Estimates one-inflated positive Poisson (OIPP) and one-inflated zero-truncated negative binomial (OIZTNB) regression models. A suite of ancillary statistical tools are also provided, including: estimation of positive Poisson (PP) and zero-truncated negative binomial (ZTNB) models; marginal effects and their standard errors; diagnostic likelihood ratio and Wald tests; plotting; predicted counts and expected responses; and random variate generation. The models and tools, as well as four applications, are shown in Godwin, R. T. (2024). "One-inflated zero-truncated count regression models" arXiv preprint <doi:10.48550/arXiv.2402.02272>.

This package provides tools to analyze and infer orthology and paralogy relationships between glutamine synthetase proteins in seed plants.

The log-rank test is performed to assess the survival outcomes between two group. When there is no proper control group or obtaining such data is cumbersome, one sample log-rank test can be applied. This package performs one sample log-rank test as described in Finkelstein et al. (2003)<doi:10.1093/jnci/djt227> and variation of the test for small sample sizes which is detailed in FD Liddell (1984)<doi:10.1136/jech.38.1.85> paper. Visualization function in the package generates Kaplan-Meier Curve comparing survival curve of the general population against that of the population of interest.

Package for estimating the parameters of a nonlinear function using iterated linearization via Taylor series. Method is based on KubÃ¡Ä ek (2000) ISBN: 80-244-0093-6. The algorithm is a generalization of the procedure given in Köning, R., Wimmer, G. and Witkovský, V. (2014) <doi:10.1088/0957-0233/25/11/115001>.

This package provides a collection of general-purpose helper functions that I (and maybe others) find useful when developing data science software. Includes tools for simulation, data transformation, input validation, and more.

Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.

Allows performing forwards prediction for the General Unified Threshold model of Survival using compiled ode code. This package was created to avoid dependency with the morse package that requires the installation of JAGS'. This package is based on functions from the morse package v3.3.1: Virgile Baudrot, Sandrine Charles, Marie Laure Delignette-Muller, Wandrille Duchemin, Benoit Goussen, Nils Kehrein, Guillaume Kon-Kam-King, Christelle Lopes, Philippe Ruiz, Alexander Singer and Philippe Veber (2021) <https://CRAN.R-project.org/package=morse>.

Transactions occurring for a UK-based and registered, non-store online retail between 01/12/2010 and 09/12/2011 (Chen et. al., 2012, <doi:10.1145/1835804.1835882>). This dataset is included in this package with the donor's permission, Dr. Daqing Chen.

This package provides tools to process raster data and apply Otsu-based thresholding for burned area mapping and other image segmentation tasks. Implements the method described by Otsu (1979) <doi:10.1109/TSMC.1979.4310076>, a data-driven technique that determines an optimal threshold by maximizing the inter-class variance of pixel intensities. It includes validation functions to assess segmentation accuracy against reference data using standard accuracy metrics such as precision, recall, and F1-score.

Computes the routing distribution, the expectation of the number of broadcasts, transmissions and receptions considering an Opportunistic transport model. It provides theoretical results and also estimated values based on Monte Carlo simulations.

Estimates optimal number of biomarkers for two-group classification based on microarray data.

This package provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

This package implements orbit counting using a fast combinatorial approach. Counts orbits of nodes and edges from edge matrix or data frame, or a graph object from the graph package.

Non-spatial and spatial open-population capture-recapture analysis.

Trains per-horizon probabilistic ensembles from a univariate time series. It supports rpart', glmnet', and kNN engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.

OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster.

Reconstructs plausible 2 by 2 contingency tables from published cohort-study summaries when the original cell counts are unavailable. Given group sample sizes and an odds ratio with partial confidence interval information, the package searches for compatible event counts, then derives corresponding relative risks and confidence intervals. It implements the methods described in Wang (2013) <doi:10.18637/jss.v055.i05> and includes summary and plotting methods for reviewing admissible scenarios.

Standardized survey outcome rate functions, including the response rate, contact rate, cooperation rate, and refusal rate. These outcome rates allow survey researchers to measure the quality of survey data using definitions published by the American Association of Public Opinion Research (AAPOR). For details on these standards, see AAPOR (2016) <https://www.aapor.org/Standards-Ethics/Standard-Definitions-(1).aspx>.

We introduce an R function one_two_sample() which can deal with one and two (normal) samples, Ying-Ying Zhang, Yi Wei (2012) <doi:10.2991/asshm-13.2013.29>. For one normal sample x, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x. For two normal samples x and y, the function reports descriptive statistics, plot, interval estimation and test of hypothesis of x and y, respectively. It also reports interval estimation and test of hypothesis of mu1-mu2 (the difference of the means of x and y) and sigma1^2 / sigma2^2 (the ratio of the variances of x and y), tests whether x and y are from the same population, finds the correlation coefficient of x and y if x and y have the same length.

This package provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) <doi:10.1145/1541880.1541882>. It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.

An implementation for computing Optimal B-Robust Estimators of two-parameter distribution. The procedure is composed of some equations that are evaluated alternatively until the solution is reached. Some tools for analyzing the estimates are included. The most relevant is covariance matrix computation using a closed formula.

High-performance implementation of 36 optimal binning algorithms (16 categorical, 20 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'. Fully integrated with the tidymodels ecosystem for building robust machine learning pipelines. Based on methods described in Siddiqi (2006) <doi:10.1002/9781119201731> and Navas-Palencia (2020) <doi:10.48550/arXiv.2001.08025>.

This package provides functions to do O2PLS-DA analysis for multiple omics data integration. The algorithm came from "O2-PLS, a two-block (X±Y) latent variable regression (LVR) method with an integral OSC filter" which published by Johan Trygg and Svante Wold at 2003 <doi:10.1002/cem.775>. O2PLS is a bidirectional multivariate regression method that aims to separate the covariance between two data sets (it was recently extended to multiple data sets) (Löfstedt and Trygg, 2011 <doi:10.1002/cem.1388>; Löfstedt et al., 2012 <doi:10.1016/j.aca.2013.06.026>) from the systematic sources of variance being specific for each data set separately.