'Compressive' Hierarchical Kernel Clustering Toolbox

Routines for efficient cluster analysis of large scale data. This package implements the 'CHICKN' clustering algorithm (see 'Permiakova' 'et' 'al.' (2020) "'CHICKN': Extraction of 'peptide' 'chromatographic' 'elution' profiles from large scale mass 'spectrometry' data by means of 'Wasserstein' 'compressive' hierarchical cluster analysis"). Functions for data compression, hierarchical clustering and post processing are provided.

Search Contributed R Packages, Sort by Package

Search contributed R packages, sort by package.

Airborne Point Cloud Data Manipulation and Visualization for Forestry Applications

Airborne point cloud interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Diffusion Non-Additive Model with Tunable Precision

Performs Diffusion Non-Additive (DNA) model proposed by Heo, Boutelet, and Sung (2025+) for multi-fidelity computer experiments with tuning parameters. The DNA model captures nonlinear dependencies across fidelity levels using Gaussian process priors and is particularly effective when simulations at different fidelity levels are nonlinearly correlated. The DNA model targets not only interpolation across given fidelity levels but also extrapolation to smaller tuning parameters including the exact solution corresponding to a zero-valued tuning parameter, leveraging a nonseparable covariance kernel structure that models interactions between the tuning parameter and input variables. Closed-form expressions for the predictive mean and variance enable efficient inference and uncertainty quantification. Hyperparameters in the model are estimated via maximum likelihood estimation.

Tools for Analyzing Remote Sensing Forest Data

Tools for analyzing remote sensing forest data, including functions for detecting treetops from canopy models, outlining tree crowns, and calculating textural metrics.

Read and Write 'las' and 'laz' Binary File Formats Used for Remote Sensing Data

Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing < https://community.asprs.org/leadership-restricted/leadership-content/public-documents/standards>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.4 < https://laszip.org/>.

R Interface to the 'Protocol Buffers' 'API' (Version 2 or 3)

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal 'RPC' protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our 'JSS' paper (2016, . A sufficiently recent version of 'Protocol Buffers' library is required; currently version 3.3.0 from 2017 is the tested minimum.

Simple but Efficient Rowwise Jobs

Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.

Quick Serialization of R Objects

Provides functions for quickly writing and reading any R object to and from disk.