Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP  <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.

Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000)  <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980)  <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.

Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.