This package implements panel cointegration tests allowing for structural breaks and cross-section dependence following the methodology of Banerjee and Carrion-i-Silvestre (2015) <doi:10.1002/jae.2348>. The package provides iterative factor-break estimation, individual ADF tests on defactored residuals, standardized panel test statistics, and the Bai and Ng (2004) <doi:10.1111/j.1468-0262.2004.00528.x> MQ test for identifying common stochastic trends. Supports five model specifications with varying deterministic components and break structures.

This package provides tools to analyze datasets previous to any statistical modeling. Has various functions designed to find inconsistencies and understanding the distribution of the data.

The xtdml package implements partially linear panel regression (PLPR) models with high-dimensional confounding variables and an exogenous treatment variable within the double machine learning framework. The package is used to estimate the structural parameter (treatment effect) in static panel data models with fixed effects using the approaches established in Clarke and Polselli (2025) <doi:10.1093/ectj/utaf011>. xtdml is built on the object-oriented package DoubleML (Bach et al., 2024) <doi:10.18637/jss.v108.i03> using the mlr3 ecosystem.

Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.

Grammatical evolution (see O'Neil, M. and Ryan, C. (2003,ISBN:1-4020-7444-1)) uses decoders to convert linear (binary or integer genes) into programs. In addition, automatic determination of codon precision with a limited rule choice bias is provided. For a recent survey of grammatical evolution, see Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>.

Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones. The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, <doi:10.3354/meps13814>).

Compute surrogate explanation groves for predictive machine learning models and analyze complexity vs. explanatory power of an explanation according to Szepannek, G. and von Holt, B. (2023) <doi:10.1007/s41237-023-00205-2>.

The US Census Bureau provides a seasonal adjustment program now called X-13ARIMA-SEATS building on both earlier programs called X-11 and X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau offers both source and binary versions -- which this package integrates for use by other R packages.

This package provides a simple XML tree parser/generator. It includes functions to read XML files into R objects, get information out of and into nodes, and write R objects back to XML code. It's not as powerful as the XML package and doesn't aim to be, but for simple XML handling it could be useful. It was originally developed for the R GUI and IDE RKWard <https://rkward.kde.org>, to make plugin development easier.

Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.

This package provides tools for reading, parsing and visualizing simulation data stored in xvg'/'xpm file formats (commonly generated by GROMACS molecular dynamics software). Streamlines post-processing and analysis of molecular dynamics ('MD') simulation outputs, enabling efficient exploration of molecular stability and conformational changes. Supports import of trajectory metrics ('RMSD', energy, temperature) and creation of publication-ready visualizations through integration with ggplot2'.

Create beautifully color-coordinated and customized themes for your xaringan slides, without writing any CSS. Complete your slide theme with ggplot2 themes that match the font and colors used in your slides. Customized styles can be created directly in your slides R Markdown source file or in a separate external script.

XML package for creating and reading and manipulating XML', with an object model based on Reference Classes'.

Dataset from the young elite swimmers study.

Simplify working with the YouTube Analytics API <https://developers.google.com/youtube/analytics>. Collect data for your channel including geography, traffic sources, time period, etc.

Compute the standard expected years of life lost (YLL), as developed by the Global Burden of Disease Study (Murray, C.J., Lopez, A.D. and World Health Organization, 1996). The YLL is based on comparing the age of death to an external standard life expectancy curve. It also computes the average YLL, which highlights premature causes of death and brings attention to preventable deaths (Aragon et al., 2008).

Analyzing performances of cricketers and cricket teams based on yaml match data from Cricsheet <https://cricsheet.org/>.

This package provides helper functions to perform Bayesian model averaging using Markov chain Monte Carlo samples from separate models. Calculates weights and obtains draws from the model-averaged posterior for quantities of interest specified by the user. Weight calculations can be done using marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay, R. (2010) <doi:10.1016/j.ijforecast.2009.08.001>.

Nonparametric estimation of discount functions and yield curves from transaction data of coupon paying bonds. Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> describe an application of this package using the Center for Research in Security Prices (CRSP) Bond Data and document its implementation.

This package provides a YAML-based mechanism for working with table metadata. Supports compact syntax for creating, modifying, viewing, exporting, importing, displaying, and plotting metadata coded as column attributes. The yamlet dialect is valid YAML with defaults and conventions chosen to improve readability. See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.

Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.

This package provides functions to manage taxonomy when lineages are described with strings and ranks separated with special patterns like "|*__" or ";*__".

Another implementation of general regression neural network in R based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the functional approximation or the classification.

Analyze data from behavioral experiments conducted using MED-PC software developed by Med Associates Inc. Includes functions to fit exponential and hyperbolic models for delay discounting tasks, exponential mixtures for inter-response times, and Gaussian plus ramp models for peak procedure data, among others. For more details, refer to Alcala et al. (2023) <doi:10.31234/osf.io/8aq2j>.