R: 'a la Carte' on Text (ConText) Embedding Regression

# 'a la Carte' on Text (ConText) Embedding Regression

## Documentation for package ‘conText’ version 3.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package README

## Help Pages

bootstrap_contrast | Bootstrap similarity and ratio computations
bootstrap_nns | Bootstrap nearest neighbors
bootstrap_similarity | Boostrap similarity vector
build_conText | build a 'conText-class' object
build_dem | build a 'dem-class' object
build_fem | build a 'fem-class' object
compute_contrast | Compute similarity and similarity ratios
compute_similarity | Compute similarity vector (sub-function of bootstrap_similarity)
compute_transform | Compute transformation matrix A
conText | Embedding regression
contrast_nns | Contrast nearest neighbors
cos_sim | Compute the cosine similarity between one or more ALC embeddings and a set of features.
cr_glove_subset | GloVe subset
cr_sample_corpus | Congressional Record sample corpus
cr_transform | Transformation matrix
dem | Build a document-embedding matrix
dem_group | Average document-embeddings in a dem by a grouping variable
dem_sample | Randomly sample documents from a dem
embed_target | Embed target using either: (a) a la carte OR (b) simple (untransformed) averaging of context embeddings
feature_sim | Given two feature-embedding-matrices, compute "parallel" cosine similarities between overlapping features.
fem | Create an feature-embedding matrix
find_cos_sim | Find cosine similarities between target and candidate words
find_nns | Return nearest neighbors based on cosine similarity
get_context | Get context words (words within a symmetric window around the target word/phrase) sorrounding a user defined target.
get_cos_sim | Given a tokenized corpus, compute the cosine similarities of the resulting ALC embeddings and a defined set of features.
get_grouped_similarity | Get averaged similarity scores between target word(s) and one or two vectors of candidate words.
get_local_vocab | Identify words common to a collection of texts and a set of pretrained embeddings.
get_ncs | Given a set of tokenized contexts, find the top N nearest contexts.
get_nns | Given a tokenized corpus and a set of candidate neighbors, find the top N nearest neighbors.
get_nns_ratio | Given a corpus and a binary grouping variable, computes the ratio of cosine similarities over the union of their respective N nearest neighbors.
get_seq_cos_sim | Calculate cosine similarities between target word and candidates words over sequenced variable using ALC embedding approach
ncs | Given a set of embeddings and a set of tokenized contexts, find the top N nearest contexts.
nns | Given a set of embeddings and a set of candidate neighbors, find the top N nearest neighbors.
nns_ratio | Computes the ratio of cosine similarities for two embeddings over the union of their respective top N nearest neighbors.
permute_contrast | Permute similarity and ratio computations
plot_nns_ratio | Plot output of 'get_nns_ratio()'
prototypical_context | Find most "prototypical" contexts.
run_ols | Run OLS
tokens_context | Get the tokens of contexts sorrounding user defined patterns