This package provides a collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic.

Discrete event simulation (DES) involves modeling of systems having discrete, i.e. abrupt, state changes. For instance, when a job arrives to a queue, the queue length abruptly increases by 1. This package is an R implementation of the event-oriented approach to DES; see the tutorial in Matloff (2008) <http://heather.cs.ucdavis.edu/~matloff/156/PLN/DESimIntro.pdf>.

Utilities for mixed frequency data. In particular, use to aggregate and normalize tabular mixed frequency data, index dates to end of period, and seasonally adjust tabular data.

Designed for genomic and proteomic data analysis, enabling unbiased PubMed searching, protein interaction network visualization, and comprehensive data summarization. This package aims to help users identify novel targets within their data sets based on protein network interactions and publication precedence of target's association with research context based on literature precedence. Methods in this package are described in detail in: Douglas (Year) <to-be-added DOI or link to the preprint>. Key functionalities of this package also leverage methodologies from previous works, such as: - Szklarczyk et al. (2023) <doi:10.1093/nar/gkac1000> - Winter (2017) <doi:10.32614/RJ-2017-066>.

This package provides functions to calculate Divisia monetary aggregates index as given in Barnett, W. A. (1980) (<DOI:10.1016/0304-4076(80)90070-6>).

This package provides functions to pipe data from R to DataGraph', a graphing and analysis application for mac OS. Create a live connection using either .dtable or .dtbin files that can be read by DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.

Fit logistic functions to observed dose-response continuous data and evaluate goodness-of-fit measures. See Malyutina A., Tang J., and Pessia A. (2023) <doi:10.18637/jss.v106.i04>.

Reverse and model the effects of changing deposition rates on geological data and rates. Based on Hohmann (2018) <doi:10.13140/RG.2.2.23372.51841> .

Given a set of predictive quantiles from a distribution, estimate the distribution and create `d`, `p`, `q`, and `r` functions to evaluate its density function, distribution function, and quantile function, and generate random samples. On the interior of the provided quantiles, an interpolation method such as a monotonic cubic spline is used; the tails are approximated by a location-scale family.

This package provides a Bayesian hierarchical model for clustering dissimilarity data using the Dirichlet process. The latent configuration of objects and the number of clusters are automatically inferred during the fitting process. The package supports multiple models which are available to detect clusters of various shapes and sizes using different covariance structures. Additional functions are included to ensure adequate model fits through prior and posterior predictive checks.

Function to test spatial segregation and association based in contingency table analysis of nearest neighbour counts following Dixon (2002) <doi:10.1080/11956860.2002.11682700>. Some Fortran code has been included to the original dixon2002() function of the ecespa package to improve speed.

This package provides a set of functions to estimate the controlled direct effect of treatment fixing a potential mediator to a specific value. Implements the sequential g-estimation estimator described in Vansteelandt (2009) <doi:10.1097/EDE.0b013e3181b6f4c9> and Acharya, Blackwell, and Sen (2016) <doi:10.1017/S0003055416000216> and the telescope matching estimator described in Blackwell and Strezhnev (2020) <doi:10.1111/rssa.12759>.

This package provides a Graphical User Interface (GUI) to import, save, detrend and perform standard tree-ring analyses. The interactive detrending allows the user to check how well the detrending curve fits each time-series and change it when needed.

This package implements the Improved Expectation Maximisation EM* and the traditional EM algorithm for clustering big data (gaussian mixture models for both multivariate and univariate datasets). This version implements the faster alternative-EM* that expedites convergence via structure based data segregation. The implementation supports both random and K-means++ based initialization. Reference: Parichit Sharma, Hasan Kurban, Mehmet Dalkilic (2022) <doi:10.1016/j.softx.2021.100944>. Hasan Kurban, Mark Jenne, Mehmet Dalkilic (2016) <doi:10.1007/s41060-017-0062-1>.

This package provides a tool to sample data with the desired properties.Samples can be drawn by purposive sampling with determining distributional conditions, such as deviation from normality (skewness and kurtosis), and sample size in quantitative research studies. For purposive sampling, a researcher has something in mind and participants that fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) <doi:10.11648/j.ajtas.20160501.11>.Purposive sampling can be useful for answering many research questions (Klar & Leeper, 2019) <doi:10.1002/9781119083771.ch21>.

Hash an expression with its dependencies and store its value, reloading it from a file as long as both the expression and its dependencies stay the same.

Designed to support the visualization, numerical computation, qualitative analysis, model-data fusion, and stochastic simulation for autonomous systems of differential equations. Euler and Runge-Kutta methods are implemented, along with tools to visualize the two-dimensional phaseplane. Likelihood surfaces and a simple Markov Chain Monte Carlo parameter estimator can be used for model-data fusion of differential equations and empirical models. The Euler-Maruyama method is provided for simulation of stochastic differential equations. The package was originally written for internal use to support teaching by Zobitz, and refined to support the text "Exploring modeling with data and differential equations using R" by John Zobitz (2021) <https://jmzobitz.github.io/ModelingWithR/index.html>.

Fit and explore Drift Diffusion Models (DDMs), a common tool in psychology for describing decision processes in simple tasks. It can handle both time-independent and time-dependent DDMs. You either choose prebuilt models or create your own, and the package takes care of model predictions and parameter estimation. Model predictions are derived via the numerical solutions provided by Richter, Ulrich, and Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).

This package provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees. Provides methods to 1) reconstruct unmutated ancestral sequences, 2) build B cell phylogenetic trees using multiple methods, 3) visualize trees with metadata at the tips, 4) reconstruct intermediate sequences, 5) detect biased ancestor-descendant relationships among metadata types Workflow examples available at documentation site (see URL). Citations: Hoehn et al (2022) <doi:10.1371/journal.pcbi.1009885>, Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.

This dataset includes Background and Pathway data used in package DysPIA'.

The goal of dlr is to provide a friendly wrapper around the common pattern of downloading a file if that file does not already exist locally.

This package provides functions are provided that facilitate the analysis of SNP (single nucleotide polymorphism) data to answer questions regarding captive breeding and relatedness between individuals. dartR.captive is part of the dartRverse suit of packages. Gruber et al. (2018) <doi:10.1111/1755-0998.12745>. Mijangos et al. (2022) <doi:10.1111/2041-210X.13918>.

Intelligently assign samples to batches in order to reduce batch effects. Batch effects can have a significant impact on data analysis, especially when the assignment of samples to batches coincides with the contrast groups being studied. By defining a batch container and a scoring function that reflects the contrasts, this package allows users to assign samples in a way that minimizes the potential impact of batch effects on the comparison of interest. Among other functionality, we provide an implementation for OSAT score by Yan et al. (2012, <doi:10.1186/1471-2164-13-689>).

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.