Simulation and estimation of Exponential Random Graph Models (ERGMs)
  for small networks using exact statistics as shown in Vega Yon et al. (2020)
  <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm'
  package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator
  (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs
  for small networks. As exhaustive enumeration is computationally feasible for
  small networks, this R package takes advantage of this and provides tools for
  calculating likelihood functions, and other relevant functions, directly,
  meaning that in many cases both estimation and simulation of ERGMs for
  small networks can be faster and more accurate than simulation-based
  algorithms.

Provides several validator functions for checking if arguments
    passed by users have valid types, lengths, etc. and for generating
    informative and well-formatted error messages in a consistent style. Also
    provides tools for users to create their own validator functions. The
    error message style used is adopted from
    <https://style.tidyverse.org/error-messages.html>.

Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.

Estimate the effective reproduction number from wastewater
    and clinical data sources.

Functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).