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rio - A Swiss-Army Knife for Data I/O

Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data format from the file extension, reasonable defaults are used for data import and export, web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly, and fast import packages are used where appropriate. An additional convenience function, 'convert()', provides a simple method for converting between file types.

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csvcsvydatadata-scienceexcelioriosasspssstata

16.87 score 619 stars 70 dependents 12k scripts 49k downloads

readODS - Read and Write ODS Files

Read ODS (OpenDocument Spreadsheet) into R as data frame. Also support writing data frame into ODS file.

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cpp

12.80 score 59 stars 27 dependents 1.1k scripts 16k downloads

rang - Reconstructing Reproducible R Computational Environments

Resolve the dependency graph of R packages at a specific time point based on the information from various 'R-hub' web services <https://blog.r-hub.io/>. The dependency graph can then be used to reconstruct the R computational environment with 'Rocker' <https://rocker-project.org>.

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reproducibilityreproducible-research

7.26 score 80 stars 15 scripts 241 downloads

oolong - Create Validation Tests for Automated Content Analysis

Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009, <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. 2021) <doi:10.1017/pan.2021.33> tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.

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textanalysistopicmodelingvalidation

7.17 score 59 stars 28 scripts 338 downloads

grafzahl - Supervised Machine Learning for Textual Data Using Transformers and 'Quanteda'

Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

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6.24 score 43 stars 5 scripts 176 downloads

minty - Minimal Type Guesser

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

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cpp

6.04 score 29 dependents 8 scripts 13k downloads

sweater - Speedy Word Embedding Association Test and Extras Using R

Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.

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bias-detectiontextanalysiswordembeddingcpp

4.78 score 32 stars 19 scripts 264 downloads

ngramrr - A Simple General Purpose N-Gram Tokenizer

A simple n-gram (contiguous sequences of n items from a given sequence of text) tokenizer to be used with the 'tm' package with no 'rJava'/'RWeka' dependency.

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4.48 score 10 stars 2 dependents 5 scripts 496 downloads

sehrnett - A Very Nice Interface to 'WordNet'

A very nice interface to Princeton's 'WordNet' without 'rJava' dependency. 'WordNet' data is not included. Princeton University makes 'WordNet' available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).

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wordnet

3.48 score 6 stars 3 scripts 188 downloads