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Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)
Comfortable ways to work with hyperspectral data sets. I.e. spatially or time-resolved spectra, or spectra with any other kind of information associated with each of the spectra. The spectra can be data as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS, etc. More generally, any data that is recorded over a discretized variable, e.g. absorbance = f(wavelength), stored as a vector of absorbance values for discrete wavelengths is suitable.
Minimal Type Guesser
Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').
R to Symbolic Data Analysis
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Working with Sets the Tidy Way
Implements a class and methods to work with sets, doing intersection, union, complementary sets, power sets, cartesian product and other set operations in a "tidy" way. These set operations are available for both classical sets and fuzzy sets. Import sets from several formats or from other several data structures.
Estimates and Plots Single-Level and Multilevel Latent Class Models
Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025)
Mark-Recapture Analysis
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972)
Subset Searching Algorithm Using DPP Greedy MAP
Given item set, item representation vector, and item ratings,
find a subset with better relevance-diversity trade-off. Also provide
machine learning algorithm to learn item representations maximizing
log likelihood under DPP assumption.
References: [1]Laming Chen, Guoxin Zhang, and Hanning Zhou(2017)< https://lsrs2017.files.wordpress.com/2017/08/lsrs_2017_lamingchen.pdf>
[2]Laming Chen, Guoxin Zhang, and Hanning Zhou(2018)< https://papers.nips.cc/paper/2018/file/dbbf603ff0e99629dda5d75b6f75f966-Paper.pdf>
[3]Wilhelm, Mark & Ramanathan, Ajith & Bonomo, Alexander & Jain, Sagar & Chi, Ed & Gillenwater, Jennifer(2018)
Reproducible Data Retrieval from the Roper Center Data Archive
Reproducible, programmatic retrieval of datasets from the Roper Center data archive. The Roper Center for Public Opinion Research < https://ropercenter.cornell.edu> maintains the largest archive of public opinion data in existence, but researchers using these datasets are caught in a bind. The Center's terms and conditions bar redistribution of downloaded datasets, but to ensure that one's work can be reproduced, assessed, and built upon by others, one must provide access to the raw data one employed. The `ropercenter` package cuts this knot by providing registered users with programmatic, reproducible access to Roper Center datasets from within R.
'Rcpp' Bindings for 'Redis' using the 'hiredis' Library
Connection to the 'Redis' (or 'Valkey') key/value store using the C-language client library 'hiredis' (included as a fallback) with 'MsgPack' encoding provided via 'RcppMsgPack' headers. It now also includes the pub/sub functions from the 'rredis' package.
A Fast and Flexible Bayesian Tool for Estimating Epidemiological Parameters
Estimation of epidemiological parameters with
Laplacian-P-splines following the methodology of Gressani et al. (2022)