Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.

This package creates interactive trees that can be included in Shiny apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the shinyTree package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in Shiny when a node is selected. Also provides a Shiny gadget allowing to manipulate one or more folders, and a Shiny module allowing to navigate in the server side file system.

Estimation of extended joint models with shared random effects. Longitudinal data are handled in latent process models for continuous (Gaussian or curvilinear) and ordinal outcomes while proportional hazard models are used for the survival part. We propose a frequentist approach using maximum likelihood estimation. See Saulnier et al, 2022 <doi:10.1016/j.ymeth.2022.03.003>.

An R package that implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2024). <Journal of Computational and Graphical Statistics, 33(3), 763-773>]. It aims at solving the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively.

Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.

We provide tools to estimate the individualized interval-valued dose rule (I2DR) that maximizes the expected beneficial clinical outcome for each individual and returns an optimal interval-valued dose, by using the jump Q-learning (JQL) method. The jump Q-learning method directly models the conditional mean of the response given the dose level and the baseline covariates via jump penalized least squares regression under the framework of Q learning. We develop a searching algorithm by dynamic programming in order to find the optimal I2DR with the time complexity O(n2) and spatial complexity O(n). To alleviate the effects of misspecification of the Q-function, a residual jump Q-learning is further proposed to estimate the optimal I2DR. The outcome of interest includes the best partition of the entire dosage of interest, the regression coefficients of each partition, and the value function under the estimated I2DR as well as the Wald-type confidence interval of value function constructed through the Bootstrap.

An implementation of fast cluster-based permutation analysis (CPA) for densely-sampled time data developed in Maris & Oostenveld, 2007 <doi:10.1016/j.jneumeth.2007.03.024>. Supports (generalized, mixed-effects) regression models for the calculation of timewise statistics. Provides both a wholesale and a piecemeal interface to the CPA procedure with an emphasis on interpretability and diagnostics. Integrates Julia libraries MixedModels.jl and GLM.jl for performance improvements, with additional functionalities for interfacing with Julia from R powered by the JuliaConnectoR package.

This package provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.

This package implements penalised multivariate regression (i.e., for multiple outcomes and many features) by stacked generalisation (<doi:10.1093/bioinformatics/btab576>). For positively correlated outcomes, a single multivariate regression is typically more predictive than multiple univariate regressions. Includes functions for model fitting, extracting coefficients, outcome prediction, and performance measurement. For optional comparisons, install remMap from GitHub (<https://github.com/cran/remMap>).

This package provides a highly configurable jQuery plugin offering a simple interface to create complex queries/filters in Shiny'. The outputted rules can easily be parsed into a set of R and/or SQL queries and used to filter data. Custom parsing of the rules is also supported. For more information about jQuery QueryBuilder see <https://querybuilder.js.org/>.

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

This package provides statistical methods for auditing as implemented in JASP for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240>, Derks et al. (2024) <doi:10.2308/AJPT-2021-086>, Derks et al. (2022) <doi:10.31234/osf.io/8nf3e> Derks et al. (2024) <doi:10.31234/osf.io/tgq5z>, and Derks et al. (2025) <doi:10.31234/osf.io/b8tu2>.

The goal of jetty is to execute R functions and code snippets in an isolated R subprocess within a Docker container and return the evaluated results to the local R session. jetty can install necessary packages at runtime and seamlessly propagates errors and outputs from the Docker subprocess back to the main session. jetty is primarily designed for sandboxed testing and quick execution of example code.

Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using tensorflow'.

This package contains a selection of color palettes and ggplot2 themes designed by the package author.

Aids in the calculation and visualization of regions of non-significance using the Johnson-Neyman technique and its extensions as described by Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5> to assess the influence of categorical and continuous moderators. Allows correcting for phylogenetic relatedness.

This package implements an S4 distribution system and estimation methods for parameters of common distribution families. The common d, p, q, r function family for each distribution is enriched with the ll, e, and v counterparts, computing the log-likelihood, performing estimation, and calculating the asymptotic variance - covariance matrix, respectively. Parameter estimation is performed analytically whenever possible.

This package provides a small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the survey', sampling or laeken packages. In addition, entropy balancing via the ebal package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by BerÄ sewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Set of common functions used for manipulating colors, detecting and interacting with RStudio', modeling, formatting, determining users operating system, feature scaling, and more!

Minimal and memory efficient implementation of the junction tree algorithm using the Lauritzen-Spiegelhalter scheme; S. L. Lauritzen and D. J. Spiegelhalter (1988) <https://www.jstor.org/stable/2345762?seq=1>. The jti package is part of the paper <doi:10.18637/jss.v111.i02>.

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death. Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model. The methods are applicable for meta-analytic data containing individual-patient information from several studies. Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression). Methodologies were published in Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>, Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>, Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>. See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>. Survival data from ovarian cancer patients are also available.

Fast extrapolation of univariate and multivariate time features using K-Nearest Neighbors. The compact set of hyper-parameters is tuned via grid or random search.

Uses least squares optimisation to estimate the parameters of the best-fitting JohnsonSU distribution for a given dataset, with the possibility of the distributions corresponding to the limiting cases of the JohnsonSU distribution. The code for the Golden Section Search used in the optimisation has been adapted from E. Cai. This package has been created as an extension of my Master's thesis. E. Cai (2013, "Scripts and Functions: Using R to Implement the Golden Section Search Method for Numerical Optimization", <https://chemicalstatistician.wordpress.com/2013/04/22/using-r-to-implement-the-golden-bisection-method/>).