Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution

Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.

Latent Variable Models for Networks

Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.

Geographically-Weighted Models

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms.

A Vincent Van Gogh Color Palette Generator

Palettes generated from Vincent van Gogh's paintings.

Disaggregate Variable Costs

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

Indices for Single-Case Research

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

Collection of Machine Learning Datasets for Supervised Machine Learning

Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020) and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.

Allow Misspellings of Length Function

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

Automated Moderated Nonlinear Factor Analysis Using 'M-plus'

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.

Analysis of MEDITS-Like Survey Data

Set of functions working with survey data in the format of the MEDITS project < https://www.sibm.it/SITO%20MEDITS/principaleprogramme.htm>. In this version, functions use TA, TB and TC tables respectively containing haul, catch and aggregated biological data.