An advanced version of package s2dverification'. Intended for seasonal to decadal (s2d) climate forecast verification, but also applicable to other types of forecasts or general climate analysis. This package is specifically designed for comparing experimental and observational datasets. It provides functionality for data retrieval, post-processing, skill score computation against observations, and visualization. Compared to s2dverification', s2dv is more compatible with the package startR', able to use multiple cores for computation and handle multi-dimensional arrays with a higher flexibility. The Climate Data Operators (CDO) version used in development is 1.9.8. Implements methods described in Wilks (2011) <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) <doi:10.1007/s00382-003-0350-4>.

This package provides efficient R and C++ routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

Transformation of sea currents to connectivity data. Two files of horizontal and vertical currents flows are transformed into connectivity data in the form of sfnetwork', shapefile, edge list and adjacency matrix. An application example is shown at Nagkoulis et al. (2025) <doi:10.1016/j.dib.2024.111268>.

This package provides a set of functions allowing to implement the SpiceFP approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" <https://hal.archives-ouvertes.fr/hal-03298977>.

You can easily add advanced cohort-building component to your analytical dashboard or simple Shiny app. Then you can instantly start building cohorts using multiple filters of different types, filtering datasets, and filtering steps. Filters can be complex and data-specific, and together with multiple filtering steps you can use complex filtering rules. The cohort-building sidebar panel allows you to easily work with filters, add and remove filtering steps. It helps you with handling missing values during filtering, and provides instant filtering feedback with filter feedback plots. The GUI panel is not only compatible with native shiny bookmarking, but also provides reproducible R code.

Starting from a given object representing a fitted model (within a certain set of model classes) whose (non-)linear predictor includes some ordered factor(s) among the explanatory variables, a new model is constructed and fitted where each named factor is replaced by a single numeric score, suitably chosen so that the new variable produces a fit comparable with the standard methodology based on a set of polynomial contrasts. Two variants of the present approach have been developed, one in each of the next references: Azzalini (2023) <doi:10.1002/sta4.624>, (2024) <doi:10.48550/arXiv.2406.15933>.

This package provides an application that acts as a GUI for the stm text analysis package.

This package provides tools for splitting large Excel worksheets into multiple smaller sheets based on a specified number of rows per chunk. The package reads Excel files, partitions the data efficiently using the data.table package, and writes the resulting subsets into a single workbook with multiple sheets using the openxlsx package. This is useful for handling large datasets, preparing data for reporting, and exporting manageable Excel files for downstream analysis.

Lexicons and tools to perform sentiment analysis on Italian texts. Lexicons included: Sentix 3.0, MAL, ElIta VAD and basic emotions (Plutchik's wheel of emotions). For more details about the lexicons, see Basile & Nissim (2013), "Sentiment Analysis on Italian Tweets", <https://aclanthology.org/W13-1614/>; Vassallo et al. (2019), "The Tenuousness of Lemmatization in Lexicon-based Sentiment Analysis", <https://aclanthology.org/2019.clicit-1.79/>; Di Palma (2024), "ELIta: A New Italian Language Resource for Emotion Analysis", <https://aclanthology.org/2024.clicit-1.36/>.

This package provides a set of functions to interpret changes in compositional data based on a network representation of all pairwise ratio comparisons: computation of all pairwise ratio, construction of a p-value matrix of all pairwise tests of these ratios between conditions, conversion of this matrix to a network.

This package provides a collection of self-labeled techniques for semi-supervised classification. In semi-supervised classification, both labeled and unlabeled data are used to train a classifier. This learning paradigm has obtained promising results, specifically in the presence of a reduced set of labeled examples. This package implements a collection of self-labeled techniques to construct a classification model. This family of techniques enlarges the original labeled set using the most confident predictions to classify unlabeled data. The techniques implemented can be applied to classification problems in several domains by the specification of a supervised base classifier. At low ratios of labeled data, it can be shown to perform better than classical supervised classifiers.

Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.

This package provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.

This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using ShrinkShape to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented. Note that all future developments will appear in this package and that PatternClass will eventually be archived.

This package implements spectral clustering algorithms for community detection in sparse networks under the stochastic block model ('SBM') and degree-corrected stochastic block model ('DCSBM'), following the methods of Lei and Rinaldo (2015) <doi:10.1214/14-AOS1274>. Provides a regularized normalized Laplacian embedding, spherical k-median clustering for DCSBM', standard k-means for SBM', simulation utilities for both models, and a misclustering rate evaluation metric. Also includes the NCAA college football network of Girvan and Newman (2002) <doi:10.1073/pnas.122653799> as a benchmark dataset, and the Bethe-Hessian community number estimator of Hwang (2023) <doi:10.1080/01621459.2023.2223793>.

This package provides statistical process control tools for stochastic textured surfaces. The current version supports the following tools: (1) generic modeling of stochastic textured surfaces. (2) local defect monitoring and diagnostics in stochastic textured surfaces, which was proposed by Bui and Apley (2018a) <doi:10.1080/00401706.2017.1302362>. (3) global change monitoring in the nature of stochastic textured surfaces, which was proposed by Bui and Apley (2018b) <doi:10.1080/00224065.2018.1507559>. (4) computation of dissimilarity matrix of stochastic textured surface images, which was proposed by Bui and Apley (2019b) <doi:10.1016/j.csda.2019.01.019>.

Package including functions and interactive shiny application for the psychometric analysis of educational tests, psychological assessments, health-related and other types of multi-item measurements, or ratings from multiple raters.

Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.

This package provides tools to record, validate, and analyse soil tillage depth and erosion across years and field treatments. Includes functions for year-wise tillage operation summaries, erosion depth tracking, compaction detection, soil loss estimation, and visualisation of temporal changes in tillage and erosion profiles. Methods follow Lal (2001) <doi:10.1201/9780203739280> and Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE)" <https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/PB97153704.xhtml>.

We introduce improved methods for statistically assessing birth seasonality and intra-annual variation. The first method we propose is a new idea that uses a nonparametric clustering procedure to group individuals with similar time series data and estimate birth seasonality based on the clusters. One can use the function SCEM() to implement this method. The second method estimates input parameters for use with a previously-developed parametric approach (Tornero et al., 2013). The relevant code for this approach is makeFits_OLS(), while makeFits_initial() is the code to implement the same method but with given initial conditions for two parameters. The latter can be used to show the disadvantage of the existing approach. One can use the function makeFits() to generate parametric birth seasonality estimates using either initialization. Detailed description can be found here: Chazin Hannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan (2019) <doi:10.1111/arcm.12432> "New Statistical Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality in Studies of Herd Animals".

This package provides a port of the spReapportion package, using Simple Features in order to lose the dependencies to the retired maptools and rgeos packages.

Time series area-level models for small area estimation. The package supplements the functionality of the sae package. Specifically, it includes EBLUP fitting of the Rao-Yu model in the original form without a spatial component. The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing the assumption of stationarity. Both univariate and multivariate applications are supported. Of particular note is the allowance for covariance of the area-level sample estimates over time, as encountered in rotating panel designs such as the U.S. National Crime Victimization Survey or present in a time-series of 5-year estimates from the American Community Survey. Key references to the methods include J.N.K. Rao and I. Molina (2015, ISBN:9781118735787), J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.

Compiles and displays the available data sets regarding the Italian school system, with a focus on the infrastructural aspects. Input datasets are downloaded from the web, with the aim of updating everything to real time. The functions are divided in four main modules, namely Get', to scrape raw data from the web Util', various utilities needed to process raw data Group', to aggregate data at the municipality or province level Map', to visualize the output datasets.