Optimal Binning Methods for Predictive Modeling and Analytics

Native R tools for optimal binning workflows in predictive modeling. The package provides APIs for binary, multi-class and continuous targets, with multi-variable binning and scorecard workflows. Methods are informed by Navas-Palencia (2020) and Navas-Palencia (2021) .

Binning and Visualizing NMR Spectra in Environmental Samples

A reproducible workflow for binning and visualizing NMR (nuclear magnetic resonance) spectra from environmental samples. The 'nmrrr' package is intended for post-processing of NMR data, including importing, merging and, cleaning data from multiple files, visualizing NMR spectra, performing binning/integrations for compound classes, and relative abundance calculations. This package can be easily inserted into existing analysis workflows by users to help with analyzing and interpreting NMR data.

A Shiny Application for Allele Binning in Microsatellite Markers

Provides allele binning functionality for SSR/microsatellite markers using least-squares minimization (Idury & Cardon (1997) ). Includes a 'Shiny' application for interactive use, summary statistics, and visualization.

Optimal Binning and Weight of Evidence Framework for Modeling

High-performance implementation of 37 optimal binning algorithms (16 categorical, 21 numerical) for Weight of Evidence ('WoE') transformation, credit scoring, and risk modeling. Includes advanced methods such as Mixed Integer Linear Programming ('MILP'), Genetic Algorithms, Simulated Annealing, and Monotonic Regression. Features automatic method selection based on Information Value ('IV') maximization, strict monotonicity enforcement, and efficient handling of large datasets via 'Rcpp'. Provides automated variable screening by Information Value strength and bin ordering, and generation of the equivalent 'SQL' 'CASE' expressions for in-database scoring. Fully integrated with the 'tidymodels' ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) and Navas-Palencia (2020) .

Finite Mixture Modeling for Raw and Binned Data

Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.

Estimation of Hawkes Processes from Binned Observations

Implements an estimation method for Hawkes processes when count data are only observed in discrete time, using a spectral approach derived from the Bartlett spectrum, see Cheysson and Lang (2020) . Some general use functions for Hawkes processes are also included: simulation of (in)homogeneous Hawkes process, maximum likelihood estimation, residual analysis, etc.

Supervised Weight of Evidence Binning of Numeric Variables and Factors

Implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.

Bayesian BIN (Bias, Information, Noise) Model of Forecasting

A recently proposed Bayesian BIN model disentangles the underlying processes that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into three components: bias, partial information, and noise. By describing the differences between two groups of forecasters, the model allows the user to carry out useful inference, such as calculating the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information. It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient extraction of valid information from the environment improves forecasting accuracy. This package provides easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov, Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN Model of Forecasting" .

Estimation of Group Means and SDs from Binned Count Data

Education agencies often report school or district score distributions as the number of students scoring in each of several score ranges, or bins, separated by threshold scores, or cuts. The functions in the binest package translate those bin counts into estimates of the mean and standard deviation (SD). They do so using the heteroskedastic ordered probit (HETOP) model, which assumes that scores follow a normal distribution within each school or district, each of which has its own mean and SD. The binest package includes the fast_hetop() function, which fits the model much more quickly than previous implementations. The model is described by Reardon, Shear, Castellano and Ho (2017) ; a Bayesian variant is described by Lockwood, Castellano and Shear (2018) .

Infer Community Assembly Mechanisms by Phylogenetic-Bin-Based Null Model Analysis

To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as 'iCAMP' (Ning et al 2020) . It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) . It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for 'QPEN' and added function 'icamp.cate()' to summarize 'iCAMP' results for different categories of taxa (e.g. core versus rare taxa).