Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.

Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery, Peck
             and Vining.
             Some additional data sets and functions are also included.

Useful functions to analyze proteomic workflows including
    number of identifications, data completeness, missed cleavages,
    quantitative and retention time precision etc. Various software
    outputs are supported such as 'ProteomeDiscoverer', 'Spectronaut',
    'DIA-NN' and 'MaxQuant'.

Complements the book 
    "Using R for Modelling and Quantitative Methods in Fisheries"
    ISBN 9780367469894, published in 
    2021 by Chapman & Hall in their "Using R series". 
    There are numerous functions and data-sets that are used in the 
    book's many practical examples.

Parametric modeling of M-quantile regression coefficient functions.

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and balanced horizontal pleiotropy simultaneously. See Xu S., Wang P., Fung W.K. and Liu Z. (2022) <doi:10.1111/biom.13732>.