This package provides functions to perform the following analyses: i) inferring epistasis from RNAi double knockdown data; ii) identifying gene pairs of multiple mutation patterns; iii) assessing association between gene pairs and survival; and iv) calculating the smallworldness of a graph (e.g., a gene interaction network). Data and analyses are described in Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. <doi:10.1038/ncomms5828>.

This package provides analytical methods for analyzing CRISPR screen data at different levels of gene expression. Multi-component normal mixture models and EM algorithms are used for modeling.

Monitor and trace changes in clustering solutions of accumulating datasets at successive time points. The clusters can adopt External and Internal transition at succeeding time points. The External transitions comprise of Survived, Merged, Split, Disappeared, and newly Emerged candidates. In contrast, Internal transition includes changes in location and cohesion of the survived clusters. The package uses MONIC framework developed by Spiliopoulou, Ntoutsi, Theodoridis, and Schult (2006)<doi:10.1145/1150402.1150491> .

Providing data to quickly visualize and analyze data from several cryptocurrencies.

Estimates group-, subgroup-, and individual-level dynamic structures from multivariate intensive longitudinal data using continuous-time state-space models. The subgrouping procedure combines iterative shared-path searches with recurrent-evidence feature screening and partitioning around medoids. The continuous-time group iterative multiple model estimation method is described in Park et al. (2025) <doi:10.1080/10705511.2024.2429544>.

This package provides a flexible and robust joint test of the single nucleotide polymorphism (SNP) main effect and genotype-by-treatment interaction effect for continuous and binary endpoints. Two analytic procedures, Cauchy weighted joint test (CWOT) and adaptively weighted joint test (AWOT), are proposed to accurately calculate the joint test p-value. The proposed methods are evaluated through extensive simulations under various scenarios. The results show that the proposed AWOT and CWOT control type I error well and outperform existing methods in detecting the most interesting signal patterns in pharmacogenetics (PGx) association studies. For reference, see Hong Zhang, Devan Mehrotra and Judong Shen (2022) <doi:10.13140/RG.2.2.28323.53280>.

Assembles two or more gene copies from short-read Next-Generation Sequencing data. Works best when there are only two gene copies and read length >=250 base pairs. High and relatively even coverage are important.

This package provides tools for working with the International Classification of Diseases ('ICD-10 Chile official MINSAL'/'DEIS v2018). Includes optimized SQL search with SQLite', fuzzy matching of medical terms ('Jaro-Winkler'), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS <https://deis.minsal.cl/centrofic/>.

This package implements the expectation-maximization (EM) algorithm as described in Fiksel et al. (2022) <doi:10.1111/biom.13465> for transformation-free linear regression for compositional outcomes and predictors.

This package provides tools for interpretable conditional forecasting and scenario analysis in reduced-form vector autoregressive (VAR) models. Implements a Kalman smoothing framework to generate forecasts under path restrictions on selected variables. The package enables decomposition of conditional forecasts into variable-specific contributions, and extraction of observation weights. It also computes measures of overall and marginal variable importance to enhance the economic interpretation of forecast revisions. The framework is structurally agnostic and suited for policy analysis, stress testing, and macro-financial applications. The methodology is described in more detail in Caspi and Ginker (2026) <doi:10.13140/RG.2.2.25225.51040>.

This package contains the probability density function, cumulative distribution function, quantile function, and random number generator for composite and discrete composite distributions with Pareto tails. The detailed description of the methods and the applications of the methods can be found in Bowen Liu, Malwane M.A. Ananda (2023) <arXiv:2309.16443>.

This package provides a simple interface to pull County Health Rankings & Roadmaps (CHR&R) county-level health data and metadata directly from Zenodo <doi:10.5281/zenodo.18157681>. Users can retrieve data for CHR&R release years 2010 through 2025. CHR&R data support research and decision-making to promote health equity and policies that help all communities thrive.

Several functions are available for calculating the most widely used effect sizes (ES), along with their variances, confidence intervals and p-values. The output includes ES's of d (mean difference), g (unbiased estimate of d), r (correlation coefficient), z (Fisher's z), and OR (odds ratio and log odds ratio). In addition, NNT (number needed to treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are computed. This package uses recommended formulas as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009). A free web application is available at <https://acdelre.github.io/apps/compute_es/>.

This package provides tools for modeling time-to-event data with a cure fraction under correlated destructive negative binomial cure rate models. The models assume multiple latent competing causes with possible dependence and allow for elimination (inactivation) of some initial causes. Estimation is performed via an Expectation-Maximization algorithm, and diagnostic tools based on Cox-Snell residuals are provided.

Infrastructure for task views to CRAN-style repositories: Querying task views and installing the associated packages (client-side tools), generating HTML pages and storing task view information in the repository (server-side tools).

This package provides a systematic biology tool was developed to identify cell infiltration via Individualized Cell-Cell interaction network. CITMIC first constructed a weighted cell interaction network through integrating Cell-target interaction information, molecular function data from Gene Ontology (GO) database and gene transcriptomic data in specific sample, and then, it used a network propagation algorithm on the network to identify cell infiltration for the sample. Ultimately, cell infiltration in the patient dataset was obtained by normalizing the centrality scores of the cells.

It is an open source insurance claim simulation engine sponsored by the Casualty Actuarial Society. It generates individual insurance claims including open claims, reopened claims, incurred but not reported claims and future claims. It also includes claim data fitting functions to help set simulation assumptions. It is useful for claim level reserving analysis. Parodi (2013) <https://www.actuaries.org.uk/documents/triangle-free-reserving-non-traditional-framework-estimating-reserves-and-reserve-uncertainty>.

This package implements the iterated RMCD method of Cerioli (2010) for multivariate outlier detection via robust Mahalanobis distances. Also provides the finite-sample RMCD method discussed in the paper, as well as the methods provided in Hardin and Rocke (2005) <doi:10.1198/106186005X77685> and Green and Martin (2017) <https://christopherggreen.github.io/papers/hr05_extension.pdf>. See also Chapter 2 of Green (2017) <https://digital.lib.washington.edu/researchworks/handle/1773/40304>.

The bivariate copula mixed model for meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos (2015) <doi:10.1002/sim.6595> and Nikoloulopoulos (2018) <doi:10.1007/s10182-017-0299-y>. The vine copula mixed model for meta-analysis of diagnostic test accuracy studies accounting for disease prevalence in Nikoloulopoulos (2017) <doi:10.1177/0962280215596769> and also accounting for non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1515/ijb-2019-0107>. The hybrid vine copula mixed model for meta-analysis of diagnostic test accuracy case-control and cohort studies in Nikoloulopoulos (2018) <doi:10.1177/0962280216682376>. The D-vine copula mixed model for meta-analysis and comparison of two diagnostic tests in Nikoloulopoulos (2019) <doi:10.1177/0962280218796685>. The multinomial quadrivariate D-vine copula mixed model for meta-analysis of diagnostic tests with non-evaluable subjects in Nikoloulopoulos (2020) <doi:10.1177/0962280220913898>. The one-factor copula mixed model for joint meta-analysis of multiple diagnostic tests in Nikoloulopoulos (2022) <doi:10.1111/rssa.12838>. The multinomial six-variate 1-truncated D-vine copula mixed model for meta-analysis of two diagnostic tests accounting for within and between studies dependence in Nikoloulopoulos (2024) <doi:10.1177/09622802241269645>. The 1-truncated D-vine copula mixed models for meta-analysis of diagnostic accuracy studies without a gold standard (Nikoloulopoulos, 2025) <doi:10.1093/biomtc/ujaf037>.

This package implements the chain binomial model for analysis of infectious disease data. Contains functions for calculating probabilities of the final size of infectious disease outbreaks using the method from D. Ludwig (1975) <doi:10.1016/0025-5564(75)90119-4> and for outbreaks that are not concluded, from Lindstrøm et al. (2024) <doi:10.48550/arXiv.2403.03948>. The package also contains methods for estimation and regression analysis of secondary attack rates.

It provides functions to bootstrap Credit Curves from market quotes (Credit Default Swap - CDS - spreads) and price Credit Default Swaps - CDS.

Find multiple solutions of a nonlinear least squares problem. Cluster Gauss-Newton method does not assume uniqueness of the solution of the nonlinear least squares problem and compute multiple minimizers. Please cite the following paper when this software is used in your research: Aoki et al. (2020) <doi:10.1007/s11081-020-09571-2>. Cluster Gaussâ Newton method. Optimization and Engineering, 1-31. Please cite the following paper when profile likelihood plot is drawn with this software and used in your research: Aoki and Sugiyama (2024) <doi:10.1002/psp4.13055>. Cluster Gauss-Newton method for a quick approximation of profile likelihood: With application to physiologically-based pharmacokinetic models. CPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based helper bot available at <https://chatgpt.com/g/g-684936db9e748191a2796debb00cd755-cluster-gauss-newton-method-helper-bot> .

This package provides a program for reconstructing local fire histories from high-resolution, continuously sampled lake-sediment charcoal records. CharAnalysis decomposes a charcoal record into low- and high-frequency components and uses locally defined thresholds to separate fire signal from noise, following the approach of Higuera et al. (2009) <doi:10.1890/07-2019.1>, with underlying assumptions and rationale described in Higuera et al. (2010) <doi:10.1071/WF09134>. The package is designed for macroscopic charcoal records with contiguous sampling fine enough to resolve individual fire events, and is not appropriate for low-resolution or discontinuously sampled records. See the package URL for the User's Guide and application examples.

Cluster analysis is performed using pairwise distance information and a random partition distribution. The method is implemented for two random partition distributions. It draws samples and then obtains and plots clustering estimates. An implementation of a selection algorithm is provided for the mass parameter of the partition distribution. Since pairwise distances are the principal input to this procedure, it is most comparable to the hierarchical and k-medoids clustering methods. The method is Dahl, Andros, Carter (2022+) <doi:10.1002/sam.11602>.