Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

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.

Minimal Type Guesser

Port the type guesser from 'readr' (so-called 'readr' first edition parsing engine, now superseded by 'vroom').

Mark-Recapture Analysis

Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) ; Jolly (1965) ; Seber (1965) ) and Huggin's (1989) closed population. Link functions include logit, sine, and hazard. Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) estimator.

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 (2023) .

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) .

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).

Person-Centered Analysis

Provides an easy-to-use yet adaptable set of tools to conduct person-center analysis using a two-step clustering procedure. As described in Bergman and El-Khouri (1999) , hierarchical clustering is performed to determine the initial partition for the subsequent k-means clustering procedure.