Global Adaptive Generative Adjustment Algorithm for Generalized Linear Models

Fits linear regression, logistic and multinomial regression models, Poisson regression, Cox model via Global Adaptive Generative Adjustment Algorithm. For more detailed information, see Bin Wang, Xiaofei Wang and Jianhua Guo (2022) . This paper provides the theoretical properties of Gaga linear model when the load matrix is orthogonal. Further study is going on for the nonorthogonal cases and generalized linear models. These works are in part supported by the National Natural Foundation of China (No.12171076).

Prepare and Explore Data for Palaeobiological Analyses

Provides functionality to support data preparation and exploration for palaeobiological analyses, improving code reproducibility and accessibility. The wider aim of 'palaeoverse' is to bring the palaeobiological community together to establish agreed standards. The package currently includes functionality for data cleaning, binning (time and space), exploration, summarisation and visualisation. Reference datasets (i.e. Geological Time Scales < https://stratigraphy.org/chart>) and auxiliary functions are also provided. Details can be found in: Jones et al., (2023) .

Distance Sampling Detection Function and Abundance Estimation

A simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) for more information on methods and < https://distancesampling.org/resources/vignettes.html> for example analyses.

Distance Metrics for Image Color Similarity

Loads and displays images, selectively masks specified background colors, bins pixels by color using either data-dependent or automatically generated color bins, quantitatively measures color similarity among images using one of several distance metrics for comparing pixel color clusters, and clusters images by object color similarity. Uses CIELAB, RGB, or HSV color spaces. Originally written for use with organism coloration (reef fish color diversity, butterfly mimicry, etc), but easily applicable for any image set.

Create United States Uniform Cartogram Heatmaps

The 'cartogram' heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (< http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

VPC Percentiles and Prediction Intervals

Perform a Visual Predictive Check (VPC), while accounting for stratification, censoring, and prediction correction. Using piping from 'magrittr', the intuitive syntax gives users a flexible and powerful method to generate VPCs using both traditional binning and a new binless approach Jamsen et al. (2018) with Additive Quantile Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) prediction correction.

Fully Automatic Generation of Scorecards

Provides an efficient suite of R tools for scorecard modeling, analysis, and visualization. Including equal frequency binning, equidistant binning, K-means binning, chi-square binning, decision tree binning, data screening, manual parameter modeling, fully automatic generation of scorecards, etc. This package is designed to make scorecard development easier and faster. References include: 1. < http://shichen.name/posts/>. 2. Dong-feng Li(Peking University),Class PPT. 3. < https://zhuanlan.zhihu.com/p/389710022>. 4. < https://www.zhangshengrong.com/p/281oqR9JNw/>.

Metagenomic Clustering

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

Estimate the Correlation Between Two Irregular Time Series

Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).

Hexbin Plots with Triangles

Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.