Raw and processed versions of the data from De Cock (2011) <http://ww2.amstat.org/publications/jse> are included in the package.

Developed for Computing the probability density function, cumulative distribution function, random generation, estimating the parameters of asymmetric exponential power distribution, and robust regression analysis with error term that follows asymmetric exponential power distribution. The asymmetric exponential power distribution studied here is a special case of that introduced by Dongming and Zinde-Walsh (2009) <doi:10.1016/j.jeconom.2008.09.038>.

Several cubic spline interpolation methods of H. Akima for irregular and regular gridded data are available through this package, both for the bivariate case (irregular data: ACM 761, regular data: ACM 760) and univariate case (ACM 433 and ACM 697). Linear interpolation of irregular gridded data is also covered by reusing D. J. Renkas triangulation code which is part of Akimas Fortran code. A bilinear interpolator for regular grids was also added for comparison with the bicubic interpolator on regular grids. Please note that most of the functions are now also covered in package interp, which is a re-implementation from scratch under a free license.

This package implements the Arvind distribution and five novel stochastic regression models that replace the traditional Gaussian error assumption with Arvind'-distributed errors. The Arvind distribution is a flexible single-parameter continuous distribution on the positive real line characterised by a polynomial numerator with Gaussian-type decay. The package provides complete distribution functions (darvind(), parvind(), qarvind(), rarvind()), maximum likelihood estimation via fit_arvind_mle(), and five model-fitting routines: Random Walk on Coefficients via fit_rw1(), Time-Varying Coefficient Linear Model via fit_tvlm(), Simulation-Extrapolation via fit_simex(), Mixed-Effects Regression via fit_mixed(), and Regime-Switching Hidden Markov Model via fit_hmm(). Additionally provides Monte Carlo forecasting with prediction intervals via forecast_arvind(), comprehensive goodness-of-fit diagnostics (21 metrics and 25 plots) via diagnostics_arvind() and plot_arvind(), k-fold and rolling-window cross-validation via cv_arvind(), and unified model comparison via summary_arvind(). For more details see Pandey, Singh, Tyagi, and Tyagi (2024) "Modelling climate, COVID-19, and reliability data: A new continuous lifetime model under different methods of estimation", Statistics and Applications, 22(2), <https://ssca.org.in/journal.html>.

Flat text files provide a robust, compressible, and portable way to store tables from databases. This package provides convenient functions for exporting tables from relational database connections into compressed text files and streaming those text files back into a database without requiring the whole table to fit in working memory.

This software solves an Advection Bi-Flux Diffusive Problem using the Finite Difference Method FDM. Vasconcellos, J.F.V., Marinho, G.M., Zanni, J.H., 2016, Numerical analysis of an anomalous diffusion with a bimodal flux distribution. <doi:10.1016/j.rimni.2016.05.001>. Silva, L.G., Knupp, D.C., Bevilacqua, L., Galeao, A.C.N.R., Silva Neto, A.J., 2014, Formulation and solution of an Inverse Anomalous Diffusion Problem with Stochastic Techniques. <doi:10.5902/2179460X13184>. In this version, it is possible to include a source as a function depending on space and time, that is, s(x,t).

Increasingly powerful techniques for high-throughput sequencing open the possibility to comprehensively characterize microbial communities, including rare species. However, a still unresolved issue are the substantial error rates in the experimental process generating these sequences. To overcome these limitations we propose an approach, where each sample is split and the same amplification and sequencing protocol is applied to both halves. This procedure should allow to detect likely PCR and sequencing artifacts, and true rare species by comparison of the results of both parts. The AmpliconDuo package, whereas amplicon duo from here on refers to the two amplicon data sets of a split sample, is intended to help interpret the obtained read frequency distribution across split samples, and to filter the false positive reads.

Automated methods to assemble population PK (pharmacokinetic) and PKPD (pharmacodynamic) datasets for analysis in NONMEM (non-linear mixed effects modeling) by Bauer (2019) <doi:10.1002/psp4.12404>. The package includes functions to build datasets from SDTM (study data tabulation module) <https://www.cdisc.org/standards/foundational/sdtm>, ADaM (analysis dataset module) <https://www.cdisc.org/standards/foundational/adam>, or other dataset formats. The package will combine population datasets, add covariates, and create documentation to support regulatory submission and internal communication.

This package provides three stability-validated pipelines for computing an Aggregated Latent Space Index (ALSI): a binary MCA pipeline (alsi_workflow()), an ordinal pipeline using homals alternating least squares optimal scaling (alsi_workflow_ordinal()), and a continuous ipsatized SVD pipeline (calsi_workflow()). All three pipelines share a common bootstrap dual-criterion stability framework (principal angles and Tucker congruence phi) for determining the number of dimensions to retain before index construction. The package is designed to complement Segmented Profile Analysis (SEPA) and is intended for psychometric scale construction and dimensional reduction in survey and clinical research.

Collect your data on digital marketing campaigns from Apple Search Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

Dilate, permute, project, reflect, rotate, shear, and translate 2D and 3D points. Supports parallel projections including oblique projections such as the cabinet projection as well as axonometric projections such as the isometric projection. Use grid's "affine transformation" feature to render illustrated flat surfaces.

Geographic, use, and property related data on airports.

An implementation of the additive polynomial (AP) design matrix. It constructs and appends an AP design matrix to a data frame for use with longitudinal data subject to seasonality.

This package provides nine computational algorithms for dimensionality reduction via Principal Component Analysis (PCA), built using an object-oriented (S3) architecture. The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) <doi:10.1007/BF02288367>, Power Iteration based on Hotelling (1933) <doi:10.1037/h0071325>, QR Algorithm based on Francis (1961) <doi:10.1093/comjnl/4.3.265>, Jacobi Algorithm based on Jacobi (1846) <doi:10.1515/crll.1846.30.51>, Arnoldi Iteration based on Arnoldi (1951) <doi:10.1090/qam/42792>, NIPALS based on Wold (1975) <doi:10.1017/S0021900200047604>, Alternating Least Squares (ALS) based on Kolda and Bader (2009) <doi:10.1137/07070111X>, Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) <doi:10.1111/1467-9868.00196>, and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) <doi:10.1016/0893-6080(89)90044-0>.

Auto-GO is a framework that enables automated, high quality Gene Ontology enrichment analysis visualizations. It also features a handy wrapper for Differential Expression analysis around the DESeq2 package described in Love et al. (2014) <doi:10.1186/s13059-014-0550-8>. The whole framework is structured in different, independent functions, in order to let the user decide which steps of the analysis to perform and which plot to produce.

Fast processing of ArcGIS FeatureCollection protocol buffers in R. It is designed to work seamlessly with httr2 and integrates with sf'.

This package provides functions to simulate data sets from hierarchical ecological models, including all the simulations described in the two volume publication Applied Hierarchical Modeling in Ecology: Analysis of distribution, abundance and species richness in R and BUGS by Marc Kéry and Andy Royle: volume 1 (2016, ISBN: 978-0-12-801378-6) and volume 2 (2021, ISBN: 978-0-12-809585-0), <https://www.mbr-pwrc.usgs.gov/pubanalysis/keryroylebook/>. It also has all the utility functions and data sets needed to replicate the analyses shown in the books.

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) <doi:10.1080/00401706.2017.1345701>. For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes LatticeKrig package.

Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. accrualPlot provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.

Visual exploration and presentation of networks should not be difficult. This package includes functions for plotting networks and network-related metrics with sensible and pretty defaults. It includes ggplot2'-based plot methods for many popular network package classes. It also includes some novel layout algorithms, and options for straightforward, consistent themes.

Getting and parsing data of location geocode/reverse-geocode and administrative regions from AutoNavi Maps'<https://lbs.amap.com/api/webservice/summary> API.

Government Analysis Function recommended colours for use in charts on gov.uk to help meet accessibility guidance.

Helps enable adaptive management by codifying knowledge in the form of models generated from numerous analyses and data sets. Facilitates this process by storing all models and data sets in a single object that can be updated and saved, thus tracking changes in knowledge through time. A shiny application called AM Model Manager (modelMgr()) enables the use of these functions via a GUI.

This package provides an S3 class to represent graph adjacency lists using vctrs'. Allows for creation, subsetting, combining, and pretty printing of these lists. Adjacency lists can be easily converted to zero-indexed lists, which allows for easy passing of objects to low-level languages for processing.