This package provides a set of segregation-based indices and randomization methods to make robust environmental inequality assessments, as described in Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities: Insights from the Residential Segregation Literature" <doi:10.1016/j.ecolecon.2019.05.009>.

This package provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.

The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.

Access statistical information on welfare and health in Finland from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.

The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.

This package provides a set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.

This package provides functions to compute split generalized linear models. The approach fits generalized linear models that split the covariates into groups. The optimal split of the variables into groups and the regularized estimation of the coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. Example applications can be found in Christidis et al. (2021) <doi:10.48550/arXiv.2102.08591>.

This package provides functions for retrieving general and specific data from the Norwegian Parliament, through the Norwegian Parliament API at <https://data.stortinget.no>.

The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) <doi:10.48550/arXiv.2312.02110> and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) <doi:10.1198/jcgs.2009.08076> for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.

This package provides functions to enumerate and reference figures, tables and equations in R Markdown documents that do not support these features (thus not bookdown or quarto'. Supporting functions for using Sweave and Knitr with LyX'.

This package provides a tidy approach to spatial network analysis, in the form of classes and functions that enable a seamless interaction between the network analysis package tidygraph and the spatial analysis package sf'.

Facilitates secret management by storing credentials in a dedicated file, keeping them out of your code base. The secrets are stored without encryption. This package is compatible with secrets stored by the SecretsProvider Python package <https://pypi.org/project/SecretsProvider/>.

This package provides functions for the collection of 3D points and curves using a stereo camera setup.

Adds variable-selection functions for Beta regression models (both mean and phi submodels) so they can be used within the SelectBoost algorithm. Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, gamlss'-based LASSO/Elastic-Net, a pure glmnet iterative re-weighted least squares-based selector with an optional standardization speedup, and C++ helpers for iterative re-weighted least squares working steps and precision updates. Also provides a fastboost_interval() variant for interval responses, comparison helpers, and a flexible simulator simulation_DATA.beta() for interval-valued data. For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>.

This package provides functions to perform the Sequential Probability Ratio Test (SPRT) for hypothesis testing in Binomial, Poisson and Normal distributions. The package allows users to specify Type I and Type II error probabilities, decision thresholds, and compare null and alternative hypotheses sequentially as data accumulate. It includes visualization tools for plotting the likelihood ratio path and decision boundaries, making it easier to interpret results. The methods are based on Wald (1945) <doi:10.1214/aoms/1177731118>, who introduced the SPRT as one of the earliest and most powerful sequential analysis techniques. This package is useful in quality control, clinical trials, and other applications requiring early decision-making.The term SPRT is an abbreviation and used intentionally.

This package provides a sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate uncertainty about the sensitivity parameters and sampling variability. Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference. For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.

Ordinary and modified statistics for symmetrical linear regression models with small samples. The supported ordinary statistics include Wald, score, likelihood ratio and gradient. The modified statistics include score, likelihood ratio and gradient. Diagnostic tools associated with the fitted model are implemented. For more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.

Computes likelihood ratio test (LRT) p-values for free parameters in a structural equation model. Currently supports models fitted by the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>.

This package provides a suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)<https://github.com/Lujun995/DiSC_Replication_Code>.

This package provides a set of functions to support experimentation in the utility of partially synthetic data sets. All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.

This package implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.

It is often useful to produce short, quasi-unique identifiers (SQUIDs) without the benefit of a central authority to prevent duplication. Although Universally Unique Identifiers (UUIDs) provide for this, these are also unwieldy; for example, the most used UUID, version 4, is 36 characters long. SQUIDs are short (8 characters) at the expense of having more collisions, which can be mitigated by combining them with human-produced suffixes, yielding relatively brief, half human-readable, almost-unique identifiers (see for example the identifiers used for Decentralized Construct Taxonomies; Peters & Crutzen, 2024 <doi:10.15626/MP.2022.3638>). SQUIDs are the number of centiseconds elapsed since the beginning of 1970 converted to a base 30 system. This package contains functions to produce SQUIDs as well as convert them back into dates and times.

This package implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardà a M, López-Vizcaà no E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

This package provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.