Efficient implementations of cross-validation techniques for linear and ridge regression models, leveraging C++ code with Rcpp', RcppParallel', and Eigen libraries. It supports leave-one-out, generalized, and K-fold cross-validation methods, utilizing Eigen matrices for high performance. Methodology references: Hastie, Tibshirani, and Friedman (2009) <doi:10.1007/978-0-387-84858-7>.

Facilitate Pharmacokinetic (PK) and Pharmacodynamic (PD) modeling and simulation with powerful tools for Nonlinear Mixed-Effects (NLME) modeling. The package provides access to the same advanced Maximum Likelihood algorithms used by the NLME-Engine in the Phoenix platform. These tools support a range of analyses, from parametric methods to individual and pooled data, and support integrated use within the Pirana pharmacometric workbench <doi:10.1002/psp4.70067>. Execution is supported both locally or on remote machines.

This package contains functions for testing for significant differences between multiple coefficients of variation. Includes Feltz and Miller's (1996) <DOI:10.1002/(SICI)1097-0258(19960330)15:6%3C647::AID-SIM184%3E3.0.CO;2-P> asymptotic test and Krishnamoorthy and Lee's (2014) <DOI:10.1007/s00180-013-0445-2> modified signed-likelihood ratio test. See the vignette for more, including full details of citations.

Connect to the California Data Exchange Center (CDEC) Web Service <http://cdec.water.ca.gov/>. CDEC provides a centralized database to store, process, and exchange real-time hydrologic information gathered by various cooperators throughout California. The CDEC Web Service <http://cdec.water.ca.gov/dynamicapp/wsSensorData> provides a data download service for accessing historical records.

Offers tools to estimate the climate representativeness of reference polygons and quantifies its transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) <doi:10.32800/abc.2018.41.0333> and Mingarro and Lobo (2022) <doi:10.1017/S037689292100014X>.

Univariate feature selection and compound covariate methods under the Cox model with high-dimensional features (e.g., gene expressions). Available are survival data for non-small-cell lung cancer patients with gene expressions (Chen et al 2007 New Engl J Med) <DOI:10.1056/NEJMoa060096>, statistical methods in Emura et al (2012 PLoS ONE) <DOI:10.1371/journal.pone.0047627>, Emura & Chen (2016 Stat Methods Med Res) <DOI:10.1177/0962280214533378>, and Emura et al (2019)<DOI:10.1016/j.cmpb.2018.10.020>. Algorithms for generating correlated gene expressions are also available. Estimation of survival functions via copula-graphic (CG) estimators is also implemented, which is useful for sensitivity analyses under dependent censoring (Yeh et al 2023 Biomedicines) <DOI:10.3390/biomedicines11030797> and factorial survival analyses (Emura et al 2024 Stat Methods Med Res) <DOI:10.1177/09622802231215805>.

Retrieve cancer screening data for cervical, breast and colorectal cancers from the Kenya Health Information System <https://hiskenya.org> in a consistent way.

In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the CDsampling package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.

This package provides a tidied subset of the US College Scorecard dataset, containing institutional characteristics, enrollment, student aid, costs, and student outcomes at institutions of higher education in the United States.

Perform censored quantile regression of Huang (2010) <doi:10.1214/09-AOS771>, and restore monotonicity respecting via adaptive interpolation for dynamic regression of Huang (2017) <doi:10.1080/01621459.2016.1149070>. The monotonicity-respecting restoration applies to general dynamic regression models including (uncensored or censored) quantile regression model, additive hazards model, and dynamic survival models of Peng and Huang (2007) <doi:10.1093/biomet/asm058>, among others.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

Check for namespace collisions between a string input (your function or package name) and half a million packages and functions on CRAN.

Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

This package provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

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".

Collect marketing data from Campaign Manager using the Windsor.ai API <https://windsor.ai/api-fields/>.

The causalsens package provides functions to perform sensitivity analyses and to study how various assumptions about selection bias affects estimates of causal effects.

Routines for the graphical representation of correlation matrices by means of correlograms, MDS maps and biplots obtained by PCA, PFA or WALS (weighted alternating least squares); See Graffelman & De Leeuw (2023) <doi: 10.1080/00031305.2023.2186952>.

It provides functions that calculate Mahalanobis distance, Euclidean distance, Manhattan distance, Chebyshev distance, Hamming distance, Canberra distance, Minkowski dissimilarity (distance defined for p >= 1), Cosine dissimilarity, Bhattacharyya dissimilarity, Jaccard distance, Hellinger distance, Bray-Curtis dissimilarity, Sorensen-Dice dissimilarity between each pair of species in a list of data frames. These statistics are fundamental in various fields, such as cluster analysis, classification, and other applications of machine learning and data mining, where assessing similarity or dissimilarity between data is crucial. The package is designed to be flexible and easily integrated into data analysis workflows, providing reliable tools for evaluating distances in multidimensional contexts.

Data cleaning functions for classes logical, factor, numeric, character, currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.

This package implements weighted estimation in Cox regression as proposed by Schemper, Wakounig and Heinze (Statistics in Medicine, 2009, <doi:10.1002/sim.3623>) and as described in Dunkler, Ploner, Schemper and Heinze (Journal of Statistical Software, 2018, <doi:10.18637/jss.v084.i02>). Weighted Cox regression provides unbiased average hazard ratio estimates also in case of non-proportional hazards. Approximated generalized concordance probability an effect size measure for clear-cut decisions can be obtained. The package provides options to estimate time-dependent effects conveniently by including interactions of covariates with arbitrary functions of time, with or without making use of the weighting option.

Column Text Format (CTF) is a new tabular data format designed for simplicity and performance. CTF is the simplest column store you can imagine: plain text files for each column in a table, and a metadata file. The underlying plain text means the data is human readable and familiar to programmers, unlike specialized binary formats. CTF is faster than row oriented formats like CSV when loading a subset of the columns in a table. This package provides functions to read and write CTF data from R.

Generate project files and directories following a pre-made template. You can specify variables to customize file names and content, and flexibly adapt the template to your needs. cookiecutter for R implements a subset of the excellent cookiecutter package for the Python programming language (<https://github.com/cookiecutter/>), and aims to be largely compatible with the original cookiecutter template format.