A low-dependency implementation of GSIF::mpspline() <https://r-forge.r-project.org/scm/viewvc.php/pkg/R/mpspline.R?view=markup&revision=240&root=gsif>,
  which applies a mass-preserving spline to soil attributes. Splining soil data 
  is a safe way to make continuous down-profile estimates of attributes measured
  over discrete, often discontinuous depth intervals.

Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).

Fitting and testing multinomial processing tree (MPT) models, a
  class of nonlinear models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).

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.