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.

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.

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.

Tools for Data Diagnosis, Exploration, Transformation

A collection of tools that support data diagnosis, exploration, and transformation. Data diagnostics provides information and visualization of missing values, outliers, and unique and negative values to help you understand the distribution and quality of your data. Data exploration provides information and visualization of the descriptive statistics of univariate variables, normality tests and outliers, correlation of two variables, and the relationship between the target variable and predictor. Data transformation supports binning for categorizing continuous variables, imputes missing values and outliers, and resolves skewness. And it creates automated reports that support these three tasks.

Evaluation Metrics for Spaced Repetition Schedulers

Calibration and discrimination metrics for spaced-repetition memory models. Provides the sample-weighted binned root mean squared error (RMSE(bins)) used to rank schedulers in the open spaced repetition benchmark, together with log loss, the area under the ROC curve, and calibration curves.

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). A description of the method and package is provided in Polanco-Martínez et al. (2019), .

Statistical and Geometrical Tools

A collection of statistical and geometrical tools including the aligned rank transform (ART; Higgins et al. 1990 ; Peterson 2002 ; Wobbrock et al. 2011 ), 2-D histograms and histograms with overlapping bins, a function for making all possible formulae within a set of constraints, amongst others.

Hexbin Plots with Triangles

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

Utility Functions for R Histograms

Provides a number of utility functions useful for manipulating large histograms. This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning. It also provides a protocol buffer representation of R's native histogram class to allow histograms over large data sets to be computed and combined in distributed analytical pipelines. Implements bin-by-bin histogram distance measures described in Rubner, Tomasi and Guibas (2000) , Swain and Ballard (1991) , and Puzicha, Hofmann and Buhmann (1997) , and average shifted histograms as in Scott (2015, ISBN:9781118575536).