Penalised Maximum Likelihood Estimation for Hidden Semi-Markov Models

Provides tools for penalised maximum likelihood estimation of hidden semi-Markov models (HSMMs) with flexible state dwell-time distributions. These include functions for model fitting, model checking and state-decoding. The package considers HSMMs for univariate time series with state-dependent gamma, normal, Poisson or Bernoulli distributions. For details, see Pohle, J., Adam, T. and Beumer, L.T. (2021): Flexible estimation of the state dwell-time distribution in hidden semi-Markov models. .

R to Symbolic Data Analysis

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

User Interface for Collecting and Analysing Social Networks

A 'Shiny' application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using 'vosonSML'.

Multivariate VAM Fitting

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) and Broatch and Lohr (2012) , with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) , is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) . This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

Inference for Linear Models with Nuisance Parameters

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Predictive (Classification and Regression) Models Homologator

Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004) , Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) , ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) , Extreme Gradient Boosting Chen & Guestrin (2016) , Random Forest Breiman (2001) , Neural Networks Venables, W. N., & Ripley, B. D. (2002) , Support Vector Machines Bennett, K. P. & Campbell, C. (2000) , Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) , Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) , Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) .

Hive Plots of R Package Function Calls

Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers). Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank. FuncMap should be useful for comparing packages at a high level for their overall design. Plus, it's just plain fun. The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package. Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.

Build Tables for Publication

Functions for building customized ready-to-export tables for publication.

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.

Predict Fish Hatch and Emergence Timing

Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) ). 'hatchR' offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.