Contingency Table Analysis Based on ML Fitting of MPH Models
c(person("Joseph B.", "Lang", role = "aut",
email = "joseph-lang@uiowa.edu"),
person("Qiansheng", "Zhu", role = c("aut", "cre"),
email = "qiansheng-zhu@uiowa.edu"))
Joseph B. Lang [aut],
Qiansheng Zhu [aut, cre]
Contingency table analysis is performed based on maximum likelihood (ML) fitting of
multinomial-Poisson homogeneous (MPH) and homogeneous linear predictor (HLP) models.
See Lang (2004) <doi:10.1214/aos/1079120140> and Lang (2005) <doi:10.1198/016214504000001042>
for MPH and HLP models. Objects computed include model goodness-of-fit statistics; likelihood-
based (cell- and link-specific) residuals; and cell probability and expected count estimates along
with standard errors. This package can also compute test-inversion--e.g. Wald, profile likelihood,
score, power-divergence--confidence intervals for contingency table estimands, when table
probabilities are potentially subject to equality constraints. For test-inversion intervals, see
Lang (2008) <doi:10.1002/sim.3391> and Zhu (2020) <doi:10.17077/etd.005331>.

A Procedure to Clean, Decompose and Aggregate Timeseries
person("Francois", "Ritter", email = "ritter.francois@gmail.com", role = c("cre","aut"),comment = c(ORCID = "0000-0001-6123-2145"))
Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose and aggregate time series, Hydrol. Earth Syst. Sci. Discuss. [preprint], <doi:10.5194/hess-2021-609>, in review.
Francois Ritter [cre, aut] (<https://orcid.org/0000-0001-6123-2145>)

A collection of classical tests, including the Bartlett,
Fisher, Kruskal-Wallis, Kolmogorov-Smirnov, and Wilcoxon tests.

person("Clark", "Fitzgerald", role = c("aut", "cre"),
email = "fitzgerald@csus.edu",
comment = c(ORCID = "0000-0003-3446-6389"))
Column Text Format (CTF) is a new tabular data format designed for simplicity and performance.
CTF is the simplest column store you can imagine: plain text files for each column in a table, and a metadata file.
The underlying plain text means the data is human readable and familiar to programmers, unlike specialized binary formats.
CTF is faster than row oriented formats like CSV when loading a subset of the columns in a table.
This package provides functions to read and write CTF data from R.
Clark Fitzgerald [aut, cre] (<https://orcid.org/0000-0003-3446-6389>)