This package provides methods for calculating gradient surface metrics for continuous analysis of landscape features.

This package provides a collection of functions for processing Gen5 2.06 exported data. Gen5 is an essential data analysis software for BioTek plate readers <https://www.biotek.com/products/software-robotics-software/gen5-microplate-reader-and-imager-software/>. This package contains functions for data cleaning, modeling and plotting using exported data from Gen5 version 2.06. It exports technically correct data defined in (Edwin de Jonge and Mark van der Loo (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf>) for customized analysis. It contains Boltzmann fitting for general kinetic analysis. See <https://www.github.com/yanxianUCSB/gen5helper> for more information, documentation and examples.

Genomic signatures represent unique features within a species DNA, enabling the differentiation of species and offering broad applications across various fields. This package provides essential tools for calculating these specific signatures, streamlining the process for researchers and offering a comprehensive and time-saving solution for genomic analysis.The amino acid contents are identified based on the work published by Sandberg et al. (2003) <doi:10.1016/s0378-1119(03)00581-x> and Xiao et al. (2015) <doi:10.1093/bioinformatics/btv042>. The Average Mutual Information Profiles (AMIP) values are calculated based on the work of Bauer et al. (2008) <doi:10.1186/1471-2105-9-48>. The Chaos Game Representation (CGR) plot visualization was done based on the work of Deschavanne et al. (1999) <doi:10.1093/oxfordjournals.molbev.a026048> and Jeffrey et al. (1990) <doi:10.1093/nar/18.8.2163>. The GC content is calculated based on the work published by Nakabachi et al. (2006) <doi:10.1126/science.1134196> and Barbu et al. (1956) <https://pubmed.ncbi.nlm.nih.gov/13363015>. The Oligonucleotide Frequency Derived Error Gradient (OFDEG) values are computed based on the work published by Saeed et al. (2009) <doi:10.1186/1471-2164-10-S3-S10>. The Relative Synonymous Codon Usage (RSCU) values are calculated based on the work published by Elek (2018) <https://urn.nsk.hr/urn:nbn:hr:217:686131>.

This package provides a collection of functions to set up Google Public Data Explorer <https://www.google.com/publicdata/> data visualization tool with your own data, building automatically the corresponding DataSet Publishing Language file, or DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to be published in Public Data Explorer'.

Allows users to generate a gendered language score according to the gendered language dictionary in Roberts and Utych (2019) <doi:10.1177/1065912919874883>.

Automates delta log-normal boosted regression tree abundance prediction. Loops through parameters provided (LR (learning rate), TC (tree complexity), BF (bag fraction)), chooses best, simplifies, & generates line, dot & bar plots, & outputs these & predictions & a report, makes predicted abundance maps, and Unrepresentativeness surfaces. Package core built around gbm (gradient boosting machine) functions in dismo (Hijmans, Phillips, Leathwick & Jane Elith, 2020 & ongoing), itself built around gbm (Greenwell, Boehmke, Cunningham & Metcalfe, 2020 & ongoing, originally by Ridgeway). Indebted to Elith/Leathwick/Hastie 2008 Working Guide <doi:10.1111/j.1365-2656.2008.01390.x>; workflow follows Appendix S3. See <https://www.simondedman.com/> for published guides and papers using this package.

This package provides stat_isotonic() to add weighted univariate isotonic regression curves.

This package provides functions are provided for quantifying evolution and selection on complex traits. The package implements effective handling and analysis algorithms scaled for genome-wide data and calculates a composite statistic, denoted Ghat, which is used to test for selection on a trait. The package provides a number of simple examples for handling and analysing the genome data and visualising the output and results. Beissinger et al., (2018) <doi:10.1534/genetics.118.300857>.

Convert Ensembl gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including Entrez', HGNC', and UniProt'.

The function gggap() streamlines the creation of segments on the y-axis of ggplot2 plots which is otherwise not a trivial task to accomplish.

The gene-set distance analysis of omic data is implemented by generalizing distance correlations to evaluate the association of a gene set with categorical and censored event-time variables.

Create correlation heatmaps with ggplot2 and customise them with flexible annotation and clustering. Symmetric heatmaps can use triangular or mixed layouts, removing redundant information or displaying complementary information in the two halves. There is also support for general heatmaps not displaying correlations.

Integer programming models to assign students to groups by maximising diversity within groups, or by maximising preference scores for topics.

Run grass growth simulations using a grass growth model based on ModVege (Jouven, M., P. Carrère, and R. Baumont "Model Predicting Dynamics of Biomass, Structure and Digestibility of Herbage in Managed Permanent Pastures. 1. Model Description." (2006) <doi:10.1111/j.1365-2494.2006.00515.x>). The implementation in this package contains a few additions to the above cited version of ModVege, such as simulations of management decisions, and influences of snow cover. As such, the model is fit to simulate grass growth in mountainous regions, such as the Swiss Alps. The package also contains routines for calibrating the model and helpful tools for analysing model outputs and performance.

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 the Generalized Method of Wavelet Moments with Exogenous Inputs estimator (GMWMX) presented in Voirol, L., Xu, H., Zhang, Y., Insolia, L., Molinari, R. and Guerrier, S. (2024) <doi:10.48550/arXiv.2409.05160>. The GMWMX estimator allows to estimate functional and stochastic parameters of linear models with correlated residuals in presence of missing data. The gmwmx2 package provides functions to load and plot Global Navigation Satellite System (GNSS) data from the Nevada Geodetic Laboratory and functions to estimate linear model model with correlated residuals in presence of missing data.

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.

Simplifies the creation, management, and updating of local databases using data extracted from Google Earth Engine ('GEE'). It integrates with GEE to store, aggregate, and process spatio-temporal data, leveraging SQLite for efficient, serverless storage. The geeLite package provides utilities for data transformation and supports real-time monitoring and analysis of geospatial features, making it suitable for researchers and practitioners in geospatial science. For details, see Kurbucz and Andrée (2025) "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package" <https://hdl.handle.net/10986/43165>.

This package provides functions for drawing scene trees representing scenes that have been drawn using grid graphics.

Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.

This package provides a collection of methods to extract gene programs from single-cell gene expression data using non-negative matrix factorization (NMF). GeneNMF contains functions to directly interact with the Seurat toolkit and derive interpretable gene program signatures.

This package provides functions to fit two-dimensional Gaussian functions, predict values from fits, and produce plots of predicted data via either ggplot2 or base R plotting.

Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).

Providing publication-ready graphs for Multiple sequence alignment. Moreover, it provides a unique solution for visualizing the multiple sequence alignment without the need to do the alignment in each run which is a big limitation in other available packages.