Assessing and Visualizing Fragility of Clinical Results with Binary Outcomes

A collection of user-friendly functions for assessing and visualizing fragility of individual studies (Walsh et al., 2014 ; Lin, 2021 ), conventional pairwise meta-analyses (Atal et al., 2019 ), and network meta-analyses of multiple treatments with binary outcomes (Xing et al., 2020 ). The included functions are designed to: 1) calculate the fragility index (i.e., the minimal event status modifications that can alter the significance or non-significance of the original result) and fragility quotient (i.e., fragility index divided by sample size) at a specific significance level; 2) give the cases of event status modifications for altering the result's significance or non-significance and visualize these cases; 3) visualize the trend of statistical significance as event status is modified; 4) efficiently derive fragility indexes and fragility quotients at multiple significance levels, and visualize the relationship between these fragility measures against the significance levels; and 5) calculate fragility indexes and fragility quotients of multiple datasets (e.g., a collection of clinical trials or meta-analyses) and produce plots of their overall distributions. The outputs from these functions may inform the robustness of clinical results in terms of statistical significance and aid the interpretation of fragility measures. The usage of this package is illustrated in Lin et al. (2023 ) and detailed in Lin and Chu (2022 ).

S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations)

An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo's key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.

Fast and Robust Hierarchical Clustering with Noise Points Detection

A retake on the Genie algorithm (Gagolewski, 2021 ) - a robust hierarchical clustering method (Gagolewski, Bartoszuk, Cena, 2016 ). Now faster and more memory efficient; determining the whole hierarchy for datasets of 10M points in low dimensional Euclidean spaces or 100K points in high-dimensional ones takes only 1-2 minutes. Allows clustering with respect to mutual reachability distances so that it can act as a noise point detector or a robustified version of 'HDBSCAN*' (that is able to detect a predefined number of clusters and hence it does not dependent on the somewhat fragile 'eps' parameter). The package also features an implementation of inequality indices (the Gini, Bonferroni index), external cluster validity measures (e.g., the normalised clustering accuracy and partition similarity scores such as the adjusted Rand, Fowlkes-Mallows, adjusted mutual information, and the pair sets index), and internal cluster validity indices (e.g., the Calinski-Harabasz, Davies-Bouldin, Ball-Hall, Silhouette, and generalised Dunn indices). See also the 'Python' version of 'genieclust' available on 'PyPI', which supports sparse data, more metrics, and even larger datasets.

Time Indexes and Time Indexed Series

Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.

Read and Write Rectangular Text Data Quickly

The goal of 'vroom' is to read and write data (like 'csv', 'tsv' and 'fwf') quickly. When reading it uses a quick initial indexing step, then reads the values lazily , so only the data you actually use needs to be read. The writer formats the data in parallel and writes to disk asynchronously from formatting.

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).

Sparse Lightweight Arrays and Matrices

Data structures and algorithms for sparse arrays and matrices, based on index arrays and simple triplet representations, respectively.

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/>.

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.

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.