Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.

This package provides function, wget_set(), to change the method (default to wget -c') using in download.file(). Using wget -c allowing continued downloading, which is especially useful for slow internet connection and for downloading large files. User can run wget_unset() to restore previous setting.

This package implements a method to analyze single-cell RNA-seq data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions.

The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets.

This package implements the differential equations associated to different versions of Allometric Trophic Models (ATN) to estimate the temporal dynamics of species biomasses in food webs. It offers several features to generate synthetic food webs and to parametrise models as well as a wrapper to the ODE solver deSolve.

Whole-genome regression methods on Bayesian framework fitted via EM or Gibbs sampling, single step (<doi:10.1534/g3.119.400728>), univariate and multivariate (<doi:10.1186/s12711-022-00730-w>, <doi:10.1093/genetics/iyae179>), with optional kernel term and sampling techniques (<doi:10.1186/s12859-017-1582-3>).

Support for import from and export to the CSVY file format. CSVY is a file format that combines the simplicity of CSV (comma-separated values) with the metadata of other plain text and binary formats (JSON, XML, Stata, etc.) by placing a YAML header on top of a regular CSV.

This package provides methods for fitting and inspection of Bayesian Multinomial Logistic Normal Models using MAP estimation and Laplace Approximation as developed in Silverman et. Al. (2022) <https://www.jmlr.org/papers/v23/19-882.html>. Key functionality is implemented in C++ for scalability. fido replaces the previous package stray'.

Social Relations Analysis with roles ("Family SRM") are computed, using a structural equation modeling approach. Groups ranging from three members up to an unlimited number of members are supported and the mean structure can be computed. Means and variances can be compared between different groups of families and between roles.

This package provides GPU enabled functions for R objects in a simple and approachable manner. New gpu* and vcl* classes have been provided to wrap typical R objects (e.g. vector, matrix), in both host and device spaces, to mirror typical R syntax without the need to know OpenCL'.

Linear regression when covariates include missing values by embedding the correlation information between covariates. Especially for block missing data, it works well. ILSE conducts imputation and regression simultaneously and iteratively. More details can be referred to Huazhen Lin, Wei Liu and Wei Lan. (2021) <doi:10.1080/07350015.2019.1635486>.

This is an Automatic Item Generator for Psychological Assessment. Items created with the IMak package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.

Apply the marginal classification method to achieve the purpose of providing the point and interval estimates for the minimal clinically important difference based on the classical anchor-based method. For more details of the methodology, please see Zehua Zhou, Leslie J. Bisson and Jiwei Zhao (2021) <arXiv:2108.11589>.

Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data (Lee et al., Biometrics, 2017 <doi:10.1111/biom.12557>) and zero-inflated count data (Lee et al., Biostatistics, 2020 <doi:10.1093/biostatistics/kxy067>).

Specification and estimation of multinomial logit models. Large datasets and complex models are supported, with an intuitive syntax. Multinomial Logit Models, Mixed models, random coefficients and Hybrid Choice are all supported. For more information, see Molloy et al. (2021) <https://www.research-collection.ethz.ch/handle/20.500.11850/477416>.

This package implements methods introduced in Chen, Christensen, and Kankanala (2024) <doi:10.1093/restud/rdae025> for estimating and constructing uniform confidence bands for nonparametric structural functions using instrumental variables, including data-driven choice of tuning parameters. All methods in this package apply to nonparametric regression as a special case.

Segmentation of short text sequences - like hashtags - into the separated words sequence, done with the use of dictionary, which may be built on custom corpus of texts. Unigram dictionary is used to find most probable sequence, and n-grams approach is used to determine possible segmentation given the text corpus.

This package implements recursive construction methods for balanced incomplete block designs (BIBDs), their second generation, resolvable BIBDs (RBIBDs), and uniform designs (UDs) derived from projective geometries over GF(2). It enables extraction of nested structures in multiple stages and supports recursive resolution processes, as introduced in Boudraa et al. (2013).

This package implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2308.04325>, and allows for the statistical modeling of multi-group rank data in combination with object variables. The package also allows for the simulation of synthetic multi-group rank data.

Computes scores of outlyingness for data sets consisting of nominal variables and includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness. The scores of nominal outlyingness are computed based on the framework of Costa and Papatsouma (2025) <doi:10.48550/arXiv.2408.07463>.

The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays.

This package contains three functions that query AuriQ Systems Essentia Database and return the results in R. essQuery takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. read.essentia takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. capture.essentia takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.

This package contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure.

Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with multcomp', SimComp', nparcomp', or MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.