Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.

Estimation of two-state (survival) models and irreversible illness- death models with possibly interval-censored, left-truncated and right-censored data. Proportional intensities regression models can be specified to allow for covariates effects separately for each transition. We use either a parametric approach with Weibull baseline intensities or a semi-parametric approach with M-splines approximation of baseline intensities in order to obtain smooth estimates of the hazard functions. Parameter estimates are obtained by maximum likelihood in the parametric approach and by penalized maximum likelihood in the semi-parametric approach.

Inspired by space-time regressions often performed to assess the expansion of the Neolithic from the Near East to Europe (Pinhasi et al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant correlations between the (earliest) radiocarbon dates of archaeological sites and their respective distances from a hypothetical center of origin. Both ordinary least squares (OLS) and reduced major axis (RMA) methods are supported (Russell et al. 2014 <doi:10.1371/journal.pone.0087854>). It is also possible to iterate over many sites to identify the most likely origin.

Streamlines geographic data transformation, storage and publication, simplifying data preparation and enhancing interoperability across formats and platforms.

Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.

Connecting to databases requires boilerplate code to specify connection parameters and to set up sessions properly with the DBMS. This package provides a simple tool to fill two purposes: abstracting connection details, including secret credentials, out of your source code and managing configuration for frequently-used database connections in a persistent and flexible way, while minimizing requirements on the runtime environment.

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>.

Send syslog protocol messages to a remote syslog server specified by host name and TCP network port.

Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim <https://www.worldclim.org/>,, ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and MARSPEC <http://www.marspec.org/>.

Use the R console as an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.

Computes multivariate normal (MVN) densities, and samples from MVN distributions, when the covariance or precision matrix is sparse.

Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems relative operational supremacy.

This package provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The rjags package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean.

Geostatistical modeling and kriging with gridded data using spatially separable covariance functions (Kronecker covariances). Kronecker products in these models provide shortcuts for solving large matrix problems in likelihood and conditional mean, making snapKrig computationally efficient with large grids. The package supplies its own S3 grid object class, and a host of methods including plot, print, Ops, square bracket replace/assign, and more. Our computational methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.

The goal of the SwimmeR package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end SwimmeR allows results to be read in from .html sources, like Hy-Tek real time results pages, .pdf files, ISL results, Omega results, and (on a development basis) .hy3 files. Once read in, SwimmeR can convert swimming times (performances) between the computationally useful format of seconds reported to the 100ths place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. SwimmeR can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', SCM', SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.

This package provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species. (i) Age class determination. (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH). <https://github.com/forestscientist/StemAnalysis>.

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) <doi:10.2136/sssaj1992.03615995005600020005x>. It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) <doi:10.2136/sssaj1992.03615995005600050012x>.

This package provides a Shiny app including the Monaco editor. The Monaco editor is the code editor which powers VS Code'. It is particularly well developed for JavaScript'. In addition to the Monaco editor features, the app provides prettifiers and minifiers for multiple languages, SCSS and TypeScript compilers, code checking for C and C++ (requires cppcheck').

This package provides the hyphenation algorithm used for TeX'/'LaTeX and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the koRpus package, but later released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).

Transfers/imputes statistics among Spanish spatial polygons (census sections or postal code areas) from different moments in time (2001-2023) without need of spatial files, just linking statistics to the ID codes of the spatial units. The data available in the census sections of a partition/division (cartography) into force in a moment of time is transferred to the census sections of another partition/division employing the geometric approach (also known as areal weighting or polygon overlay). References: Goerlich (2022) <doi:10.12842/WPIVIE_0322>. Pavà a and Cantarino (2017a, b) <doi:10.1111/gean.12112>, <doi:10.1016/j.apgeog.2017.06.021>. Pérez and Pavà a (2024a, b) <doi:10.4995/CARMA2024.2024.17796>, <doi:10.38191/iirr-jorr.24.057>. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACIO/2023/031), Consellerà a de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Economà a e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre for supporting this research.

It is a toolbox for Sequential Probability Ratio Tests (SPRT), Wald (1945) <doi:10.2134/agronj1947.00021962003900070011x>. SPRTs are applied to the data during the sampling process, ideally after each observation. At any stage, the test will return a decision to either continue sampling or terminate and accept one of the specified hypotheses. The seq_ttest() function performs one-sample, two-sample, and paired t-tests for testing one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>). The seq_anova() function allows to perform a sequential one-way fixed effects ANOVA (Steinhilber et al. (2023) <doi:10.31234/osf.io/m64ne>). Learn more about the package by using vignettes "browseVignettes(package = "sprtt")" or go to the website <https://meikesteinhilber.github.io/sprtt/>.