This package contains a function, also called cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.

It computes full conformal, split conformal and multi split conformal prediction regions when the response has functional nature. Moreover, the package also contain a plot function to visualize the output of the split conformal. To guarantee consistency, the package structure mimics the univariate conformalInference package of professor Ryan Tibshirani. The main references for the code are: Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2102.06746>, Diquigiovanni, Fontana, and Vantini (2021) <arXiv:2106.01792>, Solari, and Djordjilovic (2021) <arXiv:2103.00627>.

Numerical integration of cause-specific survival curves to arrive at cause-specific cumulative incidence functions, with three usage modes: 1) Convenient API for parametric survival regression followed by competing-risk analysis, 2) API for CFC, accepting user-specified survival functions in R, and 3) Same as 2, but accepting survival functions in C++. For mathematical details and software tutorial, see Mahani and Sharabiani (2019) <DOI:10.18637/jss.v089.i09>.

Estimate bivariate common mean vector under copula models with known correlation. In the current version, available copulas are the Clayton, Gumbel, Frank, Farlie-Gumbel-Morgenstern (FGM), and normal copulas. See Shih et al. (2019) <doi:10.1080/02331888.2019.1581782> and Shih et al. (2021) <under review> for details under the FGM and general copulas, respectively.

Data sets used for copula modeling in addition to those in the R package copula'. These include a random subsample from the US National Education Longitudinal Study (NELS) of 1988 and nursing home data from Wisconsin.

Interacting with binary files can be difficult because R's types are a subset of what is generally supported by C'. This package provides a suite of functions for reading and writing binary data (with files, connections, and raw vectors) using C type descriptions. These functions convert data between C types and R types while checking for values outside the type limits, NA values, etc.

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.

Solves discrete location objectives on a distance matrix or Euclidean coordinate set. The Max-Min Diversity (MMDP / p-dispersion) objective, which maximizes the minimum pairwise distance within a selection of k items, is solved by farthest-first selection (Gonzalez 1985) <doi:10.1016/0304-3975(85)90224-5>; the DropAdd tabu-search heuristic (Porumbel, Hao & Glover 2011) <doi:10.1007/s10479-011-0898-z>, GRASP with path-relinking (Resende, Marti, Gallego & Duarte 2010) <doi:10.1016/j.cor.2008.05.011>, and an exact node-packing integer program (Sayyady & Fathi 2016) <doi:10.1016/j.ejor.2016.02.026>. The Max-Mean Dispersion objective, which selects a subset of unrestricted size maximising the sum of its pairwise distances divided by the number of selected elements, is solved by reinforcement-learning-guided tabu search (Nijimbere et al. 2020) <doi:10.3934/jimo.2020115>. The discrete k-centre (min-max covering / facility location) objective, which chooses k centres to minimise the largest distance from any point to its nearest centre, is solved via the CDSh heuristic (Garcia-Diaz et al. 2017 <doi:10.1007/s10732-017-9345-x>, 2019 <doi:10.1109/ACCESS.2019.2933875>), and an exact minimum-cover integer program. The maximum-entropy (maxdet) objective, which maximises the log-determinant of a similarity kernel built from the distances (Shewry & Wynn 1987 <doi:10.1080/02664768700000020>; the mode of a determinantal point process, Kulesza & Taskar 2012 <doi:10.1561/2200000044>), is solved by greedy pivoted-Cholesky selection and, for small instances, exact enumeration.

Assesses whether cure models are appropriate for right-censored survival data, where a fraction of subjects may never experience the event of interest. Implements a two-stage workflow combining Kaplan-Meier visualization and comparison of parametric cure and non-cure models by the Akaike information criterion with formal diagnostics for sufficient follow-up and for the presence of a cured fraction. The diagnostics include the statistics of Maller and Zhou (1992) <doi:10.1093/biomet/79.4.731> and Maller and Zhou (1994) <doi:10.1080/01621459.1994.10476889>, the test of Shen (2000) <doi:10.1016/S0167-7152(00)00063-8>, and the ratio estimation of censored uncured subjects ('RECeUS') method of Selukar and Othus (2023) <doi:10.1002/sim.9610>.

Estimation of 2-level factor copula-based regression models for clustered data where the response variable can be either discrete or continuous.

Estimating mutation and selection coefficients on synonymous codon bias usage based on models of ribosome overhead cost (ROC). Multinomial logistic regression and Markov Chain Monte Carlo are used to estimate and predict protein production rates with/without the presence of expressions and measurement errors. Work flows with examples for simulation, estimation and prediction processes are also provided with parallelization speedup. The whole framework is tested with yeast genome and gene expression data of Yassour, et al. (2009) <doi:10.1073/pnas.0812841106>.

Cohort plAtform Trial Simulation whereby every cohort consists of two arms, control and experimental treatment. Endpoints are co-primary binary endpoints and decisions are made using either Bayesian or frequentist decision rules. Realistic trial trajectories are simulated and the operating characteristics of the designs are calculated.

Implementation of the CNAIM standard in R. Contains a series of algorithms which determine the probability of failure, consequences of failure and monetary risk associated with electricity distribution companies assets such as transformers and cables. Results are visualized in an easy-to-understand risk matrix.

This package implements the estimation and inference methods for counterfactual analysis described in Chernozhukov, Fernandez-Val and Melly (2013) <DOI:10.3982/ECTA10582> "Inference on Counterfactual Distributions," Econometrica, 81(6). The counterfactual distributions considered are the result of changing either the marginal distribution of covariates related to the outcome variable of interest, or the conditional distribution of the outcome given the covariates. They can be applied to estimate quantile treatment effects and wage decompositions.

This package provides a series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) <DOI:10.1186/s12898-019-0246-8>. The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations.

The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the lme4 package and robust estimators from the rlmer() function in the robustlmm package, as well as the varComprob() function in the robustvarComp package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

This package provides the ability to create interaction maps, discover CNV map domains (edges), gene annotate interactions, and create interactive visualizations of these CNV interaction maps.

CHAP-GWAS (Chromosomal Haplotype-Integrated Genome-Wide Association Study) provides a dynamically adaptive framework for genome-wide association studies (GWAS) that integrates chromosome-scale haplotypes with single nucleotide polymorphism (SNP) analysis. The method identifies and extends haplotype variants based on their phenotypic associations rather than predefined linkage blocks, enabling high-resolution detection of quantitative trait loci (QTL). By leveraging long-range phased haplotype information, CHAP-GWAS improves statistical power and offers a more comprehensive view of the genetic architecture underlying complex traits.

CemCO algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

Facilitates the identification of counterfactual queries in structural causal models via the ID* and IDC* algorithms by Shpitser, I. and Pearl, J. (2007, 2008) <doi:10.48550/arXiv.1206.5294>, <https://jmlr.org/papers/v9/shpitser08a.html>. Provides a simple interface for defining causal diagrams and counterfactual conjunctions. Construction of parallel worlds graphs and counterfactual graphs is carried out automatically based on the counterfactual query and the causal diagram. See Tikka, S. (2023) <doi:10.32614/RJ-2023-053> for a tutorial of the package.

This package implements a Ward-like hierarchical clustering algorithm including soft spatial/geographical constraints.

This package provides tools for producing data-driven curriculum vitae documents from structured data stored in an Excel workbook. The core workflow reads CV content from a workbook, converts it into Typst layout blocks, and renders a polished PDF via the Quarto publishing system. Includes functions for reading and cleaning CV data, building Typst section headings and entries, rendering CV sections from data frames, and scaffolding new CV projects with a standard folder structure and template workbook. Designed to separate content from layout: CV data lives in the spreadsheet, rendering configuration lives in Quarto', and transformation logic lives in small, reusable R functions. See the Typst typesetting system at <https://typst.app> and the Quarto publishing system at <https://quarto.org>. Inspired by the vitae package <https://CRAN.R-project.org/package=vitae> and the Awesome CV LaTeX template <https://github.com/posquit0/Awesome-CV>.

Flexible tools to fit, tune and obtain absolute risk predictions from regularized cause-specific cox models with elastic-net penalty.

This package implements several string comparison algorithms, including calACS (count all common subsequences), lenACS (calculate the lengths of all common subsequences), and lenLCS (calculate the length of the longest common subsequence). Some algorithms differentiate between the more strict definition of subsequence, where a common subsequence cannot be separated by any other items, from its looser counterpart, where a common subsequence can be interrupted by other items. This difference is shown in the suffix of the algorithm (-Strict vs -Loose). For example, q-w is a common subsequence of q-w-e-r and q-e-w-r on the looser definition, but not on the more strict definition. calACSLoose Algorithm from Wang, H. All common subsequences (2007) IJCAI International Joint Conference on Artificial Intelligence, pp. 635-640.