This package provides a simple interface for extracting various elements from the publicly available PubMed XML files, incorporating PubMed's regular updates, and combining the data with the NIH Open Citation Collection. See Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.

Power and sample size calculation for bulk tissue and single-cell eQTL analysis based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size for testing the association of a SNP to a continuous type phenotype. Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.

Identifies the entries with patterned responses for psychometric scales. The patterns included in the package are identical (a, a, a), ascending (a, b, c), descending (c, b, a), alternative (a, b, a, b / a, b, c, a, b, c).

Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow.

This package provides tools for analyzing differential abundance in proteomics experiments. Implements S3 classes for data management and supports Generalized Linear Models (GLM; Nelder and Wedderburn (1972) <doi:10.2307/2344614>), Aligned Rank Transform (ART; Wobbrock et al. (2011) <doi:10.1145/1978942.1978963>), and pairwise test methods for statistical analysis. Includes visualization functions for Principal Component Analysis (PCA), volcano plots, and heatmaps.

This package implements a novel predictive model, Partially Interpretable Estimators (PIE), which jointly trains an interpretable model and a black-box model to achieve high predictive performance as well as partial model. See the paper, Wang, Yang, Li, and Wang (2021) <doi:10.48550/arXiv.2105.02410>.

Robust penalized (adaptive) elastic net S and M estimators for linear regression. The adaptive methods are proposed in Kepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the non-adaptive methods in Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E. (2019) <doi:10.1214/19-AOAS1269>. The package implements robust hyper-parameter selection with robust information sharing cross-validation according to Kepplinger & Wei (2025) <doi:10.1080/00401706.2025.2540970>.

This package provides an R interface to the PCATS API <https://pcats.research.cchmc.org/api/__docs__/>, allowing R users to submit tasks and retrieve results.

Improving graphics by ameliorating order effects, using Eulerian tours and Hamiltonian decompositions of graphs. References for the methods presented here are C.B. Hurley and R.W. Oldford (2010) <doi:10.1198/jcgs.2010.09136> and C.B. Hurley and R.W. Oldford (2011) <doi:10.1007/s00180-011-0229-5>.

Plots with high flexibility and easy handling, including informative regression diagnostics for many models.

Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show enriched regions or peaks where proteins are bound. This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels.

Design parameters of the optimal two-period multiarm platform design (controlling for either family-wise error rate or pair-wise error rate) can be calculated using this package, allowing pre-planned deferred arms to be added during the trial. More details about the design method can be found in the paper: Pan, H., Yuan, X. and Ye, J. (2022) "An optimal two-period multiarm platform design with new experimental arms added during the trial". Manuscript submitted for publication. For additional references: Dunnett, C. W. (1955) <doi:10.2307/2281208>.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Household questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

Like similar profiling tools, the proffer package automatically detects sources of slowness in R code. The distinguishing feature of proffer is its utilization of pprof', which supplies interactive visualizations that are efficient and easy to interpret. Behind the scenes, the profile package converts native Rprof() data to a protocol buffer that pprof understands. For the documentation of proffer', visit <https://r-prof.github.io/proffer/>. To learn about the implementations and methodologies of pprof', profile', and protocol buffers, visit <https://github.com/google/pprof>. <https://protobuf.dev>, and <https://github.com/r-prof/profile>, respectively.

Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.

Personalize drug regimens using individual pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) profiles. By combining therapeutic drug monitoring (TDM) data with a population model, posologyr offers accurate posterior estimates and helps compute optimal individualized dosing regimens. The empirical Bayes estimates are computed following the method described by Kang et al. (2012) <doi:10.4196/kjpp.2012.16.2.97>.

Implementation of the Pearson distribution system, including full support for the (d,p,q,r)-family of functions for probability distributions and fitting via method of moments and maximum likelihood method.

Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.

An implementation of a formal grammar and parser for R Markdown documents using the Boost Spirit X3 library. It also includes a collection of high level functions for working with the resulting abstract syntax tree.

Connects to the API of <https://pushshift.io/> to search for Reddit comments and submissions.

This package provides tools for calculating and viewing topological properties of phylogenetic trees.

This package implements the Panel Smooth Transition Regression (PSTR) framework for nonlinear panel data modelling. The modelling procedure consists of three stages: Specification, Estimation and Evaluation. The package provides tools for model specification testing, to do PSTR model estimation, and to do model evaluation. The implemented tests allow for cluster dependence and are heteroskedasticity-consistent. The wild bootstrap and wild cluster bootstrap tests are also implemented. Parallel computation (as an option) is implemented in some functions, especially the bootstrap tests. The package supports parallel computation, which is useful for large-scale bootstrap procedures.

This package provides a collection of software provides R support for ADMB (Automatic Differentiation Model Builder) and a GUI interface facilitates the conversion of ADMB template code to C code followed by compilation to a binary executable. Stand-alone functions can also be run by users not interested in clicking a GUI'.

Three-dimensional systematic conservation planning, conducting nested prioritization analyses across multiple depth levels and ensuring efficient resource allocation throughout the water column. It provides a structured workflow designed to address biodiversity conservation and management challenges in the 3 dimensions, while facilitating usersâ choices and parameterization (Doxa et al. 2025 <doi:10.1016/j.ecolmodel.2024.110919>).