Fast calculation of Area Under Curve (AUC) metric of a Receiver Operating Characteristic (ROC) curve, using the algorithm of Fawcett (2006) <doi:10.1016/j.patrec.2005.10.010>. Therefore it is appropriate for large-scale AUC metric calculations.

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. This is a revised version of the lineup package, to be more general and not tied to the qtl package.

Local Mean Decomposition is an iterative and self-adaptive approach for demodulating, processing, and analyzing multi-component amplitude modulated and frequency modulated signals. This R package is based on the approach suggested by Smith (2005) <doi:10.1098/rsif.2005.0058> and the Python library PyLMD'.

This package provides a collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.

Data, scripts and code from chunks used as examples in the book "Learn R: As a Language" 1ed and 2ed by Pedro J. Aphalo. ISBN 9780367182533 (pbk 1ed); ISBN 9780367182557 (hbk 1ed); ISBN 9780429060342 (ebk 1ed).

This package implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively. The method is documented in Kumar et al (2013) <https://www.nature.com/articles/jes201352)>.

Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

This package provides functions to access and test results from a linear model.

Here we provide an implementation of the linear and logistic regression-based Reliable Change Index (RCI), to be used with lm and binomial glm model objects, respectively, following Moral et al. <https://psyarxiv.com/gq7az/>. The RCI function returns a score assumed to be approximately normally distributed, which is helpful to detect patients that may present cognitive decline.

Interpretable nonparametric modeling of longitudinal data using additive Gaussian process regression. Contains functionality for inferring covariate effects and assessing covariate relevances. Models are specified using a convenient formula syntax, and can include shared, group-specific, non-stationary, heterogeneous and temporally uncertain effects. Bayesian inference for model parameters is performed using Stan'. The modeling approach and methods are described in detail in Timonen et al. (2021) <doi:10.1093/bioinformatics/btab021>.

The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.

Converts video files to mp3', merges multiple audio files and trims audio files using FFmpeg', which is dynamically downloaded to avoid bundling any third-party binaries. Users must ensure compliance with the license terms of FFmpeg when using the package. See <https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-win64-gpl.zip> for details.

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).

Detect feedback loops (cycles, circuits) between species (nodes) in ordinary differential equation (ODE) models. Feedback loops are paths from a node to itself without visiting any other node twice, and they have important regulatory functions. Loops are reported with their order of participating nodes and their length, and whether the loop is a positive or a negative feedback loop. An upper limit of the number of feedback loops limits runtime (which scales with feedback loop count). Model parametrizations and values of the modelled variables are accounted for. Computation uses the characteristics of the Jacobian matrix as described e.g. in Thomas and Kaufman (2002) <doi:10.1016/s1631-0691(02)01452-x>. Input can be the Jacobian matrix of the ODE model or the ODE function definition; in the latter case, the Jacobian matrix is determined using numDeriv'. Graph-based algorithms from igraph are employed for path detection.

This package implements the letter value boxplot which extends the standard boxplot to deal with both larger and smaller number of data points by dynamically selecting the appropriate number of letter values to display.

The reference implementation of model equations and default parameters for the toxicokinetic-toxicodynamic (TKTD) model of the Lemna (duckweed) aquatic plant. Lemna is a standard test macrophyte used in ecotox effect studies. The model was described and published by the SETAC Europe Interest Group Effect Modeling. It is a refined description of the Lemna TKTD model published by Schmitt et al. (2013) <doi:10.1016/j.ecolmodel.2013.01.017>.

Analyze graph/network data using L1 centrality and prestige. Functions for deriving global, local, and group L1 centrality/prestige are provided. Routines for visual inspection of a graph/network are also provided. Details are in Kang and Oh (2025a) <doi:10.1080/01621459.2025.2520467>, Kang and Oh (2025b) <doi:10.1080/00031305.2025.2563730>, and Kang (2025) <doi:10.23170/snu.000000188358.11032.0001856>.

This package provides a utility to facilitate the logging and review of R programs in clinical trial programming workflows.

Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han S., et al. (2019) <doi:10.1093/bib/bby065>.

"Lessons in Statistical Thinking" D.T. Kaplan (2014) <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. LSTbook supports the student-centered, tidy, pipeline-oriented computing style featured in the book.

Improve your text analysis with languagelayer <https://languagelayer.com>, a powerful language detection API.

This package provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata. The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and R color names, making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible. The functions use nearest color matching based on Euclidean distance in RGB space. Naming conventions for color mapping include those from BrickLink (<https://www.bricklink.com>), The Lego Group (<https://www.lego.com>), LDraw (<https://www.ldraw.org/>), and Peeron (<http://www.peeron.com/>).

Allows identification of palettes derived from LTER (Long Term Ecological Research) photographs based on user criteria. Also facilitates extraction of palettes from users photos directly.

This package provides Shiny widgets and theme that support a Library Computer Access/Retrieval System (LCARS) aesthetic for Shiny apps. The package also includes functions for adding a minimal LCARS theme to static ggplot2 graphs. More details about LCARS can be found at <https://en.wikipedia.org/wiki/LCARS>.