Implementation of methodology designed to perform: (i) variable selection, (ii) effect estimation, and (iii) uncertainty quantification, for high-dimensional survival data. Our method uses a spike-and-slab prior with Laplace slab and Dirac spike and approximates the corresponding posterior using variational inference, a popular method in machine learning for scalable conditional inference. Although approximate, the variational posterior provides excellent point estimates and good control of the false discovery rate. For more information see Komodromos et al. (2021) <arXiv:2112.10270>.

Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.

The main functionalities of wrappedtools are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

RStudio is an integrated development environment (IDE) for the R programming language. Some of its features include: Customizable workbench with all of the tools required to work with R in one place (console, source, plots, workspace, help, history, etc.); syntax highlighting editor with code completion; execute code directly from the source editor (line, selection, or file); full support for authoring Sweave and TeX documents. RStudio can also be run as a server, enabling multiple users to access the RStudio IDE using a web browser.

GNU Emacs is an extensible and highly customizable text editor. It is based on an Emacs Lisp interpreter with extensions for text editing. Emacs has been extended in essentially all areas of computing, giving rise to a vast array of packages supporting, e.g., email, IRC and XMPP messaging, spreadsheets, remote server editing, and much more. Emacs includes extensive documentation on all aspects of the system, from basic editing to writing large Lisp programs. It has full Unicode support for nearly all human languages.

This package provides a re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>. With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems. In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.

Penalized variable selection tools for the Cox proportional hazards model with interval censored and possibly left truncated data. It performs variable selection via penalized nonparametric maximum likelihood estimation with an adaptive lasso penalty. The optimal thresholding parameter can be searched by the package based on the profile Bayesian information criterion (BIC). The asymptotic validity of the methodology is established in Li et al. (2019 <doi:10.1177/0962280219856238>). The unpenalized nonparametric maximum likelihood estimation for interval censored and possibly left truncated data is also available.

An implementation of the quantitative ethnobotany indices in R. The goal is to provide an easy-to-use platform for ethnobotanists to assess the cultural significance of plant species based on informant consensus. The package closely follows the paper by Tardio and Pardo-de-Santayana (2008). Tardio, J., and M. Pardo-de-Santayana, 2008. Cultural Importance Indices: A Comparative Analysis Based on the Useful Wild Plants of Southern Cantabria (Northern Spain) 1. Economic Botany, 62(1), 24-39. <doi:10.1007/s12231-007-9004-5>.

This package produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).

An open source platform for validation and process control. Tools to analyze data from internal validation of forensic short tandem repeat (STR) kits are provided. The tools are developed to provide the necessary data to conform with guidelines for internal validation issued by the European Network of Forensic Science Institutes (ENFSI) DNA Working Group, and the Scientific Working Group on DNA Analysis Methods (SWGDAM). A front-end graphical user interface is provided. More information about each function can be found in the respective help documentation.

This package extends the fitdistr function of the MASS package with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left-censored, right-censored and interval-censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.

Implementation in R of the alpha-shape of a finite set of points in the three-dimensional space. The alpha-shape generalizes the convex hull and allows to recover the shape of non-convex and even non-connected sets in 3D, given a random sample of points taken into it. Besides the computation of the alpha-shape, this package provides users with functions to compute the volume of the alpha-shape, identify the connected components and facilitate the three-dimensional graphical visualization of the estimated set.

This package provides a varied array of mathematical derivations from various titrimetric and colorimetric methods for analyzing water quality parameters were condensed and integrated for the better physicochemical analysis. It is indispensable for managing any aquatic ecosystem, including aquaculture facilities. By substituting titrant and spectrophotometric absorbance readings, accurate determination of the concentrations of critical parameters such as Dissolved Oxygen, Free Carbon Dioxide, Total Alkalinity, Water Hardness, Hydrogen Sulfide, Total Ammonia Nitrogen, Nitrite, Nitrate, Chlorinity, Salinity, Inorganic Phosphate, and Transparency can be facilitated APHA(2017,ISBN:9780875532875).

This package provides tools for processing, analyzing, and visualizing spectral data collected from 3D laser-based scanning systems. Supports applications in agriculture, forestry, environmental monitoring, industrial quality control, and biomedical research. Enables evaluation of plant growth, productivity, resource efficiency, disease management, and pest monitoring. Includes statistical methods for extracting insights from multispectral and hyperspectral data and generating publication-ready visualizations. See Zieschank & Junker (2023) <doi:10.3389/fpls.2023.1141554> and Saric et al. (2022) <doi:10.1016/J.TPLANTS.2021.12.003> for related work.

This package provides software to facilitate the design, testing, and operation of computer models. It focuses particularly on tools that make it easy to construct and edit a customized graphical user interface ('GUI'). Although our simplified GUI language depends heavily on the R interface to the Tcl/Tk package, a user does not need to know Tcl/Tk'. Examples illustrate models built with other R packages, including PBSmapping', PBSddesolve', and BRugs'. A complete user's guide PBSmodelling-UG.pdf shows how to use this package effectively.

This package provides a collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.

Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.

The geographic dimension plays a fundamental role in multidimensional systems. To define a geographic dimension in a star schema, we need a table with attributes corresponding to the levels of the dimension. Additionally, we will also need one or more geographic layers to represent the data using this dimension. The goal of this package is to support the definition of geographic dimensions from layers of geographic information related to each other. It makes it easy to define relationships between layers and obtain the necessary data from them.

This package provides a collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research. The package is described in Marin-Franch and Swanson (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].

Data screening is an important first step of any statistical analysis. dataReporter auto generates a customizable data report with a thorough summary of the checks and the results that a human can use to identify possible errors. It provides an extendable suite of test for common potential errors in a dataset. See Petersen AH, Ekstrøm CT (2019). "dataMaid: Your Assistant for Documenting Supervised Data Quality Screening in R." _Journal of Statistical Software_, *90*(6), 1-38 <doi:10.18637/jss.v090.i06> for more information.

This package provides a collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods. Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics. Supported by grant NSF DMS 1712839. See Kolassa and Seifu (2013) <doi:10.1016/j.acra.2013.03.006>.

This package provides a collection of functions to facilitate analysis of proteomic data from Olink, primarily NPX data that has been exported from Olink Software. The functions also work on QUANT data from Olink by log- transforming the QUANT data. The functions are focused on reading data, facilitating data wrangling and quality control analysis, performing statistical analysis and generating figures to visualize the results of the statistical analysis. The goal of this package is to help users extract biological insights from proteomic data run on the Olink platform.

This package provides functions to calculate power and sample size for testing main effect or interaction effect in the survival analysis of epidemiological studies (non-randomized studies), taking into account the correlation between the covariate of the interest and other covariates. Some calculations also take into account the competing risks and stratified analysis. This package also includes a set of functions to calculate power and sample size for testing main effect in the survival analysis of randomized clinical trials and conditional logistic regression for nested case-control study.

This package provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis. This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package.