Prints code that can be used to recreate R objects. In a sense it is similar to base::dput() or base::deparse() but constructive strives to use idiomatic constructors.

Access Cloudstor via their WebDAV API. This package can read, write, and navigate Cloudstor from R.

Generate balance tables and plots for covariates of groups preprocessed through matching, weighting or subclassification, for example, using propensity scores. Includes integration with MatchIt', WeightIt', MatchThem', twang', Matching', optmatch', CBPS', ebal', cem', sbw', and designmatch for assessing balance on the output of their preprocessing functions. Users can also specify data for balance assessment not generated through the above packages. Also included are methods for assessing balance in clustered or multiply imputed data sets or data sets with multi-category, continuous, or longitudinal treatments.

Calculating silhouette information for clusters on circular or linear data using fast algorithms. These algorithms run in linear time on sorted data, in contrast to quadratic time by the definition of silhouette. When used together with the fast and optimal circular clustering method FOCC (Debnath & Song 2021) <doi:10.1109/TCBB.2021.3077573> implemented in R package OptCirClust', circular silhouette can be maximized to find the optimal number of circular clusters; it can also be used to estimate the period of noisy periodical data.

This package provides functions to perform statistical inference of data organized in contingency tables. This package is a companion to the "Statistical Analysis of Contingency Tables" book by Fagerland et al. <ISBN 9781466588172>.

This package provides tools to extract word frequencies from the CHILDES (Child Language Data Exchange System) corpus. The main function allows users to input a list of words and receive speaker-role-specific frequency counts and a summary of the dataset. The output includes Excel-formatted tables of word counts and metadata summaries such as number of speakers, transcripts, children, and token counts. Useful for researchers studying early language acquisition, corpus linguistics, and speaker role variation. The CHILDES database is maintained at <https://childes.talkbank.org/>.

Call the DeOldify <https://github.com/jantic/DeOldify> image colorization API on DeepAI'<https://deepai.org/machine-learning-model/colorizer> to colorize black and white images.

An automated and streamlined workflow for predictive climate mapping using climate station data. Works within an environment the user provides a destined path to - otherwise it's tempdir(). Quick and relatively easy creation of resilient and reproducible climate models, predictions and climate maps, shortening the usually long and complicated work of predictive modelling. For more information, please find the provided URL. Many methods in this package are new, but the main method is based on a workflow from Meyer (2019) <doi:10.1016/j.ecolmodel.2019.108815> and Meyer (2022) <doi:10.1038/s41467-022-29838-9> , however, it was generalized and adjusted in the context of this package.

Composite likelihood parameter estimate and asymptotic covariance matrix are calculated for the spatial ordinal data with replications, where spatial ordinal response with covariate and both spatial exponential covariance within subject and independent and identically distributed measurement error. Parameter estimation can be performed by either solving the gradient function or maximizing composite log-likelihood. Parametric bootstrapping is used to estimate the Godambe information matrix and hence the asymptotic standard error and covariance matrix with parallel processing option. Moreover, the proposed surrogate residual, which extends the results of Liu and Zhang (2017) <doi: 10.1080/01621459.2017.1292915>, can act as a useful tool for model diagnostics.

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

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).

The implemented functions allow the query, download, and import of remotely-stored and version-controlled data items. The inherent meta-database maps data files and import code to programming classes and allows access to these items via files deposited in public repositories. The purpose of the project is to increase reproducibility and establish version tracking of results from (paleo)environmental/ecological research.

This package provides a framework is provided to develop R packages using Rust <https://www.rust-lang.org/> with minimal overhead, and more wrappers are easily added. Help is provided to use Cargo <https://doc.rust-lang.org/cargo/> in a manner consistent with CRAN policies. Rust code can also be embedded directly in an R script. The package is not official, affiliated with, nor endorsed by the Rust project.

Utilities that support the usage of pyDarwin (<https://certara.github.io/pyDarwin/>) for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine.

Indicators and measures by country and time describe what happens at economic and social levels. This package provides functions to calculate several measures of convergence after imputing missing values. The automated downloading of Eurostat data, followed by the production of country fiches and indicator fiches, makes possible to produce automated reports. The Eurofound report (<doi:10.2806/68012>) "Upward convergence in the EU: Concepts, measurements and indicators", 2018, is a detailed presentation of convergence.

This package provides methods and tools for performing multistep-ahead time series forecasting using conformal prediction methods including classical conformal prediction, adaptive conformal prediction, conformal PID (Proportional-Integral-Derivative) control, and autocorrelated multistep-ahead conformal prediction. The methods were described by Wang and Hyndman (2024) <doi:10.48550/arXiv.2410.13115>.

This package implements a basis function or functional data analysis framework for several techniques of multivariate analysis in continuous-time setting. Specifically, we introduced continuous-time analogues of several classical techniques of multivariate analysis, such as principal component analysis, canonical correlation analysis, Fisher linear discriminant analysis, K-means clustering, and so on. Details are in Biplab Paul, Philip T. Reiss and Erjia Cui (2023) "Continuous-time multivariate analysis" <doi:10.48550/arXiv.2307.09404>.

Variable selection for Gaussian model-based clustering as implemented in the mclust package. The methodology allows to find the (locally) optimal subset of variables in a data set that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting mclust models. By default the algorithm uses a sequential search, but parallelisation is also available.

Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.

Reads and writes CSV with selected conventions. Uses the same generic function for reading and writing to promote consistent formats.

While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) <doi:10.1002/sim.1657> and Li, Shen, Wu, and Li (2011) <doi:10.1093/aje/kwr096>), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) <doi:10.1002/sim.7298>).

This package performs adjustments of a user-supplied independence loglikelihood function using a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions or for performing inferences that are robust to certain types of model misspecification. Functions for profiling the adjusted loglikelihoods are also provided, as are functions for calculating and plotting confidence intervals, for single model parameters, and confidence regions, for pairs of model parameters. Nested models can be compared using an adjusted likelihood ratio test.

This package provides a lightweight data validation and testing toolkit for R. Its guiding philosophy is that adding code-based data checks to users existing workflow should be both quick and intuitive. The suite of functions included therefore mirror the common data checks many users already perform by hand or by eye. Additionally, the checkthat package is optimized to work within tidyverse data manipulation pipelines.

Allows Brownian motion, fractional Brownian motion, and integrated Ornstein-Uhlenbeck process components to be added to linear and non-linear mixed effects models using the structures and methods of the nlme package.