This package provides a convenient interface with the OpenAI ChatGPT API <https://openai.com/api>. gptr allows you to interact with ChatGPT', a powerful language model, for various natural language processing tasks. The gptr R package makes talking to ChatGPT in R super easy. It helps researchers and data folks by simplifying the complicated stuff, like asking questions and getting answers. With gptr', you can use ChatGPT in R without any hassle, making it simpler for everyone to do cool things with language!

This package provides a function built on ggplot2 that visualizes pairwise BLAST alignment results as chord diagrams, intuitively displaying homologous regions between query and subject sequences.

An event-Based framework for building Shiny apps. Instead of relying on standard Shiny reactive objects, this package allow to relying on a lighter set of triggers, so that reactive contexts can be invalidated with more control.

This package provides a fast and flexible general-purpose implementation of Particle Swarm Optimization (PSO) and Differential Evolution (DE) for solving global minimization problems is provided. It is designed to handle complex optimization tasks with nonlinear, non-differentiable, and multi-modal objective functions defined by users. There are five types of PSO variants: Particle Swarm Optimization (PSO, Eberhart & Kennedy, 1995) <doi:10.1109/MHS.1995.494215>, Quantum-behaved particle Swarm Optimization (QPSO, Sun et al., 2004) <doi:10.1109/CEC.2004.1330875>, Locally convergent rotationally invariant particle swarm optimization (LcRiPSO, Bonyadi & Michalewicz, 2014) <doi:10.1007/s11721-014-0095-1>, Competitive Swarm Optimizer (CSO, Cheng & Jin, 2015) <doi:10.1109/TCYB.2014.2322602> and Double exponential particle swarm optimization (DExPSO, Stehlik et al., 2024) <doi:10.1016/j.asoc.2024.111913>. For the DE algorithm, six types in Storn, R. & Price, K. (1997) <doi:10.1023/A:1008202821328> are included: DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, DE/rand_to-best/1 and DE/rand_to-best/2.

The Greymodels Shiny app is an interactive interface for statistical modelling and forecasting using grey-based models. It covers several state-of-the-art univariate and multivariate grey models. A user friendly interface allows users to easily compare the performance of different models for prediction and among others, visualize graphical plots of predicted values within user chosen confidence intervals. Chang, C. (2019) <doi:10.24818/18423264/53.1.19.11>, Li, K., Zhang, T. (2019) <doi:10.1007/s12667-019-00344-0>, Ou, S. (2012) <doi:10.1016/j.compag.2012.03.007>, Li, S., Zhou, M., Meng, W., Zhou, W. (2019) <doi:10.1080/23307706.2019.1666310>, Xie, N., Liu, S. (2009) <doi:10.1016/j.apm.2008.01.011>, Shao, Y., Su, H. (2012) <doi:10.1016/j.aasri.2012.06.003>, Xie, N., Liu, S., Yang, Y., Yuan, C. (2013) <doi:10.1016/j.apm.2012.10.037>, Li, S., Miao, Y., Li, G., Ikram, M. (2020) <doi:10.1016/j.matcom.2019.12.020>, Che, X., Luo, Y., He, Z. (2013) <doi:10.4028/www.scientific.net/AMM.364.207>, Zhu, J., Xu, Y., Leng, H., Tang, H., Gong, H., Zhang, Z. (2016) <doi:10.1109/appeec.2016.7779929>, Luo, Y., Liao, D. (2012) <doi:10.4028/www.scientific.net/AMR.507.265>, Bilgil, H. (2020) <doi:10.3934/math.2021091>, Li, D., Chang, C., Chen, W., Chen, C. (2011) <doi:10.1016/j.apm.2011.04.006>, Chen, C. (2008) <doi:10.1016/j.chaos.2006.08.024>, Zhou, W., Pei, L. (2020) <doi:10.1007/s00500-019-04248-0>, Xiao, X., Duan, H. (2020) <doi:10.1016/j.engappai.2019.103350>, Xu, N., Dang, Y. (2015) <doi:10.1155/2015/606707>, Chen, P., Yu, H.(2014) <doi:10.1155/2014/242809>, Zeng, B., Li, S., Meng, W., Zhang, D. (2019) <doi:10.1371/journal.pone.0221333>, Liu, L., Wu, L. (2021) <doi:10.1016/j.apm.2020.08.080>, Hu, Y. (2020) <doi:10.1007/s00500-020-04765-3>, Zhou, P., Ang, B., Poh, K. (2006) <doi:10.1016/j.energy.2005.12.002>, Cheng, M., Li, J., Liu, Y., Liu, B. (2020) <doi:10.3390/su12020698>, Wang, H., Wang, P., Senel, M., Li, T. (2019) <doi:10.1155/2019/9049815>, Ding, S., Li, R. (2020) <doi:10.1155/2020/4564653>, Zeng, B., Li, C. (2018) <doi:10.1016/j.cie.2018.02.042>, Xie, N., Liu, S. (2015) <doi:10.1109/JSEE.2015.00013>, Zeng, X., Yan, S., He, F., Shi, Y. (2019) <doi:10.1016/j.apm.2019.11.032>.

This is a wrapper for the command line tool googler', which can be found at the following URL: <https://github.com/jarun/googler>.

This package performs end-to-end analysis of gene clustersâ such as photosynthesis, carbon/nitrogen/sulfur cycling, carotenoid, antibiotic, or viral marker genes (e.g., capsid, polymerase, integrase)â from genomes and metagenomes. It parses Basic Local Alignment Search Tool (BLAST) results in tab-delimited format produced by tools like NCBI BLAST+ and Diamond BLASTp, filters Open Reading Frames (ORFs) by length, detects contiguous clusters of reference genes, optionally extracts genomic coordinates, merges functional annotations, and generates publication-ready arrow plots. The package works seamlessly with or without the coding sequences input and skips plotting when no functional groups are found. For more details see Li et al. (2023) <doi:10.1038/s41467-023-42193-7>.

This package provides a suite of function-building tools centered around a (forward) composition operator, %>>>%, which extends the semantics of the magrittr %>% operator and supports Tidyverse quasiquotation. It enables you to construct composite functions that can be inspected and transformed as list-like objects. In conjunction with %>>>%, a compact function constructor, fn(), and a partial-application constructor, partial(), are also provided; both support quasiquotation.

Extends classical linear and quadratic discriminant analysis by incorporating permutation group symmetries into covariance matrix estimation. The package leverages methodology from the gips framework to identify and impose permutation structures that act as a form of regularization, improving stability and interpretability in settings with symmetric or exchangeable features. Several discriminant analysis variants are provided, including pooled and class-specific covariance models, as well as multi-class extensions with shared or independent symmetry structures. For more details about gips methodology see and Graczyk et al. (2022) <doi:10.1214/22-AOS2174> and Chojecki, Morgen, KoÅ odziejek (2025, <doi:10.18637/jss.v112.i07>).

Aligning multiple visualisations by utilising generalised orthogonal Procrustes analysis (GPA) before combining coordinates into a single biplot display as described in Nienkemper-Swanepoel, le Roux and Lubbe (2023)<doi:10.1080/03610918.2021.1914089>. This is mainly suitable to combine visualisations constructed from multiple imputations, however, it can be generalised to combine variations of visualisations from the same datasets (i.e. resamples).

Derivative Free Gradient Projection Algorithms for Factor Rotation. For more details see ?GPArotateDF. Theory for these functions can be found in the following publications: Jennrich (2004) <doi:10.1007/BF02295647>. Bernaards and Jennrich (2005) <doi:10.1177/0013164404272507>.

Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).

This package creates diagrams with an object-oriented approach. Geometric objects have computed properties with information about themselves (e.g., their area) or about their relationships with other objects (e.g, the distance between their edges). The objects have methods to convert them to geoms that can be plotted in ggplot2'.

This package provides methods for automatic calculation of gene scores from gene count tables, including a Z-score method that requires a table of samples being scored and a count table with control samples; a geometric mean method that does not rely on control samples; and a principal component-based method that summarizes gene expression using user-selected principal components. The Z-score and geometric mean approaches are described in Kim et al. (2018) <doi:10.1089/jir.2017.0127>.

This package provides a way to log ggplot component calls, which can be useful for debugging and understanding how ggplot objects are created. The logged calls can be printed, saved, and re-executed to reproduce the original ggplot object.

This package provides functions for survey data including svydesign objects from the survey package that call ggplot2 to make bar charts, histograms, boxplots, and hexplots of survey data.

Reads annual and quarterly financial reports from companies traded at B3, the Brazilian exchange <https://www.b3.com.br/>. All data is downloaded and imported from CVM's public ftp site <https://dados.cvm.gov.br/dados/CIA_ABERTA/>.

This package implements genetic algorithm and particle swarm algorithm for real-valued functions. Various modifications (including hybridization and elitism) of these algorithms are provided. Implemented functions are based on ideas described in S. Katoch, S. Chauhan, V. Kumar (2020) <doi:10.1007/s11042-020-10139-6> and M. Clerc (2012) <https://hal.archives-ouvertes.fr/hal-00764996>.

Helper to add insets based on geom_sf() from ggplot2'. This package gives you a drop-in replacement for geom_sf() that supports adding a zoomed inset map without having to create and embed a separate plot.

Functional denoising and functional ANOVA through wavelet-domain Markov groves. Fore more details see: Ma L. and Soriano J. (2018) Efficient functional ANOVA through wavelet-domain Markov groves. <arXiv:1602.03990v2 [stat.ME]>.

This package implements a geographically weighted non-negative principal components analysis, which consists of the fusion of geographically weighted and sparse non-negative principal components analyses <doi:10.17608/k6.auckland.9850826.v1>.

R Interface to C API of GLPK, depends on GLPK Version >= 4.42.

This package provides functions to compute various germination indices such as germinability, median germination time, mean germination time, mean germination rate, speed of germination, Timson's index, germination value, coefficient of uniformity of germination, uncertainty of germination process, synchrony of germination etc. from germination count data. Includes functions for fitting cumulative seed germination curves using four-parameter hill function and computation of associated parameters. See the vignette for more, including full list of citations for the methods implemented.

This package provides functions to estimate the disparities across categories (e.g. Black and white) that persists if a treatment variable (e.g. college) is equalized. Makes estimates by treatment modeling, outcome modeling, and doubly-robust augmented inverse probability weighting estimation, with standard errors calculated by a nonparametric bootstrap. Cross-fitting is supported. Survey weights are supported for point estimation but not for standard error estimation; those applying this package with complex survey samples should consult the data distributor to select an appropriate approach for standard error construction, which may involve calling the functions repeatedly for many sets of replicate weights provided by the data distributor. The methods in this package are described in Lundberg (2021) <doi:10.31235/osf.io/gx4y3>.