Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". <arXiv:2207.02815>.

Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints 
  Stoer NC and Samuelsen SO (2016) <doi:10.32614/rj-2016-030>.

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including CUPED (Controlled Experiments Utilizing Pre-Experiment Data), multiple comparison adjustment, are illustrated.

Tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.

An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) 
    and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for 
    multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes 
    are able to be calculated for either null model, and subsequently classified into one of 
    four different interaction classifications (e.g., antagonistic or synergistic interactions). 
    Analyses can be conducted on data for single experiments through to large meta-analytical datasets. 
    Minimal input (or statistical knowledge) is required, with any output easily understood. 
    Summary figures are also able to be easily generated.