The Open Data Format (ODF) is a new, non-proprietary, multilingual, metadata enriched, and zip-compressed data format with metadata structured in the Data Documentation Initiative (DDI) Codebook standard. This package allows reading and writing of data files in the Open Data Format (ODF) in R, and displaying metadata in different languages. For further information on the Open Data Format, see &lt;<a href="https://opendataformat.github.io/" target="_top">https://opendataformat.github.io/</a>&gt;.

A framework used to detect and handle outliers during data analysis workflows. Outlier detection is a statistical concept with applications in data analysis workflows, highlighting records that are suspiciously high or low. Outlier detection in distribution models was initiated by Chapman (1991) (available at &lt;<a href="https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data" target="_top">https://www.researchgate.net/publication/332537800_Quality_control_and_validation_of_point-sourced_environmental_resource_data</a>&gt;), who developed the reverse jackknifing method. The concept was further developed and incorporated into different R packages, including 'flexsdm' (Velazco et al., 2022, &lt;<a href="https://doi.org/10.1111%2F2041-210X.13874" target="_top">doi:10.1111/2041-210X.13874</a>&gt;) and 'biogeo' (Robertson et al., 2016 &lt;<a href="https://doi.org/10.1111%2Fecog.02118" target="_top">doi:10.1111/ecog.02118</a>&gt;). We compiled various outlier detection methods obtained from the literature, including those elaborated in Dastjerdy et al. (2023) &lt;<a href="https://doi.org/10.3390%2Fgeotechnics3020022" target="_top">doi:10.3390/geotechnics3020022</a>&gt; and Liu et al. (2008) &lt;<a href="https://doi.org/10.1109%2FICDM.2008.17" target="_top">doi:10.1109/ICDM.2008.17</a>&gt;. In this package, we introduced the ensembling aspect, where multiple outlier detection methods are used to flag the record as either an absolute outlier. The concept can also be applied in general data analysis, as well as during the development of species distribution models.

Extends the 'SelectBoost' approach to Generalized Additive Models for Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC(). Includes optional standardization of predictors and helper functions for corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) &lt;<a href="https://hal.science/hal-05352041" target="_top">https://hal.science/hal-05352041</a>&gt; that highlights correlation-aware resampling to improve variable selection for GAMLSS and quantile regression when predictors are numerous and highly correlated.

Trains per-horizon probabilistic ensembles from a univariate time series. It supports 'rpart', 'glmnet', and 'kNN' engines with flexible residual distributions and heteroscedastic scale models, weighting variants by calibration-aware scores. A Gaussian/t copula couples the marginals to simulate joint forecast paths, returning quantiles, means, and step increments across horizons.

The tools provide an interface for inter-process communication ('IPC') between 'R' and 'Go' using the 'Nanomsg' library. It vendors on the 'Go' side the 'mangos' library &lt;<a href="https://github.com/nanomsg/mangos" target="_top">https://github.com/nanomsg/mangos</a>&gt; and 'arrow-go' &lt;<a href="https://github.com/apache/arrow-go" target="_top">https://github.com/apache/arrow-go</a>&gt; and uses 'nanonext' and 'nanoarrow' on the 'R' side.

An implementation of generalized linear models (GLMs) for studying relationships among attributes in connected populations, where responses of connected units can be dependent, as introduced by Fritz et al. (2025) &lt;<a href="https://doi.org/10.1080%2F01621459.2025.2565851" target="_top">doi:10.1080/01621459.2025.2565851</a>&gt;. 'igml' extends GLMs for independent responses to dependent responses and can be used for studying spillover in connected populations and other network-mediated phenomena.

A faster implementation of Bayesian Causal Forests (BCF; Hahn et al. (2020) &lt;<a href="https://doi.org/10.1214%2F19-BA1195" target="_top">doi:10.1214/19-BA1195</a>&gt;), which uses regression tree ensembles to estimate the conditional average treatment effect of a binary treatment on a scalar output as a function of many covariates. This implementation avoids many redundant computations and memory allocations present in the original BCF implementation, allowing the model to be fit to larger datasets. The implementation was originally developed for the 2022 American Causal Inference Conference's Data Challenge. See Kokandakar et al. (2023) &lt;<a href="https://doi.org/10.1353%2Fobs.2023.0024" target="_top">doi:10.1353/obs.2023.0024</a>&gt; for more details.

Companion package of Arnaud Barat, Andreu Sansó, Maite Arilla-Osuna, Ruth Blasco, Iñaki Pérez-Fernández, Gabriel Cifuentes-Alcobenda, Rubén Llorente, Daniel Vivar-Ríos, Ella Assaf, Ran Barkai, Avi Gopher, &amp; Jordi Rosell-Ardèvol (2025), "Quantifying Diversity through Entropy Decomposition. Insights into Hominin Occupation and Carcass Processing at Qesem cave".

Implements algorithms for analyzing Cayley graphs of permutation groups, with a focus on the TopSpin puzzle and similar permutation-based combinatorial puzzles. Provides methods for cycle detection, state space exploration, and finding optimal operation sequences in permutation groups generated by shift and reverse operations.

You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.

These functions provide a convenient interface for downloading data from the U.S. Bureau of Labor Statistics &lt;<a href="https://www.bls.gov" target="_top">https://www.bls.gov</a>&gt;. The functions in this package utilize flat files produced by the Bureau of Labor Statistics, which contain full series history. These files include employment, unemployment, wages, prices, industry and occupational data at a national, state, and sub-state level, depending on the series. Individual functions are included for those programs which have data available at the state level. The core functions provide direct access to the Current Employment Statistics (CES) &lt;<a href="https://www.bls.gov/ces/" target="_top">https://www.bls.gov/ces/</a>&gt;, Local Area Unemployment Statistics (LAUS) &lt;<a href="https://www.bls.gov/lau/" target="_top">https://www.bls.gov/lau/</a>&gt;, Occupational Employment and Wage Statistics (OEWS) &lt;<a href="https://www.bls.gov/oes/" target="_top">https://www.bls.gov/oes/</a>&gt; and Alternative Measures of Labor Underutilization (SALT) &lt;<a href="https://www.bls.gov/lau/stalt.htm" target="_top">https://www.bls.gov/lau/stalt.htm</a>&gt; data produced by the Bureau of Labor Statistics.

Getting 'TikTok' data (&lt;<a href="https://www.tiktok.com/" target="_top">https://www.tiktok.com/</a>&gt;) through the official and unofficial APIs—in other words, you can track 'TikTok'.