'Rcpp' implementation of the multivariate Kim filter, which combines the Kalman and Hamilton filters for state probability inference.
The filter is designed for state space models and can handle missing values and exogenous data in the observation and state equations.
Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.

A collection of useful functions not found anywhere else, mainly
for programming: Generalized lagged differences, checking containment in an
interval, creating a factor where the levels maintain the order of
appearance, sampling rows from a data frame, converting seconds from
midnight to and from H:M:S format, choosing the first non-NA value,
transposing lists of lists, returning the name of the file currently
sourced, smart named lists and vectors, and an alternative interface to
assign().
'coalesce_na.R' 'export.R' 'gdiff.R' 'hms_to_seconds.R'
'in_interval.R' 'kimisc-package.R' 'nlist.R' 'ofactor.R'
'sample_data_frame.R' 'seconds_to_hms.R' 'thisfile.R' 'tll.R'

person(given = "Kirill", family = "Müller", role = c("aut",
"cre"), email = "krlmlr+r@mailbox.org")
A collection of useful functions not found anywhere else,
mainly for programming: Pretty intervals, generalized lagged
differences, checking containment in an interval, creating a
factor where the levels maintain the order of appearance,
sampling rows from a data frame, converting seconds from
midnight to and from H:M:S format, choosing the first non-NA
value, transposing lists of lists, returning the name of the
file currently sourced, smart named lists and vectors, and an
alternative interface to assign().

A collection of useful functions not found anywhere else,
mainly for programming: Pretty intervals, generalized lagged
differences, checking containment in an interval, and an
alternative interface to assign().

Victor Moreno, Nilanjan Chatterjee, Bhramar Mukherjee
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific
cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
retrospectively built from relatives of probands for whom the genotype is known. Currently
the method of moments and marginal maximum likelihood are implemented. Confidence intervals
are calculated from bootstrap samples.
Most of the code is a translation from previous matlab code by Chatterjee.