stats::nlm() to
minpack.lm::nlsLM() function for estimating parameters in
estimate_k(), estimate_k_1RM(),
estimate_kmod(), estimate_kmod_1RM(),
estimate_klin(), and estimate_klin_1RM()
functions.estimate_k_generic() and
estimate_k_generic_1RM(). These functions return the model
object, but use the default k value of 0.0333estimate_k_generic_1RM_mixed() which uses generic
k value of 0.0333 to predict the 0RMestimate_k_generic_1RM_quantile() which uses
generic k value of 0.0333 to predict the
0RMday) in the
strength_training_log dataset. This will be used for an
example on how to use the rolling estimationestimate_rolling_1RM() function. This is used to
implement “embedded testing” using training logs to estimate both
reps-max profiles and 1RMsscheme_rep_acc() - now the END rep and
step is used, and the reps are counted backwards as intendedvertical_generic() and
vertical_rep_accumulation() to avoid generating rep
accumulation schme using those two functions, but rather using
scheme_rep_acc()scheme_() functionsprogression_perc_drop in all scheme_()
functionsvertical_const in
all scheme_() functionsscheme_light_heavy() - now it takes the
highest rep and use that to estimate %1RMsscheme_ladder() set and rep scheme.vertical_rep_accumulation.post() function. This
functions is to be applied AFTER scheme is generated. Other options is
to use scheme_rep_acc() function, that is flexible enough
to generate most schemes, except for the scheme_ladder()
and scheme_light_heavy()vertical_block_undulating() vertical planning
function. This is a combination of Block Variant (undulation in the
steps) and Undulating (undulation in reps)scheme_generic(), where
vertical_set_accumulation didn’t repeat the adjustments,
which cause problems if only single set is accumulated. This is because
the adjustments were not accumulated, but rather “recycled”.accumulate_rep to
accumulate_set in vertical_set_accumulation()
and vertical_set_accumulation_reverse() functionsvertical_set_accumulation() and
vertical_set_accumulation_reverse() (see sequence
argument)adjustment in the
scheme_ functions. Now they are flexible, depending on the
reps argument, but follow the general logic of a given
scheme.plot_scheme() function. Removed {ggstance} from package
dependenciesfont_size arguments to plot_scheme()
and plot_progression_table() functionsgenerate_progression_table(),
create_example(), plot_progression_table()
functionsplot_vertical() function for plotting vertical
planSTMr_scheme class (subclass of data frame), and
now scheme can be plotted by using simple S3 plot method.
plot_scheme() function is now deprecated. Added three types
of plots: bar, vertical, and
fraction. The STMr_scheme class has now the
following columns: index, step,
set, reps, adjustment, and
perc_1RM.STMr_vertical constructor. Now the
vertical_ functions return STMr_vertical data
frame object with following column names: index,
step, set, set_id, and
reps. set_id is needed to sort out an issue
(see above) for the vertical_set_accumulation() and
vertical_set_accumulation_reverse() vertical plans when
adjustment is applied inside scheme_generic() functionscheme_light_heavy() and
scheme_ladder() functions, I have set
adjustment to NA since to avoid confusing the
user. This is because due to the modifications that these functions does
to the “light” sets, the adjustment is not applicable and not related to
selected progression table{ggfittext} package dependency, so the plot
labels are now flexible and fit the “container”. This can be useful when
set accumulation is used, so the labels do not go outside of the
barsreps_change to
vertical_set_accumulation() and
vertical_set_accumulation_reverse(), making them really
flexible functionsscheme_manual() for manual generation of the
scheme, which provides for the ultimate flexibilityperc_str argument to plot() S3
method, which allows the user to remove “%” and thus have more space for
labelrelease function and S3 plot
method for merging multiple schemes (i.e., blocks or phases) into one
release. This is used to inspect how multiple back-to-back phases mold
togetherperc_1RM argument to scheme_manual()
for the user to provide manual 1RM percentages, rather than to be
estimatedscheme_perc_1RM() which is simpler
scheme_manual() for manually entering 1RM percentages. For
example creating simple warm-up scheme+ method for STMr_scheme objects.
This allows for easy modular adding of the schemescreate_example(),
get_perc_1RM(), get_reps()progression_rel_int() functionnRM_testing dataset to
RTF_testing, as well as renamed the columns to be more
descriptiveestimate_k_mixed(),
estimate_k_1RM_mixed(), estimate_kmod_mixed(),
estimate_kmod_1RM_mixed(),
estimate_klin_mixed(), and
estimate_klin_1RM_mixed(). These are implemented using the
{nlme} package and nlme::nlme() functionstrength_training_log dataset. eRIR
ratings are now halved, and everything over 5 is now
NAget_reps() function
documentationAdded different weighting options for the estimate_
family of functions. These include
1/reps weighting)1/(eRIR+1) weighting)Added strength_training_log dataset. Single
individual performing two strength training sessions per week, over the
course of 12 weeks (4 phases, each 3 weeks long). Individual eRIR
(estimated reps-in-reserve) subjective rating is included in the
dataset. This dataset is used to demonstrate techniques for
embedded testing of the 1RM and individual profiles
Added estimate_k_quantile(),
estimate_kmod_quantile(), and
estimate_klin_quantile() functions to implement non-linear
quantile estimation of the parameters
REWRITTEN the whole package. This version will have compatibility issues with the previous version due to different naming of the functions. The package is now more modular, flexible, and can be parameterized more easily
The functions are organized in the following manner:
estimate_)max_). Epley’s, Modified
Epley’s and Linear/Brzycki’s model are implementedadj_). Deducted
Intensity (DI), Relative Intensity (RelInt), Reps In Reserve (RIR), and
% Max Reps (%MR) methods are implementedget_reps() and
get_perc_1RM() are implemented to combine reps-max models
as well as progression (adjustment) functions into easy to use
formatprogression_) are
implemented and allow easy parameterization to involve specific model
and their estimated parameter valuesvertical_)scheme_)generate_progression_table(),
plot_progression_table(), plot_scheme(), and
create_example()Fixed few typos in citation()
Added sample data set nRM_testing, which contains
reps max testing of 12 athletes using 70, 80, and 90% 1RM
1RM in estimate_xxx_1RM()
functions is now in the second place in coefficient ordercreate_example() function for quickly creating
example using selected progression tableget_max_perc_1RM()). Also see
get_max_perc_1RM_k() functionsget_max_perc_1RM_k(),
get_max_reps_k(), and get_predicted_1RM_k()
functions that uses user defined k value/parameter.
Together with the previous functionality, use is not able to easily
create custom max-reps table functions with extra arguments. This
provides great flexibilityget_max_perc_1RM_kmod(),
get_max_reps_kmod(), and
get_predicted_1RM_kmod() functions that uses user defined
kmod value/parameter for the modified Epley’s equation
Added get_max_perc_1RM_klin(),
get_max_reps_klin(), and
get_predicted_1RM_klin() functions that uses user defined
klin value/parameter for the linear equationestimate_ family of functions to estimate
Epley’s, modified Epley’s, and linear equation parameters, as well as
novel estimation functions that uses absolute weight to estimate both
k, kmod, klin and
1RM parametersfont_size when plotting adjustments using
plot_progression_table()NEWS.md file to track changes to the
package.