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Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Multiverse Analysis of Multinomial Processing Tree Models
Statistical or cognitive modeling usually requires a number of more or less
arbitrary choices creating one specific path through a 'garden of forking paths'.
The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016,
Analyze Multinomial Processing Tree Models
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.
Fast Implementation of the Diffusion Decision Model
Provides the probability density function (PDF), cumulative
distribution function (CDF), the first-order and second-order partial
derivatives of the PDF, and a fitting function for the diffusion decision
model (DDM; e.g.,
Ratcliff & McKoon, 2008,
Statistics for Holland's Theory of Vocational Choice
Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment.
Multivariate ARIMA and ARIMA-X Analysis
Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).
Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'
Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.
Track Estimation using YAPS (Yet Another Positioning Solver)
Estimate tracks of animals tagged with acoustic transmitters. 'yaps' was introduced in 2017 as a transparent open-source tool to estimate positions of fish (and other aquatic animals) tagged with acoustic transmitters. Based on registrations of acoustic transmitters on hydrophones positioned in a fixed array, 'yaps' enables users to synchronize the collected data (i.e. correcting for drift in the internal clocks of the hydrophones/receivers) and subsequently to estimate tracks of the tagged animals. The paper introducing 'yaps' is available in open access at Baktoft, Gjelland, Økland & Thygesen (2017)
Convert Gene IDs Between Each Other and Fetch Annotations from Biomart
Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the 'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.
Rasch Model Parameters by Pairwise Algorithm
Performs the explicit calculation -- not estimation! -- of the Rasch item parameters for dichotomous and polytomous item responses, using a pairwise comparison approach. Person parameters (WLE) are calculated according to Warm's weighted likelihood approach.