Streamlines analysis of qualitative data exported from Dedoose <https://www.dedoose.com>. Supports monitoring thematic saturation, calculating code frequencies, organizing excerpts, generating dynamic codebooks, and producing code network maps within R'.

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.

Provide tools for drought monitoring based on univariate and multivariate drought indicators.Statistical drought prediction based on Ensemble Streamflow Prediction (ESP), drought risk assessments, and drought propagation are also provided. Please see Hao Zengchao et al. (2017) <doi:10.1016/j.envsoft.2017.02.008>.

In tumor tissue, underlying genomic instability can lead to DNA copy number alterations, e.g., copy number gains or losses. Sporadic copy number alterations occur randomly throughout the genome, whereas recurrent alterations are observed in the same genomic region across multiple independent samples, perhaps because they provide a selective growth advantage. This package implements the DiNAMIC procedure for assessing the statistical significance of recurrent DNA copy number aberrations (Bioinformatics (2011) 27(5) 678 - 685).

Dose Titration Algorithm Tuning (DTAT) is a methodologic framework allowing dose individualization to be conceived as a continuous learning process that begins in early-phase clinical trials and continues throughout drug development, on into clinical practice. This package includes code that researchers may use to reproduce or extend key results of the DTAT research programme, plus tools for trialists to design and simulate a 3+3/PC dose-finding study. Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and Norris (2017c) <doi:10.1101/240846>.

This package provides a wrapper for the ZEIT ONLINE Content API, available at <http://developer.zeit.de>. diezeit gives access to articles and corresponding metadata from the ZEIT archive and from ZEIT ONLINE. A personal API key is required for usage.

Collects libphonenumber jars required for the dialr package.

Density estimation for possibly large data sets and conditional/unconditional random number generation or bootstrapping with distribution element trees. The function det.construct translates a dataset into a distribution element tree. To evaluate the probability density based on a previously computed tree at arbitrary query points, the function det.query is available. The functions det1 and det2 provide density estimation and plotting for one- and two-dimensional datasets. Conditional/unconditional smooth bootstrapping from an available distribution element tree can be performed by det.rnd'. For more details on distribution element trees, see: Meyer, D.W. (2016) <arXiv:1610.00345> or Meyer, D.W., Statistics and Computing (2017) <doi:10.1007/s11222-017-9751-9> and Meyer, D.W. (2017) <arXiv:1711.04632> or Meyer, D.W., Journal of Computational and Graphical Statistics (2018) <doi:10.1080/10618600.2018.1482768>.

Automatic differentiation is achieved by using dual numbers without providing hand-coded gradient functions. The output value of a mathematical function is returned with the values of its exact first derivative (or gradient). For more details see Baydin, Pearlmutter, Radul, and Siskind (2018) <https://jmlr.org/papers/volume18/17-468/17-468.pdf>.

Demonstration code showing how (univariate) kernel density estimates are computed, at least conceptually, and allowing users to experiment with different kernels, should they so wish. The method used follows directly the definition, but gains efficiency by replacing the observations by frequencies in a very fine grid covering the sample range. A canonical reference is B. W. Silverman, (1998) <doi: 10.1201/9781315140919>. NOTE: the density function in the stats package uses a more sophisticated method based on the fast Fourier transform and that function should be used if computational efficiency is a prime consideration.

This package provides a systematic biology tool was developed to repurpose drugs via a subpathway crosstalk network. The operation modes include 1) calculating centrality scores of SPs in the context of gene expression data to reflect the influence of SP crosstalk, 2) evaluating drug-disease reverse association based on disease- and drug-induced SPs weighted by the SP crosstalk, 3) identifying cancer candidate drugs through perturbation analysis. There are also several functions used to visualize the results.

Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>.

You can load a schema from a DTR (data type registry) as an R object. Use this schema to write your data in JSON-LD (JavaScript Object Notation for Linked Data) format to make it machine readable.

Parse, format, and validate international phone numbers using Google's libphonenumber java library, <https://github.com/google/libphonenumber>.

This package provides functionality for users who are learning R or the techniques of data analysis. Written as a collection of wrapper functions, the DTwrapper package facilitates many core operations of data processing. This is achieved with relatively few requirements about the order of the processing steps or knowledge of specialized syntax. DTwrappers creates coding results along with translations to data.table's code. This enables users to benefit from the speed and efficiency of data.table's calculations. Furthermore, the package also provides the translated code for educational purposes so that users can review working examples of coding syntax and calculations.

Alpha and beta diversity for taxonomic (TD), functional (FD), and phylogenetic (PD) dimensions based on rasters. Spatial and temporal beta diversity can be partitioned into replacement and richness difference components. It also calculates standardized effect size for FD and PD alpha diversity and the average individual traits across multilayer rasters. The layers of the raster represent species, while the cells represent communities. Methods details can be found at Cardoso et al. 2022 <https://CRAN.R-project.org/package=BAT> and Heming et al. 2023 <https://CRAN.R-project.org/package=SESraster>.

This package provides database-backed spatial geometry classes and methods for working with vector spatial data in DuckDB'. The package supports loading, converting, querying, joining, and measuring spatial geometries through familiar sf'-style interfaces while keeping geometry columns lazy inside the database. It integrates with dbProject to preserve database paths, live connections, and spatial table metadata across interactive sessions. The package follows the Simple Features framework described by Pebesma (2018) <doi:10.32614/RJ-2018-009> and uses DuckDB's spatial extension <https://duckdb.org/docs/stable/core_extensions/spatial/overview.html>.

Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al. (2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified.

This package provides functions for demographic analysis including lifetable calculations; Lee-Carter modelling; functional data analysis of mortality rates, fertility rates, net migration numbers; and stochastic population forecasting.

This package provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at <https://www.openarchives.org/ore/>. Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at <https://datatracker.ietf.org/doc/html/draft-kunze-bagit-08>.

Decorrelates a set of summary statistics (i.e., Z-scores or P-values per SNP) via Decorrelation by Orthogonal Transformation (DOT) approach and performs gene-set analyses by combining transformed statistic values; operations are performed with algorithms that rely only on the association summary results and the linkage disequilibrium (LD). For more details on DOT and its power, see Olga (2020) <doi:10.1371/journal.pcbi.1007819>.

This package contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086> (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, the heterogeneity test, the marginal-change test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>, and the strength test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns.

This package provides a collection of functions for calculating the M2 model fit statistic for diagnostic classification models as described by Liu et al. (2016) <DOI:10.3102/1076998615621293>. These functions provide multiple sources of information for model fit according to the M2 statistic, including the M2 statistic, the *p* value for that M2 statistic, and the Root Mean Square Error of Approximation based on the M2 statistic.