Constructs a shiny app function with interactive displays for conditional visualization of models, data and density functions. An extended version of package condvis'. Catherine B. Hurley, Mark O'Connell,Katarina Domijan (2021) <doi:10.1080/10618600.2021.1983439>.

Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

This package provides functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes Epi package Lexis data.

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

This package provides a uniform statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<DOI:10.1177/0962280214541724> and Guo et al. (2021)<DOI:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.

This package contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. CASMI is a fundamental concept of a series of methods. For more information about CASMI and CASMI'-related methods, please refer to the corresponding publications (e.g., a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author for the latest updates.

Compare baseline characteristics between two or more groups. The variables being compared can be factor and numeric variables. The function will automatically judge the type and distribution of the variables, and make statistical description and bivariate analysis.

This package contains the prepared data that is needed for the shiny application examples in the canvasXpress package. This package also includes datasets used for automated testthat tests. Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al. (2008) <doi:10.1002/gcc.20577>. Davis S, Meltzer PS (2007) <doi:10.1093/bioinformatics/btm254>.

Retail shopping transactions for 2,469 households over one year. Originates from the 84.51° Complete Journey 2.0 source files <https://www.8451.com/area51> which also includes useful metadata on products, coupons, campaigns, and promotions.

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with known variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a given value. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval has the following three practical applications. Firstly, if the error variance has been accurately estimated from previous data then it may be treated as being effectively known. Secondly, for sufficiently large (dimension of the response vector) minus (dimension of regression parameter vector), greater than or equal to 30 (say), if we replace the assumed known value of the error variance by its usual estimator in the formula for the confidence interval then the resulting interval has, to a very good approximation, the same coverage probability and expected length properties as when the error variance is known. Thirdly, some more complicated models can be approximated by the linear regression model with error variance known when certain unknown parameters are replaced by estimates. This confidence interval is described in Mainzer, R. and Kabaila, P. (2019) <doi:10.32614/RJ-2019-026>, and is a member of the family of confidence intervals proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

Fits hidden Markov models of discrete character evolution which allow different transition rate classes on different portions of a phylogeny. Beaulieu et al (2013) <doi:10.1093/sysbio/syt034>.

Compute covariate-adjusted specificity at controlled sensitivity level, or covariate-adjusted sensitivity at controlled specificity level, or covariate-adjust receiver operating characteristic curve, or covariate-adjusted thresholds at controlled sensitivity/specificity level. All statistics could also be computed for specific sub-populations given their covariate values. Methods are described in Ziyi Li, Yijian Huang, Datta Patil, Martin G. Sanda (2021+) "Covariate adjustment in continuous biomarker assessment".

Checks that students have the correct version of R', R packages, RStudio and other dependencies installed, and that the recommended RStudio configuration has been applied.

The Certifiably Optimal RulE ListS (Corels) learner by Angelino et al described in <doi:10.48550/arXiv.1704.01701> provides interpretable decision rules with an optimality guarantee, and is made available to R with this package. See the file AUTHORS for a list of copyright holders and contributors.

The currentSurvival package contains functions for the estimation of the current cumulative incidence (CCI) and the current leukaemia-free survival (CLFS). The CCI is the probability that a patient is alive and in any disease remission (e.g. complete cytogenetic remission in chronic myeloid leukaemia) after initiating his or her therapy (e.g. tyrosine kinase therapy for chronic myeloid leukaemia). The CLFS is the probability that a patient is alive and in any disease remission after achieving the first disease remission.

Download, cache and read municipality-level address data from the Cadastro Nacional de Enderecos para Fins Estatisticos (CNEFE) of the 2022 Brazilian Census, published by the Instituto Brasileiro de Geografia e Estatistica (IBGE) <https://ftp.ibge.gov.br/Cadastro_Nacional_de_Enderecos_para_Fins_Estatisticos/>. Beyond data access, provides spatial aggregation of addresses, computation of land-use mix indices, and dasymetric interpolation of census tract variables using CNEFE dwelling points as ancillary data. Results can be produced on H3 hexagonal grids or user-supplied polygons, and heavy operations leverage a DuckDB backend with extensions for fast, in-process execution.

This package implements the three-step workflow for robust analysis of change in two repeated measurements of continuous outcomes, described in Ning et al. (in press), "Robust estimation of the effect of an exposure on the change in a continuous outcome", BMC Medical Research Methodology.

This package provides functions for cobin and micobin regression models, a new family of generalized linear models for continuous proportional data (Y in the closed unit interval [0, 1]). It also includes an exact, efficient sampler for the Kolmogorov-Gamma random variable. For details, see Lee et al. (2025+) <doi:10.48550/arXiv.2504.15269>.

The number of bird or bat fatalities from collisions with buildings, towers or wind energy turbines can be estimated based on carcass searches and experimentally assessed carcass persistence times and searcher efficiency. Functions for estimating the probability that a bird or bat that died is found by a searcher are provided. Further functions calculate the posterior distribution of the number of fatalities based on the number of carcasses found and the estimated detection probability.

Use three methods to estimate parameters from a mediation analysis with a binary misclassified mediator. These methods correct for the problem of "label switching" using Youden's J criteria. A detailed description of the analysis methods is available in Webb and Wells (2024), "Effect estimation in the presence of a misclassified binary mediator" <doi:10.48550/arXiv.2407.06970>.

Perform likelihood estimation and corresponding analysis under the copula-based Markov chain model for serially dependent event times with a dependent terminal event. Available are statistical methods in Huang, Wang and Emura (2020, JJSD accepted).

Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

As a chronomètre is a stopwatch', this package offers a simple stopwatch, and in particular one that can be shared with Python (using the corresponding package of the same name available via PyPi') such that both interpreters operate on the same object instance and shown in the demo file, as well as in the unit tests.

Computes p-values using the largest root test using an approximation to the null distribution by Johnstone (2008) <DOI:10.1214/08-AOS605>.