This package provides functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for viewing the details (size, time created, time modified, etc.) of a folder's top level contents.

Enables high-dimensional penalized regression across heterogeneous subgroups. Fusion penalties are used to share information about the linear parameters across subgroups. The underlying model is described in detail in Dondelinger and Mukherjee (2017) <arXiv:1611.00953>.

Implementation of several generalized F-statistics. The current version includes a generalized F-statistic based on the flexible isotonic/monotonic regression or order restricted hypothesis testing. Based on: Y. Lai (2011) <doi:10.1371/journal.pone.0019754>.

Generalized promotion time cure model (GPTCM) via Bayesian hierarchical modeling for multiscale data integration (Zhao et al. (2025) <doi:10.48550/arXiv.2509.01001>). The Bayesian GPTCMs are applicable for both low- and high-dimensional data.

Leveraging information-theoretic measures like mutual information and v-measure to quantify spatial associations between patterns (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>; Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>).

Infix operators to detect, subset, and replace the elements matched by a given condition. The functions have several variants of operator types, including subsets, ranges, regular expressions and others. Implemented operators work on vectors, matrices, and lists.

Implementations of estimation algorithm of low rank plus sparse structured VAR model by using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). It relates to the algorithm in Sumanta, Li, and Michailidis (2019) <doi:10.1109/TSP.2018.2887401>.

Fits sparse generalized linear models using an adaptive ridge approximation to an L0 penalty. Supported model families include Gaussian, logistic, Poisson, gamma, and inverse Gaussian regression. The package also provides cross-validation for selecting the penalty parameter.

Imputes missing values of an incomplete data matrix by minimizing the Mahalanobis distance of each sample from the overall mean [Labita, GJ.D. and Tubo, B.F. (2024) <doi:10.24412/1932-2321-2024-278-115-123>].

This package implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models, see Deldossi and Nai Ruscone (2020) <doi:10.18637/jss.v094.i02>.

Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

This package provides a system contains easy-to-use tools for the conditional estimation of the prevalence of an emerging or rare infectious diseases using the methods proposed in Guerrier et al. (2023) <arXiv:2012.10745>.

Full text, in data frames containing one row per verse, of the Qur'an in Arabic (with and without vowels) and in English (the Yusuf Ali and Saheeh International translations), formatted to be convenient for text analysis.

Stochastic frontier analysis with advanced methods. In particular, it applies the approach proposed by Latruffe et al. (2017) <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical inefficiency effects when one input is endogenous.

Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).

Generate Venn plots, summary tables, and ellipse paths for polygon clipping. Provides direct access to subsets of interest and offers flexible customization of Venn diagrams. Summary tables are also available when Venn diagram visualization is not suitable.

List of english scrabble words as listed in the OTCWL2014 <https://www.scrabbleplayers.org/w/Official_Tournament_and_Club_Word_List_2014_Edition>. Words are collated from the Word Game Dictionary <https://www.wordgamedictionary.com/word-lists/>.

Diagnostics for non-linear mixed-effects (population) models from NONMEM <https://www.iconplc.com/solutions/technologies/nonmem/>. xpose facilitates data import, creation of numerical run summary and provide ggplot2'-based graphics for data exploration and model diagnostics.

This package contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. Calibration uses a 1st to 5th order polynomial as suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>. Data can be processed individually or batch processed for all files paired with a given calibration file. RACiR is a trademark of LI-COR Biosciences, and used with permission.

This package tracks reading and writing within R scripts that are organized into a directed acyclic graph. It contains an interactive Shiny application adaprApp(). It uses Git and file hashes to track version histories of inputs and outputs.

This package provides implementations of the family of map() functions from the purrr package that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem.

This package computes various confidence intervals (CI) for the Kaplan-Meier estimator, namely: Petos CI, Rothman CI, CIs based on Greenwoods variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner.

CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.