Showing 15 of total 15 results (show query)
bioc
BERT:High Performance Data Integration for Large-Scale Analyses of Incomplete Omic Profiles Using Batch-Effect Reduction Trees (BERT)
Provides efficient batch-effect adjustment of data with missing values. BERT orders all batch effect correction to a tree of pairwise computations. BERT allows parallelization over sub-trees.
Maintained by Yannis Schumann. Last updated 2 months ago.
batcheffectpreprocessingexperimentaldesignqualitycontrolbatch-effectbioconductor-packagebioinformaticsdata-integrationdata-science
74.1 match 2 stars 5.40 score 18 scripts
psychbruce
FMAT:The Fill-Mask Association Test
The Fill-Mask Association Test ('FMAT') <doi:10.1037/pspa0000396> is an integrative and probability-based method using Masked Language Models to measure conceptual associations (e.g., attitudes, biases, stereotypes, social norms, cultural values) as propositions in natural language. Supported language models include 'BERT' <doi:10.48550/arXiv.1810.04805> and its variants available at 'Hugging Face' <https://huggingface.co/models?pipeline_tag=fill-mask>. Methodological references and installation guidance are provided at <https://psychbruce.github.io/FMAT/>.
Maintained by Han-Wu-Shuang Bao. Last updated 5 months ago.
aiartificial-intelligencebertbert-modelbert-modelscontextualized-representationfill-in-the-blankfill-maskhuggingfacelanguage-modellanguage-modelslarge-language-modelsmasked-language-modelsnatural-language-processingnatural-language-understandingnlppretrained-modelstransformertransformers
33.5 match 12 stars 4.82 score 2 scriptspsychbruce
PsychWordVec:Word Embedding Research Framework for Psychological Science
An integrative toolbox of word embedding research that provides: (1) a collection of 'pre-trained' static word vectors in the '.RData' compressed format <https://psychbruce.github.io/WordVector_RData.pdf>; (2) a series of functions to process, analyze, and visualize word vectors; (3) a range of tests to examine conceptual associations, including the Word Embedding Association Test <doi:10.1126/science.aal4230> and the Relative Norm Distance <doi:10.1073/pnas.1720347115>, with permutation test of significance; (4) a set of training methods to locally train (static) word vectors from text corpora, including 'Word2Vec' <arXiv:1301.3781>, 'GloVe' <doi:10.3115/v1/D14-1162>, and 'FastText' <arXiv:1607.04606>; (5) a group of functions to download 'pre-trained' language models (e.g., 'GPT', 'BERT') and extract contextualized (dynamic) word vectors (based on the R package 'text').
Maintained by Han-Wu-Shuang Bao. Last updated 1 years ago.
bertcosine-similarityfasttextglovegptlanguage-modelnatural-language-processingnlppretrained-modelspsychologysemantic-analysistext-analysistext-miningtsneword-embeddingsword-vectorsword2vecopenjdk
11.5 match 22 stars 4.04 score 10 scripts
bertvanderveen
minic:Minimization Methods for Ill-Conditioned Problems
Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), <doi:10.1007/s12532-023-00238-4>).
Maintained by Bert van der Veen. Last updated 6 months ago.
8.0 match 1 stars 3.40 score
cran
nlme:Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Maintained by R Core Team. Last updated 2 months ago.
1.5 match 6 stars 13.00 score 13k scripts 8.7k dependentscran
addScales:Adds Labeled Center Line and Scale Lines/Regions to Trellis Plots
Modifies trellis objects by adding horizontal and/or vertical reference lines or shaded regions that provide visual scaling information. This is mostly useful in multi-panel plots that use the relation = 'free' option in their 'scales' argument list.
Maintained by Bert Gunter. Last updated 5 years ago.
9.8 match 2.00 scorecran
stripless:Structured Trellis Displays Without Strips for Lattice Graphics
For making Trellis-type conditioning plots without strip labels. This is useful for displaying the structure of results from factorial designs and other studies when many conditioning variables would clutter the display with layers of redundant strip labels. Settings of the variables are encoded by layout and spacing in the trellis array and decoded by a separate legend. The functionality is implemented by a single S3 generic strucplot() function that is a wrapper for the Lattice package's xyplot() function. This allows access to all Lattice graphics capabilities in the usual way.
Maintained by Bert Gunter. Last updated 9 years ago.
9.4 match 1 stars 2.00 scorecran
remindR:Insert and Extract "Reminders" from Function Comments
Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.
Maintained by Bert Gunter. Last updated 8 years ago.
9.4 match 2.00 score
ropensci
pangoling:Access to Large Language Model Predictions
Provides access to word predictability estimates using large language models (LLMs) based on 'transformer' architectures via integration with the 'Hugging Face' ecosystem. The package interfaces with pre-trained neural networks and supports both causal/auto-regressive LLMs (e.g., 'GPT-2'; Radford et al., 2019) and masked/bidirectional LLMs (e.g., 'BERT'; Devlin et al., 2019, <doi:10.48550/arXiv.1810.04805>) to compute the probability of words, phrases, or tokens given their linguistic context. By enabling a straightforward estimation of word predictability, the package facilitates research in psycholinguistics, computational linguistics, and natural language processing (NLP).
Maintained by Bruno Nicenboim. Last updated 4 days ago.
nlppsycholinguisticstransformers
3.4 match 8 stars 4.90 score
jenniniku
gllvm:Generalized Linear Latent Variable Models
Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).
Maintained by Jenni Niku. Last updated 1 days ago.
1.6 match 51 stars 10.52 score 176 scripts 1 dependentscran
memify:Constructing Functions That Keep State
A simple way to construct and maintain functions that keep state i.e. remember their argument lists. This can be useful when one needs to repeatedly invoke the same function with only a small number of argument changes at each invocation.
Maintained by Bert Gunter. Last updated 4 years ago.
9.8 match 1.00 score
macmillancontentscience
wordpiece.data:Data for Wordpiece-Style Tokenization
Provides data to be used by the wordpiece algorithm in order to tokenize text into somewhat meaningful chunks. Included vocabularies were retrieved from <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed into an R-friendly format.
Maintained by Jon Harmon. Last updated 3 years ago.
2.3 match 3.18 score 5 scripts 1 dependentsmacmillancontentscience
wordpiece:R Implementation of Wordpiece Tokenization
Apply 'Wordpiece' (<arXiv:1609.08144>) tokenization to input text, given an appropriate vocabulary. The 'BERT' (<arXiv:1810.04805>) tokenization conventions are used by default.
Maintained by Jonathan Bratt. Last updated 3 years ago.
0.5 match 8 stars 4.60 score 7 scripts