remstimate 2.3.9
CRAN release: 2024-05-13
- minor fix on titles of Schoenfeld’s residuals plots;
- correction on learning rate parameter of “GDADAMAX” method.
remstimate 2.3.8
CRAN release: 2023-12-20
DESCRIPTION file, removed quotes from acronyms;
documentation (return values added where missing);
tests (removed test on ‘ncores’ argument);
vignette (switched to ‘html_document’, edited seed value and parameters for
"HMC"
method). . # remstimate 2.3.7 (initial CRAN release)trace plot for posterior draws (only via
"HMC"
method) with highest posterior density intervals;histograms of posterior draws (for both
"BSIR"
and"HMC"
method) with highest posterior density intervals;the content of
summary.remstimate()
can be saved by assigning it to a variable;Watanabe-Akaike’s Information Criterion (WAIC) calculation available. New argument, logical value,
WAIC
, with defaultFALSE
. IfWAIC=TRUE
, then an optional argumentnsimWAIC
(number of draws to estimate the WAIC) is set by default to500
(the user can supply a different value);updated parallelization of routines as to the computation of likelihood, gradient and hessian in the actor-oriented modeling framework;
updated parallelization of routines for computing model residuals and WAIC;
added new tests, increased coverage.
remstimate 2.2.0:
- major updates of Rcpp functions;
- now using
tinytest
for testing (all tests are converted fromtestthat
); - new examples to all functions in the package;
- remove Rcpp functions (
emp_dist_longest_batch()
, for tuning L parameter in"HMC"
method); - vignette created for actor-oriented modeling framework.
remstimate 2.1.0:
- new remstimate logo;
- update of functions’ description;
- major update of
remstimate()
function:- removed input argument
model
; - initial controls of input argument;
- output structure;
-
"HMC"
for actor-oriented framework; - added initial values of
"HMC"
method (experimental);
- removed input argument
- major updates of Rcpp functions:
- new experimental Rcpp function
set_seed
; - input argument
edgelist
changed withdyad
; - new input arguments
actor1
andactor2
(for actor-oriented routines); - removed old Rcpp functions:
getDyadIndex()
,getDyadComposition()
; - added dependency of exported C++ routines from
remify
, using nowremify::getDyadIndex()
andremify::getDyadComposition()
;
- new experimental Rcpp function
- added new tests using
testthat
. Tests are divided in multiple .R scripts depending on what is tested; - update summary and print of actor-oriented framework in
summary.remstimate
; -
predict.remstimate
andplot.remstimate
function are not yet available; - added dependencies:
remify
andremstats
; - added data for tie-oriented and actor-oriented examples;
- new README.md with badges.
remstimate 2.0.0:
- This version is adapted to the latest changes coming from
remify 2.0.0
and it can estimate a Tie-Oriented model as well as an Actor-Oriented model. Models can be estimated by means of different methods:"MLE"
,"GDADAMAX"
(replacing the former"GD"
and"GDADAM"
),"BSIR"
and"HMC"
. Methods like"BSIR"
and"HMC"
are ready-to-use but still under a continuous development in order to improve the user-experience; - removed experimental “fast method” to compute the likelihood;
- added dependencies
trust
andparallel
; - experimental Rcpp functions
posteriorRank()
andremDerivativesStandard_lambdas()
(added but finally removed in the version 2.0.0); - added new Rcpp functions:
getDyadIndex()
,getDyadComposition()
.
remstimate 1.0.0:
-
11/12/2020:
- Methods working with the function
remstimate()
are:"MLE"
,"GD"
,"GDADAM"
,"BSIR"
,"HMC"
. However the output lacks of a structure attributes and methods;
- Methods working with the function
-
04/09/2020 :
- messages becomes again an Rcpp file. This exstension appears to suit better the intent of the content/aim of error and warning messages;
-
remstimate.R contains the main function
remstimate()
which is aimed to run either a Frequentist or a Bayesian approach by using different optimization/methods. It also includes a switch to the “fast method” to compute the likelihood. The “fast method” is run if the actual improvement (percentage of improvement) is higher than a threshold set by the user (default is0.5
); -
reh.cpp : contains
getRisksetMatrix()
,getRisksetCube()
,convertInputREH()
,getBinaryREH()
andreh()
. This last function is the one that preprocesses the input given by the user which consists in: edgelist, riskset and covariates. intereventTime variable and covariates input still need to be preprocessed via specific utility functions; -
remstimate.cpp : contains
remDerivatives()
(which returns the value of loglikelihood, gradient, hessian at a specific parameter value),lpd()
(log-pointwise density), utility functions for the fast method (cube2matrix()
,getUniqueVectors()
,computeTimes()
,computeOccurrencies()
) which is run with the functionremDerivativesFast()
; - Since
compute_stats()
is not an exported function inremstats
, getStats.R / compute_stats.cpp / compute_stats.h are temporary files so as to calculate statistics, run the estimation and compare estimates withrelevent::rem()
.getStats()
is the alias ofremstats::remstats()
with some modifications at the stage of the preprocessing of the network;
-
07/07/2020 :
-
reh()
will be the only preprocessing function and it is coded in Rcpp (see reh.cpp file) whereas the R function and the reh.R file are removed; - utility functions called inside
reh()
are added inside the reh.cpp file, before thereh()
function itself; - messages.cpp becomes a header file messages.h and the aim/content remains the same;
-
-
03/07/2020 :
-
reh.h changed to reh.cpp and it contains utility functions used in reh.R within the R function
reh(...)
; -
messages.cpp will contain functions
errorMessage(cond)
andwarningMessage(cond)
that will return appropriate error/warning messages according to the cond argument;
-
reh.h changed to reh.cpp and it contains utility functions used in reh.R within the R function
-
11/06/2020 :
- Created reh.h were the utility functions to preprocess data will be developed;
- remstimateBoost.h will contain the routines that speed up the computation of the loglikelihood and its first and second derivatives;
-
20/04/2020 :
- created repository with first commit;
- package only contains three functions:
remCpp(...)
(uses optim to find the maximum likelihood estimates of REM),nllik(...)
(returns the negative log-likelihood value for an observed event sequence, by specifying a vector of parameters and statistics),lpd(...)
(calculates the same asnllik(...)
but only for a specific time point and without taking the negative of the value).