Motifs within biological sequences show a significant role. This package utilizes a user-defined threshold value (window size and similarity) to create consensus segments or motifs through local alignment of dynamic programming with gap and it calculates the frequency of each identified motif, offering a detailed view of their prevalence within the dataset. It allows for thorough exploration and understanding of sequence patterns and their biological importance.

This package provides a distributed framework for simulating and estimating skew factor models under various skewed and heavy-tailed distributions. The methods support distributed data generation, aggregation of local estimators, and evaluation of estimation performance via mean squared error, relative error, and sparsity measures. The distributed principal component (PC) estimators implemented in the package include IPC (Independent Principal Component),'PPC (Project Principal Component), SPC (Sparse Principal Component), and other related distributed PC methods. The methodological background follows Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

This package provides a set of functions to perform Raju, van der Linden and Fleer's (1995, <doi:10.1177/014662169501900405>) Differential Functioning of Items and Tests (DFIT) analyses. It includes functions to use the Monte Carlo Item Parameter Replication approach (Oshima, Raju, & Nanda, 2006, <doi:10.1111/j.1745-3984.2006.00001.x>) for obtaining the associated statistical significance tests cut-off points. They may also be used for a priori and post-hoc power calculations (Cervantes, 2017, <doi:10.18637/jss.v076.i05>).

Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials. For details of the methodology, please refer to D.O. Dixon and R. Simon (1991), Biometrics, 47: 871-881.

Apply the Deductive Rational Method to a monthly series of flow or precipitation data to fill in missing data. The method is as described in: Campos, D.F., (1984, ISBN:9686194444).

Kevin Dowd's book Measuring Market Risk is a widely read book in the area of risk measurement by students and practitioners alike. As he claims, MATLAB indeed might have been the most suitable language when he originally wrote the functions, but, with growing popularity of R it is not entirely valid. As Dowd's code was not intended to be error free and were mainly for reference, some functions in this package have inherited those errors. An attempt will be made in future releases to identify and correct them. Dowd's original code can be downloaded from www.kevindowd.org/measuring-market-risk/. It should be noted that Dowd offers both MMR2 and MMR1 toolboxes. Only MMR2 was ported to R. MMR2 is more recent version of MMR1 toolbox and they both have mostly similar function. The toolbox mainly contains different parametric and non parametric methods for measurement of market risk as well as backtesting risk measurement methods.

Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the disclapmix package that uses an EM algorithm.

Use dynamic programming method to solve l1 convex clustering with identical weights.

Perform tree-ring analyses such as detrending, chronology building, and cross dating. Read and write standard file formats used in dendrochronology.

Infer progression of circadian rhythms in transcriptome data in which samples are not labeled with time of day and coverage of the circadian cycle may be incomplete. See Shilts et al. (2018) <doi:10.7717/peerj.4327>.

An implementation of the differentiable lasso (dlasso) and SCAD (dSCAD) using iterative ridge algorithm. This package allows selecting the tuning parameter by AIC, BIC, GIC and GIC.

This package performs emulation of dynamic simulators using Gaussian process via one-step ahead approach. The package implements a flexible framework for approximating time-dependent outputs from computationally expensive dynamic systems. It is specifically designed for nonlinear dynamic systems where full simulations may be costly. The underlying Gaussian process model accounts for temporal dependency through the one-step-ahead formulation, allowing for accurate emulation of complex dynamics. Hyperparameters are estimated via maximum likelihood. For methodological details, see Heo (2025, <doi:10.48550/arXiv.2503.20250>) for exact method, and Mohammadi, Challenor, and Goodfellow (2019, <doi:10.1016/j.csda.2019.05.006>) for Monte Carlo method.

Create shareable data sets from raw data files that contain protected elements. Relying on master crosswalk files that list restricted variables, package functions warn users about possible violations of data usage agreement and prevent writing protected elements.

Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips, using base R graphics. Note that the ggdist package offers a similar set of tools for illustrating distributions, based on ggplot2'.

This package implements the deflist class, a read-only list-like object that accesses its elements via a function. The deflist class can be used to model deferred access to data or computations by routing indexed list access to a function. This approach is particularly useful when sequential list-like access to data is required but holding all the data in memory at once is not feasible. The package also provides utilities for memoisation and caching to optimize access to frequently requested elements.

This package provides methods for analyzing the dispersion of tabular datasets with batched and ordered samples. Based on convex hull or integrated covariance Mahalanobis, several indicators are implemented for inter and intra batch dispersion analysis. It is designed to facilitate robust statistical assessment of data variability, supporting applications in exploratory data analysis and quality control, for such datasets as the one found in metabololomics studies. For more details see Salanon (2024) <doi:10.1016/j.chemolab.2024.105148> and Salanon (2025) <doi:10.1101/2025.08.01.668073>.

This package provides a collection of functions to preprocess data and organize them in a format amenable to use by chevron.

This package provides functions to import multiple files of multiple data file types ('.xlsx', .xls', .csv', .txt') from a given directory into R data frames.

This package provides a collection of data-limited management procedures that can be evaluated with management strategy evaluation with the MSEtool package, or applied to fishery data to provide management recommendations.

This package performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

Models for analyzing site occupancy and count data models with detection error, including single-visit based models (Lele et al. 2012 <doi:10.1093/jpe/rtr042>, Moreno et al. 2010 <doi:10.1890/09-1073.1>, Solymos et al. 2012 <doi:10.1002/env.1149>, Denes et al. 2016 <doi:10.1111/1365-2664.12818>), conditional distance sampling and time-removal models (QPAD) (Solymos et al. 2013 <doi:10.1111/2041-210X.12106>, Solymos et al. 2018 <doi:10.1650/CONDOR-18-32.1>), and single bin QPAD (SQPAD) models (Lele & Solymos 2025 <doi:10.1093/ornithapp/duaf078>). Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.

This R function implements the nonstationary Kriging model proposed by Tuo, Wu and Yu (2014) <DOI:10.1080/00401706.2013.842935> for analyzing multi-fidelity computer outputs. This function computes the maximum likelihood estimates for the model parameters as well as the predictive means and variances of the exact solution.

Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries.

This package provides data transformations, estimation utilities, predictive evaluation measures and simulation functions for discrete time survival analysis.