Convenience functions and datasets to be used with Practical Multilevel Modeling using R. The package includes functions for calculating group means, group mean centered variables, and displaying some basic missing data information. A function for computing robust standard errors for linear mixed models based on Liang and Zeger (1986) <doi:10.1093/biomet/73.1.13> and Bell and McCaffrey (2002) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2002002/article/9058-eng.pdf?st=NxMjN1YZ> is included as well as a function for checking for level-one homoskedasticity (Raudenbush & Bryk, 2002, ISBN:076191904X).

This package provides a downstream bioinformatics tool to construct and assist curation of microhaplotypes from short read sequences.

Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the well-known early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or cross-validation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).

Fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one random effect in each category of the response variable (Lopez-Vizcaino,E. et al., 2013) <doi:10.1177/1471082X13478873>; Model 2, introducing independent time effect; Model 3, introducing correlated time effect. mme calculates direct and parametric bootstrap MSE estimators (Lopez-Vizcaino,E et al., 2014) <doi:10.1111/rssa.12085>.

Modeling microstructures of human tooth dentin and horizontal serial-sectioning of the dentin. Corresponding age range of dentin serial sections, that is used in stable isotope analyses, can be calculated by using this package.

The stepwise variable selection procedure (with iterations between the forward and backward steps) can be used to obtain the best candidate final regression model in regression analysis. All the relevant covariates are put on the variable list to be selected. The significance levels for entry (SLE) and for stay (SLS) are usually set to 0.15 (or larger) for being conservative. Then, with the aid of substantive knowledge, the best candidate final regression model is identified manually by dropping the covariates with p value > 0.05 one at a time until all regression coefficients are significantly different from 0 at the chosen alpha level of 0.05.

Given the maximum available sample size (N) for an experiment, and the target levels of Type I and II error probabilities, this package designs a modified SPRT (MSPRT). For any designed MSPRT the package can also obtain its operating characteristics and implement the test for a given sequentially observed data. The MSPRT is defined in a manner very similar to Wald's initial proposal. The proposed test has shown evidence of reducing the average sample size required to perform statistical hypothesis tests at specified levels of significance and power. Currently, the package implements one-sample proportion tests, one and two-sample z tests, and one and two-sample t tests. A brief user guidance for this package is provided below. One can also refer to the supplemental information for the same.

Metric halfspace depth for object data, generalizing Tukey's depth for Euclidean data. Implementing the method described in Dai and Lopez-Pintado (2022) <doi:10.1080/01621459.2021.2011298>.

It can be used to create/encode molecular "license-plates" from sequences and to also decode the "license-plates" back to sequences. While initially created for transfer RNA-derived small fragments (tRFs), this tool can be used for any genomic sequences including but not limited to: tRFs, microRNAs, etc. The detailed information can reference to Pliatsika V, Loher P, Telonis AG, Rigoutsos I (2016) <doi:10.1093/bioinformatics/btw194>. It can also be used to annotate tRFs. The detailed information can reference to Loher P, Telonis AG, Rigoutsos I (2017) <doi:10.1038/srep41184>.

Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server.

Visualizes multiple sequence alignments dynamically within the Shiny web application framework.

This package contains a dataset of morphological and structural features of Medicinal LEAves (MedLEA)'. The features of each species is recorded by manually viewing the medicinal plant repository available at (<http://www.instituteofayurveda.org/plants/>). You can also download repository of leaf images of 1099 medicinal plants in Sri Lanka.

Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, directory structures, and other hierarchical structured documents into mind map widgets or Freemind codes or Mermaid mind map codes, and vice versa. Freemind mind map ('.mm') files can be opened by or imported to common mind map software such as Freemind (<https://freemind.sourceforge.io/wiki/index.php/Main_Page>). Mermaid mind map codes (<https://mermaid.js.org/>) can be directly embedded in documents.

You can apply image processing effects that modifies the perceived material properties of objects in photos, such as gloss, smoothness, and blemishes. This is an implementation of the algorithm proposed by Boyadzhiev et al. (2015) "Band-Sifting Decomposition for Image Based Material Editing". Documentation and practical tips of the package is available at <https://github.com/tsuda16k/materialmodifier>.

We introduce a generalized factor model designed to jointly analyze high-dimensional multi-modality data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among modality variables with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors. More details can be referred to Liu et al. (2025) <doi:10.48550/arXiv.2507.09889>.

To calculate the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.

Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales.

This package contains auxiliary routines for influx software. This packages is not intended to be used directly. Influx was published here: Sokol et al. (2012) <doi:10.1093/bioinformatics/btr716>.

Extends multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm>, which allows users perform to create explorable multiverse analysis in R. This extension provides an additional level of abstraction to the multiverse package with the aim of creating user friendly syntax to researchers, educators, and students in statistics. The mverse syntax is designed to allow piping and takes hints from the tidyverse grammar. The package allows users to define and inspect multiverse analysis using familiar syntax in R.

Designs plots in terms of core structure. See example(metaplot)'. Primary arguments are (unquoted) column names; order and type (numeric or not) dictate the resulting plot. Specify any y variables, x variable, any groups variable, and any conditioning variables to metaplot() to generate density plots, boxplots, mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() to arrange plots in grids. Wherever present, scalar column attributes label and guide are honored, producing fully annotated plots with minimal effort. Attribute guide is typically units, but may be encoded() to provide interpretations of categorical values (see ?encode'). Utility unpack() transforms scalar column attributes to row values and pack() does the reverse, supporting tool-neutral storage of metadata along with primary data. The package supports customizable aesthetics such as such as reference lines, unity lines, smooths, log transformation, and linear fits. The user may choose between trellis and ggplot output. Compact syntax and integrated metadata promote workflow scalability.

This package provides tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.

Fits Bayesian dose-response model-based network meta-analysis (MBNMA) that incorporate multiple doses within an agent by modelling different dose-response functions, as described by Mawdsley et al. (2016) <doi:10.1002/psp4.12091>. By modelling dose-response relationships this can connect networks of evidence that might otherwise be disconnected, and can improve precision on treatment estimates. Several common dose-response functions are provided; others may be added by the user. Various characteristics and assumptions can be flexibly added to the models, such as shared class effects. The consistency of direct and indirect evidence in the network can be assessed using unrelated mean effects models and/or by node-splitting at the treatment level.

Stand-alone HTTP capable R-package repository, that fully supports R's install.packages() and available.packages(). It also contains API endpoints for end-users to add/update packages. This package can supplement miniCRAN', which has functions for maintaining a local (partial) copy of CRAN'. Current version is bare-minimum without any access-control or much security.

Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.