This package provides a Bayesian framework for parameter inference in differential equations. This approach offers a rigorous methodology for parameter inference as well as modeling the link between unobservable model states and parameters, and observable quantities. Provides templates for the DE model, the observation model and data likelihood, and the model parameters and their prior distributions. A Markov chain Monte Carlo (MCMC) procedure processes these inputs to estimate the posterior distributions of the parameters and any derived quantities, including the model trajectories. Further functionality is provided to facilitate MCMC diagnostics and the visualisation of the posterior distributions of model parameters and trajectories.

This package provides methods to estimate dynamic treatment regimes using Interactive Q-Learning, Q-Learning, weighted learning, and value-search methods based on Augmented Inverse Probability Weighted Estimators and Inverse Probability Weighted Estimators. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine, Tsiatis, A. A., Davidian, M. D., Holloway, S. T., and Laber, E. B., Chapman & Hall/CRC Press, 2020, ISBN:978-1-4987-6977-8.

This package implements a Bayesian Optimal Phase II design (DTE-BOP2) for trials with delayed treatment effects, particularly relevant to immunotherapy studies where treatment benefits may emerge after a delay. The method builds upon the BOP2 framework and incorporates uncertainty in the delay timepoint through a truncated gamma prior, informed by expert knowledge or default settings. Supports two-arm trial designs with functionality for sample size determination, interim and final analyses, and comprehensive simulation under various delay and design scenarios. Ensures rigorous type I and II error control while improving trial efficiency and power when the delay effect is present. A manuscript describing the methodology is under development and will be formally referenced upon publication.

This package provides a Bayesian clustering method for replicated time series or replicated measurements from multiple experimental conditions, e.g., time-course gene expression data. It estimates the number of clusters directly from the data using a Dirichlet-process prior. See Fu, A. Q., Russell, S., Bray, S. and Tavare, S. (2013) Bayesian clustering of replicated time-course gene expression data with weak signals. The Annals of Applied Statistics. 7(3) 1334-1361. <doi:10.1214/13-AOAS650>.

This package provides novel dendroclimatological methods, primarily used by the Tree-ring research community. There are four core functions. The first one is daily_response(), which finds the optimal sequence of days that are related to one or more tree-ring proxy records. Similar function is daily_response_seascorr(), which implements partial correlations in the analysis of daily response functions. For the enthusiast of monthly data, there is monthly_response() function. The last core function is compare_methods(), which effectively compares several linear and nonlinear regression algorithms on the task of climate reconstruction.

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>.

Constructs dynamic optimal shrinkage estimators for the weights of the global minimum variance portfolio which are reconstructed at given reallocation points as derived in Bodnar, Parolya, and Thorsén (2021) (<arXiv:2106.02131>). Two dynamic shrinkage estimators are available in this package. One using overlapping samples while the other use nonoverlapping samples.

Implementation of different statistical tools for the description and analysis of gene expression data based on the concept of data depth, namely, the scale curves for visualizing the dispersion of one or various groups of samples (e.g. types of tumors), a rank test to decide whether two groups of samples come from a single distribution and two methods of supervised classification techniques, the DS and TAD methods. All these techniques are based on the Modified Band Depth, which is a recent notion of depth with a low computational cost, what renders it very appropriate for high dimensional data such as gene expression data.

Ecological Metadata Language or EML is a long-established format for describing ecological datasets to facilitate sharing and re-use. Because EML is effectively a modified xml schema, however, it is challenging to write and manipulate for non-expert users. delma supports users to write metadata statements in R Markdown or Quarto markdown format, and parse them to EML and (optionally) back again.

Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.

This package contains the normalizing and variance stabilizing Data-Driven Haar-Fisz algorithm. Also contains related algorithms for simulating from certain microarray gene intensity models and evaluation of certain transformations. Contains cDNA and shipping credit flow data.

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and DESeq2 (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools. These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping. Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data. They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods. Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation; thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed. Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation. These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.

This package provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.

Enhancing cross-language compatibility within the RStudio environment and supporting seamless language understanding, the deepRstudio package leverages the power of the DeepL API (see <https://www.deepl.com/docs-api>) to enable seamless, fast, accurate, and affordable translation of code comments, documents, and text. This package offers the ability to translate selected text into English (EN), as well as from English into various languages, namely Japanese (JA), Chinese (ZH), Spanish (ES), French (FR), Russian (RU), Portuguese (PT), and Indonesian (ID). With much of the text being written in English, the emphasis is on compatibility from English. It is also designed for developers working on multilingual projects and data analysts collaborating with international teams, simplifying the translation process and making code more accessible and comprehensible to people with diverse language backgrounds. This package uses the rstudioapi package and DeepL API, and is simply implemented, executed from addins or via shortcuts on RStudio'. With just a few steps, content can be translated between supported languages, promoting better collaboration and expanding the global reach of work. The functionality of this package works only on RStudio using rstudioapi'.

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.

An open, multi-algorithmic pipeline for easy, fast and efficient analysis of cellular sub-populations and the molecular signatures that characterize them. The pipeline consists of four successive steps: data pre-processing, cellular clustering with pseudo-temporal ordering, defining differential expressed genes and biomarker identification. More details on Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements extensions of the work published by Ghannoum et. al. (2019) <doi:10.1101/700989>.

This package provides functions for computing the density, distribution, and random generation of the Decision Diffusion model (DDM), a widely used cognitive model for analysing choice and response time data. The package allows model specification, including the ability to fix, constrain, or vary parameters across experimental conditions. While it does not include a built-in optimiser, it supports likelihood evaluation and can be integrated with external tools for parameter estimation. Functions for simulating synthetic datasets are also provided. This package is intended for researchers modelling speeded decision-making in behavioural and cognitive experiments. For more information, see Voss, Rothermund, and Voss (2004) <doi:10.3758/BF03196893>, Voss and Voss (2007) <doi:10.3758/BF03192967>, and Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>.

Mixed model analysis for quantitative genetics with multi-trait responses and pedigree-based partitioning of individual variation into a range of environmental and genetic variance components for individual and maternal effects. Method documented in dmmOverview.pdf; dmm is an implementation of dispersion mean model described by Searle et al. (1992) "Variance Components", Wiley, NY. Dmm() can do MINQUE', bias-corrected-ML', and REML variance and covariance component estimates.

Plots dependency logos from a set of aligned input sequences.

Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

Data quality assessments guided by a data quality framework introduced by Schmidt and colleagues, 2021 <doi:10.1186/s12874-021-01252-7> target the data quality dimensions integrity, completeness, consistency, and accuracy. The scope of applicable functions rests on the availability of extensive metadata which can be provided in spreadsheet tables. Either standardized (e.g. as html5 reports) or individually tailored reports can be generated. For an introduction into the specification of corresponding metadata, please refer to the package website <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.

Estimate and return the needed parameters for visualizations designed for OpenBudgets.eu <http://openbudgets.eu/> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the OpenBudgets.eu data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to other datasets, to extract visualization parameters, convert them to JSON format and use them as input in a different graphical interface.

Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).