Various cladogenesis-related calculations that are slow in pure R are implemented in C++ with Rcpp. These include the calculation of the probability of various scenarios for the inheritance of geographic range at the divergence events on a phylogenetic tree, and other calculations necessary for models which are not continuous-time markov chains (CTMC), but where change instead occurs instantaneously at speciation events. Typically these models must assess the probability of every possible combination of (ancestor state, left descendent state, right descendent state). This means that there are up to (# of states)^3 combinations to investigate, and in biogeographical models, there can easily be hundreds of states, so calculation time becomes an issue. C++ implementation plus clever tricks (many combinations can be eliminated a priori) can greatly speed the computation time over naive R implementations. CITATION INFO: This package is the result of my Ph.D. research, please cite the package if you use it! Type: citation(package="cladoRcpp") to get the citation information.

Generates a visualization of binary classifier performance as a grid of diagnostic plots with just one function call. Includes ROC curves, prediction density, accuracy, precision, recall and calibration plots, all using ggplot2 for easy modification. Debug your binary classifiers faster and easier!

Various statistical methods and models which are typically used for the estimation of outstanding claims reserves in general insurance, including those to estimate the claims development result as required under Solvency II.

Calculates the credit debt for the next period based on the available data using the cross-classification credibility model.

Calculate confidence and consistency that measure the goodness-of-fit and transferability of predictive/potential distribution models (including species distribution models) as described by Somodi & Bede-Fazekas et al. (2024) <doi:10.1016/j.ecolmodel.2024.110667>.

This package provides a simple interface for multivariate correlation analysis that unifies various classical statistical procedures including t-tests, tests in univariate and multivariate linear models, parametric and nonparametric tests for correlation, Kruskal-Wallis tests, common approximate versions of Wilcoxon rank-sum and signed rank tests, chi-squared tests of independence, score tests of particular hypotheses in generalized linear models, canonical correlation analysis and linear discriminant analysis.

An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.

Supplies higher-order coordinatized data specification and fluid transform operators that include pivot and anti-pivot as special cases. The methodology is describe in Zumel', 2018, "Fluid data reshaping with cdata'", <https://winvector.github.io/FluidData/FluidDataReshapingWithCdata.html> , <DOI:10.5281/zenodo.1173299> . This package introduces the idea of explicit control table specification of data transforms. Works on in-memory data or on remote data using rquery and SQL database interfaces.

Method for visualizing proportions between objects of different sizes. The proportions are drawn as circles with different diameters, which makes them ideal for visualizing proportions between planets.

Estimation of changepoints using an "S-curve" approximation. Formation of confidence intervals for changepoint locations and magnitudes. Both abrupt and gradual changes can be modeled.

Imputes missing glucose values in repeated-measures continuous glucose monitoring (CGM) data. Workflows create time-series features from raw timestamps, support model selection, and return the user's original columns plus an imputed glucose column. Methods include multiple imputation by chained equations (MICE; Azur et al. (2011) <doi:10.1002/mpr.329>), Random Forest regression (Breiman (2001) <doi:10.1023/A:1010933404324>), k-nearest-neighbor regression (Zhang (2016) <doi:10.21037/atm.2016.03.37>), XGBoost (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>), LightGBM (Ke et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>), and ARIMA forecasting with the forecast framework (Hyndman and Khandakar (2008) <doi:10.18637/jss.v027.i03>). A Python-compatible backend uses reticulate to call pandas', scikit-learn', statsmodels', Python xgboost', and optional Python lightgbm'.

Computes a composite year-on-year index for bank performance assessment using the CAMEL framework (Capital Adequacy, Asset Quality, Management Efficiency, Earnings, Liquidity). The multivariate weighting scheme employs factor analysis with robust covariance estimation to derive communality-based weights from the correlation matrix of CAMEL ratios. Provides functions for index computation, visualization, and comparison across banks and time periods.The methodology is described in Ayimah et al. (2023a) <doi:10.9734/bpi/mono/978-81-19315-32-1> and Ayimah et al. (2023b) <https://ajtem.com/index.php/ajtem/article/view/53>.

Interface to the Google Cloud Machine Learning Platform <https://cloud.google.com/vertex-ai>, which provides cloud tools for training machine learning models.

Get insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020) <doi:10.1007/s00357-019-09350-4>.

This package provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weià , C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.

Interact with Condor from R via SSH connection. Files are first uploaded from user machine to submitter machine, and the job is then submitted from the submitter machine to Condor'. Functions are provided to submit, list, and download Condor jobs from R. Condor is an open source high-throughput computing software framework for distributed parallelization of computationally intensive tasks.

This package provides functions to perform matching algorithms for causal inference with clustered data, as described in B. Arpino and M. Cannas (2016) <doi:10.1002/sim.6880>. Pure within-cluster and preferential within-cluster matching are implemented. Both algorithms provide causal estimates with cluster-adjusted estimates of standard errors.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

This package implements higher order likelihood-based inference for logistic and loglinear models.

Implementation of case-control data analysis using likelihood ratio approaches and logistic regression for the classification of variants of uncertain significance (VUS) in breast, ovarian, or custom cancer susceptibility genes.

This package provides different datasets parsed from Drugbank <https://www.drugbank.ca/covid-19> database using dbparser package. It is a smaller version from dbdataset package. It contains only information about COVID-19 possible treatment.

Calculates equitable overload compensation for college instructors based on institutional policies, enrollment thresholds, and regular teaching load limits. Compensation is awarded only for credit hours that exceed the regular load and meet minimum enrollment criteria. When enrollment is below a specified threshold, pay is prorated accordingly. The package prioritizes compensation from high-enrollment courses, or optionally from low-enrollment courses for fairness, depending on user-defined strategy. Includes tools for flexible policy settings, instructor filtering, and produces clean, audit-ready summary tables suitable for payroll and administrative reporting.

This package provides tools to extract, label, and read microdata from the 2022 National Census of Population, Households and Dwellings of Argentina stored in REDATAM databases officially distributed by INDEC. Implements a complete province-by-province extraction pipeline with efficient memory management, reconstruction of hierarchical identifiers, automatic variable labeling from official INDEC dictionaries, and integrity verification against published totals. Allows working with census data directly in R without knowledge of REDATAM syntax, and supports export to multiple formats including Parquet, CSV, SPSS and SAS. Census data must be downloaded directly from the official INDEC portal (<https://www.indec.gob.ar>). This package does not distribute census data. Duran (2026) <doi:10.5281/zenodo.19560728>.

This package provides a versatile R package for creating and pricing custom derivatives to suit your financial needs.