Edit docs

7 files changed, 58 insertions, 60 deletions

diff --git a/R/ID.R b/R/ID.R
index b5fc8c2..cd991a4 100644
--- a/R/ID.R
+++ b/R/ID.R
@@ -9,15 +9,14 @@
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.

 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.

 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.

-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.

+#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.

 #'

-#' @param NT Vector of case counts

-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are

-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case

-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)

+#' @param NT Vector of case counts.

+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts

+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one If case

+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.

 #'

-#' @return \code{ID} returns a list containing the following components:  \code{Rhat} is the estimate of R0 and

-#'         \code{inputs} is a list of the original input variables \code{NT, mu}.

+#' @return \code{ID} returns a single value, the estimate of R0.

 #'

 #' @examples

 #' ## ===================================================== ##

diff --git a/R/IDEA.R b/R/IDEA.R
index 53cfaa7..20a9401 100644
--- a/R/IDEA.R
+++ b/R/IDEA.R
@@ -10,15 +10,14 @@
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic.

 #' The method assumes that the mean of the serial distribution (sometimes called the serial interval) is known.

 #' The final estimate can be quite sensitive to this value, so sensitivity testing is strongly recommended.

-#' Users should be careful about units of time (e.g. are counts observed daily or weekly?) when implementing.

+#' Users should be careful about units of time (e.g., are counts observed daily or weekly?) when implementing.

 #'

-#' @param NT Vector of case counts

-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are

-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case

-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)

+#' @param NT Vector of case counts.

+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts

+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one. If case

+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.

 #'

-#' @return \code{IDEA} returns a list containing the following components: \code{Rhat} is the estimate of R0 and

-#'         \code{inputs} is a list of the original input variables \code{NT, mu}.

+#' @return \code{IDEA} returns a single value, the estimate of R0.

 #'

 #' @examples

 #' ## ===================================================== ##

diff --git a/R/WP.R b/R/WP.R
index 61f5cd1..7597c49 100644
--- a/R/WP.R
+++ b/R/WP.R
@@ -19,7 +19,7 @@ source("WP_unknown.R")
 #' is strongly recommended. If the serial distribution is unknown (i.e., \code{mu} is \code{NA}), the
 #' likelihood function can be flat near the maximum, resulting in numerical instability of the optimizer.
 #' When \code{mu} is \code{NA}, the implementation takes considerably longer to run. Users should be careful
-#' about units of time (e.g. are counts observed daily or weekly?) when implementing.  
+#' about units of time (e.g., are counts observed daily or weekly?) when implementing.  
 #'
 #' The model developed in White and Pagano (2008) is discrete, and hence the serial distribution is finite 
 #' discrete. In our implementation, the input value \code{mu} is that of a continuous distribution. The
@@ -28,27 +28,26 @@ source("WP_unknown.R")
 #' user notices that the input \code{mu} and output mean of \code{SD} are different, this is to be expected,
 #' and is caused by the discretization.
 #'
-#' @param NT Vector of case counts
+#' @param NT Vector of case counts.
 #' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case
 #'           counts are weekly and the serial distribution has a mean of seven days, then \code{mu} should be
 #'           set to one). The default value of \code{mu} is set to \code{NA}.
-#' @param search List of default values for the grid search algorithm; the list includes three elements: the
-#'               first is \code{B} which is the length of the grid in one dimension, the second is
-#'               \code{scale.max} which is the largest possible value of the scale parameter, and the third is
-#'               \code{shape.max} which is the largest possible value of the shape parameter; defaults to
+#' @param search List of default values for the grid search algorithm. The list includes three elements: the
+#'               first is \code{B}, which is the length of the grid in one dimension; the second is
+#'               \code{scale.max}, which is the largest possible value of the scale parameter; and the third
+#'               is \code{shape.max}, which is the largest possible value of the shape parameter. Defaults to
 #'               \code{B=100, scale.max=10, shape.max=10}. For both shape and scale, the smallest possible
-#'               value is 1/\code{B}.  
+#'               value is 1/\code{B}.
 #' @param tol Cutoff value for cumulative distribution function of the pre-discretization gamma serial
-#'            distribution, defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the
+#'            distribution. Defaults to 0.999 (i.e. in the discretization, the maximum is chosen such that the
 #'            original gamma distribution has cumulative probability of no more than 0.999 at this maximum).
 #'
-#' @return WP returns a list containing the following components:  \code{Rhat} is the estimate of R0, \code{SD}
-#'            is either the discretized serial distribution (if \code{mu} is not \code{NA}) or the estimated
-#'            discretized serial distribution (if \code{mu} is \code{NA}), and \code{inputs} is a list of the
-#'            original input variables \code{NT, mu, method, search, tol}. The list also returns the variable
-#'            \code{check}, which is equal to the number of non-unique maximum likelihood estimators. The serial
-#'            distribution \code{SD} is returned as a list made up of \code{supp} the support of the distribution
-#'            and \code{pmf} the probability mass function.  
+#' @return \code{WP} returns a list containing the following components:  \code{Rhat} is the estimate of R0,
+#'         and \code{SD} is either the discretized serial distribution (if \code{mu} is not \code{NA}), or the
+#'         estimated discretized serial distribution (if \code{mu} is \code{NA}). The list also returns the
+#'         variable \code{check}, which is equal to the number of non-unique maximum likelihood estimators.
+#'         The serial distribution \code{SD} is returned as a list made up of \code{supp} (the support of
+#'         the distribution) and \code{pmf} (the probability mass function).
 #'
 #' @examples
 #' ## ===================================================== ##
diff --git a/R/WP_known.R b/R/WP_known.R
index 563b7ae..2c54bce 100644
--- a/R/WP_known.R
+++ b/R/WP_known.R
@@ -3,9 +3,10 @@
 #' This is a background/internal function called by \code{WP}. It computes the maximum
 #' likelihood estimator of R0 assuming that the serial distribution is known and finite discrete.
 #'
-#' @param NT vector of case counts
-#' @param p discretized version of the serial distribution
-#' @return The function returns \code{Rhat}, the maximum likelihood estimator of R0.
+#' @param NT Vector of case counts.
+#' @param p Discretized version of the serial distribution.
+#'
+#' @return The function returns the maximum likelihood estimator of R0.
 #'
 #' @export
 WP_known <- function(NT, p) {
diff --git a/R/WP_unknown.R b/R/WP_unknown.R
index 569a880..542f0f7 100644
--- a/R/WP_unknown.R
+++ b/R/WP_unknown.R
@@ -8,11 +8,11 @@ source("WP_known.R")
 #' The function then implements a simple grid search algorithm to obtain the maximum likelihood estimator
 #' of R0 as well as the gamma parameters.
 #'
-#' @param NT vector of case counts
-#' @param B length of grid for shape and scale (grid search parameter)
-#' @param shape.max maximum shape value (grid search parameter)
-#' @param scale.max maximum scale value (grid search parameter)
-#' @param tol cutoff value for cumulative distribution function of the serial distribution, defaults to 0.999
+#' @param NT Vector of case counts.
+#' @param B Length of grid for shape and scale (grid search parameter).
+#' @param shape.max Maximum shape value (grid \code{search} parameter).
+#' @param scale.max Maximum scale value (grid \code{search} parameter).
+#' @param tol cutoff value for cumulative distribution function of the serial distribution (defaults to 0.999).
 #'
 #' @return The function returns \code{Rhat}, the maximum likelihood estimator of R0, as well as the maximum
 #'         likelihood estimator of the discretized serial distribution given by \code{p} (the probability mass
@@ -20,8 +20,8 @@ source("WP_known.R")
 #'         The function also returns \code{resLL} (all values of the log-likelihood) at \code{shape} (grid for
 #'         shape parameter) and at \code{scale} (grid for scale parameter), as well as \code{resR0} (the full
 #'         vector of maximum likelihood estimators), \code{JJ} (the locations for the likelihood for these), and
-#'         \code{J0} (the location for the maximum likelihood estimator \code{Rhat}).  If \code{JJ} and \code{J0}
-#'         are not the same, this means that the maximum likelihood estimator is not unique. 
+#'         \code{J0} (the location for the maximum likelihood estimator \code{Rhat}). If \code{JJ} and \code{J0}
+#'         are not the same, this means that the maximum likelihood estimator is not unique.
 #'
 #' @export
 WP_unknown <- function(NT, B=100, shape.max=10, scale.max=10, tol=0.999) {
diff --git a/R/computeLL.R b/R/computeLL.R
index 6acfc78..9047777 100644
--- a/R/computeLL.R
+++ b/R/computeLL.R
@@ -2,10 +2,11 @@
 #'
 #' This is a background/internal function called by \code{WP}.  It computes the log-likelihood.
 #'
-#' @param NT vector of case counts
-#' @param p discretized version of the serial distribution
-#' @param R0 basic reproductive ratio
-#' @return The function returns the variable \code{LL} which is the log-likelihood at the input variables and parameters.
+#' @param NT Vector of case counts.
+#' @param p Discretized version of the serial distribution.
+#' @param R0 Basic reproductive ratio.
+#'
+#' @return This function returns the log-likelihood at the input variables and parameters.
 #'
 #' @export
 computeLL <- function(p, NT, R0) {
diff --git a/R/seqB.R b/R/seqB.R
index 7938311..a64b598 100644
--- a/R/seqB.R
+++ b/R/seqB.R
@@ -7,43 +7,42 @@
 #' The distribution of R0 is then updated sequentially, with one update for each new case count observation.
 #' The final estimate of R0 is \code{Rhat}, the mean of the (last) posterior distribution.
 #' The prior distribution is the initial belief of the distribution of R0; which in this implementation is the uninformative uniform
-#' distribution with values between zero and \code{kappa}. Users can change the value of kappa only (ie. the prior distribution
+#' distribution with values between zero and \code{kappa}. Users can change the value of /code{kappa} only (i.e., the prior distribution
 #' cannot be changed from the uniform).  As more case counts are observed, the influence of the prior distribution should lessen on
 #' the final estimate \code{Rhat}.
 #'
 #' This method is based on an approximation of the SIR model, which is most valid at the beginning of an epidemic. The method assumes
 #' that the mean of the serial distribution (sometimes called the serial interval) is known. The final estimate can be quite sensitive
-#' to this value, so sensitivity testing is strongly recommended. Users should be careful about units of time (e.g. are counts observed
+#' to this value, so sensitivity testing is strongly recommended. Users should be careful about units of time (e.g., are counts observed
 #' daily or weekly?) when implementing.  
 #'
 #' Our code has been modified to provide an estimate even if case counts equal to zero are present in some time intervals. This is done
 #' by grouping the counts over such periods of time. Without grouping, and in the presence of zero counts, no estimate can be provided.
 #'
-#' @param NT Vector of case counts
-#' @param mu Mean of the serial distribution (needs to match case counts in time units; for example, if case counts are
-#'           weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one, if case
-#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven)
-#' @param kappa Largest possible value of uniform prior, defaults to 20. This describes the prior belief on ranges of R0,
-#'              so should be set to a higher value if R0 is believed to be larger.  
+#' @param NT Vector of case counts.
+#' @param mu Mean of the serial distribution. This needs to match case counts in time units. For example, if case counts
+#'           are weekly and the serial distribution has a mean of seven days, then \code{mu} should be set to one. If case
+#'           counts are daily and the serial distribution has a mean of seven days, then \code{mu} should be set to seven.
+#' @param kappa Largest possible value of uniform prior (defaults to 20). This describes the prior belief on ranges of R0,
+#'              and should be set to a higher value if R0 is believed to be larger.  
 #'
-#' @return secB returns a list containing the following components: \code{Rhat} is the estimate of R0 (the posterior mean),
-#'              \code{posterior} is the posterior distribution of R0 from which alternate estimates can be obtained (see examples),
-#'              \code{group} is an indicator variable (if \code{group=TRUE}, zero values of NT were input and grouping was done to
-#'              obtain \code{Rhat}), and \code{inputs} is a list of the original input variables \code{NT, gamma, kappa}. The variable
-#'              \code{posterior} is returned as a list made up of \code{supp} the support of the distribution and \code{pmf} the
-#'              probability mass function.
+#' @return \code{secB} returns a list containing the following components: \code{Rhat} is the estimate of R0 (the posterior mean),
+#'         \code{posterior} is the posterior distribution of R0 from which alternate estimates can be obtained (see examples),
+#'         and \code{group} is an indicator variable (if \code{group=TRUE}, zero values of NT were input and grouping was done 
+#'         to obtain \code{Rhat}). The variable \code{posterior} is returned as a list made up of \code{supp} (the support of
+#'         the distribution) and \code{pmf} (the probability mass function).
 #'
 #' @examples
 #' ## ===================================================== ##
 #' ## Illustrate on weekly data                             ##
 #' ## ===================================================== ##
 #'
-#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)	
+#' NT <- c(1, 4, 10, 5, 3, 4, 19, 3, 3, 14, 4)
 #' ## obtain Rhat when serial distribution has mean of five days
-#' res1 <- seqB(NT=NT, mu=5/7)	
+#' res1 <- seqB(NT=NT, mu=5/7)
 #' res1$Rhat
 #' ## obtain Rhat when serial distribution has mean of three days
-#' res2	<- seqB(NT=NT, mu=3/7)	
+#' res2	<- seqB(NT=NT, mu=3/7)
 #' res2$Rhat
 #'
 #' ## ============================================================= ##