This tool is for parsing public drug databases such as DrugBank XML database <https://go.drugbank.com/>. The parsed data are then returned in a proper R object called dvobject'.

This package creates the "table one" of bio-medical papers. Fill it with your data and the name of the variable which you'll make the group(s) out of and it will make univariate, bivariate analysis and parse it into HTML. It also allows you to visualize all your data with graphic representation.

Bayesian inference algorithms based on the population-based "differential evolution" (DE) algorithm. Users can obtain posterior mode (MAP) estimates via DEMAP, posterior samples via DEMCMC, and variational approximations via DEVI.

Explore data related to the Doctor Who TV series.

This package provides a small package containing helper utilities for creating functions for computing statistics.

Computes discrete fast Fourier transform of river discharge data and the derived metrics. The methods are described in J. L. Sabo, D. M. Post (2008) <doi:10.1890/06-1340.1> and J. L. Sabo, A. Ruhi, G. W. Holtgrieve, V. Elliott, M. E. Arias, P. B. Ngor, T. A. Räsänsen, S. Nam (2017) <doi:10.1126/science.aao1053>.

This package provides a toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.

All datasets and functions required for the examples and exercises of the book "Data Science for Psychologists" (by Hansjoerg Neth, Konstanz University, 2026, <doi:10.5281/zenodo.7229812>), freely available at <https://hneth-ds4psy.share.connect.posit.cloud/>. The book and corresponding courses introduce principles and methods of data science to students of psychology and other biological or social sciences. The ds4psy package primarily provides datasets, but also functions for data generation and manipulation (e.g., of text and time data) and graphics that are used in the book and its exercises. All functions included in ds4psy are designed to be explicit and instructive, rather than efficient or elegant.

The truncated factor model is a statistical model designed to handle specific data structures in data analysis. DTFM is a powerful tool designed to efficiently process and analyze distributed datasets. The philosophy of the package is described in Guo et al. (2023) <doi:10.1007/s00180-022-01270-z>.

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>.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.

Flexible and efficient cleaning of data with interactivity. datacleanr facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in R'.

This package provides a simple way of fitting detection functions to distance sampling data for both line and point transects. Adjustment term selection, left and right truncation as well as monotonicity constraints and binning are supported. Abundance and density estimates can also be calculated (via a Horvitz-Thompson-like estimator) if survey area information is provided. See Miller et al. (2019) <doi:10.18637/jss.v089.i01> for more information on methods and <https://distancesampling.org/resources/vignettes.html> for example analyses.

Employ time-calibrated phylogenies and trait/range data to test for differences in diversification rates over evolutionary time. Extend the STRAPP test from BAMMtools::traitDependentBAMM() to any time step along phylogenies. See inst/COPYRIGHTS for details on third-party code.

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Testing and documenting code that communicates with remote databases can be painful. Although the interaction with R is usually relatively simple (e.g. data(frames) passed to and from a database), because they rely on a separate service and the data there, testing them can be difficult to set up, unsustainable in a continuous integration environment, or impossible without replicating an entire production cluster. This package addresses that by allowing you to make recordings from your database interactions and then play them back while testing (or in other contexts) all without needing to spin up or have access to the database your code would typically connect to.

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.

This package contains the support functions for the Time Series Analysis book. We present a function to calculate MSE and MAE for inputs of actual and forecast values. We also have the code for disaggregation as found in Wei and Stram (1990, <doi:10.1111/j.2517-6161.1990.tb01799.x>), and Hodgess and Wei (1996, "Temporal Disaggregation of Time Series").

Draw, manipulate, and evaluate directed acyclic graphs and simulate corresponding data, as described in International Journal of Epidemiology 50(6):1772-1777.

Draws stylized choropleth maps -- hexagonal maps and triangular multiclass hex maps -- for New Zealand District Health Boards and Regional Council areas. These allow faceted, coloured displays of quantitative information for comparison across District Health Boards or Regional Councils. The preprint Lumley (2019) <arXiv:1912.04435> is based on the methods in this package.

It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

This package provides a collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) <doi:10.1214/aos/1024691468>, Qiu, P. and Yandell, B. (1997) <doi: 10.1080/10618600.1997.10474746>, Qiu, P. (2009) <doi: 10.1007/s10463-007-0166-9>, Kang, Y. and Qiu, P. (2014) <doi: 10.1080/00401706.2013.844732>, Qiu, P. and Kang, Y. (2015) <doi: 10.5705/ss.2014.054>, Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) <doi: 10.1080/00401706.2017.1415975>, Kang, Y. (2020) <doi: 10.1080/10618600.2019.1665536>.

The new (dQTG.seq1 and dQTG.seq2) and existing (SmoothLOD, G', deltaSNP and ED) bulked segregant analysis methods are used to identify various types of quantitative trait loci for complex traits via extreme phenotype individuals in bi-parental segregation populations (F2, backcross, doubled haploid and recombinant inbred line). The numbers of marker alleles in extreme low and high pools are used in existing methods to identify trait-related genes, while the numbers of marker alleles and genotypes in extreme low and high pools are used in the new methods to construct a new statistic Gw for identifying trait-related genes. dQTG-seq2 is feasible to identify extremely over-dominant and small-effect genes in F2. Li P, Li G, Zhang YW, Zuo JF, Liu JY, Zhang YM (2022, <doi: 10.1016/j.xplc.2022.100319>).

Discrete splines are a class of univariate piecewise polynomial functions which are analogous to splines, but whose smoothness is defined via divided differences rather than derivatives. Tools for efficient computations relating to discrete splines are provided here. These tools include discrete differentiation and integration, various matrix computations with discrete derivative or discrete spline bases matrices, and interpolation within discrete spline spaces. These techniques are described in Tibshirani (2020) <doi:10.48550/arXiv.2003.03886>.