Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices.
    It uses mixed and random model least squares analysis to estimate the heritability of
    traits and genetic correlation between traits. The package uses the sire model as it is considered as
    random effect. The genetic and phenotypic (co)variances along with the relative economic values are 
    used to construct the selection index for any number of traits. It also estimates the accuracy of the
    index and the genetic gain expected for different traits. Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>.

Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.

Style sheets and JavaScript assets for 'shiny.semantic' package.

It offers functions for creating dashboard with Fomantic UI.

Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). 'SemanticDistance' computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. 'SemanticDistance' can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). 'SemanticDistance' includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network).