Draws gene or genome maps and comparisons between these, in a publication-grade manner. Starting from simple, common files, it will draw postscript or PDF files that can be sent as such to journals.

Easily explore data by plotting graphs with a few lines of code. Use these ggplot() wrappers to quickly draw graphs of scatter/dots with box-whiskers, violins or SD error bars, data distributions, before-after graphs, factorial ANOVA and more. Customise graphs in many ways, for example, by choosing from colour blind-friendly palettes (12 discreet, 3 continuous and 2 divergent palettes). Use the simple code for ANOVA as ordinary (lm()) or mixed-effects linear models (lmer()), including randomised-block or repeated-measures designs, and fit non-linear outcomes as a generalised additive model (gam) using mgcv(). Obtain estimated marginal means and perform post-hoc comparisons on fitted models (via emmeans()). Also includes small datasets for practising code and teaching basics before users move on to more complex designs. See vignettes for details on usage <https://grafify.shenoylab.com/>. Citation: <doi:10.5281/zenodo.5136508>.

This package performs generalized Susceptible-Exposed-Infected-Recovered (SEIR) modeling to predict epidemic curves. The method is described in Peng et al. (2020) <doi:10.1101/2020.02.16.20023465>.

The proposed Generalized Autoregressive Conditional Heteroskedasticity (GARCH)-informed Long Short-Term Memory (LSTM) model follows the concept of physics-informed machine learning (PIML) by integrating established econometric knowledge of price volatility into a data-driven forecasting framework. In the model, conditional volatility estimated from the GARCH process is incorporated as an additional explanatory signal or volatility-based weighting component within the LSTM architecture. This enables the LSTM to learn nonlinear temporal dependencies while remaining informed by the underlying characteristics of agricultural price series, including volatility clustering, heteroscedasticity and market uncertainty. The optimized weighting parameter, lambda, controls the contribution of the GARCH-derived volatility information to the final prediction. Thus, the model combines the statistical interpretability of GARCH with the nonlinear learning capability of LSTM, producing a hybrid PIML framework that is more responsive to both normal price movements and periods of extreme market volatility. The methodology is motivated by hybrid forecasting framework proposed by Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

This package provides a simple and intuitive high-level language for music representation. Generates and embeds music scores and audio files in RStudio', R Markdown documents, and R Jupyter Notebooks'. Internally, uses MusicXML <https://github.com/w3c/musicxml> to represent music, and MuseScore <https://musescore.org/> to convert MusicXML'.

Integrating applied psychological and psychometric methods into geographical analysis. With the emergence of geo-referenced questionnaires, spatially explicit psychological and psychometric methods can offer a geographically contextualised approach that reflects latent traits and processes at a more local scale, leading to more tailored research and decision-making processes. The implemented methods include Geographically Weighted Cronbach's alpha and its bandwidth selection. See Zhang & Li (2025) <doi:10.1111/gean.70021>.

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Presentation quality charts built on ggplot2 that the package itself does not provide. The bar chart race interpolates values on a uniform time grid, ranks every frame on its own values and eases bars into new positions, so a reordering field stays readable. Frames are ordinary ggplot2 objects laid out on a fixed pixel grid, which keeps the axis and label column from drifting between them, and are encoded to GIF or MP4. Circular images, such as the bundled country flags, can be placed at the end of each bar. Causal diagrams are drawn as directed acyclic graphs whose nodes and arrows each carry a rationale and references, shown on hover and opened with clickable links on click, through ggiraph'; the same diagram is also available as a static ggplot2 object. Network plots for network meta-analysis are drawn from arm level data, with the baseline characteristics and outcomes of every arm shown side by side on click. Forest plots for metafor and meta fits carry each study's record and risk of bias traffic lights, and replay a cumulative meta-analysis as an animation; funnel plots shade where each study would be significant and carry the pooled estimate without it, trim and fill and the tests for small-study effects; league tables for netmeta fits show the direct and indirect evidence behind every estimate. Kaplan-Meier plots read survival and the hazard ratio at any time under the pointer, beside a risk table and proportional hazards tests, and swimmer plots give each patient a lane with their responses, progression and death. Nomograms of regression models, from linear and generalized linear models to mixed, Cox, parametric survival, ordinal and multinomial models, have a handle per predictor and compute each prediction with its confidence interval in the page. Choropleth maps of the world or of any sf map step or play through the years, with several measures side by side for the same year. Causal diagrams can also show which paths between an exposure and an outcome an adjustment set leaves open, by the backdoor criterion; Kaplan-Meier plots give the restricted mean survival time up to a horizon the reader can move; league tables and network plots show where each network estimate's evidence comes from; and swimmer plots can carry a waterfall of best change and each patient's course beside the lanes. Four explorers put a threshold or an assumption in the reader's hands: the cutoff of a diagnostic test, with what it means for 1,000 people at any prevalence; the strength of unmeasured confounding, with E-values; the choices of a multiverse of analyses; and the threshold that defines a responder. Plots after CINeMA judge the confidence in each estimate of a network meta-analysis in six domains, with every judgment's reason and source, and show what lies behind them: each study's contribution, the estimates against a movable range of little difference, direct against indirect evidence, and a league table and a network marked with the judgments.

Defines classes and methods that can be used to implement genetic algorithms for feature selection. The idea is that we want to select a fixed number of features to combine into a linear classifier that can predict a binary outcome, and can use a genetic algorithm heuristically to select an optimal set of features.

This package provides a fully automated workflow for calibrating and analyzing light-level geolocation ('GLS') data from seabirds and other wildlife. The glscalibrator package auto-discovers birds from directory structures, automatically detects calibration periods from the first days of deployment, processes multiple individuals in batch mode, and generates standardized outputs including position estimates, diagnostic plots, and quality control metrics. Implements the established threshold workflow internally, following the methods described in SGAT (Wotherspoon et al. (2016) <https://github.com/SWotherspoon/SGAT>), GeoLight (Lisovski et al. (2012) <doi:10.1111/j.2041-210X.2012.00185.x>), and TwGeos (Lisovski et al. (2019) <https://github.com/slisovski/TwGeos>).

This package provides a not-so-comprehensive list of methods for estimating graphon, a symmetric measurable function, from a single or multiple of observed networks. For a detailed introduction on graphon and popular estimation techniques, see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>. It also contains several auxiliary functions for generating sample networks using various network models and graphons.

This package implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hersh, C.P., et al. (2017). <DOI:10.1002/gepi.22037>. Gene-based segregation method for identifying rare variants for family-based sequencing studies. Genet Epidemiol 41(4):309-319. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.

This package provides a collection of I/O tools for handling the most commonly used genomic datafiles, like fasta/-q, bed, gff, gtf, ped/map and vcf.

Ease the transition between R vectors and markdown text. With gluedown and rmarkdown', users can create traditional vectors in R, glue those strings together with the markdown syntax, and print those formatted vectors directly to the document. This package primarily uses GitHub Flavored Markdown (GFM), an offshoot of the unambiguous CommonMark specification by John MacFarlane (2019) <https://spec.commonmark.org/>.

Toolset to create perpendicular profile graphs and swath profiles. Method are based on coordinate rotation algorithm by Schaeben et al. (2024) <doi:10.1002/mma.9823>.

This package provides a word cloud text geom for ggplot2'. Texts are placed so that they do not overlap as in ggrepel'. The algorithm used is a variation around the one of wordcloud2.js'.

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), One-Step Estimator Paths for Concave Regularization', <doi:10.48550/arXiv.1308.5623>.

This package provides ggplot2 geoms analogous to geom_col() and geom_bar() that allow for treemaps using treemapify nested within each bar segment. Also provides geometries for subgroup bordering and text annotation.

Interact with Google Cloud Storage <https://cloud.google.com/storage/> API in R. Part of the cloudyr <https://cloudyr.github.io/> project.

Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).

Create groups of ggplot2 layers that can be easily migrated from one plot to another, reducing redundant code and improving the ability to format many plots that draw from the same source ggpacket layers.

Facilitates efficient visualization of Relative Synonymous Codon Usage patterns across species. Based on analytical outputs from codonW', MEGA', and Phylosuite', it supports multi-species RSCU comparisons and allows users to explore visual analysis of structurally similar datasets.

Intended for both technical and non-technical users to create interactive data visualizations through a web browser GUI without writing any code.

Create a user-friendly plotting GUI for R'. In addition, one purpose of creating the R package is to facilitate third-party software to call R for drawing, for example, Phoenix WinNonlin software calls R to draw the drug concentration versus time curve.