Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.

This package provides a helper function, to bulk read SQL code from separate files and load it into an R list, where the list elements contain the individual statements and queries as strings. This works by annotating the SQL code with a name comment, which also will be the name of the list element.

Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) <https://codar.com/images/about/patents/05990834.PDF>; CODAR Ocean Sensors (2002) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/Informative/FirstOrder_Settings.pdf>; Lipa et al. (2006) <doi:10.1109/joe.2006.886104>; Paolo et al. (2007) <doi:10.1109/oceans.2007.4449265>; CODAR Ocean Sensors (2009a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_AntennaPattern.pdf>; CODAR Ocean Sensors (2009b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_CrossSpectraReduced.pdf>; CODAR Ocean Sensors (2016a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/File_Cross_Spectra_V6.pdf>; CODAR Ocean Sensors (2016b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/FIle_Reduced_Spectra.pdf>; CODAR Ocean Sensors (2016c) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/Application_Guides/Guide_SpectraPlotterMap.pdf>; Bushnell and Worthington (2022) <doi:10.25923/4c5x-g538>.

Transcribe audio files using the Speechmatics speech-to-text API <https://www.speechmatics.com/>. Supports custom vocabulary, speaker diarization, punctuation control, and audio filtering.

This package provides functions connecting to the Salesforce Platform APIs (REST, SOAP, Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. "API" is an acronym for "application programming interface". Most all calls from these APIs are supported as they use CSV, XML or JSON data that can be parsed into R data structures. For more details please see the Salesforce API documentation and this package's website <https://stevenmmortimer.github.io/salesforcer/> for more information, documentation, and examples.

Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weià , C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

This package implements the SISAL algorithm by Tikka and Hollmén. It is a sequential backward selection algorithm which uses a linear model in a cross-validation setting. Starting from the full model, one variable at a time is removed based on the regression coefficients. From this set of models, a parsimonious (sparse) model is found by choosing the model with the smallest number of variables among those models where the validation error is smaller than a threshold. Also implements extensions which explore larger parts of the search space and/or use ridge regression instead of ordinary least squares.

This package provides a function sfc() to compute the substance flow with the input files --- "data" and "model". If sample.size is set more than 1, uncertainty analysis will be executed while the distributions and parameters are supplied in the file "data".

This package contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.

Simulates sea-level rise and fall from raster-based elevation models through functions for relative vertical datum transformation of Digital Terrain Models (DTMs) and integration with bathymetric data to produce continuous terrestrialâ marine Digital Elevation Models (DEMs). Supports coastal exposure modeling, paleogeographic reconstruction, submerged landscape analysis, and climate change impact assessment. Optional gap-filling and threshold-based terrain filtering facilitate reconstruction of incomplete elevation surfaces and scenario-based landscape simulation. Applications span coastal engineering, geomorphology, archaeology, environmental modeling, and geospatial analysis.

This package provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>, Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and any other extended models involving multiple latent class variables.

Corrects the spelling of a given word in English using a modification of Peter Norvig's spell correct algorithm (see <http://norvig.com/spell-correct.html>) which handles up to three edits. The algorithm tries to find the spelling with maximum probability of intended correction out of all possible candidate corrections from the original word.

This package performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.

Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).

Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2024) <doi:10.1016/j.cam.2023.115379>; Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).

Create mixed models with repeated measures using natural cubic splines applied to an observed continuous time variable, as described by Donohue et al. (2023) <doi:10.1002/pst.2285>. Iterate through multiple covariance structure types until one converges. Categorize observed time according to scheduled visits. Perform subgroup analyses.

This package implements a suite of shrinkage estimators for multivariate linear regression to improve estimation stability and predictive accuracy. Provides methods including the Stein estimator, Diagonal Shrinkage, the general Shrinkage estimator (solving a Sylvester equation), and Slab Regression (Simple and Generalized). These methods address Stein's paradox by introducing structured bias to reduce variance without requiring cross-validation, except for ShrinkageRR where the intensity is chosen by minimizing an explicit Mean Squared Error (MSE) criterion. Methods are based on Asimit, V., Cidota, M. A., Chen, Z., and Asimit, J. (2025) <https://openaccess.city.ac.uk/id/eprint/35005/>.

The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004) <DOI:10.1198/016214504000000430>.

Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on kohonen and openair packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf> heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.

This package provides a pipeline for the comparative analysis of collective movement data (e.g. fish schools, bird flocks, baboon troops) by processing 2-dimensional positional data (x,y,t) from GPS trackers or computer vision tracking systems, discretizing events of collective motion, calculating a set of established metrics that characterize each event, and placing the events in a multi-dimensional swarm space constructed from these metrics. The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.

This package provides a base dependency solution with basic argument parsing for use with Rscript'.

Support for reading and writing files in StatDataML---an XML-based data exchange format.