temporal and transition networks. Every generator returns batches of reproducible data sets for the evaluation of statistical methods, the testing of models and software, and teaching; simulated transition networks are native 'tna' models.

[mohsaqr] Nestimate 0.9.26

saqr@saqr.me (Mohammed Saqr) — Mon, 12 Oct 2026 13:18:07 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] cooccure 0.4.1

saqr@saqr.me (Mohammed Saqr) — Thu, 08 Oct 2026 18:38:18 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, ).

[mohsaqr] Saqrlab 0.4.1

saqr@saqr.me (Mohammed Saqr) — Thu, 08 Oct 2026 18:38:18 GMT

Simulation tools for temporal network analysis (TNA) research. Generates synthetic datasets with known ground-truth parameters for testing and validating statistical methods: Markov chain sequences, latent profiles, latent classes, regression, factor analysis, and sequence cluster structures. All simulation functions return list(data, params) for JSON-serializable access to ground-truth parameters.

[mohsaqr] Dynet 0.7.3

saqr@saqr.me (Mohammed Saqr) — Thu, 08 Oct 2026 14:58:15 GMT

Building, analysis and visualisation of temporal networks. Supports several formats of relational data: interval data with explicit start and end times, contact data of instantaneous events, threaded interactions such as forum or chat data, and co-presence logs. Every analysis returns a tidy data frame carrying proper vertex labels. Metrics cover time-varying centrality, graph-level structure, edge formation and dissolution, burstiness, time-respecting paths, reachability and mixing. Offers a wide variety of network visualisations and animations, as well as plots of temporal network metrics across time.

[mohsaqr] simulab 0.4.10

saqr@saqr.me (Mohammed Saqr) — Sun, 04 Oct 2026 14:43:52 GMT

[mohsaqr] Saqrmisc 0.9.8

saqr@saqr.me (Mohammed Saqr) — Mon, 28 Sep 2026 20:27:04 GMT

A comprehensive R package providing miscellaneous functions for data processing, statistical analysis, and visualization. Includes functions for latent profile clustering via the 'latents' package, comparison plots with statistical testing, and mosaic plot analysis for categorical variables. The package offers robust error handling, comprehensive documentation, and user-friendly interfaces for complex statistical tasks.

[mohsaqr] carmar 0.8.6

saqr@saqr.me (Mohammed Saqr) — Tue, 15 Sep 2026 14:55:10 GMT

A single-file notebook for 'R' that runs on the 'R' installation already on the computer, and a 'Quarto' filter that makes the code chunks of a rendered web page runnable against each reader's own local 'R'. The notebook is a self-contained web page opened in the default browser; code is evaluated by a separate local 'R' process (the kernel) that the page reaches over a loopback socket, so closing the calling session does not close the notebook. Nothing is installed elsewhere on the system: on the user's explicit call the kernel process is started, its state is written under the directory returned by tools::R_user_dir(), and one small discovery record is written under '~/.carmar/run' and removed on a clean stop.

[mohsaqr] tsn 1.4.1

saqr@saqr.me (Mohammed Saqr) — Thu, 03 Sep 2026 07:51:47 GMT

Constructs distance, visibility, and state-transition networks from univariate time series. The core constructor provides whole-series and sliding-window distance networks, natural and horizontal visibility graphs following Lacasa et al. (2008) and Luque et al. (2009) , and optional state aggregation. Companion verbs expose discretization, trend classification, and an optional bridge to the 'Nestimate' transition-network models. Core network construction uses only packages distributed with R.

[mohsaqr] idiographic 0.3.4

saqr@saqr.me (Mohammed Saqr) — Sun, 23 Aug 2026 16:48:48 GMT

Person-specific and within-person network estimation from intensive longitudinal and panel data. Estimators include ordinary vector autoregression (VAR), graphical vector autoregression (graphical VAR), multilevel vector autoregression (mlVAR), rolling ordinary and graphical VAR, native Bayesian VAR and multilevel Bayesian VAR, unified Structural Equation Modeling (uSEM), and Group Iterative Multiple Model Estimation (GIMME). All estimators are native clean-room implementations. All functions are validated against authoritative literature. Also provides preprocessing audits, edge-stability diagnostics, model-comparison reports, and rolling forecast validation. Methods are described in and .

[mohsaqr] bibnets 0.6.0

saqr@saqr.me (Mohammed Saqr) — Thu, 18 Jun 2026 09:59:52 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) .