Calculate the Open Bodem Index (OBI) Score

The Open Bodem Index (OBI) is a method to evaluate the quality of soils of agricultural fields in The Netherlands and the sustainability of the current agricultural practices. The OBI score is based on four main criteria: chemical, physical, biological and management, which consist of more than 21 indicators. By providing results of a soil analysis and management info the 'OBIC' package can be use to calculate he scores, indicators and derivatives that are used by the OBI. More information about the Open Bodem Index can be found at < https://openbodemindex.nl/>.

Calculation of the Standardized Precipitation-Evapotranspiration Index

A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).

Determining the Best Number of Clusters in a Data Set

It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.

Factor Analysis for All

Provides a comprehensive Shiny-based graphical user interface for conducting a wide range of factor analysis procedures. 'FAfA' (Factor Analysis for All) guides users through data uploading, assumption checking (descriptive statistics, collinearity, multivariate normality, outliers), data wrangling (variable exclusion, data splitting), exploratory factor analysis (EFA) with various rotation and extraction methods, confirmatory factor analysis (CFA), reliability analysis (e.g., Cronbach's Alpha, McDonald's Omega), and measurement invariance testing across groups. Factor retention methods include parallel analysis following Horn (1965) , optimized parallel analysis following Timmerman and Lorenzo-Seva (2011) , permutation parallel analysis for categorical variables following Lubbe (2019) , the Hull method following Lorenzo-Seva et al. (2011) , minimum average partial criteria following Velicer (1976) and O'Connor (2000) , and the empirical Kaiser criterion following Braeken and van Assen (2017) . Exploratory graph analysis follows Golino and Epskamp (2017) , with bootstrap stability assessment following Christensen and Golino (2021) . Internal split-sample EFA replication follows Osborne and Fitzpatrick (2012) . Model-specific dynamic fit index cutoffs for CFA follow McNeish and Wolf (2023) . Item weighting follows Kılıç (2026) . Analyses use established R packages such as 'lavaan' and 'psych'. Results are presented in tables and plots with downloadable outputs. Analysis projects can be saved and restored, and reproducible R, HTML, and PDF workflow reports can be generated.

Access to Fragile Families Metadata

A collection of functions that allows users to retrieve metadata for the Fragile Families challenge via a Web API (< http://api.metadata.fragilefamilies.princeton.edu>). Users can select and search metadata for relevant variables by filtering on different attribute names.

Compute Neural Fragility for Ictal iEEG Time Series

Provides tools to compute the neural fragility matrix from intracranial electrocorticographic (iEEG) recordings, enabling the analysis of brain dynamics during seizures. The package implements the method described by Li et al. (2017) and includes functions for data preprocessing ('Epoch'), fragility computation ('calcAdjFrag'), and visualization.

Clock of Regimes for Regime-Switching Fragility Analysis

Implements the Clock of Regimes (KRONX) framework for regime-switching fragility analysis of financial time series. The package fits Gaussian and Student-t Hidden Markov Models (HMMs) to return data, constructs a hazard-adjusted transition operator Q, derives the associated generator K = Q - I, and computes the fundamental matrix N = -K inverse to characterize expected residence times under structural fragility.

Flexible Procedures for Clustering

Various methods for clustering and cluster validation. Fixed point clustering. Linear regression clustering. Clustering by merging Gaussian mixture components. Symmetric and asymmetric discriminant projections for visualisation of the separation of groupings. Cluster validation statistics for distance based clustering including corrected Rand index. Standardisation of cluster validation statistics by random clusterings and comparison between many clustering methods and numbers of clusters based on this. Cluster-wise cluster stability assessment. Methods for estimation of the number of clusters: Calinski-Harabasz, Tibshirani and Walther's prediction strength, Fang and Wang's bootstrap stability. Gaussian/multinomial mixture fitting for mixed continuous/categorical variables. Variable-wise statistics for cluster interpretation. DBSCAN clustering. Interface functions for many clustering methods implemented in R, including estimating the number of clusters with kmeans, pam and clara. Modality diagnosis for Gaussian mixtures. For an overview see package?fpc.

Random Cluster Generation (with Specified Degree of Separation)

We developed the clusterGeneration package to provide functions for generating random clusters, generating random covariance/correlation matrices, calculating a separation index (data and population version) for pairs of clusters or cluster distributions, and 1-D and 2-D projection plots to visualize clusters. The package also contains a function to generate random clusters based on factorial designs with factors such as degree of separation, number of clusters, number of variables, number of noisy variables.

Submission Confidence Index Engine

Converts standardized R4SUB (R for Regulatory Submission) evidence into indicator scores, pillar scores, and a Submission Confidence Index (SCI). Provides sensitivity analysis, explainability tables, and decision band classification to answer the question: are we ready for regulatory submission.