Use leaf physiognomic methods to reconstruct mean annual temperature (MAT), mean annual precipitation (MAP), and leaf dry mass per area (Ma), along with other useful quantitative leaf traits. Methods in this package described in Lowe et al. (in review).

Traditional phasing programs are limited to diploid organisms. Our method modifies Li and Stephens algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haplotype searches in a multiple infection setting. This package is primarily developed as part of the Pf3k project, which is a global collaboration using the latest sequencing technologies to provide a high-resolution view of natural variation in the malaria parasite Plasmodium falciparum. Parasite DNA are extracted from patient blood sample, which often contains more than one parasite strain, with unknown proportions. This package is used for deconvoluting mixed haplotypes, and reporting the mixture proportions from each sample.

Efficient Global Optimization (EGO) algorithm as described in "Roustant et al. (2012)" <doi:10.18637/jss.v051.i01> and adaptations for problems with noise ("Picheny and Ginsbourger, 2012") <doi:10.1016/j.csda.2013.03.018>, parallel infill, and problems with constraints.

Interactively train neural networks on Numerai, <https://numer.ai/>, data. Generate tournament predictions and write them to a CSV.

Extends package distr by functionals, distances, and conditional distributions.

Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.

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>).

Detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or an estimating equation approach (Li et al., 2025, <doi:10.1175/JCLI-D-24-0193.1>; Ma et al., 2023, <doi:10.1175/JCLI-D-22-0681.1>). Provides shrinkage estimators for the covariance matrix following Ledoit and Wolf (2004, <doi:10.1016/S0047-259X(03)00096-4>) and Ledoit and Wolf (2017, <doi:10.2139/ssrn.2383361>).

Simple helpers for GDAL data source names ('DSN'), prefix and suffix and other handling. GDAL is the Geospatial Data Abstraction Library <https://gdal.org/>, not used by this package directly.

Discovers small binary-classification biomarker panels from count or expression matrices while prioritizing detectability, expression stability, and univariate discrimination. Candidate filtering and panel selection can be repeated inside nested cross-validation to reduce information leakage. The package provides shared resampling splits, exhaustive small-panel search, logistic model fitting with an automatic ridge fallback for unstable separation-prone fits, out-of-fold evaluation, selection-frequency summaries, and optional DESeq2 differential-expression support. The nested model-selection workflow follows Varma and Simon (2006) <doi:10.1186/1471-2105-7-91>, and the optional differential-expression analysis uses Love, Huber, and Anders (2014) <doi:10.1186/s13059-014-0550-8>.

This package provides programmatic access to the Dark Sky API <https://darksky.net/dev/docs>, which provides current or historical global weather conditions.

This package provides the ability to display something analogous to Python's docstrings within R. By allowing the user to document their functions as comments at the beginning of their function without requiring putting the function into a package we allow more users to easily provide documentation for their functions. The documentation can be viewed just like any other help files for functions provided by packages as well.

This package performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones. See Bodhisattva Sen and Mary C Meyer (2016) <doi:10.1111/rssb.12178> for more details.

Perform model selection using distribution and probability-based methods, including standardized AIC, BIC, and AICc. These standardized information criteria allow one to perform model selection in a way similar to the prevalent "Rule of 2" method, but formalize the method to rely on probability theory. A novel goodness-of-fit procedure for assessing linear regression models is also available. This test relies on theoretical properties of the estimated error variance for a normal linear regression model, and employs a bootstrap procedure to assess the null hypothesis that the fitted model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023) <arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available.

Includes functions that researchers or practitioners may use to clean raw data, transferring html, xlsx, txt data file into other formats. And it also can be used to manipulate text variables, extract numeric variables from text variables and other variable cleaning processes. It is originated from a author's project which focuses on creative performance in online education environment. The resulting paper of that study will be published soon.

Adopts the general least squares-based data-driven normalization strategy developed by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250> to correct for technical variance in gene expression data generated via digital polymerase chain reaction (dPCR). Performs normalization of raw copy numbers and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

This package provides a suite of functions for analyzing and visualizing the health economic outputs of mathematical models. This package was developed with funding from the National Institutes of Allergy and Infectious Diseases of the National Institutes of Health under award no. R01AI138783. The content of this package is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The theoretical underpinnings of dampack''s functionality are detailed in Hunink et al. (2014) <doi:10.1017/CBO9781139506779>.

This package provides high-level functions to compute estimates (means, proportions, totals, ratios and quantiles) for complex survey designs, with automatic classification of statistical reliability, between-year significance tests and standardised report generation in Excel format (via openxlsx'). It was developed for the Social Observatory ('Observatorio Social') of the Chilean Ministry of Social Development and implements its data-quality criteria, but it can be applied to any complex-survey design (for example the Chilean CASEN household survey, included as example data). The reliability criteria follow Division Observatorio Social (2023) <https://bidat.gob.cl/details/ficha/dato/manual-para-la-investigacion-casen-2022> and Instituto Nacional de Estadisticas (2020) <https://www.ine.gob.cl/inicio/documentos-de-trabajo/documento/fundamentos-del-est%C3%A1ndar-para-la-evaluaci%C3%B3n-de-la-calidad-de-las-estimaciones-en-encuestas-de-hogares>; complex-survey estimation methods follow Lumley (2010, ISBN:9780470284308).

Modeling the zero coupon yield curve using the dynamic De Rezende and Ferreira (2011) <doi:10.1002/for.1256> five factor model with variable or fixed decaying parameters. For explanatory purposes, the package also includes various short datasets of interest rates for the BRICS countries.

This package provides tools for fitting parametric mortality curves. Implements multiple optimisation strategies to enhance robustness and stability of parameter estimation. Offers tools for forecasting mortality rates guided by mortality curves. For modelling details see: Tabeau (2001) <doi:10.1007/0-306-47562-6_1>, Renshaw and Haberman (2006) <doi:10.1016/j.insmatheco.2005.12.001>, Cairns et al. (2009) <doi:10.1080/10920277.2009.10597538>, Li and Lee (2005) <doi: 10.1353/dem.2005.0021>.

An interface to Docling', a document-understanding library that converts PDF', DOCX', PPTX', HTML and image documents into structured, AI-ready data. The package wraps the Docling Python package through reticulate to extract layout-aware text, tables and metadata, export to Markdown or JSON', and split documents into context-rich chunks suitable for retrieval-augmented generation (RAG) and embedding pipelines.

Detects regions of differential abundance in single-cell transcriptomic data by applying a pre-trained neural network model to the labels of each cell's nearest neighbours. Tests for both local and global differential abundance, controlling the false discovery rate with the Benjamini-Yekutieli procedure. The method is described in Hall and Castellano (2023) <doi:10.1101/2023.05.05.539427>.

This package provides the user with an interactive application which can be used to facilitate the planning of dose finding studies by applying the theory of optimal experimental design.

Creating, optimizing and refining data nuggets. Data nuggets reduce a large dataset into a small collection of nuggets of data, each containing a center (location), weight (importance), and scale (variability) parameter. Data nugget centers are selected based on a space-filling maximum-entropy scheme. Data nugget weights are created by counting the number observations closest to a given data nugget center. We then say the data nugget contains these observations and the data nugget center is recalculated as the mean of these observations. Data nugget scales are created by calculating the trace of the covariance matrix of the observations contained within a data nugget divided by the dimension of the dataset. The optimal number of data nuggets is determined data-driven based on the relative second-order differences of propensity score indices. Data nuggets are refined by splitting data nuggets which have high scales or elongated shapes (defined as the ratio of the two largest eigenvalues of the covariance matrix of the observations contained within the data nugget).