Helping psychologists and other behavioural scientists to analyze mouse movement (and other 2-D trajectory) data. Bundles together several functions that compute spatial measures (e.g., maximum absolute deviation, area under the curve, sample entropy) or provide a shorthand for procedures that are frequently used (e.g., time normalization, linear interpolation, extracting initiation and movement times). For more information on these dependent measures, see Wirth et al. (2020) <doi:10.3758/s13428-020-01409-0>.

Computation of standardized interquartile range (IQR), Huber-type skipped mean (Hampel (1985), <doi:10.2307/1268758>), robust coefficient of variation (CV) (Arachchige et al. (2019), <doi:10.48550/arXiv.1907.01110>), robust signal to noise ratio (SNR), z-score, standardized mean difference (SMD), as well as functions that support graphical visualization such as boxplots based on quartiles (not hinges), negative logarithms and generalized logarithms for ggplot2 (Wickham (2016), ISBN:978-3-319-24277-4).

Microbial growth is often measured by growth curves i.e. a table of population sizes and times of measurements. This package allows to use such growth curve data to determine the duration of "microbial lag phase" i.e. the time needed for microbes to restart divisions. It implements the most commonly used methods to calculate the lag duration, these methods are discussed and described in Opalek et.al. 2022. Citation: Smug, B. J., Opalek, M., Necki, M., & Wloch-Salamon, D. (2024). Microbial lag calculator: A shiny-based application and an R package for calculating the duration of microbial lag phase. Methods in Ecology and Evolution, 15, 301â 307 <doi:10.1111/2041-210X.14269>.

To assist biological researchers in assembling taxonomically and marker focused molecular sequence data sets. MACER accepts a list of genera as a user input and uses NCBI-GenBank and BOLD as resources to download and assemble molecular sequence datasets. These datasets are then assembled by marker, aligned, trimmed, and cleaned. The use of this package allows the publication of specific parameters to ensure reproducibility. The MACER package has four core functions and an example run through using all of these functions can be found in the associated repository <https://github.com/rgyoung6/MACER_example>.

Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.

Computes indirect effects, conditional effects, and conditional indirect effects in a structural equation model or path model after model fitting, with no need to define any user parameters or label any paths in the model syntax, using the approach presented in Cheung and Cheung (2024) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap confidence intervals by doing bootstrapping only once and reusing the bootstrap estimates in all subsequent computations. Supports bootstrap confidence intervals for standardized (partially or completely) indirect effects, conditional effects, and conditional indirect effects as described in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui, and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by structural equation modeling using lavaan() or regression using lm().

Generates efficient balanced mixed-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has EfNOD efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient mixed-level k-circulant design through a progress bar. The progress of 100 per cent means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs. For more details, please see Mandal, B.N., Gupta V. K. and Parsad, R. (2011). Construction of Efficient Mixed-Level k-Circulant Supersaturated Designs, Journal of Statistical Theory and Practice, 5:4, 627-648, <doi:10.1080/15598608.2011.10483735>.

Analyzes subject-level data in clinical trials using the metalite data structure. The package simplifies the workflow to create production-ready tables, listings, and figures discussed in the subject-level analysis chapters of "R for Clinical Study Reports and Submission" by Zhang et al. (2022) <https://r4csr.org/>.

This package provides a collection of functions for conducting a meta-analysis with mean differences data. It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

Simulate, manage, visualize, and analyze spatially and temporally explicit datasets of mating potential. Implements methods to calculate synchrony, proximity, and compatibility.Synchrony calculations are based on methods described in Augspurger (1983) <doi:10.2307/2387650>, Kempenaers (1993) <doi:10.2307/3676415>, Ison et al. (2014) <doi:10.3732/ajb.1300065>, and variations on these, as described.

The Society of Actuaries (SOA) provides an extensive online database called Mortality and Other Rate Tables ('MORT') at <https://mort.soa.org/>. This database contains mortality, lapse, and valuation tables that cover a variety of product types and nations. Users of the database can download any tables in Excel', CSV', or XML formats. This package provides convenience functions that read XML formats from the database and return R objects.

This package provides compact tools for missing-data analysis, including artificial amputation, chained single and multiple imputation, statistical and machine-learning-based imputation methods, diagnostic evaluation, and post-imputation pooling.

This package provides functions for the creation/generation and analysis of multilayer social networks <doi:10.18637/jss.v098.i08>.

Visualize confounder control in meta-analysis. metaconfoundr is an approach to evaluating bias in studies used in meta-analyses based on the causal inference framework. Study groups create a causal diagram displaying their assumptions about the scientific question. From this, they develop a list of important confounders'. Then, they evaluate whether studies controlled for these variables well. metaconfoundr is a toolkit to facilitate this process and visualize the results as heat maps, traffic light plots, and more.

This is a tool for epidemiologist, medical data analyst, medical or public health professionals. It contains three domains of functions: 1) data management, 2) statistical analysis and 3) calculating epidemiological measures.

Function and support for medication and dosing information extraction from free-text clinical notes. Medication entities for the basic medExtractR implementation that can be extracted include drug name, strength, dose amount, dose, frequency, intake time, dose change, and time of last dose. The basic medExtractR is outlined in Weeks, Beck, McNeer, Williams, Bejan, Denny, Choi (2020) <doi: 10.1093/jamia/ocz207>. The extended medExtractR_tapering implementation is intended to extract dosing information for more tapering schedules, which are far more complex. The tapering extension allows for the extraction of additional entities including dispense amount, refills, dose schedule, time keyword, transition, and preposition.

This package provides a range of functions for computing both global and local mark correlation functions for spatial point patterns in either Euclidean spaces or on linear networks, with points carrying either real-valued or function-valued marks. For a review of mark correlation functions, see Eckardt and Moradi (2024) <doi:10.1007/s13253-024-00605-1>.

This package provides a graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test, Royston test and Henze-Zirkler test.

Stateful helpers for building Matrix (<https://matrix.org>) chat clients in R. Builds on the low-level mx.api Client-Server API bindings, adding local configuration persistence, room resolution, sync cursor handling, sync-event extraction, invite acceptance, a conservative Markdown-to-HTML converter for formatted messages, and Olm'/'Megolm end-to-end encryption orchestration over the optional mx.crypto package.

With foundations on the work by Goutali and Chebana (2024) <doi:10.1016/j.envsoft.2024.106090>, this package contains various univariate and multivariate trend tests. The main functions regard the Multivariate Dependence Trend and Multivariate Overall Trend tests as proposed by Goutali and Chebana (2024), as well as a plotting function that proves useful as a summary and complement of the tests. Although many packages and methods carry univariate tests, the Mann-Kendall and Spearman's rho test implementations are included in the package with an adapted version to hydrological formulation (e.g. as in Rao and Hamed 1998 <doi:10.1016/S0022-1694(97)00125-X> or Chebana 2022 <doi:10.1016/C2021-0-01317-1>). For better understanding of the example use of the functions, three datasets are included. These are synthetic data and shouldn't be used beyond that purpose.

Random Forest Spatial Interpolation (RFSI, SekuliÄ et al. (2020) <doi:10.3390/rs12101687>) and spatio-temporal geostatistical (spatio-temporal regression Kriging (STRK)) interpolation for meteorological (Kilibarda et al. (2014) <doi:10.1002/2013JD020803>, SekuliÄ et al. (2020) <doi:10.1007/s00704-019-03077-3>) and other environmental variables. Contains global spatio-temporal models calculated using publicly available data.

Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The Gurobi software and its R interface package are required for one of the package functions (match.2x()) and can be obtained at <https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) <doi:10.1080/10618600.2022.2074429> "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) <doi:10.1057/palgrave.jors.2601723> "Local search heuristics for multi-index assignment problems with decomposable costs".